Reviewerly/exHarmony huggingface dataset:
Identifikasi Kemiripan Teks Menggunakan Class Indexing Based dan Cosine Similarity Untuk Klasifikasi Dokumen Pengaduan

Report handling on "LAPOR!" systemdepends on the system administrator who manually reads every incoming report [3]. Read manually can lead to errors&lt;br /&gt;in handling complaints [4] if the data flow is very large and grows rapidly it can take at least three days and sensitive to inconsistencies [3]. In this study, the authors propose a model that can measure and identify the similarity of document reports computerized that can identify the similarity between the Query (Incoming) with Document (Archive). In this study, the authors employed term weighting scheme Class-Based Indexing, and Cosine Similarity to analyze document similarities. CoSimTFIDF, CoSimTFICF and CoSimTFIDFICF values are defined as feature sets for the text classification process using the KNearest&lt;br /&gt;&lt;br /&gt;Neighbor (K-NN) method. The optimum result&lt;br /&gt;evaluation with preprocessing employ Stemming and the best&lt;br /&gt;result of all features is 75% training data ratio and 25% test&lt;br /&gt;data on the CoSimTFIDF feature that is 84%. Value k = 5&lt;br /&gt;has a high accuracy of 84.12%
A method for dynamic segmentation of a pair of sequental video-frames

An algorithm of dynamic segmentation of sequential frame pairs was proposed. A comparative analysis of segmentation quality when finding shifts and affine inter-frame transformations for the segments was conducted. In addition, we compared the performance of the proposed method with some static segmentation approaches.
Bursting the Bitcoin Bubble: Assessing the Fundamental Value and Social Costs of Bitcoin

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Satellite image derived spectral modeling to assess the grades of hematite deposits: a study on Noamundi area in West Singhbhum district, Jharkhand

The mineralogical composition of the hematite deposited-surface is investigated by a spectral model. The model involved in the gradation of hematite ore from the Hyperion satellite image. The analysis is restricted within the visible near-infrared wavelength range. Pre-processing was carried out for radiance to reflectance conversion, dimensionality reduction, and minimizes scanning errors of image data. Typical image spectral modeling follows the steps of continuum removal, peak and absorption position of the spectra, band-depth calculation, spectral slope, and full width half maximum. Well defined relationships are obvious between the concentrations of iron oxide and (a) slope between peak and absorption trough (R2:0.729); (b) FWHM (R2:0.853). Therefore, the Lucey’s model was utilized to generate the hematite abundance map. The result was validated by observed relationships between predicted and actual Fe with R2:0.80 and average error ±3.81%. This study exhibits the possibility of evaluations of iron grades in view of image-spectral parameters.
GRIP: Generative Robust Inference and Perception for Semantic Robot Manipulation in Adversarial Environments

Recent advancements have led to a proliferation of machine learning systems used to assist humans in a wide range of tasks. However, we are still far from accurate, reliable, and resource-efficient operations of these systems. For robot perception, convolutional neural networks (CNNs) for object detection and pose estimation are recently coming into widespread use. However, neural networks are known to suffer overfitting during training process and are less robust within unseen conditions, which are especially vulnerable to adversarial scenarios. In this work, we propose Generative Robust Inference and Perception (GRIP) as a two-stage object detection and pose estimation system that aims to combine relative strengths of discriminative CNNs and generative inference methods to achieve robust estimation. Our results show that a second stage of sample-based generative inference is able to recover from false object detection by CNNs, and produce robust estimations in adversarial conditions. We demonstrate the efficacy of GRIP robustness through comparison with state-of-the-art learning-based pose estimators and pick-and-place manipulation in dark and cluttered environments.
vcftoolz: a Python package for comparing and evaluating Variant Call Format files.

The analysis of next-generation sequence data often involves variant calling -the process of identifying differences between genomes.The standard output file format of variant callers is the Variant Call Format (VCF) (https://github.com/samtools/hts-specs).Researchers need to view and compare VCF files when comparing the behavior of different variant calling algorithms (and even the same algorithm with different parameters).Additionally, the performance of a variant calling algorithm can be evaluated by comparing against a known truth VCF dataset.Here, we present vcftoolz, software to facilitate comparing and evaluating the variant calls in VCF files.The core functionality of vcftoolz is the capability to compare two or more VCF files, producing a report, Venn Diagrams, and a spreadsheet identifying the concordance between the VCF files.The artifacts produced by vcftoolz are not available from other tools.The vcftoolz software is designed to work with bacterial variant files.It has been tested in a 3-way comparison of VCF files, with each file having 200 samples and 500 snps per sample for a total of 100,000 variants in each file.
Evaluating the satisfaction index using automated interaction service and customer knowledgebase: a big data approach to CRM

Organisations need to understand their customer's requirements to outlive in this competitive world. Today, the customer interaction bots, which can handle multiple customers anywhere-anytime are attracting many business communities to have better customer relationship management (CRM). Searching for specific information seems to be interesting to provide a real value to customers, but the challenge is to get the reliable information for the customer's queries. The implementation of customer interaction bot is carried out using text document dataset. We have used LUIS, which provides a platform to build intelligent customer-computer applications that can understand the customer's requirements and responds to their queries. Text document data is indexed; the database is connected to direct line bot framework. The knowledgebase is implemented for customer queries based on needs, expectations, wants/desires, and complaints/problems. The proposed system evaluates the customer satisfaction based on customer bot interaction knowledgebase to achieve a better CRM.
Using of interactive content in the electronic educational resources in the educational process of a modern school

Elaboration and implementation of electronic educational resources and its main component electronic content is an integral part of information and communication technologies development. An overview of properties of information, which fills the resource, enables to identify different types of electronic content: text, graphic, interactive and video one. The article focuses on the research of interactive content, since it provides opportunities for continuous communication between participants in the educational area and facilitates their interaction. An analysis of existing online services and platforms is carried out in the article. The classification of electronic educational resources is developed based on the types of interactive content through the lens of their potential capabilities: tests and crosswords; quizzes, games and polls; interactive posters; multiservices. The description of H5P and LearningApps multiservices is provided; their advantages and disadvantages when using in the mathematics lessons are specified. The practical basis of the research is development of interactive posters through H5P and Thinglink services. This allowed to outline the principles of their creation and most appropriate ways to use in the educational process. In addition, the blogs and authors’ interactive posters developed by mathematics teachers during the certification program "Pedagogical program tool "Designer of Interactive Poster" are analysed. The importance to select the interactive content based on the educational goal and objectives is emphasized.
Grammatical errors of writing in EFL class

The current paper is descriptive qualitative research which aimed at describing grammatical errors of students’ writing in English as a Foreign Language (EFL) Class. Data were gained from documents written by students of State Polytechnic in Kupang City, East Nusa Tenggara- Indonesia. The results have shown that the highest percentage of grammatical errors occurred in using the verb group. It consisted of 65.0 %. Following these errors, students made errors in the use of prepositions (12.8 %). The third errors that were usually made by students were the errors in the use of pronouns. It took 9.8 %. After errors in the use of pronouns, students made errors in pluralization (5.1 %). The next lower frequency of errors made by students were errors in the use of articles. It took 4.3 % of the whole text. Following that, students made errors in the use of conjunctions. The errors occurred at 1.7 %. The last errors made by students were errors in the use of subject-verb agreement. The errors occurred only 1.3 %. The results of the current analysis are hoped to be a reflection of EFL lecturers in Indonesia. Furthermore, EFL lecturers of State Polytechnic Kupang are hoped to focus more on teaching the use of verbs in writing class.
Video Game-Induced Theta Rhythm

Smartphones and other personal electronic devices present novel cortical processing tasks with potential for identification of novel EEG waveforms. A 17-year-old patient with epilepsy manifested as recurrent myoclonic seizures, absence seizures, and a single generalized tonic-clonic seizure was hospitalized to undergo video-EEG monitoring for seizure quantification and classification of the epilepsy syndrome. During the monitoring session, a frontocentral predominant 5 to 6 Hz theta rhythm was identified only when the patient was actively texting or playing a video game on his smartphone. Previously, patients with focal epilepsy have been found to have a frontocentral theta rhythm on EEG while texting on mobile devices. We report similar EEG findings in a patient with genetic generalized epilepsy during smartphone gaming to expand the population and triggers for this theta waveform. Given the young age and type of epilepsy, we suggest that the waveform represents the EEG manifestation of the attention-visuomotor pathway that is stimulus independent.
Prediction of Criminal Suspects Based on Association Rules and Tag Clustering

To date, not many studies have been conducted on criminal prediction.In this study, the criminal data related to city S is divided into a training data set and a validation data set at a 1:1 ratio in light of the personal tag data and the travel and accommodation data of criminals and ordinary people in city S. Firstly, the FP-growth algorithm is adopted to calculate association rules between the criminals and the ordinary people in their travel and hotel accommodation data, in order to discover criminal suspects based on association rules.Secondly, the DBSCAN algorithm is employed for clustering of the tag data of the criminals and the ordinary people, followed by similarity calculation, in order to discover criminal suspects based on tag clustering.Lastly, intersection operation is performed on the above two sets of criminal suspects, and the resulting intersection is verified against the criminal validation set for elimination of criminals who appear in the intersection so as to obtain final criminal suspects.Results show that a set of 648 criminal suspects is retrieved based on the association rules calculated by the FP-growth algorithm, while a set of 973 criminal suspects is retrieved based on DBSCAN clustering and cosine similarity of the personal tags; the number of criminal suspects is narrowed down to 567 after the intersection operation of the two sets, and 419 of the 567 criminal suspects are further verified to be criminals using the validation set, thereby leaving the other 148 to be the final criminal suspects and giving a prediction accuracy of 73.9%.The data mining method of criminal suspects based on association rules and tag clustering in this study has been successfully applied to the police system of city S, and the experiment proves the effectiveness of this method in detecting criminal suspects.
PrivStream

Event monitoring and detection in real-time systems is crucial. Protecting users' data while reporting an event in almost real-time will increase the level of this challenge. In this work, we adopt the strong notion of differential privacy to private stream counting for event detection with the aim of minimizing false positive and false negative rates as our utility metrics.
Development of an Educational Kit for Learning IoT

The primary purpose of this paper is to provide an overview of existing education solutions for IoT and develop proposals for their improvement. The study draws analysis of current conditions of the educational IoT sphere, a comparative analysis of educational products used for teaching of undergraduate students. With that the article describes the architecture of our own software and hardware platform for learning IOT. Moreover, this paper reviews methods and technical instruments employed to design software and hardware appliances.
Analysis of Library Management Information System in Muhammadiyah 2 Vocational High School, Malang

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Keyword Search on RDF Datasets

In the last years, the Resource Description Framework (RDF) has gained popularity as the de-facto representation format for heterogeneous structured data on the Web. RDF datasets are interrogated via the SPARQL language, which is often not intuitive for a user since it requires the knowledge of the syntax, the underlying structure of the dataset and the IRIs. On the other hand, today users are accustomed to Web-based search facilities that propose simple keyword-based interfaces to interrogate data. Hence, in order to ease the access to the data to users, we aim to develop of an effective and efficient system for keyword search over RDF graphs. Furthermore, we propose a methodology to properly evaluate these systems. Finally, we aim to address the problem of the explainability of the information contained in the answers to non-expert users.
On ELFs, Deterministic Encryption, and Correlated-Input Security

We construct deterministic public key encryption secure for any constant number of arbitrarily correlated computationally unpredictable messages. Prior works required either random oracles or non-standard knowledge assumptions. In contrast, our constructions are based on the exponential hardness of DDH, which is plausible in elliptic curve groups. Our central tool is a new trapdoored extremely lossy function, which modifies extremely lossy functions by adding a trapdoor.
Immersive spatial narratives as a framework for augmenting creativity in foresight-based learning systems

Purpose The ability to project oneself into a future landscape is a critical aspect for studying and practicing the science of foresight and foresight-based learning systems. The purpose of this paper is to discuss how we can construct immersive spatial narratives through multimedia-enhanced learning approaches, to increase deeper learner immersion and levels of creativity to transport the learner into a simulated 2030 landscape by reducing the distance between the projected reality and the Self. Design/methodology/approach The author designed a foresight-based course on the Future of Mobile Learning underpinned by a new learning system that embraced the concept of immersive spatial narratives, combining physical, virtual and cognitive learning spaces, which enable students to explore complex, undiscovered or unstructured knowledge. Practicing was carried out on 35 students who had completed the course during the preceding three years through a questionnaire and interviews to establish increased levels of creativity in a simulated future landscape. Findings The paper established that the addition of multimedia learning environments and tools to foresight-based learning creates immersive spatial narratives that increase creativity and learner ability to project him/herself into a simulated future landscape. In all, 75 per cent of the respondents stated that having to think about the future and place themselves in a practicing landscape increased their creative skills. Originality/value A new, foresight-based learning system driven by the concept of immersive spatial narratives, enhanced with student-created multimedia learning tools. The system demonstrated how this approach helps to increase learner creativity and the ability to transition from their Present Self to their Future Self.
Streamlined Dense Video Captioning

Dense video captioning is an extremely challenging task since accurate and coherent description of events in a video requires holistic understanding of video contents as well as contextual reasoning of individual events. Most existing approaches handle this problem by first detecting event proposals from a video and then captioning on a subset of the proposals. As a result, the generated sentences are prone to be redundant or inconsistent since they fail to consider temporal dependency between events. To tackle this challenge, we propose a novel dense video captioning framework, which models temporal dependency across events in a video explicitly and leverages visual and linguistic context from prior events for coherent storytelling. This objective is achieved by 1) integrating an event sequence generation network to select a sequence of event proposals adaptively, and 2) feeding the sequence of event proposals to our sequential video captioning network, which is trained by reinforcement learning with two-level rewards at both event and episode levels for better context modeling. The proposed technique achieves outstanding performances on ActivityNet Captions dataset in most metrics.
Vector Space Model

The vector space model is a widely used model in computer science. Its wide use is due to the simplicity of the model and its very clear conceptual basis that corresponds to the human intuition in processing information and data. The idea behind the model is very simple, and it is an answer to the question, how can we compare objects in a formal way? It seems that the only way to describe the objects is to use a representation with features (characteristics) and their values. It is a universal idea, and it even seems to be the only possible way to work with formal objects.
The Development of English Reading Materials Integrated into Character Values on English Subject through Scientific Approach at Grade XI of MA KM.M Padang Panjang

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Blind Super-Resolution With Iterative Kernel Correction

Deep learning based methods have dominated super-resolution (SR) field due to their remarkable performance in terms of effectiveness and efficiency. Most of these methods assume that the blur kernel during downsampling is predefined/known (e.g., bicubic). However, the blur kernels involved in real applications are complicated and unknown, resulting in severe performance drop for the advanced SR methods. In this paper, we propose an Iterative Kernel Correction (IKC) method for blur kernel estimation in blind SR problem, where the blur kernels are unknown. We draw the observation that kernel mismatch could bring regular artifacts (either over-sharpening or over-smoothing), which can be applied to correct inaccurate blur kernels. Thus we introduce an iterative correction scheme -- IKC that achieves better results than direct kernel estimation. We further propose an effective SR network architecture using spatial feature transform (SFT) layers to handle multiple blur kernels, named SFTMD. Extensive experiments on synthetic and real-world images show that the proposed IKC method with SFTMD can provide visually favorable SR results and the state-of-the-art performance in blind SR problem.
Brief Paper: A Design of the Platform Technology for the Smart Shopping Mall Using NFC: A Software Engineering Approach

An IoT-based server system for store management was developed in this study. Its client-server structure having a product categorization function allows the system to read the NFC tags attached to individual products and provides information about them to customers automatically. The system also provides an effective promotional effect as it not only offers necessary information about the items the customers are seeking but also displays the advertisements related to them. This server system was designed specifically designed for the use in a smart shopping Mall or a department store so that the store owners can manage their sales operation more effectively while their customers enjoy shopping more conveniently. It is expected that the technology used for this IoT-utilized server system can be one of the efficient and effective platform technologies in the current and future store management systems.
Deep Feature Representation for Face Sketch Recognition

Face sketch recognition aims at matching face sketch images to face photo images.The main challenge lies in modality discrepancy between face photo and sketch images.In this work, we propose a new facial sketch-to-photo recognition approach by adopting VGG-Face deep learning network, with which face images can be represented by compact and highly discriminative feature vectors.Different from existing VGG-Face based methods which directly match face sketches to photos, we firstly transform the gallery photos to sketches for decreasing the modality difference.Experimental results on multiple face photo-sketch datasets indicate the superiority of our method.
Collection Development and the Historical Record: Are We Forgetting Monographs as Primary Sources?

Contemporary academic libraries’ collection development practices are influenced by limited space, complicated access models, and shrinking budgets that are competing for interests in print and electronic, and other outside forces. Librarians responsible for collections make tough decisions that unintentionally threaten to change how academic libraries build and curate collections for longevity. Much of today’s discussion deals with immediate problems and offers few considerations about how these collection decisions will affect the availability and potentially the existence of historical evidence. Among the issues highlighted in this commentary are definitions of primary sources, researchers-used historical materials, and solutions for the identified problems.
Participatory water conservation education on social media in Amathole District local municipalities, Eastern Cape, South Africa

Social media has been attributed as beneficial by aiding fast and reliable communication which cuts across time and geographical boundaries. In South Africa, reports indicate that most people have access to social media. Such attributes can be harnessed by municipalities to educate about water conservation, especially considering the drought and water crisis that was experienced in Eastern Cape, South Africa. This paper focused on local municipalities in Amathole District in the Eastern Cape Province, which was declared a high risk zone due to the drought that prevailed in 2016 till 2017. The Government to Citizen Model (G2C) was used as a theoretical framework which supports participation. Purposive sampling was used, with water messages being prioritised. Qualitative content analysis was used to analyse content on the local municipalities’ social media platforms. The researchers found that local municipalities in Amathole District, excluding the district municipality, were not using social media platforms for participatory water conservation education.