nils-herrmann/clean_scirepeval huggingface dataset:
PHD: A Probabilistic Model of Hybrid Deep Collaborative Filtering for Recommender Systems

Collaborative Filtering (CF), a well-known approach in producing recommender systems, has achieved wide use and excellent performance not only in research but also in industry. However, problems related to cold start and data sparsity have caused CF to attract an increasing amount of attention in efforts to solve these problems. Traditional approaches adopt side information to extract effective latent factors but still have some room for growth. Due to the strong characteristic of feature extraction in deep learning, many researchers have employed it with CF to extract effective representations and to enhance its performance in rating prediction. Based on this previous work, we propose a probabilistic model that combines a stacked denoising autoencoder and a convolutional neural network together with auxiliary side information (i.e, both from users and items) to extract users and items’ latent factors, respectively. Extensive experiments for four datasets demonstrate that our proposed model outperforms other traditional approaches and deep learning models making it state of the art.
Effects of Early and Late Nocturnal Sleep on Declarative and Procedural Memory

Recall of paired-associate lists (declarative memory) and mirror-tracing skills (procedural memory) was assessed after retention intervals defined over early and late nocturnal sleep. In addition, effects of sleep on recall were compared with those of early and late retention intervals filled with wakefulness. Twenty healthy men served as subjects. Saliva cortisol concentrations were determined before and after the retention intervals to determine pituitary-adrenal secretory activity. Sleep was determined somnopolygraphically. Sleep generally enhanced recall when compared with the effects of corresponding retention intervals of wakefulness. The benefit from sleep on recall depended on the phase of sleep and on the type of memory: Recall of paired-associate lists improved more during early sleep, and recall of mirror-tracing skills improved more during late sleep. The effects may reflect different influences of slow wave sleep (SWS) and rapid eye movement (REM) sleep since time in SWS was 5 times longer during the early than late sleep retention interval, and time in REM sleep was twice as long during late than early sleep (p < 0.005). Changes in cortisol concentrations, which independently of sleep and wakefulness were lower during early retention intervals than late ones, cannot account for the effects of sleep on memory. The experiments for the first time dissociate specific effects of early and late sleep on two principal types of memory, declarative and procedural, in humans.
Local connectome phenotypes predict social, health, and cognitive factors

The unique architecture of the human connectome is defined initially by genetics and subsequently sculpted over time with experience. Thus, similarities in predisposition and experience that lead to similarities in social, biological, and cognitive attributes should also be reflected in the local architecture of white matter fascicles. Here we employ a method known as local connectome fingerprinting that uses diffusion MRI to measure the fiber-wise characteristics of macroscopic white matter pathways throughout the brain. This fingerprinting approach was applied to a large sample (N = 841) of subjects from the Human Connectome Project, revealing a reliable degree of between-subject correlation in the local connectome fingerprints, with a relatively complex, low-dimensional substructure. Using a cross-validated, high-dimensional regression analysis approach, we derived local connectome phenotype (LCP) maps that could reliably predict a subset of subject attributes measured, including demographic, health, and cognitive measures. These LCP maps were highly specific to the attribute being predicted but also sensitive to correlations between attributes. Collectively, these results indicate that the local architecture of white matter fascicles reflects a meaningful portion of the variability shared between subjects along several dimensions. SummaryThe local connectome is the pattern of fiber systems (i.e., number of fibers, orientation, and size) within a voxel, and it reflects the proximal characteristics of white matter fascicles distributed throughout the brain. Here we show how variability in the local connectome is correlated in a principled way across individuals. This intersubject correlation is reliable enough that unique phenotype maps can be learned to predict between-subject variability in a range of social, health, and cognitive attributes. This work shows, for the first time, how the local connectome has both the sensitivity and the specificity to be used as a phenotypic marker for subject-specific attributes.
Agent-Agnostic Human-in-the-Loop Reinforcement Learning

Providing Reinforcement Learning agents with expert advice can dramatically improve various aspects of learning. Prior work has developed teaching protocols that enable agents to learn efficiently in complex environments; many of these methods tailor the teacher’s guidance to agents with a particular representation or underlying learning scheme, offering effective but specialized teaching procedures. In this work, we explore protocol programs, an agent-agnostic schema for Human-in-the-Loop Reinforcement Learning. Our goal is to incorporate the beneficial properties of a human teacher into Reinforcement Learning without making strong assumptions about the inner workings of the agent. We show how to represent existing approaches such as action pruning, reward shaping, and training in simulation as special cases of our schema and conduct preliminary experiments on simple domains.
Executive Functions Predict the Success of Top-Soccer Players

While the importance of physical abilities and motor coordination is non-contested in sport, more focus has recently been turned toward cognitive processes important for different sports. However, this line of studies has often investigated sport-specific cognitive traits, while few studies have focused on general cognitive traits. We explored if measures of general executive functions can predict the success of a soccer player. The present study used standardized neuropsychological assessment tools assessing players' general executive functions including on-line multi-processing such as creativity, response inhibition, and cognitive flexibility. In a first cross-sectional part of the study we compared the results between High Division players (HD), Lower Division players (LD) and a standardized norm group. The result shows that both HD and LD players had significantly better measures of executive functions in comparison to the norm group for both men and women. Moreover, the HD players outperformed the LD players in these tests. In the second prospective part of the study, a partial correlation test showed a significant correlation between the result from the executive test and the numbers of goals and assists the players had scored two seasons later. The results from this study strongly suggest that results in cognitive function tests predict the success of ball sport players.
Human Activity Recognition from Sensor Data

Human Activity Recognition (HAR) refers to the problem of automatically identifying human activities by learning from data collected either from the individual or from the environment surrounding that individual. The form of the data might be inertial measurements, physiological signals from devices like wearable sensors or those such as image, audio and video data collected from environmental devices/sensors. HAR has been a well studied problem for its potential capability to mine human behavioral patterns and for monitoring human activity for security and health purposes.
Specific Clinical Findings of Secondary Syphilis In The Oral Mucosa: ASeries of Six Case Reports

Syphilis (SyS) is a systemic infectious disease caused by the anaerobic spirochetes Treponema pallidum. This condition is further divided into primary, secondary latent and tertiary stages. Oral manifestations are most common in secondary stage and frequently are the only clinical sign of the disease. These lesions are characterized by having high rates of infection and peculiar clinical characteristics. Therefore, the purpose of this article is to highlight the common and not-so-common clinical appearances of secondary SyS in adults. Tongue, lips, jugal mucosa and palate lesions were selected to illustrate the similarity of oral SyS with other diseases. The diagnosis was performed by clinical and serological correlation. Furthermore, it was emphasized the importance of the knowledge of different forms of oral SyS lesions by clinicians in order to provide early diagnosis and faster treatment for infected patients.
Ultra-Compact and Robust Physically Unclonable Function Based on Voltage-Compensated Proportional-to-Absolute-Temperature Voltage Generators

This paper presents a technique for designing an ultra-compact and robust physically unclonable function for security-oriented applications. The circuits are based on pairs of analog circuits whose output voltage is supply voltage compensated and proportional to absolute temperature (PTAT). The difference between two outputs of a PTAT pair is digitized to produce 1 b output which is sensitive mostly only to random transistor threshold voltage variations. Fabricated in a 65 nm, the proposed 256 b PUF array takes an area of 3.07 μm2/bit, consumes 0.548 pJ/bit at a throughput of 10 Mb/s, while showing desirable robustness against temperature and supply voltage variations with 3.5% and 1.004% bit-instability across 0 to 80°C and 0.6 to 1.2 V, respectively. The unpredictability and uniqueness of the 256 b PUF output are verified by NIST randomness test and Hamming Distance between keys. As compared with the state of the art, the proposed design has an 8.3× smaller area/bit or 2× better robustness against noise and environmental variations.
Joint Binary Neural Network for Multi-label Learning with Applications to Emotion Classification

Recently the deep learning techniques have achieved success in multi-label classification due to its automatic representation learning ability and the end-to-end learning framework. Existing deep neural networks in multi-label classification can be divided into two kinds: binary relevance neural network (BRNN) and threshold dependent neural network (TDNN). However, the former needs to train a set of isolate binary networks which ignore dependencies between labels and have heavy computational load, while the latter needs an additional threshold function mechanism to transform the multi-class probabilities to multi-label outputs. In this paper, we propose a joint binary neural network (JBNN), to address these shortcomings. In JBNN, the representation of the text is fed to a set of logistic functions instead of a softmax function, and the multiple binary classifications are carried out synchronously in one neural network framework. Moreover, the relations between labels are captured via training on a joint binary cross entropy (JBCE) loss. To better meet multi-label emotion classification, we further proposed to incorporate the prior label relations into the JBCE loss. The experimental results on the benchmark dataset show that our model performs significantly better than the stateof-the-art multi-label emotion classification methods, in both classification performance and computational efficiency.
Intravesical stone formation on intrauterine contraceptive device

Since more than 30 years, intrauterine contraceptive devices (IUCD) have been used for a contraceptive opportunity. Although they are termed to be a safe and effective method for contraception, they also have some type of complications and uterine perforation, septic abortion, pelvic abscess are the serious complications of these devices. The incidence of uterine perforation is very low, but in the literature nearly 100 cases were reported about the extra uterine localization of IUCD. Migration may occur to the adjacent organs. We here in describe a case of a 31 year-old woman who had an IUCD with stone formation in the bladder. In the literature all of the cases were reported as IUCD migration, but although it seems technically impossible, IUCD placement into the bladder should also be considered in misplaced IUCDs.
A Systematic Review of Data Mining Approaches to Credit Card Fraud Detection

Credit card fraud is a serious and ever-growing problem with billions of dollars lost every year due to fraudulent transactions. Fraud has always been present and will always be. It is also ever changing, as the technology and usage patterns change over time, which makes CCFD (credit card fraud detection) a particularly hard problem. Traditionally, fraud detection relied solely on domain experts’ detection rules, but in the past decade or two, such solutions are being augmented with data mining models for fraud detection. The progress in this area is impeded both by the sensitive nature of the data and great commercial potential – the industrial solutions are understandably kept secret and authentic datasets are rare and few. In this paper we study the CCFD problem with its typical problems and state of the art solution. We survey the recent literature and bring a structured overview of relevant fraud detection features and data mining approaches to this problem.  credit card, fraud detection, machine learning, systematic review
Towards Online-Recognition with Deep Bidirectional LSTM Acoustic Models

Online-Recognition requires the acoustic model to provide posterior probabilities after a limited time delay given the online input audio data. This necessitates unidirectional modeling and the standard solution is to use unidirectional long short-term memory (LSTM) recurrent neural networks (RNN) or feedforward neural networks (FFNN). It is known that bidirectional LSTMs are more powerful and perform better than unidirectional LSTMs. To demonstrate the performance difference, we start by comparing several different bidirectional and unidirectional LSTM topologies. Furthermore, we apply a modification to bidirectional RNNs to enable online-recognition by moving a window over the input stream and perform one forwarding through the RNN on each window. Then, we combine the posteriors of each forwarding and we renormalize them. We show in experiments that the performance of this online-enabled bidirectional LSTM performs as good as the offline bidirectional LSTM and much better than the unidirectional LSTM.
An Efficient and Secure Automotive Wireless Software Update Framework

Future vehicles will be wirelessly connected to nearby vehicles, to the road infrastructure, and to the Internet, thereby becoming an integral part of the Internet of Things. New comfort features, safety functions, and a number of new vehicle-specific services will be integrated in future smart vehicles. These include a fast, secure, and reliable way to diagnose and reconfigure a vehicle, as well as the installation of new software (SW) on its integrated electronic control units (ECUs). Such wireless SW updates are beneficial for both automotive carmakers and customers, as they allow us to securely enable new features on the vehicle and to fix SW bugs by installing a new SW version over the air. A secure and dependable wireless SW update process is valuable in the entire lifetime of a modern vehicle as it can be used already during vehicle development and manufacturing process on the assembly line, as well as during vehicle maintenance in a service center. Additionally, future vehicles will allow us to remotely download up-to-date SW on the ECUs. To support this process over the entire vehicle's lifetime, a generic framework is needed. In this paper, SecUp, a generic framework enabling secure and efficient wireless automotive SW updates is proposed. SecUp utilizes IEEE 802.11s as wireless medium to interconnect vehicles and diagnostic devices in a dependable and fast way. Additionally, SecUp is enabling beneficial wireless SW update features such as parallel and partial SW updates to increase the efficiency, and comprises advanced security mechanisms to prevent abuse and attacks.
A multi-level encoder for text summarization

Neural sequence-to-sequence model have been successfully applied for abstractive text summarization. However, there is an obvious hierarchic phenomenon when we do a summarization that we always need to read the source text several times and abstract information at multiple level, but in the basic sequence-to-sequence model there is not a corresponding multiple structure. We propose a novel multi-level encoder that get the information of the text at different level to address that problem. The experiment shows that our model outperform the baseline 2 ROUGE points.
Stock Trend Prediction Using Regression Analysis – A Data Mining Approach

Organizations have been collecting data for decades, building massive data warehouses in which to store the data. Even though this data is available, very few of these organizations have been able to realize the actual value stored in it. The question these organizations are asking is how to extract meaningful data and uncover patterns and relationship from their databases. This paper presents a study of regression analysis for use in stock price prediction. Data were obtained from the daily official list of the prices of all shares traded on the stock exchange published by the Nigerian Stock Exchange using banking sector of Nigerian economy with three banks namely:First Bank of Nigeria Plc, Zenith Bank Plc, and Skye Bank Plc to build a database. A data mining software tool was used to uncover patterns and relationships and also to extract values of variables from the database to predict the future values of other variables through the use of time series data that employed moving average method. The tools were found capable technique to describe the trends of stock market prices and predict the future stock market prices of three banks sampled.
Agile versus CMMI - process template selection and integration with microsoft team foundation server

Increasingly many software development organizations must pursue at least a level 3 CMMI certification to compete in the marketplace. Not all software organizations are under this constraint, but the CMMI model offers benefit beyond just a marketing advantage. Agile methods have taken hold in many of the same organizations that stand to gain benefit from CMMI. CMMI adds some stress to the Agile approach by nature of more detailed planning and tracking. Microsoft and other software development organizations have endeavored to automate the more tedious aspects of CMMI, thus making it easier to support agile approaches within more rigid frameworks. The Microsoft Solutions Framework (MSF) initially founded on Microsoft software best practices observed internally and externally has expanded to integrate automated software configuration for team projects. The Team Foundation Server (TFS) product leverages MSF and supports both CMMI and Agile as process templates and provides tools around these templates to automate the use of either approach. In this paper, we explore how Microsoft has integrated these approaches into TFS, the rationale and implications for selecting one approach over another and how agile practices can be integrated into a CMMI framework.
Bangla Speech Recognition for Voice Search

In this work, different Gaussian Mixture Model-Hidden Markov Model(GMM-HMM) based and Deep Neural Network (DNN-HMM) based models have been analyzed for speech recognition in Bangla language to build a voice search module for search engine pipilika 1. A small corpus of 9 hours of speech recordings from 49 different speakers was prepared for this work consisting of a vocabulary of 500 unique words. The lowest Word Error Rate(WER) for (GMM-HMM) based model was 3.96% and for (DNN-HMM) based model was 5.30%. To our best knowledge, this is the lowest WER for Bangla speech recognition for such vocabulary size.
Hypolipidemic activity of Moringa oleifera Lam., Moringaceae, on high fat diet induced hyperlipidemia in albino rats

The leaves of Moringa oleifera Lam., Moringaceae, are used by the Indians in their herbal medicine as a hypolipidemic agent in obese patients. Albino Wistar rats were fed with methanolic extract of M. oleifera (150, 300 and 600 mg/kg, p.o.) and simvastatin (4 mg/ kg, p.o.) along with hyperlipidemic diet for 30 days. Moringa oleifera and simvastatin were found to lower the serum cholesterol, triacylglyceride, VLDL, LDL, and atherogenic index, but were found to increase the HDL as compared to the corresponding high fed cholesterol diet group (control). The Moringa oleifera methanolic extract was also investigated for its mechanism of action by estimating HMG CO-A reductase activity. Moringa oleifera was found to increase the excretion of fecal cholesterol. Thus, the study demonstrates that M. oleifera possesses a hypolipidemic effect.
Sensorless control of high-speed PM BLDC motor

In the paper were examined the possibilities to control a high-speed sensorless PM BLDC motor, with a target speed 100 000 rpm and power 1 kW, designed in the Department of Power Electronics, Electrical Drives and Robotics - KENER, Silesian University of Technology. The article describes methods based on phase voltages integration and the third harmonic, also presents a method of open-loop starting and ways of determining the position of the stopped rotor. Described sensorless control methods were examined by computer simulation, in order to test the possibilities of their implementation in the high-speed motor project.
Modeling Driver Behavior in a Cognitive Architecture

This paper explores the development of a rigorous computational model of driver behavior in a cognitive architecture--a computational framework with underlying psychological theories that incorporate basic properties and limitations of the human system.



Computational modeling has emerged as a powerful tool for studying the complex task of driving, allowing researchers to simulate driver behavior and explore the parameters and constraints of this behavior.



An integrated driver model developed in the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture is described that focuses on the component processes of control, monitoring, and decision making in a multilane highway environment.



This model accounts for the steering profiles, lateral position profiles, and gaze distributions of human drivers during lane keeping, curve negotiation, and lane changing.



The model demonstrates how cognitive architectures facilitate understanding of driver behavior in the context of general human abilities and constraints and how the driving domain benefits cognitive architectures by pushing model development toward more complex, realistic tasks.



The model can also serve as a core computational engine for practical applications that predict and recognize driver behavior and distraction.
Development of a General Knowledge Management Maturity Model

As investments in Knowledge Management (KM) initiatives grow, there is an increasing need for coherent and comprehensible principles and practices to guide KM implementation efforts. Academics and practitioners have proposed various KM Maturity Models (KMMM) to formally capture the KM development process by assessing the extent to which KM is explicitly defined, managed, controlled, and effective. However, the proliferation of definitions and assumptions and lack of clear description of assessment methods have made their selection and application difficult for practitioners and their study complex for researchers. Based on such motivations, this paper reviews, compares, assesses and integrates existing KMMMs to propose an General KMMM (G-KMMM), which focuses on assessing the maturity of people, process and technology aspects of KM development in organizations. An accompanying assessment tool is also developed to facilitate practical application. Avenues for further research and practice are discussed.