Reviewerly/exHarmony huggingface dataset:
Transformer-based Contrastive Learning for Unsupervised Person Re-Identification

Unsupervised Re-identification (Re-ID) methods have been dominated by convolutional neural networks (CNN) for many years. Most of these current methods apply pseudo-label-based contrastive learning (CL) and achieve great progress. However, they have limited capacity to represent global fea-tures, suffer from severe performance drops when training with limited computing resources, and are unable to effectively use pseudo-label information when training with CL. To tackle these problems, we propose a Transformer-based Contrastive Learning (TransCL) method to enhance the performance of CL and improve the feature representation ability of Re-ID, in which a batch and memory contrast (BMC) strategy is developed to optimize multi-level CL tasks concurrently to fully use the pseudo-label information. Additionally, a GCN aggregated clustering (GAC) scheme is designed to assist in generating more effective pseudo labels for CL. Extensive experimental results indicate that GAC and BMC work with vision transformer (ViT) achieves better training performance and enhances the representation ability of the Re-ID model. TransCL surpasses the state-of-the-art CNN method by 8.0% in mAP on the challenging MSMT17 dataset.
Understanding issues in cloud forensics

For a long time, cloud computing has obtained appeal and is currently utilized to sustain different human course aspects. Cloud forensics has been presented to assist forensic detectives in discovering possible reasons versus cloud convict tasks and keeping the safety and sincerity of the material held in the cloud. While an excellent study in this field has been accomplished worrying obstacles and remedies, the survey of techniques and structures is still in its early stage. This write-up concentrates on the technical elements of cloud forensics. It thoughtfully assesses cloud forensics' existing difficulties and remedies. Besides, it checks out, based upon a comprehensive testimonial of the study. This entire job has been performed both in digital and even cloud forensic techniques to sustain the examination of protection occurrences in the cloud. The in-depth contrast exposes the resemblances and disadvantages of the existing approaches offering some different future research study instructions.
Interactive technologies of teaching disciplines of the natural science cycle as a means of forming a socially adapted student’s personality

The problem of socialization acquires a special educational significance precisely in adolescence, when the activity of a teenager in interpersonal communication expands, and activities that contribute to the enrichment of social experience, the development of communicative competence, the formation of the ability to independent social practice come to the fore. The development of a stable moral identity in older adolescents in an educational environment depends on the optimization of the socialization process, which provides ample opportunities for the formation of various levels of social maturation (identification, individualization and personalization), where conditions for combining the social positions of “I am in society” are opened for adolescents, in search of the best ways to assert themselves. The purpose of this article is to study the main contributions of the practical application of interactive technologies of teaching disciplines of the natural science cycle on the example of a biology course for the development of a socially adapted student’s personality through the formation of motivational-value and activity-practical components. In the article the authors present the results of correlation of data on the use of interactive technologies of teaching biological disciplines in the experimental group, using the Fisher Criterion (φ-criterion), proving the positive dynamics of improving communication, citizenship and creativity as the main characteristics of the criteria of the selected components of the formation of a socialized student’s personality.
An In-Depth Evaluation of Externally Amplified Coupling (EAC) Attacks—A Concrete Threat for Masked Cryptographic Implementations

Masking is a systematic countermeasure to achieve side-channel security for cryptographic algorithms. However, its secure implementation relies on an independence assumption that can be violated by signal coupling. It has been established that coupling induced within a device can be detrimental. It was demonstrated on a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1^{st}$ </tex-math></inline-formula> -order secure design (i.e., with two shares) that an adversary who can manipulate the design’s power-measurement setup can externally induce significant coupling. It can thus concretely reduce the “effective-security-order”, i.e., make <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1^{st}$ </tex-math></inline-formula> -order leakages as significant as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{nd}$ </tex-math></inline-formula> -order ones with fewer measurements. This paper explores the impact of such external amplification phenomena on fabricated hardware test cases for the first time. We designed a dedicated ASIC to extend the empirical results for demonstrating impact up to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4^{th}$ </tex-math></inline-formula> order. We have systematically evaluate d factors related to adversarial control, e.g., the external measurement resistance. We also investigate d their relative influence compared to intra-design ones, i.e., internal power-grid resistance and transistors’ inherent resistance. Our study demonstrates that externally amplified coupling scale s up to concrete masked hardware designs with various amounts of shares and is not very sensitive to intra-design parameters. Therefore, providing experimental evidence that such coupling should be considered during masking validation.
Few-Shot Learning Network for Out-of-Distribution Image Classification

Image classification in real-world applications is a challenging task due to the lack of labeled data. Many few-shot learning techniques have been developed to tackle this problem. However, existing few-shot learning techniques will fail if new classes are added to the model without any labeled data (out-of-distribution). The existing techniques will classify the new classes as one of the existing classes and will not be able to detect them as new classes. Also, if all data are unlabeled, existing few-shot learning techniques will not work (because existing techniques are supervised learning). This article proposes a novel few-shot learning network [knowledge transfer network (KTNet)] that can learn from unlabeled data and assigns a pseudolabel to these data. These pseudolabeled data will either be added to the existing labeled data (in-distribution) to increase the number of shots or added as new classes if the data are out-of-distribution. The proposed KTNet technique can work in all cases, such as a small amount of labeled data exist for all classes, labeled data exist for a subset of all classes, and if existing data for all classes are unlabeled. KTNet is evaluated using two benchmark datasets (mini-ImageNet and fewshot-CIFAR). The results show that the proposed network outperforms state-of-the-art models in both datasets with respect to classification accuracy. Also, KTNet is better than existing techniques at detecting and clustering the out-of-distribution classes.
Virtual Characters Meet the Uncanny Valley: A Literature Review Based on the Web of Science Core Collection (2007-2022)

Following the robots, whether virtual characters (VC) will cause the uncanny valley (UV) effect has become a new topic of UV studies, and also a topic that cannot be avoided by VC design and human-VC interaction. Based on the Web of Science core collection (WoSCC), this paper reviews literature on the topics of both the VC and UV (2007-2022) by summarizing the trends and methods research. It groups studies into three categories: verifying the UV and its influencing factors; suggestions for avoiding the UV; possibilities for crossing the UV. We suggest that, under current technological conditions, finding a proper balance between abstraction and high realism to avoid the risk of the UV is a sound choice for VC design, while also providing suggestions for future research.
Demonstrator of Decentralized Autonomous Organizations for Tax Credit’s Tracking

Tax credit stimulus and fiscal bonuses had a very important impact on Italian economy in the last decade. Along with a huge expansion in constructions a relevant increase in scams and frauds has come too. The demonstrator implements a possible system to track and control the whole Superbonus 110 tax credit process from its generation to its redeem through a Decentralized Autonomous Organization architecture enriched with a Multi Agent Systems to implement controllers.
Real-time Ship Object Detection with YOLOR

Real-time object detection technology is a key technology for USVs to perceive the environment. Accurately and quickly detecting the position and type of ship targets in images is the basis for intelligent navigation of USVs. In 2021, the YOLOR (You Only Learn One Representation) model outperformed all other real-time object detection models on the COCO dataset. The YOLOR model is a multi-task model obtained by adding implicit knowledge modeling on the basis of YOLOv4-csp (You Only Look Once version 4 -csp) and modifying the first CSPDark layer of YOLOv4-csp to a Dark layer, reducing the amount of computation by 40%. However, implicit knowledge modeling only adds less than ten thousand parameters and computation. In this paper, we trained four models using the public marine ship dataset Seaships (7000), investigate the effect of the YOLOR model on real-time ship object detection, and demonstrated that implicit knowledge modeling can significantly increase the model's detection accuracy. The experimental results indicate that of the model with implicit knowledge modeling is 96.7, and is 71.2%, which is 3.5% and 24.3% higher than the YOLOv4-csp model, respectively. Additionally, we discovered that implicit knowledge modeling significantly improves model detection accuracy at medium and low resolutions ( and ), but not at high resolutions ().
Hybrid Optimization for Developing Human Like Chess Playing System

Chess is considered as the “Drosophila of Artificial Intelligence (AI)” since the research in AI began with the concept of game playing and the game of chess became favourite topic in the Artificial Intelligence research history. This article introduces various traditional techniques used for design and development of chess playing system and also proposes the improved hybrid optimization technique for deciding the best move in chess. Most state of the art chess engines relied upon more traditional search techniques like Minimax and Alpha beta till the recent success of systems which are based on machine learning concept. Most chess engines are designed in such a way that they do not play intuitively like humans, but calculate as much variations as possible and base their decision upon that. Recent developments in game playing showed how AlphaZero used new approach based on deep reinforcement learning. Chess engines can be improved in terms of intuitive feel for the position by training the model with top human games and best move can be derived using our proposed hybrid optimization technique. Our hybrid based optimization model is evaluated in terms of parameters like accuracy, sensitivity, specificity and found to be more effective when compared with other techniques.
ID Card Storage System using Optical Character Recognition (OCR) on Android-based Smartphone

The Indonesian Identity Card (ID card) has been widely used as authentication in various fields. One of the most common uses of ID cards is registration on various business processes that require some personal privacy data, including an ID number, full name, date of birth, address, and many more. This research focuses on extracting the ID card image characters into an editable format by implementing the Optical Character Recognition (OCR) in the mobile platform. There are three significant steps of the proposed algorithm: image pre-processing, character recognition, and character extraction. The output of this research is an application that enables the user to recognize some important information on the ID card image and save the data into a file by utilizing an Android-based smartphone. The system proposed achieved the best accuracy of 87.5% from 50 samples of Indonesian National ID cards with 2.137 characters recognized (the average is 43 words).
A Weighted Combination of Semantic and Syntactic Word Image Representations

In contrast to traditional keyword spotting, semantic word spotting allows users to search not only for word images with the same transcription as the keyword, but also for concepts which are latent or hidden inside a query. However, it has been shown that mapping word images to semantic representations proves to be a difficult task. As semantic embeddings do not consider syntactic similarity, it is common to find search results with highly ranked semantically similar word images, while words with the same transcription as the search query appear in lower ranks. To counteract this problem, a combination of semantic and syntactic representations usually provides a good trade-off w.r.t. semantic and syntactic metrics. In this work, we present methods for realizing a weighted combination of semantic and syntactic information. This allows users to focus more on semantic or syntactic aspects and thus provides new insights to their document collections. Thereby, our proposed methods are not limited to the use of word spotting, but also aim to address the optimization of recognition-free NLP downstream tasks.
Security-Focused Prototyping: A Natural Precursor to Secure Development

Secure development is often thought of as a proactive approach to cyber security. Rather than building a technological solution and then securing it in retrospect, secure development strives to embed good security practices throughout the development process and reduce risk. Unfortunately, evidence suggests secure development is complex, costly, and limited in practice. This article therefore introduces security-focused prototyping as a natural precursor to secure development. Security-focused prototyping embeds security at the beginning of the development process, can be used to discover domain-specific security requirements through active learning, and can help communicate the complexity of secure development to organizations such that the resources and commitment it requires are better understood. A case study considering the application layer of an Internet of Things system is presented and shows that security-focused prototyping has the potential to facilitate further secure development through the achievement of well-established prototyping objectives, such as communication, active learning, and reduced time/costs. Future work could build on this work by conducting additional case studies to further explore the potential of security-focused prototyping and investigating the importance of fidelity with regards to security-focused prototypes.
Decentralised Agent-Based Medical Image Reconstruction

In this paper we propose a solution to a challenging task of magnetic resonance image reconstruction in a scalable and flexible way. The proposal elaborates task slicing and its distribution using market-based approach, where participating nodes perform task processing using their own spare computing capacities. The distributed task execution is orchestrated within multi-agent environment where agents offer the execution services by bidding for the offered tasks. Proposed negotiation protocol includes local and cloud-based resources where the offered services are exchanged for an award based on the negotiated award value function. The proposed solution considers simplicity and expressiveness of SDLMAS language and the advantages of corresponding platform for rapid design and development of multi-agent systems.
Private and Accurate Decentralized Optimization via Encrypted and Structured Functional Perturbation

We propose a decentralized optimization algorithm that preserves the privacy of agents’ cost functions without sacrificing accuracy, termed EFPSN. The algorithm adopts Paillier cryptosystem to construct zero-sum functional perturbations. Then, based on the perturbed cost functions, any existing decentralized optimization algorithm can be utilized to obtain the accurate solution. We theoretically prove that EFPSN is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(\epsilon, \delta)$ </tex-math></inline-formula> -differentially private and can achieve infinitesimally small <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon,\delta $ </tex-math></inline-formula> under deliberate parameter settings. Numerical experiments further confirm the effectiveness of the algorithm.
High-order Correlation Network for Video Recognition

How to model global video representation is an important research content of video recognition. Among current convolutional neural network(CNN) based methods, only using first-order representations (i.e. global average pooling) has limitations in capturing spatiotemporal features of videos. Recent studies have shown that high-order statistics are more suitable to model complex feature distributions. To better characterize the spatiotemporal structure for video recognition, we propose a novel High-order Correlation Network (HoCNet) in this work, the core of which is to explore high-order video representations through correlation computation and covariance pooling. HoC-Net leverages the correlation module to obtain complex temporal dynamic information of frames via computing dot product of features in the fixed sliding window of two adjacent frames. As an approximate high-order calculation, the correlation module can be inserted into any stage of the deep network to model high-order representations in various spatial resolutions. Additionally, a robust high-order pooling module, i.e., iterative matrix square root normalization of covariance pooling (iSQRT-COV), is also introduced at the end of the network, and this further boosts modeling complex spatiotemporal distributions of video features. Experiments conducted on four widely used video benchmarks demonstrate the effectiveness of HoCNet, which achieves the comparable performance with the state-of-the-art models.
InSAR Phase Unwrapping Using Dynamics of Coupled Phase Oscillators of the Kuramoto Model

In this paper, we propose an interferometric synthetic aperture radar (InSAR) phase unwrapping method by use of dynamics of the Kuramoto model that is one of phase oscil-latory networks. The network generates a quality map which induces a proper unwrapping path by solving singular points (SPs) or residues. We show that our proposal unwraps in-terferometric phase with higher accuracy compared to other model-algorithm-based unwrapping methods.
A Decentralized Ride-Hailing Mode Based on Blockchain and Attribute Encryption

With the development of smart transportation, ride-hailing applications have become an essential part of people’s lives. These ride-hailing apps provide convenience of contacting taxi for passengers. However, most present ride-hailing or ride-sharing systems rely on a trusted third party. It makes them be attacked vulnerably. A decentralized block-chain-based ride-hailing mode with attribute encryption is proposed in this paper. Attribute-based encryption is applied to ensure the drivers who meet the passenger’s requirements can obtain the passenger’s order in this mode. After the transaction has completed, the transaction information is saved on the blockchain. This mode supports the investigation of historical records via the blockchain technology. Besides, a new payment protocol is used in this mode. The new payment protocol is based on trip distance. It applies smart contract and zero-knowledge set membership proof. The reputation of drivers based on drivers’ past behavior is designed. The driver’s reputation will be updated after the transaction is completed. Passengers can choose a driver with high reputation. Each phase of this mode is simulated in our test net of Ethereum. The results prove that this ride-hailing mode is efficient.
Web-Based Face Recognition using Line Edge Detection and Euclidean Distance Method

A face recognition system is to perform a match face image using the face extraction method. There are many applications used in various algorithms and implemented in many programming languages, but still difficult to implement on web-based applications using the PHP programming language. The purpose of this research is to produce a website-based application focused on the face recognition section. The author will limit the system only to detect the front view of the face, the main goal is to get a fairly high level of accuracy. There will be a feature to find where the face is located. The research method used is a laboratory experiment where the search system scheme based on face recognition will produce the appearance of several faces that have the closest Euclidean distance values from grades 1st to 5th. The results from the comparison of the test image with the training image based on Line Edge Detection and Euclidean Distance calculation concluded that the system can be implemented in searching of similarity and recognizing the face.
Multiclass Labelling For Bug Report Severity Prediction Using Semantic Analysis And Machine Learning

Bug reporting is a feature that is provided with the intent that the user can submit a bug report regarding software malfunction. Most of the sizeable open source software is equipped with this feature. Such reporting systems are generally termed “issue tracking systems.” One of the typical attributes of a bug report is severity, which is often marked as critical by users in most bug reports. However, not all bugs are critical, and the ones that are actually critical can be overlooked in such a situation. Likewise, there are some other types of classifications of bug reports, some of which require more critical attention in one context or the other. For example, the bug report might be labeled as one of the non-functional requirements like robustness, security, efficiency, etc., or might be in some other class domain like UI, Database, and Notifications, etc. Often, for large software projects, we are interested in two or more of such class labels for the consideration of bug reports, so as to determine the priority of assignment to the developer for resolution. This paper considers the Eclipse software database for the determination of multiclass labels for the purpose of the determination of priorities in changing contexts.