Reviewerly/exHarmony huggingface dataset:
Medchain for Securing Data in Decentralized Healthcare System Using Dynamic Smart Contracts

In today's era the current medical application accepts and analyze the patient medical data. Developing a system to acquire data that can "Efficient Data Security System for Storing Medical system data". The medical data are obtained by a user, Lab technician (for X-ray, blood test), and hospital. In the proposed research, security is one of the component in optimization of services using Cyber Physical System. The digital records are accessible only to the authorized persons. This paper consists of a medical application that accepts and analyze a patient's medical data to give a pre-diagnosis to check if he/she is having specific disease, ensuring to efficient data security system to secure the patient's medical data stored and accessed in the cloud. The paper aims to design and develop a Block Chain based Security Model for smart healthcare systems to enable Confidentiality, Integrity, Authentication and Authorization for the medical data.To propose the Dynamic Smart Contract Ledgers (DSMCL) for Confidentiality and Integrity handling Sensitive data of patient and DSMCL for Authentication and Authorization handling medical Images(scan, X-ray). The outcome of this paper is to secure the medical data and image using blockchain and smart contract technology. Only the authorized users will be allowed to access patient detail after validation and verification during emergency situation using Key generation which holds individual patient data inside a block. Finally, the accuracy of the execution is compared with the algorithms not involving blockchain and the proposed system is much better than the conventional ones.
Tailoring a Forward Looking Vocal Assistant to Older Adults

Human-machine speech and interaction is becoming increasingly important. In particular, one of the aspects that is gaining attention concerns the possibility to interact in a natural way and, more specifically, in natural language. Being able to support a believable speech-based conversation is particularly relevant especially for tools that assist older people in their daily life. This paper introduces our goal to develop a vocal assistant for different assistive scenarios such as information point and cognitive rehabilitation. In particular, the article describes a planning-based system which integrates the natural language understanding features of a state-of-the-art tool in the attempt of obtaining forward-looking functionalities to support end-users longer in time. Our planning-based system is able to both achieve forward-looking functionalities and embrace elements related to the user’s personality and current mood, helping older users to maintain a natural communication flow and become more confident with the technology.
An Efficient Key Frame Extraction from Surveillance Videos for Real-World Anomaly Detection

Technology development has entrenched video analysis in numerous applications. Video analysis is important to obtain the data contents rather than its characteristics. Instead of examining the entire data content analyzing the essential portions as key frame reduces the computation time and memory. Extracting the important features from the dataset is termed as key frame extraction and this research work presents an efficient methodology to extract important frames from the video instances using HSV histogram and k-means clustering algorithm. The raw video data is preprocessed and then data points are clustered using k-means clustering. The proposed k-means clustering based key frame extraction technique is experimented with using UCF Crime dataset and the key frames are extracted from seven categories of videos. Detailed analysis of actual features and extracted features are comparatively analyzed and the observations are presented in the experimental section.
Developing a Tool to Classify Lethal Weapons by Analyzing Images

This paper shows how modified VGG16 and a customized model can be used for a weapon classifier system. The basic concept is to detect weapon type by its shape, length, angle, rotation degree, etc. The layered structure of VGG16 has been primarily used along with some extra layers for step-by-step classification. The images have been preprocessed with image augmentation using parameters such as shear, size, and rotation. The VGG16 model has been used for its prediction accuracy with less time complexity. The VGG19 model has better accuracy compared to the VGG16 but the time complexity is higher. The better accuracy of VGG19 is insignificant and negligible. VGG16 has 1 × 1 convolutional layers. VGG16 has small-sized convolution filters allowing the model to have a large number of weight layers. Instead of using a large number of hyper-parameters, the model uses a 3 × 3 convolution layer and a 2 × 2 max pool layer making the layer arrangement more consistent throughout the architecture. The trained mode acquired a test accuracy of 96.5% when tested with new input images.
CRIC: A VQA Dataset for Compositional Reasoning on Vision and Commonsense

Alternatively inferring on the visual facts and commonsense is fundamental for an advanced visual question answering (VQA) system. This ability requires models to go beyond the literal understanding of commonsense. The system should not just treat objects as the entrance to query background knowledge, but fully ground commonsense to the visual world and imagine the possible relationships between objects, e.g., "fork, can lift, food". To comprehensively evaluate such abilities, we propose a VQA benchmark, Compositional Reasoning on vIsion and Commonsense(CRIC), which introduces new types of questions about CRIC, and an evaluation metric integrating the correctness of answering and commonsense grounding. To collect such questions and rich additional annotations to support the metric, we also propose an automatic algorithm to generate question samples from the scene graph associated with the images and the relevant knowledge graph. We further analyze several representative types of VQA models on the CRIC dataset. Experimental results show that grounding the commonsense to the image region and joint reasoning on vision and commonsense are still challenging for current approaches. The dataset is available at https://cricvqa.github.io.
HADA: An automated tool for hardware dimensioning of AI applications

In recent years, the uptake of Artificial Intelligence (AI) in industry is increasing. For many AI techniques, like Deep Learning, optimization, planning, etc., computational and storage requirements are significant. The problem of determining what is the right hardware (HW on premise or on the cloud) architecture and its dimensioning for AI algorithms is still crucial. Searching for the optimal solution is often challenging, as it is not trivial to anticipate the behavior of an algorithm on diverse architectures. This is especially true if the AI application must respect quality-of-service constraints or budgets. In this scenario, having an automated decision support tool to match algorithms, user constraints and HW resources would be a great advantage for companies and practitioners working with AI applications. In this paper, we tackle this challenge with an approach that relies on the Empirical Model Learning paradigm, based on the integration of Machine Learning (ML) models into an optimization problem. The key idea is to integrate domain knowledge held by experts with data-driven models that learn the relationships between HW requirements and AI algorithm performances. In particular, the approach starts with benchmarking multiple AI algorithms on different HW resources, generating data used to train ML models; then, optimization is used to find the best HW configuration that respects user-defined constraints (e.g., budget, time, solution quality). In the experimental evaluation we validate our approach on a complex problem, namely online algorithms for energy systems, an area characterized by uncertainty and tight HW and real-time constraints. Results show the effectiveness of our approach and its flexibility: We can train the ML models only once and reuse them in the optimization model to tackle a variety of problems, determined by different data instances and user-defined constraints.
A Web Service composition Method Based on Improved Whale Optimization Algorithm

To solve the optimization problem of service composition, a WOA based algorithm is proposed that called iWOA in this paper. The iWOA uses AHP and SAW to confirm parameters for fitness function, and uses tent map and OBL to initialize population. Then a diversity mutation operation is performed to reduce the probability of premature convergence in the iterations. The experiment results shows that the iWOA is effective.
Digital Twin Oriented Visual Saliency Analysis on 360-Degree Panoramic Image

Digital product definition (DPD) has experienced the development from two-dimensional (2D) to three-dimensional (3D). Recently, the experience of domestic and foreign manufacturing industries has shown that the product model defined by 3D digitalization has matured, and its benefits have been repeatedly verified. Panoramic Image is a fundamental technique to reflect the real-world scenario in the form of a 360-degree image, providing an immersive experience to users. However, observers may be only interested in part of 360-degree images and neglect other parts. Visual saliency analysis becomes essential to understanding user behavior. Saliency in the image represents the region that attracts human attention to certain areas of the image. The approach to extracting the areas and information that users are concerned about from pictures has gradually become a challenge in computer vision. Visual saliency analysis can effectively utilize computer resources to allocate bits to the more critical pixels in the image. Different from regular plane pictures, panoramic pictures contain more visual information about the real-world scenario. Images from other angles are actually projected onto a plane, which may cause severe distortion of plane panoramic images via projection conversion. It will cause serious distortion if the segmentation obtained by the saliency prediction model is directly spliced. Herein, we propose a novel panoramic image Mosaic method based on the Truncated Square Pyramid Projection scheme (TSP). Simulation results show the effectiveness of the proposed scheme.
The Effect of Chatbot-customer Interaction on Consumer Brand Advocacy: Exploring the Role of Chatbots

Chatbot-customer interaction is becoming increasingly prevalent. However, prior literature has not focused on the influence of chatbot-customer interaction on brand advocacy. In this paper, we study the influence mechanism of chatbot-customer interaction on consumer brand advocacy. Through a questionnaire survey of 312 e-commerce users who used artificial intelligence-powered chatbot platforms, our study shows that the functional anthropomorphic interactions significantly affect brand advocacy. Furthermore, customer brand experiences is an intermediary role between chatbot-customer interaction and brand advocacy.
Combining AI/ML and PHY Layer Rule Based Inference - Some First Results

In 3GPP New Radio (NR) Release 18 we see the first study item starting in May 2022, which will evaluate the potential of artificial intelligence and machine learning (AI/ML) methods for Radio Access Network (RAN) 1, i.e., for mobile radio PHY and MAC layer applications. We use the profiling method for an accurate iterative estimation of the parameters of the dominant multipath components, as it promises a large channel prediction horizon. We investigate options to partly or fully replace some functionalities of rule based PHY layer algorithms by AI/ML inferences, with the goal to achieve either a higher performance, lower latency, or reduced processing complexity. We provide first results for noise reduction, then a combined scheme for model order selection, compare options to infer multipath component start parameters, and provide an outlook on a possible channel prediction framework.
Semantic-based Thai Recipe Recommendation

Nowadays, people are constantly affected by epidemics such as COVID-19. To reduce the risk of acquiring germs in the community, people's lifestyles have been changed, and they are more inclined to cook for themselves. Typically, people can usually quickly and easily find recipe information via websites and applications. The resulting recipes consist of ingredients as specified by the user. Unfortunately, users often have ingredients that disappear in available cooking recipes. This makes the system is unable to recommend all relevant recipes to users, although the users can use the existing ingredients instead of the ingredients specified in the recipes. Based on this limitation, this research proposes a semantic-based Thai cooking recipe recommendation system which can recommend recipes based on the ingredient substitutes. This research uses existing Thai food ontology to retrieve substitute ingredients based on three different ingredient properties, such as smell, taste, and texture. To recommend cooking recipes, the system expands the given user queries with substitute ingredients and then calculates similarities between all queries and each cooking recipe. Recipes with high similarities are presented and ranked to users. To evaluate the performances, precision, recall and f-measure are applied. The experiments demonstrate that the proposed method performs well with 0.96, 0.72, and 0.82 in precision, recall, and f-measure respectively.
A Novel Deep BiGRU-ResNet Model for Human Activity Recognition using Smartphone Sensors

Human activity recognition (HAR) employing wearable sensors is utilized in several implementations, including remote health monitoring and exercise performance. The most widely used HAR research is inspired by traditional machine learning and developing methodologies using deep learning. Whereas machine learning techniques have proven effective in resolving HAR, these require human feature extraction. Consequently, deep learning methods have been designed to circumvent this constraint autonomously rather than manually extracting information. This paper provides an innovative deep residual learning approach based on LSTM-CNN and deep residual modeling techniques. The objective of the proposed model, BiGRUResNet, was to increase accuracy while decreasing the number of parameters. Two BiGRU layers, three residual layers, one global average pooling layer, and one softmax layer are present. Utilizing a publicly recognized UCI-HAR dataset, the proposed model was analyzed. Results of the experiment indicate that the proposed model outperforms previous deep learning-based models in 5-fold cross-validation, with a 99.09% accuracy and 99.15% F1 score.
Question Generation in the Thai Language Using MT5

There are numerous publications of Question Generation (QG) in English but few in Thai. More than a million question-answer pairs are available in the English language, compared with only around 12,000 question-answer pairs in the Thai language. This paper presents a method to improve automatic Thai answer-agnostic QG from a dataset of insufficient size. Our evaluation showed that a QG model which was trained by the pre-trained model MT5 from a Thai dataset achieved a BLEU-1 score of 56.19. We proposed a method to generate synthetic data and an additional mechanism by using a single pre-trained model. Our best model outperformed the previous model by achieving a BLEU-1 score of 59.03. The results from the human evaluation in fluency score was 4.40, the relevance score 4.65, and the answer-ability score 4.7 out of 5.0.
A component recommendation model for issues in software projects

In modern software development projects, developer teams usually adopt an issue-driven approach to increase their productivity. The component of an issue report implicitly or-ganize issues in a software project (e.g, defects, new feature requests, and tasks) into a group of issues that have similar characteristics. A component of an issue report is an important attribute needed to be identified in an issue triaging process. Thus, assigning the correct component(s) to an issue is crucial in issue resolution. However, it is a challenging task since large-scale projects contain a considerable amount of components (e.g. almost one-hundred components in the Bamboo project) and it can increase significantly as the project evolves over time. In this paper, we propose an approach that uses textual feature extraction and machine learning techniques with Binary Relevance (BR) to develop a component recommendation model to support the task of assigning component(s) to an issue. The empirical evaluation over 60,000 issue reports shows that our proposed models outperform the baseline benchmarks and other techniques by achieving on average 0.480 Precision@1, 0.616 Recall@3, 0.432 MAP, and 0.596 MRR.
Strategy control of drive mechatronic system for Virtusphere with two actuators

The studies performed by the authors on Virtusphere (VS) platform led to identify solution to improve the feeling of immersion provided by a virtual environment. It was concluded in a previous paper that this feeling could be achieved by upgrading the VS with a mechatronic system. First experiments were performed using one brushless DC (BLDC) motor to determine if the actuation method and the control strategy suit application requirements. These were validated by comparing the results of the MATLAB / Simulink simulations with the practical results. To further enhance the system’s effectiveness, the authors propose an additional actuator. This paper presents improvements to the drive mechatronic system for Virtusphere (DMSVS) and a strategy to synchronize the two motors with the user’s movement. Using two in-wheel motors to reduce power transmission elements, a series of experiments were performed to implement the synchronization equations. At the end of this article will be presented the results of these experiments and conclusions about the control algorithm with simple wheels and with the new omnidirectional wheel design developed by the authors.
Smart Cloud-Edge Video Surveillance System

As the world advances it becomes increasingly technology-dependent, bringing together infrastructure and technology to improve the quality of life for the citizens. Smart cities have become the future of urbanization. Since the priority of a city is to protect its citizens, a video surveillance system is required to ensure their safety. This paper proposes a multi-camera cloud-Edge surveillance system for smart cities and homes. Multiple units of Raspberry Pi act as the Edge Computing device that streams and summarizes the processed video footage. After summarizing the video to reduce its length and size, it sends the videos to the cloud (virtual machine). The cloud applies resource-intensive computer vision algorithms such as detecting motion, objects including humans, weapons, and fire. Furthermore, it manages the recorded surveillance videos, stores them in the database, and alerts the user if a threat occurs. The experimental results show that the time taken to perform these tasks was reduced by an average of 83% for the object detection models.
Design Understanding: Identifying Instruction Pipelines in Hardware Designs

Automated tools help a designer to reduce the time and the effort required to understand details of an unfamiliar design. In this paper we show an approach using static analysis to identify instruction pipelines, which are key structures in processor hardware designs. We present two algorithms which identify pipeline structures. The first algorithm is based on a structural analysis using a graph representation of the design, while the second algorithm uses terms and phrases for pipelines as they are found in literature for a name-matching approach. The two algorithms successfully identified pipelines for e.g. Y86 and edge processor designs.
Optimizing Savitzky-Golay Filter on GPU and FPGA Accelerators for Financial Applications

Over the last few years, computational power and intelligence are becoming more and more necessary in the sector of finance. More specifically, computational finance turns into a very popular topic for both academia and industry, where numerous published works from this field and especially investment and risk management, showcase the effects of these technological advancements. At the same time, the ever-increased computational demands have led to the deployment of various accelerators in order to meet both latency and power constraints for financial applications that vary from special purpose, made by economists, to general purpose Digital Signal Processing (DSP) applied in financial time-series. One of the most widely used applications, belonging to the 2nd category, is the Savitzky-Golay algorithm, a filter used for smoothing time-series data. In this work, we propose a mechanism that automatically creates different accelerated Savitzky-Golay filters for GPUs and FPGAs, based on a set of pre-accelerated templates. By evaluating the provided templates with a set of real use-case parameters, a speedup of x33.5 on the NVIDIA T4 GPU and x21.9 on the Alveo U50 FPGA is achieved compared with an Intel Xeon Gold 5218R CPU as a baseline, while achieving a decrease in power consumption of 89% and 70% respectively, disclosing a real latency-power trade-of between both accelerators.
A Novel Piecewise Chaotic Map for Image Encryption

This paper investigates the issue of chaos-based image encryption. A new one-dimensional piecewise chaotic map based on the z-shaped fuzzy number is proposed and investigated, exhibiting regions of constant chaos and high Lyapunov exponent values. A pseudorandom bit generator based on the novel chaotic map is designed and successfully passes the National Institute of Standards and Technology (NIST) statistical tests. This PRBG is applied to the problem of image encryption, introducing a new image encryption scheme by using row and column permutation, chaotic pixel shuffling, and the exclusive OR operation. To demonstrate the robustness of this image encryption technique, various tests, such as differential attack and correlation, entropy and histogram analysis are performed.
Analysis of Unsupervised Statistical Machine Translation Using Cross-Lingual Word Embedding for English–Hindi

Machine translation (MT) is automatically converting a piece of text from one language to another. The work provides a comparison and analysis of the phrase-based statistical machine translation (PBSMT) system using monolingual corpora that interpret English text to Hindi. MT quality has improved dramatically over the previous two decades, making it appealing for usage in the translation industry. The importance of MT cannot be overstated. The application of MT systems has been bounded due to the dependency on the bilingual corpus for a range of language pairings. The current paper presents a comparison of two possible cross-lingual word embedding mapping approaches utilizing monolingual datasets for the SMT methodology. When translating from a language to some other language without supervision, inter-lingual word embedding is crucial. We have implemented and compared two cross-lingual mapping approaches are employed in this paper: adversarial training and self-learning methods. The experimental findings for several evaluation methodologies, such as BLEU, METEOR, METEOR-Hi, TER, WER, MER, and NIST, show that the PBSMT approach delivers superior translation using the self-learning method than the adversarial training method for English–Hindi.