Reviewerly/exHarmony huggingface dataset:
Conformal isotonic separation for binary classification

Isotonic Separation (IS) is a non-parametric classification algorithm which constructs an isotonic function as a model from given data with monotonic nature. This paper proposes an approach in which a novel conformal prediction framework is deployed in two setups to improve the classification performance of isotonic separation: Transductive and Inductive method. This paper introduces a new Non-Conformity Measure (NCM) which is specific to isotonic separation to compute the uniqueness of a data point to a set of data points. This measure is deployed on two different forms of conformal prediction methods: Transductive Conformal Isotonic Separation (TCIS) and Inductive Conformal Isotonic Separation (ICIS). Experiments are conducted on different monotonic data sets using transductive and inductive isotonic separation techniques. Experimental and statistical results reveal that transductive and inductive conformal isotonic separation give significant results in terms of performance measures.
A Reliable and Tamper-Free Double-Layered Vaccine Production and Distribution: Blockchain Approach

Vaccination in COVID-19 pandemics nurtures the various security and safety issues in diverse countries. Vaccination is the process of injecting the biological product called immunization and contains a disease-causing microorganism which tonics the body’s immune system to realize the viruses as a threat and destroy it. World Health Organization (WHO) keeps track of these vaccination details. Vaccination production in big companies submits only the production records while selling. These records can be easily manipulated according to needs. This holds us back from getting exact data and knowledge about them. The existing blockchain-based vaccine production system fails to achieve the vaccine expiry issues, high operating costs, ineffective auditing, fraud possibilities, and third-party intervention. The proposed vaccine blockchain system aims to address the aforementioned security issues through end-usage details of the vaccine of batch packing record (BPR)/batch manufacturing record (BMR) maintenance during the process of vaccine circulation. The double-layered blockchain structure is designed in such a way that the first layer as private data regarding the production, circulation details, and the relating hash, and the next layer of public data of production records hash, circulation records hash, and vaccine information. The proposed Ethereum smart contracts with new multi-node cooperate consensus mechanism implements the improved vaccine detectable with time efficiency and DoS attack prevention.
A Novel High-Capacity Behavioral Steganographic Method Combining Timestamp Modulation and Carrier Selection Based on Social Networks

Behavioral steganography is a method used to achieve covert communication based on the sender’s behaviors. It has attracted a great deal of attention due to its robustness and wide application scenarios. Current behavioral steganographic methods are still difficult to apply in practice because of their limited embedding capacity. To this end, this paper proposes a novel high-capacity behavioral steganographic method combining timestamp modulation and carrier selection based on social networks. It is a steganographic method where the embedding process and the extraction process are symmetric. When sending a secret message, the method first maps the secret message to a set of high-frequency keywords and divides them into keyword subsets. Then, the posts containing the keyword subsets are retrieved on social networks. Next, the positions of the keywords in the posts are modulated as the timestamps. Finally, the stego behaviors applied to the retrieved posts are generated. This method does not modify the content of the carrier, which ensures the naturalness of the posts. Compared with typical behavioral steganographic methods, the embedding capacity of the proposed method is 29.23∼51.47 times higher than that of others. Compared to generative text steganography, the embedding capacity is improved by 16.26∼23.94%.
SalGFCN: Graph Based Fully Convolutional Network for Panoramic Saliency Prediction

The saliency prediction of panoramic images is dramatically affected by the distortion caused by non-Euclidean geometry characteristic. Traditional CNN based saliency pre-diction algorithms for 2D images are no longer suitable for 360-degree images. Intuitively, we propose a graph based fully convolutional network for saliency prediction of 360-degree images, which can reasonably map panoramic pixels to spherical graph data structures for representation. The saliency prediction network is based on residual U-Net architecture, with dilated graph convolutions and attention mechanism in the bottleneck. Furthermore, we design a fully convolutional layer for graph pooling and unpooling operations in spherical graph space to retain node-to-node features. Experimental results show that our proposed method outperforms other state-of-the-art saliency models on the large-scale dataset.
Design of Basketball Shot Automatic Recognition System Based on Background Difference Method

Nowadays, the era is moving towards intelligence and data. As a popular sport in the world, basketball should also keep pace with the times. Because artificial judgment of goals and action standards requires high precision in the fleeting shooting movement, it is not an easy task for people with their subjective will. Therefore, in order to improve accuracy and objectivity, the development of automatic identification systems is a major change in the basketball world. For this reason, this article proposes to design a shooting automatic recognition system based on the background difference method in view of the current situation. In this paper, the design and application of the system are tested by method experimental analysis method, sampling survey method and data analysis method. First of all, this article collects the relevant information of the background difference method, and understands the algorithm flow and modeling method of the background difference method. Secondly, design related tests according to the characteristics of shooting. Then design an automatic identification system for experimental research. Eight basketball players were selected for data observation and analysis through sampling, and the results were obtained. Experiments show that the accuracy of the basketball shot automatic identification system reaches 89%, which can basically meet the requirements. However, in large-scale competitions, the recognition system needs to be further improved.
Research on Lightweight of Docking Structure Based on ANSYS

Abstract The docking structure has broad application prospects in the construction of tower assembly. However, the docking structure has a relatively large quality, which has a greater impact on the operations of high-altitude workers. This paper studies the impact force on docking structure during the docking process. The finite element analysis (FEA) model of the docking structure and the tower section is established and the impact force of the docking structure under various wording conditions is calculated. Based on the calculation results of the impact force the lightweight research of the docking structure is carried out and the optimization constraint conditions are proposed. The simulation model is established by ANSYS and the optimization design of guiding tools and vertical limit tools are completed by adjusting the structural parameters. After the optimization design, the docking structure is trial-produced and tested. Through the method of FEA and experiment, a butting device that meets the requirements of strength and rigidity and is lighter in weight is obtained. Compared with the existing docking structure, the weight of the optimized docking structure is reduced by about 39%. The research results can provide a reference for the design of the docking structure.
Weak Password Scanning System for Penetration Testing

Nowadays, many network security related personnel are accustomed to using simple passwords or default passwords set by system. Based on this kind of weak password vulnerabilities, the hackers can gain access to the systems easily. Weak password scanning is an important part of penetration testing. In order to enable penetration testers to discover weak passwords in the system more conveniently, this paper proposes a system for weak password scanning. This system includes five modules, namely the interface module, data reading processing module, IP address survival detection module, task scheduling module, and the weak password scanning plugin module. Furthermore, this system is developed based on the Go language, which has the characteristics of supporting high concurrency from the language level. We test this system by using the environment built by Docker. The experimental results validate the effectiveness of this system. In the actual penetration testing, this system can save a lot of time and energy for personnel, and has a certain practical value.
They Would Do Better If They Worked Together: Interaction Problems Between Password Managers and the Web

We identified and examined a multitude of interaction problems between popular password managers (PWMs) and websites in a two-part analysis. Although standards for authentication implementation exist, many identified interactions pose nontrivial obstacles for PWMs.
A Data Science Perspective on Computational Psychometrics

Digitally based learning and assessment systems generate large volumes of complex process data. The next generation psychometricians need to acquire new data science skills to meet the data challenge. In this chapter, we summarize data science skills and identify the subset that psychometricians need to prioritize. We introduce an evidence identification centered data design (EICDD) process during the task design, as an important way to address the data challenges from digitally based assessments. We describe some specific data techniques to parse and process complex process data with example codes in Python programming language. We also outline the general methodological strategies when dealing with process data from digitally based assessments.
IoT-oriented Secure Data Sharing Using Public Cloud

The number of IoT devices is continuously increasing. Secure data sharing governed by appropriate access control is required to safely utilize data generated by IoT devices. Storing data in a public cloud is suitable for deploying services with distributed data sharing on a large scale. However, this raises security concerns since even when the data are encrypted, an adverse third party may access them if a decryption key is stored within the same environment (key escrow problem). Conventional methods are not supposed to be used in the IoT environment or have issues with security, key distribution, and changing access authority. We propose a novel approach to securely share the data generated by IoT devices within a public cloud. Our method enables 1) addressing the key escrow problem; 2) providing forward secrecy; 3) ensuring indistinguishability under Adaptive Chosen Ciphertext Attack (safety equivalent to IND-CCA2); 4) changing access authority easily; and 5) saving computational resources of IoT devices. We implemented this method and evaluated its performance. The experimental results show that it has comparable or better performance compared with conventional methods. Furthermore, we confirm that resource consumption in our method is more practical even in the large-scale IoT environment.
Arabic language investigation in the context of unimodal and multimodal sentiment analysis

Sentiment analysis is gaining high importance as a field of study in academia and industry. It becomes one of the most active research field in Artificial Intelligence and web mining. Most of the work on sentiment analysis has focused on the English language. On the other hand, few studies are done on other languages that also dominate the Internet, including Arabic which is currently ranked as the fourth language used in the Internet. Research on this language remains weak and still suffers from several limitations and challenges. Moreover, people in several social media tend to use other kinds of data to express their opinions, mood and sentiment, including images, videos and audios. Thus, it is crucial to mine, understand and identify sentiments from different modalities. In this paper, we first present an outline of the progress of unimodal and multimodal sentiment analysis, most notably the Arabic language, and then we present the different limits and challenges of the Arabic in this domain.
Learning Scene-Aware Spatio-Temporal GNNs for Few-Shot Early Action Prediction

We aim to address a new task named few-shot early action prediction (FS-EAP) that learns classifiers for novel actions from only a few partially observed videos. We argue that the task is extremely challenging since the partially observed videos do not contain enough action information in a few-shot environment. To tackle this task, in this paper, we propose a scene-aware spatio-temporal graph neural network (SA-STGNN) by leveraging the fine-grained spatio-temporal interactions in the video scenes. Specifically, we first generate a spatio-temporal graph corresponding to the partially observed video to capture comprehensive spatio-temporal correlations. Then we utilize the spatio-temporal graph as the input of our SA-STGNN and predict the augmented video features corresponding to the complete video. The architecture uses several scene-aware learning blocks, which are a combination of edge fusion graph neural layers and temporal gated convolutional layers to jointly model spatial and temporal dependencies. Finally, we employ an early action predictor to exploit the learned video features for predicting actions in the few-shot setting. Extensive experimental results on two widely adopted video datasets demonstrate the effectiveness of our approach and its superior performance over the state-of-the-art approaches.
An extension of the framework types-to-sets for Isabelle/HOL

In their article titled From Types to Sets by Local Type Definitions in Higher-Order Logic and published in the proceedings of the conference Interactive Theorem Proving in 2016, Ondřej Kunčar and Andrei Popescu propose an extension of the logic Isabelle/HOL and an associated algorithm for the relativization of the type-based theorems to more flexible set-based theorems, collectively referred to as Types-To-Sets. One of the aims of their work was to open an opportunity for the development of a software tool for applied relativization in the implementation of the logic Isabelle/HOL of the proof assistant Isabelle. In this article, we provide a description of a software framework for the interactive automated relativization of definitions and theorems in Isabelle/HOL, developed as an extension of the proof language Isabelle/Isar, building upon some of the ideas for further work expressed in the original article on Types-To-Sets by Ondřej Kunčar and Andrei Popescu and the subsequent article Smooth Manifolds and Types to Sets for Linear Algebra in Isabelle/HOL, which was written by Fabian Immler and Bohua Zhan and published in the proceedings of the International Conference on Certified Programs and Proofs in 2019.
Sparse fuzzy classification for profiling online users and relevant user-generated content

Extracting information and knowledge from users’ online activity is of great significance for a variety of practical purposes. Yet, existing research suffers from limitations including requiring prior knowledge and poor interpretability. In this study, we develop a novel classification algorithm based on the dual concepts of fuzzy set and sparsity regularization. Specifically, the proposed algorithm introduces two types of fuzzy sets designed to fuzzify samples, then the membership criteria from resultant fuzzy sets is further cast as the soft feature for training a sparse classifier. To demonstrate the practical benefits of this process and the performance of the proposed classification algorithm, we carefully examine its application to several benchmarking datasets, in addition to a unique real-world data resource containing 49,252 users worldwide and their 55,539 online historical records collected over a ten-month period. Experimental results demonstrate that the proposed algorithm outperforms other state-of-the-art methods by generating a more accurate classification. In turn, our findings can be utilized to shape industry efforts to identify users’ preferences and to personalize recommendation services.
Development of On-Line Learning Materials on Sustainable Food Education for Elementary School Students Using the 'Food Classroom' Platform

제3차 국가 식생활교육기본계획에서는 지속가능한 식생활의 확산으로 우리 농업&#8231;환경의 가치 인식과 국민의 건강·사회·환경의 지속가능성을 달성하고자 추진 중이다. 이의 하나로 2020년 구축된 지속가능한 식생활교육 플랫폼인 ‘식생활교실’에는 다양한 콘텐츠가 탑재되어 있으나 아직 초등학생을 대상으로 한 통합적 정보 제공 및 활용에 대한 평가는 없었으며, 또한 코로나19와 함께 시작된 온라인학습이라는 환경적 요구에 부응할 필요가 대두되었다. 이에 본 연구에서는 플랫폼 탑재자료를 활용하여 온라인 학습자료를 개발하고 현장에 시범 적용하여 그 효과를 평가해 보고자 하였다. 먼저 2015 개정 실과교육과정과 국가 식생활교육 기본계획의 초등고학년 성취기준을 비교하여 관련한 내용 요소를 추출하고 ‘식생활교실’ 플랫폼 콘텐츠를 바탕으로 초등 5학년에 적용할 온라인 교육자료 총 18차시를 개발하였다. 환경, 건강, 배려의 지속가능한 식생활교육에서 여러 주제를 통합하고 온라인 학습환경에서 학생들이 활동 중심으로 참여하도록 내용을 구성하였다. 개발된 자료는 2021년 3~5월, 서울 B 초등학교 5학년 전체 학생(172명)에게 적용하였다. 플랫폼에 탑재된 지속가능한 식생활역량 진단도구를 활용하여 그 효과를 평가한 결과, 총 15개의 식생활역량 진단 항목 중 14개의 평균 점수가 향상되었고, 그중 12개 항목이 유의미하게 향상된 것을 확인하여 개발한 교육자료가 환경, 건강, 배려의 영역에서 학생들의 지속가능한 식생활역량 향상에 이바지함을 확인할 수 있었다(9개 항목 p<0.001, 2개 항목 p<0.01, 1개 항목 p<0.05). 학생의 수업 참여와 결과물 관찰에서 약 90% 이상이 긍정적인 반응을 보였으며 자신의 생활에 곧바로 적용하는 모습이 관찰되었다. 따라서 본 연구에서 개발된 교육자료가 온라인 시대에 지속가능한 식생활교육 자료로 효과적으로 널리 활용될 수 있을 것이다.The 3rd National Food Education Plan of Korea is being conducted for the promotion of the value of agriculture and environment, and for the achievement of the sustainability of public health, society and environment. ‘Food Classroom’ was opened in 2020 as a platform for the sustainable food education. Despite the diverse contents in the platform, there has been no evaluation of those as a resource of sustainable food education for the elementary students. Also, the pandemic of COVID-19 has driven us to establish on-line educational environment. Therefore, this study developed on-line sustainable food education materials by using ‘Food Classroom’ contents, and the effects were measured for the elementary students. For these purposes, core elements were selected by comparison of the achievement standards of the National curriculum of Practical Arts and those of National Food Education Plan, and a total of 18 classes of learning materials were developed by combining related themes of environment, health and gratitude, and providing hands-on experience in on-line environment. These were applied to a total of 172 students in the 5th grade of B elementary school located in Seoul during the period of March to May in 2021. As a result, the food competency of the students was increased in 14 items among 15, and 12 items showed statistical significance (9 items p<0.001, 2 items p<0.01, 1 item p<0.05) when measured by the diagnosis tool uploaded in the platform. Observation of students’ participation and their outputs in the class also showed very positive responses. Therefore, the learning materials developed in this study could be utilized effectively for sustainable food education in the on-line environment.
Hybrid Autoscaling Strategy on Container-Based Cloud Platform

The container has several advantages over the traditional virtual machine technology such as light-weight, fast booting time, and fast recovery. Kubernetes is one the most outstanding container management and deployment platforms. The Kubernetes provides autoscaling function, which will increase and decrease the hardware resources to adapt with the current traffic load situation to keep the user experience. Two popular autoscaling methods are horizontal autoscaling and vertical autoscaling. Based on the monitoring resource utilization, horizontal autoscaling will increase the number of PoDs (point of deployment) or vertical autoscaling will increase the hardware resources of each PoD to achieve the target utilization. In this paper, we present a hybrid solution that combines the advantages of both autoscaling solutions and proposes a bandwidth-efficient scheduler strategy. By numerical analysis, our hybrid approach is better than the normal HPA approach in terms of bandwidth cost and has lower autoscaling latency than the VPA approach
Transformed K-means Clustering

In this work we propose a clustering framework based on the paradigm of transform learning. In simple terms the representation from transform learning is used for K-means clustering; however, the problem is not solved in such a naïve piecemeal fashion. The K-means clustering loss is embedded into the transform learning framework and the joint problem is solved using the alternating direction method of multipliers. Results on document clustering show that our proposed approach improves over the state-of-the-art.
Exploiting Unblocking Checkpoint for Fault-Tolerance in Pregel-Like Systems

With the explosive growth of graph size, a series of Pregel-like systems have emerged. Typically, these systems employ checkpointing and rollback mechanisms to achieve fault-tolerance in either blocking or unblocking manner. The blocking checkpointing pauses the iterative processing while checkpointing, whereas the unblocking checkpointing writes the checkpoints in parallel with the iterative processing. The unblocking checkpointing decreases the checkpointing overhead, but incurs resource contention due to checkpointing concurrently. Hence, it may prolong the time on execution and checkpointing. In this work, we propose a queuing strategy to alleviate the contention. This strategy employs a checkpoint queue to store all the pending checkpoints, which allows to concurrently write a certain number of checkpoints at most from the queue following a First-In-First-Out (FIFO) policy. To further utilize the characteristics of checkpoint in Pregel-like systems, we define checkpoint staleness and checkpoint tardiness, and then propose staleness/tardiness-aware skipping policy to replace the FIFO policy. Extensive experiments verified that the queuing strategy with the skipping policy outperforms blocking and unblocking checkpointing in Pregel-like systems.
Privacy-Preserving Framework to Facilitate Shared Data Access for Wearable Devices

Wearable devices are emerging as effective modalities for the collection of individuals’ data. While this data can be leveraged for use in several areas ranging from health-care to crime investigation, storing and securely accessing such information while preserving privacy and detecting any tampering attempts are significant challenges. This paper describes a decentralized system that ensures an individual’s privacy, maintains an immutable log of any data access, and provides decentralized access control management. Our proposed framework uses a custom permissioned blockchain protocol to securely log data transactions from wearable devices in the blockchain ledger. We have implemented a proof-of-concept for our framework, and our preliminary evaluation is summarized to demonstrate our proposed framework’s capabilities. We have also discussed various application scenarios of our privacy-preserving model using blockchain and proof-of-authority. Our research aims to detect data tampering attempts in data sharing scenarios using a thorough transaction log model.