Reviewerly/exHarmony huggingface dataset:
A Template Approach for Summarizing Restaurant Reviews

In the era of rapid development of social networks, user reviews of restaurant review websites have grown rapidly. In order to allow users to quickly grasp the key points of review information on review sites, this paper provides an abstractive multi-text summary method that can automatically generate template-based review summaries based on predefined topics and sentiments. In particular, for each predefined topic and each type of sentiment (positive or negative), this study uses the TextRank algorithm to find the most representative sentences to form a summary. This method allows users to quickly grasp the positive and negative opinions of each important aspect of the restaurant. The previous research on generating abstracts from reviews either did not generate abstracts based on topics, or they were based on topics generated by random models. However, the latter method cannot guarantee that the topics generated by the random model are really the topics that the user needs. For a restaurant review, some topics are indispensable. In order to ensure that abstracts can be generated for these essential topics, our method predefines the topics that must be generated, and then generates abstracts for these topics. In the evaluation, this study compared the template method with the Refresh and Gensim systems based on criteria such as informativeness, clarity, usefulness and likes. The results show that the method proposed in this paper is superior to the other two summary methods.
A Personalised Recommendation Framework for Ubiquitous Learning System

The traditional e-learning has been developed into personalised and ubiquitous learning, in which the learners find learning materials (LMs) that are suitable to their contextual requirements, and can access them from anywhere and anytime. In this paper, we propose a framework for a personalised recommendation in a ubiquitous learning platform, following a knowledge-based approach. The framework comprises modules like query processing, information storage and retrieval, and learner context mapping and reasoning. Learner's implicit and explicit contexts are used for assessing the preference and suitability and mapping with the LMs that are retrieved based on the learner's query analysis, with the help of educational metadata. Selecting suitable LMs based on different factors is a multi-criteria decision making (MCDM) problem. For prioritising the selection factors, we use SWARA, and for multi-objective decision making, we apply MOORA. Utilising these two techniques, the LMs are ranked and are recommended accordingly.
Contact tracing: Achieving equilibrium between blockchain solutions and privacy amid the novel coronavirus pandemic

In moments of scepticism and hopelessness, the drive to satisfy collective needs pushes humanity to accomplish incredible feats to ensure our survival on earth. Indeed, the current global crisis caused by the novel coronavirus is no exception. We are struggling with the abrupt spread of this illness and continuously striving to find efficient solutions. This has been André Salem Alégo’s primary inspiration since the crisis hit his country in March 2020. He is the chief executive officer of Blockforce, a blockchain-based SocialTech. Having observed the effects of novel coronavirus, André examined various approaches that could help contain the spread of the virus and keep society safe. He came up with the idea for an innovative service called Desviralize, which supports pandemic crisis monitoring through contact-tracing with the use of blockchain technology. This teaching case covers how an emergent technology evolves amid the pandemic. It draws attention to important facts related to the digital era such as data security and privacy in the context of Desviralize and Blockforce’s chief executive officer’s strategies for boosting platform growth within the constraints imposed by data protection regulation, the critical pandemic situation, and the ethical aspects of the application of technology.
Breast Cancer Detection in Histology Images Using Convolutional Neural Network

Breast cancer has been one of the leading causes of death among women in the world. The death rates due to breast cancer can be reduced by early detection. The normal symptoms of breast cancer are masses or lumps that feel different from the other tissues, and there are two types of masses- benign and malignant. Benign masses are abnormal growths which are not cancerous, whereas malignant masses are cancerous. Various methods have been proposed to detect breast cancer. A detailed survey has been done in this paper. Deep learning techniques are now widely used for cancer detection, and in deep learning, features are extracted from input data using multiple layers. In this paper, transfer learning techniques have been used to detect breast cancer, and pretrained models like InceptionResnetV2, VGG-16 and VGG-19 are used. In transfer learning, pretrained models are used to solve a new problem where training can be done using a small amount of data. A comparison of those techniques have also been done. Publicly available dataset named BreaKHis dataset has been used for the study.
A Study of Analogical Density in Various Corpora at Various Granularity

In this paper, we inspect the theoretical problem of counting the number of analogies between sentences contained in a text. Based on this, we measure the analogical density of the text. We focus on analogy at the sentence level, based on the level of form rather than on the level of semantics. Experiments are carried on two different corpora in six European languages known to have various levels of morphological richness. Corpora are tokenised using several tokenisation schemes: character, sub-word and word. For the sub-word tokenisation scheme, we employ two popular sub-word models: unigram language model and byte-pair-encoding. The results show that the corpus with a higher Type-Token Ratio tends to have higher analogical density. We also observe that masking the tokens based on their frequency helps to increase the analogical density. As for the tokenisation scheme, the results show that analogical density decreases from the character to word. However, this is not true when tokens are masked based on their frequencies. We find that tokenising the sentences using sub-word models and masking the least frequent tokens increase analogical density.
An Analysis of Augmented Reality Aided Vehicle Operation

In this study, we investigated the effect of augmented reality aided dashboard information projected on the windshield on car driving performance. As augmented reality technology becomes more popular and accessible, essential driving information such as the current velocity can be projected to where the traffic happens in terms of location and distance. In comparison with a Heads Up Display (HUD) on a windshield, augmented reality devices offer the possibility to place the data farther in front of the car. Because of this, the eyes of the driver do not need to re-focus constantly to a closer object, when most of the imminent attention needs to be set to the space in front of the vehicle. To test in a safe environment, we developed a virtual reality car parkour and conducted a user study. Splitting our participants in two equally big groups, one with a conservative dashboard and the other with the velocity displayed 10 m ahead and over the horizon. Assessing the participant's performance in areas such as maintaining the speed limit, distance to the traffic, and time to match up with the given speed limit, we found striking positive results using the augmented reality solution.
The Prediction of the High-Cost Non-Cumulative Discounted Gain and Precision Performance Metrics in Information Retrieval Evaluation

Information retrieval systems play the critical role of meeting information needs of users. Therefore, high effectiveness is expected from these systems since users make decisions based on the retrieval results. The effectiveness of these systems varies and is only known through retrieval evaluation and the main approach of evaluating these systems is the test collection model which comprises of a corpus of documents, topics and relevance judgments. One limitation of this approach is the cost of generating the relevance judgments. A recent solution to this limitation is the prediction of performance metrics at the high evaluation depths of documents using the system scores of other performance metrics computed at the low evaluation depths. However, this solution has a drawback of the inaccurate predictions of the non-cumulative discounted gain (nDCG) and the precision performance metrics at the high evaluation depths of documents using other performance metrics computed at the low evaluation depths. Therefore, this study addresses this drawback by proposing an approach that predicts the nDCG and precision performance metrics at the high evaluation depths of documents using topic scores of other performance metrics computed at the low evaluation depths. This study has shown that the proposed approach performs better predictions of the nDCG and precision performance metrics than the existing method.
Semi-supervised Extreme Learning Machinebased Method for Moving Cast Shadow Detection

The moving cast shadows often have a negative effect on computer vision and image processing. In this paper, we propose a moving shadow detection method based on semi-supervised extreme learning machine to effectively distinguish shadows from the foreground. Firstly, pixel-level features and region-level features are extracted from the foreground. Secondly, S-ELM (semi-supervised extreme learning machine, S-ELM) is trained by using the features extracted above to classify the object and shadow pixels. Finally, in order to overcome the influence of noises and improve the accuracy of the results, the post-processing procedures are carried out. A substantial number of experiments on two common datasets show that the proposed method has good performance and is superior to some existing well-known methods.
CARS: A Hybrid Security Approach to Secure Cloud Data Through Mobile Devices

Today, there is a surge in the usage of smartphones in the world mainly because of smartphone penetration and affordable price. In 2016, the number of smartphone users in the world were 2.1 billion and is estimated to grow to around 2.5 billion by 2019. So, it has become very common to use apps on the phone and store our data on the cloud. Unfortunately, most of the data that is stored on cloud is in unencrypted form which makes it vulnerable to attacks. So, a hybrid security algorithm is proposed in this paper which will encrypt the data before it reaches the cloud server.
Re-establishment and Regarding Trust and Transparency, Blockchain’s Contribution to the Solution of a Thousand-Year Problem

Trust is the most important key element in the history of humanity and its success. We see and witness an emerging and exciting technology called Blockchain, eliminating losses caused by anti-trust and relieves pain in our social-build environment. Although the atmosphere in business, management, and society today has been heavily altered and involved, trust stays the same. This requirement leads companies in various industries to use new techniques to ensure their customer's business is in safe hands. Unfortunately, most of the current research is heavily based on the Bitcoin currency rather than its underlying technology, which focuses on revealing and improving Blockchain's limitations from managerial value perspectives. Many of the proposed solutions lack a concrete evaluation of their effectiveness. Many other Blockchain scalability related challenges, including throughput and latency, have been left unstudied. In this study, enabling functions is studied, and Blockchain's contribution to organisations' management is discussed. Besides, applicability in today's economic environment in various industries is studied. It is understood that after the development of technologies that eliminate centralised structures such as Blockchain, the business world is keen to adapt to this change. In this context, it is concluded that including new technologies such as Blockchain increased trust in various industries, and in the majority of the studies, Blockhain considered being an element to reduce costs and create a more transparent environment. It can be said that this technology contributes to the development and better understanding of the application area of the industries mentioned in the study.
An Efficient Identity-based Forward Secure Signature Scheme from Lattices

With the use of a large number of mobile devices, the problem of key leakage becomes more and more serious. In view of the excellent characteristics of lattice cipher and forward-secure digital signature scheme, the construction of identity-based forward-secure digital signature based on lattice technology has become a research hotspot. However, the identity-based forward secure digital signature scheme on the existing grid has the disadvantage of excessive signature length. This paper uses the technique (without trapdoors) of Lyubashevsky and extended Samplepre, an efficient identity-based forward secure signature scheme from lattice is proposed. Its security is based on the Small Integer Solution (SIS) difficulty assumption, and the strong non-forgery of the signature scheme is achieved. Low computing overhead; The analysis results show that, compared with the existing schemes, the key and signature are smaller in size, more efficient in computing, able to resist quantum attacks, and more practical.
Learning to Discover a Unified Architecture for Low-Level Vision

Neural Architecture Search (NAS) has pioneered various constructive principles to push forward the development of deep learning and achieved dramatic performances for diverse tasks recently. Existing NAS methods mainly focus on a single specific task to discover the architecture automatically. But actually, these methods lack ample exploitation and exploration for the latent ability of architecture search mechanism, e.g., from diverse cross-task distributions to discover a unified architecture automatically. In this work, we propose a Cross-task Differentiable ARchiTecture Search (Cross-DARTS for short) framework to discover a unified architecture for different low-level vision tasks automatically, to further widen the capacity of NAS. Specifically, we establish a new model to bridge different low-level vision tasks under the architecture search perspective. By performing a new data construction that integrates multi-task distributions, Cross-DARTS is obtained based on the differentiable search scheme. A multi-scale fusion cell with powerful contextual representation capacity is designed as the basic component of search space towards the low-level vision. Consistent achievements of promising results on three vision tasks, including noise, rain, joint rain and haze removal fully show our superiority.
An Analysis of Emotional Speech Recognition for Tamil Language Using Deep Learning Gate Recurrent Unit

Designing the interaction among human language and a registered emotional database enables us to explore how the system performs and has multiple approaches for emotion detection in patient services. As of now, clustering techniques were primarily used in many prominent areas and in emotional speech recognition, even though it shows best results a new approach to the design is focused on Long Short-Term Memory (LSTM), Bi-Directional LSTM and Gated Recurrent Unit (GRU) as an estimation method for emotional Tamil datasets is available in this paper. A new approach of Deep Hierarchal LSTM/BiLSTM/GRU layer is designed to obtain the best result for long term learning voice dataset. Different combinations of deep learning hierarchal architecture like LSTM &amp; GRU (DHLG), BiLSTM &amp; GRU (DHBG), GRU &amp; LSTM (DHGL), GRU &amp; BiLSTM (DHGB) and dual GRU (DHGG) layer is designed with introduction of dropout layer to overcome the learning problem and gradient vanishing issues in emotional speech recognition. Moreover, to increase the design outcome within each emotional speech signal, various feature extraction combinations are utilized. From the analysis an average classification validity of the proposed DHGB model gives 82.86%, which is slightly higher than other models like DHGL (82.58), DHBG (82%), DHLG (81.14%) and DHGG (80%). Thus, by comparing all the models DHGB gives prominent outcome of 5% more than other four models with minimum training time and low dataset.
Video Inpainting by Frame Alignment with Deformable Convolution

Video inpainting is a task of filling missing regions in videos. In this task, it is important to efficiently use information from other frames and generate plausible results with sufficient temporal consistency. In this paper, we present a video inpainting method jointly using affine transformation and deformable convolutions for frame alignment. The former is responsible for frame-scale rough alignment and the latter performs pixel-level fine alignment. Our model does not depend on 3D convolutions, which limits the temporal window, or troublesome flow estimation. The proposed method achieves improved object removal results and better PSNR and SSIM values compared with previous learning-based methods.
Sign Language Recognition Using Deep Learning

Hand Movement is one of the important part of Human- System interplay with a wide range of plays in the automotive sector, consumer electronics and others. In recent spaces, accurate and fruitful deep learnedness models have been proposed. Notwithstanding, in the inquisition community, the current intimately available datasets demand real- world fundamentals required to make responsive and fruitful systems. Communication is the essential channel to participate debates. As of late the hard of hearing and visually crabbed unfortunate casualties expanded. Since fair fully senseless cannot speak with ordinary object. New situation is the place the idiotic, hard of hail speak and to hear. Signal acknowledgment is the scientific explanation of a mortal movement by a registering gimmick. Gesture predicated communication give trim correspondence stage to the counsel hindered and moronic individuals to speak with ordinarily. The Deaf and dumb use hand shifts to convey though luminosity integers can hear just the voice and correspondence through voice. So as to draw a stage nearer to these objective plays we use CNN math with Deep knowledge and Tensor inflow methodology, Image processing and information mining ideas to accept beneficence as hand signals and produce unmistakable yields as content and with Good fineness and above through proposed work.
The Nature and Structure of a Forensic Linguist’s Specialized Knowledge

The article addresses one of the current problems of forensic linguistics - the problem of a forensic linguist’s specialized knowledge. Basing on the study of the concept of “specialized knowledge” in legislation and the theory of forensic science and the practice of forensic linguistic analysis, the author demonstrates that a forensic linguist’s specialized knowledge is inhomogeneous in its nature. It is not limited to linguistic knowledge only but has a complex structure, which includes background knowledge, awareness of forensic science theory, and understanding of substantive and procedural law. It seems that at the present stage, such a structure of specialized knowledge of a forensic linguist most fully contributes to solving the tasks of forensic linguistics and implementing the principles of forensic expert activity regulated by Federal Law No. 73-FZ of 31.05.2001.
Machine learning based meta-models for sensorless thermal load prediction

Lifetime of electronic systems depends various factors determined during product development stage, e.g. design, materials, quality of components and manufacturing. In the field, the load of the system leads to degradation affects the remaining useful life of the product. In this work, we focus on thermal load conditions. The thermal load is induced externally by ambient temperature and internally due to heat generated by operation of components. In most consumer use cases external load is a constant room temperature. In industry, e.g. automotive or aviation, the external load shows strong fluctuation and appears in cycles. The heat loss during operation causes superposition of internal and external thermal loads distributed over the system. Our goal is to create a meta-model that allows the calculation of temperature at various points of interest in the system based on virtual sensors in these points. The model is trained based on experimental data with attached thermal sensors in the points of interest. A temperature chamber provides ambient temperature cycles in a range of -50 to 50 °C, resulting in maximum CPU temperatures of 95 °C. A program running on the system stresses the CPU of the system to certain load levels. These load levels cause heat loss in the CPU, which is distributed to the system.The results are time series for the CPU load, CPU temperature, ambient temperature and temperatures of the attached thermos couples. We test several mathematical and machine learning approach to obtain the temperature in the points of interest, which are the outputs of the models. CPU load, ambient temperature (and in some cases CPU temperature) serve as inputs. In further research attempts, we run the experiment to failure of components. Based on these data, models can be extended to calculate the remaining useful life of the system.
A Decentralized Explanatory System for Intelligent Cyber-Physical Systems

The Internet of Things (IoT) has been a prominent application for Intelligent Systems in recent years. While the increasing demand for explanations led to many advances in Explainable Artificial Intelligence (XAI), most solutions focus on systems where a single agent takes all decisions to be explained. However within the IoT context, Cyber-Physical Systems (CPS) are decentralized, with multiple agents coordinating their decisions to control the overall CPS. By contrast users expect coherent system-wide explanations, as if they were generated by a single agent. We propose a decentralized Explanation System generating such explanations while preserving the advantages of decentralized control: separation of concerns, heterogeneity, flexibility, ...Our architecture relies on: i) decentralized XAI Component specialists for providing partial explanations based on local knowledge; ii) a central generic Spotlight composing local explanations into a global explanation. We illustrate and qualitatively evaluate our approach via a proof-of-concept implementation for a smart home system.