Reviewerly/exHarmony huggingface dataset:
Enforcing ethical goals over reinforcement-learning policies

Abstract Recent years have yielded many discussions on how to endow autonomous agents with the ability to make ethical decisions, and the need for explicit ethical reasoning and transparency is a persistent theme in this literature. We present a modular and transparent approach to equip autonomous agents with the ability to comply with ethical prescriptions, while still enacting pre-learned optimal behaviour. Our approach relies on a normative supervisor module, that integrates a theorem prover for defeasible deontic logic within the control loop of a reinforcement learning agent. The supervisor operates as both an event recorder and an on-the-fly compliance checker w.r.t. an external norm base. We successfully evaluated our approach with several tests using variations of the game Pac-Man, subject to a variety of “ethical” constraints.
Contrastive semantic disentanglement in latent space for generalized zero-shot learning

The target of generalized zero-shot learning (GZSL) is to train a model that can classify data samples from both seen categories and unseen categories under the circumstances that only the labeled samples from seen categories are available. In this paper, we propose a GZSL approach based on conditional generative models that adopts a contrastive disentanglement learning framework to disentangle visual information in the latent space. Specifically, our model encodes original and generated visual features into a latent space in which these visual features are disentangled into semantic-related and semantic-unrelated representations. The proposed contrastive learning framework leverages class-level and instance-level supervision, where it not only formulates contrastive loss based on semantic-related information at the instance level, but also exploits semantic-unrelated representations and the corresponding semantic information to form negative sample pairs at the class level to further facilitate disentanglement. Then, GZSL classification is performed by training a supervised model (e.g, softmax classifier) based only on semantic-related representations. The experimental results show that our model achieves state-of-the-art performance on several benchmark datasets, especially for unseen categories. The source code of the proposed model is available at: https://github.com/fwt-team/GZSL.
CSIP: A Compact Scrypt IP design with single PBKDF2 core for Blockchain mining

Developing compact and energy-efficient Scrypt hardware for power-constrained devices is necessary to balance the distribution of blockchain networks. However, existing Scrypt circuits are challenging to achieve in a compact area and energy-efficient since they focus only on maximizing hash performance. Therefore, this paper proposes a Compact Scrypt IP (CSIP) architecture to reduce power consumption while maintaining hashing performance for blockchain mining. Specifically, CSIP uses only one SHA-256 core inside one PBKDF2 core to minimize hardware resources, thus decreasing power consumption significantly. Furthermore, CSIP supports the configuration of parameters to suit the various requirements of blockchain mining. The CSIP design is successfully implemented and verified on a Xilinx Zynq UltraScale+ MPSoC ZCU102 FPGA at a system-on-chip level. Accordingly, the energy efficiency of the CSIP on ZCU102 FPGA is 322 times and 9 times higher than Intel i9-10940X CPU and Nvidia Tesla V100 GPU, respectively. Finally, the experimental results on Xilinx Virtex-7 VC707 FPGA show that the proposed CSIP is significantly better than existing Scrypt architectures in area, power, and energy efficiency.
Robust projection twin extreme learning machines with capped <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si6.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>-norm distance metric

In this paper, we incorporate the idea of projection twin support vector machines (PTSVM) into the basic framework of twin extreme learning machines (TELM) and first propose a novel binary classifier named projection twin extreme learning machines (PTELM). PTELM is to seek two projection directions in the TELM feature space, such that the projected samples of one class are well separated from those of the other class. Compared with the PTSVM, PTELM tackles nonlinear cases without using several fixed kernel functions, thus PTELM is less sensitive to use specified parameters and can get better classification accuracy. Then, a new capped L1-norm PTELM (CL1-PTELM) is proposed by introducing capped L1-norm distance metric in PTELM to reduce the effect of outliers. CL1-PTELM overcomes the disadvantages of L2-norm distance metric and hinge loss. Thus, CL1-PTELM enhances the robust performance of our PTELM. Finally, two effective algorithms are designed to solve the problem of PTELM and to deal with the challenge of CL1-PTELM brought by non-convex optimization problem, respectively. Simultaneously, we theoretically prove the convergence and local optimality of CL1-PTELM algorithm. Numerical experiments on three synthetic datasets and several UCI datasets show the feasibility and effectiveness of our proposed methods.
An Architecture of MySQL Storage Engines to Increase the Resource Utilization

Big data storage and retrieval issues have been tackled in various ways. Making a robust model is challenging, requiring the architects and designers to review the requirements and decide on a solution. Relational databases still have the potential to overcome significant data processing and storage issues. An essential component of any relational database is the storage engine. It directly influences how resources are used and how well queries perform. MySQL’s Storage engine ensures that all basic operations are carried out effectively. Our primary goal in this research is to find the most appropriate storage engine in terms of memory and CPU usage. We also examine each engine’s query performance To assist the organization in selecting the best storage engine for their application, this article also examines the most widespread applications of InnoDB, MyISAM, and ARCHIVE.
Optical three-dimensional shape measurement based on structured light and a binocular vision system

Three-dimensional shape measurement based on structured light is affected by two factors: the number of fringe patterns and the phase unwrapping process. Although one-shot technology can get the wrapped phase, it is not suitable for measuring complex surface. Moreover, phase unwrapping also affects measurement speed and accuracy. To overcome these problems, a two-dimensional wavelet transform with binocular vision system is proposed. Wavelet transform is used to get the wrapped phase based on the Morlet wavelet. In order to get a three-dimensional shape without phase unwrapping, a binocular vision system is used. The increase matching accuracy, the preliminary disparity, and the sub-pixel optimization are calculated, respectively. Based on the calibration parameters, three-dimensional information can be obtained directly from the wrapped phase. In addition, the average phase is calculated based on ambient pixels to confirm wrapped phase boundary. Experimental results demonstrate the feasibility and advantage of the proposed method. Compared with traditional methods, both measurement accuracy and measurement speed can be increased.
Operator-Friendly UAV Control System with HMI Using Speech and Gesture Recognition

UAVs (Unmanned Aerial Vehicles) and drones have been used in various fields such as delivery of goods, fire suppression, and traffic monitoring. As a result, the number of beginners in UAV control is also increasing. Traditional methods of UAV control include manual flight using twin-stick controllers or automatic flight by uploading Waypoint-based missions to the FC (Flight Controller) in advance using GCS (Ground Control System) software. However, these conventional UAV control methods take a long time for beginners to get used to it. In manual flight, considerable proficiency is required for UAV control, which increases the control fatigue of novice operators. For beginners, it may be difficult not only to control UAV, but also to handle other tasks such as manipulating a gimbal-camera or starting/pausing missions at the same time as UAV operations. Especially in emergency situations, simultaneous work can be very burdensome because the ability to respond quickly is required. Therefore, it is necessary to develop an operator-friendly control system with HMI (Human Machine Interface) (Zimmer D, Rhodes D (2006) Human–machine interfaces. IEEE Indus Appl Mag 12:29–35) that reduces the operator's controlling stress and does not require high-level control capabilities. We have developed the UAV control system with speech recognition, which is used for AI speakers that are easily seen around us, and gesture recognition, which is used for motion recognition cameras like KINECT, based on the existing control systems. We have implemented speech recognition techniques using PyAudio and SpeechRecognition, speech recognition open-source libraries, and gesture recognition techniques using YOLOv3, an object-detecting open-source library. First of all, to select speech and gesture commands, we analyze the UAV operation procedure and divided step by step. Second, the commands are adopted according to the divided UAV operation steps. Finally, we develop modules to process these commands and do the SITL (Simulation In The Loop). In this paper, we have designed a prototype of an Operator-friendly Control System with HMI (Human Machine Interface) capable of simple speech and gesture commands, and proceed with the SITL. Based on our UAV Control System with HMI, we can identify which sensors and which commands are Operator-friendly. The entire process is simulated using ROS Melodic and gazebo9 in Ubuntu 18.04 environments. As a result, we expect that the people who have no experience in operating UAVs can operate the UAV with HMI control system using speech and gesture recognition in the simulation.
State of the Art of Automation in Sign Language: A Systematic Review

Sign language is the fundamental communication language of deaf people. Efforts to develop sign language generation systems can make the life of these people smooth and effortless. Despite the importance of sign language generation systems, there is a paucity of a systematic literature review. This is the foremost recognizable scholastic literature review of sign language generation systems. It presents a scholastic database of the literature between 1998 and 2020 and suggests classification criteria to systematize research studies. Four hundred fourteen research studies were recognized and reviewed for their direct pertinence to sign language generation systems. One hundred sixty-two research studies were subsequently chosen, examined, and classified. Each of the 162 chosen research papers was categorized based on 30 sign languages and was further comparatively analyzed based on seven comparison parameters (input form, translation technologies, application domain, use of parsers/grammars, manual/non-manual features, accuracy, and output form). It is evident from our research findings that the majority of research on sign language generation was carried out using data-driven approaches in the absence of proper grammar rules and generated only manual signs. This research study may provide researchers a roadmap toward future research directions and facilitate the compilation of information in the field of sign language generation.
Toward Visual Behavior and Attention Understanding for Augmented 360 Degree Videos

Augmented reality (AR) overlays digital content onto reality. In an AR system, correct and precise estimations of user visual fixations and head movements can enhance the quality of experience by allocating more computational resources for analyzing, rendering, and 3D registration on the areas of interest. However, there is inadequate research to help in understanding the visual explorations of the users when using an AR system or modeling AR visual attention. To bridge the gap between the saliency prediction on real-world scenes and on scenes augmented by virtual information, we construct the ARVR saliency dataset. The virtual reality (VR) technique is employed to simulate the real-world. Annotations of object recognition and tracking as augmented contents are blended into omnidirectional videos. The saliency annotations of head and eye movements for both original and augmented videos are collected and together constitute the ARVR dataset. We also design a model that is capable of solving the saliency prediction problem in AR. Local block images are extracted to simulate the viewport and offset the projection distortion. Conspicuous visual cues in the local block images are extracted to constitute the spatial features. The optical flow information is estimated as an important temporal feature. We also consider the interplay between virtual information and reality. The composition of the augmentation information is distinguished, and the joint effects of adversarial augmentation and complementary augmentation are estimated. The Markov chain is constructed with block images as graph nodes. In the determination of the edge weights, both the characteristics of the viewing behaviors and the visual saliency mechanisms are considered. The order of importance for block images is estimated through the state of equilibrium of the Markov chain. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method.
Comparison Based Analysis and Prediction for Earlier Detection of Breast Cancer Using Different Supervised ML Approach

Breast cancer is one of the most leading diseases in women which leads to an increase in the mortality rate unexpectedly. The tremendous growth of breast cells forms tumors that sense as lumps. Generally, two categories are most important Benign (non-cancerous) and Malignant (cancerous). In medical science, different treatment strategies are there to identify which kind of tumor it is (Benign or Malignant). Image tests (X-rays, CT scan), ultrasound, MRI, radiation therapy, chemotherapy, immune therapy, surgery, biopsy are the most common techniques that are used in hospitals depending upon different stages of lumps which is very important but expensive in breast cancer detection. As a solution in our paper, we have discussed a comparison-based analysis of different machine learning approaches on Wisconsin Breast Cancer (WBC) dataset for measuring the variance (deviation between training accuracy and test accuracy) for early detection of breast cancer. But the question is what happens when the dataset is noisy or binary classification (Benign or Malignant) is crucial. Support vector machine (SVM) is an improvement over accuracy measure with respect to others. Binary classification is not possible always for higher-dimensional data, sometimes data that is not separable in 2D may be separable in 3D or higher dimension using transformation known as Kernel trick. In our literature, we have computed variance against nine machine learning models, three out of them [Gaussian naive Baye’s (0.014330739682853), SVM-polynomial (0.019316054532322), SVM-sigmoid (0.000344725696839)] provide positive variances, whereas others are provided negative variances [Logistic regression (−0.046554384582553), K-nearest neighbor (−0.018483863554286), decision tree (−0.083916083916084), random forest (−0.009291178305352), SVM-linear (−0.023227945763157), SVM-RBF (−0.018533110082405)]. Positive variance is an improvement of test accuracy, whereas negative variance is a loss of test accuracy. Our goal is to consider only positive cases among which give the best result (where the variance vanishes or approximately tends to zero).
The application of 360°VR for training sports officials: a constraints-led approach

Rationale New technologies are an important tool for facilitating development of sports officials, particularly in response to the COVID-19 pandemic. 360°VR presents an innovative opportunity for amateur officiating organisations to develop more game-like (i.e. representative) training opportunities outside of competition.Design This commentary paper outlines 360°VR from a constraints-led approach.Findings This article provides a practical guide of how to design, develop, and deliver this innovative technology for officials. 360°VR affords the opportunity to deliver more individualised training for officials, incorporating key environmental constraints such as crowd noise, and reproducing head movements that closely simulate scanning in a game. The rationale for 360°VR as a more representative training tool is discussed, with future avenues for applying this technology described, which include psychological training, reflective practice, and developing decision-making skills.Practical implications 360°VR is a tool that can be used in a range of areas for developing sports officiating. While there are some limitations of this technology, there are many opportunities to engage with amateur officials using this technology which is considered more enjoyable and relevant.Research contribution This commentary paper provides a theoretical rationale for using 360°VR in sports officials from a constraints-led perspective, and is the first paper to do so in sports officiating.
Multimodal Sarcasm Detection via Hybrid Classifier with Optimistic Logic

This work aims to provide a novel multimodal sarcasm detection model that includes four stages: pre-processing, feature extraction, feature level fusion, and classification. The pre-processing uses multimodal data that includes text, video, and audio. Here, text is pre-processed using tokenization and stemming, video is pre-processed during the face detection phase, and audio is pre-processed using the filtering technique. During the feature extraction stage, such text features as TF-IDF, improved bag of visual words, n-gram, and emojis as well on the video features using improved SLBT, and constraint local model (CLM) are extraction. Similarly the audio features like MFCC, chroma, spectral features, and jitter are extracted. Then, the extracted features are transferred to the feature level fusion stage, wherein an improved multilevel canonical correlation analysis (CCA) fusion technique is performed. The classification is performer using a hybrid classifier (HC), e.g. bidirectional gated recurrent unit (Bi-GRU) and LSTM. The outcomes of Bi-GRU and LSTM are averaged to obtain an effective output. To make the detection results more accurate, the weight of LSTM will be optimally tuned by the proposed opposition learning-based aquila optimization (OLAO) model. The MUStARD dataset is a multimodal video corpus used for automated sarcasm Discovery studies. Finally, the effectiveness of the proposed approach is proved based on various metrics.
Bidirectional Statistical Feature Extraction Based on Time Window for Tor Flow Classification

The anonymous system Tor uses an asymmetric algorithm to protect the content of communications, allowing criminals to conceal their identities and hide their tracks. This malicious usage brings serious security threats to public security and social stability. Statistical analysis of traffic flows can effectively identify and classify Tor flow. However, few features can be extracted from Tor traffic, which have a weak representational ability, making it challenging to combat cybercrime in real-time effectively. Extracting and utilizing more accurate features is the key point to improving the real-time detection performance of Tor traffic. In this paper, we design an efficient and real-time identification scheme for Tor traffic based on the time window method and bidirectional statistical characteristics. In this paper, we divide the network traffic by sliding the time window and then calculate the relative entropy of the flows in the time window to identify Tor traffic. We adopt a sequential pattern mining method to extract bidirectional statistical features and classify the application types in the Tor traffic. Finally, extensive experiments are carried out on the UNB public dataset (ISCXTor2016) to validate our proposal’s effectiveness and real-time property. The experiment results show that the proposed method can detect Tor flow and classify Tor flow types with an accuracy of 93.5% and 91%, respectively, and the speed of processing and classifying a single flow is 0.05 s, which is superior to the state-of-the-art methods.
To pre-filter, or not to pre-filter, that is the query: A multi-campus big data study

Library discovery platforms, which provide searchable user interfaces as their front-facing layer, aggregate tremendous amounts of metadata from multiple data streams describing a wide variety of print and electronic resources. Complicating the matter further, resources may differ in availability or delivery time depending not only on their media but also upon the source of the data stream describing them. How should libraries structure end users’ options for searching discovery platforms in light of the many options available? This study used a nonexperimental design and quantitative methods to analyze users’ revealed preferences for query type in twenty-four academic libraries in a data set containing metadata, sans queries, for over 64 million searches. Libraries studied were all located in California, used the same discovery layer software, and served similar user and faculty constituencies; however, the number of query types and pre-filtering options available differed between institutions. Results show that, when users were presented with the choice between search options, most conducted simple, more broad searches rather than complex and specific searches. When search options were highly constrained by the default choice architecture, but complex searches were possible, few users opted out of the default simple search. Implications for usability of discovery layers and the motivations of librarians in choice architecture are nontrivial and are discussed. The desires of librarians and “power user” faculty must be balanced with the fact that most users are novices and users of all abilities are largely habituated to commercial search products which emphasize post-search results filtering.
An effective Reinforcement Learning method for preventing the overfitting of Convolutional Neural Networks

Convolutional Neural Networks are machine learning models that have proven abilities in many variants of tasks. This powerful machine learning model sometimes suffers from overfitting. This paper proposes a method based on Reinforcement Learning for addressing this problem. In this research, the parameters of a target layer in the Convolutional Neural Network take as a state for the Agent of the Reinforcement Learning section. Then the Agent gives some actions as forming parameters of a hyperbolic secant function. This function’s form is changed gradually and implicitly by the proposed method. The inputs of the function are the weights of the layer, and its outputs multiply by the same weights to updating them. In this study, the proposed method is inspired by the Deep Deterministic Policy Gradient model because the actions of the Agent are into a continuous domain. To show the proposed method’s effectiveness, the classification task is considered using Convolutional Neural Networks. In this study, 7 datasets have been used for evaluating the model; MNIST, Extended MNIST, small-notMNIST, Fashion-MNIST, sign language MNIST, CIFAR-10, and CIFAR-100.