Reviewerly/exHarmony huggingface dataset:
A nonparametric-learning visual servoing framework for robot manipulator in unstructured environments

Current visual servoing methods used in robot manipulation require system modeling and parameters, only working in structured environments. This paper presents a nonparametric visual servoing for a robot manipulator operated in unstructured environments. A Gaussian-mapping likelihood process is used in Bayesian stochastic state estimation (SSE) for Robotic coordination control, in which the Monte Carlo sequential importance sampling (MCSIS) algorithm and a learning-remedied method are created for robotic visual-motor mapping estimation. The self-learning strategy described takes advantage of remedy the particles deterioration to maintain the robust performance at a low rate of particle sampling, rather than likes MCSIS rely on enlarge the sampling variance to cover the whole state distribution. Additionally, the servoing controller is deduced for robotic coordination directly by visual observation. The stability of the proposed framework is illustrated by Lyapunov theory and applied to a manipulator with eye-in-hand configuration no system parameters. Finally, the simulation and experimental results demonstrate consistently that the proposed algorithm involving learning-remedied outperforms traditional visual servoing approaches.
Immersive Virtual Reality Training for Inspecting Flagger Work zones

Construction and maintenance work on roads pose safety risks to both drivers and workers. The responsible agencies regularly inspect work zones for compliance with traffic control and signage standards. The current training practice is to review documents related to temporary traffic control and reports from previous inspections, typically Power Point files with pictures. It would be beneficial if a new mechanism for training could be developed that is as effective as field visits but without the amount of time and effort required to visit multiple field sites. This study developed an immersive training module for transportation agency staff that inspect flagger operations in road construction and maintenance work zones. Human flaggers are commonly used to control traffic at work zones on two lane highways (one lane in each direction). The main objective of the proposed training is to deliver a realistic experience to trainees in an immersive virtual environment using the current traffic control protocols and standards. The module creation consisted of three steps. First, the roadway geometrics, work zone signage, traffic control devices, and the natural environment was created. Second, motion capture technology was used to replicate the actual movement of a human flagger directing traffic in a work zone. The environment and flagger avatar created in the first two steps were integrated and implemented in a simulation in the third step. The module was demonstrated to inspection staff at one state department of transportation (DOT) and revised based on their feedback. The state DOT staff were highly receptive to the use of virtual reality for training and commented on the benefits of the immersive experience that is lacking in their current training practices.
Chatting Without Borders: Assessment as the First Step in Cultivating an Accessible Chat Reference Service

As distance education programs grow at college and universities across the country, libraries must ensure virtual reference services are prepared to meet the needs of patrons in these programs. This article describes the process and results of a 2018 chat analysis conducted at a midsize research university with a large distance education program. The authors discuss the implications of their findings, as well as their process of closing the assessment loop. By using data to inform changes to virtual reference services, chat becomes more accessible and approachable to all users regardless of location.
What Does Not Fit Can Be Made to Fit! Trade-Offs in Distributed Ledger Technology Designs

Distributed ledger technology (DLT), including blockchain, enables secure processing of transactions between untrustworthy parties in a decentralized system. However, DLT is available in different designs that exhibit diverse characteristics. Moreover, DLT characteristics have complementary and conflicting interdependencies. Hence, there will never be an ideal DLT design for all DLT use cases; instead, DLT implementations need to be configured to contextual requirements. Successful DLT configuration requires, however, a sound understanding of DLT characteristics and their interdependencies. In this manuscript, we review DLT characteristics and organize them into six groups. Furthermore, we condense interdependencies of DLT characteristics into trade-offs that should be considered for successful deployment of DLT. Finally, we consolidate our findings into DLT archetypes for common design objectives, such as security, usability, or performance. Our work makes extant DLT research more transparent and fosters understanding of interdependencies and trade-offs between DLT characteristics.
Enhanced Convergent PNP Algorithms For Image Restoration

Image restoration has long been one of the key research topics in image processing. Many mathematical approaches have been developed to solve this problem, e.g., variational methods, wavelet techniques, or Bayesian methods. With the widespread of neural network (NN) models in all the subdomains of data science, the performance limits of these methods are further pushed. One of the most successful strategies consists of plugging NNs in existing optimization algorithms. However, so doing raises several mathematical and practical challenges. One of the main issues is to secure the convergence of the resulting iterative scheme. Further questions concerning the characterization of the reached limit are also worth being addressed. In this paper, we show that the theory of maximally monotone operators allows us to bring insightful answers to these problems and to design firmly nonexpansive NNs; combining these with postprocessing NNs leads to excellent global restoration quality.
Design Principles for Robust Fraud Detection: The Case of Stock Market Manipulations

We address the challenge of building an automated fraud detection system with robust classifiers that mitigate countermeasures from fraudsters in the field of information-based securities fraud.Our work involves developing design principles for robust fraud detection systems and presenting corresponding design features.We adopt an instrumentalist perspective that relies on theory-based linguistic features and ensemble learning concepts as justificatory knowledge for building robust classifiers.We perform a naive evaluation that assesses the classifiers' performance to identify suspicious stock recommendations, and a robustness evaluation with a simulation that demonstrates a response to fraudster countermeasures.The results indicate that the use of theory-based linguistic features and ensemble learning can significantly increase the robustness of classifiers and contribute to the effectiveness of robust fraud detection.We discuss implications for supervisory authorities, industry, and individual users.
An Electric Vehicle Charging Reservation Approach based on Blockchain

The popularity of electric vehicles depends on convenient and efficient charging services. At present, none of existing charging services allow users to reach charging stations at desirable time and charge immediately when they arrive without waiting. This paper proposes a charging reservation service approach based on the consortium blockchain and smart contract technology. Users can choose the charging station and charging time period with no charging congestion, which is based on the charging information recorded in the consortium blockchain composed of stations located in distributed regions in a city. To ensure a user arrives at the charging station on time and charge within due time as he/she has reserved, a personalized pricing scheme for reward and punishment by utilizing smart contract is proposed. We take the past charging behavior into consideration when deciding current charging price of each user, which can provide individualized prices for different users. This approach can not only greatly reduce the user's waiting time, but also offer high cost-effective charging services for good behavior users. We carry out experimental verification under multiple sets of parameter settings, illustrate the variations in three aspects including user's initial score, violation rate and intensity of reward and punishment, thus the feasibility of our approach is proved. Our work is a credible charging paradigm based on trust mechanism via blockchain, which has the potential to become an industry service standard for electric vehicle charging.
Attend and represent

Today, a plethora of different software verification tools exist. When having a concrete verification task at hand, software developers thus face the problem of algorithm selection. Existing algorithm selectors for software verification typically use handpicked program features together with (1) either manually designed selection heuristics or (2) machine learned strategies. While the first approach suffers from not being transferable to other selection problems, the second approach lacks interpretability, i.e., insights into reasons for choosing particular tools.
A Secure and Computable Blockchain-Based Data Sharing Scheme in IoT System

The internet of things (IoT) devices are expected to collect vast amounts of data that support different kinds of applications such as health monitor, smart home, and traffic management. However, its characteristics such as resource-constrained nature, dynamicity, and large-scale growth bring challenges to secure IoT data sharing. Nowadays, blockchain-based ciphertext-policy attribute-based encryption (CP-ABE) was proposed to realize secure IoT data sharing. In blockchain-based CP-ABE data sharing schemes, the data are encrypted and stored in the cloud. Once users want to process the data, they should download and then decrypt the ciphertext in the client-end, and after processing the data, users encrypt and upload the ciphertext onto the cloud. This outweighs the advantage of using cloud computing resources. Fully homomorphic encryption (FHE) and homomorphic signature technology may be adopted to realize ciphertext computation and for correctness checking of ciphertext computation results. In this paper, we propose a secure and computable IoT data sharing system to ensure users enjoying the computation convenience of the cloud-end. Specifically, the proposed system integrates CP-ABE and FHE to realize secure IoT data sharing and ciphertext computation. In addition, we generated homomorphic signatures of ciphertexts to enable users to check the correctness of the ciphertext computation results. Moreover, to supervise the cloud, providing the honest IoT data access control, storage, and computing services for users, we recorded the access policy of the data, the hash of the data, the signature of the ciphertext, and the homomorphic signature of the ciphertext on the blockchain. The performance evaluation and security analysis show the proposed scheme is practical and secure.
Experimental study of neuromorphic node based on a multiwaveband emitting two-section quantum dot laser

In this work, we present experimental results concerning excitability in a multiband emitting quantum-dot-based photonic neuron. The experimental investigation revealed that the same two-section quantum dot laser can be tuned through a simple bias adjustment to operate either as a leaky integrate and fire or as a resonate and fire neuron. Furthermore, by exploiting the inherent multiband emission of quantum-dot devices revealed by the existence of multiple lasing thresholds, a significant enhancement in the neurocomputational capabilities, such as spiking duration and firing rate, is observed. Spike firing rate increased by an order of magnitude that leads to an enhancement in processing speed and, more importantly, neural spike duration was suppressed to the picosecond scale, which corresponds to a significant temporal resolution enhancement. These new regimes of operation, when combined with thermal insensitivity, silicon cointegration capability, and the fact that these multiband mechanisms are also present in miniaturized quantum-dot devices, render these neuromorphic nodes a proliferating platform for large-scale photonic spiking neural networks.
An Even/Odd Error Detection Based Low-Complexity Chase Decoding for Low-Latency RS Decoder Design

This letter presents a modified low-complexity chase (LCC) algorithm, where a fewer number of vectors can be tested with minor error correction performance degradation. The proposed LCC decoding pre-determines whether the number of errors in the received codeword is even or odd, and it processes only necessary test vectors. As a result, the number of test vectors can be reduced by half compared to the conventional LCC decoding. The Reed-Solomon (255,239) decoder with the proposed LCC algorithm has been implemented using 65nm CMOS process. The hardware implementation results show that the proposed decoder shows 48.5% reduced latency with 0.06 dB of coding gain decrease at 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> codeword error rate compared to the state-of-the-art LCC decoder.
A Proposed Image Compression Technique Based on DWT and Predictive Techniques

The idea behind the suggested developed image compression system depends on the application of DWT on error prediction values (Residual Image) instead of the original image. The error prediction values have simpler values compared to the original image, to reduce the coefficient values of all the wavelet bands. As a result, the detail bands can overcome for the first decomposition level and all / some of detail bands for the second decomposition level. In addition, the effect of quantization process is less. The system has been applied on the natural and gray-scale images. The results obtained was similar for both image types. Seven types of different wavelet for four families were compared: {dbl, db3, db5, syml, coifl, bior 2.2, bi02.4.}. The db3 wavelet produced the best (PSNR), which nearest from Coifl wavelet.
Learning A Wafer Feature With One Training Sample

In this work, we consider learning a wafer plot recognizer where only one training sample is available. We introduce an approach called Manifestation Learning to enable the learning. The underlying technology utilizes the Variational AutoEncoder (VAE) approach to construct a so-called Manifestation Space. The training sample is projected into this space and the recognition is achieved through a pre-trained model in the space. Using wafer probe test data from an automotive product line, this paper explains the learning approach, its feasibility and limitation.
A Lightweight Scheme Exploiting Social Networks for Data Minimization According to the GDPR

In many application domains, there is a need to ensure that users satisfy some requirements to use a service: for example, there is a minimum age to buy alcoholic beverages or to watch some videos on YouTube. In these situations, organizations typically collect more personal information than necessary to provide a better service. The consequence is a personal data leakage that violates the data minimization principle stated by the General Data Protection Regulation 2016/679. This article proposes a new approach for allowing individuals to maintain control over the disclosure of their data, deciding which information to disclose and for how long. Our approach is based on the use of social networks, and implementation on Facebook is presented to show that the proposed solution is effective, cheap, friendly, and simple to adopt.
Text Summarization Using Text Frequency Ranking Sentence Prediction

In the era of information technology, data plays significant role. The data which prevails on the internet are unstructured and are not in a concise manner. To make the raw data into a structured, readable, coherent and concise and to extract the summary of data, the text summarization concept is introduced. The text summarization involves in providing asummary of the useful information from the raw data without dissolving the main theme of the data. Nowadays readers face the challenge of reading comments, reviews, news articles, blogs, etc., as they are too informal and noisy. Retrieving the correct gist of the text which is necessary for all the readers is a quite difficult task. In order to overcome the problems faced by the readers, TFRSP (Text Frequency Ranking Sentence Prediction) algorithmis proposedto generate a precise summary that uses supervised and unsupervised learning algorithms. The proposed approach uses the combination of TF-IDF-TR (Term Frequency – Inverse Document Frequency – Text Rank) as an unsupervised learning algorithm and Seq2Seq (Sequence to Sequence) model as a supervised learning algorithm to obtain the benefits of both extractive and abstractive summarization. The results of the proposed TFRSPapproach is compared with the existing methods of text summarization using the Recall Oriented Understudy of Gisting Evaluation (ROUGE) and attains a high ROUGE score, hence achieves high accuracy ofsummary.
Semantic-based topic representation using frequent semantic patterns

Topic modeling discovers the hidden topics in a document collection. Most of the existing topic models focus only on word usage and generate the topics based on the word frequency and co-occurrence without considering the meaning of the text. In this paper, we propose a novel approach to generate a semantic pattern-based topic representation based on the meaning of the text to represent the topics in a document collection. The proposed approach considers both the semantics and co-occurrence of words to generate a set of frequent semantic patterns to represent each topic. The semantics are captured by matching the words in each topic with concepts in the Probase ontology. A set of frequent semantic patterns in each topic is generated based on the co-occurrence of the matched words to represent the topic. Hence, our approach differs from traditional topic models because of the meaningful frequent semantic patterns generated based on the ontology. The proposed topic representation was evaluated in terms of topic quality and information filtering performance against a set of state-of-the-art systems. Perplexity, coherence, and topic word distribution were examined in the topic quality evaluation. The generated frequent semantic patterns were used as features for the information filtering evaluation. Our topic representation outperformed in all the evaluations.
SlidAR+: Gravity-aware 3D object manipulation for handheld augmented reality

Accurately placing virtual objects in a scene is a challenging tasks in handheld augmented reality (HAR). To add and arrange virtual objects in HAR, users must manipulate 6 degrees of freedom (DoFs) of the virtual object, namely: position (3) and orientation (3). However, it is difficult to manipulate all DoFs with the two-dimensional display of the handheld device. We present SlidAR+, a method for controlling the position and orientation of objects in HAR. SlidAR+ is an extension of SlidAR [1], a technique that allows users to control the position of a virtual object by manipulating only 1 DoF. We use the direction of gravity as a constraint to improve the user’s control and reduce the time it takes to adjust the orientation. Upon comparing it with a state-of-the-art object manipulation method, using SlidAR+, user were able to complete the tasks faster under our expected conditions and were also preferred by most participants.
Affective and Cognitive Factors of Internet User Behaviour

The article examines the specifics of digital network user behaviour in the context of personality theory and psychological theory of social attitude. The authors highlight the emotional perception of cyber reality and the experience of the value of interactions in the network as a subconscious tendency to choose a particular strategy of behaviour, in assessing information and resolving a problem situation associated with internal tension or conflict. The goal is to investigate the emotional state of a digital network user, to correlate the indicators of the user’s subconscious inclination to a specific mechanism of psychological protection in a difficult life situation and the choice of behaviour strategy in network interaction. Research methods involve a survey of student groups, focused on identifying the specifics of their perception of Internet interactions, creating a generalized socio-psychological portrait of the user through the prism of subconscious attitudes, in particular, to comfort, depression, self-control, distraction. A survey of students was carried out using a psycho-diagnostic methodology focused on identifying the specifics of the psychological defence of a person in the information field of the network and in overcoming stressful situations associated with internal conflict. The research was carried out among the students and post-graduate students of St. Petersburg universities of different directions and levels of training. The results of an empirical study of the young people’s attitudes towards using the Internet show statistically significant differences in the attitudes of young people in the emotional assessment of interactions in the digital network. The relationship between the cognitive orientations of the individual and behavioural strategies in overcoming problematic life situations has also been established.
Causes and Impacts of Personal Health Information (PHI) Breaches: A Scoping Review and Thematic Analysis

In light of the recent high-profile data breach incidents, and newly updated mandatory data breach notification laws, considerable global attention is forming around the protection of Personal health information (PHI). PHI breaches, generally described as an impermissible use or disclosure of protected personal health information, are extremely consequential for healthcare organizations and their patients and customers. This paper reports on a scoping review and thematic analysis of the literature studying the causes and impacts of PHI breaches. We started our review by identifying over 900 relevant articles, and through a rigorous process, included 28 articles for a detailed synthesis. Our findings highlight a number of direct and indirect causes of PHI breaches and their behavioral and operational impacts. Based on these findings, gaps in the literature are identified and implications for future research are discussed.
AdvCapsNet: To defense adversarial attacks based on Capsule networks

Convolutional neural networks have achieved the state-of-the-art results across numerous applications, but recent work finds that these models can be easily fooled by adversarial perturbations. This is partially due to gradient calculation instability, which may be amplified throughout network layers (Liao et al., 2018). To address this issue, we propose a novel AdvCapsNet derived from Capsule (Sabour et al., 2017), which utilizes a significantly more complicated non-linearity, to defend against adversarial attacks. In this paper, we focus on the transfer-based black-box adversarial attacks, which are more practical than their white-box counterparts. Specifically, we investigate vanilla Capsule’s robustness and boost its performance by introducing an adversarial loss function as regularization. The weight updating between capsule layers is implemented via dynamic routing regularized by the additional adversarial term. Extensive experiments demonstrate that the proposed AdvCapsNet can significantly boost Capsule’s robustness and that AdvCapsNet is far more resistance to adversarial attacks than alternative baselines, including both CNN- and Capsule-based defense models.