Reviewerly/exHarmony huggingface dataset:
A social evaluation of the perceived goodness of explainability in machine learning

Machine learning in decision support systems already outperforms pre-existing statistical methods. However, their predictions face challenges as calculations are often complex and not all model predictions are traceable. In fact, many well-performing models are black boxes to the user who– consequently– cannot interpret and understand the rationale behind a model’s prediction. Explainable artificial intelligence has emerged as a field of study to counteract this. However, current research often neglects the human factor. Against this backdrop, we derived and examined factors that influence the goodness of a model’s explainability in a social evaluation of end users. We implemented six common ML algorithms for four different benchmark datasets in a two-factor factorial design and asked potential end users to rate different factors in a survey. Our results show that the perceived goodness of explainability is moderated by the problem type and strongly correlates with trustworthiness as the most important factor.
An IoT-Based Secure Vaccine Distribution System through a Blockchain Network

COVID-19 is an extremely dangerous disease because of its highly infectious nature. In order to provide quick and immediate identification of infection, proper and immediate clinical support is needed. Researchers have proposed various machine learning and smart IoT-based schemes for categorizing COVID-19 patients. Artificial neural networks (ANNs), which are inspired by the biological concept of neurons, are generally used in various applications including healthcare systems. The ANN scheme provides a viable solution in the decision making process for managing healthcare information. The aim of this article is to provide secure COVID-19 vaccine distribution through IoT-based systems. The level-wise blockchain network is used to ensure security among IoT devices while distributing the vaccines. The proposed phenomenon is analyzed and verified over synthesized data where vaccine units are supplied by various distributors. The proposed approach is validated over accurate report generation and data alteration parameters against existing methods.
Enhancement and Development of Next Generation Data Mining Photolithographic Mechanism

Abstract The analytical data of project management was established. In a stereolithography method, the APC system was already implemented in essential dimensions and overlays. Productivity and system efficiency have been enhanced. The new APC, however, is created on the inspection information where the method anomalies are blended with the fluctuation of the system and which have to evaluate very small quantities, and it has the impact cap. The inspection data for the CD, overlay and log information of the acquaintance tool in ainteractive data base have been compiled and processed. We have also investigated how the earlier in this thread problem can be paid and resolved. First of all, in the enormous tool log data we have extracted ties between inspection informationbesides several parameters, particularly factor loadings. We then discussed problems with big relationships and have, thus, gathered valuable knowledge which did not come out of the traditional system. In order to show the stabilising machine fluctuation effect, we developed, along with APC, a second-generationinformation mining system.
Newly Elaborated Hybrid Algorithm for Optimization of Robot Arm’s Trajectory in Order to Increase Efficiency and Provide Sustainability in Production

Nowadays, resources for production (raw materials, human, energy, etc.) are limited, while population, consumption and environmental damage are continuously increasing. Consequently, the current practices of resource usage are not sustainable. Therefore, manufacturing companies have to change to environmentally friendly and innovative technologies and tools, e.g., industrial robots. Robots are necessary in the production sector and also in terms of sustainability: (1) the application of robots is needed to compensate for the lack of human resources; (2) robots can increase productivity significantly; and (3) there are several hazardous (e.g., chemical, physical) industrial tasks for which robots are more adequate than human workforce. This article introduces a newly elaborated Hybrid Algorithm for optimization of a robot arm’s trajectory by the selection of that trajectory that has the smallest cycle time. This Hybrid Algorithm is based on the Tabu Search Algorithm and also uses two added methods—Point Insertion and Grid Refinement—simultaneously to find more precisely the optimal motion path of the robot arm in order to further reduce the cycle time and utilize the joints’ torque more efficiently. This Hybrid Algorithm is even more effective than applying the Tabu Search method alone and results in even higher efficiency improvement. The Hybrid Algorithm is executed using MATLAB software by creating a dynamic model of a 5 degree-of-freedom robot arm. The main contribution of the research is the elaboration of the new Hybrid Algorithm, which results in the minimization of robot arms’ motion cycle times, causing a significant increase in productivity and thus a reduction in specific production cost; furthermore, obstacles in the workspace can be avoided. The efficiency of the Hybrid Algorithm is validated by a case study showing that application of the new algorithm resulted in 32% shorter motion cycle time.
A Comprehensive Study on Offline Signature Verification

Abstract Handwritten Signatures are special types of behavioural biometric which are used in many applications such as banks, credit cards, passport, check processing, and financial documentation, etc. Verification of these signatures is a challenging task especially in the case of offline where there is no information of signing process. So there is a need for a system that can distinguish between the genuine and the forged signature to avoid the chances of theft or fraud. Many types of researches have been done in this area in the last three decades. Earlier this task was performed by handcrafted features and recently deep learning techniques have been employed for this task, but still, there is a chance of enhancement in the accuracy of the system. In this paper, we present a comprehensive study of the work done in the field of offline signature verification and also the challenges which are still present in this area.
Manipulating and Evaluating Levels of Personality Perceptions of Voice Assistants through Enactment-Based Dialogue Design

We present an enactment-based dialogue design approach to imbue voice assistants with different levels of personality. In two focus groups, we asked amateur actors to animate different voice assistant personalities by writing and enacting dialogues between a voice assistant and a user. We realised the resulting dialogues using Amazon Alexa and presented them to N=156 participants in an online survey to investigate whether the personality levels were successfully synthesised and if user personality influences the preference for specific personality levels. Our results indicate that participants ranked the personality levels as intended but that high personality levels were perceived as less pronounced than expected. Furthermore, we found a small relationship between extraversion and a preference for a voice assistant that is social-entertaining whilst conscientious participants tended to reject a confrontational voice assistant. We discuss implications for researchers and practitioners on how to manipulate voice assistant personalities and adapt them to users.
Anonysign: Novel Human Appearance Synthesis for Sign Language Video Anonymisation

The visual anonymisation of sign language data is an essential task to address privacy concerns raised by large-scale dataset collection. Previous anonymisation techniques have either significantly affected sign comprehension or required manual, labour-intensive work. In this paper, we formally introduce the task of Sign Language Video Anonymisation (SLVA) as an automatic method to anonymise the visual appearance of a sign language video whilst retaining the meaning of the original sign language sequence. To tackle SLVA, we propose Anonysign, a novel automatic approach for visual anonymisation of sign language data. We first extract pose information from the source video to remove the original signer appearance. We next generate a photo-realistic sign language video of a novel appearance from the pose sequence, using image-to-image translation methods in a conditional variational autoencoder framework. An approximate posterior style distribution is learnt, which can be sampled from to synthesise novel human appearances. In addition, we propose a novel style loss that ensures style consistency in the anonymised sign language videos. We evaluate ANONYSIGN for the SLVA task with extensive quantitative and qualitative experiments highlighting both realism and anonymity of our novel human appearance synthesis. In addition, we formalise an anonymity perceptual study as an evaluation criteria for the SLVA task and showcase that video anonymisation using ANONYSIGN retains the original sign language content.
The impact of activation functions on training and performance of a deep neural network

Artificial Neural Networks (ANN) are modelled on the workings of the human brain where information is transported through the synapses formed between individual neurons. The activation function decides the signal to be transmitted from a node of the ANN to the others that are connected, so we can interpret the value of the function as the "activation" of a neuron. There are many activation functions available, each with its advantages and disadvantages when it comes to learning rate and computational load. The current article presents the most commonly used activation functions and their applications in ANNs. We study the effect of using different activation functions for a convolutional neural network used for image classification. Model performance for the train and test set are presented for each fold of the k-fold cross-validation, as well as accuracy scores for the trained network.
USE INTERACTIVE MEDIA TO ENHANCE CREATIVITY OF DESIGNERS BY STIMULATING THE SENSES IN THE CONTEXT OF ART DESIGN EDUCATION

Abstract Creativity is very important for designers, and methods to stimulate designers' creativity are the long-term focus of art design education. The senses are an important channel for designers to receive information and define core issues. Stimulating the designer's senses can help enhance their perception and creativity, and is of great benefit for the quality and efficiency of the design outcome. Today's interactive media technology provides more possibilities and advantages for designers' perception and sensation. The purpose of this research is to explore a way to stimulate the designer's senses through the use of interactive media, thereby improving the designer's design thinking and creativity, and providing designers with innovative design support. By means of interactive ground projection and experiments, and discussion of the advantages of interactive media to stimulate designers' senses, this research proposes innovations in art design educational media, which is valuable for the training and learning of designers and the development of virtual education environment in the future.
LiveROI

Virtual reality (VR) streaming can provide immersive video viewing experience to the end users but with huge bandwidth consumption. Recent research has adopted selective streaming to address the bandwidth challenge, which predicts and streams the user's viewport of interest with high quality and the other portions of the video with low quality. However, the existing viewport prediction mechanisms mainly target the video-on-demand (VOD) scenario relying on historical video and user trace data to build the prediction model. The community still lacks an effective viewport prediction approach to support live VR streaming, the most engaging and popular VR streaming experience. We develop a region of interest (ROI)-based viewport prediction approach, namely LiveROI, for live VR streaming. LiveROI employs an action recognition algorithm to analyze the video content and uses the analysis results as the basis of viewport prediction. To eliminate the need of historical video/user data, LiveROI employs adaptive user preference modeling and word embedding to dynamically select the video viewport at runtime based on the user head orientation. We evaluate LiveROI with 12 VR videos viewed by 48 users obtained from a public VR head movement dataset. The results show that LiveROI achieves high prediction accuracy and significant bandwidth savings with real-time processing to support live VR streaming.
DNN Is Not All You Need: Parallelizing Non-neural ML Algorithms on Ultra-low-power IoT Processors

Machine Learning (ML) functions are becoming ubiquitous in latency- and privacy-sensitive IoT applications, prompting a shift toward near-sensor processing at the extreme edge and the consequent increasing adoption of Parallel Ultra-low-power (PULP) IoT processors. These compute- and memory-constrained parallel architectures need to run efficiently a wide range of algorithms, including key Non-neural ML kernels that compete favorably with Deep Neural Networks in terms of accuracy under severe resource constraints. In this article, we focus on enabling efficient parallel execution of Non-neural ML algorithms on two RISCV-based PULP platforms, namely, GAP8, a commercial chip, and PULP-OPEN, a research platform running on an FPGA emulator. We optimized the parallel algorithms through a fine-grained analysis and intensive optimization to maximize the speedup, considering two alternative Floating-point (FP) emulation libraries on GAP8 and the native FPU support on PULP-OPEN. Experimental results show that a target-optimized emulation library can lead to an average 1.61× runtime improvement and 37% energy reduction compared to a standard emulation library, while the native FPU support reaches up to 32.09× and 99%, respectively. In terms of parallel speedup, our design improves the sequential execution by 7.04× on average on the targeted octa-core platforms leading to energy and latency decrease up to 87%. Last, we present a comparison with the ARM Cortex-M4 microcontroller, a widely adopted commercial solution for edge deployments, which is 12.87× slower than PULP-OPEN.
Cyclic codes over a non-chain ring R, and their application to LCD codes

Let Fq be a finite field of order q, a prime power integer, such that q=et+1 where t≥1,e≥2 are integers. In this paper, we study cyclic codes of length n over a non-chain ring Re,q=Fq[u]/〈ue−1〉. We define a Gray map φ and obtain many maximum-distance-separable (MDS) and optimal Fq-linear codes from the Gray images of cyclic codes. Under certain conditions we determine linear complementary dual (LCD) codes of length n when gcd⁡(n,q)≠1 and gcd⁡(n,q)=1, respectively. It is proved that a cyclic code C of length n is an LCD code if and only if its Gray image φ(C) is an LCD code of length en over Fq. Among others, we present the conditions for existence of free and non-free LCD codes. Moreover, we obtain many optimal LCD codes as the Gray images of non-free LCD codes over Re,q.
Communication-Adaptive Stochastic Gradient Methods for Distributed Learning

This paper targets developing algorithms for solving distributed learning problems in a communication-efficient fashion, by generalizing the recent method of lazily aggregated gradient (LAG) to deal with stochastic gradient - justifying the name of the new method LASG. While LAG is effective at reducing communication without sacrificing the rate of convergence, we show it only works with deterministic gradients. We introduce new rules and analysis for LASG that are tailored for stochastic gradients, so it effectively saves downloads, uploads, or both for distributed stochastic gradient descent. LASG achieves impressive empirical performance - it typically saves total communication by an order of magnitude. LASG can be used together with gradient quantization to bring more savings.
Applications of Blockchain Technology in the Construction Industry

Modern technology can be used to transform buildings and infrastructure that we live in. The ability of blockchain technologies to record, enable and secure large amount and types of transactions makes it an important technological innovation of the 21st century. Several studies have been carried out to review the applications of blockchain technology in the construction industry. However, these studies were limited to literature reviews with no empirical data to back up the submissions. To this end, this study evaluates the different applications of blockchain technology in the construction industry. To achieve this, a well-structured questionnaire survey of building professionals (Quantity surveyors, architects, engineers, construction managers) and information technology experts was undertaken. Retrieved data was analysed using descriptive statistics. Findings revealed that blockchain technology can be very useful in contract management, supply chain management, blockchain-based BIM, financial management among other applications in the construction industry.
Homogenization of incoming solar radiation measurements over Poland with satellite and climate reanalysis data

Well-maintained and regularly calibrated measuring instruments provide the most accurate solar radiation data. This extremely valuable research material makes it possible, among others, to analyse variability in solar radiation over the long term and its dependence on other atmospheric state elements such as cloud cover and atmospheric aerosol concentration. Unfortunately, ground-based measurements of solar radiation are often subject to various errors which are very difficult to detect. This is why quality control procedures and homogenisation of data are essential and should be performed prior to further analyses. This paper presents a method for quality control and homogenization of solar radiation data, which builds on the bias-based quality control (BQC) method (Urraca et al., 2017), and is tailored specially for detecting single erroneous daily values, and very long periods of small errors. The method was tested for 16 ground-based stations located in Poland for the period 1991–2015. In comparison with the number of errors detected by the BQC method, the number of detected errors increased significantly: 130 to 2890 more erroneous days were detected at each station. Consequently, the number of inhomogeneous data sets was reduced from 8 to 3 stations. The values on the days considered as erroneous were replaced with debiased values originating from the Surface Solar Radiation Data Set – Heliosat, Edition 2 (SARAH-2). The presented methodology can be also of use in any other places, especially those with many single erroneous days and no metadata publicly available.
Efficient Robot Motion Planning via Sampling and Optimization

Robot motion planning is one of the important elements in robotics. In environments full of obstacles, it is always challenging to find a collision-free and dynamically-feasible path between the robot's initial configuration and goal configuration. While many motion planning algorithms have been proposed in the past, each of them has its pros and cons. This work presents a benchmark which implements and compares existing planning algorithms on a variety of problems with extensive simulation. Based on that, we also propose a hybrid planning algorithm, RRT*-CFS, that combines the merits of sampling-based planning methods and optimization-based planning methods. The first layer, RRT*, quickly samples a semi-optimal path. The second layer, CFS, performs sequential convex optimization given the reference path from RRT*. The proposed RRT*-CFS has feasibility and convergence guarantees. Simulation results show that RRT*-CFS benefits from the hybrid structure and performs robustly in various scenarios including the narrow passage problems.
Constrained mutual convex cone method for image set based recognition

In this paper, we propose convex cone-based frameworks for image-set classification. Image-set classification aims to classify a set of images, usually obtained from video frames or multi-view cameras, into a target object. To accurately and stably classify a set, it is essential to accurately represent structural information of the set. There are various image features, such as histogram-based features and convolutional neural network features. We should note that most of them have non-negativity and thus can be effectively represented by a convex cone. This leads us to introduce the convex cone representation to image-set classification. To establish a convex cone-based framework, we mathematically define multiple angles between two convex cones, and then use the angles to define the geometric similarity between them. Moreover, to enhance the framework, we introduce two discriminant spaces. We first propose a discriminant space that maximizes gaps between cones and minimizes the within-class variance. We then extend it to a weighted discriminant space by introducing weights on the gaps to deal with complicated data distribution. In addition, to reduce the computational cost of the proposed methods, we develop a novel strategy for fast implementation. The effectiveness of the proposed methods is demonstrated experimentally by using five databases.
A Semantic Model for Indexing in the Hidden Web

We argue that that the use of the Carnap - Bar-Hillel principle of indexed expressions provides a suitable cognitive model to undergo the spread of information processes in the Semantic Net. To understand the stratification of the net into the surface Web and the hidden Web, a model structure is proposed that provides navigation through the net as a whole. For logical expressions, a relativized evaluation map is given, which is parameterized by the indexing system. It is allowed to use concepts that are generated using partial functions, the variables of which run over the range of possible individual information processes. A generalized operator providing virtualization is presented for individuals. The process is accompanied with the dynamic generation of Web-pages, which triggers their indexing. This way they are thereby transferred to the surface layer of the network. A commutative diagram is proposed that establishes a cognitive model of semantic constructions of variable domains, the connection of virtual individuals with actual ones which corresponds to transitions over the stages of knowledge. The knowledge stages are modeled by indexing systems.