Reviewerly/exHarmony huggingface dataset:
Usability Heuristics and Guidelines for RESTful APIs and Application to a Case Study

Usability refers to a software's capability for being understood, learned, used, and attractive to the user under specific conditions. The main goal of this paper is to develop a set of usability heuristics and guidelines for designing RESTful APIs. This is done through a comprehensive analysis of REST principles using an extended, general-purpose usability model as a guide. The efficacy of these heuristics and guidelines is tested with a case study, namely, the REST API for Twitter developers.
On the generalized <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si125.svg" display="inline" id="d1e2320"><mml:mi>k</mml:mi></mml:math>-cosine arithmetic-mean curvature for multi-scale corner detection

In multi-scale feature extraction, image feature cues extracted over a set of scales are normally fused by computing their geometric mean (GM). In this paper, it has been theoretically shown that from a signal processing point of view, the arithmetic mean (AM) should be used instead of the GM; this is beneficial in conducting multi-scale corner detection. First, the generalized k-cosine is developed and exploited to compute the AM curvature. Compared to the GM curvature, the AM curvature can be more efficient in yielding higher saliency for corner points than that of insignificant local image structures. On the other hand, our developed generalized k-cosine curvature measurement can incorporate the contour’s convexity and concavity into the corner detection process, while enjoying reduced computational complexity. To further improve corner detection performance, a curvature smoothing technique, with a two-stage curvature thresholding, is developed for removing noise from the computed AM curvatures. Extensive experimental results have demonstrated that our proposed AM-curvature-based corner detector can clearly outperform a number of state-of-the-art corner detection methods. In particular, the average F-score achieved on five benchmark datasets has been increased by 3.8% on clean images and 3.2% on quality-degraded images, respectively.
Cross-view graph contrastive learning with hypergraph

Graph contrastive learning (GCL) provides a new perspective to alleviate the reliance on labeled data for graph representation learning. Recent efforts on GCL leverage various graph augmentation strategies, i.e., node dropping and edge masking, to create augmented views of the original graph, and then contrast the representations in these augmented views to learn expressive graph embeddings. Nevertheless, the contrasting is still conducted between small variations of the original graph, where limited information can be extracted. In this work, for the first time, we propose to use hypergraph to establish a new view for graph contrastive learning. Specifically, for each graph, we construct its corresponding hypergraph, and then contrast the graph representations learned in the hypergraph view and the original graph view, by which the high-order information of a graph can be captured to produce graph representations of higher quality. Furthermore, to bridge the potential gap between the graph and hypergraph representations, we utilize the diffusion model to exchange the information contained in these two views, enabling better graph contrastive learning. We evaluate our proposal with a collection of experiments, and the empirical results validate that the proposed model can improve node and graph classification performance.
DEDACS: Decentralized and dynamic access control for smart contracts in a policy-based manner

Distributed Ledger Technology (DLTs) or blockchains have been steadily emerging and providing innovation in the past decade for several use cases, ranging from financial networks, to notarization, or trustworthy execution via smart contracts. DLTs are enticing due to their properties of decentralization, non-repudiation, and auditability (transparency). These properties are of high potential to access control systems that can be implemented on-chain, and are executed without infringement and full transparency.
Feature Selection as a Hedonic Coalition Formation Game for Arabic Topic Detection

Arabic topic detection is a Natural Language Processing (NLP) task that aims to assign a topic or a set of topics to a new document based on its content and selected features. Feature selection is an essential topic detection technique used to capture discriminative and descriptive features in documents to enhance the performance of topic detection. This paper proposes a novel feature selection approach formulated as a Hedonic Coalition Formation Game. We use the proposed method to describe each topic with a unique Topic Oriented Vocabulary (TOV) vector. We inspect the performances of the vocabularies obtained by solving the Hedonic game in topic detection over several Arabic corpora. In addition, the proposed feature selection method is compared with a Mutual Information (MI) based vocabulary vector generation method. The reported results show the effectiveness of the proposed feature selection approach by generating new vocabularies with highly discriminating features and reduced dimensions.
Subject review: Image encryption techniques

Security is one of the core areas of study in recent days. Encryption of the image is widely known as an effective method for its secure transmission. The objective of any image encryption method is to obtain a top quality hidden image in order to keep information secret. This paper is a study of research authors in the field of image encryption with a research review to see how the field of image encryption has progressed and the best algorithms in their performance.
Hybrid Encryption Model for Protecting the Stakeholders’ Data in NGWN Environment

Supporting diverse smart requests that rely on frequent data interactions and collaborative data processing is a primary goal of next-generation wireless networks (NGWN). An integral part of enabling NGWN is data management (DM), which includes data gathering, storage, sharing, and computing. Yet, there are two major obstacles in the way of DM for NGWN: the difficulty of managing stakeholders' data across diverse trust domains in a dispersed network architecture, and the increasing stringency of privacy preservation requirements for personal data in light of new privacy rules. Data and information security can be achieved by a variety of methods, including cryptography, steganography, etc. In this research, we use AES and RSA to implement a hybrid cryptographic system. Improved security is provided by hybrid cryptography since the symmetrical key used to encrypt messages is itself encrypted. This article also includes an encrypted hash value of a message as a digital signature. This digital signature is used for authenticity verification on the receiving end. The final step is to assemble the encrypted message, symmetric key, and digest into a decipherable form. In this case, the security provided by a hybrid cryptosystem is superior to that of a single cryptographic model. This algorithm has the unique ability to verify the integrity of sent messages. The practicality of this approach has been demonstrated by successful simulations.
An Energy Saving Control Strategy Based on Multi-Agent Q-Learning Algorithm for Data Center

Abstract In recent years, the application of green renewable energy to data centers has become an important trend. Traditional solutions lack the consideration of matching tasks to renewable energy supplies. Therefore, in the face of diverse real-time computing tasks, how to reduce the total energy cost while ensuring the quality of service is an important challenge for the data center in the future. In this paper, our focus is on using the information on renewable energy supply and task characteristics as input states to assign tasks that maximize user satisfaction while meeting the minimum total cost of energy consumption. We consider the diversity of real-time tasks and design three different task types: the most crucial task, the crucial task and the non-crucial task. According to the different characteristics of these tasks, we propose a scheduling algorithm based on multi-agent, which uses multiple sets of agents with different initial positions to parallel search in different dimensions of the parameter space to find the optimal solution. To further optimize the algorithm, we eliminate the centralized noise solution based on the Pareto sorting method and sort the multiple optimal solutions to highlight the most suitable solution. The experimental results show that the proposed algorithm compared with other algorithms can reduce the total energy consumption by 11% and increase the customer satisfaction by 13% on average, and has better performance and applicability.
PERANCANGAN BANGKU PENDEK (DINGKLIK) BERBAHAN PLASTIK DENGAN BANTUAN SIMULASI CAD/CAE

Injection Molding is a thermoplastic material processing method in which the material that melts due to heating is injected by a plunger into the mold and then cooled until the material is solid, and removed from the mold.&#x0D; &#x0D; The purpose of this design is to determine the maximum load that can be accepted by the short bench (Dingklik), and to design the dimensions of the bench, while the design stages are as follows: starting from the design of the bench using CAD/CAE software, and machining simulation using can CAM software.&#x0D; &#x0D; Data input simulation is performed using the CAM software engine. The maximum weight of the respondent is 95 kg, and the load chosen to be safe in this design is 190 Kg (1862,9 N)&#x0D; &#x0D; The results of the design of the bench obtained the following data: length 350 mm, width 250 mm and height 180 mm, the material used is Polypropylene, the surface area of the object is 409923 mm2, the total volume with the gatting system is 776493,9461 mm3, and the maximum load that the material can withsatand is 2551.3995 Newtons.
A Mathematical Framework for Enriching Human–Machine Interactions

This paper presents a conceptual mathematical framework for developing rich human–machine interactions in order to improve decision-making in a social organisation, S. The idea is to model how S can create a “multi-level artificial cognitive system”, called a data analyser (DA), to collaborate with humans in collecting and learning how to analyse data, to anticipate situations, and to develop new responses, thus improving decision-making. In this model, the DA is “processed” to not only gather data and extend existing knowledge, but also to learn how to act autonomously with its own specific procedures or even to create new ones. An application is given in cases where such rich human–machine interactions are expected to allow the DA+S partnership to acquire deep anticipation capabilities for possible future changes, e.g., to prevent risks or seize opportunities. The way the social organization S operates over time, including the construction of DA, is described using the conceptual framework comprising “memory evolutive systems” (MES), a mathematical theoretical approach introduced by Ehresmann and Vanbremeersch for evolutionary multi-scale, multi-agent and multi-temporality systems. This leads to the definition of a “data analyser–MES”.
Stability analysis of intelligent English translation system based on model predictive control algorithm

English translation systems often require manual input to convert speech into text document mode, which leads to poor translation results. In order to improve the intelligence of the intelligent English translation system, based on the machine learning algorithm, this paper constructs an intelligent English translation system based on the model predictive control algorithm and combines the self-triggering MPC with the robust control to propose a corresponding control solution. That is, a robust self-triggering MPC method is proposed for linear systems with constraints. Moreover, this paper studies the stability and robustness of MPC in continuous time systems and describes the interfaces to be used in the system and the performance requirements of the system. In addition, this paper analyzes and describes the feasibility of the system development plan. Finally, this paper designs experiments to analyze the model performance from the system translation accuracy rate, system login security and system stability. The research results show that the model constructed in this paper has certain practical effects.
A Frequency Attention-Based Dual-Stream Network for Image Inpainting Forensics

The rapid development of digital image inpainting technology is causing serious hidden danger to the security of multimedia information. In this paper, a deep network called frequency attention-based dual-stream network (FADS-Net) is proposed for locating the inpainting region. FADS-Net is established by a dual-stream encoder and an attention-based blue-associative decoder. The dual-stream encoder includes two feature extraction streams, the raw input stream (RIS) and the frequency recalibration stream (FRS). RIS directly captures feature maps from the raw input, while FRS performs feature extraction after recalibrating the input via learning in the frequency domain. In addition, a module based on dense connection is designed to ensure efficient extraction and full fusion of dual-stream features. The attention-based associative decoder consists of a main decoder and two branch decoders. The main decoder performs up-sampling and fine-tuning of fused features by using attention mechanisms and skip connections, and ultimately generates the predicted mask for the inpainted image. Then, two branch decoders are utilized to further supervise the training of two feature streams, ensuring that they both work effectively. A joint loss function is designed to supervise the training of the entire network and two feature extraction streams for ensuring optimal forensic performance. Extensive experimental results demonstrate that the proposed FADS-Net achieves superior localization accuracy and robustness on multiple datasets compared to the state-of-the-art inpainting forensics methods.
SMART DOCTORS ASSISTANT- AN ADVANCED APPOINTMENT BOOKING SYSTEM FOR HOSPITALS

Smart Doctors Assistant System is an advanced and contactless method especially built to avoid long wait-time in hospitals to book an appointment with the doctor. It aims to make the process of booking an appointment swift and hassle-free. Patients struggled a lot during the COVID-19 pandemic as they had to physically fill out the forms in the hospitals to schedule an appointment and then consult the doctor. This increased the chances of them being exposed to the virus. Patients who cannot fill out forms alone can do it without assistance using our system. Our proposed system uses face recognition and voice commands, thus making it effortless even for illiterates to schedule an appointment with the doctor. A time slot is allocated to the patient which is sent to his/her registered email address. The registered user can log in to our system at the scheduled time and directly consult the doctor.
Research on a class-incremental learning method based on sonar images

Due to the low resolution and the small number of samples of sonar images, the existing class incremental learning networks have a serious problem of catastrophic forgetting of historical task targets, resulting in a low average recognition rate of all task targets. Based on the framework model of generated replay, an improved class incremental learning network is proposed in this paper, and a new deep convolution generative adversarial network is designed and built to replace the variational autoencoder as the reconstruction model of generated replay incremental network to improve the effect of image reconstruction; a new convolution neural network is constructed to replace the multi-layer perception as the recognition network of generated replay incremental network to improve the performance of image classification and recognition. The results show that the improved generated replay incremental network alleviates the problem of catastrophic forgetting of historical task targets, and the average recognition rate for all task targets is significantly improved.
Characteristic analysis and filtering algorithm design for UNGM model

The univariate non-stationary growth model (UNGM) is widely used in the verification of nonlinear filters, and the unscented Kalman filter (UKF) is often used as the reference filter for comparative analysis when using this model to evaluate the filter performance. However, due to the strong nonlinearity of UNGM and the change of model properties with different parameter settings, the estimation misalignment problem due to different reasons will occur when UKF is used for filtering. To solve these problems, this paper analyzes the complex characteristics of UNGM in filtering process, and proposes an UKF with sliding sampling module(SSUKF). The algorithm is optimized on the basis of UKF, and can effectively deal with the complex characteristics of UNGM by sampling and analyzing the filtering information in the filtering process and correcting the distribution of Sigma points in real time. SSUKF is applied to UNGM under different parameters and compared with UKF and bootstrap particle filter(BPF). The simulation results show that SSUKF can effectively solve the misalignment problem when UKF is applied to UNGM, and the calculation speed is better than BPF. Compared with UKF, SSUKF is suitable as a benchmark filter for evaluating the performance of nonlinear filters using UNGM.
Robust Intelligent Multimodal Biometric Authentication Systems for a Secured EHR

Nowadays, a new smart approach is required to maintain confidentiality and seclusion of Electronic Health Records (EHR) of patients using a multimodal biometric authentication system. To authorize access to medical records, multimodal-based encryption solutions require the patient to be present at all times. Because there are known cases where patients’ health data have been stolen and misused, patient privacy and confidentiality is a big issue in EHR. Another challenge with EHR is how to give proper therapy while also having access to relevant information, particularly in pre-hospital settings. As a result, the system was created to access patient records in the hospital utilizing multimodal biometrics of patients. Cloud computing and SQL databases were used as software tools. By restricting the amount of data available to users, the system safeguards privacy and confidentiality while yet providing reliable access to critical aspects of patients’ EHR data via patient fingerprint and voice recognition.
A Comparison Between Position-Based and Image-Based Multi-Layer Graphical user Authentication System

System security is very important, especially in the age that we live in. One of the ways to secure data is by creating a password that makes it difficult for unauthorized user to gain access to the system. However, what makes it difficult for the system to be attacked is directly dependent on approach used to create it, and how secured it is. Text based approach is the oldest authentication approach. It requires that the user supplies textual password in order to gain access to the system. However, this approach has shown a significant drawback and several vulnerabilities, one of which is the difficulty in recalling or remembering textual passwords. Several other attacks that textual passwords are vulnerable to include brute force attacks, shoulder spying, dictionary attacks etc. The introduction of graphical schemes made things a lot better. Graphical passwords make use of images. However, most graphical schemes are vulnerable to shoulder surfing attacks. In this research work, we developed two systems; A position-based multi-layer graphical user authentication system and an Image-based multi-layer graphical user authentication system. The reason behind this research work is to compare the two systems, and evaluate them based on three major performance metrics: (1) Security, (2) Reliability (3) Individual preference.
Digital navigation pilots in four public libraries

This article is written by Christine Gauvreau, a Consultant with the Connecticut State Library’s Division of Library Development. By supplying pilot project subawards of $100,000 in Institute of Museum and Library Services American Rescue Plan Act funds to four public libraries, the Connecticut State Library facilitated the collection of qualitative data demonstrating the need for and impact of libraries offering digital navigation appointments to residents in need of an internet connection, a laptop, technical skills to operate the computer, and/or digital literacy skills. Libraries learned that three part-time digital navigators for a nine-month period could serve and satisfy hundreds of residents, contribute greatly to enlarging the digital ecosystem in a town, and lay the foundation for sustainable programs.
“You have my sword.” “You have my bow.” “And my axe.”: Creating a popular reading collection through interdepartmental collaboration

In Spring 2021, the Georgia Tech Library received unexpectedly robust support for the redevelopment of the Library’s previously removed Popular Reading Collection. The project had a strict four-month deadline to encumber funds and to ensure that materials were available before the Fall 2021 return to campus. A cross-functional team was established to create and implement a plan to achieve this goal. The successful project resulted in widespread positive feedback from students, Institute partners and leaders, and Library leadership. This case study demonstrates the importance of context in deciding library projects, the utility of cross-functional teams in establishing new collections, and the psychological value of physical materials to campus communities.
Traffic Sign Detection and Recognition Using Multi-Frame Embedding of Video-Log Images

The detection and recognition of traffic signs is an essential component of intelligent vehicle perception systems, which use on-board cameras to sense traffic sign information. Unfortunately, issues such as long-tailed distribution, occlusion, and deformation greatly decrease the detector’s performance. In this research, YOLOv5 is used as a single classification detector for traffic sign localization. Afterwards, we propose a hierarchical classification model (HCM) for the specific classification, which significantly reduces the degree of imbalance between classes without changing the sample size. To cope with the shortcomings of a single image, a training-free multi-frame information integration module (MIM) was constructed, which can extract the detection sequence of traffic signs based on the embedding generated by the HCM. The extracted temporal detection information is used for the redefinition of categories and confidence. At last, this research performed detection and recognition of the full class on two publicly available datasets, TT100K and ONCE. Experimental results show that the HCM-improved YOLOv5 has a mAP of 79.0 in full classes, which exceeds that of state-of-the-art methods, and achieves an inference speed of 22.7 FPS. In addition, MIM further improves model performance by integrating multi-frame information while only slightly increasing computational resource consumption.