Correlating forensic data for enhanced network crime investigations: Techniques for packet sniffing, network forensics, and attack detection

Abstract: In today’s digitally saturated world, digital devices are frequently involved in criminal events as targets, mediums, or witnesses. Forensic investigations encompass the collection, recovery, analysis, and presentation of information stored on network devices, with specific relevance to network crimes. Such investigations often necessitate the use of diverse analysis tools and methods. This study introduces techniques that support digital investigators in correlating and presenting information derived from forensic data, with a primary focus on packet sniffing, network forensics, and attack detection. By leveraging these methodologies, investigators aim to achieve more valuable reconstructions of events or actions, resulting in enhanced case conclusions. The study emphasizes the importance of understanding how malware operates within the context of the Internet. It explores packet sniffing techniques to capture and analyze network data, enabling investigators to detect and trace the origins of malicious activities. Additionally, it delves into the realm of network forensics, proposing effective methods for gathering evidence from network devices and reconstructing digital events. Furthermore, the study covers the significance of attack detection in network crime investigations. It highlights techniques to identify and analyze attack patterns, facilitating the identification of perpetrators and their motivations. By correlating information obtained from forensic data, investigators can obtain comprehensive insights into the nature and impacts of network crimes. Overall, this study aims to arm digital investigators with the knowledge and tools necessary to navigate the complexities of packet sniffing, network forensics, and attack detection. By incorporating these techniques into their investigations, investigators can achieve more robust reconstructions of events, draw well-informed conclusions, and contribute to the successful resolution of network crime cases.


Professional IT Ethics in Hacktivism - Case IT Army of Ukraine

Abstract: This short paper is motivated by the current developments in the Russo-Ukrainian conflict and especially the broad emergence of cyber-based activities in it. The rise of crowdsourced activity in response to the conflict in the form of hacktivist groups such as the IT Army of Ukraine presents an ethical dilemma for IT professionals that is reflected in this paper with the help of the ACM Code of Ethics and Professional Conduct. Even though hacktivism as an activity could for the most part be seen as contradictory to the ACM Code of Ethics, the General Ethical Principles give surprising support for the activities of the IT Army of Ukraine in the current socio-political context of the Russo-Ukrainian conflict.


Some new quantum MDS codes with large minimum distance

Abstract: Abstract The construction of quantum Maximum Distance Separable (MDS) error-correcting codes is of great significance in practical applications, as it plays a vital role in enhancing the reliability and efficiency of quantum communication and quantum computing. As a result, it remains an active research topic in the field of quantum information science. In this paper, based on generalized Reed-Solomon (GRS) codes and Hermitian construction, we construct two new classes of quantum MDS codes. Our constructed quantum MDS codes are novel in the sense that their parameters differ from all previously known codes. Furthermore, most of our codes exhibit significantly larger minimum distance compared to the existing ones of the same length.


Cloud Computing Transformed IT Organizations With Its On-Demand Model, Notably Infrastructure as a Service (IaaS). Cloud Providers Manage Extensive Physical Devices, Consuming Substantial Energy and Bandwidth for Data Traffic During Virtual Machine D

Abstract:
Cloud computing transformed IT organizations
 with its on-demand model, notably Infrastructure
 as a Service (IaaS). Cloud providers manage
 extensive physical devices, consuming
 substantial energy and bandwidth for data traffic
 during virtual machine deployment. Balancing
 energy and bandwidth are critical. This thesis
 presents BOHGOA integrated with ACO to
 optimize virtual machine placement in clouds,
 simultaneously considering bandwidth and
 energy. It yields Pareto-optimal solutions for this
 trade-off. Using CloudSim, BOHGOA
 outperformed GA, ACO, and FFD algorithms,
 reducing bandwidth consumption by 54.14%,
 32.11%, 57.47% (240 VMs) and 38.12%,
 22.76%, 47.05% (500 VMs) respectively.
 Additionally, it decreased physical machine
 energy usage by 37.70%, 34.01%, 40.14% (240
 VMs) and 27.50%, 22.28%, 30.52% (500 VMs)
 respectively. These results underscore
 BOHGOA&#039;s effectiveness in optimizing cloud
 VM placement.


Institutional Redesign and Dynamics of Collaborative Ties in the Educational Ecosystem

Abstract:
As the landscape of education evolves, there is a growing need to understand and adapt to the changing dynamics of partnerships and collaborations within the educational sector. While previous studies have predominantly focused on spiral models, social networks, and platform models, little attention has been given to the emerging new institutional design in education. Through an extensive analysis of open sources, this research identifies the roles played by both conventional and non-conventional actors in shaping the educational landscape and their contributions to community development.
 The study reveals the evolving roles of universities, highlighting their active engagement in fostering entrepreneurship, creating vibrant communities, and supporting lifelong learning. As a result of our study, we propose a mapped scheme illustrating the relationships between the university and other actors within the educational space and illustrating the university’s potential to gain ecosystem leadership.
 In summary, by capitalizing on the identified opportunities, both parties (universities and non-conventional actors) can foster a thriving ecosystem, driving community development, lifelong learning, and local entrepreneurship.


Exploration of the Integrated Training Model for Information Technology Teachers

Abstract: With the acceleration of the social information process, information awareness and information skills have become the basic qualities of every citizen. The establishment of the training mechanism for scientific and technological innovation talents from the beginning of higher education is insufficient to meet the needs of the development of the times. It is imperative to improve the training of information technology innovation talents and explore a new training model. This paper describes the general situation of the development of education in the field of information technology from a domestic and international perspective. It then analyzes the existing problems, explores new exploration models and implementation suggestions, and puts forward prospects at the end of the paper.


ANALISA KEPUASAN PENGGUNA WEBSITE TOP UP VOUCHER GAMES ONLINE MENGGUNAKAN WEBQUAL 4.0

Abstract: The online gaming industry generates revenue through the sale of games that customers pay to play. In Indonesia, there are numerous programs that offer online game vouchers at different costs and with various payment options. The objective of this study is to identify the variables that influence user satisfaction on web applications that offer online game coupons. The study used the WebQual 4.0 methodology as a tool for evaluating the caliber of web apps. The research was conducted online via the Google Form application. The data collection period spanned from June 11, 2023 to June 29, 2023, and we got 130 respondents. The data was evaluated using descriptive statistical analysis with the aid of SPSS tools. The findings of this study indicate that the variable of Interaction Quality (X3) has a significant role in determining user satisfaction for online game voucher websites, however the factors of Usability (X1) and Information Quality (X2) do not exert any influence on user satisfaction. Therefore, this research shows that hypotheses H1 and H2 are rejected for all data groups. This research provides recommendations to online game voucher application owners to focus on interaction quality variables in further web application development.


Snail Homing and Mating Search Algorithm: A Novel Bio-Inspired Metaheuristic Algorithm

Abstract: Abstract In this paper, a novel Snail Homing and Mating Search (SHMS) algorithm is proposed. It is inspired from the biological behaviour of the snails. Snails continuously travels to find food and a mate, leaving behind a trail of mucus that serves as a guide for their return. Snails tend to navigate by following the available trails on the ground and responding to cues from nearby shelter homes. The proposed SHMS algorithm is investigated by solving several unimodal and multimodal functions. The solutions are validated using standard statistical tests such as two-sided and pairwise signed rank Wilcoxon test and Friedman rank test. The solution obtained from the SHMS algorithm exhibited superior robustness as well as search space exploration capabilities within the less computational cost. The real-world application of SHMS algorithm is successfully demonstrated in the engineering design domain by solving three cases of design and economic optimization shell and tube heat exchanger problem. The objective function value and other statistical results obtained using SHMS algorithm are compared with other well-known metaheuristic algorithms.


Fostering cultural heritage awareness through VR serious gaming: A study on promoting Saudi heritage among the younger generation

Abstract: This study employs a virtual reality (VR) game to examine the role of VR gaming in learning Saudi cultural heritage. By creating 3D (Three-dimensional) virtual heritage buildings, the game immerses players in cultural scenes, fostering a lasting appreciation for art history. Objectives include making heritage information dissemination engaging, blending learning and entertainment in a 3D environment, designing a gamified setting for active learning, and igniting interest in culture, tradition, architecture, and art history. This paper further highlights the significance of serious gaming in promoting the Saudi cultural heritage among the younger generation. The research involved immersing 59 participants into a heritage building environment using a VR game and then probing their experience of the environment through a questionnaire. Results indicate positive participant experiences, increased interest in Saudi cultural heritage and appreciation for VR technology. The study demonstrates the potential of VR games to make heritage accessible and enjoyable for the younger generation, motivating further exploration and learning. Valuable resources are provided for individuals and researchers interested in using VR gaming for cultural heritage engagement.


Methods Used in YouTube for Teaching and Learning English Language: A Systematic Literature Review

Abstract: Challenges await in giving and receiving English instructions. One way globalization proposes to create a fun, engaging, and fulfilling setting for English language education is the appropriate use of YouTube. This paper is a systematic review of literature on the use of YouTube in teaching and learning the English language. It also followed thematic content analysis via manual coding. Eligibility criteria were prepared before going to Google Scholar and ERIC databases to look for studies that could be used. PRISMA Flowchart was used to show how the researcher only had 46 articles for this study. PICO Framework was utilized to summarize the data gathered and aid the researcher in asking the question, “How was YouTube used for teaching and learning the English language? and “What specific skills are developed in the use of YouTube for teaching and learning English?”. The results constructed Horizontal, Intersecting, Vertical, Parallel, and Spiral methods for English instruction. It also revealed that speaking, pronunciation, listening, self-learning, comprehension, grammar, reading and writing improved. Future researchers can work on guidelines for selecting content or using YouTube for teaching and learning the English language.


KLASTERISASI ANGKATAN KERJA DI INDONESIA BERDASARKAN USIA MENGGUNAKAN METODE ALGORITMA K-MEANS

Abstract: The concept of the population is divided into two groups, namely the working age population and the population not working age. Indonesia, which has 34 provinces, has an unequal distribution of labor force due to the level of economic growth that is still not evenly distributed in several sectors. Labor is the most important and influential element in managing and controlling the economic system. In this study the method used in the grouping of provinces was based on the workforce in 34 provinces using the K-Means algorithm. The purpose of grouping data is done to get a province grouping that has a workforce in Indonesia by grouping / clustering into 3 groups based on age groups using the K-Means algorithm. Based on the calculations, the results of cluster 0 were 6 provinces, cluster 1 as many as 3 provinces and cluster 2 were 25 provinces. The K-Means algorithm can be used to understand the workforce problems and make it easier to describe the characteristics or characteristics of each group. Based on these results, the local government can give more attention to the regions with the smallest workforce such as the Province of Central Sulawesi, East Kalimantan, Jambi so that economic growth in various sectors can be increased so that the welfare of the workforce, especially in terms of work in the field of work can be easily obtained.


Kinetic Model Evaluation of Arsenic and Selenium Sources and Mobility in Waste Rock of the Powder River Basin, USA

Abstract: Abstract Groundwater quality can be impacted through the backfilling of coal pits with waste rock containing new mineral surfaces and transportable particles. Reaction fronts may propagate with the saturation of these backfill aquifers as the previously bound minerals and newly created nanomaterials provide additional release of potential contaminants. This study was implemented to identify newly available arsenic and selenium sources and effectively model their release with the weathering of waste rock from the Cordero Rojo Mine in the Powder River Basin, Wyoming. Basic forward models were constructed to replicate the typical modeling scenario for the identified mineral sources of arsenic and selenium in the Powder River Basin overburden—pyrite and gypsum, respectively. The basic forward models were unable to capture the arsenic and selenium trends recorded for leachate from a kinetic column loaded with waste rock from the Cordero Rojo Mine. Enhanced models were constructed to capture nanomaterial contributions to supplement the initially identified processes of the oxidation of arsenic-bearing pyrite and dissolution of selenium-bearing gypsum. Incorporation of the nanomaterial contributions produced modeled arsenic and selenium trends similar to the observed trends in the column leachate. The identification and modeling of newly available contaminant sources in backfill waste rock is necessary to predict the exceedance of water quality criteria for overburden formations that have not previously shown the potential for water quality contamination.


The Influence of Organizational Culture and the Role of Internal Audit on Fraud Prevention

Abstract: The rise of fraud or fraud that occurs in organizations can cause losses to the organization so that the organization is difficult to develop, so there is a need for awareness of the importance of fraud prevention to minimize losses due to fraud. This article aims to examine and review research related to the factors that influence fraud prevention, namely organizational culture and the role of internal audit. This literature review also has the aim of developing hypotheses about the influence between variables to be used in further research. The results of this research article conclude that organizational culture, and the role of internal audit have a positive effect on fraud prevention. This indicates that the better the organizational culture and internal audit, the better the implementation of fraud prevention in the organization


Secure Digital Signature Using Block Chain Technology

Abstract: The lack of authentication and ant forge mechanisms severely compromises the security details of the authorizations issuing the certification. We employ blockchain technology to verify people in a way that is akin to a digital signature with their identity and access authorization, so resolving the issue of certificate forgery. Blockchain technology is an open distributed ledger that ensures that every transaction cannot be altered and holds unquestionable information in a highly secure and encrypted manner. A high standard for the procedure that may guarantee that the data in such a certificate is authentic indicates that the document is authentic and has not been forged, having come from a reliable source. The interplanetary file system uses the content address as the only means of uniquely identifying every file inside a global namespace that includes all computing devices. A bi-dimensional barcode that provides data as black and white dots is called a Quick Response (QR) code. The system consists of black squares that can be photographed by a camera or other image device, arranged in a square framework on a white background.


COVID-19 and Advice Speech Act: A Syntactic-Pragmatic Study

Abstract: Advising is a significant activity in a number of organizational health settings, including face-to-face counseling and the dissemination of information via pamphlets, posters, television, and Internet. Health-related advice includes anything from medicine to stopping smoking, getting immunized, and modifying one's diet. A strange sickness initially manifested itself in Wuhan, China, in late 2019. This disease spread rapidly over the world and was eventually named coronavirus disease (COVID-19). Due to the disease's global expansion, the World Health Organization (WHO) has declared it a pandemic, which indicates that it is spreading among individuals in a large number of nations, resulting in mortality. In early May, the WHO announced that more than 200 thousand individuals had died. The situation is worse, as the number of reported cases continues to rise daily. The purpose of this study is to shed light on the language of acts and advising actions, as well as the right method of advising. The advising act verbs and COVID-19 can be investigated syntactically and pragmatically. There is an effective function for advice speeches in relation to COVID-19 announcements and advice. Additionally, there is a strong correlation between the number of mistakes and accuracy in the eight questions across the eight groups. The study's findings indicate that Iraqi students do not follow health recommendations supplied by foreign health organizations. Concerning the COVID-19 pandemic, WHO is one of the leaders capable of effectively managing its concerns and taking proactive measures to contain the disease by issuing directives addressing the viruses as contagious agents. By including these characteristics of syntactic-pragmatic advising acts, these sorts of actions may be comprehended and executed by everyone in any scenario when speaking. Iraqi EFL learners had an unsatisfactory performance evaluation result as a result of inadequate teaching of PV in connection with public advice during the COVID-19 epidemic.


An Entrepreneurial Skills Model for Project-Based Learning in Programming

Abstract: This research aims to: 1) analyse the development needs of the Project Based-Learning based Entrepreneurial Skills Programming model; 2) design a model that supports the integration of entrepreneurial skills into programming learning; 3) assess the validity of the Project Based-Learning based Entrepreneurial Skills Programming model; 4) pilot the model to evaluate the effectiveness of teaching and developing entrepreneurial skills; 5) examine the effectiveness of the Entrepreneurial Skills Programming model; and 6) identify student responses to the Entrepreneurial Skills Programming model. These skills are important to equip students with relevant and appropriate abilities for the dynamic world of work and rapid technological development. The method used is Research and Development (R&D) with a 4D development model approach: Define, Design, Develop, and Disseminate. Data were collected through literature studies, field surveys, and in-depth interviews with stakeholders. The research stages included needs identification, model design, prototype development, and model dissemination. The validity and reliability of the instruments were tested using the Validity Coefficient (Va) and percentage of agreement (PA). The results showed that the developed entrepreneurship model was effective in improving students' entrepreneurial skills, evidenced by the significant improvement in students' learning outcomes and their positive response to the model. The model is considered to add value and increase students' enthusiasm for learning. This confirms the importance of integrating entrepreneurial skills in programming learning to prepare students for the challenges of today's world of work.


Mutual encoder-decoder with bi-gated convolution for image inpainting

Abstract: Partial convolution and gated convolution (GC) have been widely used to solve the limitations of vanilla convolution. However, both approaches have their respective drawbacks. For example, the single-channel binary mask used in partial convolution restricts its flexibility, and the accuracy of the gating values learned from GC cannot be guaranteed. To overcome these limitations, we propose an approach called bi-gated convolution. It adaptively integrates the binary mask and the gating values learned from the network to obtain refined gating values that effectively characterize the features, thereby increasing the recognition accuracy of the gating value. Furthermore, we propose a feature-adaptive supplementation operation designed specifically for repairing damaged areas within the encoder features. Finally, we present a mutual encoder-decoder architecture with bi-gated convolution. Experiments conducted on two benchmark datasets show that the proposed method has the capability to generate visually plausible results.


MiDaS: a large-scale Minecraft dataset for non-natural image benchmarking

Abstract: Reinforcement learning (RL) has recently made several significant advances using video games as a testbed. While many of these games are relatively self-contained, there has been a recent push to develop agents capable of tackling massive, open-ended environments that are more reminiscent of the real world. One of the most popular of these platforms is Minecraft, but to attain human-level performance, agents must be able to learn, plan, and reason using high-dimensional image input. Commonly, an agent will attempt to extract lower-dimensional features that assist with downstream tasks. However, representation learning techniques have primarily been applied to real-world, natural image datasets, and it is unclear how these same methods might translate to an artificial world with non-natural images. We therefore present MiDaS, a novel large-scale Minecraft dataset featuring 36,000 labeled images across 60 classes. MiDaS contains information about both the blocks in the image, critical to solving the game, as well as auxiliary information such as time of day and biome. Further, we perform an evaluation of various models to benchmark performance on this new dataset. Since RL agents must be capable of learning features without labels, we include benchmarks of various self-supervised learning approaches on the dataset. Our results indicate that self-supervised methods perform best in the linear evaluation paradigm, particularly in low-label settings with a ResNet-based backbone, whereas ImageNet-pretraining assists more in the fine-tuning setting. The full dataset is available at https://github.com/MinecraftDataset/MiDaS.


VALIDITY OF CONTEXTUAL TEACHING AND LEARNING (CTL) COMIC LEARNING MEDIA

Abstract: The lack of student interest in learning mathematics is caused by the teaching materials used not being able to make students active so that student scores have not reached the Minimum Completeness Criteria (KKM). School teaching materials contain many questions with almost the same level of difficulty and solution methods so that students get a little bored of doing them. This research aims to develop mathematics learning media in the form of comics with Contextual Teaching and Learning (CTL) nuances to improve the mathematics learning outcomes of class VII students at SMP N 20 Jambi City. In this development it is expected to obtain valid mathematics learning media results. This type of research is development research that produces a product in the form of learning comics. The development of this comic uses the ADDIE model which consists of 5 stages, namely the Analyze, Design, Development, Implementation, Evaluation stages. The research subjects were class VII students of SMP N 20 Jambi City. Learning device validation was carried out by mathematics education experts and Indonesian language education experts. The overall validation results for comic media are very valid with a success rate of 85.52%. With this it can be concluded that the development of comic media with Contextual Teaching and Learning (CTL) nuances can be used as teaching material.


Attentive Implicit Relation Embedding for Event Recommendation in Event-based Social Network

Abstract: The event-based social network (EBSN) is a new type of social network that combines online and offline networks, and its primary goal is to recommend appropriate events to users. Most studies do not model event recommendations on the EBSN platform as graph representation learning, nor do they consider the implicit relationship between events, resulting in recommendations that are not accepted by users. Thus, we study graph representation learning, which integrates implicit relationships between social networks and events. First, we propose an algorithm that integrates implicit relationships between social networks and events based on a multiple attention model. The graph structure that integrates implicit relationships between social networks and events is divided into user modeling and event modeling: modeling the interactive information of user events, user social relationships, and implicit relationships between users in user modeling; modeling user information and implicit relationships between events in event modeling; and deeply mining high-level transfer relationships between users and events. Then, the user modeling and event modeling models are fused using a multiattention joint learning mechanism to capture the different impacts of social and implicit relationships on user preferences, improving the recommendation quality of the recommendation system. Finally, the effectiveness of the proposed algorithm is verified in real datasets.


New version of commonmeta-py library for scholarly metadata

Abstract: Yesterday I released version 0.13 of the commonmeta-py library on PyPi. The major new feature is a command-line interface for all metadata conversions, and the metadata validation with the Commonmeta JSON Schema has finally been fixed. The command-line interface takes any supported persistent identifier or file as input, and returns the metadata in the desired format as output.


Analisis Pengenalan Literasi Digital pada Anak di Lingkungan Keluarga

Abstract: Human interaction patterns with digital media are becoming more intense as a result of digital disruption. Digital media can be beneficial, but it can also be detrimental. Therefore, digital literacy skills are needed to help people use digital media correctly, especially children. This can start from the family environment as the smallest organization closest to the child. This research aims to analyze the introduction of digital literacy to children in the family environment. This research was conducted by conducting a systematic literature review using a qualitative approach. Data collection techniques by examining Google Scholar-based journal article sources that are relevant to the research study. After collecting and obtaining data that is in accordance with the research study, the data is then analyzed using the content analysis method. The results of research on introducing digital literacy to children in the family environment can be divided into 4 parts, including 1) children are good imitators, 2) parents' digital literacy skills, 3) guidance and control, 4) developing digital literacy habits.


Bruker to buy robotics firm Chemspeed

Abstract: The scientific instrument firm Bruker will acquire Chemspeed Technologies, a Swiss provider of robotics and automation equipment for chemistry, pharmaceutical, and materials research labs. Bruker says Chemspeed had sales of more than $50 million last year and was profitable. Known as a developer of nuclear magnetic resonance and mass spectrometers, Bruker has been seeking to diversify. In an announcement, the company says the purchase will accelerate its entry into lab automation, digitalization, and scientific software.


Merck partners with Variational AI

Abstract: Merck & Co. has partnered with Variational AI to explore using artificial intelligence in drug discovery. Variational AI CEO Handol Kim says Merck will test Variational’s Enki platform in finding new small molecules, though the firms did not identify specific targets. This platform, which Kim has compared to DALL-E and Midjourney, is meant to generate small-molecule structures in response to a target product profile.


Rearranging Equations to Develop Physics Reasoning

Abstract: Abstract Researchers generally agree that physics experts use mathematics in a way that blends mathematical knowledge with physics intuition. However, the use of mathematics in physics education has traditionally tended to focus more on the computational aspect (manipulating mathematical operations to get numerical solutions) to the detriment of building conceptual understanding and physics intuition. Several solutions to this problem have been suggested; some authors have suggested building conceptual understanding before mathematics is introduced, while others have argued for the inseparability of the two, claiming instead that mathematics and conceptual physics need to be taught simultaneously. Although there is a body of work looking into how students employ mathematical reasoning when working with equations, the specifics of how physics experts use mathematics blended with physics intuition remain relatively underexplored. In this paper, we describe some components of this blending, by analyzing how physicists perform the rearrangement of a specific equation in cosmology. Our data consist of five consecutive forms of rearrangement of the equation, as observed in three separate higher education cosmology courses. This rearrangement was analyzed from a conceptual reasoning perspective using Sherin’s framework of symbolic forms. Our analysis clearly demonstrates how the number of potential symbolic forms associated with each subsequent rearrangement of the equation decreases as we move from line to line. Drawing on this result, we suggest an underlying mechanism for how physicists reason with equations. This mechanism seems to consist of three components: narrowing down meaning potential, moving aspects between the background and the foreground and purposefully transforming the equation according to the discipline’s questions of interest. In the discussion section we highlight the potential that our work has for generalizability and how being aware of the components of this underlying mechanism can potentially affect physics teachers’ practice when using mathematics in the physics classroom.


Comprehensibility at a Phonological Level

Abstract: This chapter focuses on the more traditional scope of the comprehensibility/intelligibility construct and takes an analytical approach to examining evidence related to phonemic and prosodic linguistic components affecting comprehensibility of spoken language. An overview of the features that have been identified by previous studies is presented and categorised (e.g. Isaacs & Harding, 2017; Isaacs & Trofimovich, 2012), including those occurring at phoneme level (articulation), word level (segmental units, word stress) and sentence level (intonation, pitch, fluency). The features are then examined from three key perspectives: a) reported impact on comprehensibility b) how the features are reflected in rating scales c) whether the features are L1-dependent.


An Investigation of DRM Procedures and Best Strategies Carried Out to Safeguard Sound Documents in on the Web

Abstract: Abstract: The study of data security has turned into a worry of numerous specialists, whose endeavors are attempting to concoct arrangements and innovations that guarantee the exchange of data in a safer way through the organization, particularly the Web, with practically no entrance of that data. Because of the significance of computerized information sent between the two gatherings through an unreliable channel. Subsequently, there are numerous advances and techniques that are as of now utilized in data security. This paper subtleties the examination into utilizing sound sign handling strategies to give confirmation and ID administrations to the motivation of upgrading digital protection in voice applications. Sound is a developing space for network protection innovation. It is visualized that throughout the following 10 years, the essential point of interaction for giving orders to customer web empowered gadgets will be voice. Progressively, gadgets like work stations, savvy speakers, vehicles, television's, telephones and Web of Things (IOT) gadgets all have underlying voice collaborators and voice enacted highlights. This exploration frames a way to deal with safely distinguish and verify clients of sound and voice worked frameworks.


On the power of linear programming for K-means clustering

Abstract: In [SIAM J. Optim., 2022], the authors introduced a new linear programming (LP) relaxation for K-means clustering. In this paper, we further investigate the theoretical properties of this relaxation. We focus on K-means clustering with two clusters, which is an NP-hard problem. As evident from our numerical experiments with both synthetic and real-world data sets, the proposed LP relaxation is almost always tight; i.e. its optimal solution is feasible for the original nonconvex problem. To better understand this unexpected behaviour, we obtain sufficient conditions under which the LP relaxation is tight. We further analyze the sufficient conditions when the input is generated according to a popular stochastic model and obtain recovery guarantees for the LP relaxation. Finally, we construct a family of inputs for which the LP relaxation is never tight.


FedShift: Tackling Dual Heterogeneity Problem of Federated Learning via  Weight Shift Aggregation

Abstract: Federated Learning (FL) offers a compelling method for training machine learning models with a focus on preserving data privacy. The presence of system heterogeneity and statistical heterogeneity, recognized challenges in FL, arises from the diversity of client hardware, network, and dataset distribution. This diversity can critically affect the training pace and the performance of models. While many studies address either system or statistical heterogeneity by introducing communication-efficient or stable convergence algorithms, addressing these challenges in isolation often leads to compromises due to unaddressed heterogeneity. In response, this paper introduces FedShift, a novel algorithm designed to enhance both the training speed and the models' accuracy in a dual heterogeneity scenario. Our solution can improve client engagement through quantization and mitigate the adverse effects on performance typically associated with quantization by employing a shifting technique. This technique has proven to enhance accuracy by an average of 3.9% in diverse heterogeneity environments.


An Empirical Analysis of Diversity in Argument Summarization

Abstract: Presenting high-level arguments is a crucial task for fostering participation in online societal discussions. Current argument summarization approaches miss an important facet of this task -- capturing diversity -- which is important for accommodating multiple perspectives. We introduce three aspects of diversity: those of opinions, annotators, and sources. We evaluate approaches to a popular argument summarization task called Key Point Analysis, which shows how these approaches struggle to (1) represent arguments shared by few people, (2) deal with data from various sources, and (3) align with subjectivity in human-provided annotations. We find that both general-purpose LLMs and dedicated KPA models exhibit this behavior, but have complementary strengths. Further, we observe that diversification of training data may ameliorate generalization. Addressing diversity in argument summarization requires a mix of strategies to deal with subjectivity.


A Survey of Integrating Deep Learning-Based Missing Person Detection Model into CCTV Systems for Enhanced Identification

Abstract: This study looks at the possibility of increasing identification abilities in closed-circuit video (CCTV) systems using a deep learning-based missing person detection model. Missing individuals are becoming increasingly common, necessitating the development of novel search tactics. This study uses deep learning to augment traditional CCTV systems by using an improved model that can reliably identify and track persons who go missing. Taking a detailed look at current techniques to missing person identification, the literature review shows the shortcomings of existing systems and the potential for improving them through the use of deep learning. The paper examines past methodologies, including facial recognition accuracy, tracking robustness, and system scalability.The integration of computer vision with missing person identification, object tracking technologies, and facial recognition algorithms are among the main topics. The research also investigates privacy, moral, and legal implications of deploying such technology in public.


London solidifies sector-based approach to AI rules

Abstract: Headline UK: London solidifies sector-based approach to AI


Learn from Restoration: Exploiting Oriented Knowledge Distillation in Self-Supervised Learning for Person Re-Identification

Abstract: Abstract Person Re-IDentification (person ReID) aims to identify such individuals across diverse surveillance scenarios, which plays a pivotal role in centralized monitoring. However very recent studies pre-trained their models on ImageNet and fine-tuned on specific downstream ReID dataset, which leads to restricted generalization capability of person centered tasks. Addressing this limitation, this paper introduces a Self-Supervised Learning (SSL) model with Oriented Token Prior guided Knowledge Distillation. The proposed approach adopts the teacher-student network and attempts to not only restore the masked or low-quality samples, but also to align the semantics of the patches in feature domain. Within this framework, our model is pre-trained on a person-specific dataset LUPerson, which indicates a larger data scale with person-centered samples. Extensive experiments carried out on ReID datasets Market1501, MSMT17 and Occluded-Duke shows that the proposed method yield the state-of-the-art performance among the supervised and SSL ReID methods. Moreover, the proposed method could obtain the remarkable performance of partial supervised and unsupervised learning, which further indicates the strong generalizability and the robustness of our method. The code is publicly available at \href{https://github.com/ICT-CVlab/Oriented-KD-SSL}{https://github.com/ICT-CVlab/Oriented-KD-SSL}


Speech act of flaming: A pragmatic analysis of Twitter trolling in Pakistan

Abstract: This study analyzed twitter trolling as a speech act of flaming. Trolling are deliberate disruptive practices of individuals or of particular group to sensationalize, commoditize, or intensify the reaction in online communication. Twitter API account was used to collect the tweets generated in Pakistan in English. The tweets were manually annotated with the help of a framework proposed by the Nitin, et al., 2011. and Lingam in UAM corpus tool. The finding revealed that tweets are trolled to flame by criticizing, name-calling, speculating, defaming, and degrading. The direct or intentional flaming showed derogatory behavior based on assumptions and aimed at inciting degenerative polarized responses. Moreover, this study also implies that there are serious moral and social implications of Twitter trolling.


Regressive class models for machine learning algorithms to predict trajectories of repeated multinomial outcomes: an application to the activity of daily living of elderly data

Abstract: Due to the advancement of electronic data capturing, the amount of repeated categorical data being collected and stored has increased. This massive amount of data is complex and poses significant statistical challenges in methodology and computation. To analyze such big data, the divide and recombine method is commonly used. First, a large data set is partitioned into subsets, and each subset is analyzed separately. Then, the results are recombined in a manner that produces statistically valid output. However, available literature can only accommodate cross-sectional data. We propose a new simpler approach to analyze large, repeated categorical data using a joint modeling framework. In the proposed method, follow-up time is a natural conditioning variable that allows big data to be divided into subsets. Then, using the relationship between joint, marginal, and conditional probabilities, we can recombine the results in a statistically valid way. Several machine learning algorithms for cross-sectional data are extended for repeated outcomes to predict trajectories using the proposed framework. As an illustration, the proposed methodology is used to analyze repeatedly measured activity of daily living (ADL) data from the Health and Retirement Study (HRS), USA. We also check model performances under multiple machine learning algorithms using bootstrap simulations.


Diffusion Cross-domain Recommendation

Abstract: It is always a challenge for recommender systems to give high-quality outcomes to cold-start users. One potential solution to alleviate the data sparsity problem for cold-start users in the target domain is to add data from the auxiliary domain. Finding a proper way to extract knowledge from an auxiliary domain and transfer it into a target domain is one of the main objectives for cross-domain recommendation (CDR) research. Among the existing methods, mapping approach is a popular one to implement cross-domain recommendation models (CDRs). For models of this type, a mapping module plays the role of transforming data from one domain to another. It primarily determines the performance of mapping approach CDRs. Recently, diffusion probability models (DPMs) have achieved impressive success for image synthesis related tasks. They involve recovering images from noise-added samples, which can be viewed as a data transformation process with outstanding performance. To further enhance the performance of CDRs, we first reveal the potential connection between DPMs and mapping modules of CDRs, and then propose a novel CDR model named Diffusion Cross-domain Recommendation (DiffCDR). More specifically, we first adopt the theory of DPM and design a Diffusion Module (DIM), which generates user's embedding in target domain. To reduce the negative impact of randomness introduced in DIM and improve the stability, we employ an Alignment Module to produce the aligned user embeddings. In addition, we consider the label data of the target domain and form the task-oriented loss function, which enables our DiffCDR to adapt to specific tasks. By conducting extensive experiments on datasets collected from reality, we demonstrate the effectiveness and adaptability of DiffCDR to outperform baseline models on various CDR tasks in both cold-start and warm-start scenarios.


A fast and gridless ORKA algorithm for tracking moving and deforming  objects

Abstract: Identifying objects in given data is a task frequently encountered in many applications. Finding vehicles or persons in video data, tracking seismic waves in geophysical exploration data, or predicting a storm front movement from meteorological measurements are only some of the possible applications. In many cases, the object of interest changes its form or position from one measurement to another. For example, vehicles in a video may change its position or angle to the camera in each frame. Seismic waves can change its arrival time, frequency, or intensity depending on the sensor position. Storm fronts can change its form and position over time. This complicates the identification and tracking as the algorithm needs to deal with the changing object over the given measurements. In a previous work, the authors presented a new algorithm to solve this problem - Object reconstruction using K-approximation (ORKA). The algorithm can solve the problem at hand but suffers from two disadvantages. On the one hand, the reconstructed object movement is bound to a grid that depends on the data resolution. On the other hand, the complexity of the algorithm increases exponentially with the resolution. We overcome both disadvantages by introducing an iterative strategy that uses a resampling method to create multiple resolutions of the data. In each iteration the resolution is increased to reconstruct more details of the object of interest. This way, we can even go beyond the original resolution by artificially upsampling the data. We give error bounds and a complexity analysis of the new method. Furthermore, we analyze its performance in several numerical experiments as well as on real data. We also give a brief introduction on the original ORKA algorithm. Knowledge of the previous work is thus not required.


PoCo: Policy Composition from and for Heterogeneous Robot Learning

Abstract: Training general robotic policies from heterogeneous data for different tasks is a significant challenge. Existing robotic datasets vary in different modalities such as color, depth, tactile, and proprioceptive information, and collected in different domains such as simulation, real robots, and human videos. Current methods usually collect and pool all data from one domain to train a single policy to handle such heterogeneity in tasks and domains, which is prohibitively expensive and difficult. In this work, we present a flexible approach, dubbed Policy Composition, to combine information across such diverse modalities and domains for learning scene-level and task-level generalized manipulation skills, by composing different data distributions represented with diffusion models. Our method can use task-level composition for multi-task manipulation and be composed with analytic cost functions to adapt policy behaviors at inference time. We train our method on simulation, human, and real robot data and evaluate in tool-use tasks. The composed policy achieves robust and dexterous performance under varying scenes and tasks and outperforms baselines from a single data source in both simulation and real-world experiments. See https://liruiw.github.io/policycomp for more details .


Evading Data Contamination Detection for Language Models is (too) Easy

Abstract: Large language models are widespread, with their performance on benchmarks frequently guiding user preferences for one model over another. However, the vast amount of data these models are trained on can inadvertently lead to contamination with public benchmarks, thus compromising performance measurements. While recently developed contamination detection methods try to address this issue, they overlook the possibility of deliberate contamination by malicious model providers aiming to evade detection. We argue that this setting is of crucial importance as it casts doubt on the reliability of public benchmarks. To more rigorously study this issue, we propose a categorization of both model providers and contamination detection methods. This reveals vulnerabilities in existing methods that we exploit with EAL, a simple yet effective contamination technique that significantly inflates benchmark performance while completely evading current detection methods.


Precedence-Constrained Winter Value for Effective Graph Data Valuation

Abstract: Data valuation is essential for quantifying data's worth, aiding in assessing data quality and determining fair compensation. While existing data valuation methods have proven effective in evaluating the value of Euclidean data, they face limitations when applied to the increasingly popular graph-structured data. Particularly, graph data valuation introduces unique challenges, primarily stemming from the intricate dependencies among nodes and the exponential growth in value estimation costs. To address the challenging problem of graph data valuation, we put forth an innovative solution, Precedence-Constrained Winter (PC-Winter) Value, to account for the complex graph structure. Furthermore, we develop a variety of strategies to address the computational challenges and enable efficient approximation of PC-Winter. Extensive experiments demonstrate the effectiveness of PC-Winter across diverse datasets and tasks.


Improving Hardenability Modeling: A Bayesian Optimization Approach to Tuning Hyperparameters for Neural Networks Regression

Abstract: This study investigates the application of regression neural networks, in particular the fitrnet model, in predicting the hardness of steels. The experiments involve extensive tuning of hyperparameters using Bayesian optimization and employ 5-fold and 10-fold cross-validation schemes. The trained models are rigorously evaluated, and their performances are compared using various metrics such as mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results provide valuable insights into the effectiveness of the models and their ability to generalize to unseen data. In particular, Model 4208 (8-85-141-1) emerges as the top performer with an impressive RMSE of 1.0790 and an R² of 0.9900. The research paper contains an illustrative example that demonstrates the practical application of the developed model in determining the hardenability band for a specific steel grade and shows the effectiveness of the model in predicting and optimizing heat treatment results.


Efficacy of virtual purification-based error mitigation on quantum metrology

Abstract: Noise is the main source that hinders us from fully exploiting quantum advantages in various quantum informational tasks. However, characterizing and calibrating the effect of noise is not always feasible in practice. Especially for quantum parameter estimation, an estimator constructed without precise knowledge of noise entails an inevitable bias. Recently, virtual purification-based error mitigation (VPEM) has been proposed to apply for quantum metrology to reduce such a bias occurring from unknown noise. While it was demonstrated to work for particular cases, whether VPEM always reduces a bias for general estimation schemes is unclear. For more general applications of VPEM to quantum metrology, we study factors determining whether VPEM can reduce the bias. We find that the closeness between the dominant eigenvector of a noisy state and the ideal quantum probe (without noise) with respect to an observable determines the reducible amount of bias by VPEM. Next, we show that one should carefully choose the reference point of the target parameter, which gives a smaller bias than others because the bias depends on the reference point. Otherwise, even if the dominant eigenvector and the ideal quantum probe are close, the bias of the mitigated case could be larger than the nonmitigated one. Finally, we analyze the error mitigation for a phase estimation scheme under various noises. Based on our analysis, we predict whether VPEM can effectively reduce a bias and numerically verify our results.


Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object  Detector

Abstract: This paper addresses the challenge of cross-domain few-shot object detection (CD-FSOD), aiming to develop an accurate object detector for novel domains with minimal labeled examples. While transformer-based open-set detectors e.g., DE-ViT~\cite{zhang2023detect} have excelled in both open-vocabulary object detection and traditional few-shot object detection, detecting categories beyond those seen during training, we thus naturally raise two key questions: 1) can such open-set detection methods easily generalize to CD-FSOD? 2) If no, how to enhance the results of open-set methods when faced with significant domain gaps? To address the first question, we introduce several metrics to quantify domain variances and establish a new CD-FSOD benchmark with diverse domain metric values. Some State-Of-The-Art (SOTA) open-set object detection methods are evaluated on this benchmark, with evident performance degradation observed across out-of-domain datasets. This indicates the failure of adopting open-set detectors directly for CD-FSOD. Sequentially, to overcome the performance degradation issue and also to answer the second proposed question, we endeavor to enhance the vanilla DE-ViT. With several novel components including finetuning, a learnable prototype module, and a lightweight attention module, we present an improved Cross-Domain Vision Transformer for CD-FSOD (CD-ViTO). Experiments show that our CD-ViTO achieves impressive results on both out-of-domain and in-domain target datasets, establishing new SOTAs for both CD-FSOD and FSOD. All the datasets, codes, and models will be released to the community.


High-dimensional Bayesian Optimization via Covariance Matrix Adaptation  Strategy

Abstract: Bayesian Optimization (BO) is an effective method for finding the global optimum of expensive black-box functions. However, it is well known that applying BO to high-dimensional optimization problems is challenging. To address this issue, a promising solution is to use a local search strategy that partitions the search domain into local regions with high likelihood of containing the global optimum, and then use BO to optimize the objective function within these regions. In this paper, we propose a novel technique for defining the local regions using the Covariance Matrix Adaptation (CMA) strategy. Specifically, we use CMA to learn a search distribution that can estimate the probabilities of data points being the global optimum of the objective function. Based on this search distribution, we then define the local regions consisting of data points with high probabilities of being the global optimum. Our approach serves as a meta-algorithm as it can incorporate existing black-box BO optimizers, such as BO, TuRBO, and BAxUS, to find the global optimum of the objective function within our derived local regions. We evaluate our proposed method on various benchmark synthetic and real-world problems. The results demonstrate that our method outperforms existing state-of-the-art techniques.


Linguistic features for sentence difficulty prediction in ABSA

Abstract: One of the challenges of natural language understanding is to deal with the subjectivity of sentences, which may express opinions and emotions that add layers of complexity and nuance. Sentiment analysis is a field that aims to extract and analyze these subjective elements from text, and it can be applied at different levels of granularity, such as document, paragraph, sentence, or aspect. Aspect-based sentiment analysis is a well-studied topic with many available data sets and models. However, there is no clear definition of what makes a sentence difficult for aspect-based sentiment analysis. In this paper, we explore this question by conducting an experiment with three data sets: "Laptops", "Restaurants", and "MTSC" (Multi-Target-dependent Sentiment Classification), and a merged version of these three datasets. We study the impact of domain diversity and syntactic diversity on difficulty. We use a combination of classifiers to identify the most difficult sentences and analyze their characteristics. We employ two ways of defining sentence difficulty. The first one is binary and labels a sentence as difficult if the classifiers fail to correctly predict the sentiment polarity. The second one is a six-level scale based on how many of the top five best-performing classifiers can correctly predict the sentiment polarity. We also define 9 linguistic features that, combined, aim at estimating the difficulty at sentence level.


Aligner: Achieving Efficient Alignment through Weak-to-Strong Correction

Abstract: Efforts to align Large Language Models (LLMs) are mainly conducted via Reinforcement Learning from Human Feedback (RLHF) methods. However, RLHF encounters major challenges including training reward models, actor-critic engineering, and importantly, it requires access to LLM parameters. Here we introduce Aligner, a new efficient alignment paradigm that bypasses the whole RLHF process by learning the correctional residuals between the aligned and the unaligned answers. Our Aligner offers several key advantages. Firstly, it is an autoregressive seq2seq model that is trained on the query-answer-correction dataset via supervised learning; this offers a parameter-efficient alignment solution with minimal resources. Secondly, the Aligner facilitates weak-to-strong generalization; finetuning large pretrained models by Aligner's supervisory signals demonstrates strong performance boost. Thirdly, Aligner functions as a model-agnostic plug-and-play module, allowing for its direct application on different open-source and API-based models. Remarkably, Aligner-7B improves 11 different LLMs by 21.9% in helpfulness and 23.8% in harmlessness on average (GPT-4 by 17.5% and 26.9%). When finetuning (strong) Llama2-70B with (weak) Aligner-13B's supervision, we can improve Llama2 by 8.2% in helpfulness and 61.6% in harmlessness. See our dataset and code at https://aligner2024.github.io


TensoSDF: Roughness-aware Tensorial Representation for Robust Geometry  and Material Reconstruction

Abstract: Reconstructing objects with realistic materials from multi-view images is problematic, since it is highly ill-posed. Although the neural reconstruction approaches have exhibited impressive reconstruction ability, they are designed for objects with specific materials (e.g., diffuse or specular materials). To this end, we propose a novel framework for robust geometry and material reconstruction, where the geometry is expressed with the implicit signed distance field (SDF) encoded by a tensorial representation, namely TensoSDF. At the core of our method is the roughness-aware incorporation of the radiance and reflectance fields, which enables a robust reconstruction of objects with arbitrary reflective materials. Furthermore, the tensorial representation enhances geometry details in the reconstructed surface and reduces the training time. Finally, we estimate the materials using an explicit mesh for efficient intersection computation and an implicit SDF for accurate representation. Consequently, our method can achieve more robust geometry reconstruction, outperform the previous works in terms of relighting quality, and reduce 50% training times and 70% inference time.


A Survey on Effective Invocation Methods of Massive LLM Services

Abstract: Language models as a service (LMaaS) enable users to accomplish tasks without requiring specialized knowledge, simply by paying a service provider. However, numerous providers offer massive large language model (LLM) services with variations in latency, performance, and pricing. Consequently, constructing the cost-saving LLM services invocation strategy with low-latency and high-performance responses that meet specific task demands becomes a pressing challenge. This paper provides a comprehensive overview of the LLM services invocation methods. Technically, we give a formal definition of the problem of constructing effective invocation strategy in LMaaS and present the LLM services invocation framework. The framework classifies existing methods into four different components, including input abstract, semantic cache, solution design, and output enhancement, which can be freely combined with each other. Finally, we emphasize the open challenges that have not yet been well addressed in this task and shed light on future research.


Modified K-means with Cluster Assignment -- Application to COVID-19 Data

Abstract: Text extraction is a highly subjective problem which depends on the dataset that one is working on and the kind of summarization details that needs to be extracted out. All the steps ranging from preprocessing of the data, to the choice of an optimal model for predictions, depends on the problem and the corpus at hand. In this paper, we describe a text extraction model where the aim is to extract word specified information relating to the semantics such that we can get all related and meaningful information about that word in a succinct format. This model can obtain meaningful results and can augment ubiquitous search model or a normal clustering or topic modelling algorithms. By utilizing new technique called two cluster assignment technique with K-means model, we improved the ontology of the retrieved text. We further apply the vector average damping technique for flexible movement of clusters. Our experimental results on a recent corpus of Covid-19 shows that we obtain good results based on main keywords.


One-shot Neural Face Reenactment via Finding Directions in GAN's Latent  Space

Abstract: In this paper, we present our framework for neural face/head reenactment whose goal is to transfer the 3D head orientation and expression of a target face to a source face. Previous methods focus on learning embedding networks for identity and head pose/expression disentanglement which proves to be a rather hard task, degrading the quality of the generated images. We take a different approach, bypassing the training of such networks, by using (fine-tuned) pre-trained GANs which have been shown capable of producing high-quality facial images. Because GANs are characterized by weak controllability, the core of our approach is a method to discover which directions in latent GAN space are responsible for controlling head pose and expression variations. We present a simple pipeline to learn such directions with the aid of a 3D shape model which, by construction, inherently captures disentangled directions for head pose, identity, and expression. Moreover, we show that by embedding real images in the GAN latent space, our method can be successfully used for the reenactment of real-world faces. Our method features several favorable properties including using a single source image (one-shot) and enabling cross-person reenactment. Extensive qualitative and quantitative results show that our approach typically produces reenacted faces of notably higher quality than those produced by state-of-the-art methods for the standard benchmarks of VoxCeleb1 & 2.


Continual Domain Adversarial Adaptation via Double-Head Discriminators

Abstract: Domain adversarial adaptation in a continual setting poses a significant challenge due to the limitations on accessing previous source domain data. Despite extensive research in continual learning, the task of adversarial adaptation cannot be effectively accomplished using only a small number of stored source domain data, which is a standard setting in memory replay approaches. This limitation arises from the erroneous empirical estimation of $\gH$-divergence with few source domain samples. To tackle this problem, we propose a double-head discriminator algorithm, by introducing an addition source-only domain discriminator that are trained solely on source learning phase. We prove that with the introduction of a pre-trained source-only domain discriminator, the empirical estimation error of $\gH$-divergence related adversarial loss is reduced from the source domain side. Further experiments on existing domain adaptation benchmark show that our proposed algorithm achieves more than 2$\%$ improvement on all categories of target domain adaptation task while significantly mitigating the forgetting on source domain.


A Survey of Privacy Threats and Defense in Vertical Federated Learning:  From Model Life Cycle Perspective

Abstract: Vertical Federated Learning (VFL) is a federated learning paradigm where multiple participants, who share the same set of samples but hold different features, jointly train machine learning models. Although VFL enables collaborative machine learning without sharing raw data, it is still susceptible to various privacy threats. In this paper, we conduct the first comprehensive survey of the state-of-the-art in privacy attacks and defenses in VFL. We provide taxonomies for both attacks and defenses, based on their characterizations, and discuss open challenges and future research directions. Specifically, our discussion is structured around the model's life cycle, by delving into the privacy threats encountered during different stages of machine learning and their corresponding countermeasures. This survey not only serves as a resource for the research community but also offers clear guidance and actionable insights for practitioners to safeguard data privacy throughout the model's life cycle.


Sentiment-enhanced Graph-based Sarcasm Explanation in Dialogue

Abstract: Sarcasm Explanation in Dialogue (SED) is a new yet challenging task, which aims to generate a natural language explanation for the given sarcastic dialogue that involves multiple modalities (i.e., utterance, video, and audio). Although existing studies have achieved great success based on the generative pretrained language model BART, they overlook exploiting the sentiments residing in the utterance, video and audio, which are vital clues for sarcasm explanation. In fact, it is non-trivial to incorporate sentiments for boosting SED performance, due to three main challenges: 1) diverse effects of utterance tokens on sentiments; 2) gap between video-audio sentiment signals and the embedding space of BART; and 3) various relations among utterances, utterance sentiments, and video-audio sentiments. To tackle these challenges, we propose a novel sEntiment-enhanceD Graph-based multimodal sarcasm Explanation framework, named EDGE. In particular, we first propose a lexicon-guided utterance sentiment inference module, where a heuristic utterance sentiment refinement strategy is devised. We then develop a module named Joint Cross Attention-based Sentiment Inference (JCA-SI) by extending the multimodal sentiment analysis model JCA to derive the joint sentiment label for each video-audio clip. Thereafter, we devise a context-sentiment graph to comprehensively model the semantic relations among the utterances, utterance sentiments, and video-audio sentiments, to facilitate sarcasm explanation generation. Extensive experiments on the publicly released dataset WITS verify the superiority of our model over cutting-edge methods.


Expressivity of Geometric Inhomogeneous Random Graphs -- Metric and  Non-Metric

Abstract: Recently there has been increased interest in fitting generative graph models to real-world networks. In particular, Bl\"asius et al. have proposed a framework for systematic evaluation of the expressivity of random graph models. We extend this framework to Geometric Inhomogeneous Random Graphs (GIRGs). This includes a family of graphs induced by non-metric distance functions which allow capturing more complex models of partial similarity between nodes as a basis of connection - as well as homogeneous and non-homogeneous feature spaces. As part of the extension, we develop schemes for estimating the multiplicative constant and the long-range parameter in the connection probability. Moreover, we devise an algorithm for sampling Minimum-Component-Distance GIRGs whose runtime is linear both in the number of vertices and in the dimension of the underlying geometric space. Our results provide evidence that GIRGs are more realistic candidates with respect to various graph features such as closeness centrality, betweenness centrality, local clustering coefficient, and graph effective diameter, while they face difficulties to replicate higher variance and more extreme values of graph statistics observed in real-world networks.


Deep MSFOP: Multiple Spectral filter Operators Preservation in Deep  Functional Maps for Unsupervised Shape Matching

Abstract: We propose a novel constraint called Multiple Spectral filter Operators Preservation (MSFOR) to compute functional maps and based on it, develop an efficient deep functional map architecture called Deep MSFOP for shape matching. The core idea is that, instead of using the general descriptor preservation constraint, we require our maps to preserve multiple spectral filter operators. This allows us to incorporate more informative geometrical information, contained in different frequency bands of functions, into the functional map computing. This can be confirmed by that some previous techniques like wavelet preservation and LBO commutativity are actually our special cases. Moreover, we also develop a very efficient way to compute the maps with MSFOP constraint, which can be conveniently embedded into the deep learning, especially having learnable filter operators. Utilizing the above results, we finally design our Deep MSFOP pipeline, equipped with a suitable unsupervised loss jointly penalizing the functional map and the underlying pointwise map. Our deep functional map has notable advantages, including that the functional map is more geometrically informative and guaranteed to be proper, and the computing is numerically stable. Extensive experimental results on different datasets demonstrate that our approach outperforms the existing state-of-the-art methods, especially in challenging settings like non-isometric and inconsistent topology datasets.


Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation

Abstract: In recent advancements in medical image analysis, Convolutional Neural Networks (CNN) and Vision Transformers (ViT) have set significant benchmarks. While the former excels in capturing local features through its convolution operations, the latter achieves remarkable global context understanding by leveraging self-attention mechanisms. However, both architectures exhibit limitations in efficiently modeling long-range dependencies within medical images, which is a critical aspect for precise segmentation. Inspired by the Mamba architecture, known for its proficiency in handling long sequences and global contextual information with enhanced computational efficiency as a State Space Model (SSM), we propose Mamba-UNet, a novel architecture that synergizes the U-Net in medical image segmentation with Mamba's capability. Mamba-UNet adopts a pure Visual Mamba (VMamba)-based encoder-decoder structure, infused with skip connections to preserve spatial information across different scales of the network. This design facilitates a comprehensive feature learning process, capturing intricate details and broader semantic contexts within medical images. We introduce a novel integration mechanism within the VMamba blocks to ensure seamless connectivity and information flow between the encoder and decoder paths, enhancing the segmentation performance. We conducted experiments on publicly available MRI cardiac multi-structures segmentation dataset. The results show that Mamba-UNet outperforms UNet, Swin-UNet in medical image segmentation under the same hyper-parameter setting. The source code and baseline implementations are available.


Democratizing Large Language Models via Personalized Parameter-Efficient  Fine-tuning

Abstract: Personalization in large language models (LLMs) is increasingly important, aiming to align LLM's interactions, content, and recommendations with individual user preferences. Recent advances in LLM personalization have spotlighted effective prompt design, by enriching user queries with non-parametric knowledge through behavior history retrieval and textual profiles. However, these approaches were limited due to a lack of model ownership, resulting in constrained customization and privacy issues. Moreover, they often failed to accurately capture user behavior patterns, especially in cases where user data were complex and dynamic. To address these shortcomings, we introduce One PEFT Per User (OPPU), which employs personalized parameter-efficient fine-tuning (PEFT) modules, to store user-specific behavior patterns and preferences. By plugging in users' personal PEFT parameters, they can own and use their LLMs personally. OPPU integrates parametric user knowledge in the personal PEFT parameters with the non-parametric knowledge acquired through retrieval and profile. This integration adapts individual LLMs to user behavior shifts. Experimental results demonstrate that OPPU significantly outperforms existing prompt-based methods across seven diverse tasks in the LaMP benchmark. Further in-depth studies reveal OPPU's enhanced capabilities in handling user behavior shifts, modeling users at different active levels, maintaining robustness across various user history formats, and displaying versatility with different PEFT methods.


De-amplifying Bias from Differential Privacy in Language Model  Fine-tuning

Abstract: Fairness and privacy are two important values machine learning (ML) practitioners often seek to operationalize in models. Fairness aims to reduce model bias for social/demographic sub-groups. Privacy via differential privacy (DP) mechanisms, on the other hand, limits the impact of any individual's training data on the resulting model. The trade-offs between privacy and fairness goals of trustworthy ML pose a challenge to those wishing to address both. We show that DP amplifies gender, racial, and religious bias when fine-tuning large language models (LLMs), producing models more biased than ones fine-tuned without DP. We find the cause of the amplification to be a disparity in convergence of gradients across sub-groups. Through the case of binary gender bias, we demonstrate that Counterfactual Data Augmentation (CDA), a known method for addressing bias, also mitigates bias amplification by DP. As a consequence, DP and CDA together can be used to fine-tune models while maintaining both fairness and privacy.


V2VSSC: A 3D Semantic Scene Completion Benchmark for Perception with  Vehicle to Vehicle Communication

Abstract: Semantic scene completion (SSC) has recently gained popularity because it can provide both semantic and geometric information that can be used directly for autonomous vehicle navigation. However, there are still challenges to overcome. SSC is often hampered by occlusion and short-range perception due to sensor limitations, which can pose safety risks. This paper proposes a fundamental solution to this problem by leveraging vehicle-to-vehicle (V2V) communication. We propose the first generalized collaborative SSC framework that allows autonomous vehicles to share sensing information from different sensor views to jointly perform SSC tasks. To validate the proposed framework, we further build V2VSSC, the first V2V SSC benchmark, on top of the large-scale V2V perception dataset OPV2V. Extensive experiments demonstrate that by leveraging V2V communication, the SSC performance can be increased by 8.3% on geometric metric IoU and 6.0% mIOU.


Open-Vocabulary Calibration for Vision-Language Models

Abstract: Vision-language models (VLMs) have emerged as formidable tools, showing their strong capability in handling various open-vocabulary tasks in image recognition, text-driven visual content generation, and visual chatbots, to name a few. In recent years, considerable efforts and resources have been devoted to adaptation methods for improving downstream performance of VLMs, particularly on parameter-efficient fine-tuning methods like prompt learning. However, a crucial aspect that has been largely overlooked is the confidence calibration problem in fine-tuned VLMs, which could greatly reduce reliability when deploying such models in the real world. This paper bridges the gap by systematically investigating the confidence calibration problem in the context of prompt learning and reveals that existing calibration methods are insufficient to address the problem, especially in the open-vocabulary setting. To solve the problem, we present a simple and effective approach called Distance-Aware Calibration (DAC), which is based on scaling the temperature using as guidance the distance between predicted text labels and base classes. The experiments with 7 distinct prompt learning methods applied across 11 diverse downstream datasets demonstrate the effectiveness of DAC, which achieves high efficacy without sacrificing the inference speed.


Mesh-based Gaussian Splatting for Real-time Large-scale Deformation

Abstract: Neural implicit representations, including Neural Distance Fields and Neural Radiance Fields, have demonstrated significant capabilities for reconstructing surfaces with complicated geometry and topology, and generating novel views of a scene. Nevertheless, it is challenging for users to directly deform or manipulate these implicit representations with large deformations in the real-time fashion. Gaussian Splatting(GS) has recently become a promising method with explicit geometry for representing static scenes and facilitating high-quality and real-time synthesis of novel views. However,it cannot be easily deformed due to the use of discrete Gaussians and lack of explicit topology. To address this, we develop a novel GS-based method that enables interactive deformation. Our key idea is to design an innovative mesh-based GS representation, which is integrated into Gaussian learning and manipulation. 3D Gaussians are defined over an explicit mesh, and they are bound with each other: the rendering of 3D Gaussians guides the mesh face split for adaptive refinement, and the mesh face split directs the splitting of 3D Gaussians. Moreover, the explicit mesh constraints help regularize the Gaussian distribution, suppressing poor-quality Gaussians(e.g. misaligned Gaussians,long-narrow shaped Gaussians), thus enhancing visual quality and avoiding artifacts during deformation. Based on this representation, we further introduce a large-scale Gaussian deformation technique to enable deformable GS, which alters the parameters of 3D Gaussians according to the manipulation of the associated mesh. Our method benefits from existing mesh deformation datasets for more realistic data-driven Gaussian deformation. Extensive experiments show that our approach achieves high-quality reconstruction and effective deformation, while maintaining the promising rendering results at a high frame rate(65 FPS on average).


Experimental implementation of quantum-walk-based portfolio optimisation

Abstract: Abstract The application of quantum algorithms has attracted much attention as it holds the promise of solving practical problems that are intractable to classical algorithms. One such application is the recent development of a quantum-walk-based optimisation algorithm approach to portfolio optimisation under the modern portfolio theory framework. In this paper, we demonstrate an experimental realisation of the alternating phase-shift and continuous-time quantum walk unitaries that underpin this quantum algorithm using optical networks and single photons. The experimental analysis conﬁrms that the probability of states corresponding to high-quality solutions is eﬃciently ampliﬁed by increasing the number of phase-shift and quantum walk iterations. This work provides strong evidence for practical applications of quantum-walk-based algorithms such as ﬁnancial portfolio optimisation.&#xD;


Cluster-weighted modeling with measurement error in covariates

Abstract: The cluster-weighted model (CWM) is a model-based clustering approach that utilizes a mixture of regression models to cluster data points based on both a response variable Y and covariates 𝑿, where the covariates are assumed to be random. The Gaussian CWM (GCWM) is the most commonly used member of the CWM family, where the Gaussian distribution is adopted for both the covariates and the response given the covariates. In mixture of regression, assignment of data points to the clusters is based on the conditional distribution of the response variable given covariates and is independent of the covariates’ distribution. In CWM, to increase clustering performance, the covariates’ distribution is also used to assign data points to the clusters. Existing researches on CWMs are limited to the directly observed covariates, which may not reflect real-world scenarios where measurement errors (MEs) occur. The measurement error can lead to inconsistent estimates, consequently, produce spurious or obscure clusters. In this article, we assume that random covariates 𝑿 are latent, observed with an independent ME that has the Gaussian distribution. A new generalized expectation maximization algorithm is defined for estimating model parameters. The performance of the proposal is illustrated and compared with the GCWM using both simulated and real data.


Use of Chat GPT in English for Engineering Classes: Are Students’ and Teachers’ Views on Its Opportunities and Challenges Similar?

Abstract: Mobile technologies have become increasingly important in the field of education, providing innovative ways to engage students and enrich their learning experiences. ChatGPT is an innovative tool that can be used in English for Specific Purposes (ESP) classes to engage students, enhance their knowledge and skills, and add variety and interest to the traditional process of learning engineering English, whether accessed through computers or mobile devices. In light of this, the study aims to investigate and compare the attitudes of technical university students and teachers towards using ChatGPT in English classes, as well as their perspectives on academic integrity issues and the strategies they employ when using ChatGPT. The study involved 22 teachers and 60 students from a Ukrainian university. Quantitative research, which involved collecting data through surveys, showed that students exhibited positive attitudes, while teachers took a more neutral approach. Both groups agree that ChatGPT plays a supplementary role in enhancing both hard and soft skills, as well as English language communication skills. However, they emphasized its impact on the development of research skills. The study highlights critical issues related to academic integrity, specifically the differing perceptions of possible violations between students and teachers. The authors present ideas on integrating ChatGPT into ESP classes, enabling teachers to leverage artificial intelligence (AI) tools for language learning and adapt to changing educational landscapes. The study emphasizes the importance of integrating ChatGPT effectively into engineering English classes to support students’ learning. While emphasizing the importance of guidelines for academic integrity, it also advocates for the creative use of AI tools by teachers and students. Addressing the disparity in attitudes suggests providing teacher professional development to ensure proficient use of AI tools.


Education and Technology: Construction of a Distance Education System Beyond the COVID-19 Crisis

Abstract: The COVID-19 pandemic has revealed a multidimensional problem. Added to the health crisis was the lack of social interaction, which directly affected education. Therefore, a number of strategies were required to compensate for the lack of face-to-face learning, considering the fact that most institutions had regulations and practices of that kind. This experience has established the need to develop a systemic approach, in which social interaction mediated by technology is clearly promoted. This work seeks to establish the general lines of future distance education models, considering the adaptations and opportunities that derive from it.


History of generative Artificial Intelligence (AI) chatbots: past,  present, and future development

Abstract: This research provides an in-depth comprehensive review of the progress of chatbot technology over time, from the initial basic systems relying on rules to today's advanced conversational bots powered by artificial intelligence. Spanning many decades, the paper explores the major milestones, innovations, and paradigm shifts that have driven the evolution of chatbots. Looking back at the very basic statistical model in 1906 via the early chatbots, such as ELIZA and ALICE in the 1960s and 1970s, the study traces key innovations leading to today's advanced conversational agents, such as ChatGPT and Google Bard. The study synthesizes insights from academic literature and industry sources to highlight crucial milestones, including the introduction of Turing tests, influential projects such as CALO, and recent transformer-based models. Tracing the path forward, the paper highlights how natural language processing and machine learning have been integrated into modern chatbots for more sophisticated capabilities. This chronological survey of the chatbot landscape provides a holistic reference to understand the technological and historical factors propelling conversational AI. By synthesizing learnings from this historical analysis, the research offers important context about the developmental trajectory of chatbots and their immense future potential across various field of application which could be the potential take ways for the respective research community and stakeholders.


Leveraging AI for Enhanced Software Effort Estimation: A Comprehensive  Study and Framework Proposal

Abstract: This paper presents an extensive study on the application of AI techniques for software effort estimation in the past five years from 2017 to 2023. By overcoming the limitations of traditional methods, the study aims to improve accuracy and reliability. Through performance evaluation and comparison with diverse Machine Learning models, including Artificial Neural Network (ANN), Support Vector Machine (SVM), Linear Regression, Random Forest and other techniques, the most effective method is identified. The proposed AI-based framework holds the potential to enhance project planning and resource allocation, contributing to the research area of software project effort estimation.


Improving Token-Based World Models with Parallel Observation Prediction

Abstract: Motivated by the success of Transformers when applied to sequences of discrete symbols, token-based world models (TBWMs) were recently proposed as sample-efficient methods. In TBWMs, the world model consumes agent experience as a language-like sequence of tokens, where each observation constitutes a sub-sequence. However, during imagination, the sequential token-by-token generation of next observations results in a severe bottleneck, leading to long training times, poor GPU utilization, and limited representations. To resolve this bottleneck, we devise a novel Parallel Observation Prediction (POP) mechanism. POP augments a Retentive Network (RetNet) with a novel forward mode tailored to our reinforcement learning setting. We incorporate POP in a novel TBWM agent named REM (Retentive Environment Model), showcasing a 15.4x faster imagination compared to prior TBWMs. REM attains superhuman performance on 12 out of 26 games of the Atari 100K benchmark, while training in less than 12 hours. Our code is available at \url{https://github.com/leor-c/REM}.


Denoising CSAMT signals in the time domain base on long short-term memory

Abstract: Abstract Controlled-Source Audio Frequency Magnetotellurics (CSAMT) is an artificial-source electromagnetic technique that partially mitigates the limitations of weak natural field signals. However, practical field surveys inevitably encounter strong interference, severely affecting signal quality. Traditional methods like Fourier transformation, which directly computes apparent resistivity from frequency-domain information, are inadequate for this context, so we need alternative denoising approaches. However, research on CSAMT denoising is currently limited. Given the excellent performance of Long Short-Term Memory (LSTM) neural networks in the processing of Magnetotelluric (MT) data, as demonstrated by previous studies, this paper proposes the use of LSTM to denoise CSAMT signals in the time domain. Unlike traditional denoising methods, we aim to directly extract the target frequency signal from the time series data for denoising. For MT data, target frequency signals and noise are all mixed together, so noise suppression can only be achieved by identifying noise characteristics in the time series. However, unlike MT data, CSAMT data has an artificial transmitting source, and the frequency of the valid signal is fixed within a time interval. This allows for the direct extraction of target frequency signals without considering the complex characteristics of noise. In this study, we developed a neural network based on bidirectional LSTM to accomplish the task of noise suppression. After conducting both simulated and measured data tests, this method was able to, on average, improve the signal-to-noise ratio (SNR) of CSAMT data by approximately 20dB and partially address the challenge of denoising when the data's SNR falls below 0dB.


A Conceptual Instructional Design Model for Executive Education

Abstract: This paper presents a conceptual instructional design (ID) model for executive education derived from business school good practice and education theory. A review of executive education literature finds that today’s executive education offerings are required to be real-world oriented, closely aligned with the demands of the post-pandemic workplace, attuned to the changing needs and circumstances of adult learners and sufficiently able to deliver immersive learning. A reconnaissance investigation of 14 top-ranked business schools identifies the most frequently adopted teaching and learning approaches in executive education considered as good practice for the sector. These include experiential learning, collaborative learning, and technology-enabled learning. The proposed conceptual instructional design model is rooted in these approaches alongside three key principles that underpin experiential learning, namely authenticity, reflection and collaboration. Moreover the model proposes ADDIE supported teaching approaches to ensure the realisation of these principles within an appropriate constructive learning environment (CLE). Informed by activity theory, an ‘activity system’ is presented as the outer shell of the model to ensure that an executive education-oriented CLE is informed by learner needs and authentic context.


Analysis of Social Interaction of Deaf Children in Special Schools

Abstract: Deaf is a term used to describe the symptoms and symptoms of someone who is hearing impaired or deaf. Deaf children interact through a variety of methods, including sign language, facial expressions, and body, as well as the use of hearing aid technology. They can also utilize visual communication approaches, peer support, and participation in group activities to strengthen social skills. Social interaction is a dynamic in which individuals or groups communicate, act, or influence each other in a particular environment or situation. Social interaction involves the exchange of information, emotions, ideas, or behaviors between individuals or groups. This research makes a valuable contribution to improving our understanding of the benefits of social interaction for deaf children in Special Schools, opening up opportunities for the development of more inclusive educational strategies and supporting their holistic development. This study aimed to analyze social interactions between deaf children. An observational approach and interview, this study explores the dynamics of communication, information exchange, and emotional understanding in interactions between both groups of children. Qualitative analysis methods are used to identify unique communication patterns and understand the challenges faced by deaf children in communicating. The subjects in this study were interviewing homeroom teachers and counseling teachers at the school, the number of subjects to be studied were children with deafness. The results highlight adaptation efforts and communication strategies developed by deaf children to interact. The results of these findings can provide valuable insights for educators, parents, and health practitioners in supporting the development of social skills of deaf children so that they can engage more effectively in society.


Research on Chinese Animal Disease Text Named Entity Recognition Based on Data Augmentation

Abstract: Animal disease-related information serves as an important reference. This paper focuses on the study of Named Entity Recognition (NER) in animal disease texts using a dataset constructed from animal disease books. However, the dataset exhibits imbalanced distribution of different types of entities, which affects the learning effectiveness of the models. In this method, firstly, a small amount of data was enhanced based on the template data augmentation method, and then the overall data was enhanced based on the improved EDA method. On the enhanced dataset, the accuracy of NER based on the BiLSTM network and CRF model increased by 3%. The recall rate saw a significant improvement of 17%, and the F1 value increased by 10%. The experimental findings suggest that the data augmentation technique presented in this paper can improve the model’s learning effectiveness on the dataset related to livestock and poultry diseases.


AI Assistance for UX: A Literature Review Through Human-Centered AI

Abstract: Recent advancements in HCI and AI research attempt to support user experience (UX) practitioners with AI-enabled tools. Despite the potential of emerging models and new interaction mechanisms, mainstream adoption of such tools remains limited. We took the lens of Human-Centered AI and presented a systematic literature review of 359 papers, aiming to synthesize the current landscape, identify trends, and uncover UX practitioners' unmet needs in AI support. Guided by the Double Diamond design framework, our analysis uncovered that UX practitioners' unique focuses on empathy building and experiences across UI screens are often overlooked. Simplistic AI automation can obstruct the valuable empathy-building process. Furthermore, focusing solely on individual UI screens without considering interactions and user flows reduces the system's practical value for UX designers. Based on these findings, we call for a deeper understanding of UX mindsets and more designer-centric datasets and evaluation metrics, for HCI and AI communities to collaboratively work toward effective AI support for UX.


Learn To be Efficient: Build Structured Sparsity in Large Language  Models

Abstract: Large Language Models (LLMs) have achieved remarkable success with their billion-level parameters, yet they incur high inference overheads. The emergence of activation sparsity in LLMs provides a natural approach to reduce this cost by involving only parts of the parameters for inference. Existing methods only focus on utilizing this naturally formed activation sparsity, overlooking the potential for further amplifying this inherent sparsity. In this paper, we hypothesize that LLMs can learn to be efficient by achieving more structured activation sparsity.To achieve this, we introduce a novel algorithm, Learn-To-be-Efficient (LTE), designed to train efficiency-aware LLMs to learn to activate fewer neurons and achieve a better trade-off between sparsity and performance. Furthermore, unlike SOTA MoEfication methods, which mainly focus on ReLU-based models, LTE can also be applied to LLMs like GPT and LLaMA with soft activation functions. We evaluate LTE on four models and eleven datasets. The experiments show that LTE achieves a better trade-off between sparsity and task performance. For instance, LTE with LLaMA provides a 1.83x-2.59x FLOPs speed-up on language generation tasks, outperforming the state-of-the-art methods.


Determining the upper bound of code distance of quantum stabilizer codes  through Monte Carlo method based on fully decoupled belief propagation

Abstract: Code distance is an important parameter for quantum stabilizer codes (QSCs). Directly precisely computing it is an NP-complete problem. However, the upper bound of code distance can be computed by some efficient methods. In this paper, employing the idea of Monte Carlo method, we propose the algorithm of determining the upper bound of code distance of QSCs based on fully decoupled belief propagation. Our algorithm shows high precision - the upper bound of code distance determined by the algorithm of a variety of QSCs whose code distance is known is consistent with actual code distance. Besides, we explore the upper bound of logical X operators of Z-type Tanner-graph-recursive-expansion (Z-TGRE) code and Chamon code, which is a kind of XYZ product code constructed by three repetition codes. The former is consistent with the theoretical analysis, and the latter implies the code distance of XYZ product codes can very likely achieve $O(N^{2/3})$, which supports the conjecture of Leverrier et al..