Reviewerly/exHarmony huggingface dataset:
Evaluating Online Products Using Text Mining: A Reliable Evidence-Based Approach

To address the uncertainty about the quality of online merchandise, e-commerce sites often provide product review ranking services to help customers make purchasing decisions. Such services can be very useful, but they are not necessarily reliable when the ranking results are based on ratings without considering their reliability. In this paper, we propose a reliable evidence-based approach to online product evaluation by using text mining to analyze product reviews while taking into account the reliability of each review. We parse the product reviews and classify the opinion orientations for each recognized product feature as positive or negative. Then, we weigh the classified opinion orientations by their reliability and use them as independent evidence to calculate the belief values of the product using Dempster–Shafer (DS) theory. Based on the belief values of a list of similar products, we can calculate their product effectiveness and cost-effectiveness values for product ranking. The case studies show that our approach can greatly help customers make better decisions when choosing the right online products.
Users’ Rating Predictions Using Collaborating Filtering Based on Users and Items Similarity Measures

The social media has made the world a global world and we, in addition to, as part of physical society, are now part of the virtual society as well. There has been the generation of a large amount of information over the social web. By way of increasing online information, new opportunities emerged, and diverse issues have been raised, which have attracted researchers to address these research problems. In this current age, where online business and e-commerce are part of our daily lives, recommender systems (RSs) are very effective for information filtering. RSs play a significant role in our lives by assisting users in recommending items and services what they may be interesting in to purchase or avail. In this research work, our goal is to predict the users' ratings for various items, which are an active research area in collaborative filtering (CF). In this work, we have explored various similarity measures based on user-user and item-item rating predictions on different datasets by applying collaborative filtering approaches. The comparison of item-item and user-user CF algorithms such as user K-Nearest Neighbour using cosine; similarity, Pearson correlation as well as item-based K-NN using these measures with baseline approaches and matrix-based methods such as Matrix factorization (MF), biased MF, and factor wise MF has been carried out. For empirical-based comparison analysis, diverse approaches have been selected such as slope one, random, and global average, and it revealed that item-item K-NN using Pearson correlation has outperformed all other applied approaches. For the experiments, three real world and widely used datasets of MovieLens 1M, CiaoDVD, and MovieLens 100k have been used. The empirical-based results have been evaluated by using standard performance evaluation measures of RMSE and MAE.
KVMIveggur: Flexible, secure, and efficient support for self-service virtual machine introspection

Virtual machine introspection (VMI) has evolved into a widely used technique for purposes such as digital forensics, intrusion detection, and malware analysis. The recent integration of enhanced VMI capabilities into KVM further facilitates the use of VMI. A significant obstacle, however, remains: VMI usually requires highly privileged access to the host system. Existing research prototypes that address this issue either target only the Xen hypervisor, are extremely slow, offer only a subset of the desired functionality, or are hard to deploy in real-life systems. We present our flexible KVMIveggur architecture as a novel solution to these challenges. It offers three flavors of isolation (using containers, virtual machines, and network remote access) that all enable access control for secure self-service VMI in cloud environments. It enables the full use of passive and active VMI, supports continuous monitoring also during live VM migration, and can be tailored for low overhead and minimal resource utilization on the host system. The experimental evaluation of our prototype demonstrates the feasibility and the efficiency of our approach and provides detailed insights into the differences between the three flavors.
The Effect of Body Position on IR-UWB Radar Sensor-based Hand Gesture Speed Recognition and Classification System

When it comes to Human-Computer Interface (HCI), Hand Gesture Recognition (HGR) is a kind of HCI that does not need users to make physical contact with the computer or instrument with which they communicate. Recently, there has been an upsurge in interest in developing methodologies for radar-based HGR which is based on Ultra-Wideband (UWB) transmission state of the art. It utilizes a transmitter-receiver pair that operates across a large frequency spectrum and has a short pulse length, making it ideal for use in urban environments. This study investigates the influence of body position on hand gesture speed recognition and classification system utilizing an IR-UWB radar sensor. Overall, the study outcomes show that the system performs differently at identifying hand gestures and the corresponding speeds in two different body positions.
Research on a simple wide-angle optical modality based on computational imaging technology

With the rapid development of information technology, people's demands for large-angle coverage and high-information acquisition of optical systems are increasing in industrial production and daily life. Therefore, the requirements for imaging quality of wide-angle lenses are improving. Aiming at solving the severe aberrations in the design of the wide-angle lens, traditional optical designs often introduce complex structures. However, a complex structure will bring several drawbacks such as large volume, heavyweight and high cost. Computational imaging technology(CIT) takes information-driven as the core, breaks the imaging mode of independent optimization of optical design and image restoration, realizes global integrated optimization design, and breaks the limit of traditional imaging. Based on the CIT theory, a simple wide-angle optical system design method is proposed and demonstrated in this paper. Firstly, in the optical design process of the wideangle lens, the requirements for suppressing aberrations are relaxed, and the image quality is worse. Then, the cross-channel image restoration algorithm is used to remove the residual aberrations to obtain a high-quality image. Finally, the system is simplified. This method can not only obtain high-quality images but also reduce the complexity of a wide-angle optical system.
Synergistic Scheduling of Learning and Allocation of Tasks in Human-Robot Teams

We consider the problem of completing a set of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> tasks with a human-robot team using minimum effort. In many domains, teaching a robot to be fully autonomous can be counterproductive if there are finitely many tasks to be done. Rather, the optimal strategy is to weigh the cost of teaching a robot and its benefit- how many new tasks it allows the robot to solve autonomously. We formulate this as a planning problem where the goal is to decide what tasks the robot should do autonomously (act), what tasks should be delegated to a human (delegate) and what tasks the robot should be taught (learn) so as to complete all the given tasks with minimum effort. This planning problem results in a search tree that grows expo-nentially with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> - making standard graph search algorithms intractable. We address this by converting the problem into a mixed integer program that can be solved efficiently using off-the-shelf solvers with bounds on solution quality. To predict the benefit of learning, we use an approximate simulation model of the tasks to train a precondition model that is parameterized by the training task. Finally, we evaluate our approach on peg insertion and Lego stacking tasks- both in simulation and real-world, showing substantial savings in human effort.
Research on an Interactive Question Answering System of Artificial Intelligence Customer Service Based on Word2Vec

In order to reduce the labor cost pressure of telecom operators' customer service and improve the service quality, the natural language analysis technology based on artificial intelligence technology will realize the automatic question and answer customer service.This paper proposes to obtain word vectors based on Word2vec model. By comparing the word vectors under different training model parameters, the results show that the low-frequency word threshold plays a better role in controlling the number of the final trained word vectors. The training results of SKIP-GRAM model are better than that of CBOW and the word list is more regular. Under the condition of making full use of the existing customer service knowledge resources, the new system will realize the goal of innovating service means, expanding customer service channels, diverting customer service pressure and improving service efficiency.
A goal-oriented framework for ontology reuse

Ontologies have been successfully used to assign semantics in the Semantic Web context, to support integration of data from different systems or different sources, and to enable reasoning. However, building ontologies is not a trivial task. Ontology reuse can help in this matter. The search and selection of ontologies to be reused should consider the alignment between their scope and the scope of the ontology being developed. In this paper, we discuss how goal modeling can be helpful in this context and we present GO-FOR, a framework in which goals are the central elements to promote ontology reuse. GO-FOR comprises a conceptual architecture, a goal-oriented ontology development process and a supporting tool. In GO-FOR we introduce Goal-Oriented Ontology Patterns (GOOPs) as a new type of pattern to be applied to develop ontologies in a goal-oriented approach. Results of the use of GO-FOR to build an ontology used to integrate patient examination data are also shown in this paper.
Ratatouille: A tool for Novel Recipe Generation

Due to availability of a large amount of cooking recipes online, there is a growing interest in using this as data to create novel recipes. Novel Recipe Generation is a problem in the field of Natural Language Processing in which our main interest is to generate realistic, novel cooking recipes. To come up with such novel recipes, we trained various Deep Learning models such as LSTMs and GPT-2 with a large amount of recipe data. We present Ratatouille (https://cosylab.iiitd.edu.in/ratatouille2/), a web based application to generate novel recipes.
Malaysian Banknotes Counterfeit Detection Algorithm for Ten Ringgit and Twenty Ringgit

The counterfeit problem with Ringgit become a significant challenge especially with nowadays high definition color printing technology. Hence, watermark-based image processing techniques is crucial to the Ringgit counterfeit detection. This paper presents a Malaysian banknotes counterfeit detection algorithm using fuzzy logic and image processing techniques for ten Ringgit and twenty Ringgit. The algorithm will first identify the currency values of the inserted banknote, perform banknotes position detection and re-adjustment, detect the three watermarks (Watermark Portrait, Perfect See-Through Register, and Color Shifting Security Thread) and uses the Fuzzy IF-THEN conditional statements to inference and make decision whether the inserted banknote is a real ten Ringgit, a real twenty Ringgit or none of them.
Data placement in dynamic fog ecosystems

Dynamic data placement in distributed fog databases needs to employ different mechanisms than those typically encountered in modern cloud-hosted storage solutions to account for node instability and latency in receiving the results from queries running across multiple sites. In this work, we examine two dynamic data placement policies. The first is based on the fog node stability metadata, while the second one is driven by analytic applications running on top of distributed storages taking into account the latency, data freshness and quality objectives. Both our policies are enabled through extending Apache Ignite.
Investigating the Impact of Crosslingual Acoustic-Phonetic Similarities on Multilingual Speech Recognition

Multilingual automatic speech recognition (ASR) systems mostly benefit low resource languages but suffer degradation in performance across several languages relative to their monolingual counterparts. Limited studies have focused on understanding the languages behaviour in the multilingual speech recognition setups. In this paper, a novel data-driven approach is proposed to investigate the cross-lingual acoustic-phonetic similarities. This technique measures the similarities between posterior distributions from various monolingual acoustic models against a target speech signal. Deep neural networks are trained as mapping networks to transform the distributions from different acoustic models into a directly comparable form. The analysis observes that the languages closeness can not be truly estimated by the volume of overlapping phonemes set. Entropy analysis of the proposed mapping networks exhibits that a language with lesser overlap can be more amenable to cross-lingual transfer, and hence more beneficial in the multilingual setup. Finally, the proposed posterior transformation approach is leveraged to fuse monolingual models for a target language. A relative improvement of ~8% over monolingual counterpart is achieved.
Real-time Face Mask Types Detection to Monitor Standard Operating Procedure Compliance Using You Only Look Once-based Framework

Real-time face mask types detection using image processing and deep learning model had seen enormous promise in real-world applications. Due to the spread of Covid-19, the practice of wearing face masks in public areas is used to safeguard people from the virus. However, to manually detect the type of face masks used can be difficult, hence this project aims to design and develop a real-time face mask detection model that can detect types of face masks worn by an individual which include 1) surgical masks, 2) KF94, 3) N95, 4) cloth or 5) double-masking. It could also identify if an individual is wearing the face masks incorrectly. This project is developed using the modified waterfall methodology. There are four phases in the methodology: (i) Requirement Analysis, (ii) Design, (iii) Implementation, and (iv) Testing. The data used for training and testing in this project was collected from available images on the internet. The data were pre-processed to remove any unwanted images and each image is then annotated with appropriate classes. The detection model was built using the You Only Look Once version 3 (YOLOv3) framework.
Digital Media Ethics

Digital media and connected technologies have brought some new ethical challenges to the surface. Digital media ethics is the scientific and systematic study of ethical attitudes and problems in relation to the use of digital media. This paper discusses the role of digital media ethics in modern school education. First, it is argued that digital media ethics is best perceived as a dimension of digital competence more generally. Since the development of students' digital competence is one of the central ambitions in modern school education, there is good reason to teach digital media ethics in school. Then, the authors go on to argue that there are at least three potential benefits from teaching digital media ethics: (i) students will increase their digital competence; (ii) students will develop their ability to use digital and connected technologies such as mobile phones for learning; and (iii) students will be helped in living a good life. Finally, the authors offer some reflections on challenges that might arise when digital media ethics is integrated in school education.
Leisure Sports Behavior Recognition Algorithm Based on Deep Residual Network

Aiming at the related problems existing in the field of leisure sports computing, in order to study the behavior recognition of leisure sports by deep residual network, based on the deep residual neural network theory, the behavior recognition algorithm and the corresponding robust model are used to analyze the leisure sports related samples, and the correlation model is used to predict and analyze the leisure sports related content. The results show that the change curves of Sig and Tanh functions can be divided into slow increasing stage, linear increasing stage, and stable stage. The y value corresponding to ReLU curve shows a linear change trend with the increase of x value. The Leaky function’s corresponding curve can be divided into two stages. The function coincides with the ReLU function in the first quadrant and remains linear in the third quadrant. The activation function curves corresponding to layers 56 and 20 have a relatively large variation range, and both of them show an overall trend of gradual decline. On the whole, the curve value corresponding to layer 56 is higher than that corresponding to layer 20, indicating that the method of layer 20 is relatively good and the corresponding training error is relatively low. It can be seen from the robustness recognition rate of various methods under different training samples that Fl has the highest overall data recognition rate while Sc has relatively poor stability. However, the recognition rate of IDCC and DCC shows a relatively flat trend, indicating that these two methods have certain advantages in describing the robust recognition rate. The research results can provide theoretical support for the application of deep residual neural networks in other fields.
ssdeeper: Evaluating and improving ssdeep

This paper focuses on ssdeep, a widely used context-triggered piecewise hashing algorithm. We present an extensive discussion and evaluation of the ssdeep algorithm and its implementation and reveal several inconsistencies. We also propose various improvements, which we implemented and evaluated concerning the desired characteristics of a similarity hashing algorithm. Both, the implementations and our dataset are publicly available. While our improvements lead to more stable hash values, better runtime performance and better detection rates, we argue that there is not much room for improvement without modifying the ssdeep core algorithm.
Effectiveness of Computer-Aided Technology for Teaching English Courses in the Internet Era

Computer and Internet technologies have advanced significantly in recent years. They have been incorporated into people’s daily lives and workplaces. The traditional English teaching approach has several problems. It includes poor student learning interest and lack of active learning. English teaching courses are set up in colleges and universities for non-English majors. The contradiction between students’ needs for foreign language learning and personal interests is increasing day by day. Traditional single classroom teaching has been unable to meet the basic requirements. With the rapid advancement of Internet technology, computer-aided technology is being proposed and applied in English education to investigate the effectiveness of English instruction. This article goes into detail on the idea, classification structure, and courseware design process of computer-aided technology. It employs the AI teaching approach in English instruction, creates a similarity matrix of English learners, and assesses English learners who exhibit similar behavior. Finally, the students are tested by answering the proportion of 1000 words correctly in English teaching to measure the effect of course learning. The results show that the number of words memorized by students increases after using computer-aided technology. This article analyzes students’ capacity to compose in English and evaluates the efficacy of students’ English teaching. It proclaims that after teachers adopt computer-aided technology, the error rate in English composition is reduced. The total score in English increased from 58.6% to 69.6%.
HyperShell v2: Distributed Task Execution for HPC

HyperShell is an elegant, cross-platform, high-performance computing utility for processing shell commands over a distributed, asynchronous queue. It is a highly scalable workflow automation tool for many-task scenarios. There are several existing tools that serve a similar purpose, but lack some aspect that HyperShell provides (e.g., distributed, detailed logging, automated retries, super scale). Novel aspects of HyperShell include but are not limited to (1) cross-platform, (2) client-server design, (3) staggered launch for large scales, (4) persistent hosting of the server, and optionally (5) a database in-the-loop for restarts and persisting task metadata. HyperShell was originally created to support researchers at Purdue University, out of a specific unmet need. It has been in use for several years now. With this next release, we’ve completely re-implemented HyperShell as both an application and a library to provide new features, scalability, flexibility, robustness, and wider support. (https://github.com/glentner/hyper-shell)
Sequence-Aware API Recommendation Based on Collaborative Filtering

API recommendation is crucial to improve programmers’ productivity. A lot of work has been proposed to improve the accuracy of API recommendations. In the existing work, many metrics, such as Precision, Recall, and MAP are used to evaluate the accuracy of the recommendation. These metrics can well reflect the ability to distinguish useful APIs from the candidate set, but they cannot evaluate the ability to determine the priority of useful APIs with each other. The priority between related APIs directly determines whether the recommended results are practical for developers. From this perspective, inspired by the sequence-aware recommendation, this paper constructs an API recommendation method with sequence awareness and designs new metrics to evaluate the method’s ability to determine the priority of useful APIs. The experimental results show that, compared with the baseline, the proposed method not only achieves better results on the common widely-used metrics but also outperforms the baseline method concerning the newly proposed sequence metrics.
Information-Based Sensor Control for Multitask Planning via Multi-Bernoulli Filter

In multitarget tracking, the issue of sensor control is a challenging problem in theoretical analysis and calculation. In this paper, we study the sensor control strategies for multitask planning based on information criteria and propose two sensor control strategies according to the two different task objectives. Initially, we propose a sensor control strategy to improve the overall multitarget tracking performance within the partially observed Markov decision process (POMDP) framework, where the reward function is calculated by the Bhattacharyya distance between the prior and the posterior multitarget densities. In this strategy, we present a target-oriented multi-Bernoulli (TOMB) particle sampling method to approximate the multitarget density and then derive the solution of Bhattacharyya distance in detail. Subsequently, as another important contribution of this paper, we propose a threat-based sensor control strategy, which is still solved under the information theory where the goal is to prioritize multiple threat targets and then to track preferentially the maximum threat target. These strategies are finally used to optimize the sensor trajectory for range-bearing multitarget tracking.