Reviewerly/exHarmony huggingface dataset:
An Efficient Method for Scientific Data Retrieval Service

The sharing of scientific research data on the Internet is already the trend in academia. More and more data have been published to the public throughout the web on Internet. Due to the rapid growth of data, and the requirements of data service quality, the efficiency of data retrieval services has become an important factor affecting service quality. Based on the characteristics of scientific data, and the actual requirements of Pharmaceutical Information Center (PIC, http://pharmdata.ncmi.cn), we propose an efficient scientific data service retrieval method which can greatly improve retrieval speed and service quality. This method includes two work phases. The first phase is to obtain meaningful search keywords from scientific data using semantic analysis technology, including effective keyword sets construction, and eliminating the impact of invalid search keywords. The second phase is to construct a Hash Index Tree (HI-Tree) for valid keywords. Scientific data retrieval service will just traverse the cached HI-Tree instead of traversing the entire database to minimize the database query operation. Compared with traditional database retrieval methods, the experimental results show that our method improves the retrieval efficiency greatly and make better user experience of the data services.
Approximating the minimal lookahead needed to win infinite games

We present an exponential-time algorithm approximating the minimal lookahead necessary to win an ω-regular delay game.* This work was prepared while the author was affiliated with the University of Liverpool. 1 We refer to the introduction of [4] for a discussion of the history of delay games, including motivation and a connection to the uniformization of ω-regular relations by continous functions.
Multisensor device for emergency recognition in smart building environment

In case of serious environmental disasters, such as floods, landslides and earthquakes, the first activity that an emergency management system must carry out is the detection, recognition and report of anomalous events. In order to be able to automatically perform these actions, it is necessary to have an efficient sensor network within the building. By means of a well- organized and high-performance sensor network, data from the field are acquired and processed to produce usable information at a high level. The Building Management System (BMS) therefore has information available on the occurrence of an anomalous event, whether caused by natural or intentional causes. The purpose of our work is to design and implement a cost-effective sensorized device, managed by an ATmega microcontroller and equipped with a Wi-Fi module for easier integration, able to detect and report potentially dangerous situations within a building. In particular, fires, floods, gas leaks and earthquakes were considered. Thanks to the integration of simple data analysis and data fusion algorithms, the platform is able to recognize and report the occurrence of emergency events within a building.
A constraint-based approach to linguistic interfaces

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A New State of Art Deep Learning Approach for Bangla Handwritten Digit Recognition using SVM Classifier

Deep Convolutional Neural Network (CNN) has earned remarkable success in computer vision technology, particularly in image classification task. With the growing interest, it has been also employed to recognize Bangla handwritten digits. In this paper, we have presented a deep convolutional neural network with support vector machine (CNN-SVM) in order to recognize Bangla handwritten digits. Here, we have designed and implemented a 14 layered deep CNN architecture as a feature extractor to acquire remarkable features automatically from Bangla handwritten digits image and SVM was used as a classifier to recognize Bangla handwritten digits. We have trained and tested the proposed model with two different Bangla handwritten digit dataset namely NumtaDB and BanglaLekha-Isolated. The accuracy was found 99.53% on NumtaDB dataset and 99.59% on BanglaLekha-Isolated dataset. We also used our prepared dataset which is completely unseen to proposed CNN-SVM model in order to evaluate the generalization capability of the proposed model. The CNN-SVM model achieved a test accuracy of around 99% on our prepared dataset that indicates that the proposed model is reliable and convincing.
Unsupervised Monocular Training Method for Depth Estimation Using Statistical Masks

Recently, unsupervised monocular training methods based on convolutional neural networks have already shown surprisingly progress in improving the accuracy of depth estimation. However, the performance of these methods suffers deeply from problematic pixels such as occluded pixels, low-texture pixels, and so on. In this paper, we introduce a method to a mask by the statistic of error maps for segmenting the problematic pixels. Different from the conventional methods which use additional segmentation networks to classify problematic pixels, we use a multi-task learning architecture to generate identical mask, mean mask, and variance mask for filtering the problematic pixels. Experimental results show that our proposed method has satisfactory performance compared with other relative methods on the KITTI dataset. Moreover, we also apply our method to the UAV dataset VisDrone, and the results also indicate the effectiveness of the method in detecting moving objects.
Searching Covid‐19 by linguistic register: Parallels and warrant for a new retrieval model

Keeping informed given rapid trend in data and resources about covid-19 is a new challenge. Different user groups (researchers/doctors, practitioners, public) vary in linguistic expression and vocabulary so a new retrieval framework might likewise vary to improve retrieval, expose unanticipated concepts, and establish a sustainable research stream. In this project a document collection about covid-19 was created, parsed according to ISO12620's definition of linguistic register, and retrieval sets compared. Results suggest trends from other fields parallel register-oriented criteria; project exposes unexpected concepts across groups, uses of visualization, and warrants ling-register as a sustainable IR research stream.
On the Performance and Complexity of Crossover in Differential Evolution Algorithm

In this study, the efficiency and complexity of four different crossover variants in Differential evolution (DE) algorithm are experimentally studied. Three well-known crossover variants with a newly designed crossover are applied in nine state-of-the-art and one standard DE algorithm. The results obtained from CEC 2011 real-world problems showed a significant difference between different DE variants and crossover types in performance and time complexity. Higher time complexity is for Eigen crossover, higher efficiency s for newly designed crossover.
Web Service Translating Content into Turkish Sign Language

The essential communication tool for people with hearing loss is sign language. It is way more efficient for their communication. Existing systems for translating the text into sign language are offline and not practical. In this study, we propose a web service-based solution for online translation of content into Turkish Sign Language. We implemented the system and tested it using 32 sentences of 189 words as inputs. The correct word translation rate was 81.74% for the media or audio inputs and the correct word translation for the text inputs was 81.09% The results show the feasibility of the solution and the potential for improvements.
Patch-based partial motion blurred segmentation

Motion blur has a significant impact on image recognition. Segmentation of motion-blurred regions contributes to further identification or classification. Most existed segmentation algorithms are always universal for partially blur images, but not especially for motion-blurred ones. This paper proposes a particular algorithm aiming at motion-blurred region segmentation. Firstly, motion regions are segmented by a patch-based preprocessing. Then, the blurriness of motion regions is measured by a defined function to detect local blurred areas. Empirical thresholds are recommended according to the experimental results. The experimental results show that the motion-blurred regions can be segmented more accurately, and the speed almost doubles other algorithms. Thus we propose a more accurate and efficient segmentation method, especially for partial motion-blurred images.
Using ICT in the Classroom for Acquiring Digital Competences

This research will explore the use of ICT tools in primary school and its impact on digital competences. What digital competencies can mean in this context and how they can be integrated into the primary school curriculum are some of the questions this chapter will explore. This chapter will present three case studies with three primary school teachers who participated with their students in CRISS project (CRISS is a project financed by the European Commission, through the Horizon2020 programme, ID:732489) during which all used CRISS platform and their experience with the process of students' digital competences evaluation, obstacles that they encountered during this process, and how the use of ICT tools can improve primary school students' digital competences. The advantages and disadvantages of this type of learning method will emerge and be discussed.
RETRACTED: Classification algorithm based on semantic judgment and its application in image classification

With the rapid development of the national economy and the widespread use of computer applications platforms, we are analyzing the explosive growth of data, and, data binding and loading delays become a significant problem for this application. Asymmetry of semantic information between a source of the application and the image memory problems. In recent years, intelligent computing, has become a major research analysis of the semantic content. It is also in the order that is generating the offline and online learning characteristics, machine learning, and has become a hot research. The meaning of the image such semantic relatedness, classification, annotation, and in larger environments, tasks such as meaning analysis of such content hash mapping application image, learning. Caption of the image is an important issue in the semantic content of the image analysis. Comments can establish a relationship between the image classification and semantic content. Intelligent learning computer analyze and solve problems, to extract large amounts of data based on the views of a large image. The image semantic content analysis and visual vocabulary and metadata are combined, in planning. Hash value is a semantic metadata, additional metadata to obtain in-depth research, as well as the structure and semantic metadata management at different levels.
Composing Web Services Using a Multi-Agent Framework

Different web services can be composed to perform increasingly complex tasks (e.g., making an on-line payment). However, existing approaches compose web services with hard-coded control and data flows. To proactively and autonomously compose web services, developers can develop agents. However, the development of agents for service composition is complex, due to the reasons that: 1) developers may not have the knowledge from various domains to identify the necessary tasks to carry out the required web services; and 2) a deep understanding of the agent specific code is required in order to implement agents. To alleviate the required efforts to develop agents, we propose an approach to separate the development of agent specific code from the business logic code in the service composition. More specifically, we provide an easy-to-understand syntax that abstracts agent specific code and automatically generates executable agent code. Our experimental results show that our approach can accurately identify tasks for service composition with an Area Under the Curve (AUC) of 0.88. Our experiments also demonstrate that our approach can correctly generate agent code from seven agent specifications. Finally, our user studies reveal that developers are satisfied with our approach to develop agents for service composition.
Microscopic elastic structure mapping of frozen tuna (Thunnus orientalis) via scanning haptic microscopy

The mechanical properties of fish are closely related to texture and are generally evaluated by macroscopic tests. During freezing storage, however, mechanical damage, such as the growth of ice crystals, occurs at the microscopic level. In this study, scanning haptic microscopy (SHM) measurements were performed on refrigerated and freeze–thaw tuna fillets to observe the microscopic elastic modulus distribution in food samples. In elastic images of the refrigerated samples, areas of hard myotome membrane and soft muscle were clearly discernible. For the muscle area, the elastic modulus of the freeze–thaw sample was significantly higher than that of the refrigerated sample, which was consistent with macroscopic indentation results. In contrast, there were no significant differences in the elastic modulus due to freezing in the myotome area. Thus, we propose that SHM imaging is useful for the evaluation of microscopic mechanical damage.
HT-IWT-DCT-Based Hybrid Technique of Robust Image Watermarking

Nowadays, several malpractices are performed for violating integrity of digital multimedia. In this paper, a hybrid technique of image watermarking is proposed which finds its potential capability to overcome this issue. The proposed scheme efficaciously uses homomorphic transform (HT), integer wavelet transform (IWT), discrete cosine transform (DCT) along with singular value decomposition (SVD). Selection of reflectance component of the host image for watermark embedding ensures that robustness and imperceptibility are achieved concurrently. Embedding of watermark is done by modifying it with singular values (SV) of LH subband of the host image. The proposed scheme is highly robust and imperceptible as numerous performance metrics attain high values under attacks. Performance of the proposed algorithm is compared with other formerly reported techniques and it is found that the developed scheme is superior over them.
Research on Classification and Detection System of Common Household Tools for Home Service Robot

In recent years, the application of vision processing algorithm based on deep network in robot and other mobile devices has become a research problem that attracts wide attention. In order to solve the problems of limited storage space, long prediction time, low algorithm performance and weak computing power of object detection on mobile devices such as home service robot, this paper designs a classification detection model of common household tools based on the lightweight convolution neural network MobileNetV2[1]. Firstly, MobileNetV2 is selected as the backbone network of feature extraction. By decomposing the standard convolution into deep convolution and pointwise convolution, the multi-scale prediction part is reserved, and the parameters are effectively reduced; then, the full connection layer network and Softmax classifier are used to realize the classification and recognition of common household tools. Compared with the common classification algorithms, the network algorithm has higher prediction accuracy, smaller network model and better performance, so it is better applied to the system platform of home service robot.
ID practice and theory

Abstract This article describes some of many definitions of information design. Practical as well as theoretical aspects are important in information design. It is an applied science. We may view information design, as a “combined discipline,” as a “practical theory,” or as a “theoretical practice.” As an academic discipline, Information Design needs to incorporate theoretical contributions from other disciplines. The principles of information design are universal, and they are not tied to any unique features of a particular culture or language. One internal and six external information design theories are mentioned. Principles become the fundamental sources for development of practical guidelines.
Two-Phase Fuzzy C-Means and Genetic Algorithm for Food Distribution

Covid-19 is a pandemic that effects all around the world nowadays. Some countries decided to isolate areas to reduce the spreading of the virus. During this isolation, one of the most important things for people to get food and necessary items to survive during lockdown period. In this study, two-phase Fuzzy C-Means and Genetic Algorithms proposed for this problem. Proposed algorithm compared with genetic algorithm in a real case study in Kuwait. As a result, prosed algorithm gives better result according to total travelled distance by all trucks.
Review Restaurant with Descriptive and Predictive Mining Based on Spatiotemporal Data Analytics

At present, there are over 4.3 million restaurants in the world, so the number of restaurant reviews is very large. However, the restaurant reviews will be very difficult to understand by restaurant management. Therefore, it needs data analytics to know the quality of the restaurants. The data used in this study is restaurant data taken from the Tripadvisor website. Before conducting analytics, it will convert the review data in the form of text into numbers using sentiment analysis. In this study, it carried the sentiment analysis process out using Vader and SentiWordnet libraries. For each libraries produced sentiment value, so it will have Vader sentiment value and Sentiwordnet sentiment value. While it does the process of data analytics by two methods, namely descriptive analytics and predictive analytics. The data analytics process uses five features, including service rating, value rating, food rating, Vader sentiment value, and SentiWordnet sentiment value. The results of cluster analysis in descriptive analytics using the variance method ranged from 0 to 0.252 and testing predictive mining errors using the Mean Absolute Percentage Error method reaches 7%.
Is the "Job Map" the Next-Generation "Story Map"? Investigating the Application of Jobs-to-be-Done for Requirements Engineering in Agile Projects

Jobs-To-Be-Done (JTBD) is a powerful innovation framework used in various industries to get a better understanding of a task (i.e., a job) that is to be supported by a solution. However, JTBD has not been widely adopted in software engineering yet. In this experience report, we describe our first lessons learned from applying JTBD in a students' software development pro-ject following Scrum. Early results suggest that JTBD has the potential to complement existing software engineering ap-proaches. In particular, we learned that the application of a Job Map in addition to a Story Map can be beneficial to identify the actual user of an application and understand what the user really wants to achieve with the help of the software solution.
Effect on Reconstruction of Images by Applying Fractal Based Lossy Compression Followed by Symmetrical Encryption Techniques

Compression and encryption are two major aspects of cryptanalysis. Compression reduces the large bandwidth requirement while encryption provides security against differential unethical attacks during transmission of images over a channel. The fractal based compression algorithm is proposed to remove the redundant data as it makes the image resolution independent. Here, AES and DES symmetrical encryption techniques are used over compressed images. The efficiency of proposed compression technique is evaluated by performance evaluation parameters like PSNR, MSE and SSIM. The effects on these performance evaluation parameters, when symmetrical encryption techniques are imposed on compressed images, are also analysed. The capacity of encryption techniques to procure the information from differential attacks is also analysed by calculation of differential parameters like NPCR and UACI. This experiment is performed on standard set of five grayscale test images. This paper shows that fractal-DES cryptanalysis system uses minimum bandwidth during transmission and provides better security and efficient reconstruction of images over fractal-AES cryptanalysis system.
Text Classification Based on GNN

The phenomenon that AI researchers tend to transform some certain data into the form of graphs is prevailing. Usually, these graph-like data will be inputted into some certain artificial neutral networks which are dramatically disparate with the conventional CNN. The purpose of the algorithm employed the GNN is to extract much more detailed features that can be stored easily in the graph. However, these detailed features are much more difficult to extract in the raw data accumulated in the database, which requires the experiment to transfer the common database into the whole graph ahead of schedule. The dataset used in this paper, Cora, is commonly used in some papers whose targets aimed at semantic segmentation, while disparate with this paper as well. The result of this experiment has achieved to nearly 100% accuracy accompanied with those preprocessed data. Furthermore, this paper also attaches much focus on the effects of preprocessing operation which can be reflected on the differences of accuracy. Only by preprocessing operation can this paper achieve better results accompanied with higher accuracy when compared with other experiments.
Flush-Detector: More Secure API Resistant to Flush-Based Spectre Attacks on ARM Cortex-A9

ARM series processors are increasingly used in IoT and cloud services because of their high performance and flexibility of hardware design, especially Cortex-A9 MPCore processor. However, they also suffer from various types of security threats, typically such as flush-based cache attacks. Among these attacks, flush-based Spectre attacks(using Flush + Reload for Spectre attacks) represent a serious threat to system. They usually induce the victim to speculatively perform operations that would not occur during the correct program execution, and then leak the victim’s confidential information to the adversary via cache side channel attacks. So far, there is no widely accepted solution to defend against Spectre attacks. The proposed solutions either lead to large performance losses or sacrifice transparency. In this paper, we propose a secure flush operation API named Flush-Detector to mitigate flush-based Spectre attacks. We present the design and implement of Flush-Detector to detect and defend against flush-based Spectre attacks on ARM Cortex-A9 MPCore. The attack experimental results show that Flush-Detector can detect flush-based Spectre attacks in real time and reduce the attack success rate to less than 1%. Moreover, performance test results demonstrate that the time consumption of Flush-Detector API is about 17.7% longer than the original cache flush API.
Towards Social Facilitation in Audience Participation Games: Fighting Game AIs whose Strength Depends on Audience Responses

This paper presents two AIs that enable a fighting game to be played or live-streamed as an audience participation game. The proposed fighting game AIs imitates a social facilitation in human psychology by dynamically adjusting its strength based on audience responses during gameplay. The two AIs exploit Monte-Carlo Tree Search. They have three important mechanisms, for dynamic difficulty adjustment, human-like behavior promotion, and social facilitation integration. We developed the two AIs with different evaluation functions. The common feature is an integrated social facilitation parameter. However, the difference is that one AI is developed by generalizing an existing AI by simply adding a parameter for setting the targeted HP difference, whereas the other was particularly designed for an social facilitation by a strict control of damage score difference. Our experiment result shows that the former one yields more human-like behavior, while the latter one yields slightly better strength adjustment.