nils-herrmann/clean_scirepeval huggingface dataset:
A novel framework for SMS spam filtering

A novel framework for SMS spam filtering is introduced in this paper to prevent mobile phone users from unsolicited SMS messages. The framework makes use of two distinct feature selection approaches based on information gain and chi-square metrics to find out discriminative features representing SMS messages. The discriminative feature subsets are then employed in two different Bayesian-based classifiers, so that SMS messages are categorized as either spam or legitimate. Moreover, the paper introduces a real-time mobile application for Android™ based mobile phones utilizing the proposed spam filtering scheme, as well. Hence, SMS spam messages are silently filtered out without disturbing phone users. Effectiveness of the filtering framework is evaluated on a large SMS message collection including legitimate and spam messages. Following the evaluation, remarkably accurate classification results are obtained for both spam and legitimate messages.
A Composable Deadlock-Free Approach to Object-Based Isolation

A widely used principle in the design of concurrent programs is isolation – the property that a task can operate on shared data without interference from other tasks. In this paper, we introduce a new approach to object-based isolation that is guaranteed to be deadlockfree, while still retaining the rollback benefits of transactions. Further, our approach differentiates between read and write accesses in its concurrency control mechanisms. Finally, since the generality of our approach precludes the use of static ordering for deadlock avoidance, our runtime ensures deadlock-freedom by detecting and resolving deadlocks at runtime automatically, without involving the programmer.
RFID: a technical overview and its application to the enterprise

Radio frequency identification (RFID) offers tantalizing benefits for supply chain management, inventory control, and many other applications. Only recently, however, has the convergence of lower cost and increased capabilities made businesses take a hard look at what RFID can do for them. This article offers an RFID tutorial that answers the following questions: i) what is RFID, and how does it work? ii) What are some applications of RFID? iii) What are some challenges and problems in RFID technology and implementation? iv) How have some organizations implemented RFID?.
Remote photoplethysmography application to the analysis of time-frequency changes of human heart rate variability

In this article we present the possibilities of using remote photoplethysmography (rPPG or imaging PPG) technology to estimate time-frequency changes of human heart rate variability. We propose improvements for algorithm presented in our recent study. Algorithm modification allows to exclude skin areas with highly variable levels of lighting, thus reducing noise level and increasing duration of signal suitable for processing. Twenty healthy volunteers (males and females) aged from 20 to 25 took part in this investigation. The blood volume pulse rate estimated from the rPPG rhythmogram and cardiac pulse rate estimated from the electrocardiogram are compared. The results showed that the very low frequency hemodynamic oscillations of blood volume pulse rate estimated from the rPPG rhythmogram in the [0.04-0.003] Hz frequency band can be used to monitor functional changes of a human.
HTML web content extraction using paragraph tags

With the ever expanding use of the internet to disseminate information across the world, gathering useful information from the multitude of web page styles continues to be a difficult problem. The use of computers as a tool to scrape the desired content from a web page has been around for several decades. Many methods exist to extract desired content from web pages, such as Document Object Model (DOM) trees, text density, tag ratios, visual strategies, and fuzzy algorithms. Due to the multitude of different website styles and designs, however, finding a single method to work in every case is a very difficult problem. This paper presents a novel method, Paragraph Extractor (ParEx), of clustering HTML paragraph tags and local parent headers to identify the main content within a news article. On websites that use paragraph tags to store their main news article, ParEx shows better performance than the Boilerpipe algorithm with higher F1 scores of 97.33% to 88.53%.
Hand Gesture Recognition for Sign Language Recognition: A Review

For deaf and dumb people, Sign language is the only way of communication. With the help of sign language, these physical impaired people express their emotions and thoughts to other person. Since for the common person, it is difficult to understand these language therefore often these physically challenged has to keep the translator along with them to communicate with the world. Hence sign language recognition has become empirical task. Since sign language consist of various movement and gesture of hand therefore the accuracy of sign language depends on the accurate recognition of hand gesture. This paper present various method of hand gesture and sign language recognition proposed in the past by various researchers. SL, DCT, Self organizing Map, Camshift
Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder. Finding the biomarkers associated with ASD is extremely helpful to understand the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatment. Although Deep Neural Networks (DNNs) have been applied in functional magnetic resonance imaging (fMRI) to identify ASD, understanding the data driven computational decision making procedure has not been previously explored. Therefore, in this work, we address the problem of interpreting reliable biomarkers associated with identifying ASD; specifically, we propose a 2-stage method that classifies ASD and control subjects using fMRI images and interprets the saliency features activated by the classifier. First, we trained an accurate DNN classifier. Then, for detecting the biomarkers, different from the DNN visualization works in computer vision, we take advantage of the anatomical structure of brain fMRI and develop a frequency-normalized sampling method to corrupt images. Furthermore, in the ASD vs. control subjects classification scenario, we provide a new approach to detect and characterize important brain features into three categories. The biomarkers we found by the proposed method are robust and consistent with previous findings in the literature. We also validate the detected biomarkers by neurological function decoding and comparing with the DNN activation maps.
Word Sense Disambiguation for Free-text Indexing Using a Massive Semantic Network

Semantics-free, word-based information retrieval is thwarted by two complementary problems. First, search for relevant documents returns irrelevant items when all meanings of a search term are used, rather than just the meaning intended. This causes low precision. Second, relevant items are missed when they are indexed not under the actual search terms, but rather under related terms. This causes low recall. With semantics-free approaches there is generally no way to improve both precision and recall at the same time. Word sense disambiguation during document indexing should improve precision. We have investigated using the massive Word Net semantic network for disambigu at ion during indexing. With the unconstrained text of the SMART ret rieval environment, we have had to derive our own content description from the input text, given only part-ofspeech tagging of the input. We employ the notion of semantic distance between network nodes. Input text terms with multiple senses are disambiguated by finding the combination of senses from a set of contiguous terms which minimizes total pairwise dist ante between senses. Results so far have been encouraging. Improvement in disamblguation compared with chance is clear
Clickbait Convolutional Neural Network

With the development of online advertisements, clickbait spread wider and wider. Clickbait dissatisfies users because the article content does not match their expectation. Thus, clickbait detection has attracted more and more attention recently. Traditional clickbait-detection methods rely on heavy feature engineering and fail to distinguish clickbait from normal headlines precisely because of the limited information in headlines. A convolutional neural network is useful for clickbait detection, since it utilizes pretrained Word2Vec to understand the headlines semantically, and employs different kernels to find various characteristics of the headlines. However, different types of articles tend to use different ways to draw users’ attention, and a pretrained Word2Vec model cannot distinguish these different ways. To address this issue, we propose a clickbait convolutional neural network (CBCNN) to consider not only the overall characteristics but also specific characteristics from different article types. Our experimental results show that our method outperforms traditional clickbait-detection algorithms and the TextCNN model in terms of precision, recall and accuracy.
Code Conjurer: Pulling Reusable Software out of Thin Air

For many years, the IT industry has sought to accelerate the software development process by assembling new applications from existing software assets. However, true component-based reuse of the form Douglas Mcllroy envisaged in the 1960s is still the exception rather than the rule, and most of the systematic software reuse practiced today uses heavyweight approaches such as product-line engineering or domain-specific frameworks. By component, we mean any cohesive and compact unit of software functionality with a well-defined interface - from simple programming language classes to more complex artifacts such as Web services and Enterprise JavaBeans.
Twitter as a Corpus for Sentiment Analysis and Opinion Mining

Sentiment Analysis (SA) and summarization has recently become the focus of many researchers, because analysis of online text is beneficial and demanded in many different applications. One such application is productbased sentiment summarization of multi-documents with the purpose of informing users about pros and cons of various products. This paper introduces a novel solution to target-oriented sentiment summarization and SA of short informal texts with a main focus on Twitter posts known as “tweets”. We compare different algorithms and methods for SA polarity detection and sentiment summarization. We show that our hybrid polarity detection system not only outperforms the unigram state-of-the-art baseline, but also could be an advantage over other methods when used as a part of a sentiment summarization system. Additionally, we illustrate that our SA and summarization system exhibits a high performance with various useful functionalities and features. Sentiment classification aims to automatically predict sentiment polarity (e.g., positive or negative) of users publishing sentiment data (e.g., reviews, blogs). Although traditional classification algorithms can be used to train sentiment classifiers from manually labeled text data, the labeling work can be time-consuming and ex-pensive. Meanwhile, users often use some different words when they express sentiment in different domains. If we directly apply a classifier trained in one domain to other domains, the performance will be very low due to the differences between these domains. In this work, we develop a general solution to sentiment classification when we do not have any labels in a target domain but have some labeled data in a different domain, regarded as source domain.
Galvanometric optical laser beam steering system for microfactory application

This article presents a kinematic model and control of a galvanometric laser beam steering system for high precision marking, welding or soldering applications as a microfactory module. Galvo systems are capable of scanning laser beam with relatively high frequencies that makes them suitable for fast processing applications. For the sake of flexibility and ease of use 2D reference shapes to be processed are provided as CAD drawings. Drawings are parsed and interpolated to x - y reference data points on MATLAB then stored as arrays in C code. C header file is further included as reference data points to be used by the system. Theoretical kinematic model of the system is derived and model parameters are tuned for practical implementation and validated with respect to measured positions on rotation space with optical position sensor and image field with position sensitive device. Machining with material removal requires high power laser to be employed that makes position measurement on image field unfeasible. Therefore for closed loop applications optical position sensor embedded in galvo motors is used for position feedback. Since the model approved to be approximately linear in the range of interest by simulations, a PI controller is used for precise positioning of the galvo motors. Experimental results for tracking circular and rectangular shape references are proved to be precise with errors of less than 2%.
Extracting information from neuronal populations: information theory and decoding approaches

To a large extent, progress in neuroscience has been driven by the study of single-cell responses averaged over several repetitions of stimuli or behaviours. However,the brain typically makes decisions based on single events by evaluating the activity of large neuronal populations. Therefore, to further understand how the brain processes information, it is important to shift from a single-neuron, multiple-trial framework to multiple-neuron, single-trial methodologies. Two related approaches — decoding and information theory — can be used to extract single-trial information from the activity of neuronal populations. Such population analysis can give us more information about how neurons encode stimulus features than traditional single-cell studies.
Switched inductor boost converter for PV applications

This paper introduces a boost converter with high dc gain as a solution for partial shading of photovoltaic (PV) module. Switched inductor boost converter (SIBC) is introduced by replacing the inductor of the boost converter with a switched inductor branch. As a result, the conversion gain ratio can be increased. The proposed converter is used as an interface between the PV system and the load. A Maximum Power Point Tracking (MPPT) control is applied to extract the maximum power of the PV module. Analyses, simulation, and experimental results are provided to validate the operation of the converter.
Limitation of inductive power transfer for consumer applications

Inductive power transmission is proposed more and more also for consumer applications. In this work, limitations with respect to efficiency of the whole magnetic system are investigated. The power efficiency of a given structure is dependent on resonant matching and on the load impedance. First, the matching conditions for optimal power efficiency are derived. Then the achievable efficiency for inductive transmission structures with varying distance and size ratios are investigated. Recent publications on inductive power transmission are evaluated and discussed based on these results. As a conclusion, inductive power transmission in a larger space (e.g. a whole room) is very inefficient. On the other hand, inductive power transmission at a surface can be efficient as conventional power supplies. Based on this insight, an inductive power transmission pad has been designed and built, with the purpose to charge mobile devices like mobile phones. It can charge an arbitrary number of devices and allows free positioning of the devices on the pad. It consists of an array of planar transmitter coils and has a size of 20 cm x 26 cm. It can detect the position of a receiver and activates only the coils underneath a receiver.
Predicting task from eye movements: On the importance of spatial distribution, dynamics, and image features

Yarbus' pioneering work in eye tracking has been influential to methodology and in demonstrating the apparent importance of task in eliciting different fixation patterns. There has been renewed interest in Yarbus' assertions on the importance of task in recent years, driven in part by a greater capability to apply quantitative methods to fixation data analysis. A number of recent research efforts have examined the work has raised a number of interesting questions, with some investigations calling for closer examination of the validity of Yarbus' claims, and subsequent efforts revealing some of the nuances involved in carrying out this type of analysis including both methodological, and data related considerations. In this paper, we present an overview of prior efforts in task prediction, and assess different types of statistics drawn from fixation data, or images in their ability to predict task from gaze. We also examine the extent to which relatively general task definitions (free-viewing, object-search, saliency-viewing, explicit saliency) may be predicted by spatial positioning of fixations, features co-located with fixation points, fixation dynamics and scene structure. This is accomplished in considering the data of Koehler et al. (2014)  affording a larger scale, and qualitatively different corpus of data for task prediction relative to existing efforts. Based on this analysis, we demonstrate that both spatial position, as well as local features are of value in distinguishing general task categories. The methods proposed provide a general framework for highlighting features that distinguish behavioural differences observed across visual tasks, and we relate new task prediction results in this paper to the body of prior work in this domain. Finally, we also comment on the value of task prediction and classification models in general in understanding facets of gaze behaviour. & 2016
Profiling Student Interactions in Threaded Discussions with Speech Act Classifiers

On-line discussion is a popular form of web-based computer-mediated communication and is an important medium for distance education. Automatic tools for analyzing online discussions are highly desirable for better information management and assistance. This paper presents an approach for automatically profiling student interactions in on-line discussions. Using N-gram features and linear SVM, we developed “speech act” classifiers that identify the roles that individual messages play. The classifiers were used in finding messages that contain questions or answers. We then applied a set of thread analysis rules for identifying threads that may have unanswered questions and need instructor attention. We evaluated the results with three human annotators, and 70-75% of the predictions from the system were consistent with human answers.
DSM: A Low-Overhead, High-Performance, Dynamic Stream Mapping Approach for MongoDB

For write-intensive workloads, reclaiming free blocks in flash SSDs is expensive due to data fragmentation problem that leads to performance degradation. This paper addresses that problem in MongoDB, a popular document store in the current market, by introducing a novel stream mapping scheme that exploits unique characteristics of MongoDB and multi-streamed technology. It dynamically assigns streams for corresponding writes according to their hotness values and distinguishes writes on primary index files from writes on secondary index files. The proposed method is high-performance, low-overhead, and independent with data models or workloads. Empirical results in Linkbench benchmark show that compared to the original WiredTiger our approach improves the throughput and reduces the 99th-percentile latency by up to 65% and 46.2% respectively. Compared to the best-performance in the prior research, our approach improves the throughput and reduces the 99th-percentile latency by up to 23% and 28.5% respectively. Distinguishing writes on primary index files from writes on secondary index files enhances the throughput and the 99th-percentile latency by up to 11.7% and 15.7% respectively. Moreover, by tuning the leaf page size in B+Tree of MongoDB, we can significantly improve the throughput by 1.6x – 2.1x in Linkbench.
EEG-based automatic emotion recognition: Feature extraction, selection and classification methods

Automatic emotion recognition is an interdisciplinary research field which deals with the algorithmic detection of human affect, e.g. anger or sadness, from a variety of sources, such as speech or facial gestures. Apart from the obvious usage for industry applications in human-robot interaction, acquiring the emotional state of a person automatically also is of great potential for the health domain, especially in psychology and psychiatry. Here, evaluation of human emotion is often done using oral feedback or questionnaires during doctor-patient sessions. However, this can be perceived as intrusive by the patient. Furthermore, the evaluation can only be done in a noncontinuous manner, e.g. once a week during therapy sessions. In contrast, using automatic emotion detection, the affect state of a person can be evaluated in a continuous non-intrusive manner, for example to detect early on-sets of depression. An additional benefit of automatic emotion recognition is the objectivity of such an approach, which is not influenced by the perception of the patient and the doctor. To reach the goal of objectivity, it is important, that the source of the emotion is not easily manipulable, e.g. as in the speech modality. To circumvent this caveat, novel approaches in emotion detection research the potential of using physiological measures, such as galvanic skin sensors or pulse meters. In this paper we outline a way of detecting emotion from brain waves, i.e., EEG data. While EEG allows for a continuous, real-time automatic emotion recognition, it furthermore has the charm of measuring the affect close to the point of emergence: the brain. Using EEG data for emotion detection is nevertheless a challenging task: Which features, EEG channel locations and frequency bands are best suited for is an issue of ongoing research. In this paper we evaluate the use of state of the art feature extraction, feature selection and classification algorithms for EEG emotion classification using data from the de facto standard dataset, DEAP. Moreover, we present results that help choose methods to enhance classification performance while simultaneously reducing computational complexity.