Reviewerly/exHarmony huggingface dataset:
Visualizing Transform Relations of Multilayers in Deep Neural Networks for ISAR Target Recognition

Deep neural networks (DNNs) achieve state-of-the-art performance in many of the tasks such as image classification, speech recognition and so on, but the principle of them is like a black box. In this paper, we propose a method to combine several connected layers into one layer to visualize the transform relations represented by the connected layers. In theory, this method can visualize the transformation between any two layers in DNNs and is more efficient to analyze the changes of the transformation across different layers compared with other visualization algorithms like deconvolution or saliency maps. Furthermore, we visualize the transform relations not only for a specific input image but the class which all the input images belong to.
Exclusion zone analysis on secret key distillation over a realistic satellite-to-satellite free-space channel

Quantum cryptography is the study of unconditional information security against an all-powerful eavesdropper in secret key distillation. However, the assumption of an omnipotent eavesdropper is too strict for some realistic implementations. In this paper, we study the realistic application model of secret key distillation over a satellite-to-satellite free-space channel in which we impose a reasonable restriction on the eavesdropper by setting an exclusion zone around the legitimate receiver as a defense strategy. We first study the case where the eavesdropper’s aperture size is unlimited so their power is only restricted by the exclusion zone. Then, we limit Eve’s aperture to a finite size and study the straightforward case when her aperture is in the same plane of Bob’s, investigating how an exclusion zone can help improve security. Correspondingly, we determine the secret key rate lower bounds as well as upper bounds. Furthermore, we also apply our results on specific discrete variable (DV) and continuous variable (CV) protocols for comparison. We show that, by putting reasonable restrictions on the eavesdropper through the realistic assumptions of an inaccessible exclusion zone, we can significantly increase the key rate in comparison to those without and do so with relatively lower transmission frequency. We conclude that this model is suitable for extended analysis in many light-gathering scenarios and for different carrier wavelengths.
CAVE-SC: Inferring categories for venues using check-ins

The categories of venues are labels, such as Museum and Bar, assigned to geographic locations. They are often incomplete and new venues may constantly arise, making automatic venue labeling essential. In this study, we proposed a category-aware venue embedding model with similarity constraints (CAVE-SC), which is an automatic feature engineering method to infer category labels for venues. CAVE-SC first models the co-occurrence relations between a given venue and its two types of sequential contexts (namely, the venue context and the category context) based on a softmax loss and then projects both venues and categories into the same latent space. Furthermore, CAVE-SC models the similarity constraints posed by the relationships between venues and their categories based on a newly designed triplet loss, assuming that the distance between a venue and its category is smaller than the distance between the venue and a randomly sampled category in the embedding space. Finally, linear interpolation is used to balance the two losses, thereby generating discriminative feature representations of venues. Venue labeling can be performed based on the distances between venue representations and category representations. Extensive experiments on two check-in datasets demonstrate the superiority of CAVE-SC over the baseline methods.
Sistem Pendukung Keputusan Pemilihan Smartphone Gaming Android Terbaik Dengan Metode Weighted Product

The lack of information in choosing an effective android gaming smartphone makes it difficult for people to choose the best gaming smartphone while people only know about ordinary smartphones with criteria that are not for games. By conducting this research, it can provide information in making decisions to determine the best Android gaming smartphone. Solutions using a decision support system using the Weighted Product (WP) method, the Weighted Product method can assist in making decisions but calculations using this method only produce the largest value that will be selected as the best alternative. The targeted population is the area of North Bekasi which amounted to 329,976 people with a presentation of leeway of five percent (5%) using the slovin formula and the results obtained were 399.515 rounded up to 400 samples. The criteria that have been set based on standard specification data for gaming smartphones for playing games are (price, battery, RAM, ROM, Chipset, and GPU) as well as alternatives selected based on the best gaming cellphones in 2021, namely Asus ROG Phone 3, Black Shark 3, Realme X50 Pro 5G, OPPO Find X2 Pro, Nubia Red Magic 5G. By using a decision support system using the Weighted Product method, it is concluded that the selection of the best Smartphone Gaming at this time in the North Bekasi area and the appropriate criteria have obtained satisfactory results in the selection of the best Gaming Smartphone. In other words, Asus ROG Phone 3, Black Shark 3 and OPPO Find X2 Pro are recommended as the best Android Gaming Smartphones today.
Minimum Sample Size Estimate for Classifying Invasive Lung Adenocarcinoma

Statistical Learning Theory (SLT) plays an important role in prediction estimation and machine learning when only limited samples are available. At present, determining how many samples are necessary under given circumstances for prediction accuracy is still an unknown. In this paper, the medical diagnosis on lung cancer is taken as an example to solve the problem. Invasive adenocarcinoma (IA) is a main type of lung cancer, often presented as ground glass nodules (GGNs) in patient’s CT images. Accurately discriminating IA from non-IA based on GGNs has important implications for taking the right approach to treatment and cure. Support Vector Machine (SVM) is an SLT application and is used to classify GGNs, wherein the interrelation between the generalization and the lower bound of necessary sampling numbers can be effectively recovered. In this research, to validate the interrelation, 436 GGNs were collected and labeled using surgical pathology. Then, a feature vector was constructed for each GGN sample through the fully connected layer of AlexNet. A 10-dimensional feature subset was then selected with the p-value calculated using Analysis of Variance (ANOVA). Finally, four sets with different sample sizes were used to construct an SVM classifier. Experiments show that a theoretical estimate of minimum sample size is consistent with actual values, and the lower bound on sample size can be solved under various generalization requirements.
Accelerated Inference of Face Detection under Edge-Cloud Collaboration

Model compression makes it possible to deploy face detection models on devices with limited computing resources. Edge–cloud collaborative inference, as a new paradigm of neural network inference, can significantly reduce neural network inference latency. Inspired by these two techniques, this paper adopts a two-step acceleration strategy for the CenterNet model. Firstly, the model pruning method is used to prune the convolutional layer and the deconvolutional layer to obtain a preliminary acceleration effect. Secondly, the neural network is segmented by the optimizer to make full use of the computing resources on the edge and the cloud to further accelerate the inference of the neural network. In the first strategy, we achieve a 62.12% reduction in inference latency compared to the state-of-the-art object detection model Blazeface. Additionally, with a two-step speedup strategy, our method is only 26.5% of the baseline when the bandwidth is 500 kbps.
STRIPED: A Threat Analysis Method for IoT Systems

Currently, IoT systems display a poor level of security, as 50% of IoT devices are vulnerable to severe attacks, according to research. In an attempt to ameliorate the situation, we propose STRIPED, a threat analysis technique that focuses particularly on threat scenarios involving IoT devices that can be physically accessed by attackers. We evaluate STRIPED in a two-pronged way. First, we assess its performance compared to STRIDE (from which STRIPED is derived) in the context of a case study from the manufacturing industry. Second, we gather the feedback of 8 security experts working in a large, multinational company that specializes in secure IoT products for the domains of automotive, industrial, mobile and smart-home applications. These initial evaluation attempts provide encouraging evidence and suggest our method is a step in the right direction of facilitating security-by-design in IoT systems, especially industrial ones.
MI-GCN: Node Mutual Information-based Graph Convolutional Network

Graph Neural Networks (GNNs) have been widely used in various processing tasks for processing graphs and complex network data. However, in recent studies, GNNs cannot effectively process the structural topology information and characteristics of the nodes in the graph, or even fail to deal with the information of the nodes. For optimal node embedding aggregation and delivery, this weakness may severely affect the ability of GNNs to classify nodes. In order to overcome this issue, we propose a novel node Mutual Information-based Graph Convolutional Network (MI-GCN) for semi-supervised node classification. First, we analyze the node information entropy that measures the importance of nodes in the complex network, and further define the node joint information entropy and node mutual information in the graph data. Then, we use node mutual information to strengthen the ability of GNNs to fuse node structural information. Extensive experiments demonstrate that our MI-GCN not only retains the advantages of the most advanced GNNs, but also improves the ability to fuse node structural information. MI-GCN can achieve superior performance on node classification compared to several baselines on real-world multi-type datasets, including fixed data splits and random data splits.
HWST128

Memory safety is paramount for secure systems. Pointer-based memory safety relies on additional information (metadata) to check validity when a pointer is dereferenced. Such operations on the metadata introduce significant performance overhead to the system. This paper presents HWST128, a system to reduce performance overhead by using hardware/software co-design. As a result, the system described achieves spatial and temporal safety by utilizing microarchitecture support, pointer analysis from the compiler, and metadata compression. HWST128 is the first complete solution for memory safety (spatial and temporal) on RISC-V. The system is implemented and tested on a Xilinx ZCU102 FPGA board with 1536 LUTs (+4.11%) and 112 FFs (+0.66%) on top of a Rocket Chip processor. HWST128 is 3.74× faster than the equivalent software-based safety system in the SPEC2006 benchmark suite while providing similar or better security coverage for the Juliet test suite.
Contrastive quant

Contrastive learning learns visual representations by enforcing feature consistency under different augmented views. In this work, we explore contrastive learning from a new perspective. Interestingly, we find that quantization, when properly engineered, can enhance the effectiveness of contrastive learning. To this end, we propose a novel contrastive learning framework, dubbed Contrastive Quant, to encourage feature consistency under both differently augmented inputs via various data transformations and differently augmented weights/activations via various quantization levels. Extensive experiments, built on top of two state-of-the-art contrastive learning methods SimCLR and BYOL, show that Contrastive Quant consistently improves the learned visual representation.
CopypastaVulGuard – A browser extension to prevent copy and paste spreading of vulnerable source code in forum posts

Forums such as Stack Overflow are used by many software developers to find a solution for a given coding problem. Found solutions, i.e. forum posts containing relevant source code, are utilized in a copy and paste manner. This behavior carries the risk that vulnerabilities contained in the source code of the forum posts are spread. Software developers should be able to identify vulnerable source code at an early stage, thereby preventing copying the corresponding source code. In this paper, we introduce the tool CopypastaVulGuard that identifies vulnerable source code in forum posts and allows software developers to omit the source code by marking the forum posts as dangerous. Our tool consists of a browser extension and a management application capable to address as examples SQL injections, remote code executions and deprecated functions based on a dump of the archive.org Stack Overflow data set. We present an evaluation of our tool’s possible impact and relevance considering pros/cons and selected research questions.
TAIM

Recently, various in-memory computing accelerators for low precision neural networks have been proposed. While in-memory Binary Neural Network (BNN) accelerators achieved significant energy efficiency, BNNs show severe accuracy degradation compared to their full precision counterpart models. To mitigate the problem, we propose TAIM, an in-memory computing hardware that can support ternary activation with negligible hardware overhead. In TAIM, a 6T SRAM cell can compute the multiplication between ternary activation and binary weight. Since the 6T SRAM cell consumes no energy when the input activation is 0, the proposed TAIM hardware can achieve even higher energy efficiency compared to BNN case by exploiting input 0's. We fabricated the proposed TAIM hardware in 28nm CMOS process and evaluated the energy efficiency on various image classification benchmarks. The experimental results show that the proposed TAIM hardware can achieve ~ 3.61× higher energy efficiency on average compared to previous designs which support ternary activation.
Biomedical Word Sense Disambiguation with Contextualized Representation Learning

Representation learning is an important component in solving most Natural Language Processing (NLP) problems, including Word Sense Disambiguation (WSD). The WSD task tries to find the best meaning in a knowledge base for a word with multiple meanings (ambiguous word). WSD methods choose this best meaning based on the context, i.e., the words around the ambiguous word in the input text document. Thus, word representations may improve the effectiveness of the disambiguation models if they carry useful information from the context and the knowledge base. Most of the current representation learning approaches are that they are mostly trained on the general English text and are not domain specified. In this paper, we present a novel contextual-knowledge base aware sense representation method in the biomedical domain. The novelty in our representation is the integration of the knowledge base and the context. This representation lies in a space comparable to that of contextualized word vectors, thus allowing a word occurrence to be easily linked to its meaning by applying a simple nearest neighbor approach. Comparing our approach with state-of-the-art methods shows the effectiveness of our method in terms of text coherence.
A Spatio-Temporal Data-Driven Automatic Control Method for Smart Home Services

With the rapid development of smart home technologies, various smart devices have entered and brought convenience to people’s daily life. Meanwhile, higher demands for smart home services have gradually emerged, which cannot be well satisfied by using traditional service provisioning manners. This is because traditional smart home control systems commonly rely on manual operations and fixed rules, which cannot satisfy changeable user demands and may seriously degrade the user experience. Therefore, it is necessary to capture user preferences based on their historical behavior data. To address the above problems, a temporal knowledge graph is first proposed to support the acquisition of user-perceived environmental data and user behavior data. Next, a user-oriented smart home service prediction model is designed based on the temporal knowledge graph, which can predict the service status and automatically perform the corresponding service for each user. Finally, a prototype system is built according to a real-world smart home environment. The experimental results show that the proposed method can provide personalized smart home services and well satisfy user demands.
The cybersecurity-related ethical issues of cloud technology and how to avoid them

Nowadays, cloud technology is assuming immense significance, being treated as a critical infrastructure, and is also a buzzword. Nevertheless, the technology has also brought about a number of new adverse phenomena and threats; it has attracted criminals, as well. Whenever the questions of “good” and “bad” arise, the ethical issues arise alongside them; the cybersecurity of cloud technology is no exception. This paper deals with the ethical dilemmas of cloud technology. It discusses a collection of the ethical issues of the cloud technology presented from the perspective of cybersecurity, based on the state-of-the-art literature. The main contribution of this work is that it gathers, synthesizes and organises the cybersecurity-related ethical dilemmas of cloud technology, thus offering the most extensive collection thereof. In addition, the work presents a comprehensive list of recommendations and suggestions which may help solve or prevent these ethical issues, and are a good starting point for anyone designing an ethical cybersecurity strategy.
Raven

Debugging is an essential part of kernel development. However, debugging features are not available on RISC-V without the use of external hardware. In this paper, we leverage a security feature called Physical Memory Protection (PMP) as a debugging primitive to address this issue. Based on this debugging primitive, we design Raven, a novel kernel debugging tool with the standard functionalities (breakpoints, watchpoints, stepping, introspection). A prototype of Raven is implemented on a SiFive Unmatched development board. Our experiments show that Raven imposes a moderate but acceptable overhead to the kernel. Moreover, a real-world debugging scenario is set up to test its effectiveness.
RegVault

This paper presents RegVault, a hardware-assisted lightweight data randomization scheme for OS kernels. RegVault introduces novel cryptographically strong hardware primitives to protect both the confidentiality and integrity of register-grained data. RegVault leverages annotations to mark sensitive data and instruments their loads and stores automatically. Moreover, RegVault also introduces new techniques to protect the interrupt context and safeguard the sensitive data spilling. We implement a prototype of RegVault by extending RISC-V architecture to protect six types of sensitive data in Linux kernel. Our evaluations show that RegVault can defend against the kernel data attacks effectively with a minimal performance overhead.
Alleviating datapath conflicts and design centralization in graph analytics acceleration

Previous graph analytics accelerators have achieved great improvement on throughput by alleviating irregular off-chip memory accesses. However, on-chip side datapath conflicts and design centralization have become the critical issues hindering further throughput improvement. In this paper, a general solution, Multiple-stage Decentralized Propagation network (MDP-network), is proposed to address these issues, inspired by the key idea of trading latency for throughput. Besides, a novel High throughput Graph analytics accelerator, HiGraph, is proposed by deploying MDP-network to address each issue in practice. The experiment shows that compared with state-of-the-art accelerator, HiGraph achieves up to 2.2× speedup (1.5× on average) as well as better scalability.
A Study on the Development of Learners in Hairdressing Using Vygotsky&amp;apos;s Zone of Proximal Development

Among the hair and beauty subjects, “Upstyle practice” education is not a regular curriculum but a “separate subject” that is “opened separately by researchers” and is a subject that is not included in the national technical qualification (beautician) exam, but is cooperative learning for learners. This is because it is possible to show artistry through this, and it is judged that it is appropriate to present the development of learners because of its easy participation and high utilization, and it can be selected as a subject for research and experimental research. The purpose of this study is to promote learner development and suggest educational effects by setting scaffolding and measuring interest and learning attitudes in small-group cooperative learning of Vygotsky's Zone of Proximal Development Theory. In the theoretical study, as a literature review on Vygotsky's educational theory, the researcher used Vygotsky's Zone of Proximal Development (ZPD). was designed. Classes using Vygotsky's Zone of Proximal Development showed relatively high interest and learning attitudes compared to traditional classes. can see. In the future, it is expected that continued research will be conducted on the content of hair and beauty practice courses that can be incorporated into daily life naturally.
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Let Fq be a finite field and t be a positive integer. We first generalize the construction of algebraic-geometric codes to the setting of Fq-linear Fqt-codes. Then we show that such codes arising from the projective line yield MDS (resp. self-dual MDS) Fq-linear Fqt-codes (resp. when q is a power of 2). We also derive a tight upper bound for the maximal length of primitive MDS elliptic Fq-linear Fqt-codes with Fq-dimension k divided by t and satisfies 3≤kt≤q+1−2q20.
A Comprehensive Reliability Assessment Method for Existing Railway Bridges Based on Bayesian Theory

The increase in traffic volume and train load poses new challenges to the reliability assessment of existing railway bridges. To construct a comprehensive assessment method for the safety and reliability of existing railway bridges, firstly, the risk factors of railway bridge structures are analyzed and the evaluation criteria are determined; secondly, based on the accident tree theory, a multilevel Bayesian network model with key points is established, and the ability of the Bayesian network bidirectional reasoning and sensitivity analysis is used to evaluate the structural safety; finally, the result was applied to the marina northern Songhua River extra-large bridge to verify the applicability of the comprehensive evaluation of the reliability of an existing railway bridge. This approach provides a theoretical basis for the maintenance and reinforcement of the Songhua River Bridge along the Bin-North Line.