Reviewerly/exHarmony huggingface dataset:
Preliminary Assessment of a Novel Intraoral-Tongue Operated Assistive Technology with Computer Interface

Assistive Technologies (ATs) are dedicated for the people who have severe physical disabilities to improve the quality of their lives by utilizing the users' remaining abilities, such as head and eyeball movements. A novel intra-oral tongue operated assistive technology, called intraoral tongue drive system (iTDS), has been proposed to help the users live more independently. The latest iTDS is a stand-alone version by implementing the advanced signal processing algorithm to process the tongue commands locally and send out to the target devices, such as a personal computer. In this paper, the focus is the preliminary assessment of the stand-alone iTDS (s-iTDS). Four able-bodied subjects joined the study. Each subject completed two computer accessing tasks, center-out tapping and maze navigation, using s-iTDS for 5 sessions in total. The evaluation was compared with the previous TDS versions, including external TDS (eTDS) and multi-modal TDS (mTDS), and the golden standard, keyboard & mouse (KnM) combinations. The throughput of the center-out tapping task was reported as 2.23 bps using the latest s-iTDS, more than two times higher than the results of eTDS and mTDS. The error rate and miss target rate are 22.5% and 13.76%, respectively, demonstrating better performance among the presented TDS. In terms of the maze navigation task, 1.78 bps throughput was reported with s-iTDS.
A Simple and Efficient CCA-Secure Lattice KEM in the Standard Model

We present, to date, the most efficient public-key encapsulation mechanism from integer lattices in the standard model. Our construction achieves adaptive CCA security through a “direct” chosen-ciphertext security technique without relying on any generic transformation. The security of our construction is based on the standard learning-with-errors assumption. The efficiency of our construction is almost the same as the best known non-adaptive CCA-secure construction.
Learning Continuous Facial Actions From Speech for Real-Time Animation

Speech conveys not only the verbal communication, but also emotions, manifested as facial expressions of the speaker. In this article, we present deep learning frameworks that directly infer facial expressions from just speech signals. Specifically, the time-varying contextual non-linear mapping between audio stream and micro facial movements is realized by our proposed recurrent neural networks to drive a 3D blendshape face model in real-time. Our models not only activate appropriate facial action units (AUs), defined as 3D expression blendshapes in the FaceWarehouse database, to depict different utterance generating actions in the form of lip movements, but also, without any assumption, automatically estimate emotional intensity of the speaker and reproduces her ever-changing affective states by adjusting strength of related facial unit activations. In the baseline models, conventional handcrafted acoustic features are utilized to predict facial actions. Furthermore, we show that it is more advantageous to learn meaningful acoustic feature representation from speech spectrograms with convolutional nets, which subsequently improves the accuracy of facial action synthesis. Experiments on diverse audiovisual corpora of different actors across a wide range of facial actions and emotional states show promising results of our approaches. Being speaker-independent, our generalized models are readily applicable to various tasks in human-machine interaction and animation.
Cluster-Based Document Retrieval with Multiple Queries

The merits of using multiple queries representing the same information need to improve retrieval effectiveness have recently been demonstrated in several studies. In this paper we present the first study of utilizing multiple queries in cluster-based document retrieval; that is, using information induced from clusters of similar documents to rank documents. Specifically, we propose a conceptual framework of retrieval templates that can adapt cluster-based document retrieval methods, originally devised for a single query, to leverage multiple queries. The adaptations operate at the query, document list and similarity-estimate levels. Retrieval methods are instantiated from the templates by selecting, for example, the clustering algorithm and the cluster-based retrieval method. Empirical evaluation attests to the merits of the retrieval templates with respect to very strong baselines: state-of-the-art cluster-based retrieval with a single query and highly effective fusion of document lists retrieved for multiple queries. In addition, we present findings about the impact of the effectiveness of queries used to represent an information need on (i) cluster hypothesis test results, (ii) percentage of relevant documents in clusters of similar documents, and (iii) effectiveness of state-of-the-art cluster-based retrieval methods.
What is Information Behaviour in Design? A Novel Set of Information Dimensions

This research addresses lack of a holistic and rigorous understanding of designers' information behaviour, through outlining seven dimensions for investigation, analysis, and capture of designers' use and requirements of information within the design process. ‘Practicing designers’ and ‘people information’ are focussed upon. Four initial information dimensions are outlined through literature analysis and synthesis in Information sciences and Design. These are iteratively evaluated, refined, and detailed through four empirical studies i.e. interview and questionnaire administered to design companies (N = 9); observation of a design team in a real-world design project (N = 5); observation of three teams throughout a design competition (N = 22); and a survey of designers and design researchers (N = 89). A novel set of information dimensions is synthesized including ‘Purpose’, ‘Source’, ‘Format’, ‘Content’, ‘Attributes’, ‘Stage’ and ‘Intensity’. Furthermore, practicing designers' people information behaviour is detailed using these seven dimensions. Findings both enhance theoretical understanding of information behaviour in design, and provide new empirical data on designerly information behaviour.
Comparison of Gradient Descent and Least Squares Algorithms in Deep Model

Abstract Deep neural networks have a wide range of applications in stock forecasting, big data forecasting of influenza outbreaks, prediction of game outcomes and so on. They all involve a common problem—regression prediction problems. Deep model can be applied to solve regression prediction problems by optimizing model structure through optimization algorithm. The optimization algorithm generally uses the gradient descent method to optimize the model structure by inputting much real data. When the previous mathematical model solves the problem of regression analysis, one of the most basic optimization methods is the least squares method. This paper studies the performance of the vgg16 convolutional neural network model combined with transfer learning reconstruction when the amount of data is large. By analyzing their algorithm structure to more clearly understand how they are optimized for deep model, better algorithms can be used for future model optimization.
PAC Reinforcement Learning Algorithm for General-Sum Markov Games

This paper presents a theoretical framework for probably approximately correct (PAC) multi-agent reinforcement learning (MARL) algorithms for Markov games. Using the idea of delayed Q-learning, the paper extends the well-known Nash Q-learning algorithm to build a new PAC MARL algorithm for general-sum Markov games. In addition to guiding the design of a provably PACMARL algorithm, the framework enables checking whether an arbitrary MARL algorithm is PAC. Comparative numerical results demonstrate the algorithm's performance and robustness.
An Improved Dilated Convolutional Network for Herd Counting in Crowded Scenes

Crowd management technologies that leverage computer vision are widespread in contemporary times. There exists many security-related applications of these methods, including, but not limited to: following the flow of an array of people and monitoring large gatherings. In this paper, we propose an accurate monitoring system composed of two concatenated convolutional deep learning architectures. The first part called Front-end, is responsible for converting bi-dimensional signals and delivering high-level features. The second part, called the Back-end, is a dilated Convolutional Neural Network (CNN) used to replace pooling layers. It is responsible for enlarging the receptive field of the whole network and converting the descriptors provided by the first network to a saliency map that will be utilized to estimate the number of people in highly congested images. We also propose to utilize a genetic algorithm in order to find an optimized dilation rate configuration in the back-end. The proposed model is shown to converge 30% faster than state-of-the-art approaches. It is also shown that it achieves 20% lower Mean Absolute Error (MAE) when applied to the Shanghai data set.
An Evaluation on Competitive and Cooperative Evolutionary Algorithms for Data Clustering

Data clustering methods are important tools for exploratory data analysis in many real world applications, such as data mining, image understanding, text analysis, engineering, medicine, and so on. Partitional clustering models are the most popular clustering methods, but these approaches suffer from some limitations, like the sensibility to algorithm initialization and the lack of mechanisms to help them escaping from local minima points. Evolutionary Algorithms (EAs) are global optimization meta-heuristics known for their capabilities to find optimal solutions even when dealing with hard and complex problems. Although many EAs are based on competitive behavior among individuals, its is known that cooperation may lead to better solutions then sheer competition. In this work, we perform a comparative analysis among four state-of-the-art EAs (Genetic Algorithm, Differential Evolution, Particle Swarm Optimization and Group Search Optimization), implemented in both competitive and cooperative frameworks, in the context of data clustering problem. Experiments are executed using eleven real world benchmark datasets as the testing bed, so we could access whether competitive or cooperative behaviors would prevail. The experimental results showed that cooperative algorithms are able to find better solutions, in average, when dealing with clustering problems, than their corresponding competitive approaches, and such models also require less storage memory to keep their population in comparison to competitive methods.
Comparing content and overlap in two large academic eBook collections

This article discusses the difficulties Kutztown University's Rohrbach Library- and many libraries- face when evaluating eBook collections, and the process used to overcome some of the obstacles to making meaningful comparisons. Two multidisciplinary eBook subscription packages, EBSCO and ProQuest, are the focus of this study. Data collected from COUNTER reports and the integrated library system were input into Excel to examine the data. The authors show a comparison of the two collections while including the tremendous challenges and limitations involved in the process. This article emphasizes the need for COUNTER standardization, more consistent data reporting, and collection quality evaluation methods.
A Minimax Approach to Perturbation Attacks on Hierarchical Image Classifiers

Adversarial attacks on classification models were shown to drastically degrade the performance of simple one- stage classifiers. In this paper, we investigate the vulnerability of Hierarchical Classifiers to such attacks. We formulate a maximin problem to generate perturbations targeting these models. An algorithmic approach is proposed to alter the predicted super class - for instance, changing the prediction of a dog to a car rather than a cat, so that an `animal' is classified as a `vehicle'. With the proposed algorithm, the relative loss in classification accuracy for super labels decreases significantly in comparison to perturbations generated for simple classifiers. We also examine the amount of perturbation needed for each case.
Hierarchical erasure correction of linear codes

Linear codes over finite extension fields have widespread applications in theory and practice. In some scenarios, the decoder has a sequential access to the codeword symbols, giving rise to a hierarchical erasure structure. In this paper we develop a mathematical framework for hierarchical erasures over extension fields, provide several bounds and constructions, and discuss potential applications in distributed storage and flash memories. Our results show intimate connection to Universally Decodable Matrices, as well as to Reed-Solomon and Gabidulin codes.
Evolution of Convolution Neural Network Architectures using Genetic Algorithm

Convolution Neural Network (CNN) has become a remarkable tool in solving many real-world problems of computer vision in recent years. However, the designing of most of CNN architectures is manual, which requires a significant trial and error methodology as CNN's performance highly relies on their architectures. Thus, it gets arduous to design a promising CNN architecture without having sufficient domain knowledge and human expertise. In this paper, we attempt to explore the possibility of using Genetic Algorithm to design CNN architectures automatically. The CNN architecture proposed by the Genetic Algorithm is trained from scratch using Gradient- Descent Algorithm and evaluated on a validation set at each evolutionary step. The algorithm does not require any preprocessing of the data before its execution, nor any post-processing on the evolved CNN architecture. We propose an encoding scheme for determining the layer connectivity. This scheme allows the formation of skip connections within the CNN architecture. Along with the encoding scheme, the filter dimensions, and the number of nodes in the fully-connected layer are also genetically evolved during the subsequent evolution using standard genetic operators, namely selection, mutation, and crossover. We have specified algorithms for the formation of the CNN model by combining the information about the encoding scheme, filter dimensions, and the number of nodes of the fully-connected layer in addition to the genetic operations.The proposed algorithm is tested on the MNIST dataset for handwritten digit recognition and Fashion-MNIST dataset. Our experiments have shown that the algorithm is capable of successfully generating high-quality CNN architectures, which are less studied before.
Finding needles in a hay stream: On persistent item lookup in data streams

In a data stream composed of an ordered sequence of data items, persistent items refer to those persisting to occur over a long timespan. Compared with ordinary items, persistent ones, though not necessarily occurring more frequently, typically convey more valuable information. Persistent item lookup, the functionality to identify all persistent items, emerges as a pivotal building block in many computing and network systems. In this paper, we devise a generic persistent item lookup algorithm supporting high-speed, high-accuracy lookup with limited memory cost. The key technicalities we propose in our design are two-fold. First, our algorithm attempts to record only persistent items seen so far based on the currently available information about the stream, thus significantly reducing memory overhead, especially for real-life highly skewed data streams. Second, our algorithm balances the recording load in both time and space domains: in the time domain, we partition persistent items into approximately equal-size subsets and record only one subset in each epoch; in the space domain, we apply the state-of-the-art load balancing technique to evenly distribute recorded items across the on-die memory. By holistically integrating these components, we iron out a persistent item lookup algorithm outperforming existing solutions in a wide range of practical settings.
Fault Diagnosis of Remainders Inside PIGA Based on Pattern Recognition Algorithm

The three floated pendulous integrating gyro accelerometer (PIGA) is a key component in inertial navigation which is widely used in the aerospace field. PIGA is composed of numerous components, resulting in its complex model, high failure rate and difficulties in the failure detection. Aiming at the characteristic of PIGA, a new data-driven fault diagnosis algorithm, utilizing wavelet packet and energy entropy to extract the time-frequency distribution features of data then probabilistic neural network (PNN) and support vector machine (SVM) to classification is proposed. The method is validated in the fault data of surplus inside the PIGA. The classification accuracy of SVM based on radial basis kernel function (RBF) is 96.67% on the circumstance that the time window length is 1 second. The results demonstrate that the proposed algorithm can effectively and rapidly recognize the fault caused by remainder inside the PIGA, which does not depend on the model and can be conveniently transplanted to other kinds of inertial devices.
A multiion particle swarm optimization algorithm based on repellent and attraction forces

Summary Particle swarm optimization (PSO) is an iterative computational methods which is used for obtaining the solutions of practical optimization problems. PSO is however, prone to be ended up obtaining a local optimum. Various strategies are proposed in the related literature to address this issue. Such strategies, however, often reduce the convergence speed of the algorithms. This article proposes a multiion particle swarm optimization (MION‐PSO) algorithm which incorporates three strategies to balance the exploration and exploitation abilities of the algorithm. To improve the exploration abilities, the particles are regarded as ions with repellent/attraction forces among them. The second strategy is a multiion strategy (MIS) in which the population is divided into many subswarms, namely, particle group. Using MIS, the optimal solution within each group is then used to purposefully guide the updates of other individuals. To delete the useless particles without the capability of updating historical best location for a specific period and have a misleading impact on others, without we employ a particle elimination strategy. Twenty‐three benchmark functions and eight baseline algorithms are employed to test the performance of MION‐PSO method. The experimental results show that MION‐PSO method significantly outperforms the baseline algorithms in terms of convergence, and further exhibits high capability in finding the optimal solutions especially on unimodal functions.
Built-In Cybersecurity: Insights Into Product Security for Cyberphysical Systems at a Large Company

Security flaws in cyberphysical systems are expensive. The key to creating secure but affordable industrial cyber systems is to understand the various points at which security can be implemented to build security into a product. This article is written from the perspective of a practitioner and addresses several components of product security that work together to build security into cyberphysical systems.
Implementation of a Hardware Accelerator for a Real-time Encryption System

IoT devices are spreading everywhere and its progress increases day by day. The security of these devices must be the biggest concern. This paper is describing a complete system of a surveillance camera transceiver using two PYNQ Boards connected through Ethernet cable. On one side there is a camera, on the other side there is an HDMI screen and in between is the proposed software image processing and encryption/decryption using the Ascon algorithm (one of the winners of CAESAR Competition for the lightweight category). The aim is to get low power, low area, and low-cost design implementation.
MSChain: Blockchain based Decentralized Certificate Transparency for Microservices

Microservices architecture has become one of the most prominent software architectures in the software development processes due to its features such as scalability, maintainability, resilience, and composability. It allows developing business applications in a decentralized manner by dividing the important business logic into separate independent services. Digital certificates are used to verify the identity of microservices in most cases. However, the certificate authorities (CA) who issue the certificates to microservices cannot be trusted always since they can issue certificates without the consent of the relevant microservice. Nevertheless, existing implementations of certificate transparency are mostly centralized and has the vulnerability of the single point of failure. The distributed ledger technologies such as blockchain can be used to achieve decentralized nature in certificate transparency implementations. A blockchain-based decentralized certificate transparency system specified for microservices architecture is proposed in this paper to ensure secure communication among services. After the implementation and deployment in a cloud service, the system expressed average certificate querying time of 643 milliseconds along with the highly secured service provided.
Monitoring Cooker Activities Using a Grid-EYE Infrared Array Sensor

One of the challenges of our ageing society is to create a safe environment in which the elderly can live. Although the idea of creating an entirely risk-free society is impossible, it is feasible to monitor the health of elderly people by placing smart sensors in their houses, in order to react as soon as possible if a hazardous situation is taking place. In particular, this project focuses on the kitchen environment, by monitoring daily cooking activities and detecting anomalies in the use of the cooker, using some low-cost sensors: a Grid-EYE and a Sharp sensor.
Learning Bayesian Networks Structures with an Effective Knowledge-driven GA

Bayesian networks (BNs) are probabilistic graphical models, which are regarded as one of the most effective theoretical models in the field of representing and reasoning under uncertainty. Learning BNs structure is an NP-hard problem since the search space of structure grows super-exponentially as the increasing of the number of variables. Evolutionary algorithms (EAs) are widely used to learn BNs structure while single-solution searching methods may trap into local optima. This work aims to propose an efficient knowledge-driven Genetic algorithm (EKGA-BN) to solve the BN structure learning problem. The proposed EKGA-BN uses a novel selection operator to keep population diversity in order to learn a BN structure with higher accuracy. The idea of Hill climbing algorithm (HC) is combined in the selection operator so as to accelerate the convergence rate. A novel knowledge-driven mutation procedure is proposed to enhance the local search ability of EKGA-BN. Experimental results on four well-known benchmark networks show that the proposed method outperforms state-of-the-art algorithms in both convergence rate and the accuracy of BNs structure.