Reviewerly/exHarmony huggingface dataset:
MyResearchChallenge.digital - A Participatory Problem Discovery System for Citizen Design Science Research

To solve societal problems, it is essential to engage with actual problem owners in society in a scalable way. In this paper, we follow a Citizen Design Science Research (CDSR) paradigm proposing to more actively involve society in DSR projects. Specifically, we present MyResearchChallenge.digital, a prototypical system that supports participatory problem discovery by enabling the involvement of citizens in problem awareness in a scalable way. The system allows researchers and citizens to cooperate on the exploration of a given problem space, leveraging the creativity and wisdom of the crowd for identifying and describing relevant DSR problems. The evaluation of the prototype with 30 representative citizens points to the system’s strengths and opportunities related to its ease of use and the problem articulation feature but also reveals weaknesses and threats concerning the problem exploration features and issues about platform abuse.
Generating Classified Ad Product Image Titles with Image Captioning

Image captioning is a field of study to describe images by automatically generating semantic sentences in accordance with natural language rules. In recent years, studies in the literature of challenging problem of image captioning have gained momentum with the advances in deep learning. The reason why these studies are challenging is they use both image processing and natural language processing methods together. The subject of this study is generating the most suitable title for the product images in the shopping category of classified advertising by using state of the art deep learning image captioning methods. This is the first study which generates titles to classified ad product images in Turkish in the literature. The results of the experimental study, conducted with approximately 1.8 million images and titles on the sahibinden.com website, are fairly successful for describing product images.
Building a Language Data Set in Telugu Using Machine Learning Techniques to Address Suicidal Ideation and Behaviors in Adolescents

Taking one’s own life is a tragic reaction to stressful situations in life. There is a noticeable increase in the ratio of number of suicides every year in Telangana [1]. Most of them are adolescents and youngsters and others too. So there is an urging need of research to be done on suicidal ideation and preventive methods to support mental health professionals and psychotherapists. So this paper aims in developing technological solutions to the problem. Suicides can be prevented if we could identify the mental health conditions of a person with ideations and predict the severity in earlier [2]. So in this paper, we applied machine learning algorithms to categorize persons with suicidal ideations from the data that is maintained or recorded during visit of an adolescent with a mental health professional in textual form of questionnaires. The data is recorded in native Telugu language during the session, as most of cases are from illiterates [1, 3]. So in order to classify the patient test data with more accuracy, there is a need of language corpus in Telugu with ideations. So this paper would give a great insight into creation of suicidal language or ideation corpora in native language Telugu.
Search Heuristics and Constructive Algorithms for Maximally Idempotent Integers

Previous work established the set of square-free integers n with at least one factorization n=p¯q¯ for which p¯ and q¯ are valid RSA keys, whether they are prime or composite. These integers are exactly those with the property λ(n)∣(p¯−1)(q¯−1), where λ is the Carmichael totient function. We refer to these integers as idempotent, because ∀a∈Zn,ak(p¯−1)(q¯−1)+1≡na for any positive integer k. This set was initially known to contain only the semiprimes, and later expanded to include some of the Carmichael numbers. Recent work by the author gave the explicit formulation for the set, showing that the set includes numbers that are neither semiprimes nor Carmichael numbers. Numbers in this last category had not been previously analyzed in the literature. While only the semiprimes have useful cryptographic properties, idempotent integers are deserving of study in their own right as they lie at the border of hard problems in number theory and computer science. Some idempotent integers, the maximally idempotent integers, have the property that all their factorizations are idempotent. We discuss their structure here, heuristics to assist in finding them, and algorithms from graph theory that can be used to construct examples of arbitrary size.
Adversarial Defenses on Graphs: Towards Increasing the Robustness of Algorithms

Graph Neural Networks (GNNs) have achieved tremendous development on perceptual tasks in recent years, such as node classification, graph classification, link prediction, community detection and so on. However, recent studies show that GNNs are incredibly vulnerable to adversarial attacks, so enhancing the robustness of such models remains a significant challenge. This chapter will introduce some of the latest typical defensive measures on graph-structured data to defend against malicious attacks. More specifically, we will elaborate on the existing defensive work from the following five categories: adversarial training, graph purification, certifiable robustness, attention mechanism, and adversarial detection. We will also make a comparison and summary of this existing defense methods. Different kinds of defense methods have different application scenarios and corresponding limitations.
Analysis of Spurious Local Solutions of Optimal Control Problems: One-Shot Optimization Versus Dynamic Programming

Dynamic programming (DP) has a rich theoretical foundation and a broad range of applications, especially in the classic area of optimal control and the recent area of reinforcement learning (RL). Many optimal control problems can be solved as a single optimization problem, named one-shot optimization, or via a sequence of optimization problems using DP. However, the computation of their global optima often faces the NP-hardness issue due to the non-linearity of the dynamics and non-convexity of the cost, and thus only local optimal solutions may be obtained at best. Furthermore, in many cases arising in machine learning and model-free approaches, DP is the only viable choice, and therefore it is essential to understand when DP combined with a local search solver works. In this work, we introduce the notions of spurious local minimizers for the one-shot optimization and spurious local minimum policies for DP, and show that there is a deep connection between them. In particular, we prove that under mild conditions the DP method using local search can successfully solve the optimal control problem to global optimality if and only if the one-shot optimization is free of spurious solutions. This result paves the way to understand the performance of local search methods in optimal control and RL.
Further Non-subjective Priors for Wrapped Cauchy Distributions

Wrapped Cauchy distributions are widely used for the analysis of circular data. In an earlier article, Ghosh et al. (2019), derived reference priors and quantile matching priors for parameters of wrapped Cauchy distributions. The matching priors are designed to achieve Bayes–frequentist synthesis by providing asymptotic equivalence of the coverage probabilities of Bayesian credible intervals and frequentist confidence intervals. The present article explores the matching idea further by deriving highest posterior density matching priors as well as matching priors associated with likelihood ratio statistics. In fact, the latter is related to a very important work of Professor C.R. Rao joint with Professor Rahul Mukerjee.
Understanding the Blockchain Ecosystem with Analysis of Decentralized Applications: an Empirical Study

Blockchain technology is characterized by decentralization, traceability and tamper resistance. A decentralized application (Dapp) refers to a blockchain-based application deployed on Ethereum platform, with the aim to facilitate decentralized smart contracts and to prosper the blockchain ecosystem. In this paper, an in-depth analysis for the current decentralized applications is performed to reveal the structure and development situation of blockchain ecosystem. With a better understanding of the blockchain ecosystem, insights can be obtained for the Dapp developers as well as for the potential blockchain adopters. Furthermore, guidelines and suggestions are provided to inspire the business applications based on blockchain.
Using Block-Matching Methods to Help Navigating Visually Impaired People using Raspberry PI Platform

The paper analyses the influence of various parameters in the detection and recognition of traffic signs, to help navigating people with visual impairments. We used the Raspberry PI 4 model B + platform and functions from the OpenCV library to reach our objective. 3 template matching functions from OpenCV were compared and we used the Normalized Cross Correlation (NCC or CCORR), Normalized Inter-correlation (NIC or CCOEFF) and Sum of Squared Differences (SSD or SQDIFF) as matching metrics from the class of intensity-based methods for template matching. The goal was to identify what method gives the best results for the images with traffic signs taken in the evening.
Private Aggregation with Application to Distributed Optimization

The paper presents a fully distributed private aggregation protocol that can be employed in dynamical networks where communication is only assumed on a neighbor-to-neighbor basis. The novelty of the scheme is its low overhead in communication and computation due to a pre-processing phase that can be executed even before the participants know their input to aggregation. Moreover, the scheme is resilient to node drop-outs, and it is defined without introducing any trusted or untrusted third parties. We prove the privacy of the scheme itself and subsequently, we discuss the privacy leakage caused by the output of the scheme. Finally, we discuss implementation of the proposed protocol to solve distributed optimization problems using two versions of the alternating direction method of multipliers (ADMM).
Research on the Influencing Factors of Users’ Satisfaction with the Usability of News App Interface Under the Epidemic

Under the background of COVID-19 epidemic, starting from the perspective of user satisfaction in the process of user's using news App, the research explores the relevant factors affecting user satisfaction based on the process of user's browsing news. Through user survey and literature research, combined with factor analysis method, the research sums up 13 factors affecting user satisfaction with the interface usability of news App. It uses decision-making experiment method to analyze and research 30 pcs of online and offline questionnaire data and draws cause and effect diagrams. Finally, it concludes that the three key factors affecting the user satisfaction with interface usability of news App are the factors: image, image-text layout, interface clarity; and the factor that affects other factors to a large extent is interface clarity. Finally, it clarifies the relationship among the relevant factors affecting user satisfaction with the interface usability of news App, further analyzes the problems existing in the current news App at the level of human cognition, and puts forward some relevant suggestions for the improvement of user satisfaction with the Interface Usability of news App.
The Vocational College English Teaching in the Age of “Internet+”

The rapid development of information technology propels the innovative process of vocational college English teaching. It has become increasingly important to explore the transformation and reform of curriculum teaching from traditional to modern teaching by taking advantage of Internet technology in English teaching and by designing an information-based language teaching mode to keep up with the pace of times. This paper expounds the necessity and innovative ideas of vocational college English teaching research in the age of “Internet +”, and presents optimization strategies from three aspects: improving teachers’ information technology literacy, developing three-dimensional teaching resources and establishing diversified evaluation system.
An Improved Coral Reef Optimization-Based Scheduling Algorithm for Cloud Computing

An important problem in cloud computing faces the challenge of scheduling tasks to virtual machines to meet the cost and time demands, while maintaining the Quality of Service (QoS). Allocating tasks into cloud resources is a difficult problem due to the uncertainty of consumers’ future requirements and the diversity of providers’ resources. Previous studies, either on modeling or scheduling approaches, can no longer offer a satisfactory solution. In this paper, we establish a resource allocation framework and propose a novel task scheduling algorithm. An improved coral reef optimization (ICRO) is proposed to deal with this task scheduling problem. In ICRO, the better-offspring and multicrossover strategies increase the convergent speed and improve the quality of solutions. In addition, a novel load balance-aware mutation enhances the load balance among virtual machines and adjusts the number of resources provided to users. Experimental results show that compared with other algorithms, ICRO can significantly reduce the makespan and cost of the scheduling, while maintaining a better load balance in the system.
Criminal Networks Mining and Visualization for Crime Investigation

Online social media platforms have led to an increase in the number of cybercrimes and have facilitated the surge of other crimes as well. These platforms have become centres to develop conspiracies to execute crimes. If the information exchanged through these social media can be obtained then that would be an add-on to the investigation. In this paper, we have used natural language processing and data mining for extracting criminal networks from the chat logs obtained from confiscated systems. We capture the keywords based on the context and visualize them along with the network discovered.
It’s a Joint Effort: Understanding Speech and Gesture in Collaborative Tasks

Computers are evolving from computational tools to collaborative agents through the emergence of natural, speech-driven interfaces. However, relying on speech alone is a limitation; gesture and other non-verbal aspects of communication also play a vital role in natural human discourse. To understand the use of gesture in human communication, we conducted a study to explore how people use gesture and speech to communicate when solving collaborative tasks. We asked 30 pairs of people to build structures out of blocks, limiting their communication to either Gesture Only, Speech Only, or Gesture and Speech. We found differences in how gesture and speech were used to communicate across the three conditions and found that pairs in the Gesture and Speech condition completed tasks faster than those in Speech Only. From our results, we draw conclusions about how our work impacts the design of collaborative systems and virtual agents that support gesture.
Multi-Authority Criteria-Based Encryption Scheme for IoT

Currently, the Internet of Things (IoT) provides individuals with real-time data processing and efficient data transmission services, relying on extensive edge infrastructures. However, those infrastructures may disclose sensitive information of consumers without authorization, which makes data access control to be widely researched. Ciphertext-policy attribute-based encryption (CP-ABE) is regarded as an effective cryptography tool for providing users with a fine-grained access policy. In prior ABE schemes, the attribute universe is only managed by a single trusted central authority (CA), which leads to a reduction in security and efficiency. In addition, all attributes are considered equally important in the access policy. Consequently, the access policy cannot be expressed flexibly. In this paper, we propose two schemes with a new form of encryption named multi-authority criteria-based encryption (CE) scheme. In this context, the schemes express each criterion as a polynomial and have a weight on it. Unlike ABE schemes, the decryption will succeed if and only if a user satisfies the access policy and the weight exceeds the threshold. The proposed schemes are proved to be secure under the decisional bilinear Diffie–Hellman exponent assumption (q-BDHE) in the standard model. Finally, we provide an implementation of our works, and the simulation results indicate that our schemes are highly efficient.
Invisible Conflicts: The Challenges of Design on a Planetary Scale

The experience of the interconnected planet is the experience of an artificial totality, the result of the accelerated intersection of different invisible realms, topologies and layers that compete with one another following the irregular logics of rival interests, incompatible designs, non-human life form habits and rhythms, forms of resistance, errors, collateral effects and accidents. This issue of Temes de Disseny presents case studies and research practices that address the need to focus on these frictions from a variety of different perspectives in order to detect new forms of power and hidden inequalities as well as to envision unprecedented opportunities for intervention and counter-design.
Exploring the Limits of Zero-Shot Learning - How Low Can You Go?

Standard zero-shot learning (ZSL) methods use a large number of seen categories to predict very few unseen categories while maintaining unified data splits and evaluation metrics. This has enabled the research community to advance notably towards formulating a standard benchmark ZSL algorithm. However, the most substantial impact of ZSL lies in enabling the prediction of a large number of unseen categories from very few seen categories within a specific domain. This permits the collection and annotation of training data for only a few previously seen categories, thereby significantly mitigating the training data collection and annotation process. We address the difficult problem of predicting a large number of unseen object categories from very few previously seen categories and propose a framework that enables us to examine the limits of inferring several unseen object categories from very few previously seen object categories, i.e., the limits of ZSL. We examine the functional dependence of the classification accuracy of unseen object classes on the number and types of previously seen classes and determine the minimum number and types of previously seen classes required to achieve a prespecified classification accuracy for the unseen classes on three standard ZSL data sets. An experimental comparison of the proposed framework to a prominent ZSL technique on these data sets shows that the proposed framework achieves higher classification accuracy on average while providing valuable insights into the unseen class inference process.
Software Supports for Remote Examination on Mathematical Disciplines in Higher Education

The article discusses one of the ways to carry tests for students in mathematical disciplines in the context of distance learning. This approach involves the use of free software for issuing and receiving control tasks and organizing video conferencing. The features of control over the execution of tasks which are fair and equal for all students calculation of the time in the achieved by using password-protected archives created by means of macros. The results of the statistical analysis of the results of remote and face-to-face control activities are presented.
An Electronic Image Stabilization Algorithm Based on Improved FAST Feature Point Matching

Aiming at the problem that the video picture of the dispatch terminal of the public network digital trunking system is rotated and jittered, which affects the judgment of the command and dispatch personnel, this paper proposes an electronic image stabilization algorithm based on improved FAST feature point matching. The algorithm combines the SAD feature matching algorithm to match the feature points extracted from the current frame and the reference frame, and uses the improved RANSAC algorithm to eliminate the mismatch of feature points, thereby obtaining an accurate matching image. Theoretical analysis and experimental results show that, compared with the traditional algorithm before the improvement, the algorithm proposed in this paper can improve the accuracy of feature point matching in video images with low latency.
PERANCANGAN BUKU ILUSTRASI “SIAP SIAGA” MENGHADAPI BENCANA ALAM UNTUK ANAK USIA 7-12 TAHUN SEBAGAI MEDIA PEMBELAJARAN

Indonesian territory is the largest archipelago in the world. This region which is also located between the continents of Asia-Australia and the Indian Ocean-Pacific Ocean has 17,508 islands. Despite the richness and beauty of the extraordinary islands, the Indonesian people need to realize that this archipelago has 129 active volcanoes and is located at the confluence of three active tectonic plates in the world, namely the Indo-Australian, Eurasian and Pacific plates. So, Indonesia is declared a natural disaster prone area. Illustration book is a book that displays the visualization of an article which emphasizes the relationship between the subject and the writing. The illustrations in a book aim to decorate a story. With an illustration, the writing is easier for the reader to digest. Lack of knowledge about how to evacuate yourself when a disaster occurs with this informative and effective media is one of the problems for the creators, making it a final project entitled Preparedness for Natural Disasters. Therefore, the design of this illustration book aims to provide information, learning, ways of self-evacuation, and prevention of natural disasters to children aged 7-12 years so that they can be prepared for natural disasters. The material in this illustration book is also the result of interviews with the Regional Disaster Management Agency. Therefore this illustration book can help overcome existing problems. In addition to learning for readers, they will not be bored to read because there are attractive illustrations and colors.
Detection and Classification of Small Traffic Signs Based on Cascade Network

Research on the traffic sign detection is significant for driverless technology, which provides useful navigation information. Existing object detection methods are only applicable to large-size objects or small-scale specific types of traffic signs， and the performance of detecting traffic signs in street views is not adequate. In this regard, we propose a method to detect and classify small traffic signs by constructing a cascaded network. Specifically, the RetinaNet network is adopted firstly to integrate multi-layer information to identify small traffic signs in traffic scene images. The focal loss function is used to balance the biased distribution of traffic sign categories. Then, a two-class network is cascaded after the RetinaNet, which helps identify valid traffic signs from the first-stage prediction results. Experiments show that our cascaded network structure could achieve the balance of different categories of predictions and an improvement in precision and recall.