Reviewerly/exHarmony huggingface dataset:
Visualising and Solving a Maze Using an Artificial Intelligence Technique

This paper describes the implementation of an artificial intelligence (AI) technique known as Genetic Algorithm (GA), used to solve randomly generated mazes that are of varying sizes and complexity. To evaluate the effectiveness of the GA, several non-AI techniques are implemented such as Depth-First Search (DFS), Breadth-First Search (BFS), A-Star Algorithm (A*), Dijkstra Algorithm (DA), and Greedy Best-First Search (GBFS). Genetic algorithm is a method for solving both constrained and unconstrained problems based on a natural selection process that mimics biological evolution. The non-AI algorithms make use of graph concepts together with underlying data structures to solve randomly generated mazes. This paper also shows the results obtained from five different experiments after implementing the complete system and executing each of the non-AI algorithms as well as the GA. The strongest results after executing each algorithm, that is, the number of steps and the time taken to solve the maze were recorded and graphs were plotted to compare the performance of the GA compared to the non-AI algorithms. The GA always found the shortest path but becomes slower than the non-AI algorithms for dimensions greater than 10x 10.
Supply Chain Finance Research in Digital Bulk Commodities Service Platform Based on Blockchain

Supply chain service mode in bulk commodity has some special characteristics, such as large amount, diversification and digitalization and higher operational risk. Based on the application of the bulk commodity digital supply chain, this paper explores the use of blockchain technology in order to build a digital platform. It will reduce business operation risks, improve the service efficiency and rebuild the supply chain credit system. Then it could strengthen the management granularity of the fund side and expand the construction of the bulk commodity ecosystem.
A systematic review of the research on disruptive technology – Blockchain

In this review, a methodical mapping of Blockchain-based research is performed across the multifaceted realm. The proposed research work is aimed at studying the prevailing research perspectives, obstacles, and upcoming considerations related to Blockchain from a technological point of view to underline the role of this disruptive technology in the current sci-tech ecosystem. For this purpose, the hypothetical fundamentals of countless work published in reputed scientific publications in the past ten years, are integrated into this paper. Following the structured, methodical review and thorough study of the recognized literature, an extensive taxonomy of Blockchain-based research in various domains like security, usability, privacy, smart contracts, throughput, latency, wasted-resources, broadcast protocol, and trustworthiness is being presented, and key presentations, directions, and originating research areas are adopted. Developing on the evaluations, various unexplored research topics are discovered and future prospecting indications that are expected to be of substantial value both for theoreticians and professionals.
Development of a Differential Evolution-based Fuzzy Cognitive Maps for Data Breach in Health-care Sector Fuzzy Cognitive Maps for Data Breach

The last three decades have witnessed an increased concern in data breaches. This problem is more pronounced in patients' health information (PHI) management. Previous researchers have reported the need to address this problem from a multi-criteria perspective; however, there is sparse information on how to use fuzzy logic to integrated PHI data breach criteria. This article, therefore, presents a fuzzy-based model, which is a fuzzy cognitive map, for a data breach in health-centres using organizational and human criteria. This model combines fuzzy logic, decision-makers' opinions and cognitive maps method. Decision-makers' opinions were analysis from optimistic, pessimistic and most likely perspectives. The performance of this model has evaluated three FCM data breach forecasting scenarios. And the results obtained showed that the model predicts PHI data breach under different multi-criteria conditions. This article has been able to shed insights into the application of a fuzzy cognitive map for data breach problem in health-care centres
Applied Webservices Platform Supported Through Modified Edit Distance Algorithm: Automated Phonetic Transcription Grading Tool (APTgt)

The abstract should summarize the contents of the paper in short terms, i.e. 150–250 words. This research proposes a platform to improve the user experience of Communications Disorders (CMDS) faculty and their students. Traditionally CMDS faculty have the task of training new scholars in the field on communication disorders in the craft of phonetic transcription utilizing the using the International Phonetic Alphabet (IPA). This can be a phoneme by phoneme transcription of words that have to be analyzed in a phoneme by phoneme process. In the department of Computer Science (CS), we had to opportunity to engage in this participatory design exercise with CMDS to analyze and automate this process. This project utilizes a variant of the Edit Distance algorithm for preprocessing the distance between the phonetic transcriptions of the professor's provided key and student's response. We call this platform, the Automated Phonetic Transcription grading tool, APTgt. The platform dramatically speeds up the grading process (i.e. instant feedback) as compared to traditional manual grading. The case discussed is a complex multivariate problem in the field of Communications Disorders of using the International Phonetic Alphabet (IPA) as a transcription language to support students learning the IPA language. Our focus was to develop an engaging, easy to use platform that can support interactive online learning and reduce complexity of evaluation and feedback for transcript tasks.
Exploring the Effects of Immersive Virtual Reality on Learning Outcomes: A Two-Path Model

Immersive virtual reality (VR) has attracted widespread attention and has been increasingly adopted in education. However, the influence of immersive VR on learning outcomes is still unclear, with conflicting results emerging from the research. The contradictory research can be attributed to the fact that little research has systematically analyzed the effects of immersive VR on learning processes and ultimately on learning outcomes. Therefore, this study focuses on analyzing how immersive VR affects learning outcomes and explaining the reasons behind the contradictory research about immersive VR's effects. A survey was conducted in a laboratory setting in which the participants were asked to play an immersive VR application for learning. The results show that immersive VR affects learning outcomes through affective and cognitive paths. In the affective path, immersive VR features influence learning outcomes through the mediation of immersion and enjoyment. In the cognitive path, immersive VR features influence learning outcomes through the mediation of usefulness, control and active learning, and cognitive benefits. By providing a nuanced understanding of the effects of immersive VR on learning outcomes, this study contributes to the VR literature for learning.
COST: A Consensus-Based Oracle Protocol for the Secure Trade of Digital Goods

In e-commerce, buyers and sellers trade digital products without knowing each other personally. Due to the lack of trust, a buyer prefers to pay for a product upon receipt. Also, the seller prefers to send a product after receiving the payment. With trusted escrows as intermediaries, the payment can be processed and independent parties can confirm the authenticity of a digital product before completing the trade. However, concerning the trade of digital goods, current escrow-based solutions for the buyer-seller dilemma are prone to digital theft because they require the intermediary to validate the complete digital product. Moreover, buyers and sellers rely on the judgment of a single intermediary that they have to trust fully. This work presents a consensus-based oracle protocol for the secure and transparent trade of digital goods using a decentralized escrow service. It protects the seller and buyer against digital theft by splitting the digital product into fragments. Independent parties confirm the authenticity of each of these fragments via a distributed ledger. The protocol is designed to ensure non-fraudulent behavior among all participants and to compensate validators for their work. Compensations are distributed to the validators according to their stake, their confidence and the overall outcome of the validity check.
Medical Image Registration Based on Renyi’s Quadratic Mutual Information

In this paper, a medical image registration based on Renyi's quadratic mutual information is proposed. Firstly, the initial values (i.e. the translation and rotation parameters) for aligning the floating image with the reference image are obtained by computing the medical image centroids and moments. Then, when exploring the optimal geometric transformation parameters, the Renyi's quadratic mutual information is taken as the similarity criterion between the reference and floating images. Finally, the Simplex method, as multi-parameter optimization one, is used to search the optimal registering values in the experiments. The experimental results reveal that, the proposed method has a lower computational load, faster registration and better registration accuracy compared with the registration method based on the Shannon mutual information. It can cater to both mono-modality and multi-modality image registrations.
Accountability and Responsibility in Multiagent Organizations for Engineering Business Processes

Business processes realize a business goal by coordinating the tasks undertaken by multiple interacting parties. Given such a distributed nature, Multiagent Organizations (MAO) are a promising paradigm for conceptualizing and implementing business processes. Yet, MAO still lack of a systematic method for reporting to the right agents feedback about success or failure of a task. We claim that an explicit representation of accountability and responsibility assumptions provides the right abstractions to engineer MAO for supporting the execution of business processes. Basing our programming approach on MAO, we present two accountability patterns for developing accountable agents. To illustrate this approach we use the JaCaMo multi-agent programming platform.
Sentiment Analysis Model for Opinionated Awngi Text

Sentiments are very imperative to decide for both individuals and organizations. Due to the rapid growth of Awngi text on the web, there is no sufficiently available sentiment corpus to be used for research. We developed our corpus by collecting around one thousand five hundred posts from online sources. Even infrequent texts available on the web mostly transliterated in Latin due to lack of accessibility and convenience to typing. This paper proposed a feature-level sentiment analysis method based on machine learning for opinionated Awngi music sentiments. Thus, pre-processing techniques have been employed to clean the data, to convert transliterations to the native Ethiopic script, and to change the words to their base form by removing the inflectional morphemes. To improve the calculation method of feature selection and weighting and proposed a more suitable sentiment analysis algorithm for feature extraction named CHI and weight calculation called TF IDF, increasing the proportion and weight of sentiment words in the feature words. The experiment results show that, among the two learning setups, the accuracy of the SVM is found to be promising.
Early warning disaster-aware service protection in geo-distributed data centers

We investigate dynamic protection of cloud services supported by geo-distributed DCs (Geographically distributed Data Centers) under disaster scenarios which lead to failures of DC nodes and communication fibers. By assuming that the affected network region can be aware of an upcoming disaster for a certain early warning time before it is finally hit, and the corresponding service requests can be identified before they are disrupted, we utilize early warning time to achieve dynamic service protection by carrying out three methods: request rerouting, request relocation and service relocation. In particular, the first one reroutes requests to their original service providing DC nodes; the second one relocates requests to other service providing DC nodes; and the third one relocates requested services to backup DC nodes to support service requests. We first formulate an Integer Linear Program (ILP) model to maximize the number of disaster-affected requests that can be protected, and then propose a time-efficient heuristic solution. Extensive numerical results show that the proposed strategy can achieve good performance to protect service requests under disasters with early warning times.
Ordered directional monotonicity in the construction of edge detectors

In this paper we provide a specific construction method of ordered directionally monotone functions. We show that the functions obtained with this construction method can be used to build edge detectors for grayscale images. We compare the results of these detectors to those obtained with some other ones that are widely used in the literature. Finally, we show how a consensus edge detector can be built improving the results obtained both by our proposal and by those in the literature when applied individually.
A novel design and implementation of wheelchair navigation system using Leap Motion sensor

In this paper, a novel design for a leap motion wheelchair navigation system is proposed, and the suggested model is implemented on a prototype. The behaviour of the created prototype is closely observed during the different performance tests carried out, and the results are presented throughout this manuscript.In the prototype, a Leap Motion sensor is implemented to acquire navigation data through hand gestures of the users. This navigations system design is specifically implemented to facilitate wheelchair use for amputee users and stroke patients as it does not rely on the movement of the fingers. Through this design, wheelchair movement can be controlled through detection of finger, fist, palm or wrist (for amputees) movement by the leap motion sensor. Bluetooth connection is used as the navigation system's communication means, removing the need for constant internet connection and providing freedom of movement outside of internet-covered territory. Additionally, two Dynamixel motors are used as movement force, which yield optimal computational time and minimal delay.The performance of the designed prototype is tested by considering response time and speed resolution as evaluation metrics. Results suggest that the designed wheelchair will give movement independence to users who cannot use their fingers to control the movement of their wheelchairs, while reducing delay, being independent of internet connection, providing high resolution and minimising detection error.The promising results obtained from prototype testing suggest the possibility of real-life application of this wheelchair navigation system, which can greatly assist amputee users and rehabilitation patients.Implications for rehabilitationA novel wheelchair navigations system designed to facilitate amputee users, stroke patients and rehabilitation patients.The proposed system eliminates the reliance on finger movements, is gaze independent, and does not require voice or gesture control, creating much more freedom for users undergoing specific medical conditions or still under rehabilitation or treatment.Results demonstrate very low delay time in wheelchair command to action, allowing improved control for users and reducing the occurrence of control-related accidents.The designed wheelchair navigation system is independent of internet connection, allowing more freedom in range for wheelchair users compared to available cloud based models.
Evaluation of the potential impact of fully-immersive virtual reality on production engineering curricula

Virtual Reality, or VR, is a family of technologies defined in the 60s that aims at mimicking reality through computers with different purposes. VR is usually classified according to the level of user immersion achieved. VR has attracted increasing attention between academic and practitioners during the last decade due to technological progresses that achieved high reliability and relatively low cost. VR potential to visualize information, replace physical presence and stimulate interaction make of this technology an essential part in the toolbox of future engineers. Higher Education Institution must align their curricula to fulfill this purpose. At the same time VR can be a support to enhance the learning process itself. This work analyses the impact of fully-immersive VR technology on an average production engineering curricula based on the educational offer at KTH Royal Institute of Technology in Stockholm, Sweden. The results show that VR has a great potential to shape the production engineering curricula in the coming years both in term of content and design of the learning experiences.
A Method for Increasing User Engagement with Voice Assistant System

Voice interaction is becoming one of the major methods of human interaction with computers. Several mobile service providers have introduced various types of voice assistant systems such as Bixby from Samsung, Siri from Apple, and Google Assistant from Google that provide information including the schedule for a day, the weather, or methods to control the device to perform a task such as playing music. Although the voice assistant system provides various types of functions, generally, the users do not understand what functions the system can support. We conducted a control task analysis based on expert interviews and found that the main bottleneck of using a voice assistant system is that the user cannot know all the commands. Thus, we believe that presenting recommendable commands is an effective way to increase user engagement. Through buzz data analysis, we discovered what functions could be used and determined the relevant usage context. Subsequently, we performed context modelling and designed the user interface (UI) of a voice assistant system and conducted a case study. Through this case study, we proved that presenting commands on a UI induced more user engagement and usability.
Selective Video Encryption using Chaos for H.264 Videos Applicable to Video on Demand(VoD)

The field of encrypting a video has been evolving for a long time to increase the protection given to the video data. Although, different techniques are being proposed, yet the level of efficiency in encryption is not adequate to suit the different real-world and real-time applications. Encrypting only a few selected parameters in video shall appeal its usage in the time constraint approaches. In this work, a chaos-based encryption technique is proposed to secure the video content. The technique employs key sequence generation using the equivalence class rules in number theory. Different syntax elements like motion vectors, trailing zeroes, etc are considered for encryption. An improved logistic map is used to generate the ciphers and the results are analyzed using the various standard techniques like Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM).
On the Security of Keyed-Homomorphic PKE: Preventing Key Recovery Attacks and Ciphertext Validity Attacks

In this short note, we formally show that Keyed-Homomorphic Public Key Encryption (KH-PKE) is secure against key recovery attacks and ciphertext validity attacks that have been introduced as chosen-ciphertext attacks for homomorphic encryption.
Estimation of Interest Levels From Behavior Features via Tensor Completion Including Adaptive Similar User Selection

A method for estimating interest levels from behavior features via tensor completion including adaptive similar user selection is presented in this paper. The proposed method focuses on a tensor that is suitable for data containing multiple contexts and constructs a third-order tensor in which three modes are “products”, “users” and “user behaviors and interest levels” for these products. By complementing this tensor, unknown interest level estimation of a product for a target user becomes feasible. For further improving the estimation performance, the proposed method adaptively selects similar users for the target user by focusing on converged estimation errors between estimated interest levels and known interest levels in the tensor completion. Furthermore, the proposed method can adaptively estimate the unknown interest from the similar users. This is the main contribution of this paper. Therefore, the influence of users having different interests is reduced, and accurate interest level estimation can be realized. In order to verify the effectiveness of the proposed method, we show experimental results obtained by estimating interest levels of users holding books.
An AI-Based Approach to Automatic Waste Sorting

One of the major problems facing our cities is the disposal of the huge amount of waste produced every day. A possible solution is represented by recycling. In this article, we propose a system for automatic recognition and extraction of materials from the unsorted waste, which takes advantage of Computer Vision and Machine Learning techniques. The system can classify the material of incoming objects and grasp, and insert them into proper bins. For the material classification phase, the system analyzes the information captured by a Near-Infrared (NIR) camera and an RGB camera. Experimental tests performed on real-world datasets show encouraging accuracy values.
Sign Language Interactive Learning - Measuring the User Engagement

The knowledge of sign language by hearing people is not widespread and this may pose unpleasant barriers with the deaf. One of the biggest challenges is to raise the awareness about the importance of sign language while providing support for learning it. Our research aims at providing sign language learners with an advantageous interactive experience. In the paper we analyze the engagement of users learning through our intelligent interactive system and show that higher motivation is achieved.
Side Window Weighted Median Image Filtering

Window-based processing is one of the most common operations in image filtering. Traditional median filters, involving unweighted and weighted filters, almost always align the center of the neighboring window with the pixel being processed. In this paper, we present an improved version of the popular weighted median filter. We introduce side window weighted median filtering technique, which treats each pixel as a potential edge. Instead of aligning the center of the filtering window, the proposed algorithm aligns the sides or corners of the window with the pixels being processed. Experiments show that the proposed side window weighted median filter owns better edge preserving capabilities than traditional weighted median filter.