Reviewerly/exHarmony huggingface dataset:
Code Change Difference Analysis Tool Design and Implementation Based on Eclipse Plug-in

Aiming at the problems existing in the code difference analysis tools in eclipse, a code difference analysis tool based on Eclipse Plug-in is proposed. By ignoring the meaningless identifier text difference comparison algorithm, the redundant display of the difference results is overcome. For the display mode, the default mode based on JavaFX visualization framework is used to solve the fuzzy problem of display. The tool is designed and implemented based on Eclipse Plug-in, and the effectiveness of the tool is verified by project example.
Quick Response Code: Medication Prescription

QR code is a two-dimensional matrix barcode which is in the form of label and can be translate by machine-readable optical devices. Each QR code represents its own unique code patterns. It can be scans and retrieves using devices such as smartphones, tablets or computer cameras that have QR code scanning application. In hospitals and clinics, medication prescriptions are given to patients which display the date, patient's name, medication name, dose and instructions. However, details information of each medication prescription are not given. In such, there is a need for patient to retrieve the details of each prescription. The existing QR codes also provide limited information and exclude Material Safety Data Sheet. Importantly, the medication prescription information must be from the trusted resources. Therefore, this research objectives are to develop Medication Prescription QR Code, conduct a case study in Universiti Teknologi MARA in Kedah and test users' acceptance. The methodology includes analysis, design, implementation, testing, deployment and maintenance phases. This study introduces Medication Prescription QR code that offers efficient method for patients to retrieve prescribe medicine detailes information. It provides fast and precise information of specific medicines such as indications, interactions, special precautions, adverse reactions and potential hazards, and information from the Material Safety Data Sheet. Implementation of QR codes able to reduce the cost and resources, and can promote towards green environment. The results indicate that Medication Prescription QR Code has good potential to be used by patients and is a value added for medical practitioners and manufacturers. In addition, it has been recognized and received patent from Intellectual Property Corporation of Malaysia.
Interdisciplinary integration as a basis for forming an environment-oriented component of competencies

Most disciplines of the compulsory part of the curriculum and its part defined by education stakeholders in line with the corresponding study field have a significant resource for forming an environment-oriented component of competencies in students. The authors aim at identifying and showing the possibilities of interdisciplinary integration in the formation of the environment-oriented component of universal and professional competencies in the process of fulfilling the requirements of the Federal State Educational Standards. The article illustrates the practice of implementing interdisciplinary integration in higher education when forming the environment-oriented component of competencies by means of experimental work within the educational process at Penza State University and A.V. Khrulev Penza Branch of the Military Academy of Logistics. It is shown that the interdisciplinary integration implies not complementing the disciplines with additional ecological content, but creating a new system with emergent properties that are of interest of both environmental and field-specific education.
Research on encryption technology of sensitive information in reserved format

FPE(Format preserving encryption) can guarantee the same format of plaintext and ciphertext at the same time of encryption. The existing reserved format encryption algorithm is designed based on the international block cipher algorithm, and its efficiency needs to be improved. This paper proposes a reserved format encryption algorithm for digital data based on China's commercial block cipher algorithm SM4. After segmented processing of character type characteristic data by the special format sensitive information algorithm with segmented constraints, it encrypts the character type characteristic data according to the ID card number type. In each round of operation, the function of F function is realized by using SM4 encryption truncation, and the ciphertext after the combination of segmented encryption results is verified to obtain the encrypted reserved format ciphertext. The algorithm proposed in this paper can realize the reservation format encryption correctly, expand the application of SM4, and improve the efficiency and security of the algorithm.
End-to-End Tracing and Congestion in a Blockchain

Modern supply chain applications are complex systems that play an important role in many different sectors. Supply chain management systems are implemented to handle increasing complexity and flows of goods. However, most of these systems are also increasing the complexity of providing trust and a global view of transactions in a distributed supply chain system. Blockchain technology introduces a new architectural style to support the traceability and trust of transactions performed by participants in a network. This chapter uses this emerging technology to realize a supply chain use case from JLP Meats in the UK with improved transparency, trust, and end-to-end querying while discussing potential challenges of realizing large-scale enterprise blockchain applications. The process of farm-to-fork is implemented and tested for traceability, item recall, block analysis, congestion enabling food safety, and sustainable agriculture. Potential challenges are highlighted in complex supply chains that need heterogeneous trade compliance and scalability.
Other Algorithms and Systems

Neural networks have become very popular in computational mechanics. Nevertheless, it is well known that some other machine learning algorithms also work well as the neural networks. In addition, neural networks are often combined with other machine learning algorithms. This chapter describes several algorithms, which are often combined with or compared with neural networks, including the genetic algorithm (GA), the genetic programming (GP), the bio-inspired algorithms and the support vector machine (SVM).
Semi-Supervised Aspect-Based Sentiment Analysis for Case-Related Microblog Reviews Using Case Knowledge Graph Embedding

Aspect-Based Sentiment Analysis (ABSA), a fine-grained task of opinion mining, which aims to extract sentiment of specific target from text, is an important task in many real-world applications, especially in the legal field. Therefore, in this paper, we study the problem of limitation of labeled training data required and ignorance of in-domain knowledge representation for End-to-End Aspect-Based Sentiment Analysis (E2E-ABSA) in legal field. We proposed a new method under deep learning framework, named Semi-ETEKGs, which applied E2E framework using knowledge graph (KG) embedding in legal field after data augmentation (DA). Specifically, we pre-trained the BERT embedding and in-domain KG embedding for unlabeled data and labeled data with case elements after DA, and then we put two embeddings into the E2E framework to classify the polarity of target-entity. Finally, we built a case-related dataset based on a popular benchmark for ABSA to prove the efficiency of Semi-ETEKGs, and experiments on case-related dataset from microblog comments show that our proposed model outperforms the other compared methods significantly.
A New Design Method about the Universal Steganalysis Classifier in Digital Image

Based on a variety of commonly used steganographic algorithms in the spatial domain of digital images, a new design method is proposed for the universal steganalysis classifier. The main idea is that we use steganalysis to extract features from the cover image set and their corresponding steganographic image set for a certain kind of steganographic algorithm, and those features are used for training the corresponding classifier. Then the classifier is used to test the remaining steganographic algorithms respectively and until the cross test is completed successively. According to the cross test results, we chose three adaptive steganographic algorithms from various steganographic algorithms. Then we combine features of the selected three steganographic algorithms to train a universal steganalysis classifier in digital image. When the method used by the image to embed secret information is unknown, we can give priority to use the universal steganalysis classifier to determine whether they contain secret messages, thereby improving the overall work efficiency. Experimental results demonstrate the feasibility of our proposed method for the universal steganalysis classifier in digital image.
ConfAdvisor: An Automatic Configuration Tuning Framework for NoSQL Database Benchmarking with a Black-box Approach

Nowadays, the widely used NoSQL databases play a fundamental role for big data storage, processing and analysis. However, NoSQL databases usually consume a large amount of computing resources and always run in a low performance state, which seriously impacts the end-to-end quality of service. Therefore, tuning the performance of NoSQL databases via benchmarking tools becomes a critical issue. Unfortunately, it is non-trivial to solve this problem because there exists a considerable number of performance-related configuration parameters from both the databases (application-specific) and the operating system kernel (kernel-specific), and manually tuning methods only consider a small subset of the whole candidate configuration parameters space, leading to a sub-optimal performance. To address this challenge, we design ConfAdvisor, an automatic configuration tuning framework for NoSQL database benchmarking. Specifically, ConfAdvisor treats the database performance as a black-box function of configuration parameters and leverage an online learning method to search the best configurations. Experimental results based on several popular NoSQL databases show that: 1) Through only tuning the kernel-specific parameters can improve database performance by up to 30%, which is often overlooked in previous studies. 2) ConfAdvisor is able to achieve well-tuned configurations with a very few trials, even in a high-dimension configuration space. And compared with default configurations, the well-tuned parameters can improve database performance by up to 88%.
Novel CNN Architecture for Human Action Recognition

The visual content representation for spatial and motion features is essential to perform many video processing applications including human action recognition. The existing works used 3D CNN architecture in two stream one for spatial and another for motion or RNN or LSTM. In recent days, research has been focused to exploit the capability of 2D CNN for video data representation without RNN or LSTM. Hence this paper proposes a novel CNN architecture to learn the representation of video sequence by giving spatial information along with temporal information as input in a single stream for classifying actions in video. It is achieved by concatenating intensity image (spatial) with optical flow (motion) which is encoded by the proposed network called Actionet. The proposed Actionet was experimented with HMDB51, UCF101 and KTH datasets which outperformed well with mean classification accuracy of 98.82%, 99.96% and 81% respectively. The efficiency of representation has also been evaluated by utilizing the learnt features for training multi-SVM, KNN and ctree which also yielded good results.
Task-Specific Novel Object Characterization

In an open world, a robot encounters novel objects. It needs to be able to deal with such novelties. For instance, to characterize the object, if it is similar to a known object, or to indicate that an object is unknown or irrelevant. We present an approach for robots to deal with an open world in which objects are encountered that are not known beforehand. Our method first decides whether an object is relevant for the task at hand, if it is similar to an object that is known to be relevant. Relevancy is determined from a task-specific taxonomy of objects. If the object is relevant for the task, then it is characterized through the taxonomy. The task determines the level of detail that is needed, which relates to the levels in the taxonomy. The advantage of our method is that it only needs to model the relevant objects and not all possible irrelevant and often unknown objects that the robot may also encounter. We show the merit of our method in a real-life experiment of a search and rescue task in a messy and cluttered house, where victims (including novelties) were successfully found.
Deeply learning a discriminative spatial–temporal feature for robot action understanding

Human action recognition is a key component in modern artificial intelligent systems, such as sport analysis, video surveillance and human–computer interaction (HCI). Existing action recognition algorithms mainly depend on a predefined spatial sequence code book, which may fail to discover discriminative spatial–temporal features. In this paper, we propose to engineer the spatial–temporal action features that can deeply encode the similarity of within-class human actions and dissimilarity of between-class human actions. Specifically, given a series of training action video samples, we first segment each video into multiple key sections based on human contour. These sections of a video are related to time and space. Then, local human action and appearance information are combined to represent each video section. We quantize these extracted features into a feature vector, which can represent category-specific human actions. Subsequently, we develop an improved linear discriminative analysis to project the data points to a subspace, where data points with the same label are close while data points with different labels are far from each other. Experimental results on HMDB51 and KTH datasets have shown the effectiveness and robustness of our method. Moreover, the recognized human action sequence can guide the operation of robots in industrial area.
Integrated geophysical and geological methods to identify structure existence as a permeable zone in a geothermal field

At the development stage of a geothermal field, a comprehensive survey is needed to obtain an accurate picture of the geothermal system to identify the subsurface condition on the prospect field. The geological structure is one of the important aspects of geothermal exploration. It creates permeability, which allows geothermal fluids to flow to the surface. Locating geological structures in the prospect area will be a powerful tool to determine the drilling target for exploration or production wells. One of the methods to identify geological structure on the surface is using satellite imagery. It is based on visual interpretation of the morpho-structural aspect combined with the lithological aspect and then confirmed with field data. The remote sensing data is built from DEMNAS data and a regional geological map. DEMNAS has spatial resolution 0,27-arcsecond, vertical datum EGM2008. While the subsurface geological structure can be identified by using the Gravity Second Vertical Derivative (SVD) and First Horizontal Derivative (FHD) method. Gravity method identifies structure by slicing the SVD grid, which has suspected structure and then confirmed with FHD slicing in the same suspected area. The geological structure will be confirmed if the value of FHD maximum and SVD value 0. The results of the data processing above are then integrated to obtain the distribution of geological structures as a permeable zone in the geothermal field which then can be used as a guide in determining the location of exploration and production wells.
Research on Task Scheduling Algorithm of Cloud Computing Based on Bilateral Selection

At present, cloud data centers' high energy consumption is an urgent problem in the development of cloud computing. One reason for the high energy consumption of cloud data centers is the mismatching scheduling between tasks and resources. The existing cloud computing task scheduling algorithms assigned tasks to resource nodes by certain rules according to the task information, and they ignored that the resource nodes are also the main body of the task scheduling algorithms. This paper proposes a new task scheduling algorithm based on bilateral selection. It fully considers the task and the resource and combines the Dynamic Voltage/Frequency Scheduling (DVFS) technology to help tasks and resources to match more appropriately. It can assign tasks to resources with higher credibility. The experimental results show that the cloud computing task scheduling algorithm based on the bilateral selection can obtain better execution performance and lower execution energy consumption and solve the high energy consumption problem caused by the mismatch between tasks and resources.
Programmes design for teachers’ professional development as a way of organizational development for the human’s capital of the university

The relevance of the article is caused due to the importance of teacher’s professionalism in achieving success of university’s educational activity, and quality of higher education which defines the need for teacher’s continuous professional growth in higher school according to the pedagogical career strategy. It is determined by teacher’s individual needs and abilities, as well as the inquiries of the university. The leading role in the complex of training materials for additional professional education belongs to additional programmes of teachers’ professional development. The purpose of the article is to design additional programmes for teachers’ professional development and to identify the criteria for their efficiency. Packages of modular-based programmes, stages for design of the program for teachers’ professional development, characteristics of modular-based educational programmes, groups of quality criteria for the additional program for teachers’ professional development and a matrix of an expert assessment of programmes for teachers’ professional development (online mode) within external expertise are presented in the article. The authors emphasize the special role of personnel’s potential in the system of additional professional education. The article is intended for scientists, teachers, experts of the system of additional professional education, post-graduate students and students.
Inter-domain Routing Scheme based on Secure Multi-Party Computation over SDN Architecture

The Internet including enterprise-level networks, data center networks, and wide area networks can be divided into many autonomous domains. Autonomous domains can be managed by different administrative units or organizations. Therefore, when calculating routes between domains, autonomous domains that do not trust each other will have security and privacy considerations. In order to solve the problem of cross-domain privacy protection and optimize routing strategies, we propose an inter-domain routing method based on secure multi-party computation under the SDN architecture. Our method effectively protects the private information in each domain and accelerates the convergence of routing. We elaborated on the preliminary exploration of this idea, and the evaluation showed that our solution only brings millisecond latency.
Development of Pipelined Blowfish Algorithm with Memory based S-Box

Abstract The main aim in the design of any encryption algorithm is to provide security against unauthorized attacks. With the fast progression of digital data exchange information security has become an important issue in data communication. Encryption algorithms play an important role in information security system. These algorithms consume a significant amount of computing resources such as CPU time, memory, and battery power. The data encryption algorithms should provide security and also should not be slow in performance because it is a common practice to embed encryption algorithms in other applications. Embedding of encryption algorithms in other applications also precludes a hardware implementation, and is thus a major cause of degraded overall performance of the system. Advanced encryption standard (AES) algorithm is used for protection against various classes of wireless attacks but it consumes more core area and power. Hence, we propose a development of pipelined Blowfish algorithm as an alternative security algorithm. In this paper the pipelined blowfish algorithm with memory based S box(with different memory sizes) has been developed and simulated using Xilinx Ise 14.2 using Verilog HDL using Zynq7000. The memory-based method is used to optimize the performance of Blowfish. The performance is analyzed in terms of its area, power and throughput.
Components of Future Teachers’ Readiness to Provide Pedagogical Care

The research objective includes theoretical justification of a model of future teachers’ readiness to provide pedagogical care. The authors clarify essence of the notion “readiness to provide pedagogical care”, identify and justify components of this readiness: cognitive (rational), emotional and value, activity-oriented. Scientific originality of the study involves identifying components of future teachers’ readiness to provide pedagogical care; the suggested model serves as a scientific and methodological basis of professional pedagogical training. The research findings are as follows: the authors prove that future teachers’ readiness to provide pedagogical care includes cognitive, emotional and value, activity-oriented components.
An Adaptable Cost-sensitive Imbalanced Fault Diagnosis Method Considering Sample-significance Information

Imbalanced datasets acquired from machinery are common in real cases and some cost-sensitive learning-based methods have been presented to tackle the imbalanced fault diagnosis in recent years. However, these methods manually assign misclassification costs that remain unchanged during training for each health condition and therefore they cannot be applied generally in different scenarios. Moreover, the samplesignificance information of each sample of the same health condition is not considered, causing low diagnostic accuracies. To deal with the weaknesses, this paper proposes a new adaptable cost-sensitive imbalanced fault diagnosis method. The architecture of the proposed method includes a feature extraction module and a cost-sensitive module. Specifically, the adaptable cost-sensitive parameter and the sample-significance information loss function are introduced into the training process to pay more attention to the minority classes and the hard samples. The imbalanced dataset of gears is employed to validate the effectiveness of the proposed method. The experimental results indicate that the proposed method can achieve high diagnosis accuracy and is superior to some existing imbalanced fault diagnosis methods.
Understanding the Cotopaxi Volcano Activity with Clustering-Based Approaches

We explored four different clustering-based classifiers to categorize two different volcanic seismic events and to find possible overlapping signals that could occur at the same time or immediately after seismic events occurrence. The BFR classifier with \(k=2\) was chosen as the best out of 36 explored models statistically (\(p<0.05\)), reaching a mean of accuracy score of \(88\%\). This result represents a satisfactory and competitive classification performance when compared to the state of art methods. The CURE classifier with \(k=3\) achieved a mean of accuracy value of \(87\%\) at \(p<0.05\), allowing it to be the only model capable of detecting seismic events with overlapping signals. Therefore, the proposed clustering-based exploration was effective in providing competitive models for seismic events classification and overlapped signal detection.