Reviewerly/exHarmony huggingface dataset:
Optimal Trading Mechanism Based on Differential Privacy Protection and Stackelberg Game in Big Data Market

Big data has become a fundamental resource and a commodity in economic activities, thus, it is necessary to build a market model capable of supporting efficient data trading. However, two major challenges remain. First, researches have considered constructing data trading mechanisms, while few of them are based on the method of measuring data value in multiple dimensions. Second, a data market involved an intermediary trading platform (i.e., a third party) which is honest but curious, results may be obtained due the the leakage of private information. In this article, we design TM-OUE, a data trading mechanism based on Optimized Unary Encoding that enables reasonable trading mechanism and protects the privacy of data trading. First of all, we combine qualitative and quantitative methods to measure the value of data in multiple dimensions and formulate data trading model between the data provider and data users. Then, we utilize an Optimized Unary Encoding (OUE) protocol to protect the privacy of the data trading mechanism. Based on the above steps, we develop a two-stage single leader multi-follower Stackelberg game to jointly maximize profits of the data provider and data users. Experimental results demonstrate that TM-OUE can offer appropriate price for data and maximize benefits both for data providers and data users, which guarantees fair data trades while protecting privacy.
A Weighted Optimal Scheduling Scheme for Congestion Control in Cloud Data Center Networks

Cloud services are experiencing a remarkable increase in the number of users and the resource required over the past few years. Thus, it has become a great challenge for the internet vendors to make a robust framework to serve the customers with low cost and delay. Congestion control is one of the essential topics of routing algorithms in cloud data center networks. In this paper, we propose a weighted optimal scheduling scheme WSPR for congestion control in cloud data center networks which prevents the congestion in advance with the global view so that it can make good use of vacant network resources. We choose BCube as our network model and modify the network topology to fit software-defined networks so as to have a full view of the topology. First, we design the SP graph which contains all shortest paths between a source server and a destination server. Second, we propose WSPR to allocate the most appropriate path to each flow for congestion control. We implement a system to simulate a data center, and evaluate our proposed scheme WSPR by comparing WSPR with other classical methods. The experimental results demonstrate that our proposed scheme WSPR has the best performance in terms of the maximum delay, average delay, and throughput among all compared methods.
Landlord: Coordinating Dynamic Software Environments to Reduce Container Sprawl

Containers provide customizable software environments that are independent from the system on which they are deployed. Online services for task execution must often generate containers on the fly to meet user-generated requests. However, as the number of users grows and container environments are changed and updated over time, there is an explosion in the number of containers that must be managed, despite the fact that there is significant overlap among many of the containers in use. We analyze a trace of container launches on the public Binder service and demonstrate the performance and resource usage issues associated with container sprawl. We present <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Landlord</small> , an algorithm that coalesces related container environments, and show that it can improve container reuse and reduce the number of container builds required in the Binder trace by 40%. We perform a sensitivity analysis of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Landlord</small> using randomized synthetic workloads on a high-energy physics (HEP) software repository and demonstrate that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Landlord</small> shows benefits for container management across a wide range of usage patterns. Finally, we compare <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Landlord</small> to offline clustering, and observe that the continuous churn in software necessitates an online approach.
TS-WHT: A Two-Step Walsh–Hadamard Transform Approach for Blind Error Correcting Code Classification

Blind classification of error-correcting codes is essential for intelligent wireless communication, cognitive radio, and non-cooperative communication. The object of this letter is to investigate the differences in the Walsh spectrum between linear block code, convolutional code, and Turbo code. Based on these differences, a two-step Walsh Hadamard transform (TS-WHT) strategy is proposed for error-correcting code blind classification. Further, according to the detection theory, the peak detection threshold is detailed and optimized with the cyclic feature of CRC codes. The results indicate that when employing the optimized threshold, probabilities of correct classification are superior for linear block codes and convolutional codes. Moreover, with the help of the fault-tolerant feature of the WHT technique, the classification accuracy of the proposed method is substantially better than existing approaches under low signal-to-noise ratio (SNR) conditions.
Learning by Transference: Training Graph Neural Networks on Growing Graphs

Graph neural networks (GNNs) use graph convolutions to exploit network invariances and learn meaningful feature representations from network data. However, on large-scale graphs convolutions incur in high computational cost, leading to scalability limitations. Leveraging the graphon—the limit object of a graph—in this paper we consider the problem of learning a graphon neural network (WNN)—the limit object of a GNN—by training GNNs on graphs sampled from the graphon. Under smoothness conditions, we show that: (i) the expected distance between the learning steps on the GNN and on the WNN decreases asymptotically with the size of the graph, and (ii) when training on a sequence of growing graphs, gradient descent follows the learning direction of the WNN. Inspired by these results, we propose a novel algorithm to learn GNNs on large-scale graphs that, starting from a moderate number of nodes, successively increases the size of the graph during training. This algorithm is further benchmarked on a recommendation system and a decentralized control problem, where it retains comparable performance to its large-scale counterpart at a reduced computational cost.
AI Voice-Assisted Fitness Coach with Body Pose Recognition

The aim of this work was to develop an AI-controlled fitness trainer to tend to each user's needs. It includes an AI-based voice assistant that acts as a virtual fitness trainer to guide the user in performing a certain routine of exercises, which was implemented through the use of NLP to recognize the user's voice for commands to activate the trainer and body pose recognition to monitor the user's postures for the workouts in real-time. This work combined two different features and collectively helped the user to workout in a more efficient way. The trainer can currently perform these operations over a set of 10 workouts—7 for muscle workouts and 3 for cardio. Once the workout session is complete, a bar plot consisting of all the exercises performed during that session is constructed and stored on the user's device. The study below goes into further detail on the major insinuations for future fitness coach design and assessment.
Auxiliary Figure Presentation Associated with Sweating on a Viewer’s Hand in Order to Reduce VR Sickness

We have proposed a system that superimposes auxiliary figures on a VR scene according to viewer’s physiological signals that are responses to the viewer’s VR sickness, in order to reduce VR sickness but not interfere with viewer's sense of immersion. We conducted an experiment to find a type of physiological signals that correlated strongly with VR sickness. The results showed that sweating on a viewer’s hand was strongly correlated, and that the amount of sweating tended to increase as the VR sickness worsened. Then, we designed and developed the proposed system that controlled degree of alpha blending of color of Dots as an auxiliary figure that reduces VR sickness according to amount of the sweating and conducted an experiment to evaluate it. The results showed that the effect of the system to reduce VR sickness was found to be effective for the participants with less VR experience although there was no statistically significant difference among the experimental conditions. Additionally, the results also showed that the controlled presentation of the system was less distracting on immersion of a VR scene than the constant presentation as a previous system.
Improving the Transferability of Adversarial Attacks Through Both Front and Rear Vector Method

Deep Neural Networks (DNNs) are vulnerable to adversarial attacks, which makes adversarial attacks serve as a method to evaluate the robustness of DNNs. However, adversarial attacks have the disadvantage of high white-box attack success rates but low transferability. Therefore, many methods were proposed to improve the transferability of adversarial attacks, one of which is the momentum-based method. To improve the transferability of the existing adversarial attacks, we propose Previous-gradient as Neighborhood NI-FGSM (PN-NI-FGSM) and Momentum as Neighborhood NI-FGSM (MN-NI-FGSM), both of which are the momentum-based attacks. The results show that momentum describes the neighborhood more preciselfy than the previous gradient. Additionally, we define the front vector and the rear vector. Then, we classify momentum-based attacks into front vector attacks and rear vector attacks. Finally, we propose Both Front and Rear Vector Method (BFRVM), which combines the front vector attacks and the rear vector attacks. The experiments show that our BFRVM attacks achieve the best transferability against normally trained models and adversarially trained models under the single-model setting and ensemble-model setting, respectively.
Introduction

We start with a brief introduction to medical image computing, which can be roughly categorized into medical image reconstruction and analysis. We then present a unifying perspective that regards medical image computing as function approximation, which leads to the prevalence of the deep learning based method as it is one of the most powerful methods for function approximation. However, an open problem in using deep neural networks is the lack of network design principles, especially for medical image computing applications where data-intensive methods are often restricted and domain knowledge is important. We attempt to address this open problem. Finally, we describe the organization of this book.
Registration: 2D/3D rigid registration

We introduce how to leverage deep learning models to solve problems in medical image registration. Since medical image registration is a broad topic, we focus on the two popular directions in addressing medical image registration with deep learning, namely deep iterative registration and transformation estimation based registration. For each direction, we present two categories of approaches and introduce the choices of object functions, as well as example models for particular registration applications. In the case study, we limit the problem to the 2D/3D rigid registration and investigate how the problem can be addressed in practice. We give a formal introduction to the 2D/3D rigid registration problem setting and propose a Point-Of-INterest Tracking (POINT) based registration approach. Specifically, we provide details on how to design a U-Net based Siamese network for tracking, and on how to establish 3D point correspondences from the 2D tracking maps. We show that the proposed approach achieves better registration performance while being more computationally efficient than reinforcement learning based and optimization-based approaches.
Self-timed Masking: Implementing Masked S-Boxes Without Registers

Masking is one of the most used side-channel protection techniques. However, a secure masking scheme requires additional implementation costs, e.g. random number, and transistor count. Furthermore, glitches and early evaluation can temporally weaken a masked implementation in hardware, creating a potential source of exploitable leakages. Registers are generally used to mitigate these threats, hence increasing the implementation’s area and latency. In this work, we show how to design glitch-free masking without registers with the help of the dual-rail encoding and asynchronous logic. This methodology is used to implement low-latency masking with arbitrary protection order. Finally, we present a side-channel evaluation of our first and second order masked AES implementations.
PERANCANGAN PENGELOLAAN DATA SANGGAR SENI SAAYUN SALANGKAH KOTA BUKITTINGGI

The design of Data Management for the SaayunSalapak Art Studio serves to design an application that is expected to support the management of art gallery data, member registration and studio event schedule data which is intended to make it easy for members to get the information needed by studio members and make it easier for admins to manage schedule of events, decision making and by utilizing web programming as application software is expected to replace ways that are less effective and efficient and are expected to facilitate reporting on the results of the decision making process. Design and manufacture of data processing applications using the System Development Life Cycle (SDLC) method. Starting from problem identification, design, modeling to implementation.
Current Situation and Trend Analysis of Domestic General Upgraded Education Research Based on CiteSpace

Upgraded education is an important form of higher education in China. In recent years, the country has expanded the enrollment scale of upgraded education. Using Citespace 6.1 software, taking CNKI database as the literature source and taking 2012–2021 as the time node, 464 journal papers related to domestic general upgraded education were retrieved. The volume of papers, journal sources, authors, institutional cooperation, research hotspots and research trends were analyzed and the corresponding maps were formed, so as to show the research hotspots and research trends of domestic general upgraded education. After analysis, the following results are obtained: the domestic general college to undergraduate education research shows a gentle trend, and the research content is mainly carried out from the aspects of teaching reform, talent training, student management and so on. For this purpose, the article summarizes and ponders according to the current research situation, in order to provide reference for the reform of domestic post-secondary education.
A Document Image Quality Assessment Algorithm Based on Information Entropy in Text Region

The quality of the image is critical to Optical Character Recognition (OCR), poor quality images will lead OCR to generate unreliable results. There are relative high ratio of low quality images in practical OCR-based application scenarios, how to evaluate quality of image and filter out unqualified images by document image quality assessment (DIQA) algorithms effectively is a big challenge for these scenarios. Current DIQA algorithms mainly focus on the overall image features rather than the text region, while the quality of the text region is dominant factor for OCR. In this paper, we propose a document image quality assessment algorithm based on information entropy in text region of image. Our algorithmic framework mainly consists of three networks to detect, extract and evaluate text region in image respectively. We build a quality prediction network based on HyperNet, and use the information entropy of the text region as the score weight, so that the final score can reflect the quality of the text region better. Finally, testing results on benchmark dataset SmartDoc-QA and our constructed dataset DocImage1k demonstrate that the proposed algorithm achieves excellent performance.
No Reference Image Quality Assessment Based on Self-supervised Learning

No-Reference Image Quality Assessment (NR-IQA) is an underlying research topic that has been of interest to the research field of computer vision for a long time. Image quality assessment has a major role in image manipulation systems for algorithm analysis and verification, as well as system capability evaluation. In recent, so much researches already raised solve this subject with deep neural networks, but this problem is still a great challenge because the absence of labeled train samples. We propose a self-supervised learning-based research methods to solve the subject of lack of labeled training samples. We first use the classification prediction of the type and degree of image distortion as an auxiliary task of self-supervised learning. To enable better learning of representations, realistic distorted images and synthetic distorted images are included in the dataset selection. We use a contrastive learning approach to train a network framework to solve the proposed auxiliary task and add a mixed domain attention module to improve local distortion prediction. After training, we use a linear regressor to map the learned features to a no-reference environment to evaluate the quality for the score. We validate our findings through extensive experiments, which is indicated that the method present in this paper can also achieve high quality representations with subjective consistency without a significant amount of artificial labeled datasets.
Design of FPGA Circuit for SHA-3 Encryption Algorithm

In the age of Internet information, how to ensure the information security of users has gradually become a key subject of research. Cryptography is an important foundation for the research and protection of information security, and the proposal and design of cryptographic algorithms cannot be separated from its theoretical support. This paper studies the principle and encryption process of the SHA-3 encryption algorithm and uses the Verilog hardware programming language to realize the circuit design of the algorithm. The Virtex-5 chip of Xilinx is selected to conduct the synthesis and layout of the algorithm. The algorithm function was simulated and verified using ISE software, and the simulation result was compared with the encryption result of the official algorithm integrated in the Python standard library, so that the algorithm function was correct. In terms of performance, through the comparison with references, the hardware performance of this paper is significantly improved.
A Survey of Counterfactual Explanations: Definition, Evaluation, Algorithms, and Applications

Explainable machine learning aims to reveal the reasons why black-box models make decisions. Counterfactual explanation is an example-based post-hoc explanation method. The counterfactual explanations aims to find the minimum perturbation that changes the model output with respect to the original instance. This study’s goal is to review the literature that has already been written about counterfactual explanations and topics that are relevant to it. We provide a formal definition of counterfactual explanations and counterfactual explainer, and a summary and formulaic description of the properties of the generated counterfactual instances. In addition, we investigate the application of counterfactual explanations in two areas: model robustness, and generating feature importance. The findings demonstrate that the qualities necessary for counterfactual instances cannot be simultaneously satisfied by present methodologies. Finally, we go over potential future research directions.
Bidirectional Controlled Quantum Teleportation of Two-Qubit State via Eight-Qubit Entangled State

In this paper, a bidirectional controlled quantum teleportation via an eight-qubit state is given. In the scheme, Alice teleports a two-qubit entangled state to Bob and Bob teleport an arbitrary two-qubit state to Alice at the same time. This quantum teleportation task is supervised by a controller. Furthermore, only Bell-state measurements and single-qubit measurements are needed in our scheme.
A Data-Based Approach for Computer Domain Knowledge Representation

Representation learning is a method to compute the corresponding vectorized representations of entities or relationships. It is one of the most basic and essential natural language processing tasks. Current computer domain knowledge modeling techniques have two flaws: (1) the neglect of fine-grained knowledge hierarchies, and (2) the lack of a unified reference standard for modeling domain information. The fine-grained knowledge hierarchy includes knowledge domains, units, and topics. We use the Computer Science Guidelines as a standard to annotate an unstructured and unlabeled corpus in the computer domain with knowledge annotation and topic mapping. We organise the corpus into a computer domain knowledge system with a three-level hierarchy. We propose a knowledge representation method that incorporates contextual semantic information and topic information. The method can be applied to discover connections between knowledge of entities of different granularity. We compare it with several existing textual representation methods. Experimental results on extracting knowledge representations in computer domains show that combining contextual semantic information and topic information methods are more effective than single ones.
A Survey on Temporal Knowledge Graphs-Extrapolation and Interpolation Tasks

Current research on knowledge graphs focuses mostly on static knowledge graphs while ignoring temporal information. Recently, people have begun to study the temporal knowledge graph, which integrates temporal information into KGC, so that the modeling is constantly changing with the knowledge that evolves over time. In this survey, we summarize the existing temporal knowledge graph research, which is divided into extrapolation tasks and interpolation tasks according to time. The extrapolation task is mainly used to predict future facts and consists of three models: Temporal Point Process, Time Series, and other models. The interpolation task extends the existing KGC models to complement the lack of past temporal information, including five models: Translational Distance, Semantic Matching, Neural, Relational Rotation, and Hyperbolic Geometric models.
Quantum Voting Protocol Based on Blind Signature

Aiming at the problem of high technical difficulty in the implementation of quantum voting protocol, a quantum voting protocol based on blind signature is proposed, which avoids the use of complex quantum fingerprint function and realizes the voting process of quantum blind signature by using the five particle entangled state in quantum cryptography. Among them, voter Alice transmits voting information by measuring particle state, and Bob, Charlie and Trent complete signature, verification and audit by measuring single particle state through Z-basis. Since the signed message is invisible to the signer, it is a quantum voting protocol to protect the anonymity of signed messages. Therefore, all keys in this protocol are issued by BB84 protocol, and one-time encryption algorithm is adopted, which has unconditional security, Compared with the existing quantum voting protocols, the proposed voting protocol selects a simpler measurement base, has simple measurement operation, easy implementation and high execution efficiency. It is a safe and efficient quantum voting protocol.