Reviewerly/exHarmony huggingface dataset:
A Novel Sampling Method with Lévy Flight for Distribution-Based Discrete Particle Swarm Optimization

We have proposed a novel sampling method (NS) for controlling step sizes and incorporating Lévy flight to distribution-based discrete particle swarm optimizations (DDP-SOs), which are discrete extended variants of particle swarm optimizations handling the continuous parameters of probability distributions over the variable values instead of directly handling discrete variables. Our previous work demonstrated that NS improved all DDPSOs on function optimization problems. However, on categorical problems, the NS did not improve DDPSOs designed for integer problems. For more detailed investigations, we conducted optimization experiments on NK landscapes. The results show that NS improves the DDPSOs' efficiency and robustness to large solution space and non-separable problems. Besides, we found that NS is effective on integer DDPSOs even for categorical optimization in cases where decision variables have a few states. In addition, the proposed methods were tested on feature selection experiments and achieved superior results compared to some evolutionary algorithms designed for feature selection.
Blockchain-as-a-Service Powered Knowledge Graph Construction

The growing interest in the knowledge graph has attracted a great attention from both academia and the industry. Blockchain-based knowledge construction is deemed to be an alternative by adopting smart contract. However, the complexity of blockchain technology is an obstacle to apply blockchain solutions in practical scenarios. In this paper, we propose a Blockchain-as-a-Service (BaaS) approach to address the complexity issue. Through our BaaS system, developers can focus on knowledge graph constructions instead of struggling for mastering blockchain technology. We have implemented our BaaS system and measured the performance.
EFL SPEAKING PRACTICE IN DISTANCE LEANING DURING THE CORONAVIRUS PANDEMIC 2020-2021

Students at languages and translation departments at Saudi universities take listening and speaking courses. Before the pandemic, the students used to complete the activities in the textbooks which required them to engage in many real-life, face-to-face individual and/or collaborative small-group activities in the classroom such as role-playing, dramatization, inviting a guest, conducting interviews and others. However, due to the Coronavirus Pandemic, there was an emergency shift to distance learning at Saudi universities starting March 2020 , where all courses are being delivered online. Several platforms are being used in distance education such as Blackboard, Zoom, Microsoft Teams and others. The current study explores the types of online speaking activities that a sample of college instructors at some language and translation departments are currently using in distance education and how they engage students online in the absence of face-to-face activities, interaction, and communication. Survey results showed that EFL college instructors are using a variety of online speaking activities such as: (i) assigning a topic which the students research and prepare at home and then give an online oral presentation about it online through the platform; (ii) using online debates about some issues; (iii) answering problem-solving questions; (iv) student-created podcast on a topic of their choice and them in Speaking Center on Twitter; (v) combining listening and speaking activities; (vi) using . free online audio recording creator to record conversations and presentations; (vii) using the app and others. Students and instructors’ views on the effects of the online speaking activities on students’ speaking skill development in the distance learning environments are reported.
Overriding Learning-based Perception Systems for Control of Autonomous Unmanned Aerial Vehicles

In this paper, we demonstrate the vulnerabilities of learning-based algorithms for perception-based autonomy as applied to unmanned aerial systems (UAS). We present an attack framework to compromise the controls of a UAS, which uses a deep neural network (DNN) to track different types of targets in varying terrains. The DNN uses the UAS camera image as input to estimate the six degree-of-freedom (6D) pose of the target relative to the UAS. The UAS flight-controller uses the estimated relative 6D pose to output the motor throttle commands to maintain a specified relative height while keeping the target in the center of the camera's frame. The attacker's objective is to reduce the height of the UAS relative to the target in a controlled fashion (e.g., so as to obtain unauthorized physical access to the UAS). This is achieved by dynamically modifying the texture of the target in the environment. To this end, the attacker uses a digital poster to generate the adversarial tracking target remotely. The digital poster has an integrated camera to estimate the UAS pose relative to the poster. The digital poster continuously adapts to the UAS pose while being robust to lighting variations. The attack is effectively demonstrated on the Virtual Robotics Experimentation Platform (V-REP) in a custom-developed environment testbed.
Integrated Design and Exploration of Curriculum System of Engineering Technology related Majors for Connection between Secondary and Higher Vocational Education

Engineering technology is the practical application of technology in industrial production, and it is the means and method to use the research results of technological development to transform nature. Engineering and technical talents trained by vocational education should be proficient in the application of technology, have excellent professional skills and professional quality in line with the needs of engineering and technology. As the posts of industrial production are cross, hierarchical and integrated, it is very necessary and valuable to train engineering and technical talents in the way of stratification and connection. According to the current situation of connection between secondary and higher vocational education, taking engineering technology related majors as an example, an integrated framework of curriculum system for connection between secondary and higher vocational education is analyzed from three aspects: personnel cultivation objectives and specifications, educational system model and teaching plan. The integrated curriculum is divided into four modules: the common basic curriculum module, the professional platform curriculum module, the professional direction curriculum module and the independent direction curriculum module. On this basis, the strategy of curriculums connection between secondary and higher vocational education is expounded from the determination of the interface standard and the path of the module connection. Taking construction machinery product - paver as a typical case, the idea of curriculum connection is described. It effectively promoted the integrated design of curriculum system for the connection between secondary and higher vocational education, improved the training level of students' engineering technology application ability, and laid a good foundation for other aspects of cohesion between secondary and higher vocational education.
Coflow Scheduling With Unknown Prior Information in Data Center Networks

In order to solve the problem of flow scheduling in cluster computing framework, the scheduling strategy based on coflow has become a research hot spot. A coflow is a collection of data flows between two different stages of the same parallel computing task. Coflow scheduling in the case of unknown prior information depends on the data flow information of the sent part to infer the data size of coflow and allocate the scheduling sequence for coflow, which is easy to cause congestion. In this paper, we design an effective coflow scheduling mechanism namely, Classification According to Ports Number (CAPN). In the mechanism, firstly, coflows are quickly classified according to the Few Ports Number Scheduling First (FPSF) algorithm, and then coflows with different priorities are scheduled and adjusted, which greatly reduce the average coflow completion time (CCT). Simulation results show that compared with the classical Aalo and MCS mechanisms, our CAPN mechanism can reduce the completion time of coflow by by 31.32% and 25.72%, respectively.
Comprehensive Analysis of Indian Currency Recognition System and Location Tracking for Visually Impaired

According to a survey, approximately 5.4 million people face the problem of visual impairment in the country. Blind people face problems when moving from one place to another many of them use a white cane or stick or a working dog for assistance. When they travel from one place to another, their loved ones are worried about their location. Caretakers or their loved ones find it hard to trace the real-time location of the blind person. Moreover, blind folks face difficulties in contacting their loved ones in case of emergency. They even find it hard to recognize the currencies. Despite the advantages of technology and the enormous usage of electronic cards and e-payments, banknotes are still primarily used owing to its ease. There are about 11 different Indian currencies, and each banknote is distinct from others in various aspects like colour and dimensions. Blind folks find it hard to use the cards and other electronic payments, so they find it hard in recognizing the currencies. Currency Recognition System (CRS) could be useful in identifying currency for visually impaired persons. In the proposed system, the Android-based solution to aid the loved ones to track the real-time location of the blind folks with a smart calling facility and a Currency Recognition System based on Teachable Machine with Google design to recognize various currencies. In Teachable Machine, models are trained using the technique called transfer learning. It aims to provide fast and accurate results for the given image. The goal of this proposed scheme is to make life easier for a visually impaired person.
Light deep learning models enriched with Entangled features for RGB-D semantic segmentation

Semantic segmentation is a crucial task in emerging robotic applications like autonomous driving and social robotics. State-of-the-art methods in this field rely on deep learning, with several works in the literature following the trend of using larger networks to achieve higher performance. However, this leads to greater model complexity and higher computational costs, which make it difficult to integrate such models on mobile robots. In this work we investigate how it is possible to obtain lighter performing deep models introducing additional data at a very low computational cost, instead of increasing the network complexity. We consider the features used in the 3D Entangled Forests algorithm, proposing different strategies to integrate such additional information into different deep networks. The new features allow to obtain lighter and performing segmentation models, either by shrinking the network size or improving existing networks proposed for real-time segmentation. Such result represents an interesting alternative in mobile robotics application, where computational power and energy are limited.
Ground-Truth Deficiencies in Software Engineering: When Codifying the Past Can Be Counterproductive

In software engineering, the objective function of human decision makers might be influenced by many factors. Relying on historical data as the ground truth may give rise to systems that automate software engineering decisions by mimicking past suboptimal behavior. We describe the problem and offer some strategies.
Quality assessment of screen content images based on multi-stage dictionary learning

In this paper, we propose an effective method for quality assessment of screen content images (SCIs) based on multi-stage dictionary learning. To simulate the brain’s layered processing of signals, we proposed a hierarchical feature extraction strategy, which is called multi-stage dictionary learning, to simulate the hierarchical information processing of brain. First, the standard deviation of normalized map obtained from training image is used to select the training data in a certain proportion, which can ensure the learning efficiency and reduce the training burden. Next, the reconstructed map is weighted as the input of the next-stage dictionary learning. Then using the trained dictionary, the sparse representation is applied to extract features. Meanwhile, considering that some important features may be ignored in the process of multi-stage dictionary learning, we use Log Gabor filter to extract feature maps, and then calculate the correlation between feature maps as another kind of compensation features. Final, for the two feature sets, we choose SVR and feature codebook to learn two objective scores, and then use the adaptive weighting strategy to get the final objective quality score. Experimental results show that the proposed method is superior to several mainstream SCIs metrics on two publicly available databases.
Behavioral Cloning in Atari Games Using a Combined Variational Autoencoder and Predictor Model

We explore an approach to behavioral cloning in video games. We are motivated to pursue a learning architecture that is data efficient and provides opportunity for interpreting player strategies and replicating player actions in unseen situations. To this end, we have developed a generative model that learns latent features of a game that can be used for training an action predictor. Specifically, our architecture combines a Variational Autoencoder with a discriminator mapping the latent space to action predictions (predictor). We compare our model performance to two different behavior cloning architectures: a discriminative model (a Convolutional Neural Network) mapping game states directly to actions, and a Variational Autoencoder with a predictor trained separately. Finally, we demonstrate how we can use the advantage of generative modeling to sample new states from the latent space of the Variational Autoencoder to analyze player actions and provide meaning to certain latent features.
Detection of Clickbait Thumbnails on YouTube Using Tesseract-OCR, Face Recognition, and Text Alteration

The combination of thumbnails and titles on the video-sharing website is effective for providing an overview of the content of the videos. But there are also clickbait thumbnails purposely designed to lure people into watching the videos, sometimes even misleading with the agenda of building a particular opinion. Clickbait thumbnails usually consist of a composition of photos of people and narrative text. OCR is generally used to detect text in images of similar objects, while face recognition is applied to identify people in photos. In this research, we implement OCR and face recognition to detect clickbait thumbnails. We use SVM Model to process the implementation results. Using dataset consist of 250 thumbnails, resulting in an accuracy value of 0.968, a sensitivity value of 0.968, a precision value of 0.9698, and an F1-Score of 0.9678.
Investigating Frontline Service Employees to Identify Behavioral Goals of Restaurant Service Robot: An Exploratory Study

This paper introduces the investigation of the frontline service employees in order to identify and suggest the behavioral goals of the restaurant service robots. We conducted the survey-based study to examine the optimal service action selection evaluated by the restaurant service employees. In addition, there was a video-based exploratory behavior analysis to find out the different behavioral factors between expert and novice employees in the real restaurant. It was found out that the employees have the preferred service actions to perform in each service episode in restaurant setting and the service behaviors of the frontline service robot in restaurant needed to be designed to be proactive for effective service delivery.
Discrete Wavelet Denoising via Kernel Based Nonlinear Component Analysis: Case Studies

All complex real world problems are essentially nonlinear. Linear models are relatively simple but inaccurate to describe the nonlinear aspects of dynamic system behaviors. Denoising techniques have been broadly applied to numerous applications in the spatial domain, frequency domain, and time domain. To increase the adaptability of denoising techniques to signal processing of arbitrary nonlinear systems, kernel based nonlinear component analysis is proposed to enhance wavelet denoising. In the multilevel wavelet decomposition, the low frequency approximations and high frequency details are produced at each level. Discrete wavelet transform (DWT) will help to decompose low frequency approximations exclusively at the subsequent levels, while the wavelet packet transform decomposes both approximations and high frequency details at each level. DWT is selected for wavelet denoising in this study, where details at each level and the approximation at specified level are all subject to simplification using nonlinear component analysis. Case studies of typical nonlinear denoising problems in various domains are conducted. The results manifest strong feasibility and adaptability across diverse denoising problems of nonlinear systems.
Statewide access to business databases: States, libraries and support for small business

This survey examines the business-related databases supplied remotely to the general public and funded by some combination of the US Library Services and Technology Act [LSTA] funds, state funds, or other funding streams in the 50 states and the District of Columbia. A literature review traces the development of statewide databases. The research method used is website analysis with follow-up emails and phone calls where necessary for clarification. The survey reveals that most states offer statewide databases useful to small businesses and entrepreneurs.
A compiler for multi-key homomorphic signatures for Turing machines

At SCN 2018, Fiore and Pagnin proposed a generic compiler (called "Matrioska") allowing to transform sufficiently expressive single-key homomorphic signatures (SKHSs) into multi-key homomorphic signatures (MKHSs) under falsifiable assumptions in the standard model. Matrioska is designed for homomorphic signatures that support programs represented as circuits. The MKHS schemes obtained through Matrioska support the evaluation and verification of arbitrary circuits over data signed from multiple users, but they require the underlying SKHS scheme to work with circuits whose size is exponential in the number of users, and thus can only support a constant number of users. In this work, we propose a new generic compiler to convert an SKHS scheme into an MKHS scheme. Our compiler is a generalization of Matrioska for homomorphic signatures that support programs in any model of computation. When instantiated with SKHS for circuits, we recover the Matrioska compiler of Fiore and Pagnin. As an additional contribution, we show how to instantiate our generic compiler in the Turing Machines (TM) model and argue that this instantiation allows to overcome some limitations of Matrioska: First, the MKHS we obtain require the underlying SKHS to support TMs whose size depends only linearly in the number of users. Second, when instantiated with an SKHS with succinctness poly(λ) and fast enough verification time, e.g., S⋅log⁡T+n⋅poly(λ) or T+n⋅poly(λ) (where T, S, and n are the running time, description size, and input length of the program to verify, respectively), our compiler yields an MKHS in which the time complexity of both the prover and the verifier remains poly(λ) even if executed on programs with inputs from poly(λ) users.
Towards collaborative ontology construction for learning Computer Science in Education

Computer Science in Education is a module administrated within three different under-graduating curricula offered by the CS Department at Universidade de Brasília (UnB), Brasília, Brazil. As the field of CS in Ed is a vast and constantly evolving domain, drawing a method to guide the learning process might become a challenge for instructional/learning designers. In this paper, we propose a method that is being progressively designed and deployed, and is supposed to converge from the principles of both ontology construction and inquiry-based learning.
Construction and application of Wenhua education cloud based on Huawei FusionSphere

Global education industry informatization wave with the maturing of the cloud computing force, the introduction of cloud computing is to promote the school education informatization transformation. Through the analysis of computer resources update is difficult and high cost, inconvenience unified management and maintenance, and low resource utilization, and the traditional information system itself cannot be Shared, low security, defects such as high energy consumption. To solve the above problems, FusionSphere, Huawei's commercial version cloud platform based on open source OpenStack technology, is used to build the private cloud of Mandarin Education. Improve the utilization of traditional IT facilities through virtualization technology. The backup solution, eBackup, is designed to ensure the security and reliability of school information data and IT system. ServiceCenter and Operation Center technologies are used to realize the unified management, operation and maintenance of the cloud platform, which is convenient for unified management and maintenance. Customize the desktop cloud client to use the unified Office, deploy the "One Teacher, One Excellent Class" network teaching platform of Mandarin University, etc. The significance of the construction of Mandarin Education Cloud lies in solving the practical application problems of Mandarin Education Informatization.
Depression tendency detection model for Weibo users based on Bi-LSTM

Depression will have a severe impact on social harmony and family happiness. Aiming at users Weibo users, this paper explores the use of deep learning methods. Based on the sentence sentiment analysis task, we propose a depression tendency detection model for Weibo users based on Bi-LSTM. Firstly, Use the Skip-Gram model in Word2Vec to vectorize the text. Adopt Bi-LSTM neural network layer. Through the bidirectional transmission, semantic dependence of capture context, mining the content characteristics of Weibo text. Finally, the text sentiment category is classified through the fully connected layer. The experimental results show that this method can effectively detect the depression tendency for Weibo users.
Non-interactive Secure Multiparty Computation for Symmetric Functions, Revisited: More Efficient Constructions and Extensions

Non-interactive secure multiparty computation (NIMPC) is a variant of secure computation which allows each of n players to send only a single message depending on his input and correlated randomness. Abelian programs, which can realize any symmetric function, are defined as functions on the sum of the players' inputs over an abelian group and provide useful functionalities for real-world applications. We improve and extend the previous results in the following ways: