Reviewerly/exHarmony huggingface dataset:
Recruitment-imitation mechanism for evolutionary reinforcement learning

Reinforcement learning, evolutionary algorithms and imitation learning are three principal methods to deal with continuous control tasks. Reinforcement learning is sample efficient, yet sensitive to hyperparameters settings and needs efficient exploration; Evolutionary algorithms are stable, but with low sample efficiency; Imitation learning is both sample efficient and stable, however it requires the guidance of expert data. In this paper, we propose Recruitment-imitation Mechanism (RIM) for evolutionary reinforcement learning, a scalable framework that combines advantages of the three methods mentioned above. The core of this framework is a dual-actors and single critic reinforcement learning agent. This agent can recruit high-fitness actors from the population performing evolutionary algorithms, which instructs itself to learn from experience replay buffer. At the same time, low-fitness actors in the evolutionary population can imitate behavior patterns of the reinforcement learning agent and promote their fitness level. Reinforcement and imitation learners in this framework can be replaced with any off-policy actor-critic reinforcement learner and data-driven imitation learner. We evaluate RIM on a series of benchmarks for continuous control tasks in Mujoco. The experimental results show that RIM outperforms prior evolutionary or reinforcement learning methods. The performance of RIM’s components is significantly better than components of previous evolutionary reinforcement learning algorithm, and the recruitment using soft update enables reinforcement learning agent to learn faster than that using hard update.
Perceptual Video Quality Prediction Emphasizing Chroma Distortions

Measuring the quality of digital videos viewed by human observers has become a common practice in numerous multimedia applications, such as adaptive video streaming, quality monitoring, and other digital TV applications. Here we explore a significant, yet relatively unexplored problem: measuring perceptual quality on videos arising from both luma and chroma distortions from compression. Toward investigating this problem, it is important to understand the kinds of chroma distortions that arise, how they relate to luma compression distortions, and how they can affect perceived quality. We designed and carried out a subjective experiment to measure subjective video quality on both luma and chroma distortions, introduced both in isolation as well as together. Specifically, the new subjective dataset comprises a total of $210$ videos afflicted by distortions caused by varying levels of luma quantization commingled with different amounts of chroma quantization. The subjective scores were evaluated by $34$ subjects in a controlled environmental setting. Using the newly collected subjective data, we were able to demonstrate important shortcomings of existing video quality models, especially in regards to chroma distortions. Further, we designed an objective video quality model which builds on existing video quality algorithms, by considering the fidelity of chroma channels in a principled way. We also found that this quality analysis implies that there is room for reducing bitrate consumption in modern video codecs by creatively increasing the compression factor on chroma channels. We believe that this work will both encourage further research in this direction, as well as advance progress on the ultimate goal of jointly optimizing luma and chroma compression in modern video encoders.
An efficient attribute-based hierarchical data access control scheme in cloud computing

Abstract Security issues in cloud computing have become a hot topic in academia and industry, and CP-ABE is an effective solution for managing and protecting data. When data is shared in cloud computing, they usually have multiple access structures that have hierarchical relationships. However, existing CP-ABE algorithms do not consider such relationships and just require data owners to generate multiple ciphertexts to meet the hierarchical access requirement, which would incur substantial computation overheads. To achieve fine-grained access control of multiple hierarchical files effectively, first we propose an efficient hierarchical CP-ABE algorithm whose access structure is linear secret sharing scheme. Moreover, we construct an attribute-based hierarchical access control scheme, namely AHAC. In our scheme, when a data visitor’s attributes match a part of the access control structure, he can decrypt the data that associate with this part. The experiments show that AHAC has good security and high performance. Furthermore, when the quantity of encrypted data files increases, the superiority of AHAC will be more significant.
BOSSA: A Decentralized System for Proofs of Data Retrievability and Replication

Proofs of retrievability and proofs of replication are two cryptographic tools that enable a remote server to prove that the users' data has been correctly stored. Nevertheless, the literature either requires the users themselves to perform expensive verification jobs, or relies on a “fully trustworthy” third party auditor (TPA) to execute the public verification. In addition, none of existing solutions consider the underlying incentive issues behind a rational server who is motivated to collect users' data but tries to evade the replication checking in order to save storage resources. In this article, we propose the first decentralized system for proofs of data retrievability and replication-BOSSA, which is incentive-compatible for each party and realizes automated auditing atop off-the-shelf blockchain platforms. We deal with issues such as proof enforcements to catch malicious behaviors, new metrics to measure the contributions, and reward distributions to create a fair reciprocal environment. BOSSA also incorporates privacy-enhancing techniques to prevent decentralized peers (including blockchain nodes) from inferring private information about the outsourced data. Security analysis is presented in the context of integrity, privacy, and reliability. We implement a prototype based on BOSSA leveraging the smart contracts of Ethereum blockchain. Our extensive experimental evaluations demonstrate the practicality of our proposal.
Quality and readability of internet information about stuttering

We examined the quality and readability of English-language Internet information about stuttering and evaluated the results considering recommendations by experts in health literacy. A search of Internet websites containing information about stuttering was conducted. Three key words (i.e., stuttering, stammering, speech disfluency) were entered into five country-specific versions of the most commonly used Internet search engine. A total of 79 websites were assessed. Their origin (commercial, non-profit, government, personal or university), quality [Health On the Net (HON) certification and DISCERN scores], and readability [Flesch Reading Ease (FRE) score, Flesch-Kincaid Grade Level Formula (F-KGL), and Simple Measure of Gobbledygook (SMOG)] were assessed. Of the 79 websites, 38 % were of commercial, 42 % were of nonprofit organization, 15 % were of government and 5% were of university origins, respectively. Only 13 % had obtained HON certification and the mean DISCERN scores was 3.10 in a 5-point scale. The mean reading grade levels were at 13th and 14th grade and 100 % of the websites exceeded the recommended 5th to 6th reading grade level for health information. The quality of Internet-based health information about the treatment of stuttering is generally adequate, but actual usability of the sites examined in this study may be limited due to poor readability levels. This is problematic in persons with poor literacy skills. Since the Internet can be readily accessed as a valuable consumer information resource, speech-language pathologists and other healthcare professionals have an opportunity to direct consumers to websites that provide readable information of good quality.
On the classification of exceptional scattered polynomials

Let f(X)∈Fqr[X] be a q-polynomial. If the Fq-subspace U={(xqt,f(x))|x∈Fqn} defines a maximum scattered linear set, then we call f(X) a scattered polynomial of index t. The asymptotic behavior of scattered polynomials of index t is an interesting open problem. In this sense, exceptional scattered polynomials of index t are those for which U is a maximum scattered linear set in PG(1,qmr) for infinitely many m. The classifications of exceptional scattered monic polynomials of index 0 (for q>5) and of index 1 were obtained in [1]. In this paper we complete the classifications of exceptional scattered monic polynomials of index 0 for q≤4. Also, some partial classifications are obtained for arbitrary t. As a consequence, the classification of exceptional scattered monic polynomials of index 2 is given.
Phishing Detection from URLs Using Deep Learning Approach

Today, the Internet covers worldwide. All over the world, people prefer an E-commerce platform to buy or sell their products. Therefore, cybercrime has become the center of attraction for cyber attackers in cyberspace. Phishing is one such technique where the unidentified structure of the Internet has been used by attackers/criminals that intend to deceive users with the use of the illusory website and emails for obtaining their credentials (like account numbers, passwords, and PINs). Consequently, the identification of a phishing or legitimate web page is a challenging issue due to its semantic structure. In this paper, a phishing detection system is implemented using deep learning techniques to prevent such attacks. The system works on URLs by applying a convolutional neural network (CNN) to detect the phishing webpage. In paper [19] the proposed model has achieved 97.98% accuracy whereas our proposed system achieved accuracy of 98.00% which is better than earlier model. This system doesn’t require any feature engineering as the CNN extract features from the URLs automatically through its hidden layers. This is other advantage of the proposed system over earlier reported in [19] as the feature engineering is a very time-consuming task.
Towards Real-Time Vehicle Detection on Edge Devices with Nvidia Jetson TX2

With the development of deep convolutional networks, significant advances in object detection task have been achieved. However, for applications in autonomous vehicles, it is necessary to have an efficient object detector that can process rapidly while maintaining high accuracy. This study presents our implementation and performance evaluation of two object detectors EfficientDet-Lite and Yolov3-tiny on Nvidia Jetson TX2 mobile embedded platform. Our experimental results on the KITTI dataset demonstrate that it is possible to achieve real-time and highly accurate object detection on edge devices with constrained resources.
Machine Learning for Breast Cancer Classification With ANN and Decision Tree

Breast cancer is one of the commonest cause of cancer deaths in women. It starts developing when threatening bumps start forming from the breast cells, and unfortunately most diagnoses happen in later stages, thus resulting in low chances of survival for the patient. So for early detection and prognosis, it is necessary to detect the benign or threatening nature of the bumps. In this paper, Artificial Neural Networks (ANN) and Decision Tree (DT) classifiers are used to develop a machine learning (ML) model using the Wisconsin diagnostic breast cancer (WDBC) dataset, in order to evaluate the attributes of a breast cancer development at beginning phases and classify it as malignant or benign. In the proposed scheme, feature selection and feature extraction are done to extract statistical features from the dataset and comparison between the models is provided based on their performance to identify the most suitable approach for diagnosis. The dataset apportioned into various arrangements of train-test split. The presentation of the framework is estimated, depending on accuracy, sensitivity, specificity, precision, and recall. The binary classification problem achieved a maximum accuracy of 98.55%.
Trends &amp; Methods in Chatbot Evaluation

Chatbots are computer programs aiming to replicate human conversational abilities through voice exchanges, textual dialogues, or both. They are becoming increasingly pervasive in many domains like customer support, e-coaching or entertainment. Yet, there is no standardised way of measuring the quality of such virtual agents. Instead, multiple individuals and groups have established their own standards either specifically for their chatbot project or have taken some inspiration from other groups. In this paper, we make a review of current techniques and trends in chatbot evaluation. We examine chatbot evaluation methodologies and assess them according to the ISO 9214 concepts of usability: Effectiveness, Efficiency and Satisfaction. We then analyse the methods used in the literature from 2016 to 2020 and compare their results. We identify a clear trend towards evaluating the efficiency of chatbots in many recent papers, which we link to the growing popularity of task-based chatbots that are currently being deployed in many business contexts.
Investigating the performance of MicroPython and C on ESP32 and STM32 microcontrollers

Python is a programming language that is used both by entry level programmers and advanced researchers. MicroPython is a software implementation of Python that runs on microcontrollers. This paper will investigate the MicroPython execution performance compared to similar C native code on low cost microcontrollers: STM32 and ESP32. The comparison will target: memory allocation speed; SHA-256 and CRC-32 performance and will present conclusions regarding the encountered problems and ways to improve the application performance.
Hand Gesture Recognition Using Multiple Acoustic Measurements at Wrist

This article investigates the use of acoustic signals recorded at the human wrist for hand gesture recognition. The prototype consists of 40 microphones to be worn at the wrist. The gesture recognition performance is evaluated through the identification of 36 gestures in American sign language (ASL), including 26 ASL alphabetical characters and 10 ASL numbers. The optimal area for sensor band placement (distal/proximal) is examined to reveal the location of the highest discrimination accuracy. Ten subjects are recruited to perform over ten trials for each set of hand gestures. Using mutual information-based feature selection methods, two time-domain features: difference absolute mean value and log-energy entropy, is selected from 35 commonly used time-domain features for the hand gesture classification. As a proof-of-concept, two common classification methods, linear discriminant analysis and support vector machine, is used to classify hand gestures from the acoustic measurements recorded by each subject. Results show the intrasubject average classification accuracy above 90% using the two features with all 40 microphones, while the average classification accuracy exceeding 84% is obtained using ten microphones. These results indicate that acoustic signatures from the human wrist can be utilized for hand gesture recognition, while the use of few, simple features, with low computational requirements is sufficient to characterize some hand gestures. The proposed technique demonstrates a promising means for developing a low-cost wearable hand gesture recognition device using microphones.
All the frequencies matter in the Bitcoin market: an efficiency analysis

Most studies in the Bitcoin literature are focused on daily data without considering other options. Therefore, it is necessary to analyse Bitcoin features at different frequencies. In this letter, we examine Bitcoin efficiency from 1 min to weekly data using the generalized Hurst exponent. Our results show that Bitcoin is more efficient over time regardless of the frequency. In particular, we observe that, since 2016, daily data are generally the most efficient frequency while 1 min and weekly data are the most inefficient. These results are relevant for investors and scholars since we detect the most profitable frequencies and underline the relevance of analysing different frequencies than daily data.
A-SARSA: A Predictive Container Auto-Scaling Algorithm Based on Reinforcement Learning

Due to the lightweight and flexible characteristics, containers have gradually been used for the application deployment and the basic unit for resource allocation in a cloud platform recently. Reinforcement learning (RL), as a classic algorithm, is widely used in virtual machine scheduling scenarios due to its advantages of adaptability and robustness. However, most RL methods have problems in container scheduling, such as untimely scheduling, lack of accuracy in decision-making and poor dynamics that will lead to a higher SLA violation rate. In order to solve the above problems, a predictive RL algorithm A-SARSA is proposed, which combines the ARIMA model and the neural network model. This algorithm not only ensures the predictability and accuracy of the scaling strategy, but also enables the scaling decisions to adapt to the changing workloads. Through a large number of experiments, the timeliness and effectiveness of the A-SARSA algorithm for container scheduling are verified, which can reduce the SLA violation rate dramatically while keeping the resource utilization rate at a good level.
Machine Learning Predicts the Timing and Shear Stress Evolution of Lab Earthquakes Using Active Seismic Monitoring of Fault Zone Processes

Machine learning (ML) techniques have become increasingly important in seismology and earthquake science. Lab-based studies have used acoustic emission data to predict time-to-failure and stress state, and in a few cases, the same approach has been used for field data. However, the underlying physical mechanisms that allow lab earthquake prediction and seismic forecasting remain poorly resolved. Here, we address this knowledge gap by coupling active-source seismic data, which probe asperity-scale processes, with ML methods. We show that elastic waves passing through the lab fault zone contain information that can predict the full spectrum of labquakes from slow slip instabilities to highly aperiodic events. The ML methods utilize systematic changes in P-wave amplitude and velocity to accurately predict the timing and shear stress during labquakes. The ML predictions improve in accuracy closer to fault failure, demonstrating that the predictive power of the ultrasonic signals improves as the fault approaches failure. Our results demonstrate that the relationship between the ultrasonic parameters and fault slip rate, and in turn, the systematically evolving real area of contact and asperity stiffness allow the gradient boosting algorithm to "learn" about the state of the fault and its proximity to failure. Broadly, our results demonstrate the utility of physics-informed ML in forecasting the imminence of fault slip at the laboratory scale, which may have important implications for earthquake mechanics in nature.
Optical Realization of Wave-Based Analog Computing with Metamaterials

Recently, the study of analog optical computing raised renewed interest due to its natural advantages of parallel, high speed and low energy consumption over conventional digital counterpart, particularly in applications of big data and high-throughput image processing. The emergence of metamaterials or metasurfaces in the last decades offered unprecedented opportunities to arbitrarily manipulate the light waves within subwavelength scale. Metamaterials and metasurfaces with freely controlled optical properties have accelerated the progress of wave-based analog computing and are emerging as a practical, easy-integration platform for optical analog computing. In this review, the recent progress of metamaterial-based spatial analog optical computing is briefly reviewed. We first survey the implementation of classical mathematical operations followed by two fundamental approaches (metasurface approach and Green’s function approach). Then, we discuss recent developments based on different physical mechanisms and the classical optical simulating of quantum algorithms are investigated, which may lead to a new way for high-efficiency signal processing by exploiting quantum behaviors. The challenges and future opportunities in the booming research field are discussed.
“How dare you call her a pig, I know several pigs who would be upset if they knew”*

Abstract On the occasion of the 2017 UK election campaign, Amnesty International conducted a large-scale, sentiment-based analysis of online hate speech against women MPs on Twitter ( Dhrodia 2018 ), identifying the “Top 5” most attacked women MPs as Diane Abbott, Joanna Cherry, Emily Thornberry, Jess Phillips and Anna Soubry. Taking Amnesty International’s results as a starting point, this paper investigates online misogyny against the “Top 5” women MPs, with a specific focus on the video-sharing platform YouΤube, whose loosely censored cyberspace is known as a breeding ground for antagonism, impunity and disinhibition ( Pihlaja 2014 ), and, therefore, merits investigation. By collecting and analysing a corpus of YouTube multimodal data we explore, critique and contextualize online misogyny as a techno-social phenomenon applying a Social Media Critical Discourse Studies (SM-CDS) approach ( KhosraviNik and Esposito 2018 ). Mapping a vast array of discursive strategies, this study offers an in-depth analysis on how technology-facilitated gender-based violence contributes to discursively constructing the political arena as a fundamentally male-oriented space, and reinforces stereotypical and sexist representation of women in politics and beyond.
A Comprehensive Blockchain-based solution For Academic Certificates Management Using Smart Contracts

Educational certificates serve as proof of qualification for their recepients. These certificates are subject to forgery and manipulation. The traditional way to verify the authenticity and integrity of certificates has not shown much efficiency in preventing fraud. In this paper, a reliabe and tamper-proof certificate verification system is proposed. Unlike the traditional verifcation methods, the proposed system is decentralized by design using the blockchain technology. Decentralization enhances the security and robustness features of the system by avoiding single points of failure and removing the need to put trust in any single party. The proposed design leverages blockchain-based, cryptographically protected smart contracts to automate the verification process and provide transparency and security required for building a fast, relaible and cost-effective verification system.
Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks

In the semantic segmentation of street scenes with neural networks, the reliability of predictions is of highest interest. The assessment of neural networks by means of uncertainties is a common ansatz to prevent safety issues. As in applications like automated driving, video streams of images are available, we present a time-dynamic approach to investigating uncertainties and assessing the prediction quality of neural networks. We track segments over time and gather aggregated metrics per segment, thus obtaining time series of metrics from which we assess prediction quality. This is done by either classifying between intersection over union equal to 0 and greater than 0 or predicting the intersection over union directly. We study different models for these two tasks and analyze the influence of the time series length on the predictive power of our metrics.