Reviewerly/exHarmony huggingface dataset:
Legislative Research on Personal Information Protection: Taking Face Recognition Information as an Example

With the rapid advancement of technology for artificial intelligence and big data technology, face recognition technology has entered people’s lives. The wide application of personal biological information will bring serious biological information security risks, and its abuse may cause the infringement of privacy, property rights, and other rights and interests. At present, China’s relevant legislation on personal biological information has shortcomings such as decentralized content, narrow protection scope, and unclear legal responsibilities. This paper mainly conducts relevant research on the above issues. The research process mainly adopts the case analysis method, and puts forward the existing problems and legislative suggestions through the analysis of relevant cases; Secondly, the comparative study method is used to compare the current legislative status of China with the relevant legislative status of mainstream countries such as the United States and the European Union. The protection of personal biological information needs to be paid attention to. In view of the current relevant actual situation and legislative status in China, on the one hand, it is necessary to clarify the legal attributes and concepts of personal information and personal biological information. On the other hand, it is essential to appropriately strengthen the punishment and legal liability for the infringement of personal biological information, and increase the attention to criminal liability. We will improve the regulatory content of existing relevant department laws on personal biometric information, and a more complete, clear, and effective legal system for personal biometric information security protection should be built.
SynthA1c: Towards Clinically Interpretable Patient Representations for Diabetes Risk Stratification

Early diagnosis of Type 2 Diabetes Mellitus (T2DM) is crucial to enable timely therapeutic interventions and lifestyle modifications. As the time available for clinical office visits shortens and medical imaging data become more widely available, patient image data could be used to opportunistically identify patients for additional T2DM diagnostic workup by physicians. We investigated whether image-derived phenotypic data could be leveraged in tabular learning classifier models to predict T2DM risk in an automated fashion to flag high-risk patients without the need for additional blood laboratory measurements. In contrast to traditional binary classifiers, we leverage neural networks and decision tree models to represent patient data as ‘SynthA1c’ latent variables, which mimic blood hemoglobin A1c empirical lab measurements, that achieve sensitivities as high as 87.6%. To evaluate how SynthA1c models may generalize to other patient populations, we introduce a novel generalizable metric that uses vanilla data augmentation techniques to predict model performance on input out-of-domain covariates. We show that image-derived phenotypes and physical examination data together can accurately predict diabetes risk as a means of opportunistic risk stratification enabled by artificial intelligence and medical imaging. Our code is available at https://github.com/allisonjchae/DMT2RiskAssessment .
A Cross-lingual Sentiment Embedding Model with Semantic and Sentiment Joint Learning

Unsupervised cross-lingual word embedding (CLWE) aligns monolingual embedding spaces without parallel corpora or bilingual dictionaries. However, it is unclear whether CLWE models tuned for different corpora alignment can perform well across NLP tasks. Previous researches have shown that unsupervised CLWE tends to make words have close word embedding distributions with similar syntactic but opposite sentiment polarity, leading to poor performance in cross-lingual sentiment analysis (CLSA). This work proposes an Unsupervised Cross-lingual Sentiment word Embedding (UCSentiE) model, which eliminates both linguistic and sentiment gap between two languages. UCSentiE leverages the priori sentiment information of source language and integrates them into CLWE without compromising cross-lingual word semantics. We evaluate UCSentiE on two NLP tasks across six languages (English, Chinese, German, Japanese, French and Spanish). Experimental results demonstrate UCSentiE’s stability in bilingual lexicon induction (BLI) and its superiority over unsupervised VecMap and supervised MUSE models in CLSA, with average F1 score improvements of about 6.53% and 2.23%, respectively. Visualization and clustering analysis further validates our approach’s effectiveness. Code is available at https://github.com/dwzgit/UCSentiE.git.
The family of the 21st century and the influence on the child's school performance

From a sociological perspective, „the family is a relatively permanent social group of individuals linked to each other by blood, origin, marriage or adoption, who share primary responsibility for the reproduction and care of members of society. The family is the fundamental instrumental core of the broader social structure, in the sense that all other institutions depend on its influences.” The family is also referred to as the foundation of society, later becoming a factor of social stability, being responsible for the tone it gives in relation to society, its stability also determining the stability of society. The way in which the family influences the development of children is complex and research can determine the effects of specific characteristics of parents in learning specific characteristics in children. The family climate is very important in determining educational influences and is a major role in the growth and education of the child and adolescent.
PENERAPAN DATA MINING PADA TRANSAKSI PENJUALAN MENGGUNAKAN ALGORITMA APRIORI DI ALFAMART CENTRE PARK

Along with the development of the economy from year to year, the retail industry is one of several industries that is experiencing quite good development, one of which is PT Sumber Alfaria Trijaya Tbk. or commonly known as Alfamart. Marketing activities at Alfamart that focus on buying and selling food and non-food goods with a large enough number of transactions in total daily sales, a company needs analytical tools to provide useful information for the company. Because Alfamart is a large-scale retail company, there are several problems that are often encountered daily, namely determining the layout of goods that are less strategic and easily noticed by customers, not knowing which products are most often purchased simultaneously by consumers and several types of goods in an overstock warehouse. Therefore, research was carried out with the implementation of data mining on sales at Alfamart using the Apriori algorithm and testing using the Rapidminer software to produce the highest association rules. The results of this study are the discovery of 10 association rules with a minimum support of 40% and 70% confidence which are expected to be recommendations for business actors to improve sales strategy.&#x0D; &#x0D;
ANALISIS POLA PEMBELIAN KONSUMEN MENGGUNAKAN ALGORITMA APRIORI

In order for sales transaction data to be useful and useful, data development or data mining methods are needed, especially in the use of the apriori association rule algorithm, in utilizing sales transaction data obtained from items purchased simultaneously by consumers when shopping. The data is used to find out items that are often purchased by consumers to increase their stock and reduce the stocking of goods that are not selling well. The data is also used by the store in arranging the placement of goods according to consumer behavior when shopping. Data retrieval with association rule is done through a mechanism with a minimum support of 0.17 and a minimum confidence of 0.60. The purpose of this study is to determine the results of analyzing transaction data that accumulates in clothing stores into useful information and to find out what items are bought simultaneously by consumers at clothing stores to manage inventory and can make it easier to arrange the placement of goods according to consumer interests based on the value of support and confidence. From the results of this study, items that have a relationship are Women's Tops and Men's Tops with a support value of 27.86% and a confidence value of 68.00%. This information can also be used to find out what items are in great demand and less in demand by consumers in determining what stock procurement should be prioritized in the future.
PENERAPAN DATA MINING UNTUK ANALISIS POLA PEMBELIAN KONSUMEN DENGAN ALGORITMA FP-GROWTH PADA DATA TRANSAKSI PENJUALAN SPAREPART MOTOR

The motorcycle repair shop is one of the factors that guarantees the comfort of using a motorcycle. Without a repair shop, riders will find it difficult to provide routine maintenance and repair their problem motorbikes. The data that has been collected is not only as archival data by the workshop but is utilized as data that is processed as information data that is used to increase sales of motor spare parts. This also has an impact on the utilization of existing sales transaction data because transaction data which is usually used as an archive causes accumulation of data whose benefits are not known, even though if the data is processed properly it can be useful as information used to make decisions in obtaining new knowledge about sales besides that it is difficult to make the right decision to determine the stock of goods based on spare part purchasing patterns, as well as the availability of repair shops in every corner of the city does not necessarily guarantee the comfort of motorbike users because there is no fast and accurate information. To get this information, it can be done using data mining techniques.
Efficient Multi-Authority Attribute-Based Searchable Encryption Scheme with Blockchain Assistance for Cloud-Edge Coordination

Cloud storage and edge computing are utilized to address the storage and computational challenges arising from the exponential data growth in IoT. However, data privacy is potentially risky when data is outsourced to cloud servers or edge services. While data encryption ensures data confidentiality, it can impede data sharing and retrieval. Attribute-based searchable encryption (ABSE) is proposed as an effective technique for enhancing data security and privacy. Nevertheless, ABSE has its limitations, such as single attribute authorization failure, privacy leakage during the search process, and high decryption overhead. This paper presents a novel approach called the blockchain-assisted efficient multi-authority attribute-based searchable encryption scheme (BEM-ABSE) for cloud-edge collaboration scenarios to address these issues. BEM-ABSE leverages a consortium blockchain to replace the central authentication center for global public parameter management. It incorporates smart contracts to facilitate reliable and fair ciphertext keyword search and decryption result verification. To minimize the computing burden on resource-constrained devices, BEM-ABSE adopts an online/offline hybrid mechanism during the encryption process and a verifiable edge-assisted decryption mechanism. This ensures both low computation cost and reliable ciphertext. Security analysis conducted under the random oracle model demonstrates that BEM-ABSE is resistant to indistinguishable chosen keyword attacks (IND-CKA) and indistinguishable chosen plaintext attacks (IND-CPA). Theoretical analysis and simulation results confirm that BEM-ABSE significantly improves computational efficiency compared to existing solutions.
AI-Driven FBMC-OQAM Signal Recognition via Transform Channel Convolution Strategy

With the advent of the Industry 5.0 era, the Internet of Things (IoT) devices face unprecedented proliferation, requiring higher communications rates and lower transmission delays. Considering its high spectrum efficiency, the promising filter bank multicarrier (FBMC) technique using offset quadrature amplitude modulation (OQAM) has been applied to Beyond 5G (B5G) industry IoT networks. However, due to the broadcasting nature of wireless channels, the FBMC-OQAM industry IoT network is inevitably vulnerable to adversary attacks from malicious IoT nodes. The FBMC-OQAM industry cognitive radio network (ICRNet) is proposed to ensure security at the physical layer to tackle the above challenge. As a pivotal step of ICRNet, blind modulation recognition (BMR) can detect and recognize the modulation type of malicious signals. The previous works need to accomplish the BMR task of FBMC-OQAM signals in ICRNet nodes. A novel FBMC BMR algorithm is proposed with the transform channel convolution network (TCCNet) rather than a complicated two-dimensional convolution. Firstly, this is achieved by designing a low-complexity binary constellation diagram (BCD) gridding matrix as the input of TCCNet. Then, a transform channel convolution strategy is developed to convert the image-like BCD matrix into a series-like data format, accelerating the BMR process while keeping discriminative features. Monte Carlo experimental results demonstrate that the proposed TCCNet obtains a performance gain of 8% and 40% over the traditional in-phase/quadrature (I/Q)-based and constellation diagram (CD)-based methods at a signal noise ratio (SNR) of 12 dB, respectively. Moreover, the proposed TCCNet can achieve around 29.682 and 2.356 times faster than existing CD-Alex Network (CD-AlexNet) and I/Q-Convolutional Long Deep Neural Network (I/Q-CLDNN) algorithms, respectively.
Query Preference Analysis on Cascade Inference Human–Object Interaction Detection Transformer

Detection transformers (DETR) have provided a novel solution to human–object interaction detection in a set-prediction manner, thanks to expressive learnable queries. However, few studies covered how queries implicitly affect model behaviors, which might contain clues for model improvement. Therefore, we propose two dataset-based analysis tools: score state space and query preference map. They provide data on the distribution of model predictions, which can reveal overall-level and query-level model properties. Starting with our baseline model, we find the model naturally regresses object boxes to overlap human boxes in the edge case of no-object verbs (stand, etc.), even without a related loss. We encourage this by patching the supervision with virtual objects, resulting in more stable query preference. Moreover, we show that two-stage decoders designed for cascade inference do not decouple tasks as intended. We infer this is caused by the empty instances used as negative samples, which suggests a redesign in the matching scheme. Further, we reveal how adding an oracle-query-based teacher model affects query roles with a tiny gain, indicating room for refinement. Our findings demonstrate how a simple focus on query behaviors can provide insights for improving models.
An Extensive Study and Review of Privacy Preservation Models for the Multi-Institutional Data

The deep learning models hold considerable potential for clinical applications, but there are many challenges to successfully training deep learning models. Large-scale data collection is required, which is frequently only possible through multi-institutional cooperation. Building large central repositories is one strategy for multi-institution studies. However, this is hampered by issues regarding data sharing, including patient privacy, data de-identification, regulation, intellectual property, and data storage. These difficulties have lessened the impracticality of central data storage. In this survey, we will look at 24 research publications that concentrate on machine learning approaches linked to privacy preservation techniques for multi-institutional data, highlighting the multiple shortcomings of the existing methodologies. Researching different approaches will be made simpler in this case based on a number of factors, such as performance measures, year of publication and journals, achievements of the strategies in numerical assessments, and other factors. A technique analysis that considers the benefits and drawbacks of the strategies is additionally provided. The article also looks at some potential areas for future research as well as the challenges associated with increasing the accuracy of privacy protection techniques. The comparative evaluation of the approaches offers a thorough justification for the research’s purpose.
Explainable Brain Age Prediction using coVariance Neural Networks.

In computational neuroscience, there has been an increased interest in developing machine learning algorithms that leverage brain imaging data to provide estimates of "brain age" for an individual. Importantly, the discordance between brain age and chronological age (referred to as "brain age gap") can capture accelerated aging due to adverse health conditions and therefore, can reflect increased vulnerability towards neurological disease or cognitive impairments. However, widespread adoption of brain age for clinical decision support has been hindered due to lack of transparency and methodological justifications in most existing brain age prediction algorithms. In this paper, we leverage coVariance neural networks (VNN) to propose an explanation-driven and anatomically interpretable framework for brain age prediction using cortical thickness features. Specifically, our brain age prediction framework extends beyond the coarse metric of brain age gap in Alzheimer's disease (AD) and we make two important observations: (i) VNNs can assign anatomical interpretability to elevated brain age gap in AD by identifying contributing brain regions, (ii) the interpretability offered by VNNs is contingent on their ability to exploit specific eigenvectors of the anatomical covariance matrix. Together, these observations facilitate an explainable and anatomically interpretable perspective to the task of brain age prediction.
Is an Ontology 'better' than a Relational Data Model?

Is an ontology “better” than a relational data model? “More expressive power” doesn’t always mean “better”. However, ontologies allow you to ratchet up power while keeping logic in data structures. By “relational data model”, folks typically mean “SQL model”. In the RDF world, this is roughly on par with a SHACL model, i.e. a model that expresses constraints on the shapes of entities and on the so-called “primitive”...
CF-BFT: A Dual-Mode Byzantine Fault-Tolerant Protocol Based on Node Authentication

The consensus protocol is one of the core technologies in blockchain, which plays a crucial role in ensuring the block generation rate, consistency, and safety of the blockchain system. Blockchain systems mainly adopt the Byzantine Fault Tolerance (BFT) protocol, which often suffers from slow consensus speed and high communication consumption to prevent Byzantine nodes from disrupting the consensus. In this paper, this paper proposes a new dual-mode consensus protocol based on node identity authentication. It divides the consensus process into two subprotocols: Check_BFT and Fast_BFT. In Check_BFT, the replicas authenticate the primary’s identity by monitoring its behaviors. First, assume that the system is in a pessimistic environment, Check_BFT protocol detects whether the current environment is safe and whether the primary is an honest node; Enter the fast consensus stage after confirming the environmental safety, and implement Fast_BFT protocol. It is assumed that there are nodes in total. If more than nodes identify that the primary is honest, it will enter the Fast_BFT process. In Fast_BFT, the primary is allowed to handle transactions alone, and the replicas can only receive the messages sent by the primary. The experimental results show that the CF-BFT protocol significantly reduces the communication overhead and improves the throughput and scalability of the consensus protocol. Compared with the SAZyzz protocol, the throughput is increased by 3 times in the best case and 60% in the worst case.
Injections Attacks Efficient and Secure Techniques Based on Bidirectional Long Short Time Memory Model

E-commerce, online ticketing, online banking, and other web-based applications that handle sensitive data, such as passwords, payment information, and financial information, are widely used. Various web developers may have varying levels of understanding when it comes to securing an online application. Structured Query language SQL injection and cross-site scripting are the two vulnerabilities defined by the Open Web Application Security Project (OWASP) for its 2017 Top Ten List Cross Site Scripting (XSS). An attacker can exploit these two flaws and launch malicious web-based actions as a result of these flaws. Many published articles focused on these attacks’ binary classification. This article described a novel deep-learning approach for detecting SQL injection and XSS attacks. The datasets for SQL injection and XSS payloads are combined into a single dataset. The dataset is labeled manually into three labels, each representing a kind of attack. This work implements some pre-processing algorithms, including Porter stemming, one-hot encoding, and the word-embedding method to convert a word’s text into a vector. Our model used bidirectional long short-term memory (BiLSTM) to extract features automatically, train, and test the payload dataset. The payloads were classified into three types by BiLSTM: XSS, SQL injection attacks, and normal. The outcomes demonstrated excellent performance in classifying payloads into XSS attacks, injection attacks, and non-malicious payloads. BiLSTM’s high performance was demonstrated by its accuracy of 99.26%.
Siamese Dense Pixel-Level Fusion Network for Real-Time UAV Tracking

Onboard visual object tracking in unmanned aerial vehicles (UAVs) has attracted much interest due to its versatility. Meanwhile, due to high precision, Siamese networks are becoming hot spots in visual object tracking. However, most Siamese trackers fail to balance the tracking accuracy and time within onboard limited computational resources of UAVs. To meet the tracking precision and real-time requirements, this paper proposes a Siamese dense pixel-level network for UAV object tracking named SiamDPL. Specifically, the Siamese network extracts features of the search region and the template region through a parameter-shared backbone network, then performs correlation matching to obtain the candidate region with high similarity. To improve the matching effect of template and search features, this paper designs a dense pixel-level feature fusion module to enhance the matching ability by pixel-wise correlation and enrich the feature diversity by dense connection. An attention module composed of self-attention and channel attention is introduced to learn global context information and selectively emphasize the target feature region in the spatial and channel dimensions. In addition, a target localization module is designed to improve target location accuracy. Compared with other advanced trackers, experiments on two public benchmarks, which are UAV123@10fps and UAV20L from the unmanned air vehicle123 (UAV123) dataset, show that SiamDPL can achieve superior performance and low complexity with a running speed of 100.1 fps on NVIDIA TITAN RTX.