Reviewerly/exHarmony huggingface dataset:
Institutional Redesign and Dynamics of Collaborative Ties in the Educational Ecosystem

Authors:
raw_author_name: Diana Koroleva
raw_affiliation_string: Higher School of Economics
raw_author_name: Daria Tomasova
raw_affiliation_string: Higher School of Economics

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.&#x0D; 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.&#x0D; 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

Authors:
raw_author_name: Haitao Sang
raw_author_name: Bo Chen
raw_author_name: Jing Cai

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

Authors:
raw_author_name: Anand Jayant kulkarni
raw_affiliation_string: Dr Vishwanath Karad MIT World Peace University
raw_author_name: Ishaan Kale
raw_affiliation_string: Dr Vishwanath Karad MIT World Peace University
raw_author_name: Apoorva Shastri
raw_affiliation_string: Dr Vishwanath Karad MIT World Peace University
raw_author_name: Aayush Khandekar
raw_affiliation_string: Stevens Institute of Technology

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

Authors:
raw_author_name: Ririn Restu Aria
raw_affiliation_string: Universitas Bina Sarana Informatika

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.
Secure Digital Signature Using Block Chain Technology

Authors:
raw_author_name: Bhagappa .
raw_affiliation_string: Brindavan College of Engineering, Bengaluru, Karnataka, India
raw_author_name: Suhas G K
raw_affiliation_string: Akshaya Institute of Technology, Affiliated to Visvesvaraya Technological University, Tumakuru, Karnataka, India

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

Authors:
raw_author_name: Samara M. Ahmed
raw_affiliation_string: Department of English, College of Education for Humanities, University of Tikrit

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

Authors:
raw_author_name: Hewei Yu
raw_affiliation_string: South China University of Technology, School of Computer Science and Engineering, Guangzhou, China
raw_author_name: Jingxi Yu
raw_affiliation_string: Arizona State University, Ira A. Fulton Schools of Engineering, Tempe, Arizona, United States (United States)

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

Authors:
raw_author_name: David Torpey
raw_affiliation_string: University of the Witwatersrand, School of Computer Science and Applied Mathematics, Johannesburg, South Africa (South Africa)
raw_author_name: Richard Klein
raw_affiliation_string: University of the Witwatersrand, School of Computer Science and Applied Mathematics, Johannesburg, South Africa (South Africa)
raw_author_name: Steven James
raw_affiliation_string: University of the Witwatersrand, School of Computer Science and Applied Mathematics, Johannesburg, South Africa (South Africa)

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

Authors:
raw_author_name: Yuan Liang
raw_affiliation_string: Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China; Suqian University, Suqian 223800, China

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

Authors:
raw_author_name: Martin Fenner

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.
Bruker to buy robotics firm Chemspeed

Authors:
raw_author_name: None Michael McCoy

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

Authors:
raw_author_name: M. Hemavathy

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

Authors:
raw_author_name: De Rosa, Antonio

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

Authors:
raw_author_name: Seo, Jungwon

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

Authors:
raw_author_name: van der Meer, Michiel
raw_author_name: Vossen, Piek
raw_author_name: Jonker, Catholijn M.
raw_author_name: Murukannaiah, Pradeep K.

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

Authors:
raw_author_name: None Archana J
raw_affiliation_string: Global Academy of Technology, Bangalore, Karnataka, India

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

Authors:
raw_author_name: Enze Yang
raw_affiliation_string: China Academy of Railway Sciences Corporation Limited
raw_author_name: Yuxin Liu
raw_affiliation_string: China Academy of Railway Sciences Corporation Limited
raw_author_name: Shitao Zhao
raw_affiliation_string: China Academy of Railway Sciences Corporation Limited
raw_author_name: Yiran Liu
raw_affiliation_string: China Academy of Railway Sciences Corporation Limited
raw_author_name: Shuoyan Liu
raw_affiliation_string: China Academy of Railway Sciences Corporation Limited

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}