Reviewerly/exHarmony huggingface dataset:
Joint Bilateral-Resolution Identity Modeling for Cross-Resolution Person Re-Identification

Person images captured by public surveillance cameras often have low resolutions (LRs), along with uncontrolled pose variations, background clutter and occlusion. These issues cause the resolution mismatch problem when matched with high-resolution (HR) gallery images (typically available during collection), harming the person re-identification (re-id) performance. While a number of methods have been introduced based on the joint learning of super-resolution and person re-id, they ignore specific discriminant identity information encoded in LR person images, leading to ineffective model performance. In this work, we propose a novel joint bilateral-resolution identity modeling method that concurrently performs HR-specific identity feature learning with super-resolution, LR-specific identity feature learning, and person re-id optimization. We also introduce an adaptive ensemble algorithm for handling different low resolutions. Extensive evaluations validate the advantages of our method over related state-of-the-art re-id and super-resolution methods on cross-resolution re-id benchmarks. An important discovery is that leveraging LR-specific identity information enables a simple cascade of super-resolution and person re-id learning to achieve state-of-the-art performance, without elaborate model design nor bells and whistles, which has not been investigated before.
Congruence and geometric feature-based commutative encryption-watermarking method for vector maps

The commutative encryption-watermarking (CEW), in which the encryption and watermarking are distinct and commutative, has become a promising method for protecting data security. For vector maps, current methods combine encryption with watermarking directly, but cannot satisfy the CEW properties. For example, the watermark embedding and encryption are not commutative and lack flexibility. Considering the essential characteristics of vector maps, this paper proposes a CEW method based on the congruence relationship and geometric feature for vector maps. In the proposed CEW method, the angles and the distance ratios are selected as the geometric features which provide separate spaces for cryptography and watermarking operations. They are encrypted by making the original values and encrypted values congruent. At the same time, the watermark is embedded into their residue. As the residue keeps invariant under congruence operations, the watermark can be extracted from either the plaintext or the ciphertext. Experiments are conducted to verify the commutativity between encryption and watermarking. Besides, the superiority in terms of watermark robustness and capacity of the proposed method has been also compared with other encryption-combining-watermarking methods for vector maps.
A Comprehensive Study for the Hindi Language to Implement Supervised Text Classification Techniques

A large amount of feedback, comments, and postings made every second in social networking is rapidly growing the social database. Now, enormous data must be analyzed to determine the direction of people’s opinions about a certain business and its products. The bulk of evaluations on the internet are in English, however as technology develops and people’s knowledge expands, Also the amount of online information available in Hindi languages grows. To comprehend people’s sentiments around real-world things, due to this Hindi language sentiment analysis is necessary; their reviews are equally important to us. For categorization accuracy, we used the Hindi language resource for general news headlines from several news sources. For text categorization, we utilized machine learning (ML) classification methods such as Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR), and the accuracy varied.
Semantics in Multi-objective Genetic Programming

Semantics has become a key topic of research in Genetic Programming (GP). Semantics refers to the outputs (behaviour) of a GP individual when this is run on a data set. The majority of works that focus on semantic diversity in single-objective GP indicates that it is highly beneficial in evolutionary search. Surprisingly, there is minuscule research conducted in semantics in Multi-objective GP (MOGP). In this work we make a leap beyond our understanding of semantics in MOGP and propose SDO: Semantic-based Distance as an additional criteriOn. This naturally encourages semantic diversity in MOGP. To do so, we find a pivot in the less dense region of the first Pareto front (most promising front). This is then used to compute a distance between the pivot and every individual in the population. The resulting distance is then used as an additional criterion to be optimised to favour semantic diversity. We also use two other semantic-based methods as baselines, called Semantic Similarity-based Crossover and Semantic-based Crowding Distance. Furthermore, we also use the NSGA-II and the SPEA2 for comparison too. We use highly unbalanced binary classification problems and consistently show how our proposed SDO approach produces more non-dominated solutions and better diversity, leading to better statistically significant results, using the hypervolume results as evaluation measure, compared to the rest of the other four methods.
Machine Learning for Technical Debt Identification

Technical Debt (TD) is a successful metaphor in conveying the consequences of software inefficiencies and their elimination to both technical and non-technical stakeholders, primarily due to its monetary nature. The identification and quantification of TD rely heavily on the use of a small handful of sophisticated tools that check for violations of certain predefined rules, usually through static analysis. Different tools result in divergent TD estimates calling into question the reliability of findings derived by a single tool. To alleviate this issue we use 18 metrics pertaining to source code, repository activity, issue tracking, refactorings, duplication and commenting rates of each class as features for statistical and Machine Learning models, so as to classify them as High-TD or not. As a benchmark we exploit 18.857 classes obtained from 25 Java projects, whose high levels of TD has been confirmed by three leading tools. The findings indicate that it is feasible to identify TD issues with sufficient accuracy and reasonable effort: a subset of superior classifiers achieved an F<sub>2</sub>-measure score of approximately 0.79 with an associated Module Inspection ratio of approximately 0.10. Based on the results a tool prototype for automatically assessing the TD of Java projects has been implemented.
Centralised Quality of Experience and Service Framework Using PROMETHEE-II for Cloud Provider Selection

The extensive diffusion of cloud services has fostered a business growth culture and innovation that propagate to many consumers and providers. For enabling a sustainable trusted relationship and for forming practicable successful service level agreements (SLAs), all stakeholders need a centralised Quality of Experience (QoE) and Quality of Service (QoS) repository that assists them in forming such an agreement. A cloud consumer needs a centralised QoE repository that supports them in selecting the right service provider that satisfies consumer's requirements in terms of cost, reliability, efficiency and other QoS parameters. On the other end, a cloud provider needs a reliable QoS repository that provides consumers with up-to-date information about services and enables a provider to take an optimal decision to allocate the amount of marginal resources while forming an SLA. Due to the elastic nature of a cloud and lack of proper resource management, the service provider usually caught in service violation, leading to violation penalties both in terms of trust and money. Existing literature lacks studies on a centralised repository to assist cloud providers in resource management and cloud consumer service selection. To address the issue, we discuss the idea of a Centralised Quality of Experience and Service (CQoES) repository framework. The approach uses PROMETHEE-II method where each alternatives are assessed based on consumer's custom weighted QoS attributes. The framework ensures the cloud marketplace's economic growth and helps the interacting parties build a durable and long-term trusted relationship.
A recommendation method of teaching resources based on similarity and ALS

Abstract This paper provides a collaborative filtering recommendation algorithm based on the combination of item similarity and alternating least square collaborative filtering based on the Spark platform. This is a recommended method to improve the efficiency of prediction calculations and reduce system response time. In order to solve the problem of model inaccuracy caused by the sparse data of the existing collaborative filtering recommendation scheme, which leads to the inaccuracy of recommending suitable online education and teaching resources to different users, the present invention uses the least squares collaborative filtering recommendation algorithm on the Spark big data analysis platform Optimize and use, and then use parallel methods to increase the amount of work completed per unit time and the accuracy of recommendations, and solve the problem of inaccurate recommendation of teaching resources.
A Hybrid Approach for an Interpretable and Explainable Intrusion Detection System

Cybersecurity has been a concern for quite a while now. In the latest years, cyberattacks have been increasing in size and complexity, fueled by significant advances in technology. Nowadays, there is an unavoidable necessity of protecting systems and data crucial for business continuity. Hence, many intrusion detection systems have been created in an attempt to mitigate these threats and contribute to a timelier detection. This work proposes an interpretable and explainable hybrid intrusion detection system, which makes use of artificial intelligence methods to achieve better and more long-lasting security. The system combines experts’ written rules and dynamic knowledge continuously generated by a decision tree algorithm as new shreds of evidence emerge from network activity.
Investors’ mood and herd investing: A quantile-on-quantile regression explanation from crypto market

This study uses daily data of 382 cryptocurrencies and a quantile-on-quantile regression (QQR) framework developed by Sim and Zhou (2015), to establish a link between herding behavior and investors’ mood and provide support for mood-as-information hypothesis in the crypto market. The results of QQR analysis reveal that the effect of investors’ mood on herd investing behavior is asymmetric and regime specific with a (weaker)higher (anti)herding tendency towards sad(happy) quantiles of investors’ mood. The results provide support to the portfolio managers by documenting that investors’ mood can be used as a signal to monitor the possible speculative activities in crypto market.
An Exploration And Validation of Visual Factors in Understanding Classification Rule Sets

Rule sets are often used in Machine Learning (ML) as a way to communicate the model logic in settings where transparency and intelligibility are necessary. Rule sets are typically presented as a text-based list of logical statements (rules). Surprisingly, to date there has been limited work on exploring visual alternatives for presenting rules. In this paper, we explore the idea of designing alternative representations of rules, focusing on a number of visual factors we believe have a positive impact on rule readability and understanding. We then presents a user study exploring their impact. The results show that some design factors have a strong impact on how efficiently readers can process the rules while having minimal impact on accuracy. This work can help practitioners employ more effective solutions when using rules as a communication strategy to understand ML models.
Days of future past

HTTP Adaptive Streaming (HAS) has become a predominant technique for delivering videos in the Internet. Due to its adaptive behavior according to changing network conditions, it may result in video quality variations that negatively impact the Quality of Experience (QoE) of the user. In this paper, we propose Days of Future Past, an optimization-based Adaptive Bitrate (ABR) algorithm over HTTP/3. Days of Future Past takes advantage of an optimization model and HTTP/3 features, including (i) stream multiplexing and (ii) request cancellation. We design a Mixed Integer Linear Programming (MILP) model that determines the optimal video qualities of both the next segment to be requested and the segments currently located in the buffer. If better qualities for buffered segments are found, the client will send corresponding HTTP GET requests to retrieve them. Multiple segments (i.e., retransmitted segments) might be downloaded simultaneously to upgrade some buffered but not yet played segments to avoid quality decreases using the stream multiplexing feature of QUIC. HTTP/3's request cancellation will be used in case retransmitted segments will arrive at the client after their playout time. The experimental results shows that our proposed method is able to improve the QoE by up to 33.9%.
Analysis of Detection Thresholds for Hand Redirection during Mid-Air Interactions in Virtual Reality

Avatars in virtual reality (VR) with fully articulated hands enable users to naturally interact with the virtual environment (VE). Interactions are often performed in a one-to-one mapping between the movements of the user's real body, for instance, the hands, and the displayed body of the avatar. However, VR also allows manipulating this mapping to introduce non-isomorphic techniques. In this context, research on manipulations of virtual hand movements typically focuses on increasing the user's interaction space to improve the overall efficiency of hand-based interactions.
COVID-19 Vaccination Certificates in the Darkweb

COVID-19 vaccines have been rolled out in many countries and with them a number of vaccination certificates. For instance, the EU is utilizing a digital certificate in the form of a QR-code that is digitally signed and can be easily validated throughout all EU countries. In this article, we document the current state of the COVID-19 vaccination certificate market in the darkweb with a focus on the EU Digital Green Certificate (DGC) . We investigate 17 marketplaces and 10 vendor shops that include vaccination certificates in their listings, and discover that a multitude of sellers in both types of platforms are advertising forging capabilities. According to their claims, it is possible to buy fake vaccination certificates issued in many countries worldwide. We demonstrate some examples of such sellers, including how they advertise their services, and we develop a taxonomy of EU COVID-19 certificate forging capabilities, describing the potential methods that the vendors are utilizing to generate certificates. We highlight two particular cases of vendor shops, with one of them showing an elevated degree of professionalism, showcasing forged valid certificates, the validity of which we verify using two different national mobile COVID-19 applications.
CharmFL: A Fault Localization Tool for Python

Fault localization is one of the most time-consuming and error-prone parts of software debugging. There are several tools for helping developers in the fault localization process, however, they mostly target programs written in Java and C/C++ programming languages. While these tools are splendid on their own, we must not look over the fact that Python is a popular programming language, and still there are a lack of easy-to- use and handy fault localization tools for Python developers. In this paper, we present a tool called “CharmFL” for software fault localization as a plug-in for PyCharm IDE. The tool employs Spectrum-based fault localization (SBFL) to help Python developers automatically analyze their programs and generate useful data at run-time to be used, then to produce a ranked list of potentially faulty program elements (i.e., statements, functions, and classes). Thus, our proposed tool supports different code coverage types with the possibility to investigate these types in a hierarchical approach. The applicability of our tool has been presented by using a set of experimental use cases. The results show that our tool could help developers to efficiently find the locations of different types of faults in their programs.
VLDeformer: Vision–Language Decomposed Transformer for fast cross-modal retrieval

Cross-model retrieval has emerged as one of the most important upgrades for text-only search engines (SE). Recently, with powerful representation for pairwise text–image inputs via early interaction, the accuracy of vision–language (VL) transformers has outperformed existing methods for text–image retrieval. However, when the same paradigm is used for inference, the efficiency of the VL transformers is still too low to be applied in a real cross-modal SE. Inspired by the mechanism of human learning and using cross-modal knowledge, this paper presents a novel Vision–Language Decomposed Transformer (VLDeformer), which greatly increases the efficiency of VL transformers while maintaining their outstanding accuracy. By the proposed method, the cross-model retrieval is separated into two stages: the VL transformer learning stage, and the VL decomposition stage. The latter stage plays the role of single modal indexing, which is to some extent like the term indexing of a text SE. The model learns cross-modal knowledge from early-interaction pre-training and is then decomposed into an individual encoder. The VLDeformer requires only small target datasets for supervision and achieves both 1000+ times acceleration and less than 0.6% average recall drop. VLDeformer also outperforms state-of-the-art visual-semantic embedding methods on COCO and Flickr30k.1
Natural Language Processing Applications in the Clinical Neurosciences: A Machine Learning Augmented Systematic Review

Natural language processing (NLP), a domain of artificial intelligence (AI) that models human language, has been used in medicine to automate diagnostics, detect adverse events, support decision making and predict clinical outcomes. However, applications to the clinical neurosciences appear to be limited. NLP has matured with the implementation of deep transformer models (e.g., XLNet, BERT, T5, and RoBERTa) and transfer learning. The objectives of this study were to (1) systematically review NLP applications in the clinical neurosciences, and (2) explore NLP analysis to facilitate literature synthesis, providing clear examples to demonstrate the potential capabilities of these technologies for a clinical audience. Our NLP analysis consisted of keyword identification, text summarization and document classification. A total of 48 articles met inclusion criteria. NLP has been applied in the clinical neurosciences to facilitate literature synthesis, data extraction, patient identification, automated clinical reporting and outcome prediction. The number of publications applying NLP has increased rapidly over the past five years. Document classifiers trained to differentiate included and excluded articles demonstrated moderate performance (XLNet AUC = 0.66, BERT AUC = 0.59, RoBERTa AUC = 0.62). The T5 transformer model generated acceptable abstract summaries. The application of NLP has the potential to enhance research and practice in the clinical neurosciences.
Cervical cell multi-classification algorithm using global context information and attention mechanism

Cervical cancer is the second biggest killer of female cancer, second only to breast cancer. The cure rate of precancerous lesions found early is relatively high. Therefore, cervical cell classification has very important clinical value in the early screening of cervical cancer. This paper proposes a convolutional neural network (L-PCNN) that integrates global context information and attention mechanism to classify cervical cells. The cell image is sent to the improved ResNet-50 backbone network to extract deep learning features. In order to better extract deep features, each convolution block introduces a convolution block attention mechanism to guide the network to focus on the cell area. Then, the end of the backbone network adds a pyramid pooling layer and a long short-term memory module (LSTM) to aggregate image features in different regions. The low-level features and high-level features are integrated, so that the whole network can learn more regional detail features, and solve the problem of network gradient disappearance. The experiment is conducted on the SIPaKMeD public data set. The experimental results show that the accuracy of the proposed l-PCNN in cervical cell accuracy is 98.89 %, the sensitivity is 99.9 %, the specificity is 99.8 % and the F-measure is 99.89 %, which is better than most cervical cell classification models, which proves the effectiveness of the model.
Semi-Supervised Contrastive Learning for Human Activity Recognition

Recent developments in deep learning have motivated the use of deep neural networks in mobile sensing applications. Human Activity Recognition (HAR), as one of the most important mobile sensing applications, has enjoyed great success due to the utilization of deep neural networks. Motivated by the success of self-supervised learning frameworks in computer vision and natural language processing, self-supervised models have been proposed to efficiently leverage massive unlabeled data and reduce the labeling burden of HAR applications. Current approaches use self-supervised pre-training (with unlabeled data) followed by downstream training (with labeled data). However, we claim that labeled data can still help in the pre-training process and propose SemiC-HAR, a Semi-supervised Contrastive learning framework for HAR. SemiC-HAR efficiently uses both of the labeled and unlabeled data during the pre-training process and combines the advantages of supervised and self-supervised contrastive learning frameworks. We evaluate SemiC-HAR on six HAR datasets with multiple sensing signals and show comparable performance to previous supervised and semi-supervised models seen at much lower fractions of labeled data.
Secure verifiable aggregation for blockchain-based federated averaging

IoT devices’ storage and computation capacities are constantly increasing in recent years, which brings critical challenges in data privacy protection. Federated learning (FL) and blockchain technology are two popular techniques used in IoT data aggregation, where FL enables data training with privacy protection, and blockchain provides a decentralized architecture for data storage and mining. However, very few the state-of-the-art works consider the applicability of the combination of FL and blockchain. In this paper, we adopt the federated averaging algorithm to reduce the communication overhead between the blockchain and end users to achieve higher performance. We also apply the double-mask-then-encrypt approach for end users to submit their local updates in order to protect data privacy. Finally, we propose and implement a non-interactive Public Verifiable Secret Sharing (PVSS) algorithm with Distributed Hash Table (DHT) that solves the user-drop-out problem and improves the communication efficiency between blockchain and end-users. At last, we theoretically analyze the security strengths of the proposed solution and conduct experiments to measure the execution time of PVSS on both the server and clients sides.