Reviewerly/exHarmony huggingface dataset:
The Right to Be Forgotten: Privacy and Security in Blockchain with Multi-authority Based Chameleon Hash Function MAP-ABCH Solution

General Data Protection Regulation GDPR enforce the “Right to be forgotten” as an option for the user to give privacy for Data saved or shared over internet. Blockchain implemented to be immutable that’s mean the data saved in blockchain cannot be changed or deleted. Trapdoor key value used in Chameleon Hash Function (CHF) if it is known it provides a way to deal with the immutability of blockchain in a secure manner. In this paper we are presenting a novel solution to enhance the security of Chameleon Hash Function (CHF). MAP-ABCH is a cryptographic hash function that provides the idea of multi-authority nodes that can save the trapdoor key. We used Proxy re-encryption (PRE) method to share the long-term trapdoor between the authority nodes. We add a short-term trapdoor key (Ephemeral trapdoor) to secure the function and control the editing and deleting in blockchain. We evaluate the performance for MAP-ABCH functions using different number of attributes and different attribute size.
Neuroevolution Trajectory Networks of the Behaviour Space

A network-based modelling technique, search trajectory networks (STNs), has recently helped to understand the dynamics of neuroevolution algorithms such as NEAT. Modelling and visualising variants of NEAT made it possible to analyse the dynamics of search operators. Thus far, this analysis was applied directly to the NEAT genotype space composed of neural network topologies and weights. Here, we extend this work, by illuminating instead the behavioural space, which is available when the evolved neural networks control the behaviour of agents. Recent interest in behaviour characterisation highlights the need for divergent search strategies. Quality-diversity and Novelty search are examples of divergent search, but their dynamics are not yet well understood. In this article, we examine the idiosyncrasies of three neuroevolution variants: novelty, random and objective search operating as usual on the genotypic search space, but analysed in the behavioural space. Results show that novelty is a successful divergent search strategy. However, its abilities to produce diverse solutions are not always consistent. Our visual analysis highlights interesting relationships between topological complexity and behavioural diversity which may pave the way for new characterisations and search strategies.
Computational Technologies for the Wearable Sports Training System Based on Sensor Acquisition

Aiming at the problems of long time-consuming and low precision in traditional training system, a wearable sports training system based on data mining is proposed and designed. According to the overall structure of the system, the hardware and software of the system are designed respectively. The hardware part is mainly composed of attitude measurement unit, power supply unit, main control unit, clock circuit, and reset circuit. The software part introduces the data mining method of mining sports training information data. The experimental results show that the system designed in this study effectively shortens the time-consuming time, improves the accuracy, and has certain advantages.
Handwriting Based Personality Traits Identification Using Adaptive Boosting and Textural Features

Computer analysis of personality traits through handwriting product is becoming increasingly an important thing, mainly because of the deep integration of AI techniques in many fields, such as recruitment services, pedagogy, and mental health diagnostics. Various research studies have shown that there are numerous dimensions of information which can be extracted from a writer's handwriting product. This information had helped reveal writer's gender, identity, age, and also several personality features. Hence, our work aimed at identifying the personality traits of a writer according to Five Factor Model (FFM), by exploiting the textural features of his handwriting product. Thus, the Edge Hinge technique was introduced to extract the textural information found in handwriting images. The exploited dataset for the evaluation of this work consists of a new corpus, dedicated to the experience of the personality traits problem on a group of 285 subjects by instrumentalizing FFM approach. We used several classifiers such as, the Random Forest, Support Vector Machine, and Adaboost in order to choose the best one. The experimental work resulted in higher identification rates than those in the literature, through introducing a combination of classifier technique based on Adaboost to improve performance.
An LSTM-based model for the compression of acoustic inventories for corpus-based text-to-speech synthesis systems

Large acoustic inventories must be used to produce speech close to natural quality. However, the concatenation cost space grows exponentially with the number of acoustic units in the acoustic inventory, increasing the latency of the unit selection algorithm, making algorithms unusable in real-time end-to-end systems. Even when data compression techniques are introduced, the model size is still high, representing a challenge for end-to-end systems. Thus, in this paper, we propose representing the concatenation cost space using LSTM (Long Short-Term Memory). The results show a 90% reduction in the size of the data space compared to all our previous techniques, and by an over 70% decrease in the look-up time. The proposed LSTM-based compression increases the responsiveness of the corpus-based text-to-speech systems significantly while keeping the overall speech quality at the same level.
Application of few-shot object detection in robotic perception

An object detection technique for robotic perception plays a vital role for robots to perform the task that it is functioned to do. In this paper, an efficient and accurate method for object detection for robots is proposed. The paper suggests implementing Few-shot object detection network for robotic vision using the Attention network and attention RPN module. The Multi-relation detector is used to compare two frames and eliminate negative objects from the frame which further enforces the suggested model. Using Contrastive training strategy, the robot is trained to exploit the resemblance between the few-shot support frame and query frame to detect the positive objects and eliminate the negative objects. This method is proposed to help robots perceive the object of interest to perform pick, place, and various other actions. This paper utilizes the COCO dataset to train the network which contains close to 1000 different categories. This method would help accelerate industry 4.0 and has potential in a wide range of applications.
Development of an IR Video Surveillance System Based on Fractional Order TV-Model

Due to the wide range of applications, video surveillance is known as one of the challenging tasks of computer vision which requires detecting and tracking the moving objects in a sequence of images (video). As we are aware that several environmental conditions such as fog, darkness, snow-fall, illumination, rain degrade the quality of vision system. This motivates us to develop a robust infrared (IR) surveillance system to fulfill the open-ended goals of the vision problem. The active motion region is detected by using optical flow. In this paper, an energy functional has been presented for optical flow estimation by incorporating the fractional order total variational (TV) and quadratic terms. In particular, the proposed model is convex and more robust against outliers and provides a dense flow. However, the total variation regularization term is of non-differentiable nature which makes the minimization scheme apparently difficult. The fractional derivative discretization of non-differentiable terms is performed by using Grunwald-Letnikov (GL) derivative. The Primal-dual algorithm is applied in solving the resulting minimization scheme. Finally, the resulting variational formulation is solved by using an appropriate method. The validity, efficiency, and robustness of the proposed system are tested on a variety of datasets under various conditions.
A New Medical Image Encryption Algorithm for IoMT Applications

Data security and privacy are essential for transmitting, storing, and preserving medical images. This article provides a secure chaotic framework for medical image encryption. The suggested technique has two stages: bit-level permutation and 2D SIM-based diffusion. The lossless technique is recommended for medical image encryption and decryption. It addresses the limitations of low-dimensional chaotic maps, such as small intervals and few parameters, and medical images’ unique texture and form. The keyspace of the new method is sufficiently big, and the encryption and decryption procedures are key-sensitive. Simulations and testing validate the new algorithm’s effectiveness and efficiency. According to security assessments, the algorithm is resistant to common attacks. A comparison of several encryption methods is performed. The proposed encryption method surpasses current encryption techniques when it comes to encrypting medical images.
Geochemical characteristics and indication of graphite deposits in Xinrong Region, Shanxi, China

The northeast-southwest trending graphite belt is one the richest graphite reserves in China that is found in the Xinrong region, Datong City of China and extends more than 22 km. The ore-bearing layer of the Huangtuyao Formation is a graphite-bearing gneiss. Based on major and trace element analysis data, we determined that the graphite-bearing gneiss is a parametamorphic rock, and this gneiss was formed by the regional metamorphism of carbonaceous argillite with three diagrams. The detritus of this ore deposit originated from an arc region by the K 2 O/Na 2 O-SiO 2 diagram. According to the Ta/Yb, Sr/Yb and K 2 O/Na 2 O ratios as well as the composition of Gehuyao gneiss, we concluded that this gneiss has typical low-potassium adakitic rock characteristics. Combined with the MgO/SiO 2 diagram and the characteristics of low K 2 O and high Al 2 O 3 , the genetic model of Gehuyao gneiss is melted subducted oceanic crust. Based on regional geological data and the comparison of samples, we found the inherent relationship of the main composition content between the graphite-bearing gneiss and the Gehuyao gneiss. This indicatives that the ore deposit accepted the weathering products of Gehuyao gneiss during the sedimentary period. This research provides evidences for ore body evolution and makes it possible to establish the sedimentary-metamorphic model of the graphite ore in this region. In the light of the range of ore body, we predicted that 2±1.5 km away from the boundary was the potential area for graphite mineralization in the North China Craton. Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/cc/applications-of-innovations-in-geochemical-data-analysis
A novel version of the hidden logarithm problem for post-quantum signature algorithms

In the article we propose a novel version of the hidden logarithm problem (HLP), the peculiarity of which is using the local units instead of the global unit in the HLP proposed earlier for designing post-quantum signature schemes. Two different 4-dimensional finite associative algebras with non-commutative multiplication operation are proposed as carriers of the introduced version of the HLP. One of the proposed algebras contains the global two-sided unit. The second algebra contains only local units (single-sided and two-sided ones). In the first algebra, every non-invertible element, which is not a zero-divisor, defines many local single-sided (the right-sided and the left-sided) and many local two-sided units. The both types of the single-sided units play an important role for defining the HLP of the proposed version. Formulas describing the local units of different types are derived and used to generate the public key of a developed HLP-based signature algorithm that is resistant to quantum attacks. Design criterion and security discussion of the introduced signature scheme are presented.
Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net

The conventional Close circuit television (CCTV) cameras-based surveillance and control systems require human resource supervision.Almost all the criminal activities take place using weapons mostly a handheld gun, revolver, pistol, swords etc.Therefore, automatic weapons detection is a vital requirement now a day.The current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient-Net.Real time datasets, from local surveillance department's test sessions are used for model training and testing.Datasets consist of local environment images and videos from different type and resolution cameras that minimize the idealism.This research also contributes in the making of Efficient-Net that is experimented and results in a positive dimension.The results are also been represented in graphs and in calculations for the representation of results during training and results after training are also shown to represent our research contribution.Efficient-Net algorithm gives better results than existing algorithms.By using Efficient-Net algorithms the accuracy achieved 98.12% when epochs increase as compared to other algorithms.
Recognition of American Sign Language Gestures Based On Electromyogram (EMG) Signal With XGBoost Machine Learning

This paper discusses a potential way to recognize different American Sign Language(ASL) with the help of machine learning. In order to recognize different ASL, we decided to collect the data by multi-channel surface electromyogram(EMG). All the signals are processed during the time domain. After that, we extract the features in the data. The feature extractions this paper uses are mean absolute value, standard deviation, variance, and skewness. The extracted features are going to be analyzed by a machine learning model. The machine learning model this paper uses is the XGBClassifier, and the overall accuracy is about 85%. The samples this method chooses are from different college students whose ages are between 19 to 25 years old. This method also included the muscle fatigue situation. There are two muscle tired statuses that can be distinguished in the measurement to increase the gesture recognition accuracy.
Empowering the Diversity and Individuality of Option: Residual Soft Option Critic Framework

Extracting temporal abstraction (option), which empowers the action space, is a crucial challenge in hierarchical reinforcement learning. Under a well-structured action space, decision-making agents can probe more deeply in the searching or plan efficiently through pruning irrelevant action candidates. However, automatically capturing a well-performed temporal abstraction is a nontrivial challenge due to its insufficient exploration and inadequate functionality. We consider alleviating this challenge from two perspectives, i.e., diversity and individuality. For the aspect of diversity, we propose a maximum entropy model based on ensembled options to encourage exploration. For the aspect of individuality, we propose to distinguish each option accurately, utilizing mutual formation minimization, so that each option can better express and function. We name our framework as an ensemble with soft option (ESO) critics. Furthermore, the residual algorithm (RA) with a bidirectional target network is introduced to stabilize bootstrapping, yielding a residual version of ESO. We provide detailed analysis for extensive experiments, which shows that our method boosts performance in commonly used continuous control tasks.
Dementia Scale Score Classification Based on Daily Activities Using Multiple Sensors

Early detection of age-related disease symptoms in older people by the use of daily activity data is one of the central challenges of home sensor systems. This paper focuses on dementia scale classification from daily activity data collected using sensors that can be deployed in actual residential environments. Activity data collected by four sensors (a door sensor, human motion sensor, location sensor, and sleep sensor) were obtained by recording 56 older adults living in common residences. We analyzed the effects of different types of sensor data, such as time spent in an individual room according to human motion sensors, location in a facility, and sleep patterns, on dementia detection. We then developed a feature extraction method related to daily activity patterns based on a clustering algorithm and analyzed its effectiveness. In the experimental evaluation, we trained binary classification models to classify dementia scale scores based on the Mini-Mental State Examination (MMSE) from these datasets. The experimental results show that a maximum accuracy of 0.871 was obtained with a linear support vector machine (SVM) model by fusing the door, location, and sleep features and by clustering activity patterns using the X-means algorithm.
How open science can benefit bilingualism research: A lesson in six tales

Abstract Bilingualism is hard to define, measure, and study. Sparked by the “replication crisis” in the social sciences, a recent discussion on the advantages of open science is gaining momentum. Here, we join this debate to argue that bilingualism research would greatly benefit from embracing open science. We do so in a unique way, by presenting six fictional stories that illustrate how open science practices – sharing preprints, materials, code, and data; pre-registering studies; and joining large-scale collaborations – can strengthen bilingualism research and further improve its quality.
An analysis of the Word Adjacency Network method—Part 1—The evidence of its unsoundness

Abstract A new mathematical method for authorship attribution, known as the Word Adjacency Network method, has emerged and its inventors have claimed a high success rate with it. They have announced that their method has shown that Christopher Marlowe wrote some scenes in the Henry VI trilogy of plays, an attribution which has made newspaper headlines. This first part of a two-part paper replicates the method’s success in attributing complete plays but demonstrates that it cannot be relied on to attribute individual scenes correctly, particularly where Marlowe is a candidate author. It shows by examining the method’s calculations that its unreliability is due to its mass exclusion of relevant evidence, that exclusion being necessitated by its use of a mathematical formula which can now be seen to be unsuited to the texts of early modern plays.
Challenges and Directions for Ambient Intelligence: A Cyber Physical Systems Perspective

Sensing is becoming more and more pervasive. New sensing modalities are enabling the collection of data not previously available. Artificial Intelligence (AI) and cognitive assistance technologies are improving rapidly. Cyber Physical Systems (CPS) are making significant progress in utilizing AI and Machine Learning (ML). This confluence of technologies is giving rise to the potential to achieve the vision of ambient intelligence. This paper describes some of the main challenges and research directions for ambient intelligence from a CPS perspective.
Incorporating Ranking Context for End-to-End BERT Re-ranking

Ranking context has been shown crucial for the performance of learning to rank. Its use for the BERT-based re-rankers, however, has not been fully explored. In this work, an end-to-end BERT-based ranking model has been proposed to incorporate the ranking context by modeling the interactions between a query and multiple documents in the same ranking jointly, using the pseudo relevance feedback to adjust the relevance weightings. Extensive experiments on standard TREC test collections confirm the effectiveness of the proposed model in improving the BERT-based re-ranker with low extra computation cost.
Change in social networks: Some dynamic extensions of Social Epistemic Logic

Abstract We propose dynamic extensions to the framework of Social Epistemic Logic introduced in Logic in the Community (Seligman et al., pp. 178–188, 2011). One of them introduces a notion of a semi-public announcement made by an aware agent and only transmitted to this agent’s social connections. Another extension along the epistemic dimension of the framework contains separate ‘sending’ and ‘reading’ modalities that allow for a more realistic asynchronous spreading of messages in a social network. Finally, we briefly discuss an extension along the ‘social’ dimension of the framework, which introduces an operator to break links within an epistemic social network based on information at the agent’s disposal. Completeness and decidability results are provided.
Do You Know Where Your Data Sleeps at Night? Cyber Security and Ransomware in Financial Markets

Financial markets are under constant threat of cyber attacks. We develop a principal-agent model of cyber-attacking with fee-paying clients who delegate security decisions to financial platforms. We derive testable implications about cyber attack vulnerability and fees charged. We also characterize the form of cyber attack chosen by attackers. Successful ransomware attacks are more likely than traditional attacks. When security is unobservable, platforms underinvest in security. Welfare can improve by targeting security investment through regulation (e.g. minimum security standards), or by improving transparency (e.g. security ratings). Our results support regulatory efforts to increase transparency around cyber security and cyber attacks.
Hidden Path: Understanding the Intermediary in Malicious Redirections

URL redirection has become an important tool for adversaries to cover up their malicious campaigns. In this paper, we conduct the first large-scale measurement study on how adversaries leverage URL redirection to circumvent security checks and distribute malicious content in practice. To this end, we design an iteratively running framework to mine the domains used for malicious redirections constantly. First, we use a bipartite graph-based method to dig out the domains potentially involved in malicious redirections from real-world DNS traffic. Then, we dynamically crawl these suspicious domains and recover the corresponding redirection chains from the crawler’s performance log. Based on the collected redirection chains, we analyze the working mechanism of various malicious redirections, involving the abused modes and methods, and highlight the pervasiveness of node sharing. Notably, we find a new redirection abuse, redirection fluxing, which is abused to enhance the concealment of malicious sites by introducing randomness into the redirection. Our case studies reveal the adversary’s preference for abusing JavaScript methods to conduct redirection, even by introducing time-delay and fabricating user clicks to simulate normal users.