Reviewerly/exHarmony huggingface dataset:
Research on the application of Naive Bayes and Support Vector Machine algorithm on exercises Classification

Abstract In this paper, exercises on electronic major courses of college are classified, further, compared with the accuracy of algorithms based on the Naive Bayes and Support Vector Machine (SVM) in the classification of exercises. Data sets from exercises related to the content on the course of Data Structure are selected. According to knowledge content of course and characteristics of exercises, judgment and multiple-choice questions are used in experimental data, and these exercises are divided into seven categories depending on content of chapters. Firstly, the package of Jieba participle is applied to segment text of exercises, and the proper nouns of knowledge content are identified by importing a custom dictionary. Because characteristic words of short text are quite sparse, weight method about TF-IDF is directly adopted to feature representation of text instead of feature selection. Finally, algorithms based on the Nave Bayes and SVM are employed to train and classify text of exercises. The experimental results show that algorithm based on SVM has lower error rate and higher classification accuracy than the Naive Bayes.
Forecasting Ethereum STORJ Token Prices: Comparative Analyses of Applied Bitcoin Models

The research on forecasting Ethereum STORJ token has not been widely studied compared to forecasting Bitcoin. The objective of this paper is threefold: apply existing Bitcoin price forecasting models to the Ethereum STORJ token price; evaluate the dynamics of the model predictive utility across three time horizons (h=5 days, h=20 days and h=50 days); and determine if Ethereum STORJ token clustering coefficients impact the effectiveness of the forecasting model. We choose Bitcoin forecasting models of: ARIMA, ARMA-GARCH, VAR, alpha-Sutte Indicator and NNAR. Model effectiveness is analyzed using RMSE, MAE and MAPE. We find that VAR outperforms all models in the short and mid-term horizons (h=5 and h=20 days) and NNAR outperforms all models in the long-term horizon (h=50 days). Non-linearity, the intrinsic value that Neural Network has, may strongly effect the forecast accuracy result. When adding the clustering coefficient to ARIMA, we find that the variable is significant but only marginally improves the forecasting of the Ethereum STORJ token. The VAR model, which includes the clustering coefficient is shown to better forecast Ethereum STORJ prices in the short and mid-term forecasting horizons. The NNAR model is a better model for the long-term forecasting horizon.
One time‐step particle smoothing for radio range‐based indoor position tracking

In the context of sequential estimation of radio range-based indoor position tracking, Bayesian smoothing framework is promising as involving past, present and future observations. The performance and practicability of a smoothing method greatly depend on how many and how future observations are incorporated. Aiming at real-time locating systems, the authors propose one time-step smoothing form on sequential Monte Carlo methods, including four popular Bayesian smoothers and a novel one time-step smoothed filtering (SF) algorithm. The smoothing algorithms are evaluated through two-dimensional position tracking on a real-world indoor test-bed. The authors present results that the proposed SF improves tracking performance requiring very limited computation and memory, which is applicable for real-time indoor position tracking. Moreover, the one time-step smoothing form is validated to mitigate ranging errors and smooth positioning trajectories.
SentEmoji

Emojis are gaining popularity in day-to-day computer-mediated conversations, resulting in more interactive conversations. On the other hand, traditional chatbots lack the ability to use emojis effectively for creating an engaging and empathising conversation even after recognising feelings of the conversation partner, an essential communicative skill. This inability is majorly due to the paucity of any such suitable publicly available datasets and framework for training and evaluation of chatbot. Prior work has either classified the emojis or generated empathy dialogue without the use of emojis. Through this work, we propose a new dataset SentEmoji, generated using public dataset EmpathyDialogues, and its mapping to relevant emojis using EmojiNet dataset. We present a novel approach to generate dialogue with emojis to express empathy. A study will be conducted to get user rating on three aspects - empathy/sympathy, relevance and fluency. The comparison of this user-study with prior studies will reflect the effectiveness of this approach.
Mining Spatial Co-location Patterns from Vague Set

Abstract The target of mining spatial co-location patterns is discovering spatial association rule from spatial database, which plays an important role in many fields, investigate the distribution of plants for instance. Vague set extend the concept of fuzzy set, more flexible in terms of express and process fuzzy information. Some concepts such as vague spatial neighborhood relationship, vague participation ratio and vague participation index are defined in this paper first, after that, vague data filter rule and the algorithm of mining co-location patterns form vague set are proposed, the efficient prune strategy is presented in order to improve the efficiency of algorithm finally. The significance and efficiency of the techniques we propose are verified by experiments.
D&amp;D: A Distributed and Disposable Approach to Privacy Preserving Data Analytics in User-Centric Healthcare

Benefiting from the modern information and communication technologies, user centricity has become a clear evolution trend in healthcare. Unfortunately, given the high sensitivity of health data and the uncertainty in user environments, user-centric healthcare systems inevitably suffer from more frequent privacy threats, not to mention that technologies and business of data exploitation have generally outpaced the current privacy regulations and laws. Although there exist well-defined privacy preserving mechanisms, such as Data Encryption, Data Perturbation, and De-identification, they have been considered inadequate to address the diverse privacy challenges in big healthcare data analytics. Our argument is that, before considering any sophisticated mechanism, practitioners should first try to imitate human memory's forgetting process as an intrinsic privacy preserving strategy in user-centric healthcare. Technically, we implement this strategy by changing traditional data analytics routines into a distributed and disposable manner, so as to naturally exclude the data owners' sensitive information. The technical implementation essentially acts as a concrete How-To solution to satisfying a fundamental principle of privacy law, i.e. data minimization. We have initially applied our work to a smart bed project for sleep quality analytics, and received positive feedback on the effectiveness of privacy preservation in suitable homecare scenarios.
Proposal of IoT based Learning Material and its Management System for Primary/Secondary Education

The purpose of this study was lesson support by building a system which IoT based learning material and using it. This system is assumed to use the regular curriculum of primary and secondary education in Japan. We previously examined that apply IoT technology to learning material. That learning material use when experiment with a measurement. And, we designed its management system. Here we describe the outline of IoT based learning material and its management system. And we report about concrete IoT based learning material.
A Novel Intermediate Attachment to Reduce Contamination in Reusable Core Needle Biopsy Devices

Abstract One barrier to breast cancer diagnosis in low-resource settings is that devices for core needle biopsy (CNB) are either disposable and expensive, or reusable and susceptible to internal contamination. Through interviews with field workers and verification experiments, we identified that a common, commercially available, reusable CNB device allows contaminants to enter the driver chamber during firing, necessitating laborious cleaning of the entire device after every use. We introduce a novel CNB device attachment that eliminates this contamination mode and interfaces with existing commercial reusable drivers and low-cost disposable needles. This attachment repositions the driver–needle connection to the exterior of the driver, preventing retrograde flow of blood. Using an unmodified commercial CNB, we replicate chamber contamination by firing into a body fluid-mimicking glycerol solution. Prototypes were tested for their performance in eliminating this contamination. We tested the effectiveness of a cleaning procedure to reduce trace contamination by using a fluorescent dye and measuring the intensity of fluorescence after cleaning. The device's ability to reliably and consistently biopsy tissue with the novel attachment was evaluated using breast tissue models. In these tests, a reusable CNB with our attachment exhibited no measurable internal contamination, and maintained full biopsy functionality as measured by tissue sample weight and length. Minimizing internal device contamination would simplify the cleaning process for reusable biopsy devices. This would improve the accessibility of breast cancer biopsies in low- and middle-income countries (LMICs).
Scale Invariant Super-Resolutions Methods with Application to InSAR Images

Super-Resolution is the process of generating high-resolution (HR) images from a low-resolution (LR) ones. In learning-based SR algorithms, artificial neural networks (ANN) are used. This is achieved by training the network using HR and LR image pairs and use this network later to create new HR images from LR ones. Our work postulates that the scaling process is invariant across scales. Thus, a model trained at lower scales can be used to reconstruct higher resolution images when the ground truth is not available to train the model. We call this approach Scale Invariant Super-Resolution (SINV) We evaluated SINV using different datasets, and with different upscaling factors <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> The upscaling factor is the factor by which the image resolution is increased. and showed that it outperforms conventional approaches. We have applied SINV to processing InSAR images.
SCEHMA: Speech Corpus of English, Hindi, Marathi and Arabic Language for Advance Speech Recognition Development

The database is an essential key element for speech recognition research. This research describes the development of the SCEHMA speech database dedicated to advance speech recognition applications in Hindi, English, Marathi and Arabic languages. The SCEHMA corpus is a collection of isolated word and continuous sentences of speech. For the application domain of agriculture, polyclinic and general-purpose speech recognition in Marathi language 28420 isolated words and 17470 sentences are collected from 300 male and 200 female subjects of 22–30 age groups. The corpus consists of 900 sentences in the Hindi language for accent recognition domain collected from 18 male and 12 female of 18–30 age groups. The English speech corpus was collected from 22–30 age groups of 750 isolated words and 750 sentences from 12 male and 3 female of age group 22–30 for the general domain. The Arabic speech corpus contains 4520 words and 40 sentences from 12 male and 9 female of 18–30 age groups for recognition domain. To achieve a high quality of speech corpus, the recording took place in 10 by 10 office room without a noisy sound environment. The speech utterances were recorded in 16 kHz in three recordings medium, a headset, desktop mounted microphone and Mobile phone. The data was recorded in the morning, and evening session in the room temperature and normal humidity. Speaker was asked to sit in front of the microphone with a distance of about 12–15 cm. The database is collected as per LDCIL protocol and the corpus is transcript through Google Unicode editor. Praat is used for corpus labeling and annotation. The total size of the SCEHMA corpus is 33690 isolated words and 19160 continuous sentences. The corpus will be made available to the scientific community for agricultural, polyclinic, medical, accent recognition, age group identification, gender recognition, and general-purpose recognition system after the transcription and annotation.
Computational Complexity of Dependence Estimation via Generalized Linear Models in Multidimensional Feature Spaces

Usually, when speaking about dependence estimation in big sets of empirical data, it is adopted to suggest that the set of precedents does not fit in the memory of one computer, and some technology of distributed computing is required. However, even if the entire training set can be placed in one computer, the question remains how much time the training process will take. We keep here to the generalized linear methodology of dependence estimation, which covers, in particular, both regression estimation and pattern recognition. It is assumed that the training information (empirical data set) is a rectangular objects/features table. We consider here two kinds of algorithms of regularized empirical risk minimization, which are mutually opposite in their computational complexity relative to the number of features and the number of training objects, i.e., to the two sizes of the objects/features table. The computational complexity of one of them is linear with respect to the number of objects and polynomial relative to the number of features, whereas the other algorithm is of linear complexity in the number of features and polynomial in that of training objects. Thus, for any combination of the two sizes of the objects/features table, we have an algorithm whose computational complexity is linear relative to the greater of two sizes and polynomial with respect to the smaller of them. This property is especially favorable for the typical situation when the number of available features is much greater than that of training examples.
MORPHER - A Platform to Support Modeling of Outcome and Risk Prediction in Health Research

Machine learning is rapidly becoming a mainstay in research and industry. Particularly for clinical predictive modeling, these approaches are being increasingly applied, as evidenced by the growth in the number of related publications. While different computer tools exist that support rapid prototyping, we observe that the state of the art is lacking in the extent to which the needs of research clinicians are addressed. This leads to an increase in the time needed for development and validation of such models. In this paper, we outline the requirements and challenges inherent to this domain and present a platform for rapid prototyping tailored to the specific needs of clinical modeling for outcome and risk prediction. We argue that a move towards hybrid solutions, i.e., a mix of cloud and on-premise infrastructure, constitutes a viable way to reduce the time needed to develop and validate clinical predictive models in a standardized, reproducible fashion.
Evacuation Route Mapping Based on Tsunami Hazard Area on Sawarna Tourist Village, Bayah, Lebak

Abstract Sawarna Village is a tourism village located on the southern coast of Lebak Regency, Banten and directly facing the Indonesian Ocean. The geographical location of Sawarna village might potentially affected by earthquake and tsunami disaster. Beside the local residents, tourists are also affected by the earthquake and tsunami. This research aims to determine the areas affected by the tsunami disaster and determining the evacuation route, especially the pathway from the location of attractions. Data obtained from the images of DEM IFSAR and Google Earth as well as field survey results. GIS analysis network was used as data analysis technique. The result showed the tsunami’s hazard area in Sawarna Village covers about 347.62 Ha, with a high risk level of 270.38 (77.78%). The tsunami hazard area covers all the sights and settlements along the coast, south of the village road and the border of the river. The evacuation direction of the tourist objects is straight away from the beach but away from the river. Meanwhile, the direction of evacuation in the settlement follows the road and away from the river to the nearby hill.
Negative Concord in Russian

In this article I will describe the general properties of Negative Concord in Russian, which is a strict Negative Concord language, where all negative indefinites must co-occur with sentential negation. However, there are several cases where the negation marker can be absent (like in fragment answers) or can appear in a non-standard position (like at the left of an embedded infinitival). I will take into consideration all these specific cases described by the literature on the negation system of Russian and analyse them according to current approaches to Negative Concord.
Grid Quality of Service Trustworthiness Evaluation Based on Bayesian Network

Quality of Service (QoS) is applied to evaluate the satisfaction level of users using a service and it is a measure and evaluation of the service level of service providers. As one of the significant contents of grid technologies, the QoS has gradually become a focus of current Internet researches. The grid is a network infrastructure that can aggregate decentralized resources to form a higher-level distributed resource sharing environment; the highly dynamic performance presented by the grid system is caused by resource competition and task uncertainty and providing extraordinary QoS is one of the three criteria for evaluating the grid technologies. Bayesian network is a method of graphing knowledge and is a probabilistic model which can be calculated; data from various sources can be synthesized and comprehensively reasoned through this Bayesian network. On the basis of summarizing and analyzing previous research works, this paper expounds the development background, current situation and future challenges of Bayesian network technology, presents the studying status and existing problems of grid QoS trustworthiness evaluation, introduces network optimization analysis method and QoS trustworthiness evaluation algorithm, proposes the Bayesian network-based trustworthiness model, effectiveness function and grid QoS evaluation reasoning process, therefore, establishes grid QoS trustworthiness evaluation model and analyzes Bayesian network-based QoS trustworthiness evaluation mechanism. The simulation results show that the proposed method can enhance the trustworthiness of service information and improve the rationality of service matching in a dynamic service grid environment. The study results of this paper provide a reference for the further researches on the grid QoS trustworthiness evaluation based on Bayesian network.
Consensus Graph Weighting via Trace Ratio Criterion for Multi-view Unsupervised Feature Selection

The problem of multi-view unsupervised feature selection receive more attention in recent years. Most of existing works explore the shared information across multi-views. Several embedded algorithms have been developed. All these methods use additional hyper-parameter for regularization, which makes these algorithms are less attractive in practical applications, where it is not easy to select the proper model among potential large candidate parameter spaces. In this paper, we aim at consider a Two-level Weighted Graph Matching for Unsupervised Multi-view Feature Selection. We construct view-specific graphs to capture the local structure of each view. We also compute the feature-level graphs to obtain the local structure of data characterized by single feature. Thus, we learn the optimal unknown graph from both the view-specific graphs and the feature-level graphs. The approximation between the consensus graph and the feature level graphs is achieved by the two-level weighted linear combination measured by the squared loss function. The consensus between the final graph and view-specific graphs are captured by the integrated pairwise alignment among sample pairs. Moreover, these two criteria are integrated into a unified learning procedure under the trace-ratio framework.
Energy-Efficient QoE-Driven Strategies for Context-Aware RAT Selection

This paper formulates an optimization problem that maximizes an aggregate utility that captures the “in-context” suitability of available radio access technologies (RATs) to support adaptive video streaming subject to a single-homing constraint. To efficiently solve the considered problem, a novel network-assisted quality-of-experience (QoE)-driven methodology is devised, and its impact on the end-user devices is evaluated. The proposed approach is evaluated and benchmarked against its distributed and centralized counterparts from a cost-benefit perspective. The results reveal that the proposed strategy significantly outperforms its distributed counterpart, and performs differently with respect to its centralized counterpart depending on the number of video clients. At low loads, it performs similarly with much less control overhead. At high loads, the proposed strategy scales up well, while the centralized approach gets overwhelmed by an increasing uplink signaling. A practicality analysis of the proposed strategy for battery-powered devices reveals that its gain in terms of uplink signaling outweighs its cost in terms of processing load, which results in a drastic reduction of the consumed energy. Therefore, the proposed solution provides a win-win situation, where the video clients can sustain good QoE levels at reduced energy consumption, while the network can accommodate more users with existing capacity.
Let's take a selfie

Selfies have become a global phenomenon that is omnipresent on social media networks. The practice of taking selfies in public spaces has been found to be engaging and allows for a variety of novel interactions. In this work, we propose a set of design considerations to further enhance selfie booths to increase the engagement they provide. These emerged through three iterations of a selfie booth.
Designing User Interfaces for a Wellbeing Persuasive App

Wellbeing and prevention are a daily target for an increasing number of people. Food and exercise are the two most important elements contributing to personal wellbeing and disease prevention. This paper describes the participative design process for creating the interfaces of an app designed to help shape healthy behavior in adults with pathologies, highlighting the principles driving the design choices.
A system to enforce user's preference in OSN advertising

Social network advertising is currently one of the most effective advertising types available to promote a product or a brand. The problem discussed in this paper concerns the possibility to ensure that advertising reaches really interested users, and also to prove this. At this aim, we propose the use of Blockchain to store users' interest and to obtain an assertion that a user is interested in a product before the advertising is shown. The proposal has been implemented by a Solidity smart contract in Ethereum and has been shown to be effective and cheap.