Reviewerly/exHarmony huggingface dataset:
Double Structured Nuclear Norm-Based Matrix Decomposition for Saliency Detection

Saliency detection aims at identifying the most important and informative area in a scene. Recently low rank matrix recovery (LR) theory becomes an effective tool for saliency detection. The existing LR-based methods all work under the popular low rank and sparsity pursuit framework and perform well for images with small or homogeneous objects. However, if the image is with heterogeneous objects, the sparsity property of the object cannot be guaranteed. Moreover, as a useful tool for depicting a spatially structured matrix variable, nuclear norm (corresponding to the low rank) considers only the global structure but overlooks the inherent local structure of the data. We address these problems by proposing a double structured nuclear norm-based matrix decomposition (DSNMD) model for saliency detection. In the model, a tree-structured nuclear (TSN) norm is firstly introduced to constraint both the background and foreground regions. We also empirically demonstrate that TSN norm provides stronger performance at capturing the underlying structural information of the image regions including global structure, local structure, and internal structure of each node of the tree, and it deservedly inherits the advantages of both nuclear norm and sparsity-related norms (e.g., $\ell _{1}$ -norm, group sparsity norm) for saliency detection. Comprehensive evaluations on six benchmark datasets indicate that our method universally surpasses state-of-the-art unsupervised methods and performs favorably against supervised approaches.
COMPASS: A Data-Driven Blockchain Evaluation Framwework

Blockchains or DLT (distributed Ledger Technology) receive significant attention recently. However, most of blockchain systems disclosed technical details in whitepapers only, but these whitepapers usually do not provide sufficient materials for a comprehensive evaluation. They do not provide data and do not compare with other systems. This paper proposes a data-driven model to evaluate blockchains from three dimensions: technical, team competence, and community activities with data automatically collected by crawling. As most of data are collected in an automated manner, evaluation and comparison analysis can be done with other blockchain systems in the database. We have developed this system COMPASS and evaluated more than 200 blockchains with highest market valuation. The results are available at Tiande website since December 2018 with frequent updates. The evaluation scores are calculated by combining objective data analysis with a weighting scheme, providing valuable reference for developers, scientists, and investors. The data show that most about 200 key developers dominate the industry and many systems are similar to each other
Hybrid System Identification by Incremental Fuzzy C-regression Clustering

In this paper, an approach to the identification of hybrid systems is discussed. It is based on the incremental fuzzy C-regression clustering. Based on the distance between the current measurement and the hyperplane of the local model, local models are updated. If necessary, a new local model is constructed. To increase the robustness and prevent false local models, the data are kept in the buffer temporarily. The approach produces good results as shown in two examples. The first example can be modelled as a piecewise affine dynamical system and the second one as a switched dynamical system.
Multifractal characterization of the Coniacian–Santonian OAE3 in lacustrine and marine deposits based on spectral gamma ray logs

Abstract Limited to the Atlantic and its surrounding basins, the expression of the Coniacian–Santonian oceanic anoxic event (OAE3) was discovered in the non-marine Cretaceous Songliao Basin, Eastern Asia not long ago. In this study, based on spectral gamma ray logs data recorded in three basins, the self-similarity of the OAE3 was studied through the analysis of the scaling properties of thorium–potassium and thorium–uranium distributions both in marine and terrestrial environments using the multifractal detrending fluctuation analysis. The results indicate that, in both marine and terrestrial systems, the OAE3 intervals are characterized by their multifractal nature due to long-range correlation. However, the multifractal features of the studied OAE3 intervals are different in the three basins, although some common trends were observed. By comparing the degree of multifractality of the OAE3 deposits with the clay minerals and the redox conditions, it appears that the changes of the multifractal features are controlled by local changes such as clay mineralogy and redox conditions in both milieus under different sedimentation patterns. At all sites, the left side shortened spectrum of the thorium–potassium distribution suggests the presence of local fluctuations with minor amplitudes during the OAE3. Furthermore, the shortened singularity spectrum of the thorium–uranium distribution reflects the existence of small-scale fluctuations with large amplitudes at marine sites while in the non-marine Songliao Basin, the thorium–uranium distribution suggests the presence of local fluctuations with small amplitudes during the OAE3. Therefore, a more local behavior of the event is considered although the regional character is not neglected.
Geometric Case Based Reasoning for Stock Market Prediction

Case based reasoning is a knowledge discovery technique that uses similar past problems to solve current new problems. It has been applied to many tasks, including the prediction of temporal variables as well as learning techniques such as neural networks, genetic algorithms, decision trees, etc. This paper presents a geometric criterion for selecting similar cases that serve as an exemplar for the target. The proposed technique, called geometric Case Based Reasoning, uses a shape distance method that uses the number of sign changes of features for the target case, especially when extracting nearest neighbors. Thus, this method overcomes the limitation of conventional case-based reasoning in that it uses Euclidean distance and does not consider how nearest neighbors are similar to the target case in terms of changes between previous and current features in a time series. These concepts are investigated against the backdrop of a practical application involving the prediction of a stock market index. The results show that the proposed technique is significantly better than the random walk model at p &lt; 0.01. However, it was not significantly better than the conventional CBR model in the hit rate measure and did not surpass the conventional CBR in the mean absolute percentage error.
Marketing of Library and Information Services in the Digital Age: Challenges, Strategies and Implications

This paper discussed effective marketing of library and information services in the digital age. It focused on librarians marketing themselves and library services which require the application of ICT through web 2.0 tools; these include WhatsApp, email, face book, You-tube, Twitter, LinkedIn, Telephone, etc to reach out to their clientele in order to satisfy their information needs. The study adopted a conceptual review based on previous research. It x-rays the need to market LIS to increase usage, whether onsite or remote for sustainable development. There were some challenges such as ignorance among librarians, lack of ICT infrastructure, lack of awareness of the public in their right to information, lack of fund and lack of training and retraining of library staff. However, some strategies, implications and recommendations were proffered. It was concluded that effective marketing be used to promote access to library and information services as a panacea towards sustainable Nigerian libraries in the digital age.&#x0D; Keywords: Marketingt, Library, Information Information services, Digital Agev
The Effect of Using STEAM Approach on Developing Computational Thinking Skills among High School Students in Jordan

The growing demand for combining digital technology with learning practices has surpassed the use of technology or learning how to use it into the process of enhancing learners’ intellectual levels and scaffolding their understanding by focusing on skills that include thought processes gathered in what is called computational thinking. On the other hand, educational challenges promote the search for new instructional tools and approaches. Consequently, learning shall be extended by superimposing science, technology, engineering, arts, and mathematics (STEAM) approach in the instructional practices. The aim of this paper is to show how STEAM approach can develop the computational thinking among high school learners in Jordan. The main skills of computational thinking included: algorithmic thinking, abstraction, decomposition, and generalization. The sample of this study involved 32 high school students in a private school in Amman. The experimental group studied geography skills in a STEAM approach that included the use of online resources such as LightBot maze and the Ordnance Survey maps (OS) website. The control group studied the same content but through conventional method. Findings showed a significant development in the computational thinking especially in algorithmic thinking and abstraction. Thus a STEAM approach learning environment is one of the effective methods of teaching that improved computational thinking.
Blockverse: A Cloud Blockchain-based Platform for Tracking in Affiliate Systems

Affiliate systems are a crucial piece of today's online advertising.In affiliate systems, web traffic is directed from certain sites displaying ads to the websites of those company whose products or services are advertised.The way in which these ads are monetized is diverse and can respond to different models.In many cases, affiliates establish a cost based on impressions (displays of the ad) or on clicks.However, more intricate models are becoming widespread, such as the cost per action, where the affiliate incomes are due to the users performing certain actions in the target website.In particular, in the world of iGaming, it is frequent that affiliates charges are based on registrations, deposits or money lost on bets.In this scenario, Blockverse is a tool whose objective is to record transactions occurring in affiliate systems at large scale, using a permissioned blockchain implemented atop state-of-the-art cloud technology.Additionally, the system will be able to execute smart deals that generate income for affiliates based on the agreed conditions, and to provide real-time analytics in the context of the affiliate system.
Features of genetic algorithm for plain text encryption

The data communication has been growing in present day. Therefore, the data encryption became very essential in secured data transmission and storage and protecting data contents from intruder and unauthorized persons. In this paper, a fast technique for text encryption depending on genetic algorithm is presented. The encryption approach is achieved by the genetic operators Crossover and mutation. The encryption proposal technique based on dividing the plain text characters into pairs, and applying the crossover operation between them, followed by the mutation operation to get the encrypted text. The experimental results show that the proposal provides an important improvement in encryption rate with comparatively high-speed Processing.
Towards a Microservices-Based Product Line with Multi-Objective Evolutionary Algorithms

Microservices are small and independently deployable services. They can be developed on different platforms and communicate via lightweight protocols, what makes them highly interoperable. The interoperability between microservices, as well as their reuse and customization needs make this kind of systems adequate to constitute a Software Product Line. However, there is no automatic approach to support the designing of Microservices-Based Product Lines (MBPLs). To move towards the development of MBPLs, this work presents an approach, named MOEA4MBPL, to extract Feature Models (FMs) from a set of microservices-based systems. These FMs intent to leverage interoperability, enabling the practitioners to reason about reuse and/or customization of functionalities. The proposed approach is based on multi-objective evolutionary algorithms, optimizing three objectives, namely precision and recall of products denoted by an FM, and conformance with existing dependencies between microservices. MOEA4MBPL was evaluated with six microservices-based systems, using the algorithms NSGA-II and SPEA2. Our approach was capable of finding FMs with good trade-off values of precision and recall, satisfying all dependencies among the microservices. SPEA2 found better fronts of solutions than NSGA-II, but the latter always executed faster and could find single solutions closer to an ideal solution than the former.
Investigation of steam generation performance on conical cavity receiver by different geometric concentration ratios for fresnel lens solar concentrator

The paper discusses the comparison of the performance of steam generators in large and small receivers, using a Fresnel lens concentrator.The goal is to get the best value from the efficiency of a steam generator between large and small receivers, with the following task details:a) design a conical cavity receiver that has the most efficient geometric concentration ratio;b) compare the thermal efficiency of conical cavity receivers that have different geometric concentration ratios;c) analyze the potential of the steam energy from the conical cavity receiver produced by the PMMA Fresnel lens concentrator based on the amount of average radiation directly at the study site.The study uses an experimental field research method, which is conducted outdoors. This research was conducted in the energy conversion laboratory, Universitas Brawijaya (Latitude: 7.9553° S and Longitude: 112.6145° W), in September 2019. The PMMA Fresnel lens is used for the solar thermal concentrators. The two receivers with a conical cavity that were compared were made of copper with a volume of 2 litres and 0.25 litres, respectively. They are coated with a glass wool insulator with a thickness of 10 mm. Direct Normal Irradiance (Ib) is measured by a solar power meter. The cup anemometer is used to measure wind speed (vw) around the receiver. Digi-Sense 12 Channel Scanning Benchtop Thermometer connected to the laptop is used to measure temperature. The positions of the four K-type thermocouples are as follows:1) ambient temperature (Ta);2) focal point temperature (Tf);3) receiver wall temperature (Tr);4) steam/water temperature (Tw).A pressure gauge to measure the pressure of the steam that goes to the measuring cup was used. After saturation pressure (Psat) has been reached, it will be known from the condensation process through the copper coil, which functions as a condenser.From the results of the study, the large receivers have specifications CRg=8 and a volume of 2 litres of water. Whereas, the small receiver is CRg=30 and 0.25 L. The large receivers can produce steam latent heat energy Qs=1.37 MJ per cycle with useful efficiency (utilization efficiency) ηTh=31.81 %. Whereas the small receiver can produce steam energy, Qs=579.17 kJ per cycle with useful efficiency, ηTh=33.31 %. Hence, from the two types of conical cavity receivers, small receivers have that higher effectiveness than large ones can be recommended
Incorporating External Knowledge into Unsupervised Graph Model for Document Summarization

Supervised neural network models have achieved outstanding performance in the document summarization task in recent years. However, it is hard to get enough labeled training data with a high quality for these models to generate different types of summaries in reality. In this work, we mainly focus on improving the performance of the popular unsupervised Textrank algorithm that requires no labeled training data for extractive summarization. We first modify the original edge weight of Textrank to take the relative position of sentences into account, and then combine the output of the improved Textrank with K-means clustering to improve the diversity of generated summaries. To further improve the performance of our model, we innovatively incorporate external knowledge from open-source knowledge graphs into our model by entity linking. We use the knowledge graph sentence embedding and the tf-idf embedding as the input of our improved Textrank, and get the final score for each sentence by linear combination. Evaluations on the New York Times data set show the effectiveness of our knowledge-enhanced approach. The proposed model outperforms other popular unsupervised models significantly.
Factual Question Generation for the Portuguese Language

Artificial Intelligence (AI) has seen numerous applications in the area of Education. Through the use of educational technologies such as Intelligent Tutoring Systems (ITS), learning possibilities have increased significantly. One of the main challenges for the widespread use of ITS is the ability to automatically generate questions. Bearing in mind that the act of questioning has been shown to improve the students learning outcomes, Automatic Question Generation (AQG) has proven to be one of the most important applications for optimizing this process. We present a tool for generating factual questions in Portuguese by proposing three distinct approaches. The first one performs a syntax-based analysis of a given text by using the information obtained from Part-of-speech tagging (PoS) and Named Entity Recognition (NER). The second approach carries out a semantic analysis of the sentences, through Semantic Role Labeling (SRL). The last method extracts the inherent dependencies within sentences using Dependency Parsing. All of these methods are possible thanks to Natural Language Processing (NLP) techniques. For evaluation, we have elaborated a pilot test that was answered by Portuguese teachers. The results verify the potential of these different approaches, opening up the possibility to use them in a teaching environment.
An Explainable Artificial Intelligence Methodology for Hard Disk Fault Prediction

Failure rates of Hard Disk Drives (HDDs) are high and often due to a variety of different conditions. Thus, there is increasing demand for technologies dedicated to anticipating possible causes of failure, so to allow for preventive maintenance operations. In this paper, we propose a framework to predict HDD health status according to a long short-term memory (LSTM) model. We also employ eXplainable Artificial Intelligence (XAI) tools, to provide effective explanations of the model decisions, thus making the final results more useful to human decision-making processes. We extensively evaluate our approach on standard data-sets, proving its feasibility for real world applications.
GGF

Syntax errors combined with obscure error messages generated by compilers usually annoy programmers and cause them to waste a lot of time on locating errors. The existing models do not utilize the structure in the code and just treat the code as token sequences. It causes low accuracy and poor performance on this task. In this paper, we propose a novel deep supervised learning model, called Graph-based Grammar Fix(GGF), to help programmers locate and fix the syntax errors. GGF treats the code as a mixture of the token sequences and graphs. The graphs build upon the Abstract Syntax Tree (AST) structure information. GGF encodes an erroneous code with its sub-AST structure, predicts the error position using pointer network and generates the right token. We utilized the DeepFix dataset which contains 46500 correct C programs and 6975 programs with errors written by students taking an introductory programming course. GGF is trained with the correct programs from the DeepFix dataset with intentionally injected syntax errors. After training, GGF could fix 4054 (58.12%) of the erroneous code, while the existing state of the art tool DeepFix fixes 1365 (19.57%) of the erroneous code.
Tonal Contour Generation for Isarn Speech Synthesis Using Deep Learning and Sampling-Based F0 Representation

The modeling of fundamental frequency (F0) in speech synthesis is a critical factor affecting the intelligibility and naturalness of synthesized speech. In this paper, we focus on improving the modeling of F0 for Isarn speech synthesis. We propose the F0 model for this based on a recurrent neural network (RNN). Sampled values of F0 are used at the syllable level of continuous Isarn speech combined with their dynamic features to represent supra-segmental properties of the F0 contour. Different architectures of the deep RNNs and different combinations of linguistic features are analyzed to obtain conditions for the best performance. To assess the proposed method, we compared it with several RNN-based baselines. The results of objective and subjective tests indicate that the proposed model significantly outperformed the baseline RNN model that predicts values of F0 at the frame level, and the baseline RNN model that represents the F0 contours of syllables by using discrete cosine transform.
A Sample-Efficient Algorithm for Episodic Finite-Horizon MDP with Constraints

Constrained Markov decision processes (CMDPs) formalize sequential decision-making problems whose objective is to minimize a cost function while satisfying constraints on various cost functions. In this paper, we consider the setting of episodic fixed-horizon CMDPs. We propose an online algorithm which leverages the linear programming formulation of repeated optimistic planning for finite-horizon CMDP to provide a probably approximately correctness (PAC) guarantee on the number of episodes needed to ensure a near optimal policy, i.e., with resulting objective value close to that of the optimal value and satisfying the constraints within low tolerance, with high probability. The number of episodes needed is shown to have linear dependence on the sizes of the state and action spaces and quadratic dependence on the time horizon and an upper bound on the number of possible successor states for a state-action pair. Therefore, if the upper bound on the number of possible successor states is much smaller than the size of the state space, the number of needed episodes becomes linear in the sizes of the state and action spaces and quadratic in the time horizon.