Reviewerly/exHarmony huggingface dataset:
Reconnaissance for Reinforcement Learning with Safety Constraints

As RL algorithms have grown more powerful and sophisticated, they show promise for several practical applications in the real world. However, safety is a necessary prerequisite to deploying RL systems in real world domains such as autonomous vehicles or cooperative robotics. Safe RL problems are often formulated as constrained Markov decision processes (CMDPs). In particular, solving CMDPs becomes challenging when safety must be ensured in rare, dangerous situations in stochastic environments. In this paper, we propose an approach for CMDPs where we have access to a generative model (e.g. a simulator) that can preferentially sample rare, dangerous events. In particular, our approach, termed the RP algorithm decomposes the CMDP into a pair of MDPs which we term a reconnaissance MDP (R-MDP) and a planning MDP (P-MDP). In the R-MDP, we leverage the generative model to preferentially sample rare, dangerous events and train a threat function, the Q-function analog of danger that can determine the safety level of a given state-action pair. In the P-MDP, we train a reward-seeking policy while using the trained threat function to ensure that the agent considers only safe actions. We show that our approach, termed the RP algorithm enjoys several useful theoretical properties. Moreover, we present an approximate version of the RP algorithm that can significantly reduce the difficulty of solving the R-MDP. We demonstrate the efficacy of our method over classical approaches in multiple tasks, including a collision-free navigation task with dynamic obstacles.
Privacy Aware Contact Tracing by Exploiting Social Networks in Viral Disease Outbreaks

This paper studies the privacy protection of individuals in contact tracing using social network for the viral disease outbreaks such as novel coronavirus (COVID-19). Contact tracing has been proved effective in controlling the diseases outbreaks such as Ebola virus disease in Africa. We propose the privacy protection mechanism which can be used by social networks under the assumption that the external knowledge (friendship network) is publicly available. We theoretically and experimentally analyze the proposed mechanism and show that the algorithm improves the privacy of individuals. The synthetic and real data sets are used for experimental evaluations.
Extraction Method for a Recipe's Uniqueness based on Recipe Frequency and LexRank of Procedures

Users often obtain recipes from culinary websites when they are cooking. In this case, various recipes for the same dish are displayed. Therefore, users compare each recipe and decide which one they want to use. We believe that it would be easier to select a recipe if we extract the point of uniqueness of each recipe and present it in the search results. In this paper, we propose a method of extracting the uniqueness of a recipe by analyzing the ingredients and procedures used. Specifically, we reference a basic recipe that describes the standard cooking methods of a dish and extract the differences between it and other recipes to ascertain the points of uniqueness of the recipe using procedures' importance and correspondence. As a result of implementing the proposed method with several recipes, we confirmed that the proposed method is able to extract the uniqueness.
The COVID-19 impacts on contextual privacy behavior in Kuwait

The medical, social and business disturbance associated with the COVID-19 coronavirus caused dramatical changes in the usage of mobile devices. People were forced during the lockdown to use different mobile-based context-aware application to cope their lives while staying home. Consequently, a typical user's privacy preferences and behavior were altered due to that fact that these applications at that moment are found to be an optimum solution to endure the predicament. This paper is aimed to assess how COVID-19 pandemic changes the mobile usage and contextual privacy behavior in Kuwait. Toward this goal, an information visualization system is developed to accumulate the user behavior dataset from their mobiles. The result shows the average mobile usage hours have increased by 60% and in total the user behavior changed by 27.9% in loosen their contextual privacy.
Research and application of data mining algorithm

Abstract With the breakthrough development of a series of technologies such as machine learning and internet of things, data plays an increasingly important role in everyone’s daily affairs and work. With the explosive exponential growth of data volume, data types are also quietly changing, from the traditional single, structured data to today’s diverse, semi-structured data. In the face of emerging new massive data, data mining technology has gradually become the focus of attention. Data mining was also known as knowledge discovery (KDD) at that time. It was mainly defined as a pattern hidden in massive data, which must be understood by people and bring potential benefits. In this paper, we mainly studies the basic principle and algorithm knowledge of data mining, and applies ridge regression and random forest algorithm model in real estate price forecasting. Finally, through the stacking thinking of ensemble learning in ensemble learning, we propose a fusion method of ridge regression and random forest model, and obtain a more accurate and stable prediction model.
Plurilinguismo e intercomprensione nella classe di italiano L2 a migranti adulti Dalla lingua veicolare alla lingua-ponte

This article reflects on how to activate and capitalise on adult migrant learners’ plurilingual repertoires through intercomprehension in the Italian L2 classroom. Firstly, two survey results with adult migrant learners and teachers of Italian as L2 are discussed so as to show how plurilingual practices are perceived and activated by both parties. Secondly the article discusses how studies on the intercomprehension between related languages and on the role of English as a bridge language towards Romance languages can be usefully applied in the given learning context in order to enhance the metalinguistic awareness of learners and develop teachers’ ability to capitalize their language repertoires in the teaching of the target language.
Classifier Performance Evaluation of Phishing Detection Model on Optimal Number of Clusters

The vast usage of internet among people facilitate huge amount of online transactions in day to day life. This necessitates millions of users to be involved by submitting their information such as username, password, and banking credentials through online. The availability of such huge personal information attracts and motivates cybercriminals to launch their cyber attacks in online networks to steal the information for their further financial crimes. Among these attacks, phishing is a kind of attack where phisher sets down a fake website that appears similar to legitimate website and sends bogus mail to targeted victims in order to pilfer their information. Such an act leads to potential loss of user credentials and reputation of the corresponding organization. The existing phishing detection approaches which rely on classifier performance have low detection rate due to the uncertainty present in the huge training dataset. Therefore, it is intuitive that if the uncertainty is reduced then the performance could be improved. Further, In this paper, clustering is used to obtain the inherent structure of the datapoints prior to classification. The clustering algorithms, viz., k-means and k-medians were run for different number of cluster values ‘k’. The silhouette criterion was used to find the optimal number of clusters present in the dataset. The classifiers, Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM) were trained for each optimal cluster range. The performance of the trained model for the corresponding classifier is analyzed in terms of detection rate, sensitivity, specificity, and error rate. From the experimental results, it is observed that the performance of the classifier is improved better when it was combined with the optimal number of clusters rather than with the arbitrary cluster range.
Training with a world champion: augmented reality applications in sport Design-led research

Recent and continuous innovations in the field of extended reality and, in particular, augmented reality, are able to revolutionize different aspects of the reference market sectors. At the same time, a constant evolution in the area of artificial intelligence, machine learning and deep learning, if combined with the aforementioned innovations, allows to conceive solutions able to shape new ways to inform, to improve skills and to spend time. The ability to simulate contexts, environments, actions and emotions and the possibility to use the data generated by the simulations in a disruptive way permit to imagine and create learning and strengthening paths.This developing research has been carried out within the Interdepartmental Laboratory EDME (Environmental Design Multisensory Experience), which belongs to the Design Department of Politecnico di Milano. It has been conducted by investigating the state of the art of augmented reality and artificial intelligence technologies, highlighting interesting and highly innovative case studies; from this first phase we moved on to analyze the sport sector in which an important potential for future development was recognized. The last part of the first phase of this research project consisted in the elaboration of a concept for an enabling technological system and a business model with a high innovation coefficient, whose realization is hypothesized for the year 2030.It is intended to demonstrate how a design operation, which started from emerging technologies and a sector of high interest and assumed a scenario of use over ten years, is not only extremely interesting but also, and above all, useful to consciously predict and accompany the aforementioned technological development.
Reliability-based Formation of Cloud Federations Using Game Theory

Cloud federation is one form of the cloud computing model that supports numerous types of applications through collaboration between different service providers. Cloud federation enables providers to offer more efficient services to customers by sharing their computing and storage resources. However, the reliability of cloud can be degraded if the federation is formed and executed in an unreliable fashion. In this paper, we propose a reliability-based cloud federation model. We evaluate the reliability of different service providers using our evaluation approach and then model the federation process as a hedonic coalition formation game based on a reliability-driven utility function. Our proposed federation formation algorithm enables service providers to cooperate while considering the reliability of the infrastructure and refrain from cooperating with unreliable systems. Our evaluation shows that the providers will be able to form acceptable federations through our algorithm while preserving or enhancing the reliability of their services in a reasonable amount of time.
Combining Symbolic and Statistical Knowledge for Goal Recognition in Smart Home Environments

An essential feature of pervasive, intelligent systems is the ability to dynamically adapt to their users' current needs. Hence, it is critical for such systems to be able to recognize the current goals and needs of the users based on observed past and current actions. This work addresses the problem of goal recognition in smart home environments. We investigate whether approaches for the plan recognition problem, which is a long-standing research area in the Artificial Intelligence community, can also be applied to the goal recognition problem in smart home environments. Therefore, we evaluate the application of a well-known symbolic plan recognition approach, which is based on classical planning methods, and propose to extend this approach through additional statistical knowledge to overcome some identified shortcomings of the planning-based approach. We show that the planning-based plan recognition approach indeed can be used to solve the goal recognition problem in smart home environments and show that the proposed extension outperforms the original approach as well as purely statistical goal recognition methods.
A new unsupervised learning-based process for extraction of knowledge’s and improving anomalies detection

Abstract Lately, the world confronts day by day the birth of typically big data in various fields such as health, telecom, electronic sales, communication, service provision, industry, e-learning and e-commerce. This emergence was a brick and a major reason behind the realization of immense changes in the processes of manipulation and processing of data as the system of storage, processing, analysis and visualization of data. From an application point of view, the manipulation of such data requires the implication of certain numbers of usage standards such as those related to security, credibility and optimization of data. However, it seems so difficult to ensure the security of massive and gigantic data coming from a variety of sources, and especially in real time, because there is an almost total absence of methodologies ensuring the processing and protection of a volume of data automatically. In fact, our work takes stock of a new process allowing the secure handling of big data. This is about getting more out of machine learning, security patterns and decision trees. More specifically, our approach aims to design machines able of securing large data in real time while making them learn from a mapping of security patterns (learning data). This training will allow machines to develop in terms of security, which will allow them to improve their intelligence related to the detection of anomalies caused in the data during the data exploitation operation.
Development of Ontology on Crime Investigation process

Abstract This paper is an extended version of the research paper “Ontology based crime investigation process” which deals with the working principle and the construction of an ontology based extensively on organized crime. Ontologies on various domains are created in which their assistance has been widely recognized. They are being developed for providing basis for allocation of knowledge in a domain and imparting reasonable information. In this paper we describe the structure of the ontology we created and also by validating the ontology via an online ontology evaluating tool.
Aviation Surveillance Information Fusion Based on Ensemble Learning

At present, with the comprehensive development of economy and technology in our country, the air transportation industry has also ushered in a golden period of development. The air traffic volume increases year by year, and the air route traffic volume will increase, which will lead to the need to carry more aircraft on the limited channel, resulting in the congestion of the channel and the potential safety problems. In order to ensure that the aircraft can fly safely, it is necessary for traffic management personnel to maintain the order of the aircraft in the channel according to the aviation monitoring information. Therefore, the accuracy of aviation surveillance information is particularly important. As a traditional track fusion algorithm, the Kalman filtering has the problem of requiring accurate error estimation, insensitivity to noise, and long calculation time in the case of large data volume. In this paper, a method of air surveillance information fusion based on ensemble learning is proposed, which can predict and fuse multiple air surveillance sources, i.e. multiple radar surveillance information, so as to obtain more accurate position estimation of the monitored target and reduce the error caused by the accuracy of radar itself, geographical location and surrounding environment.
Performance Gain in Low Resource MT with Transfer Learning: An Analysis concerning Language Families

Translation systems require a huge amount of parallel data to produce quality translations, but acquiring one for low-resource languages is difficult. To counter this, recent research has been shown to combine languages and use them to augment the low resource data, through transfer learning. While the gain in performance is apparent using transfer learning, we try to investigate the correlation between the performance gain and position of the concerned languages within a language family. We further probe and try to coordinate the performance gain with the degree of vocabulary sharing between the concerned languages.
Research on Testability Allocation Method based on AHP-Entropy Weight Combination

Testability allocation is a process of assigning system-level testability indexes to units according to certain rules. Aiming at the disadvantage that the traditional testability allocation method based on AHP is subject to experts, which may bring irrationality of the allocation outcomes, a testability allocation method based on the combination of AHP and entropy weight method is proposed to reduce and avoid the error because of subjective factors. A certain ship inverter is used as an example to verify the effectiveness and feasibility of the proposed allocation method.
Sort X Consignment Sorter using an Omnidirectional Wheel Array for the Logistics Industry

State of the art sorting systems operated by global freight carriers or e-commerce giants are large, complicated and cost-prohibitive. Some low-cost systems are based on pneumatics and have consignment weight limitations while manual intervention is an inefficient sorting option. In this work, we are presenting `Sort-X' as a system for low cost, flexible, and reliable consignment sorting system with MIMO (multiple inputs multiple outputs) sorting scheme. The inputs to Sort-X are through feed conveyor belts. These belts converge into a master feed where the consignments pass through a 2D scanner. The master feed connected to the sorting hub uses parameters such as postal code, address, shipping speed, and weight enlisted on the consignment in QR format to decide the sorting action. The hub is composed of numerous compact motorized displacement cells. Each cell has three programmable omnidirectional wheels to direct consignments to their respective destinations. Complex material flows can be accomplished in a small space using all the degrees of freedom on the displacement cells. Consignments of numerous shapes and sizes can be handled as long as they're underneath the maximum weight capacity. The gadget is designed to be bendy on the hardware and the firmware level to adapt to different sorting requirements. Individual Sort-X units can also be cascaded to obtain complex, multi-nodal sorting topologies. The mathematical technique utilized in machine design for a particular sorting operation is mentioned in this proposed research.
Snippet generation using textbook corpus - an NLP approach based on BERT

Abstract In today’s technology-driven world, most millennials are tech-savvy. They have neither the time nor the interest in reading textbooks, newspapers or journals. They would like to immediately get instant answers and clarifications for all their doubts and questions. On many occasions, we are unable to find the exact word or meaning which we are searching for. So, if we have a clear, concise summary of a piece of literature, and we could understand what it contains with just a glimpse, we would be able to save a lot of time. This paper dwells about utilizing Natural Language Processing (NLP) to summarize a given text/textbook/paper. The state-of-the-art technology in this field has been demonstrated by Google’s Bidirectional Encoder Representations from Transformers (BERT), one of the latest developments in NLP. BERT is believed to understand English better than other models because of its underlying bidirectional architecture. The present proposal is to use BERT as a sentence similarity extractor. By applying the TextRank algorithm, the sentences holding the most important information are extracted. This comes under the domain of extractive summarization. Abstractive summarization is much talked about, but since Google BERT is not built for generating text, we are utilizing it in a different way to achieve the requirement. This paper intends to discuss the use of BERT for the gen-next kids which will save time and initiate further interest for researchers in developing new programs continuously in the future.
Video stabilization algorithm for tunnel robots based on improved Kalman filter

Abstract The application of tunnel robots in power tunnels is becoming more and more extensive. Due to various reasons, video jitter will inevitably occur during the inspection process, which will affect the real-time processing of subsequent images. In order to reduce the impact of video jitter and meet the requirements of real-time image processing, it is necessary to study fast video image stabilization methods. The traditional Kalman filtering algorithm has less computational cost, but it will cause a large estimation error when the system motion state changes. In this regard, the paper proposes an improved Kalman filtering algorithm, which changes the corresponding filter parameters by real-time estimation of a system in motion to reduce the estimation error. And this algorithm performs Harris corner extraction in the set feature dense area, and combines the PyrLK optical flow algorithm with the homography matrix to calculate the motion trajectory, which improves the calculation speed and accuracy of the algorithm. Experimental results show that this algorithm has better video stabilization effect and faster calculation speed compared with the traditional Kalman filtering algorithm, and can better meet the real-time image processing requirements in the inspection process.
Application of Two New Feature Fusion Networks to Improve Real-time Prostate Capsula Detection

Excess prostate tissue is trimmed near the prostate capsula boundary during transurethral plasma kinetic enucleation of prostate (PKEP) and transurethral bipolar plasmakinetic resection of prostate (PKRP) surgeries. If a large portion of the tissue is removed, a prostate capsula perforation can potentially occur. As such, real-time accurate prostate capsula (PC) detection is critical for the prevention of these perforations.This study investigated the potential for using image denoising, image dimension reduction and feature fusion to improve real-time prostate capsula detection with two objectives. First, this paper mainly studied feature selection and input dimension reduction. Secondly, image denoising was evaluated, as it is of paramount importance to transient stability assessment based on neural networks.Two new feature fusion techniques, maxpooling bilinear interpolation single-shot multibox detector (PBSSD) and bilinear interpolation single shot multibox detector (BSSD) were proposed. Before original images were sent to the neural network, they were processed by principal component analysis (PCA) and adaptive median filter (AMF) for dimension reduction and image denoising.The results showed that the application of PCA and AMF with PBSSD increased the mean average precision (mAP) for prostate capsula images by 8.55% and reached 80.15%, compared with single shot multibox detector (SSD) alone. Application of PCA with BSSD increased the mAP for prostate capsula images by 4.6% compared with SSD alone.Compared with other methods, ours were proven to be more accurate for real-time prostate capsula detection. The improved mAP results suggest that the proposed approaches are powerful tools for improving SSD networks.