Reviewerly/exHarmony huggingface dataset:
Enabling Secure and Efficient Data Sharing and Integrity Auditing for Cloud-Assisted Industrial Control System

With the development of Cloud Computing, which is applying to Industrial Internet of Things, the security of industrial data is confronting enormous risk. Unfortunately, existing data sharing schemes are not suitable for the industry control system because of the heavy computation operations, latency-sensitive service, and resource-limited devices. Also, since the data is stored on the cloud, the data integrity are crucial issues related to privacy and trust. In order to protect the confidentiality and integrity of data in such an environment, we propose a secure and efficient data sharing scheme by employing Ciphertext-Policy Attribute Based Encryption (CP-ABE), which integrates data encryption, access control, decryption outsourcing and data integrity verification. Moreover, the length of ciphertext is fixed. The scheme is analyzed in detail from security, efficiency and computation cost.
Artificial Intelligence-Based Chatbot Framework with Authentication, Authorization, and Payment Features

Chatbot is a computer program that can simulate a human conversation via a chat application such as Facebook messenger, WeChat, Slack, etc., which permits businesses to deliver interactive experiences that help in client engagement. We create an artificial intelligence-based chatbot-building framework which is a subscription-based chatbot builder platform with authentication, authorization along with other major modules. It provides better intelligence for conversations compared to present flow-based chatbot builders. Brands can sign up for the platform and build their own unique chatbot based on their business needs and deploy to any messenger platforms such as Facebook, WhatsApp, Telegram, WeChat, etc.
Research on Visualization Technology of 3D Cable Harness Wiring of Complex Electromechanical Products

Rigid structural parts not only shield the position and pose of the cable harness, but also the position of electrical components interface when wiring due to the narrow interior space and the large number of parts in complex electromechanical products. More than that, it will also affect the normal operation of the device due to inaccurate cable harness installation and poor contact. Based on the above difficulties, the wiring process visualization technology of 3D cable harness was researched. And developed a corresponding prototype system of wiring process visualization for 3D cable harness based on the axis-aligned bounding box technology, and elaborated the functions of how to create, activate, and delete the simplified view of the 3D cable harness wiring. Finally, the method has been verified in the cable harness wiring of a phased array radar model, which proved that the 3D cable harness wiring simplified view created by the system can clearly display the position and pose information of cable harness and electrical connectors, and improved the wiring efficiency.
Exploring the Factors Affecting the Gap on Literacy Skills among Students with Learning Disabilities: A Case Study

The wide gap in literacy proficiency among students with learning disabilities is one of the rising issues in education field.This study is conducted to find out the factors that contribute towards the gap on literacy skills among students with learning disabilities and mainstream students.Furthermore, teachers' strategy to improve the literacy skills among learning disabilities students also inquired.Besides, this study also discussed about the interventions or programs that have been implemented to help these students.An interview with three special needs teachers at Sekolah Kebangsaan Putrajaya Presint 16(2) is conducted to investigate this issue.The findings shows that lack of awareness among parents which then causing their children to received late interventions is among the causes for unsatisfactory literacy skills.It is important for teachers to use variety of teaching strategies to reduce the gap on literacy skills.At the end of this paper, the author discussed on how to enhance the intervention programs and what teachers and parents role to fit the needs of these students.The findings of this study provide new dimension in explaining the importance in reducing the gap on literacy skills among special education needs students.
Kalman Estimators for Multi-sensor System with Colored Measurement Noises

For multi-sensor system with colored measurement noises, using the observation transformation, the system can be converted into an equivalent system with correlated measurement noises. Based on this method, using the classical Kalman filtering, this paper proposed a covariance intersection (CI) fusion Kalman estimator, which can handle the fused filtering, prediction and smoothing problems. The advantage of the proposed method is that it can avoid the computation of the cross co-variances among the local filtering errors and can reduce the computational burden significantly, as well as the CI fusion algorithm can be used in the uncertain system with unknown cross covariances. Based on classical Kalman filtering theory, the centralized fusion and three weighted fusion (weighted by matrices, scalars and diagonal) estimators are also presented respectively. Their accuracy comparisons are given. The geometric interpretations based on covariance ellipses are also given. A Monte-Carlo simulation example shows that the accuracy relations.
Extraction of Content -Free Basal Image and Its Application in Face Recognition

As a typical signal, an image can be composed of a series of basic signals, which are called basal images. In order to find the basic signals of a group of reconfigurable arbitrary images and optimize the face recognition algorithm, a feature-based basal image extraction method is proposed and applied to face recognition, so that the basal image can be extracted from any set of images and the performance of face recognition algorithm to local changes such as illumination and expression can be improved. Feature extraction algorithm is used to decompose a series of basal images from the training set image, the algorithm flow of basal image decomposition and extraction is expounded, the projection coefficient is obtained by projecting the face image into the space constituted by the basal image, the distance between the test sample and the training sample coefficient matrix is calculated, and the minimum distance classifier is used for classification. The experimental results show that the basal image obtained from any image set can be used for face recognition, and the basal image have content independence. At the same time, this method is better than the traditional feature face recognition method in light, expression, wearing accessories and other local changes.
Using FPGA-Based Quadratic Residue Code to Improve the Reliable of Wireless Robot Control System

In the field of wireless mobile robots, the control code of the robot needs to be transmitted through a reliable wireless channel with low latency, strong interference resistance, simple handshake, and short data length. Thus, we adopt the (71, 36, 11) quadratic residue (QR) error correction code to encode the control code of wireless mobile robots to improve the reliability of wireless mobile robot control system. Among them, we analyze the software encoding and decoding algorithms of (71, 36, 11) QR code, then we perform hardware-oriented optimization and give a specific FPGA-based decoding framework design. Then we implement the proposed architecture on Intel Arria10 10AX115U4F45I1SG FPGA, after this we calculate the decoding time of the proposed framework and compare it with software decoding, this acceleration framework is 2275 times faster than software decoding based on Intel i5-6500 processor. At the same time, after adopting this scheme, it will be beneficial to the power consumption and communication distance of wireless mobile robots. Meanwhile, the FPGA parallel acceleration design ensures low transmission latency.
Population Initialization In Genetic Algorithm For Traveling Salesmen Problem Using Steepest Ascent Hill Climbing Algorithm

Genetic algorithm is an artificial intelligence algorithm that is used to find optimal solutions for computational problems in order to maximize or minimize certain functions. In this research, algorithm Steepest Ascent Hill Climbing (SAHC) is the algorithm optimal locally which most effective for analyzed application, to enhance performance of the genetic set rules, in order to keep away from the genetic algorithm to the trouble of convergence, untimely, so it's expected to achieve optimal solutions in solving the Traveling Salesman Problem (TSP). In this research, there are three kinds of experiments by way of applying different parameters of Genetic algorithm. Each of these parameters consist of 10, 20, and 50 of the population, number of iterations 100, crossover probability 0.1, and mutation probability of 0.75. In the first experiment, initial values obtained for the solution are 3596.6, SAHC amounted to 31618.0 best solution, and the best solution Genetic Algorithm 2725.5. In the second experimental values obtained initial solution to SAHC at 3494.1, 2722.1 and the best SAHC for the best solution is Genetic Algorithm 2722.1. In the third experiment obtained the initial values of 3330.9 solutions for SAHC, SAHC is the best solution, and the best solution for 3202.3 Genetic Algorithm 2695.7. In this study, the greater number of population genetic algorithm applied to the obtained values the better solution. In this study, the authors compare the results obtained with similar studies that use methods of EA, GA and ACO-GA. The results obtained by the method of EA is equal to 2678, the GA of 3218 and with the ACO-GA by 3092 while with the research results obtained by the author in 2667.
Feedback Seminar Analysis - An Introductory Approach from an Intelligent Perspective

This paper presents a mixture of Artificial Intelligence Topics: Automated Learning with Deep Learning and Natural Language Processing, as an advantage provided by intelligent and automated approaches in opinion mining. The main objective of this paper is to determine if a writing review can contain a positive or a negative opinion and to discover which are the best intelligent approach to detect this issue’s objective. Data set used in our experiments contains 180 collected opinions/reviews from students related to a particular seminar. Methods used in our experiments are Multinomial Naive Bayes, Support Vector Machine with the RBF kernel, linear Support Vector Classification and Recurrent Neural Network with Long-Short Term Memory Unit. Computed metrics used to evaluate methods performance are accuracy, precision, recall and F1-score.
Computer-Bridge Software Test Platform Based on Anonymous Pipe

The significant momentum of computer-bridge brings a strong demand for quality validation and assurance for computer-bridge software. Current research work seldom discusses corresponding software testing questions and validation approaches with clear quality requirements and methods. This paper focuses on a platform based on anonymous pipes that can load different game engines for multi-program battles or man-program battles, including the essential problems in the platform, features, and process. Moreover, it presents a test process for a certain computer-software. Finally, it discusses the challenges, issues, and needs in computer-software testing.
Military Chess Game Search Algorithm based on Deep Search

Computer games are divided into complete information games and incomplete information games. Military chess games, as a kind of incomplete information games, have shortcomings in many aspects. In view of the fact that the game tree of the military chess game in the middle and late stages of attacking the banner is too large, this paper proposes an algorithm for deep searching the chessboard according to the military chess rules. In this paper, the bidirectional deep search algorithm is adopted, which reduces the time needed to calculate the approximate optimal solution by nearly 97% compared with using the algorithm of two-way deep search. Compared with the original depth search algorithm, the algorithm in this paper is highly optimized in terms of time complexity. The experiment shows that the search algorithm based on deep search can reduce the operation time of each step and achieve good results.
Ghost Detection and Removal Based on Two-Layer Background Model and Histogram Similarity

Detecting and removing ghosts is an important challenge for moving object detection because ghosts will remain forever once formed, leading to the overall detection performance degradation. To deal with this issue, we first classified the ghosts into two categories according to the way they were formed. Then, the sample-based two-layer background model and histogram similarity of ghost areas were proposed to detect and remove the two types of ghosts, respectively. Furthermore, three important parameters in the two-layer model, i.e., the distance threshold, similarity threshold of local binary similarity pattern (LBSP), and time sub-sampling factor, were automatically determined by the spatial-temporal information of each pixel for adapting to the scene change rapidly. The experimental results on the CDnet 2014 dataset demonstrated that our proposed algorithm not only effectively eliminated ghost areas, but was also superior to the state-of-the-art approaches in terms of the overall performance.
Approximate Dynamics Lead to Optimal Control: Efficient Calculation of Exact Derivatives

We discuss the importance of accurate derivatives for efficiently locally traversing and converging in optimization landscapes. In the context of quantum optimal control, we find that the feasibility of meeting this central requirement critically depends on the choice of propagation scheme and problem representation by deriving analytically exact control derivatives (gradient and Hessian). Even when exact propagation is sufficiently cheap, we find, perhaps surprisingly, that it is always much more efficient to optimize the (appropriately) approximate propagators: in a certain sense, approximations in the dynamics is traded off for significant complexity reductions in the exact derivative calculations. We quantitatively verify these claims for two concrete problems of increasing Hilbert space dimensionality and find that the best schemes obtain unit fidelity to machine precision, whereas the results for other schemes are separated consistently by orders of magnitude in computation time and in worst case 10 orders of magnitude in achievable fidelity. Since these gaps will continually increase with system size and complexity, this methodology unlocks efficient optimization of many-body dynamics operating in the unprecedented high-fidelity regime which will be published separately.
Designing, Analysis and Implementation of Open-source Visualization System for High-Definition Remote Sensing Data Processing

The big data characteristics of Marine High-Definition Remote Sensing (MHDRS) has result in the obstacle of the real-time processing, effective decision-making and low efficiency information collection. Aiming at this problem, this paper analyzes the visualization and control integrate system, and establishes a visually analytic system to process the different source of Remote Sensing (RS) data as well as its storage based on the MHDRS data. The result shows that the new method enhances the analysis and sharing with high efficiency.
Where is ‘There is’ in ‘∃’?

The paper offers a survey of four key moments in which symbolisms for quantification were first introduced: §§11–2 of Frege’s Begriffsschrift (1879); Peirce’s ‘Algebra of Logic’ (1885); Peano’s ‘Studii di Logica matematica’ (1897); and *9 (‘replaced’ by *8 in the second edition) of Whitehead and Russell’s Principia Mathematica (1910). Despite their divergent aims, these authors present substantially equivalent visions of what their differing symbolisms express. In each case, some passage suggests that one (but not the only) way to render one of the symbols into ordinary-language words (German, English and Italian) is to say that there is something or some thing(s) exist(s). Exactly how this comes out varies from language to language, but the point remains the same. As a result, in almost all recent logic manuals that introduce ‘∃’, some passage says that the symbol means ‘there is’ or ‘there exists’, and the tradition has grown up of labelling ‘∃’ ‘the existential quantifier’. Some considerations are offered for deprecating this reading of ‘∃’ and the label adopted for it, and for preferring to read it as ‘for some (at least one)’ and for speaking of the ‘particular quantifier’.
Knowledge-Based Categorization of Scientific Articles for Similarity Predictions

Staying aware of new approaches emerging within specific areas can be challenging for researchers who have to follow many feeds such as journals articles, authors’ papers, and other basic keyword-based matching algorithms. Hence, this paper proposes an information retrieval process for scientific articles aiming to suggest semantically related articles using exclusively a knowledge base. The first step categorizes articles by the disambiguation of their keywords by identifying common categories within the knowledge base. Then, similar articles are identified using the information extracted from the categorization, such as synonyms. The experimental evaluation shows that the proposed approach significantly outperforms the well known cosine similarity measure of vectors angles inherited from word2vec embeddings. Indeed, there is a difference of 30% for P@k ( $$k \in [1, 100]$$ ) in favor of the proposed approach.
Optimizing unbalanced text classification tasks by integrating critical data mining and restricted rewriting techniques

Summary Oversampling technology has been widely used to improve the classification task of unbalanced data. However, unlike structured data, the basic unit of text is words or characters, which can cause oversampling instances in digital space to lose word similarity in semantic space. To solve this problem, use text rewriting to directly generate artificial samples. Unfortunately, existing rewriting techniques usually destroy the grammatical structure and logic of the original text. In this article, we improve and limit some existing text rewriting methods, and propose an effective algorithm to mine feature words in various texts to help complete text rewriting. At the same time, by calculating the similarity between texts, various types of data are divided into key data and non‐key data, and finally different rewriting processes are designed for them. The experimental results of four unbalanced text classification tasks show that our method is superior to the previous text rewriting method, which can improve the classification accuracy of the model by 1.7% to 2.9%, and the AUC can be increased by 0.012 to 0.058. The ablation experiment also explored the effects of various variables and methods on the experimental results.
Scalable Two-Phase Top-Down Specification for Big Data Anonymization Using Apache Pig

In big data applications, data privacy is one of the most concerned issues because processing of large-scale privacy-sensitive dataset often requires computation power provided by public cloud services. Sub-tree data anonymization, which is getting a good trade-off between data utility and distortion, is a popularly adopted scheme to anonymize dataset for privacy preservation. Top-down specialization (TDS) and bottom-up generalization (BUG) are two ways to fulfill sub-tree anonymization. Anonymizing dataset via generalization to satisfy certain privacy requirements such as k-anonymity is a widely used category of privacy-preserving techniques. It is a challenge for existing anonymization approaches to accomplish privacy preservation on privacy-sensitive large-scale dataset due to their insufficiency of scalability. Several methods focus on developing algorithms to determine the best cut of the taxonomy tree, the optimal value of k and the best option of anonymization technique either by top-down specification or by bottom-up generalization. This paper proposes a scalable two-phase top-down specialization (STPTDS) to anonymize large-scale dataset using Apache Pig on Hadoop.
An Observational Study of Equivalence Links in Cultural Heritage Linked Data for agents

This article presents an observational study of the virtual graph formed by equivalence links between agent entities across 8 knowledge bases. To evaluate the potential of this linked data graph, we measured the equivalences that it could provide for a real dataset. We crawled the virtual graph by starting from references to agents we found in descriptions of objects collected from data of cultural heritage institutions in Europeana. Our study characterizes the current virtual equivalence graph, presenting statistics about the links, their type and origin. Crawling the equivalences for agent URIs required several crawling iterations on the virtual equivalence graph. The amount of gathered equivalences grows steeply in the first 3 crawling iterations and stabilizes on the 4th iteration. VIAF was the KB with the highest number of equivalences, reaching 60.7%, and it was followed by Wikidata with 34.5%.
Flow-Guided Single Object Tracking Framework in UAV Aerial Video

Tracking target in UAV aerial video is challenging for at least two reasons: (1) the target have small scale relative to the image which leads to have less feature information; and (2) compared to the traditional tracking, drones bring new challenges to the tracking methods, even the combination of several difficult scenarios. Notice that the most existing trackers only consider appearance feature of the current frame, and hardly benefit from information between time and motion sequence. In this paper, we address both issues inspired by time sequence and motion information. In particular, we proposed a network that unifies Siamese subnetwork and optical flow estimation in a framework. Given an aerial video, a CNN subnetwork is used for feature extraction and a flow subnetwork is used for flow estimation. Then according to target historical motion speed, EA module is used to reduce the redundant motion information by scale estimation and FA module is used for feature fusion. Finally each aggregated feature and template feature is fed into region proposal subnetwork. Extensive experiments are performed on popular aerial video datasets, and our proposed method achieves promising performance in comparison with state-of-the-art trackers.
Design and Implementation of Database Query Module Based on B / S Structure

Nowadays, the manufacturing execution management system (MES) has been widely used in industrial production, and the query operation in the user interface of the MES system is one of the commonly used operations. The common implementation method is to directly write the corresponding code according to the specific query requirements, but with the increasing query requirements, message frequent changes, there are practical problems such as large amount of code, repeated work, poor generality, and high system maintenance costs. In view of this situation, the common operating elements of query processing logic and human-computer interaction are extracted, and a general query module is designed and implemented by drawing on the existing general query system design ideas.
Smart Object Design by Children as Protagonists

Making children the protagonists of a design process by allowing them to express themselves through technology and mixing this knowledge with design competence and reflection opportunities has been seen in the CCI community as one way to empower them. This paper presents a card-based board-game and the way it was used in design workshops with children in order to involve them as protagonists.