Reviewerly/exHarmony huggingface dataset:
Tracking With Mutual Attention Network

Visual tracking is a visual task that tracks a specific target by only giving its first frame location and size. To punish the low-quality but high-scoring tracking results, researchers resorted to foreground reinforcement learning to suppress the scores of positive samples near edges. However, for training with negative samples, all backgrounds are equally labeled as false. In this way, the interdependence and difference between the foreground and the background are not considered. We interpret the underlying reason for drifts as the imbalance between the embedding of background and foreground information. Specifically, some catastrophic tracking results and common tracking errors should not be treated equally but should strengthen the implicit connection between the foreground and background. In this paper, we propose a Mutual Attention (MA) module to strengthen the interdependence between positive and negative samples. It can aggregate the rich contextual interdependence between the target template and the search area, thereby providing an implicit way to update the target template accordingly. As for the difference, we design a background training enhancement (BTE) mechanism to distinguish negative samples with varying degrees of error, that is, to down-weight outrageous and absurd tracking results to improve the robustness of the tracker. The results on a large number of benchmarks indicate the validity of our results, such as OTB-100, VOT-2018, VOT-2019, and LaSOT.
Boilerplate Detection via Semantic Classification of TextBlocks

We present a hierarchical neural network model called SemText to detect HTML boilerplate based on a novel semantic representation of text blocks. We train SemText on three published datasets of news webpages and fine-tune it using a small number of development data in CleanEval and GoogleTrends-2017. We show that SemText achieves the state-of-the-art accuracy on these datasets. We then demonstrate the robustness of SemText by showing that it also detects boilerplate effectively on out-of-domain community-based Q&A webpages.
Evaluations of service robots referring to Joruri puppets, and a remote evaluations system

One of the important requirements for service robots is attracting people by attractiveness and to be able to exchange messages with people. With reference to the traditional Japanese puppet show, Ningyo Joruri, we have independently developed OSONO, which is a physical robot, with high-quality choreography. In this paper, we report questionnaire evaluations on OSONO targeting a puppeteer expert group and compare with existing questionnaire results targeting the ordinary person. This shows that the method of creating OSONO and its choreography is effective in widely general. Additionally, we develop a remote evaluations system so that we can conduct questionnaires of OSONO in a short time for more evaluators. We also verify the effectiveness of this remote evaluation system. This system can be expected as a substitute for the conventional face-to-face evaluation, which is becoming difficult to conduct it due to the pandemic of COVID-19.
Quadratic Inequalities over Affine T-S Fuzzy Models

While the affine T-S fuzzy model is much more effective for describing the dynamics of the system concerned in comparison with the regular T-S fuzzy model, it encounters some difficulties when designing a controller based on the model because of the affine term within. However the difficulties can be overcome by introduction of a kind of quadratic inequalities. This paper proposes a way to yield such quadratic inequalities based on the information over the antecedent part of affine T-S fuzzy model rules. Although the introduction of such quadratic inequalities is motivated by controller designs based on the affine T-S fuzzy model, the way of the introduction can be applied to other fuzzy models.
Designing an Augmented Reality Educational Board Game Learning Activity with Dual-Scaffolding Teaching Strategy to Enhance EFL Reading Comprehension

The research developed an augmented reality educational board game teaching activity integrating the technique of augmented reality and board game with dual-scaffolding teaching strategy to promote learners' EFL reading comprehension. An action research was conducted to evaluate the proposed approach. The experimental results revealed that the proposed approach not only significantly promoted the flow and technology acceptance degree of the learners, but also significantly improved their understanding of the plot diagram of EFL story after the learning activity.
Active Trajectory Estimation for Partially Observed Markov Decision Processes via Conditional Entropy

In this paper, we consider the problem of controlling a partially observed Markov decision process (POMDP) in order to actively estimate its state trajectory over a fixed horizon with minimal uncertainty. We pose a novel active smoothing problem in which the objective is to directly minimise the smoother entropy, that is, the conditional entropy of the (joint) state trajectory distribution of concern in fixed-interval Bayesian smoothing. Our formulation contrasts with prior active approaches that minimise the sum of conditional entropies of the (marginal) state estimates provided by Bayesian filters. By establishing a novel form of the smoother entropy in terms of the POMDP belief (or information) state, we show that our active smoothing problem can be reformulated as a (fully observed) Markov decision process with a value function that is concave in the belief state. The concavity of the value function is of particular importance since it enables the approximate solution of our active smoothing problem using piecewise-linear function approximations in conjunction with standard POMDP solvers. We illustrate the approximate solution of our active smoothing problem in simulation and compare its performance to alternative approaches based on minimising marginal state estimate uncertainties.
TEESlice: slicing DNN models for secure and efficient deployment

Providing machine learning services is becoming profit business for IT companies. It is estimated that the AI-related business will bring trillions of dollars to the global economy. When selling machine learning services, companies should consider two important aspects: the security of the DNN model and the inference latency. The DNN models are expensive to train and represent precious intellectual property. The inference latency is important because modern DNN models are usually deployed to time-sensitive tasks and the inference latency affects the user's experience. Existing solutions cannot achieve a good balance between these two factors. To solve this problem, we propose TEESlice that provides a strong security guarantee and low service latency at the same time. TEESlice utilizes two kinds of specialized hardware: Trusted Execution Environments (TEE) and existing AI accelerators. When the company wants to deploy a private DNN model on the user's device, TEESlice can be used to extract the private information into model slices. The slices are attached to a public privacy-excluded backbone to form a hybrid model that has similar performance to the original model. When deploying the hybrid model, the lightweight privacy-related slice is secured by the TEE and the public backbone is put on the AI accelerators. The TEE provides a strong security guarantee on the model privacy and the accelerators reduce the computation latency of the heavy model backbone. Experimental results show that TEESlice can achieve more than 10x throughput promotion with the same level of strong security guarantee as putting the whole model inside the TEE. If the model provider wants to further verify the correctness of the accelerator's computation, TEESlice can still achieve 3-4x performance improvement.
Sentiment lexicon for cross-domain adaptation with multi-domain dataset in Indian languages enhanced with BERT classification model

Many websites are attempting to offer a platform for users or customers to leave their reviews and comments about the products or services in their native languages. The cross-domain adaptation (CDA) analyses sentiment across domains. The sentiment lexicon falls short resulting in issues like feature mismatch, sparsity, polarity mismatch and polysemy. In this research, an augmented sentiment dictionary is developed in our native regional language (Tamil) that intends to construct the contextual links between terms in multi-domain datasets to reduce problems like polarity mismatch, feature mismatch, and polysemy. Data from the source domain and target domain both labeled and unlabeled are used in the proposed dictionary. To be more specific, the initial dictionary uses normalised pointwise mutual information (nPMI) to derive contextual weight, whereas the final dictionary uses the value of terms across all reviews to compute the accurate rank score. Here, a deep learning model called BERT is used for sentiment classification. For cross-domain adaptation, a modified multi-layer fuzzy-based convolutional neural network (M-FCNN) is deployed. This work aims to build a single dictionary using large number of vocabularies for classifying the reviews in Tamil for several target domains. This extendible dictionary enhances the accuracy of CDA greatly when compared to existing baseline techniques and easily handles a large number of terms in different domains.
Deep Learning: Differential Privacy Preservation in the Era of Big Data

In recent years, deep learning (DL) has been ubiquitous in several areas, such as text recognition and data analysis, limited by this and increasingly used in security and data protection applications. Thus, the DL method has achieved remarkable big data analysis growth to avoid different attacks. This paper presents different methods for protecting privacy for DL in big data analysis. First, some possible attacks are explained, and then some basic approaches to protecting privacy in big data platforms are explained. In each section, drawbacks of the corresponding attacks are elaborated, and DL-based methods' effectiveness in privacy preservation has been discussed. Finally, an effective solution for enhancing privacy preservation in DL models is given. The several DL-based privacy preservation methods for big data analysis and their advantages and disadvantages are elaborated. At last, drawbacks of DL based methods are highlighted, and future scope is given to address these issues.
A High-Security Privacy Image Encryption Algorithm Based on Chaos and Double Encryption Strategy

Faces are widely used in information recognition and other fields. Due to the openness of the Internet, ensuring that face information is not stolen by criminals is a hot issue. The traditional encryption method only encrypts the whole area of the image and ignores some features of the face. This paper proposes a double-encrypted face image encryption algorithm. The contour features of the face are extracted, followed by two rounds of encryption. The first round of encryption algorithm encrypts the identified face image, and the second round of encryption algorithm encrypts the entire image. The encryption algorithm used is scrambling and diffusion at the same time, and the keystream of the cryptosystem is generated by 2D SF-SIMM. The design structure of this cryptosystem increases security, and the attacker needs to crack two rounds of the algorithm to get the original face image.
Deep Image Watermarking to JPEG Compression Based on Mixed-Frequency Channel Attention

Deep blind watermarking algorithms based on an end-to-end encoder-decoder architecture have recently been extensively studied as an important technology for protecting copyright. However, none of the existing algorithms can fully utilize the channel features of the image to improve the robustness against JPEG compression while obtaining high visual quality. Therefore, we propose firstly a mixed-frequency channel attention method in the encoder, which utilizes different frequency components of the 2D-DCT domain as weight coefficients during channel squeezing and excitation. Its essence is to suppress the useless feature maps and enhance the feature maps suitable for watermarking embedding by introducing frequency analysis in the channel dimension. The experimental results indicate that the PSNR of our method reaches over 38 and the BER is less than 0.01% under the JPEG compression with quality factor Q = 50. Besides, the proposed framework also obtains excellent robustness for a variety of common distortions, including Gaussian filter, crop, crop out, and drop out.
Technology Trends and Challenges for Large-Scale Scientific Visualization

Scientific visualization is a key approach to understanding the growing massive streams of data from scientific simulations and experiments. In this article, I review technology trends including the positive effects of Moore's law on science, the significant gap between processing and data storage speeds, the emergence of hardware accelerators for ray-tracing, and the availability of robust machine learning techniques. These trends represent changes to the status quo and present the scientific visualization community with a new set of challenges. A major challenge involves extending our approaches to visualize the modern scientific process, which includes scientific verification and validation. Another key challenge to the community is the growing number, size, and complexity of scientific datasets. A final challenge is to take advantage of emerging technology trends in custom hardware and machine learning to significantly improve the large-scale data visualization process.
A soft-computing based approach to overlapped cells analysis in histopathology images with genetic algorithm

Deep learning methods have shown great significance for medical image processing in recent years, including radiography, magnetic resonance imaging, nuclear medicine, and histopathology. Learning-based methods help to detect the target patterns when the network is well-trained. However, the gap between detected patterns and clinical decisions is still enormous, and soft-computing methods could help to improve the decisions based on the specific features of the problem. In this paper, the target is to detect the immunohistochemistry (IHC) positive cells in lymphoma histopathology images. We use a deep learning network to detect the positive cells in pixel level, and results show the proposed residual U-Net structure outperforms the baseline models. In the failure cases, they mainly occur when two cells are overlapped in predicted masks, which is the drawback of learning-based methods. Hence, a soft-computing based method is proposed to use ellipse formulation to represent a cellular pattern, and use genetic algorithm (GA) to determine an optimal decision of overlapped ellipses. We use two datasets: 36 images with ground truth from pathologists’ annotation, and 43 images with overlapped cells (cropped from failed predictions). A series of experiments show that the proposed deep learning model could detect most of IHC-positive cells in the proposed dataset, and our soft-computing method could detect 86.04% of the overlapped cells and the proposed GA outperforms the baseline methods.
Definition of Metrics for Work Efficiency Monitoring Based on Multi-System Usage Behaviour Analysis

this paper describes a workflow efficiency estimation solution with appropriate metrics that could be used to obtain mathematical evaluations of work activities of IT systems users. Such evaluations are of great importance in current World state where COVID pandemic forced many employees to transfer their work activities from the office to home environment. Managers and administration of organizations could use analysis of these metrics and patterns for optimizing their day-to-day workflows.
Industry-Academy Collaboration in Agile Methodology: a Systematic Literature Review

Industry-Academy Collaborative Research (IAC) in Software Engineering is being applied and developed widely in practice. These collaborative, research practices help both academic and software industry environments. As a way of observing what is being developed in SE, the objective of this article is to present an exploratory and empirical study of IAC practices in the context of Agile Software Development (ASD), exploring and characterizing solutions and practices, the challenges encountered in the application of the IAC and in collaboration. A Systematic Literature Review (SLR) was carried out in the five main academic databases, and snowballing was applied, evaluating/analyzing 8.460 articles, totaling 21 articles approved following the proposed criteria. As a result of the data analysis, 76 good practices and 37 challenges in carrying out the IAC were described. As well, practical collaborative models for the application of the IAC were detailed.
Performance Evaluation of Docker-based Apache and Nginx Web Server

Containerization is the modern trending concept for net-centric software application deployments other than the traditional virtualization. Among many container vendors, Docker is the most popular one in the industry. Launching all kinds of software based services and software applications on the Docker farm, is available. Apache and Nginx are the most highly used web servers in the practitioner. This research study aims to evaluate Apache and Nginx web server performances on the Docker platform. By using two separate Docker containers, the experimental study was conducted. Basically, six web server performance criteria were evaluated: requests per second, failed requests, availability, failed transactions, response time and transaction rate. For the requests per second measurement, Apache web server was presented with higher features than Nginx web server on the Docker farm with default configurations. But other evaluation measurements were approximately the same values for both servers. Both web servers with 100% availability and according to the all evaluated criteria, both Apache and Nginx web servers are recommended to use with default configurations on the Docker farms.
User Identification with Face Recognition : A Systematic Analysis

Towards achieving Security as a service in a cloud environment, user identity and access management is a crucial factor for ensuring secured access of resources. Authentication technique with multiple factors are considered to be more robust and provide greater difficulty for the attacker to get an unauthorized access to a secured cloud environment. Face recognition in conjunction with classical means of user authentication, as additional factor is found to be convenient using inbuilt cameras fitted in computers and mobile phones. We present a comprehensive analysis of several face detection and recognition mechanisms that might be used for user’s facial identification. Presented work gives a deep insight into the various available means of deep face inspection by means of Convolutional Neural Networking methods in order to access its suitability for being used as an effective additional factor for the multi-factor authentication.
Analysis of Different Feature Extractors for Image Captioning Using Deep Learning

With the surge in technological innovations and advancements, data is considered to be the most important commodity in today’s world. The collection and dissemination of massive volumes of data have swamped our world today. This has given rise to problems in domains like searching, indexing, and value extraction. Hence image captioning rises as a solution to all these problems. The literature survey revealed that there is a lot of fog around which architectures work best for feature extraction in image captioning. To tackle this gap we’ve proposed a centralized analysis on which algorithms give the best results in terms of image captioning in this paper. We’ve taken algorithms like Inception, ResNet, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Visual Geometry Group 16 (VGG16), etc. into account for our system. In this analysis, we take into consideration all the important aspects that affect image captioning to maximize the results at the end. Dataset taken into consideration is Flickr 8k. Hence the fundamentals of the approaches are discussed in this paper to examine their performances, strengths, and constraints.
Dense Video Captioning using BiLSTM Encoder

Video captioning has been a widely researched topic integrating visual information and natural language but performing video captioning on long untrimmed videos is still challenging as the video contains multiple events and the model has to describe each event. To address this issue, this paper discusses work on dense video captioning, a newly emerging research subject that entails presenting temporal events in a video and creating captions for each temporal event. Proposed architecture comprises an event proposal module, an EfficientNet B7 network for feature extraction from sampled frames, and BiLSTM encoder and LSTM decoder for captioning. BILSTM encoder effectively utilizes both past and future contexts from the video for generating captions. This model is trained and tested on MSVD dataset which has around 2000 videos and their corresponding captions. The proposed framework shows increased accuracy in video captioning in terms of BLEU score 0.78 and METEOR score 0.34.
Securing Smart Card Management Using Hyperledger Based Private Blockchain

In the case of single sign-on (SSO) within the companies, chip-embedded cards can offer highly secured data encryption. For instance, smart cards, a conceptual card with an encoded interconnected chip that serves as a private key, provide personally identifiable information, verification, storing data, and other types of security management. To secure an organization, blockchain technology is adopted since it represents a transparent database. Ethereum is an unauthorized, distributed blockchain system where everyone can manipulate the transaction records. Hyperledger Fabric, on the other hand, is an authorized and secret framework that incorporates the latest calculation of the asset’s properties and the background history of transactions. In classical Smart Card Management System, frequently identified concerns with existing techniques are - physical documents and properties displacement, dispersed blocks containing papers, intermediary and distributors etc. In this paper, we are focused on creating a blockchain-based smart card management system using Hyperledger. Hyperledger has a preprogrammed community of users and application usability for these particular users alone is available so that data is appropriately covered. By accessing the Historical Archive, this system will detect illegal entry and alteration, ensuring information neutrality, security, and authenticity.
Multi Class Sentiment Analysis of 5G Tweets

The rate of communication or the information exchange is directly proportional to the rate of development. 5G is one of the emerging technologies in the field of communication and it is expected to find a use case in every domain possible from day-to-day information exchange to the other emerging data intensive technologies like the Augmented and Virtual Reality. 5G is estimated to play a key role in the development of smart cities and IOT use cases. Sentiment analysis of data on user experiences and opinion is performed to improve the product or the service quality and meet the user requirements. Also it can help to identify whether users are interested in their product or not. This can improve the market performance of a product. This work intends to perform sentiment analysis on the twitter tweets to understand the willingness of people to adapt to 5G. The data hence obtained is modelled using various Machine Learning algorithms the outcome of which can be used for the technological advancement.