nils-herrmann/clean_scirepeval huggingface dataset:
Integration of Machine Learning Techniques to Evaluate Dynamic Customer Segmentation Analysis for Mobile Customers

The telecommunications industry is highly competitive, which means that the mobile providers need a business intelligence model that can be used to achieve an optimal level of churners, as well as a minimal level of cost in marketing activities. Machine learning applications can be used to provide guidance on marketing strategies. Furthermore, data mining techniques can be used in the process of customer segmentation. The purpose of this paper is to provide a detailed analysis of the C.5 algorithm, within naive Bayesian modelling for the task of segmenting telecommunication customers behavioural profiling according to their billing and socio-demographic aspects. Results have been experimentally implemented.
Deep Speaker: an End-to-End Neural Speaker Embedding System

We present Deep Speaker, a neural speaker embedding system that maps utterances to a hypersphere where speaker similarity is measured by cosine similarity. The embeddings generated by Deep Speaker can be used for many tasks, including speaker identification, verification, and clustering. We experiment with ResCNN and GRU architectures to extract the acoustic features, then mean pool to produce utterance-level speaker embeddings, and train using triplet loss based on cosine similarity. Experiments on three distinct datasets suggest that Deep Speaker outperforms a DNN-based i-vector baseline. For example, Deep Speaker reduces the verification equal error rate by 50% (relatively) and improves the identification accuracy by 60% (relatively) on a text-independent dataset. We also present results that suggest adapting from a model trained with Mandarin can improve accuracy for English speaker recognition.
Mechanical Design of "Omni-Ball": Spherical Wheel for Holonomic Omnidirectional Motion

In this paper, mechanical design of a novel spherical wheel shape for a omni-directional mobile robot is presented. The wheel is used in a omnidirectional mobile robot realizing high step-climbing capability with its hemispherical wheel. Conventional Omniwheels can realize omnidirectional motion, however they have a poor step overcoming ability due to the sub-wheel small size. The proposed design solves this drawback by means of a 4 wheeled design. "Omni-Ball" is formed by two passive rotational hemispherical wheels and one active rotational axis. An actual prototype model has been developed to illustrate the concept and to perform preliminary motion experiments, through which the basic performance of the Omnidirectional vehicle with this proposed Omni-Ball mechanism was confirmed. An prototype has been developed to illustrate the concept. Motion experiments, with a test vehicle are also presented.
The relation of moral emotion attributions to prosocial and antisocial behavior: a meta-analysis.

This meta-analytic review of 42 studies covering 8,009 participants (ages 4-20) examines the relation of moral emotion attributions to prosocial and antisocial behavior. A significant association is found between moral emotion attributions and prosocial and antisocial behaviors (d = .26, 95% CI [.15, .38]; d = .39, 95% CI [.29, .49]). Effect sizes differ considerably across studies and this heterogeneity is attributed to moderator variables. Specifically, effect sizes for predicted antisocial behavior are larger for self-attributed moral emotions than for emotions attributed to hypothetical story characters. Effect sizes for prosocial and antisocial behaviors are associated with several other study characteristics. Results are discussed with respect to the potential significance of moral emotion attributions for the social behavior of children and adolescents.
The Development of the Game Engagement Questionnaire: A Measure of Engagement in Video Game Playing: Response to Reviews

This paper begins with an argument that most measure development in the social sciences, with its reliance on correlational techniques as a tool, falls short of the requirements for constructing meaningful, unidimensional measures of human attributes. By demonstrating how rating scales are ordinal-level data, we argue the necessity of converting these to equal-interval units to develop a measure that is both qualitatively and quantitatively defensible. This requires that the empirical results and theoretical explanation are questioned and adjusted at each step of the process. In our response to the reviewers, we describe how this approach was used to develop the Game Engagement Questionnaire (GEQ), including its emphasis on examining a continuum of involvement in violent video games. The GEQ is an empirically sound measure focused on one player characteristic that may be important in determining game influence.
Cascaded DC–DC Converter Photovoltaic Systems: Power Optimization Issues

This paper investigates the issues of ensuring global power optimization for cascaded dc-dc converter architectures of photovoltaic (PV) generators irrespective of the irradiance conditions. The global optimum of such connections of PV modules is generally equivalent with performing the maximum power point tracking (MPPT) on all the modules. The most important disturbance occurs when the irradiance levels of modules happen to be sensibly different from a module to another - in this case, voltage-limitation requirements may be broken. The proposed supervisory algorithm then attempts to establish the best suboptimal power regime. Validation has been achieved by MATLAB/Simulink numerical simulation in the case of a single-phase grid-connected PV system, where individual MPPTs have been implemented by an extremum-seeking control, a robust and less-knowledge-demanding perturb-and-observe method.
ON-LINE MONITORING AND ANALYSIS OF FAULTS IN TRANSMISSION AND DISTRIBUTION LINES USING GSM TECHNIQUE

Increase in demand of electricity for entire applications in any country, need to produce consistently with advanced protection system. Many special protection systems are available based on volume of power distributed and often the load changes without prediction required an advanced and special communication based systems to control the electrical parameters of the generation. Most of the existing systems are reliable on various applications but not perfect for electrical applications. Electrical environment will have lots of disturbance in nature, Due to natural disasters like storms, cyclones or heavy rains transmission and distribution lines may lead to damage. The electrical wire may cut and fall on ground, this leads to very harmful for human beings and may become fatal. So, a rigid, reliable and robust communications like GSM technology instead of many communication techniques used earlier. This enhances speed of communication with distance independenncy. This technology saves human life from this electrical danger by providing the fault detection and automatically stops the electricity to the damaged line and also conveys the message to the electricity board to clear the fault. An Embedded based hardware design is developed and must acquire data from electrical sensing system. A powerful GSM networking is designed to send data from a network to other network. Any change in parameters of transmission is sensed to protect the entire transmission and distribution.
Serial Order: A Parallel Distributed Processing Approach

My sincere thanks to Donald Norman and David Rumelhart for their support of many years. I also wish to acknowledge the help of The views and conclusions contained in this document are those of the author and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the sponsoring agencies. Approved for public release; distribution unlimited. Reproduction in whole or in part is permitted for any purpose of the United States Government Requests for reprints should be sent to the
Noninvasive cuff'less estimation of blood pressure using Photoplethysmography without electrocardiograph measurement

The paper presents a novel approach of measuring blood pressure using Photoplethysmography (PPG). It is a non-invasive, cuffless and painless technique that deploys infrared light to detect small variation in blood volume in the tissues with each cardiac cycle. Few specific features (viz. systolic upstroke time (ST), diastolic time (DT) and the time delay between the systolic and diastolic peak (T1)) of the waveform obtained via this technique were examined and correlated with the arterial blood pressure in 22 subjects of two age groups i) 18-25 years ii) 26-50 years. It was observed that there is a good correlation of blood pressure (both systolic blood pressure (SBP) and diastolic blood pressure (DBP)) with diastolic time and also with the time delay between systolic and diastolic peak.
Illumination and Pose Invariant Face Recognition : A Technical Review

Face recognition being the most important biometric trait it still faces many challenges, like pose variation, illumination variation etc. When such variations are present in both pose and illumination, all the algorithms are greatly affected by these variations and their performance gets degraded. In this paper we are presenting a detail survey on 2D face recognition under such uncontrolled conditions. Here we have explored different techniques proposed for illumination and pose problem in addition with the classifiers that have been successfully used for face recognition in general. The objective of this review paper is to summarize and compare some of the well-known methods for better understanding of reader.
Contextualization, user modeling and personalization in the social web: from social tagging via context to cross-system user modeling and personalization

Social Web stands for the culture of participation and collaboration on the Web. Structures emerge from social interactions: social tagging enables a community of users to assign freely chosen keywords to Web resources. The structure that evolves from social tagging is called folksonomy and recent research has shown that the exploitation of folksonomy structures is beneficial to information systems. In this thesis we propose models that better capture usage context of social tagging and develop two folksonomy systems that allow for the deduction of contextual information from tagging activities. We introduce a suite of ranking algorithms that exploit contextual information embedded in folksonomy structures and prove that these contextsensitive ranking algorithms significantly improve search in Social Web systems. We setup a framework of user modeling and personalization methods for the Social Web and evaluate this framework in the scope of personalized search and social recommender systems. Extensive evaluation reveals that our context-based user modeling techniques have significant impact on the personalization quality and clearly improve regular user modeling approaches. Finally, we analyze the nature of user profiles distributed on the Social Web, implement a service that supports cross-system user modeling and investigate the impact of cross-system user modeling methods on personalization. In different experiments we prove that our cross-system user modeling strategies solve cold-start problems in social recommender systems and that intelligent re-use of external profile information improves the recommendation quality also beyond the cold-start.
Demand Response Management With Multiple Utility Companies: A Two-Level Game Approach

Demand Response Management (DRM) is a key component of the future smart grid that helps to reduce power peak load and variation. Different from most existing studies that focus on the scenario with a single utility company, this paper studies DRM with multiple utility companies. First, the interaction between utility companies and residential users is modeled as a two-level game. That is, the competition among the utility companies is formulated as a non-cooperative game, while the interaction among the residential users is formulated as an evolutionary game. Then, we prove that the proposed strategies are able to make both games converge to their own equilibrium. In addtion, the strategies for the utility companies and the residential users are implemented by distributed algorithms. Illustrative examples show that the proposed scheme is able to significantly reduce peak load and demand variation.
BitScope: Automatically Dissecting Malicious Binaries

Automatic analysis of malicious binaries is necessary in order to scale with the rapid development and recovery of malware found in the wild. The results of automatic analysis are useful for creating defense systems and understanding the current capabilities of attackers. We propose an approach for automatic dissection of malicious binaries which can answer fundamental questions such as what behavior they exhibit, what are the relationships between their inputs and outputs, and how an attacker may be using the binary. We implement our approach in a system called BitScope. At the core of BitScope is a system which allows us to execute binaries with symbolic inputs. Executing with symbolic inputs allows us to reason about code paths without constraining the analysis to a particular input value. We implement 5 analysis using BitScope, and demonstrate that the analysis can rapidly analyze important properties such as what behaviors the malicious binaries exhibit. For example, BitScope uncovers all commands in typical DDoS zombies and botnet programs, and uncovers significant behavior in just minutes. This work was supported in part by CyLab at Carnegie Mellon under grant DAAD19-02-1-0389 from the Army Research Office, the U.S. Army Research Office under the Cyber-TA Research Grant No. W911NF-06-1-0316, the ITA (International Technology Alliance), CCF-0424422, National Science Foundation Grant  [2005-S-606-02, Next Generation Prediction and Response technology for Computer and Network Security Incidents]. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation, the ARO, CMU, or the U.S. Government.
Alopecia areata: an update.

Alopecia areata (AA) is a common form of non-scarring hair loss of scalp and/or body. Genetic predisposition, autoimmunity, and environmental factors play a major role in the etiopathogenesis of AA. Patchy AA is the most common form. Atopy and autoimmune thyroiditis are most common associated conditions. Peribulbar and intrabulbar lymphocytic inflammatory infiltrate resembling "swarm of bees" is characteristic on histopathology. Treatment is mainly focused to contain the disease activity. Corticosteroids are the preferred treatments in form of topical, intralesional, or systemic therapy. Camouflage in the form of wigs may be an alternative option in refractory cases.
Customer-Base Valuation in a Contractual Setting: The Perils of Ignoring Heterogeneity

T past few years have seen increasing interest in taking the notion of customer lifetime value (CLV) and extending it to value a customer base (with subsequent links to corporate valuation). The application of standard textbook discussions of CLV leads to calculations based on a single retention rate. However, at the cohort level, retention rates typically increase over time. It has been suggested that these observed dynamics are due, in large part, to a sorting effect in a heterogeneous population. We show that failing to recognize these dynamics yields a downward-biased estimate of the residual value of the customer base (compared to an aggregate analysis that ignores these dynamics). We also explore the implications of failing to account for retention dynamics when computing retention elasticities and find that the frequently reported values underestimate the true effect of increases in underlying retention rates in a heterogeneous world.
Applying Machine Learning Techniques for Detection of Malicious Code in Network Traffic

The Early Detection, Alert and Response (eDare) system is aimed at purifying Web traffic propagating via the premises of Network Service Providers (NSP) from malicious code. To achieve this goal, the system employs powerful network traffic scanners capable of cleaning traffic from known malicious code. The remaining traffic is monitored and Machine Learning (ML) algorithms are invoked in an attempt to pinpoint unknown malicious code exhibiting suspicious morphological patterns. Decision trees, Neural Networks and Bayesian Networks are used for static code analysis in order to determine whether a suspicious executable file actually inhabits malicious code. These algorithms are being evaluated and preliminary results are encouraging.
Depth Image Inpainting: Improving Low Rank Matrix Completion With Low Gradient Regularization

We address the task of single depth image inpainting. Without the corresponding color images, previous or next frames, depth image inpainting is quite challenging. One natural solution is to regard the image as a matrix and adopt the low rank regularization just as color image inpainting. However, the low rank assumption does not make full use of the properties of depth images. A shallow observation inspires us to penalize the nonzero gradients by sparse gradient regularization. However, statistics show that though most pixels have zero gradients, there is still a non-ignorable part of pixels, whose gradients are small but nonzero. Based on this property of depth images, we propose a low gradient regularization method in which we reduce the penalty for small gradients while penalizing the nonzero gradients to allow for gradual depth changes. The proposed low gradient regularization is integrated with the low rank regularization into the low rank low gradient approach for depth image inpainting. We compare our proposed low gradient regularization with the sparse gradient regularization. The experimental results show the effectiveness of our proposed approach.
Reducing online privacy risk to facilitate e-service adoption : the influence of perceived ease of use and corporate credibility

The paper aims to examine ways to reduce privacy risk and its effects so that adoption of e-services can be enhanced.  Consumers that form a viable target market for an e-service are presented with the task of experiencing the e-service and expressing their attitudes and intentions toward it. Structural equation modeling is used to analyze the responses.  The paper finds that consumer beliefs that the e-service will be easy to use and that the e-service provider is credible and capable reduce privacy risk and its effects, thus enhancing adoption likelihood.  The focus on a financial services product (online bill paying) suggests that similar research should be conducted with other high-risk e-services (such as those dealing with healthcare) and lower-risk e-services (such as subscription services and social networks).  In addition to addressing consumers’ privacy risk directly, e-service providers can also reduce privacy risk and its effects by enhancing corporate credibility and perceived ease of use of the service. Increased assessments of privacy risk perceptions and efforts to reduce those perceptions will likely yield higher usage rates for e-services.  The use of the technology acceptance model from information systems research, combined with a multi-faceted conceptualization of privacy risk, moves the examination of privacy risk to a higher level, particularly in light of the examination of the additional factors of perceived ease of use and corporate credibility.
Extreme Learning Machine Ensemble Using Bagging for Facial Expression Recognition

An extreme learning machine (ELM) is a recently proposed learning algorithm for a single-layer feed forward neural network. In this paper we studied the ensemble of ELM by using a bagging algorithm for facial expression recognition (FER). Facial expression analysis is widely used in the behavior interpretation of emotions, for cognitive science, and social interactions. This paper presents a method for FER based on the histogram of orientation gradient (HOG) features using an ELM ensemble. First, the HOG features were extracted from the face image by dividing it into a number of small cells. A bagging algorithm was then used to construct many different bags of training data and each of them was trained by using separate ELMs. To recognize the expression of the input face image, HOG features were fed to each trained ELM and the results were combined by using a majority voting scheme. The ELM ensemble using bagging improves the generalized capability of the network significantly. The two available datasets (JAFFE and CK+) of facial expressions were used to evaluate the performance of the proposed classification system. Even the performance of individual ELM was smaller and the ELM ensemble using a bagging algorithm improved the recognition performance significantly. Bagging, Ensemble Learning, Extreme Learning Machine, Facial Expression Recognition, Histogram of Orientation Gradient
Word Similarity Datasets for Indian Languages: Annotation and Baseline Systems

With the advent of word representations, word similarity tasks are becoming increasing popular as an evaluation metric for the quality of the representations. In this paper, we present manually annotated monolingual word similarity datasets of six Indian languages – Urdu, Telugu, Marathi, Punjabi, Tamil and Gujarati. These languages are most spoken Indian languages worldwide after Hindi and Bengali. For the construction of these datasets, our approach relies on translation and re-annotation of word similarity datasets of English. We also present baseline scores for word representation models using state-of-the-art techniques for Urdu, Telugu and Marathi by evaluating them on newly created word similarity datasets.
FMA: A Dataset for Music Analysis

We introduce the Free Music Archive (FMA), an open and easily accessible dataset which can be used to evaluate several tasks in music information retrieval (MIR), a field concerned with browsing, searching, and organizing large music collections. The community’s growing interest in feature and end-to-end learning is however restrained by the limited availability of large audio datasets. By releasing the FMA, we hope to foster research which will improve the state-of-the-art and hopefully surpass the performance ceiling observed in e.g. genre recognition (MGR). The data is made of 106,574 tracks, 16,341 artists, 14,854 albums, arranged in a hierarchical taxonomy of 161 genres, for a total of 343 days of audio and 917 GiB, all under permissive Creative Commons licenses. It features metadata like song title, album, artist and genres; user data like play counts, favorites, and comments; free-form text like description, biography, and tags; together with fulllength, high-quality audio, and some pre-computed features. We propose a train/validation/test split and three subsets: a genre-balanced set of 8,000 tracks from 8 major genres, a genre-unbalanced set of 25,000 tracks from 16 genres, and a 98 GiB version with clips trimmed to 30s. This paper describes the dataset and how it was created, proposes some tasks like music classification and annotation or recommendation, and evaluates some baselines for MGR. Code, data, and usage examples are available at https://github.com/mdeff/fma.