Reviewerly/exHarmony huggingface dataset:
An Efficient Mask Generation Method for Moving Object Detection in Atmospheric Imaging

Atmospheric turbulence causes non-uniform geometric deformation of images due to random fluctuation of refractive index throughout the imaging path. The identification of moving objects in a turbulent medium is a fundamental preprocessing step in computer vision. In this paper, a new mask generation process is proposed that removes misdetection due to turbulence in the medium. In this regard, the first step is to estimate the background frame from the video and determine the difference images from the input frames with respect to the background frame. Then three different thresholding techniques: Otsu thresholding, maximum entropy based thresholding, and standard deviation and mean based thresholding, are applied on these difference images to generate three different masks. Finally, a refined mask is generated using these three masks which detects the moving objects from the degraded video. In simulation experiment, qualitative comparisons are conducted to evaluate the performance of the proposed method with a previous one and higher accuracy is obtained.
Pricing of Mining ASIC and Its Implication to the High Volatility of Cryptocurrency Prices

Mining application-specific integrated circuit (ASIC) is an essential facility for mining in Proof-of-Work (PoW) based blockchain systems. This paper regards ASIC as a financial asset and proposes a theoretical estimate of the pricing of ASIC. We show that the payouts from ASIC can be replicated by an integral of European call options. Hence, the value of ASIC is increasing in the volatility of the cryptocurrency price. Our results imply that miners may prefer to keep the high volatility to maintain the value of their ASIC; thus, they may refuse the proposals and innovations for stabilizing the price. In this sense, the high volatility of the PoW-based cryptocurrency price might be intrinsic.
Design of Blockchain Lottery for Thai Government

This research represents a model of buying and selling lotteries system on Blockchain by using Ethereum network to determines the conditions for the purchasing of the lottery and reward the prize winner. Our system uses Ethereum coins to buy and reward the prize for convenience in term of speed and also reduce the problems which is unable to be controlled by the government. For example, lottery agents (intermediaries) may sell the lottery at overprice, and reward the winners at a lower than appropriate rate. Additionally, the most important problem is the verification of the real owner of the lottery.
The use of chemistry e-book developed by 4S TMD

This study investigates how upper high school teachers' and students' views on the use of chemistry e-book developed by 4S TMD. In academia, e-book is progressively playing a central role in teaching, learning and research, as it helps to increase access timely and diverse scholarly material. In line with this purpose, the present study aimed at determining the views of teachers and students regarding e-book developed by 4S TMD. The participants of the study were three teachers and nine students. In this study, qualitative methods were used by semi-structured interview. The results revealed that the use of e-book developed by 4S TMD provides positive results in learning activities both in the views of teachers and students. The use of e-book can increase the productivity of learning, help teachers to streamline the efficiency of learning time, easy to carry, information is more concrete and allows students to be independent.
The Audio Paper: From Situated Practices to Affective Sound Encounters

The audio paper is an academic publication format that seeks to bring together academic argumentation and analysis with media, technology and situated, sensory experience. This article is a presentation of our conception of what an audio paper can be. As it follows, this conception is depended on and developed within a specific context, both when it comes to how the idea was conceived, how the audio paper takes form in practice and how it gradually is being accepted as an academic publication format. Meanwhile, we bring forth some of the publications we have experienced so far, we also argue, that the audio paper is not only a final and fixed format, but is always to be perceived situated in various modes of production.
Divisive and Separate Cluster Structures

This Chapter is about dividing a dataset or its subset in two parts. If both parts are to be clusters, this is referred to as divisive clustering. If just one part is to be a cluster, this will be referred to as separative clustering. Iterative application of divisive clustering builds a binary hierarchy of which we will be interested at a partition of the dataset. Iterative application of separative clustering builds a set of clusters, possibly overlapping. The first three sections introduce three different approaches in divisive clustering: Ward clustering, Spectral clustering and Single link clustering. Ward clustering is an extension of K-means clustering dominated by the so-called Ward distance between clusters; also, this is a natural niche for conceptual clustering in which every division is made over a single feature to attain immediate interpretability of the hierarchy branches and clusters. Spectral clustering gained popularity with the so-called Normalized Cut approach to divisive clustering. A relaxation of this combinatorial problem appears to be equivalent to optimizing the Rayleigh quotient for a Laplacian transformation of the similarity matrix under consideration. In fact, other approaches under consideration, such as uniform clustering and semi-average clustering, also may be treated within the spectral approach. Single link clustering formalizes the nearest neighbor approach and is much related to graph-theoretic concepts: components and maximum spanning trees. One may think of divisive clustering as a process for building a binary hierarchy, which goes "top-down" in contrast to agglomerative clustering (in Sect. 4.6 ), which builds a binary hierarchy "bottom-up". Two remaining sections describe two separative clustering approaches as extensions of popular approaches to the case. One tries to find a cluster with maximum inner summary similarity at a similarity matrix preprocessed according to the uniform and modularity approaches considered in Sect. 4.6.3 The other applies the encoder-decoder least-squares approach to modeling data by a one-cluster structure. It appears, criteria emerging within the latter approach are much akin to those described earlier, the summary and semi-average similarities, although parameters now can be adjusted according to the least-squares approach. This applies to a distinct direction, the so-called additive clustering approach, which can be usefully applied to the analysis of similarity data.
Salient object detection via reciprocal function filter

Salient object detection (SOD) has been attracting a lot of interest, and recently many computational models have been developed. Here, the authors formulate a SOD model, in which saliency map is computed as a combination of the colour, its distribution-based saliency and orientation saliency. Similar to traditional SODs, the proposed method is based on super-pixel segmentation and super-pixel utilises both colour and its distribution-based saliency to generate a coarse saliency map. However, distinct from traditional SODs, authors further use orientation contrast to optimise the coarse saliency map to obtain an improved saliency map. Authors’ contributions are twofold. First, the authors combine colour uniqueness and its distribution with local orientation information (LOI) used in Itti's model to effectively improve profiles of salient regions. Second, a reciprocal function is defined to substitute the Gabor function used in LOI, and the authors have proved that the substitution could detect relatively homogeneous and uniform regions at the boundary of salient object, whereas it is what the traditional models lack. Authors’ approach significantly outperforms state-of-the-art methods on four benchmark datasets, while the authors demonstrate that the proposed method runs as fast as most existing algorithms.
Complementary Siamese Networks for Robust Visual Tracking

In this paper, we propose the novel complementary Siamese networks (CoSNet) for visual tracking by exploiting complementary global and local representations to learn a matching function. In specific, the proposed CoSNet is two-fold: a global Siamese network (GSNet) and a local Siamese network (LSNet). The GSNet aims to match the target with candidates using holistic representation. By contrast, the LSNet explores partial object representation for matching. Instead of simply decomposing the object into regular patches in LSNet, we propose a novel attentional local part network, which automatically generates salient object parts for local representation and adaptively weights each part according to its importance in matching. In CoSNet, the GSNet and LSNet are jointly trained in an end-to-end manner. By coupling two complementary Siamese networks, our CoSNet learns a robust matching function which can effectively handle various appearance changes in visual tracking. Extensive experiments on a large-scale dataset with 100 sequences show that CoSNet outperforms other state-of-the-art trackers.
Evolutionary Subspace Clustering: Discovering Structure in Self-expressive Time-series Data

An evolutionary self-expressive model for clustering a collection of evolving data points that lie on a union of low-dimensional evolving subspaces is proposed. A parsimonious representation of data points at each time step is learned via a non-convex optimization framework that exploits the self-expressiveness property of the evolving data while taking into account data representation from the preceding time step. The resulting scheme adaptively learns an innovation matrix that captures changes in self-representation of data in consecutive time steps as well as a smoothing parameter reflective of the rate of data evolution. Extensive experiments demonstrate superiority of the proposed framework overs state-of-the-art static subspace clustering algorithms and existing evolutionary clustering schemes.
Improving Error Correction Stage and Expanding the Final Key using Dynamic Linear-feedback Shift Register in Sarg04 Protocol

The mechanism of quantum mechanics is considered as the principal method of quantum key distribution for transmitting a cryptographic key between users in unconditionally secure communication. In this paper, simulation and enhancement of the performance of SARG04 protocol have been done in terms of error correction stage using multiparity rather than single parity. In addition, the suitable length of subblock was selected depending on the values of quantum bit error rate and round number. Dynamic linear-feedback shift register circuits have been used to extend the final key. These circuits are dynamically generated by the final key. A unique linear circuit is created for each generated key.
Measurement of skin wrinkles differences using novel optical skin imaging system of digital fringes profilometry

Wrinkle is one of skin properties used to indicate aging or environment damage in human body. This feature can be assessed through contact and non-contact methods. Dermatology industry often utilizes tools such as dermatoscopy, however it lacks the ability to yield wrinkle's depth and requires contact with skin, which are not preferable because possibility of deforming skin's natural texture. These lead to the development of digital fringe profilometry which utilize phase-shifted fringes being projected to the skin. This method can satisfy both wrinkle's depth and non-invasive measurement. A novel system of digital fringe profilometry technique is presented, which used newly improved camera technology preferred by the dermatological industry and a projector, both are commercially available, with new configuration suitable for skin measurement. The system is configured with certain height and angle to respectively project fringes and obtain images. Images will later be processed using phase shifting interferometry (PSI) and global phase unwrapping. The processing system will produce wrapped phase and absolute phase value. In order to prove system's feasibility, experiment was done by sampling two areas from three fingers. Quantitative analysis was done through statistical paired T-Test to see the system ability to acknowledge significant difference of two groups' skin topographic condition. Based on the T-Test, P value that was obtained is 0.003915, which concluded that the system is capable of quantifying two different skin topographic conditions and provide significantly different results.
Applying Item Response Theory in Adaptive Tutoring Systems for Thai Language Learners

Traditional computerized tutoring systems typically suffer from issues of mismatched content, where the difficulty, sequence and amount of content is not matched to the pace of the learner. Adaptive algorithms can overcome these problems by using data collected by the system to reactively deliver the most appropriate content to the learner. Item response theory (IRT) is a technique from computer aided assessment for estimating student ability and predicting the outcome of student responses. While it is used extensively for assessment, the application of IRT to tutoring systems is relatively new. The contribution of this paper is to propose an algorithm for applying IRT to tutoring systems. The algorithm is validated by considering its application on the practical problem of supporting children with language learning difficulties as part of the “KidArn” project.
Cognitive clustering—How general?

Do “cognitive types”—groups of individuals with distinctive patterns of cognitive strengths and weaknesses—exist? The Texas Adoption Project (Horn & Loehlin, 2010) included 965 individuals with intelligence test subtest scores. Clusters formed from these individuals were compared with clusters found by others using the same intelligence test, and between subsamples in the present study—two random subsamples, two methods of clustering, two intelligence tests, the two sexes, and two generations. Moderate consistencies were found for the majority of the comparisons within the present sample. There was less cross-study agreement.
Robust Face Detection Using Hybrid Skin Color Matching under Different Illuminations

Recently, human skin color property incorporation is considered a way to improve face detection. There are different skin color matching techniques and each individual one is found suitable for a specific illumination condition. In this study, a robust face detection is developed considering a hybrid skin color detection method which combines complementary strength of different color detection methods. The proposed hybrid skin color detection method is tested with the most popular Haar Feature Based Face Detection (HFFD) method and identified performance on a large number of images from a benchmark face data set. From the experimental results and analysis, it is revealed that proposed face detection system improves the performance of HFFD significantly.
Enhance Active Learning in Higher Education by Using Mobile Learning

The quality of teaching in higher education is central for preparing students for their future academic and work demands. This includes the integration of information and communication technologies, since it has the potential to enhance teaching and learning processes and is thought improve both the understanding of content knowledge and the acquisition of relevant 21st-century skills. In particular, the use of mobile devices enables active learning at anytime and anywhere. This chapter describes a project of faculty development for integrating mobile learning (ML) into academic studies to accomplish pedagogical goals, as implemented at Beit Berl College, Israel. The chapter presents fundamental guidelines and principles of integrating ML into academic practices; Nine ML workshops that were developed and implemented; A detailed description of one workshop including the learning materials used; and Evaluation of the implementation. A summary of lessons learned from the implementation experience and further recommendations are presented.
Robust Subspace Clustering by Learning an Optimal Structured Bipartite Graph via Low-rank Representation

This paper addresses the subspace clustering problem based on low-rank representation. Combining with the idea of co-clustering, we proposed to learn an optimal structural bipartite graph. It's different with other classical subspace clustering methods which need spectral clustering as post-processing on the constructed graph to get the final result, our method can directly learn a structural graph with k connected components so that the different clusters are obtained easily. Furthermore, we introduce a regularization term of error matrix to our model which helps the proposed algorithm to be more effective to learn an optimal graph under the circumstances of various noise. Experimental results both on synthetic and benchmark datasets are presented to show the effectiveness and robustness of our model.
Redewiedergabe – Schritte zur automatischen Erkennung

Abstract This contribution presents a quantitative approach to speech, thought and writing representation (ST&amp;WR) and steps towards its automatic detection. Automatic detection is necessary for studying ST&amp;WR in a large number of texts and thus identifying developments in form and usage over time and in different types of texts. The contribution summarizes results of a pilot study: First, it describes the manual annotation of a corpus of short narrative texts in relation to linguistic descriptions of ST&amp;WR. Then, two different techniques of automatic detection – a rule-based and a machine learning approach – are described and compared. Evaluation of the results shows success with automatic detection, especially for direct and indirect ST&amp;WR.
Signaling versus distinguishing different preparations of same pure quantum state

Different ensembles of quantum states can have the same average nonpure state. Distinguishing between such constructions, via different mixing procedures of the same nonpure quantum state, is known to entail signaling. In parallel, different superpositions of pure quantum states can lead to the same pure state. We show that the possibility of distinguishing between such preparations, via different interferometric setups leading to the same pure quantum state, also implies signaling. The implication holds irrespective of whether the distinguishing procedure is deterministic or probabilistic.
Devanagari Character Classification Using Capsule Network

Handwritten character recognition has been a widely explored topic in computer vision since its inception and exceptional works have been proposed in this area. Evolving over the primordial works in this domain, Deep Convolutional neural networks provide an epitome solution to classification of characters but still lack orientation and spatial relationships among entities. Convolutional neural networks fail in capturing the pose, orientation and view of the images due to the inefficiency of max pooling layer. These limitations are overcome by a novel approach: Capsule Networks. Capsule Networks are made up of layers of capsules representing the instantiation parameters of entities by using the dynamic routing and route by agreement algorithms.
VRS-DB: Computation Exploration on Encrypted Database

The security problems occur in the cloud data storage environment under the DBaaS framework, in which the DO (data-owner) publicizes information over the cloud data storage SP(service-provider). For securing the confidentiality, privacy, integrity, atomicity, isolation of the information, DO just change the form of the data by encrypting it to another secure form, and then DO upload that secured data over the SP. Present encryption plans, in any case, are just halfway homomorphic as in each of them was intended to enable one particular sort of calculation to be done on encoded information. These current plans can’t be incorporated to answer genuine down to earth questions that include operations of various types. For that we proposed a new scheme VRS-DB, which offers protected query computation and exploration of encrypted database in a cloud computing. In particular, we address security problems in a cloud data storage framework, where scrambled appropriated information is queried under an unidentified sharing conventions. The framework gives an arrangement of rudimentary information interoperable administrators on scrambled information, which permits an extensive variety of SQL questions to be handled by the SP on encrypted data. This demo exhibits a VRS-DB model.
Towards Automating the Creation of OBDA Systems

In the paper, we consider automating the process of constructing and debugging ontology-based data access systems by using the information stored in external ontology resources. In particular, for the constructing stage, we propose to use a tool that suggests concepts from external repositories for those tables from a relational database, which haven’t been matched with concepts from a domain ontology by ontology alignment techniques. For the debugging stage, we show how a tool for navigation in a collection of ontologies can be used to simplify SPARQL query writing by generating lacking parts of query thus enhancing ontology engineer’s productivity. The proposed approaches have been implemented and used in our system, named Reply, which transforms traditional information systems into intelligent systems with a natural language query interface.
Visualisasi Data dan Prediksi Perkembangan Impor Barang di Indonesia Tahun 2010 – 2014

Import is one of the trade process conducted between two countries, the import process is done by a country to meet the needs of the country that is not meet by the ability of the country. Indonesia also imports many items such as fuel or rice. Therefore, to reduce the level of imports, we need to know which sector has the most import rate and whether the level of imports in that sector is increasing or not, so Indonesia can save the country's foreign exchange reserves.Import data Indonesia displayed in the form of visualization of data created using the method of Visual Data Mining (VDM) and using Power BI as a tools. Indonesian Import Prediction is done to know the growth of Indonesia import rate by using Linear Regression method and using SPSS tools.In a study of the growth of import levels in Indonesia, Indonesia has an import rate that increased between 8-20%, but in some periods experienced an increase and decrease caused by other factors such as consumption expenditure, currency exchange rates and state revenues.
Curve description by histograms of tangent directions

The authors propose a novel approach for the description of objects based on contours in their images using real‐valued feature vectors. The approach is particularly suitable when objects of interest have high contrast and texture‐free images or when the texture variations are high so textural cues are nuisance factors for classification. The proposed descriptor is suitable for nearest neighbour classification still popular in embedded vision applications when the power considerations outweigh the performance requirements. They describe object outlines purely based on the histograms of contour tangent directions mimicking many of the design heuristics of texture‐based descriptors such as scale‐invariant feature transform (SIFT). However, unlike SIFT and its variants, the proposed approach is directly designed to work with contour data and it is robust to variations inside and outside the object outline as well as the sampling of the contour itself. They show that relying on tangent direction estimation as opposed to gradient computation yields a more robust description and higher nearest neighbour classification rates in a variety of classification problems.