Reviewerly/exHarmony huggingface dataset:
Secure Digital Signature Using Block Chain Technology

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

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

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&amp;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

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

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

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

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

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

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

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

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 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

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 &amp; Harding, 2017; Isaacs &amp; 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: 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

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

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

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

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

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 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}