nils-herrmann/clean_scirepeval huggingface dataset:
IoT Forensic: Bridging the Challenges in Digital Forensic and the Internet of Things

The smart devices have been used in the most major domain like the healthcare, transportation, smart home, smart city and more. However, this technology has been exposed to many vulnerabilities, which may lead to cybercrime through the devices. With the IoT constraints and low-security mechanisms applied, the device could be easily been attacked, treated and exploited by cyber criminals where the smart devices could provide wrong data where it can lead to wrong interpretation and actuation to the legitimate users. To comply with the IoT characteristics, two approaches towards of having the investigation for IoT forensic is proposed by emphasizing the pre-investigation phase and implementing the real-time investigation to ensure the data and potential evidence is collected and preserved throughout the investigation.
A Feature Selection Method Based on Information Gain and Genetic Algorithm

With the rapid development of the Computer Science and Technology, It has become a major problem for the users that how to quickly find useful or needed information. Text categorization can help people to solve this question. The feature selection method has become one of the most critical techniques in the field of the text automatic categorization. A new method of the text feature selection based on Information Gain and Genetic Algorithm is proposed in this paper. This method chooses the feature based on information gain with the frequency of items. Meanwhile, for the information filtering systems, this method has been improved fitness function to fully consider the characteristics of weight, text and vector similarity dimension, etc. The experiment has proved that the method can reduce the dimension of text vector and improve the precision of text classification.
From BPMN Process Models to BPEL Web Services

The business process modelling notation (BPMN) is a graph-oriented language in which control and action nodes can be connected almost arbitrarily. It is supported by various modelling tools but so far no systems can directly execute BPMN models. The business process execution language for Web services (BPEL) on the other hand is a mainly block-structured language supported by several execution platforms. In the current setting, mapping BPMN models to BPEL code is a necessary step towards unified and standards-based business process development environments. It turns out that this mapping is challenging from a scientific viewpoint as BPMN and BPEL represent two fundamentally different classes of languages. Existing methods for mapping BPMN to BPEL impose limitations on the structure of the source model. This paper proposes a technique that overcomes these limitations. Beyond its direct relevance in the context of BPMN and BPEL, this technique addresses difficult problems that arise generally when translating between flow-based languages with parallelism
End-to-End Learning of Motion Representation for Video Understanding

Despite the recent success of end-to-end learned representations, hand-crafted optical flow features are still widely used in video analysis tasks. To fill this gap, we propose TVNet, a novel end-to-end trainable neural network, to learn optical-flow-like features from data. TVNet subsumes a specific optical flow solver, the TV-L1 method, and is initialized by unfolding its optimization iterations as neural layers. TVNet can therefore be used directly without any extra learning. Moreover, it can be naturally concatenated with other task-specific networks to formulate an end-to-end architecture, thus making our method more efficient than current multi-stage approaches by avoiding the need to pre-compute and store features on disk. Finally, the parameters of the TVNet can be further fine-tuned by end-to-end training. This enables TVNet to learn richer and task-specific patterns beyond exact optical flow. Extensive experiments on two action recognition benchmarks verify the effectiveness of the proposed approach. Our TVNet achieves better accuracies than all compared methods, while being competitive with the fastest counterpart in terms of features extraction time.
Email classification for Spam Detection using Word Stemming

Unsolicited emails, known as spam, are one of the fast growing and costly problems associated with the Internet today. Among the many proposed solutions, a technique using Bayesian filtering is considered as the most effective weapon against spam. Bayesian filtering works by evaluating the probability of different words appearing in legitimate and spam mails and then classifying them based on that probabilities.Most of the current spam email detection systems use keywords to detect spam emails.These keywords can be written as misspellings eg: baank or bannk instead of bank. Misspellings are changed from time to time and hence spam email detection system needs to constantly update the blacklist to detect spam emails containing misspellings. It’s impossible to predict all possible misspellings for a given keyword and add those to the blacklist. In this paper a better and more successful approach for improving E-mail content classification for spam control is proposed. It used the Word Stemming or Word Hashing Technique for improving the efficiency of the content based spam filter.The proposed system extract the base or stem of a misspelled or modified word, to detect spam emails. It considers every misspelled keyword applies a word stemming technique and passes the base word to the content based filter. Using a proposed if-then rule, we can decide whether or not this unknown mail is spam [1].This paper also provides an Email archiving solution which classifies the E-mail relating to a person, family, corporation, association,
Emotion detection and recognition using HRV features derived from photoplethysmogram signals

Detection of true human emotions has attracted a lot of interest in the recent years. The applications range from e-retail to health-care for developing effective companion systems with reliable emotion recognition. This paper proposes heart rate variability (HRV) features extracted from photoplethysmogram (PPG) signal obtained from a cost-effective PPG device such as Pulse Oximeter for detecting and recognizing the emotions on the basis of the physiological signals. The HRV features obtained from both time and frequency domain are used as features for classification of emotions. These features are extracted from the entire PPG signal obtained during emotion elicitation and baseline neutral phase. For analyzing emotion recognition, using the proposed HRV features, standard video stimuli are used. We have considered three emotions namely, happy, sad and neutral or null emotions. Support vector machines are used for developing the models and features are explored to achieve average emotion recognition of 83.8% for the above model and listed features.
SensEmbed: Learning Sense Embeddings for Word and Relational Similarity

Word embeddings have recently gained considerable popularity for modeling words in different Natural Language Processing (NLP) tasks including semantic similarity measurement. However, notwithstanding their success, word embeddings are by their very nature unable to capture polysemy, as different meanings of a word are conflated into a single representation. In addition, their learning process usually relies on massive corpora only, preventing them from taking advantage of structured knowledge. We address both issues by proposing a multifaceted approach that transforms word embeddings to the sense level and leverages knowledge from a large semantic network for effective semantic similarity measurement. We evaluate our approach on word similarity and relational similarity frameworks, reporting state-of-the-art performance on multiple datasets.
The single channel interferometer using a pseudo-Doppler direction finding system

A new technique for obtaining high performance, low power, radio direction finding (RDF) using a single receiver is presented. For man-portable applications, multichannel systems consume too much power, are too expensive, and are too heavy to easily be carried by a single individual. Most single channel systems are not accurate enough or do not provide the capability to listen while direction finding (DF) is being performed. By employing feedback in a pseudo-Doppler system via a vector modulator in the IF of a single receiver and an adaptive algorithm to control it, the accuracy of a pseudoDoppler system can be enhanced to the accuracy of an interferometer based system without the expense of a multichannel receiver. And, it will maintain audio listenthrough while direction finding is being performed all with a single inexpensive low power receiver. The use of these techniques provides performance not attainable by other single channel methods.
Effect of Antenna Placement and Diversity on Vehicular Network Communications

In this paper we present empirical results from a study examining the effects of antenna diversity and placement on vehicle-to-vehicle link performance in vehicular ad hoc networks. The experiments use roof- and in-vehicle mounted omni-directional antennas and IEEE 802.11a radios operating in the 5 GHz band, which is of interest for planned inter-vehicular communication standards. Our main findings are two-fold. First, we show that radio reception performance is sensitive to antenna placement in the 5 Ghz band. Second, our results show that, surprisingly, a packet level selection diversity scheme using multiple antennas and radios, multi-radio packet selection (MRPS), improves performance not only in a fading channel but also in line-of-sight conditions. This is due to propagation being affected by car geometry, leading to the highly non-uniform antenna patterns. These patterns are very sensitive to the exact antenna position on the roof, for example at a transmit power of 40 mW the line-of-sight communication range varied between 50 and 250 m depending on the orientation of the cars. These findings have implications for vehicular MAC protocol design. Protocols may have to cope with an increased number of hidden nodes due to the directional antenna patterns. However, car makers can reduce these effects through careful antenna placement and diversity.
Replay Attack Detection Using DNN for Channel Discrimination

Voice is projected to be the next input interface for portable devices. The increased use of audio interfaces can be mainly attributed to the success of speech and speaker recognition technologies. With these advances comes the risk of criminal threats where attackers are reportedly trying to access sensitive information using diverse voice spoofing techniques. Among them, replay attacks pose a real challenge to voice biometrics. This paper addresses the problem by proposing a deep learning architecture in tandem with low-level cepstral features. We investigate the use of a deep neural network (DNN) to discriminate between the different channel conditions available in the ASVSpoof 2017 dataset, namely recording, playback and session conditions. The high-level feature vectors derived from this network are used to discriminate between genuine and spoofed audio. Two kinds of low-level features are utilized: state-ofthe-art constant-Q cepstral coefficients (CQCC), and our proposed high-frequency cepstral coefficients (HFCC) that derive from the high-frequency spectrum of the audio. The fusion of both features proved to be effective in generalizing well across diverse replay attacks seen in the evaluation of the ASVSpoof 2017 challenge, with an equal error rate of 11.5%, that is 53% better than the baseline Gaussian Mixture Model (GMM) applied on CQCC.
Sleep and student performance at school.

To review the state of research on the association between sleep among school-aged children and academic outcomes, the authors reviewed published studies investigating sleep, school performance, and cognitive and achievement tests. Tables with brief descriptions of each study's research methods and outcomes are included. Research reveals a high prevalence among school-aged children of suboptimal amounts of sleep and poor sleep quality. Research demonstrates that suboptimal sleep affects how well students are able to learn and how it may adversely affect school performance. Recommendations for further research are discussed.
CYC: Toward Programs With Common Sense

Cyc is a bold attempt to assemble a massive knowledge base (on the order of 108 axioms) spanning human consensus knowledge. This article examines the need for such an undertaking and reviews the authos' efforts over the past five years to begin its construction. The methodology and history of the project are briefly discussed, followed by a more developed treatment of the current state of the representation language used (epistemological level), techniques for efficient inferencing and default reasoning (heuristic level), and the content and organization of the knowledge base.
Joint view expansion and filtering for automultiscopic 3D displays

Multi-view autostereoscopic displays provide an immersive, glasses-free 3D viewing experience, but they require correctly filtered content from multiple viewpoints. This, however, cannot be easily obtained with current stereoscopic production pipelines. We provide a practical solution that takes a stereoscopic video as an input and converts it to multi-view and filtered video streams that can be used to drive multi-view autostereoscopic displays. The method combines a phase-based video magnification and an interperspective antialiasing into a single filtering process. The whole algorithm is simple and can be efficiently implemented on current GPUs to yield a near real-time performance. Furthermore, the ability to retarget disparity is naturally supported. Our method is robust and works well for challenging video scenes with defocus blur, motion blur, transparent materials, and specularities. We show that our results are superior when compared to the state-of-the-art depth-based rendering methods. Finally, we showcase the method in the context of a real-time 3D videoconferencing system that requires only two cameras.
Advances in phase-aware signal processing in speech communication

During the past three decades, the issue of processing spectral phase has been largely neglected in speech applications. There is no doubt that the interest of speech processing community towards the use of phase information in a big spectrum of speech technologies, from automatic speech and speaker recognition to speech synthesis, from speech enhancement and source separation to speech coding, is constantly increasing. In this paper, we elaborate on why phase was believed to be unimportant in each application. We provide an overview of advancements in phase-aware signal processing with applications to speech, showing that considering phase-aware speech processing can be beneficial in many cases, while it can complement the possible solutions that magnitude-only methods suggest. Our goal is to show that phase-aware signal processing is an important emerging field with high potential in the current speech communication applications. The paper provides an extended and up-to-date bibliography on the topic of phase aware speech processing aiming at providing the necessary background to the interested readers for following the recent advancements in the area. Our review expands the step initiated by our organized special session and exemplifies the usefulness of spectral phase information in a wide range of speech processing applications. Finally, the overview will provide some future work directions.
Lightly-Supervised Attribute Extraction

Web search engines can greatly benefit from knowledge about a ttributes of entities present in search queries. In this paper, we introduce light ly-supervised methods for extracting entity attributes from natural language tex t. Using these methods, we are able to extract large numbers of attributes of differe nt entities at fairly high precision from a large natural language corpus. We compare o ur methods against a previously proposed pattern-based relation extractor, s h wing that the new methods give considerable improvements over that baseline. We a lso demonstrate that query expansion using extracted attributes improves retri eval performance on underspecified information-seeking queries. 1 Attributes in Web Search Web search engines receive numerous queries requesting inf ormation, often focused on a specific entity, such as a person, place or organization. These queri es are sometimes general requests, such as“bio of George Bush,”or specific requests, such as “new york mayor.” Accurately identifying the entity (new york) or related attributes ( mayor) can improve search results in several ways . For example, knowledge of attributes and entities can identify a query as being a factual request [1, 2]. Query expansion using known attributes of the entity can als o improve results . Additionally, an engine could suggest alternative queries based on attrib utes. If a user searches for just “Craig Ferguson” and “shows” is a known attribute of the entity ”Craig Ferguson”, then an alternative query suggestion could be “Craig Ferguson shows”which may guide the user to more informative results. The widely explored technique of pseudo relevance feedback can also benefit from a known list of entities and attributes . Some view entity and att ribute extraction as a primary building block for the automatic creation of large scale knowledge ba ses aimed at addressing these issues . The first step towards improving search results with attribu es is to create lists of entities and attributes. Towards that end, we propose new algorithms that, beginning with a small seed set of entities and attributes, learn to extract new entities and a ttributes from a large corpus of text. We adopt a bootstrapping approach, where the inputs for our lea rning algorithms are a large unlabeled corpus and the small seed set containing an entity type of int eres , such as seed pairs automatically extracted from query logs . The seed pairs are matched aga inst the corpus to create training instances for the learning algorithms. The algorithms expl oit a wide range of instance features to alleviate the effects of noise and sparseness. The algorith ms produce a large list of entities and associated attributes, which can be directly applied towar ds improving web search. This paper proceeds as follows. We begin with some backgroun d n attribute extraction and web search applications. We then outline our extraction algori thms. Some examples and evaluations of extracted attributes and entities follow.
Effects of Situated Mobile Learning Approach on Learning Motivation and Performance of EFL Students

This study developed a 5-step vocabulary learning (FSVL) strategy and a mobile learning tool in a situational English vocabulary learning environment and assessed their effects on the learning motivation and performance of English as a foreign language (EFL) students in a situational English vocabulary learning environment. Overall, 80 EFL students and 1 teacher participated in this study. Two learning methods were implemented. The first involved employing the FSVL strategy and the mobile learning tool in a situational English vocabulary learning environment, and the other method involved employing the FSVL strategy and traditional learning tools in a situational English vocabulary learning environment. Data were collected by conducting interviews and tests and administering the Attention, Relevance, Confidence, and Satisfaction motivation questionnaire. The results showed that the learning motivation and performance of students taught using the FSVL strategy and mobile learning tool were superior to those of students taught using the FSVL strategy and traditional learning tools in a situational English vocabulary learning environment.
Assumption-Free Anomaly Detection in Time Series

Recent advancements in sensor technology have made it possible to collect enormous amounts of data in real time. However, because of the sheer volume of data most of it will never be inspected by an algorithm, much less a human being. One way to mitigate this problem is to perform some type of anomaly (novelty /interestingness/surprisingness) detection and flag unusual patterns for further inspection by humans or more CPU intensive algorithms. Most current solutions are “custom made” for particular domains, such as ECG monitoring, valve pressure monitoring, etc. This customization requires extensive effort by domain expert. Furthermore, hand-crafted systems tend to be very brittle to concept drift. In this demonstration, we will show an online anomaly detection system that does not need to be customized for individual domains, yet performs with exceptionally high precision/recall. The system is based on the recently introduced idea of time series bitmaps. To demonstrate the universality of our system, we will allow testing on independently annotated datasets from domains as diverse as ECGs, Space Shuttle telemetry monitoring, video surveillance, and respiratory data. In addition, we invite attendees to test our system with any dataset available on the web.
Classification regions of deep neural networks

The goal of this paper is to analyze the geometric properties of deep neural network classifiers in the input space. We specifically study the topology of classification regions created by deep networks, as well as their associated decision boundary. Through a systematic empirical investigation, we show that state-of-the-art deep nets learn connected classification regions, and that the decision boundary in the vicinity of datapoints is flat along most directions. We further draw an essential connection between two seemingly unrelated properties of deep networks: their sensitivity to additive perturbations in the inputs, and the curvature of their decision boundary. The directions where the decision boundary is curved in fact characterize the directions to which the classifier is the most vulnerable. We finally leverage a fundamental asymmetry in the curvature of the decision boundary of deep nets, and propose a method to discriminate between original images, and images perturbed with small adversarial examples. We show the effectiveness of this purely geometric approach for detecting small adversarial perturbations in images, and for recovering the labels of perturbed images.