Reviewerly/exHarmony huggingface dataset:
Classification with Rejection Option Using the Fuzzy ARTMAP Neural Network

The ARTMAP networks are machine learning techniques focused on supervised learning, being known mainly for their ability to learn fast, stable, incremental and online. Despite these advantages, the Fuzzy ARTMAP (FAM) suffers from the categories proliferation problem, leading to a reduction in its performance for unknown samples. Such disadvantage is mainly caused by the overlapping region (noise) between classes. The vast majority of work on this issue has been concerned with alleviating the problem. A technique used to improve the performance of a classifier is the rejection option, which is used to retain the classification of a sample if the decision is not considered to be reliable. Therefore, in this paper, we introduce a variant of the Fuzzy ARTMAP to behave as a classifier with the rejection option. The main idea is to create a region of rejection by looking at the place where the categories proliferate since it occurs precisely in the overlapping region. The proposal was validated by conducting experiments with real datasets, as well as by comparing them with other models (MLP, SVM, and SOM) applied with the same rejection option technique.
ARFF Data Source Library for Distributed Single/Multiple Instance, Single/Multiple Output Learning on Apache Spark

Apache Spark has become a popular framework for distributed machine learning and data mining. However, it lacks support for operating with Attribute-Relation File Format (ARFF) files in a native, convenient, transparent, efficient, and distributed way. Moreover, Spark does not support advanced learning paradigms represented in the ARFF definition including learning from data comprising single/multiple instances and/or single/multiple outputs. This paper presents an ARFF data source library to provide native support for ARFF files, single/multiple instance, and/or single/multiple output learning on Apache Spark. This data source extends seamlessly the Apache Spark machine learning library allowing to load all the ARFF file varieties, attribute types, and learning paradigms. The ARFF data source allows researchers to incorporate a large number of diverse datasets, and develop scalable solutions for learning problems with increased complexity. The data source is implemented on Scala, just like the Apache Spark source code, however, it can be used from Java, Scala, and Python. The ARFF data source is free and open source, available on GitHub under the Apache License 2.0.
Multiphase Focal Liver Lesions Classification with Combined N-gram and BoVW

The bag-of-visual-words (BoVW) model has emerged as an effective approach to represent features for focal liver lesions (FLLs). However, most of the previous methods have the limitation of insufficient consideration of the spatiotemporal co-occurrence information, which provokes the low descriptive power of classic visual words. In contrast to previous work, we propose a novel model for multiphase medical image feature generation named the Bi-gram bag-of-spatiotemporal words (Bi-gram BoSTW) to capture the temporal information, as well as, the spatial co-occurrence relationship of the lesion. First, temporal co-occurrence images from multiphase images are constructed. Second, BoVW is employed to extract temporal features from the temporal co-occurrence images and generates the visual words. Finally, we introduce the N-gram schema to add spatial relation to local descriptors. To the best of our knowledge, this is the first work that introduces visual N-grams scheme to contrast-enhanced CT images, which integrates temporal information with spatial co-occurrence relationship and improves the classification performance. The effectiveness of the proposed model is verified on 132 FLLs with confirmed pathology type. The experimental results indicate that (1) the N-gram enriches the semantics and provides more complete representation; (2) the proposed model achieves the best accuracy (83%) with highest training speed (1.5 min) among several well-known methods based on BoVW model.
Video Categorisation Mimicking Text Mining

With the rapid growth of online videos on the Web, there is an increasing research interest in automatic categorisation of videos. It is essential for multimedia tasks in order to facilitate indexing, search and retrieval of available video files on the Web. In this paper, we propose a different technique for the video categorisation problem using only visual information. Entity labels extracted from each frame using a deep learning network, mimic words giving rise to manage the video classification task as a text mining problem. Experimental evaluation on two widely used datasets confirms that the proposing approach fits perfectly to video classification problems. Our approach achieves 64.30% in terms of Mean Average Precision (mAP) in CCV dataset, above other approaches that make use of both visual and audio information.
A Three-Way Clustering Algorithm via Decomposing Similarity Matrices for Multi-view Data with Noise

The multiple views of data can provide complementary information to each other, a large number of studies have demonstrated that one can achieve the better clustering performance by integrating information from multiple views than using only a single view. However, identifying the explicit cluster structure in the multi-view data with noise and reflecting uncertain relationships between objects and clusters is still a problem that has not been satisfactorily solved. To address the problem, this paper propose a three-way clustering algorithm for multi-view data with noise. The algorithm is mainly divided into two stages. In the first stage, we decompose the similarity matrix of each view into the good data and the corruptions to eliminate the noise contained in the multi-view data. In the second stage, only the clean data of each view is used to obtain the consistency information, and the final three-way clustering results are generated based on the theory of three-way decisions. The experimental results show that the proposed algorithm has better clustering performance in dealing with multi-view data with noise.
Providing an adaptive setup for media-content sources.

Abstract Cloud computing provides a versatile framework that media content suppliers may use to procure spilling sources that match the interest. Media content suppliers are charged for the amount of sources distributed (saved) inside the cloud. Most of the current cloud suppliers utilize a costs demonstrate for that saved sources that is reliant on non-straight line time-rebate levies. This sort of costs plan offers extraordinary rebates depending non-directly around the day and age where the sources are saved inside the cloud. Media spilling programs have recently pulled in a great deal of clients on the web. Utilizing the production of these transmission capacity concentrated projects, it’s monetarily inefficient to supply spilling conveyance with ensured QoS depending just on focal sources in a media content supplier. Inside this circumstance, a totally open issue is to choose both the right amount of sources saved inside the cloud, and in addition their reservation time with the goal that the financial expense around the media content supplier is limited. The results in our factual assessments and reproductions uncover that the proposed recipe extensively eliminates the financial cost of resource portions inside the cloud as in correlation with other customary plans. We exhort a simple - easy to apply - equation for resource reservation that maximally abuses marked down rates offered inside the taxes, while verifying that adequate sources are held inside the cloud. In accordance with the guess of enthusiasm for gushing limit, our equation is precisely made to forestall settling on wrong resource allotment decisions.
KEEFEKTIVAN PENGGUNAAN METODE THINK TALK WRITE DALAM PEMBELAJARAN MENULIS TEKS BIOGRAFI PADA SISWA KELAS X SMK NEGERI 1 CIMAHI

This research is based on research on curiosity towards the use of think writing method in biographical text learning in class X. The formulation of the method can be used to talk about it. in learning biographical texts? Method for learning on biographical texts on class X students from SMK Negeri 1 Cimahi. The method used is the experimental method. This study uses the design of one group pretest-posttest. The research data is quantitative data that is analyzed through statistical analysis techniques. Class X TEI-B with a total of 27 students being used as research samples. The results of the initial test and the final test were compared with the average initial test 38 and the average final test 75. So, it can be concluded that the Think Talk Write method is effectively used in learning to write biographical texts.
Intelligent Healthcare Website Design with Keyword Cues to Facilitate Online Health Information Search

With the rapid growth of the internet, several healthcare websites emerge in the format of inquiries and answers, which can be of significant value to reuse for online health information searchers in retrieving their desired records as primary references. Unfortunately, most of those websites do not consider the challenges the searchers will face, as they quite often are inexperienced searchers with limited healthcare domain knowledge. In this study, we consider designing the intelligent healthcare website by supplementing keyword cues to facilitate the website survey environments in orientation and navigation for searchers. A three-phase design is proposed, involving selection of health concepts, common search queries, and search intentions. The design aims to ease searchers’ mental loads in the search process, and achieve good outcome quality worthy of searchers’ references. A prototype website on dermatology is developed to demonstrate its practical application. We conclude that the proposed design approach is feasible to facilitate online information searchers in acquiring the desired records in a pull selection manner.
Utilization of Feature Detector Algorithms in a Mobile Signature Detector Application

A signature is a legal representation of a person. Forgers take advantage of this fact as they copy and imitate signatures for their own benefit. People could lose money or properties with their forged signatures used to close a contract, or to withdraw from banks. With this in mind, the researchers developed inSignia, a mobile application that could help possible victims of forgery. InSignia verifies signatures for authenticity and forgery using smartphone cameras and images. The study uses BRISK, or Binary Robust Invariant Scale Keypoints, a feature detector algorithm in comparing images. The mobile application allows image storage through a profile list, collecting, and saving significant signature images that can be compared to another signature image for authenticity. The study used Mobile Software Development Model (MDSM) as the project development methodology, which tolerated simultaneous testing and development of the application, as well as concept changes. The mobile application was subjected to stress and field testings. With the use of ISO/IEC 2011 25010: Systems and software engineering --- Systems and software Quality Requirements and Evaluation (SQuaRE), the application was evaluated in terms of its functionality, usability, reliability, and efficiency. Based on the evaluation results, the application satisfied the respondents and possible users, with an overall mean rating 4.16 or 'Very Acceptable'.
Adapted Computer Mouse for Cerebral Palsy Users

There are many computer pointing devices (mice) in the market for enabling people with motor impairment to use the computer. One popular system uses big push-buttons that are pressed by the user, each one corresponding to a cursor movement or a button click. However, the mechanical switches demand some force to be exerted on the buttons, which can be difficult with users with movement limitations. Also, they are prone to wearing and breaking. To overcome these problems, a system using capacitive touch sensors was designed. It uses a microcontroller which handles all hardware and firmware for touch sensing and USB mouse emulation. Sensors were made using square double-faced PCB pieces of 5 cm side, but they can be made with almost any size and shape. The system has 4 cursor movements, 4 button actions (left, right, left double-click, and left hold down/release), and a cursor speed setting. The device behaves as expected, and its design is quite simple, keeping costs at a reasonable range.
Quantum Memory in the USC Regime

In this chapter, we envisage a quantum memory cell composed of a superconducting flux qubit galvanically coupled (Bourassa et al. Phys Rev A 80:032109, 2009 [1]) to a microwave resonator in the circuit QED framework. With the proposed cell, we would like to scale-up the entire structure into a three dimensional setup (see Chap. 7 ).
NEWS

Most news summarization techniques are static, and thus do not satisfy user needs in having summaries with specific structures or details. Meanwhile, existing dynamic techniques such as query-based summarization fail to handle content-independent queries that target the type of summary information such as time, location, reasons, and consequences of reported events. The NEWS system supports multi-granular summarization along two dimensions: the level of detail and type of information. The system employs fine-grained information extraction to extract facts and their facets with type tagging. The extracted information is then modeled as a graph used to create summaries. The system incrementally expands summaries based on the nodes visited by users, folding related events into the search space.
Recent Progress in Computer-Vision-Based Human Activity Recognition and Related Areas

This paper presents some more recent developments in the research area of human activity recognition. In many cases, human activity recognition can be achieved with the support of body sensors that deliver information about the motion of a person or about acceleration and position. Such data can contribute substantially to the accuracy of the results, however wearing extra equipment is not a very popular practice for most human users and therefore camera-based methods are mostly preferred in today’s systems, which is also a result of the tremendous progress achieved in recent years in pattern recognition and machine learning. Therefore, also this paper concentrates on camera-based methods for human activity recognition. Activity recognition has a large variety of sub-research areas and has many exciting application areas, such as e.g. interaction in smart spaces, surveillance, human-robot interaction or automatic analysis of interest, emotions and human traits.
Research on background segmentation algorithm of moving image in speed skatingvideo

The background segmentation of human motion images is the first step in the process of human motion analysis. It is the low-level processing part of human motion analysis. The processing effect at this stage directly affects the progress of the follow-up work. The segmentation results have a great impact on the final human motion analysis results. An important purpose of our research is to endow the computer with the ability being similar to the human vision. So, the computer can feel the motion object in the view and apprehend the behavior of the human more easily. The paper is studied on the representative theories and algorithms of the background subtraction with human motion monocular image. And this paper analyses the predominance and deficiency of these theories and algorithm. These algorithms include differential images, Running Gaussian average, the Mixture of Gaussians and BP neural network. The basic principle and steps of realization are expounded. Also the data of the evaluation is given. Experiment shows that the proposed algorithm of background subtraction is highly effective and it can cast the reflected light, shadow and inverted image well. The algorithm improves the correct rate of target segmentation and is suitable for human motion image segmentation in this complex ice field environment.
Detecting Hand, Foot and Mouth Disease in Earlier Stage Using C4.5 Algorithm as Expert System Based on Android

Abstract Hand, Foot and Mouth Disease (HFMD) is an infectious diseases caused by enterovirus virus 71 (EV 71). The symptoms of HFMD is similar to several other disease that caused by a virus, especially disease that have a fever and rash symptoms which people usually underestimate diseases that have early symptoms like that. Therefore, in this system we classify the HFMD with the intention of detecting the disease from an earlier stage. And we use Android based application since at this present time, smartphone is the closest device that is always used by many people. The classification used in this paper is Decision Tree C4.5 Algorithm. Dataset used in this research is as many as 256 which divided into training data and testing data, that formed based on symptoms that had previously been validated by the doctor. The result shows that data partitions of 90%:10%, 80%:20% and 70%:30% has accuracy, precision and recall value are 100%. Thus, data partition 70%:30% has the best result because this partition has less training data but can still classify diseases effectively.
Ultrastrong Light–Matter Interaction

Einstein once wrote (Einstein in Annalen der physik 322:132, 1905 [1]): “The wave theory of light which operates with continuous functions in space has been excellently justified for the representation of a purely optical phenomena and it is unlikely ever to be replaced by another theory. One should, however, bear in mind that optical observations refer to time averages and not to instantaneous values and notwithstanding the complete experimental verification of the theory of diffraction, reflection, refraction, dispersion and so on, it is quite conceivable that a theory of light involving the use of continuous functions in space will lead to contradictions of experience, if it is applied to the phenomena of the creation and conversion of light”.
HOUGH-DCT Based Robust Watermarking for Medical Image

The patients’ case files may be maliciously tampered with during network transmission. Once the patients’ medical record information is altered by criminals, serious medical accidents will occur. To solve the problem of information leakage, a robust medical image watermarking algorithm based on Hough transform and discrete cosine transform (HOUGH-DCT) is proposed. This method combines the feature vector of medical image, cryptography, hash function and zero watermark technology, and it makes up for the shortcoming that the traditional digital watermark method cannot protect the medical image. It has strong robustness and invisibility which can protect the patient's private information and medical image data security at the same time. Experimental results show that the algorithm can embed the watermark information at a faster speed without changing the medical image information and extract the watermark safely with better invisibility and robustness.
Christian Higher Education in Korea: Exploring Historical Roles and Regular Christian Curricular and Non-Regular Mentoring Programs

Christianity played an important role in modern higher education in Korea. The oldest university in Korea is the Yonhee Professional College founded by Underwood, an American Christian missionary. Also the first medical school, Severance Medical School, was founded by a Christian missionary. Christian educational institutions have historically had a significant impact on Korean society in terms of the independence movement, enlightenment, literacy, and modern knowledge. Currently Christian curricula are institutionalized very well in Korean Christian universities. However there is some doubt as to whether regular Christian education curricula imbue students with spirituality, morality, and ethics in terms of internalization in Korean society. On the other hand, Christian circles, which provide an extracurricular program, are considered to have a significant impact on students in terms of spirituality, morality, ethical awareness, and character development since the 1960s and 1970s. In particular, the circles developed and implemented a discipleship training program which is a mentoring education method. The purpose of this chapter is to explore the roles of Christian higher education in Korean history and to compare regular Christian curricular and non-regular Christian mentoring programs. In order to explain Christian discipleship programs, this chapter defines religious terminology and lexicon that is germane to the Christian faith and some underlying assumptions about approaches toward evangelism. Additionally, this chapter discusses what religious higher education should do in the future.
(μ,λ)-CCMA-ES for Constrained Optimization with an Application in Pharmacodynamics

We present the algorithm CCMA-ES, an extension to CMA-ES, an evolution strategy that has shown to perform well in a broad range of black-box optimization problems. The (μ, λ)-CMA-ES effectively handles nonlinear nonconvex functions but faces difficulties in constrained optimization problems. We introduce viability boundaries to improve the search for an initial point in the valid domain and adapt the covariance matrix using normal approximations to maintain the inequality constraints. Using benchmark problems from 2006 CEC we compare the performance of CCMA-ES with a state of the art optimization algorithm (mViE) showing favorable results. Finally, CCMA-ES is applied to a pharmacodynamics problem describing tumor growth, and we demonstrate that CCMA-ES outperforms mViE in terms of the objective function value and total function evaluations.
Cyber Investigations, A Law Enforcement Needs Assessment

The purpose of this article is to assess the needs of local and state law enforcement agencies to investigate both cyber-enabled and cyber-dependent crimes. While large federal investigative agencies have the skills, tools and resources to investigate cyber-crimes, the increasing propagation of such crimes has overwhelmed their ability to investigate all but the most serious national security threats and large-scale cyber-crimes. To that end, this article assesses the current knowledge, skills, and abilities of local law enforcement to perform cyber-crime investigations and more importantly their desire to do so. As these crimes grow in ease and popularity it is posited that local law enforcement will be required to have the technical skills to investigate a myriad of cyber-crimes at the local level. As such, there will be a need for local training in cyber-crimes investigation and shared investigative resources. Additionally, this paper gauges local law enforcements' willingness to participate in such training.
Forensic Searches of Electronic Devices at the Border: United States v. Touset

"Forensic Searches of Electronic Devices at the Border: United States v. Touset." Justice System Journal, 40(1), pp. 86–87
A novel approach to compress dna repetative sequences in bio-informatics

Abstract In recent days numbers of gigabyte sequences of nucleotides are stored in a common database Genbank. All the victimization Deoxyribonucleic acid sequences for biological functions are to store the large number of Genomes in a compressed type in economically. Despite the fact that Deoxyribonucleic corrosive arrangements are put away in a packed kind, the information on Deoxyribonucleic corrosive groupings square measure hang on in science databases. For a four-letter alphabet in DNA (Adenine(A), Cytosine(C), Guanine(G) and Thymine(T)), an average description length of 2 bits per base is that the max length required to encode DNA. To reexamine the previous art of compression techniques and its merits and de merits, a novel attempt is initiated. Based on the comparative study of existing algorithms a new method proposed for DNA compression without depending on statistics of sequence set.