Reviewerly/exHarmony huggingface dataset:
Position Distribution Matters: A Graph-Based Binary Function Similarity Analysis Method

Binary function similarity analysis evaluates the similarity of functions at the binary level to aid program analysis, which is popular in many fields, such as vulnerability detection, binary clone detection, and malware detection. Graph-based methods have relatively good performance in practice, but currently, they cannot capture similarity in the aspect of the graph position distribution and lose information in graph processing, which leads to low accuracy. This paper presents PDM, a graph-based method to increase the accuracy of binary function similarity detection, by considering position distribution information. First, an enhanced Attributed Control Flow Graph (ACFG+) of a function is constructed based on a control flow graph, assisted by the instruction embedding technique and data flow analysis. Then, ACFG+ is fed to a graph embedding model using the CapsGNN and DiffPool mechanisms, to enrich information in graph processing by considering the position distribution. The model outputs the corresponding embedding vector, and we can calculate the similarity between different function embeddings using the cosine distance. Similarity detection is completed in the Siamese network. Experiments show that compared with VulSeeker and PalmTree+VulSeeker, PDM can stably obtain three-times and two-times higher accuracy, respectively, in binary function similarity detection and can detect up to six-times more results in vulnerability detection. When comparing with some state-of-the-art tools, PDM has comparable Top-5, Top-10, and Top-20 ranking results with respect to BinDiff, Diaphora, and Kam1n0 and significant advantages in the Top-50, Top-100, and Top-200 detection results.
Uji Validitas Konstruk Tes Wechsler Intelligence Scale For Children (WISC)

As one of the psychological instruments that successfully provides an overview of the level of intelligence of children, the Wechsler Intelligence Scale For Children (WISC) test needs to be evaluated its validity with psychometric test procedures. The Wechsler Intelligence Scale For Children (WISC) test consists of 10 subtests in which each subtest has many diverse items. This study included 315 kindergarten students with an age range of 5-6 years. The data obtained from it is then analyzed using confirmatory factor analysis to test the validity of the construct. This study showed that the model had a good fit with a p-value of 0.066 and an entire subtest fit.
The Relationship between Body Mass Index and Physical Activity Participation Rate Design Based on Fuzzy Breakpoint Regression Design

Obesity is a global public health problem in modern society. Body mass index (BMI) can measure whether a person’s body is in obesity and health. Body mass is the quality of human body, which is a comprehensive and relatively stable characteristic of human body structure, physiological function, and psychological factors, and body mass is the basis of heredity and acquisition. Physical exercise is a kind of physical activity that people do in their spare time in order to exercise their body and mind. In this paper, we take the physical health problem of obesity as the background and combine the fuzzy breakpoint regression method to design the body mass index and physical exercise participation rate. The relationship between the body mass index and physical exercise participation rate design based on fuzzy breakpoint regression in this paper is mainly discussed as follows: background: the obesity rate is rising and chronic diseases caused by obesity affect health; results: jumps were observed for body health indices above 24.9 kg/m2, while participation rates in physical activity decreased compared to the normal weight range; and conclusion: the greater the weight of the group, the less willingness to participate in physical activity and the lower the rate of participation in physical activity.
Fiber Based Generic Photonic Computing Unit

We present a fiber-based photonic computing concept and system which rely on incoherent data encoding and scalable architecture. Our results suggest projected performance of &gt;1,000,000 Tera operations per second (TOPs) and efficiency of &gt;1000 TOPs/Watt.
A Novel Reinforcement Learning Approach for Spark Configuration Parameter Optimization

Apache Spark is a popular open-source distributed data processing framework that can efficiently process massive amounts of data. It provides more than 180 configuration parameters for users to manually select the appropriate parameter values according to their own experience. However, due to the large number of parameters and the inherent correlation between them, manual tuning is very tedious. To solve the problem of tuning through personal experience, we designed and implemented a reinforcement-learning-based Spark configuration parameter optimizer. First, we trained a Spark application performance prediction model with deep neural networks, and verified the accuracy and effectiveness of the model from multiple perspectives. Second, in order to improve the search efficiency of better configuration parameters, we improved the Q-learning algorithm, and automatically set start and end states in each iteration of training, which effectively improves the agent's poor performance in exploring better configuration parameters. Lastly, comparing our proposed configuration with the default configuration as the baseline, experimental results show that the optimized configuration gained an average performance improvement of 47%, 43%, 31%, and 45% for four different types of Spark applications, which indicates that our Spark configuration parameter optimizer could efficiently find the better configuration parameters and improve the performance of various Spark applications.
Classification of Stroke Using K-Means and Deep Learning Methods

Stroke is a disease caused by blockage or rupture of blood vessels in the brain due to disruption of blood flow, where the blood supply to an area of the brain is suddenly interrupted. This study discusses stroke classification using the K-Means and Deep Learning methods. This study aims to segment patient data to produce patient class labels and classify the results of grouping the data to test the performance of the classification algorithm used. The 4,906 patient data used in this study were grouped using the K-Means method into multiple clusters, including 2 clusters, 3 clusters, 4 clusters, and 5 clusters, and the data grouping findings will be classified. The cluster validation method is the Davies Bouldin Index and the Silhouette Index, while the algorithm used in the classification process is the Deep Learning Algorithm. The classification results produce the most excellent accuracy value in the number of clusters tested, namely 2 clusters of 99.71%.&#x0D;
NxtPost: User To Post Recommendations In Facebook Groups

In this paper, we present NxtPost, a deployed user-to-post content based sequential recommender system for Facebook Groups. Inspired by recent advances in NLP, we have adapted a Transformer based model to the domain of sequential recommendation. We explore causal masked multi-head attention that optimizes both short and long-term user interests. From a user's past activities validated by defined safety process, NxtPost seeks to learn a representation for the user's dynamic content preference and to predict the next post user may be interested in. In contrast to previous Transformer based methods, we do not assume that the recommendable posts have a fixed corpus. Accordingly, we use an external item/token embedding to extend a sequence-based approach to a large vocabulary. We achieve 49% abs. improvement in offline evaluation. As a result of NxtPost deployment, 0.6% more users are meeting new people, engaging with the community, sharing knowledge and getting support. The paper shares our experience in developing a personalized sequential recommender system, lessons deploying the model for cold start users, how to deal with freshness, and tuning strategies to reach higher efficiency in online A/B experiments.
S2RL

Collaborative multi-agent reinforcement learning (MARL) has been widely used in many practical applications, where each agent makes a decision based on its own observation. Most mainstream methods treat each local observation as an entirety when modeling the decentralized local utility functions. However, they ignore the fact that local observation information can be further divided into several entities, and only part of the entities is helpful to model inference. Moreover, the importance of different entities may change over time. To improve the performance of decentralized policies, the attention mechanism is used to capture features of local information. Nevertheless, existing attention models rely on dense fully connected graphs and cannot better perceive important states. To this end, we propose a sparse state based MARL (S2RL) framework, which utilizes a sparse attention mechanism to discard irrelevant information in local observations. The local utility functions are estimated through the self-attention and sparse attention mechanisms separately, then are combined into a standard joint value function and auxiliary joint value function in the central critic. We design the S2RL framework as a plug-and-play module, making it general enough to be applied to various methods. Extensive experiments on StarCraft II show that S2RL can significantly improve the performance of many state-of-the-art methods.
Secure Data Deduplication System with Cyber Security Multikey Management in Cloud Storage

Object detection is essential in a video surveillance system. To recognize an item in a movie, we first examine each picture pixel by pixel. The Secure Data Deduplication system in segmentation is the process of separating distinct picture components into pixels in digital image processing. The performance of segmentation is influenced by irregular and/or poor lighting. These characteristics have a significant impact on the video surveillance system’s real-time object detection process. A multikey management system based on a modified ResNet model is presented in this research (M-ResNet). Cyber security is a suggested algorithm application that is used to improve images that are influenced by a lack of light. The experimental findings reveal a significant improvement in detecting objects in the video stream as compared to the present technique output and modification architecture of the ResNet model. The suggested model achieves superior results in measures like precision, recall, and pixel accuracy, as well as a decent increase in object recognition.
TOKEN Is a MASK: Few-shot Named Entity Recognition with Pre-trained Language Models

Transferring knowledge from one domain to another is of practical importance for many tasks in natural language processing, especially when the amount of available data in the target domain is limited. In this work, we propose a novel few-shot approach to domain adaptation in the context of Named Entity Recognition (NER). We propose a two-step approach consisting of a variable base module and a template module that leverages the knowledge captured in pre-trained language models with the help of simple descriptive patterns. Our approach is simple yet versatile, and can be applied in few-shot and zero-shot settings. Evaluating our lightweight approach across a number of different datasets shows that it can boost the performance of state-of-the-art baselines by $$2-5\%$$ F1-score.
AEP: Aligning knowledge graphs via embedding propagation

Knowledge graph alignment aims to identify entity pairs having the same meaning between different knowledge graphs, which is essential to the automated construction of a coherent knowledge base. With the development of knowledge representation learning, researchers have proposed several useful embedding-based alignment methods. In practice, some entities are easily aligned with high confidence while others are not. However, there is a lack of effective methods which boost the alignment of implicitly aligned entities based on explicitly aligned ones. This paper presents AEP, which combines several effective schemes for accurate knowledge graph alignment. First, we employ a word-embedding model to encode the semantic information contained in entities’ surface names. We propose an attention-based graph convolutional network model to incorporate the structural information via supervised embedding propagation. Besides, we develop a multi-view bootstrapping strategy to address the over-fitting problem caused by insufficient training sets. Next, an embedding-propagation-based alignment scheme is proposed to improve the alignment accuracy by propagating explicitly aligned entities’ embeddings to implicitly aligned ones in an unsupervised manner. Finally, we conduct extensive experiments to validate the superiority of AEP and evaluate the effects of proposed schemes. Experimental results show that AEP outperforms state-of-the-art methods, and the proposed schemes improve the alignment accuracy significantly.
Against Colluding Mining with Reward Sharing in MEC Empowered Mobile Blockchain System

Mobile edge computing is considered as a promising solution to mobile blockchain system, where mobile nodes with limited computing capability may participate the mining process by offloading the computing intensive mining tasks to nearby edge service providers (ESPs). To mitigate the collusion between ESP and miners, we propose a reward sharing model in this paper, where each miner will share part of the mining rewards to ESP to build a mutual cooperation group. We model the interaction among the ESP and miner nodes as a two-stage Stackelberg game. Then, we obtain the optimal edge computing demand and the corresponding price of each miner by solving the Nash equilibrium of the game iteratively with gradient descent method. We also compare the revenues under both collusion and normal mode with the proposed reward sharing scheme. Simulation results show that the more mining rewards that miners share to ESP, the higher the computing resource demands of miners and the less of the unit price of the computing resources. In addition, if the block size of the colluding miner is much larger than other miners, the ESP can obtain higher revenues rather than colluding.
Privacy-Preserving Adaptive Trajectory Storage on Blockchain for COVID-19 Contact Tracing

Privacy preservation in various contact tracing approaches for the COVID-19 or SARS-CoV-2 virus is challenging, as such applications tend to reveal users’ points of interest (POIs) and other sensitive data shared together with their location information. This paper proposes COVID-19 eavesdropping resistant tracing (COVERT)-Blockchain, a novel distributed-ledger-based platform to facilitate contact tracing without invading users’ privacy. COVERT-Blockchain enables infected users to share only their anonymized location traces on the Blockchain with a sliding window of the previous 15 days, thereby avoiding constant location information sharing with third party users. To further reduce the chances of revealing the corresponding users’ trajectories, in COVERT-Blockchain we employ an adaptive logging mechanism to store trajectory data for contact tracing only if the users stayed in a location where there is significant presence of other humans around them for a relatively long duration of time. This ensures anonymity where the trajectory is generated differently each time for each user, and such infrequent and random trajectory generation enables us to generate unidentifiable trajectories for each user and thus preserve their privacy. COVERT-Blockchain is evaluated for scalability and robustness in relation to overhead and delays in storing and retrieving data from the Blockchain. Results show it to efficiently achieve contact tracing without any breaches of privacy.
Phenotypic Parameters Estimation of Plants Using Deep Learning-Based 3-D Reconstruction From Single RGB Image

Monitoring crop growth is of great significance to obtain crop growth status information for development of smart agriculture. The traditional way to measure the phenotypic parameters of crops is labor-intensive and encounters inconvenient operations. In this study, we propose to obtain the phenotypic parameters of crops from 3-D reconstruction of plants from single RGB images using a data-driven plant phenotypic parameters estimation network (P3ES-Net) deep neural network, which enables to estimate the depth shift and camera focal length used for depth estimation and reconstruction of the 3-D model of plants. Based on the principles of the monocular ranging and pinhole imaging model, crop phenotypic parameters such as height, canopy size, and trunk diameter can then be calculated from the 3-D model. Experiments with four practical plants present that our method is able to achieve acceptable evaluation of the growth status of plants. Of more significance, it achieves particular superior depth estimation performance over a commercial depth camera, which is a very new on-sale depth camera using stereo vision and deep learning network. This potential performance throws light on the low-cost measurement of crop phenotypic parameters using RGB camera in monitoring crop growth.
Combining Variational Autoencoders and Transformer Language Models for Improved Password Generation

Password generation techniques have recently been explored by leveraging deep-learning natural language processing (NLP) algorithms. Previous work has raised the state of the art for password guessing algorithms significantly, by approaching the problem using either variational autoencoders with CNN-based encoder and decoder architectures or transformer-based architectures (namely GPT2) for text generation. In this work we aim to combine both paradigms, introducing a novel architecture that leverages the expressive power of transformers with the natural sampling approach to text generation of variational autoencoders. We show how our architecture generates state-of-the-art results in password matching performance across multiple benchmark datasets.
Qubit unitary coupled cluster with generalized single and paired double excitations ansatz for variational quantum eigensolver

Variational quantum eigensolver (VQE) with a unitary coupled cluster (UCC) ansatz has been suggested as a promising method for electronic structure calculations on future quantum computers. However, the complexity of the excitation terms for UCC with the single and double excitations (UCCSD) ansatz is up-bounded to O M − N 2 N 2 , where N is the number of electrons and M is the number of spin orbitals. The gate complexity of quantum circuit for the UCCSD ansatz is up-bounded to O M M − N 2 N 2 using the Jordan–Wigner transformation. These complexities significantly limit the implementation of UCCSD on current Noisy Intermediate-Scale Quantum (NISQ) devices. Herein, we developed a k-QUpCCGSD ansatz which is based on the generalized paired double excitation operators and the particle preserving exchange gate. The former reduces the number of the excitation operators, the latter reduces the number of qubit gates for transforming excitation operators to quantum circuit. The gate complexity of the proposed k-QUpCCGSD ansatz is up-bounded to O ( k M 2 ) , which significantly reduce the complexity of the VQE algorithm on NISQ devices. The performance of the proposed ansatz on VQE is demonstrated by calculating ground-state dissociation energy curves of the H6, LiH, H2O, and BeH2 molecules with the STO-3G minimal basis set, and the accuracy is evaluated by comparing to the full configuration interaction (FCI) benchmarks. Moreover, we compare the number of quantum gates, especially the CNOT gates, and accuracies of various ansatzes. The assessments have shown that the accuracy of qubit unitary coupled cluster (QUCC) ansatzes is slightly worse than that of the UCC ones, but the circuit complexity of QUCC is much less than that of UCC. Among the tested QUCC ansatzes, k-QUpCCGSD achieves higher accuracy with fewer quantum gates than QUCCSD, and k-QUpCCGSD is a promising ansatz for VQE calculation on NISQ devices.
An Examination of Pre-School Children's Interaction Levels and Motivation in Learning English with AR-Supported Educational Toys

In this study, the interactions of children in the English learning process with AR-supported educational toys were examined and the factors affecting their motivation were revealed. Based on the case study research design, this study included 15 volunteer children (10 girls, 5 boys) aged 5–6 years old. Children used AR-supported flashcards, match-cards, and puzzles to learn the English equivalents of vehicle names over the course of four weeks. At the end of the process, a total of 5 groups in groups of 3 were taken into the activity class separately, and their interactions while using AR applications were recorded with the camera. From the recordings, the interaction of the students with the material, their friends and their teachers, and their duration of occupation were noted on the observation form and analyzed descriptively. At the end of the implementation, interviews were conducted with 6 selected students. According to the results of the interaction, it was determined that the highest level of interaction was in the dimension of student-material interaction, and the lowest level of interaction was in the dimension of student-student interaction. In addition, the results related to motivation showed that there are many factors positively or negatively affecting preschool children's motivation in terms of attention, relevance, confidence, and satisfaction themes in the process of learning English vocabulary with AR-supported materials. It is thought that these detailed research results provide rich data for future research.
A NURBS Curve Interpolator Based on Double-Step Signal and Finite Impulse Response Filters

Abstract In order to solve the problem that the traditional trajectory planning method cannot flexibly control the velocities and the arrival time of critical points in the trajectory, this paper proposes a non-uniform rational B-spline interpolator based on double-step signal and finite impulse response filters. The interpolator consists of two modules: a pre-processing module and a real-time interpolation module. To reduce the contour error, first, the pre-processing module scans the curve for critical points and sets their velocities according to the chord error. Then, the curve is split, and for each segment, the length and inverse length functions are calculated to eliminate the length error and enhance the real-time performance. Moreover, to control the arrival time and smooth the velocity, the real-time interpolation module generates the trajectory by processing the double-step signal with three finite impulse response filters, which satisfies the constraints of velocity, acceleration, jerk, and displacement. Simulation results indicate that the proposed interpolator can accurately reach the critical points at the set arrival time with smooth velocity and low chord error, and experimental results on a SCARA robot show that the proposed interpolator is feasible and effective.