Reviewerly/exHarmony huggingface dataset:
A Fault Diagnosis Method for Hybrid Information System Based on Weight Assignment Murphy's Method

Single diagnosis method used for fault diagnosis has the characteristics of low accuracy and strong subjectivity. In order to improve the above disadvantages, this paper proposes a fault diagnosis method for hybrid information system based on weight assignment Murphy's method and builds data fusion diagnosis framework. Firstly, according to the attributes of fault information, the similarity reasoning method and the rule automatic matching method are respectively used in quantitative information and qualitative information for preliminary reasoning. Then the characteristics of probability data of different methods are analyzed and the corresponding weights are given. Finally, the probability data of the two systems are integrated by using the weight assignment Murphy's method to obtain the results. The experimental results show that the method of fault diagnosis using Murphy's method with weight distribution can effectively improve the accuracy and credibility of the diagnosis results.
The sum necessary to ensure that a degree sequence pair has an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e39" altimg="si27.svg"><mml:mi>A</mml:mi></mml:math>-connected realization

Let S=(a1,…,am;b1,…,bn), where a1,…,am and b1,…,bn are two nonincreasing sequences of nonnegative integers. The pair S=(a1,…,am;b1,…,bn) is said to be a bigraphic pair if there is a simple bipartite graph G=(X∪Y,E) such that a1,…,am and b1,…,bn are the degrees of the vertices in X and Y, respectively. Let A be an (additive) Abelian group. We define σ(A,m,n) to be the minimum integer k such that every bigraphic pair S=(a1,…,am;b1,…,bn) with am,bn≥2 and σ(S)=a1+⋯+am≥k has an A-connected realization. In this paper, we determine the values of σ(A,m,n) for |A|=k and m≥n≥2, where k≥6 is an even integer, and the values of σ(A,m,n) for |A|=4 and m≥n≥3. Therefore, together with some previous results [(Yin, 2016); (Yin and Dai, 2017); (Guan and Yin, 2018)], the values of σ(A,m,n) for |A|≥3 are determined completely.
LApps: Technological, Legal and Market Potentials of Blockchain Lightning Network Applications

Following in the footsteps of pioneer Bitcoin, many altcoins as well as coloured coins have been or are being developed and merchandised adopting blockchain as the core enabling technology. However, since interoperability and scalability due to high and capped (in particular cases) transaction latency are deep-rooted in the architecture of blockchain technology, they are by default inherited in any blockchain-based applications. Lightning Network (LN) is one of the supporting technologies developed to eliminate this impediment of blockchain technology by facilitating instantaneous transfers of cryptos. Since the potentials of LN is still relatively unknown, this paper investigates the current states of development along with possible non-monetary usage of LN, especially in settlement coloured coins such as securities, as well as creation of new business models based on Lightning Applications (LApps) and microchannel payments as well as micro-trades. The legal challenges that may act as impediments to the adoption of LN are also discussed.
The students’ approach patterns in thermodynamic problem-solving at SMA lab school UNTAD Palu

Abstract The process of problem-solving carried out by students in solving physics exercises consists of different approaches. It is reviewed based on the diversity of students’ ability levels in solving problems in physics subject. This study aims to describe the approach patterns used by students in the process of solving physics problems on the thermodynamic material at Senior High School Lab school UNTAD Palu. This type of research is qualitative research with a qualitative descriptive approach. The subjects in this study were 20 students of Grade XI Science 3 at Senior High Scool Labchool UNTAD Palu. Respondents in this study consisted of up to 6 students. The respondents were selected using the calculation of standard deviation and the average score of the results of the respondent selection test. The instruments used in this study were respondent selection tests, problem-solving tests, and interview guides. From the results of the research for the test of students’ physics, the problem-solving ability was in the poor category with the acquisition of an average value percentage of 37.3%. Meanwhile, the approach patterns used by the students in the physics problem-solving process was interesting as it tended to use two approaches, namely structured Plug and Chug, as well as unstructured Plug and Chug.
An Efficient Image Transmission Pipeline for Multimedia Services

The transmitting costs of image data strongly impact the user experience in multimedia services such as Instagram and Twitter. In this paper, we design a novel image transmission pipeline for both efficiently reducing data usage and preserving image quality in those services. Through analyzing the varying features in resized image, we found that high-frequency details achieve a major loss. Based on this impact, we build a series of mechanisms to construct our pipeline: 1) an image resampling mechanism, 2) a high-frequency feature extraction technique, and 3) an image reconstruction method based on the resampled image and its local features. Besides, we also introduce an adaptive binning approach for improving the performance of feature extraction. Finally, we conduct several experiments which show that the proposed pipeline significantly outperforms other baselines in preserving image quality and transmitting time, and is comparable in reducing data usage.
PENGEMBANGAN MEDIA PEMBELAJARAN AUDIO VISUAL BERBASIS POWTOON PADA MATA PELAJARAN IPS

This study aimed to develop audio-visual learning media based on powtoon in the social science subject at the fourth-grade of the elementary school economic diversity material in Indonesia and to know the appropriateness and student’s respond to the learning media. The researcher used research and development (R&D) as the research method which there were three stages within it. The test was conducted to 31 fourth grade students at SD Negeri Bhayangkari, Serang city 2018/2019 period. The result of a design expert’s test showed 96% for the average score with a very feasible category. The result of a material expert’s test showed 88% for the average score with a very feasible category. The result of a language expert’s test showed 80% for the average score with a very feasible category. The result of the questionnaire showed 88.5% for the average score with a very feasible category. Based on research findings, the developed audio-visual learning media based on powtoon for fourth-grade students in elementary school was very feasible. It can be used in the teaching-learning process at economic diversity material in Indonesia of Social Science subject. Keywords : developing learning media, audio-visual, social science subject
Single Dark Channel Prior Generalization of Smoggy Image

This paper proposes dark channel prior algorithm to remove smog from an image. Smog degrades the quality of outdoor pictures, and image quality has been lost considerably. An important problem is that of the elimination of hazards, which is called dehazing. The turbid material in the atmosphere (e.g., contaminants and droplets), typically degrades images of natural scenes. The decayed pictures lack color fidelity and contrast. The deterioration is spatially dependent, because the volume of dispersion depends on the scene point distance from the camera. In this work, a simple but effective picture is presented before the dark channel which is stripped. The previous dark channel is a kind of outdoor camera data. It is based on a crucial observation. A GUI is created using MATLAB in this paper. Using this GUI, image is encrypted first and then compressed (EtC) using which the thickness of the smog can be measured directly and a high-quality smog-free image can be obtained. The effectiveness of the proposed method is validated through the results obtained by dehazing the images.
Ethical and Societal Issues of Automated Dark Web Investigation: Part 1

Any online investigation of law enforcement agencies (LEAs) in the fight against terrorism takes place within the framework of society at large. As such, the society (at large and within each EU member state) has an impact on how such online investigations by LEAs may be carried out. The core aim of the following five chapters is to identify and to analyse any real and potential legal implications for online investigations carried out in the area of fighting terrorism. For the guidance created by these chapters to serve with lasting effect, it seemed appropriate to focus on the ethical and societal impact of LEAs using an automated tool first. The ethical and societal aspects are analysed in detail in Chaps. 7 and 8, while the relevant legal frameworks are scrutinised in Chaps. 9 and 10. This chapter not only creates transparency on the methodology behind these five chapters but also sets the scene and puts new technological developments like artificial intelligence and machine learning in perspective with the use of the Internet by terrorists and by LEAs alike.
Brilliant but Needy Students Selection Using Fuzzy Logic

It is famous that, one of the ways to break even the inequality between richer students and Brilliant but Needy Students(BbNSs) is via scholarships. However, the selection process of brilliant but needy students is often entangle with uncertainty, lack of transparency and unfairness from the experts and the vagueness of the experts’ language used linguistically. The fuzziness inherent in human reasoning couple with the biases of human by nature preclude some highly qualified brilliant but needy students from getting selected for scholarships. This study deploys Fuzzy Logic Approach(FLA) to develop an evaluation model for selecting brilliant but needy students for scholarships. The paper uses the Mamdani Inference algorithm with four input variables that constituted the input membership function and the output membership function is made of one output variable. The research concludes that; FLA objectively select the truly qualified brilliant but needy students for scholarships based on degree of membership.
DSR: A Deep Learning Framework Towards Modulation Signal Retrieval

At present, there are many researches on the recognition and classification of modulated signal using deep learning, but few researches involve the retrieval of modulation signal. This paper focuses on the modulation signal retrieval algorithm based on deep learning in the big data environment, and proposes a deep learning framework for modulation signal retrieval (DSR) using convolutional neural network (CNN) and metric learning. The model architecture proposed in this paper can richly and accurately represent the modulation signal characteristic information, and train the deep convolutional neural network with paired input instance. In this paper, we create a modulation signal dataset containing multiple modulation methods, and convert the signal into the contour stellar images. Experiments on this dataset show that the deep learning framework proposed by us can accurately retrieve the signal and obtain promising performance.
Structure Design of Convolutional Neural Network Based on Residual Theory for Face Recognition

Abstract Compared with the traditional face recognition methods, the deep convolution neural network model does not need to manually design complex and time-consuming feature extraction algorithms, but only needs to design an effective neural network model, and then carry out end-to-end, simple and efficient training on a large number of training samples, so as to obtain better classification accuracy. In this paper, based on the original VGG network model, combined with Residual theory, a deep-level residual convolutional neural network structure is designed and implemented, which not only reduces the computing power requirements of hardware computers, but also achieves better recognition results.
Students’ scientific articles writing ability based on lacertilia inventory results in the conservation area Bengkulu University

Abstract Writing scientific articles is a problem for teachers especially in elementary, junior and high school levels. This study aimed to determine student ability to write scientific articles based on the results of lacertilia suborder inventory in the conservation area of Bengkulu University. This study used Research and Development method. Participants in this research was teachers who being students of graduated school of science education on 2018 academic year who enroll biology environment subject. Students in 7 groups has made 7 articles based on lacertilia inventory research. From the scientific articles writing, there are 7 indicators that indicate in good score, including the ability to write title, identity, article components, abstracts, introduction, results and discussion, and conclusions with highest average score as much as 3.42 in ability to write results and discussion, and lowest average score as much as 2,14 in ability to write method.
Emotional Understanding of an Image by Applying High-level Concepts on Image Parts

This paper is the outcome of the research that has been conducted to investigate the relationship of applying High-Level Concepts (HLC) on a complete image and applying it along with its different parts. The implementation of technique includes the images that are divided into subparts followed by focusing on each part individually. To conclude our results, we combined the outcome of our mechanism and compared the results with the previous work. We have applied the object detection HLC on parts of an Image along with entire image. We also compared our results with previous findings and come up with the discovery of our results that have performed better than previously known work.
A Novel Trespassing Detection System Using Deep Networks

Trespassing is the act when someone enters any restricted area without prior permission, and it is considered to be an act of crime. It has been a serious issue for various institutes and organizations to maintain their security and surveillance to prevent the acts of vandalism. Therefore, it is an important attribute to continuously monitor the violators. Various researches have been done so far on trespassing detection, but none of the method allows the specific object of interest to enter in a particular area. The proposed method allows us to choose the object of interest, and also it will differentiate between the types of violator (car, motorcycle, truck, pedestrian, etc.) and sends an alert text message to a designated destination with the image of the violator. Object detection is done using the YOLO algorithm which is then integrated with camera and Raspberry Pi 3 along with cloud integration. The reason for this study is to develop an efficient method that allows/restricts the object of interest in a particular area, to prevent illegal trespassing. Experimental results show that the proposed method detects the trespassers with high accuracy and speed.
Distilling Knowledge in Adversarial Attack

Neural networks show great vulnerability under the threat of adversarial examples. By adding small perturbation to a clean image, neural networks with high classification accuracy can be completely fooled. Transferability which allows adversarial examples to transfer to networks of unknown structures, makes adversarial examples even more harmful. In this paper, we reveal that transferability of adversarial examples is closely related to inter-category information. With that in mind, we propose a simple technique to improve the transferability of adversarial examples. This method makes use of the ideology called knowledge distillation to obtain more information from known structure and datasets. It can be integrated into any gradient methods to generate adversarial examples. We carry out experiments on single, multiple and serialized multiple model scenarios. The results show that knowledge distillation is effective in extracting adversarial information for enhancing transferability.
Fine-Grained Generation for Zero-Shot Learning

Although zero-shot learning (ZSL) has achieved success in recognizing unseen classes images, most previous studies focus on feature projection from one domain to another, neglecting the domain shift problem caused by differences in classes. In this paper, we propose two novel methods in terms of attributes and features for ZSL problem. The first is Sample-Level Attribute Generation via GAN (SLA-GAN) that generates sample-level attributes. Considering the influence of lighting, environment, weather and other factors, the description based on class-level attributes will be biased. SLA-GAN generates sample-level attributes to describe each sample. Specially, we train a conditional Wasserstein GAN in which the generator synthesizes sample-level attributes from features and the discriminator distinguishes the sample-level from class-level attributes. Apart from that, we propose a novel method Fine-Grained Sample Generation via GAN (FGS-GAN) which can generate the unseen samples from the most similar samples of seen classes. Finally, we replace the class-level attributes with sample-level attributes in FGS-GAN and analyze the effects of sample-level attributes on synthesized features. Experiments on five benchmark data set show that our models outperform state-of-the-art methods in ZSL.
The Areas and Requirements of Competitiveness Advantage in Jordanian Universities

The present study aimed to explore the areas, which Jordanian universities should invest in to achieve a competitive advantage. It aimed to identify the requirements of achieving a competitive advantage by Jordanian universities based on the strategic plans of these universities. A qualitative approach was adopted. Interviews were conducted with 10 mangers of the top management level in private and public universities in Jordan. The researchers found that (research, innovation, finance, academic reputation, student attraction, and technology) are the most prominent areas that must be invested in to achieve a competitive advantage. The requirements of achieving competitiveness advantage include: offering programs of modern majors. They include: e-curricula, providing e-learning survives, supporting scientific research, developing the infrastructure, and attracting highly qualified professionals. They include: enjoying financial independence, achieving financial sustainability, and getting high rank on global universities ranking. Meeting such requirements shall enable Jordanian higher education institutions to achieve a competitive advantage. The present study is beneficial for decision makers and investors in the Jordanian higher education institutions. It enables those decision makers and investors to provide more attention to the aforementioned areas and requirements in order to achieve a competitive advantage. Hence, it contributes to developing national economy.
Integrating social media and warranty data for fault identification in the cyber ecosystem: A cloud-based collaborative framework

Fault identification during warranty is quite complex because of sophisticated product design and distributed manufacturing. Various supply chain facilities located at diverse geographical locations are usually utilised to manufacture a particular product. If a fault occurs in one component of a product, it may be linked with other components which are procured and manufactured by other segments of the globally distributed supply chain. Hence, in this multifaceted scenario, the information systems have to be integrated and responsive enough to respond proactively in sharing data from heterogeneous systems across the supply chain in the cyber ecosystem. To achieve this goal, in this chapter, we integrate warranty data from multiple datasets. Initially, social media dataset is used. Consumers increasingly engage in information sharing on weblogs, forums, Facebook, and Twitter, among others. This valuable information is mostly untapped by the automotive manufacturers. To explore the large amount of hidden fault-related data, we used data analytics. Then, we develop a cloud-based collaborative framework to manage the warranty data from other supply chain information systems, namely, design, manufacture and service. The framework provides integration and access of warranty data from multiple datasets of supply chain. The proposed ‘autonomous smart agents’ interaction assists to establish real-time warranty data exchange across the supply chain. The combined data can then be used for detailed expert analysis by fault learning and rectification agent. The execution of the framework is demonstrated using an illustrative execution process. Our contributions are clearly detailed, and some important managerial insights are provided for warranty management in globally distributed supply chain.
L'enunciato non verbale tra contenuto informativo ed enunciazione

The term enunciation derives from the Latin word Enuntiatio and indicates both the act of uttering something and the words that are used to do so. The origin of the term is very varied; it ranges from a logical position that has seen it equated with the concept of proposition to a more strictly linguistic – interpretative context. The objective of our analysis does not concern logic nor the subsequent linguistic conversion, but refers to the notion of act which, captured in the context of the semiotics of communication, we consider fundamental to try to find the enunciation even in non-verbal occasions.
Putting Gradual Types to Work

Abstract In this paper, we describe our experience incorporating gradual types in a statically typed functional language with Hindley-Milner style type inference. Where most gradually typed systems aim to improve static checking in a dynamically typed language, we approach it from the opposite perspective and promote dynamic checking in a statically typed language. Our approach provides a glimpse into how languages like SML and OCaml might handle gradual typing. We discuss our implementation and challenges faced—specifically how gradual typing rules apply to our representation of composite and recursive types. We review the various implementations that add dynamic typing to a statically typed language in order to highlight the different ways of mixing static and dynamic typing and examine possible inspirations while maintaining the gradual nature of our type system. This paper also discusses our motivation for adding gradual types to our language, and the practical benefits of doing so in our industrial setting.
A Tool for Annotating Homographies from Hockey Broadcast Video

In order to develop solutions for automatic ice rink localization from broadcast video, a dataset with ground truth homographies is required. Hockey broadcast video does not tend to provide camera parameters for each frame, which means that they must be gathered manually. A novel tool for collecting ground truth transforms through point correspondences between each frame and an overhead view of the ice rink is presented in this paper. Through collaboration with the users of the tool, we have added features to improve accuracy and efficiency, especially in frames with few lines on the playing surface visible. A dataset of 4,262 frames has been collected, which will be used for research into automatic camera calibration techniques.