nils-herrmann/clean_scirepeval huggingface dataset:
Red Opal: product-feature scoring from reviews

Online shoppers are generally highly task-driven: they have a certain goal in mind, and they are looking for a product with features that are consistent with that goal. Unfortunately, finding a product with specific features is extremely time-consuming using the search functionality provided by existing web sites.In this paper, we present a new search system called Red Opal that enables users to locate products rapidly based on features. Our fully automatic system examines prior customer reviews, identifies product features, and scores each product on each feature. Red Opal uses these scores to determine which products to show when a user specifies a desired product feature. We evaluate our system on four dimensions: precision of feature extraction, efficiency of feature extraction, precision of product scores, and estimated time savings to customers. On each dimension, Red Opal performs better than a comparison system.
Learning cloud computing and security through CloudSim simulation

Cloud computing is an emerging parallel and distributed service-oriented computing paradigm that provides platform service, software service, and infrastructure service through computing resource virtualization. Cloud computing is fast evolving and growing and is rapidly transforming the IT service industry. Many universities are offering or planning to offer cloud computing courses to meet the growing demand for cloud computing professionals. Some of them focus on big data processing programming using MapReduce with service charges from public cloud service, while many others teach cloud computing as a seminar type class without any hands-on lab exercises. A hands-on labware will be a big plus for teaching and learning cloud computing. This paper proposes a cloud computing labware with the open source CloudSim (Cloud modeling and simulation toolkit) and its extension projects to enhance computer science and information technology student learning. The proposed labware will support learning not only on MapReduce programming model but also on important topics including cloud principles, concepts, architecture, loadbalancing, and task scheduling. The proposed labware has been implemented in a cross-listed course (as both an undergraduate course and a graduate course) in two sections (hybrid section and fully-online section). Survey results show that participating students highly appreciate the proposed labware in promoting their interest and academic progress in studying Cloud computing/security.
Guide to Health Informatics

From the very earliest moments in the modern history of the computer, scientists have dreamed of creating an " electronic brain ". Of all the modern technological quests, this search to create artificially intelligent (AI) computer systems has been one of the most ambitious and, not surprisingly, controversial. It also seems that very early on, scientists and doctors alike were captivated by the potential such a technology might have in medicine (e.g. Ledley and Lusted, 1959). With intelligent computers able to store and process vast stores of knowledge, the hope was that they would become perfect 'doctors in a box', assisting or surpassing clinicians with tasks like diagnosis. With such motivations, a small but talented community of computer scientists and healthcare professionals set about shaping a research program for a new discipline called Artificial Intelligence in Medicine (AIM). These researchers had a bold vision of the way AIM would revolutionise medicine, and push forward the frontiers of technology. AI in medicine at that time was a largely US-based research community. Work originated out of a number of campuses, including MIT-Tufts, Pittsburgh, Stanford and Rutgers (e. The field attracted many of the best computer scientists and by any measure their output in the first decade of the field remains a remarkable achievement. In reviewing this new field in 1984, Clancey and Shortliffe provided the following definition: 'Medical artificial intelligence is primarily concerned with the construction of AI programs that perform diagnosis and make therapy recommendations. Unlike medical applications based on other programming methods, such as purely statistical and probabilistic methods, medical AI programs are based on symbolic models of disease entities and their relationship to patient factors and clinical manifestations.' Much has changed since then, and today the importance of diagnosis as a task requiring computer support in routine clinical situations receives much less emphasis (Durinck et al., 1994). The strict focus on the medical setting has now broadened across the healthcare spectrum, and instead of AIM systems, it is more typical to describe them as clinical decision support systems (CDSS). Intelligent systems today are thus found supporting medication prescribing, in clinical laboratories and educational settings, for clinical surveillance, or in data-rich areas like the intensive care setting. While there certainly have been ongoing challenges in developing such systems, they actually have proven their reliability and accuracy on repeated occasions (Shortliffe, 1987). Much of the difficulty experienced in introducing them has …
Automatic Construction of Hierarchical Hidden Markov Model Structure for Discovering Semantic Patterns in Motion Data

The objective of this paper is to automatically build a Hierarchical Hidden Markov Model (HHMM) (Fine et al., 1998) structure to detect semantic patterns from data with an unknown structure by exploring the natural hierarchical decomposition embedded in the data. The problem is important for effective motion data representation and analysis in a variety of applications: film and game making, military, entertainment, sport and medicine. We propose to represent the patterns of the data as an HHMM built utilising a two-stage learning algorithm. The novelty of our method is that it is the first fully automated approach to build an HHMM structure for motion data. Experimental results on different motion features (3D and angular pose coordinates, silhouettes extracted from the video sequence) demonstrate the approach is effective at automatically constructing efficient HHMM with a structure which naturally represents the underlying motion that allows for accurate modelling of the data for applications such as tracking and motion resynthesis.
Minimum Inhibitory Concentration ( MIC ) Of various synthetic and natural antimicrobial agents using E coli screened from VIT sewage treatment plant *

The Minimum Inhibitory Concentration (MIC) Test is an important evaluating tool in the field of microbiology. It gives us an idea of the microbial activity assessment in any source sample containing microbes. Validating the effectiveness of disinfection and decontamination is a vital and often challenging task. In clinical and laboratory environments, presence of sterile conditions is a compulsion and various disinfectants are used to sterilize the surroundings from bacteria, fungi and other microorganisms. The broth dilution and agar diffusion methods of MIC Test tell us about the efficiency of the disinfectant under use. By subjecting a given disinfectant sample to the MIC Test, we can find out its efficiency in lysing the microorganisms present in the inoculums. Thus two or more disinfectants can be compared also at the same time to test their lysing capacity under normal conditions. The MIC Test also helps to choose the more economic disinfectant from a given batch of disinfectants. The results obtained from the MIC Test can also be used to prepare a new anti microbial agent which has greater efficiency in lysing the microorganisms and is economic financially. Minimum Inhibitory Concentration (MIC) of various synthetic like chloramphenical, chloroxylenol and 10% hydrochloric acid with butyl oleylamine in aqueous solution), and natural antimicrobial agents like Sample C (Grass extract) and Sample D (Grass+Neem extract) using E coli screened from VIT sewage treatment plant will be discussed.
Analysis of configuration of planar cable-driven parallel robot on natural frequency

This paper studies the configuration of a planar cable-driven parallel robot to reduce the vibration of an end-effector. Since cables are more flexible than rigid links, external disturbances may cause a significant vibration during operation. The characteristics of the vibration are closely related to the natural frequency of the end-effector. And, we consider that the shapes of the frame and the end-effector, and the connection methods of the cables can affect the vibration of the cable-driven parallel robot. Therefore, the first natural frequencies in a required workspace are analyzed by the configuration change of a planar cable-driven parallel robot. The simulation shows that planar cable robots can be designed to reduce vibration by utilizing robots configurations.
Exploring Open-Source Deep Learning ASR for Speech-to-Text TV program transcription

Deep Neural Networks (DNN) are fundamental part of current ASR. State-of-the-art are hybrid models in which acoustic models (AM) are designed using neural networks. However, there is an increasing interest in developing end-to-end Deep Learning solutions where a neural network is trained to predict character/grapheme or sub-word sequences which can be converted directly to words. Though several promising results have been reported for end-to-end ASR systems, it is still not clear if they are capable to unseat hybrid systems. In this contribution, we evaluate open-source state-ofthe-art hybrid and end-to-end Deep Learning ASR under the IberSpeech-RTVE Speech to Text Transcription Challenge. The hybrid ASR is based on Kaldi and Wav2Letter will be the endto-end framework. Experiments were carried out using 6 hours of dev1 and dev2 partitions. The lowest WER on the reference TV show (LM-20171107) was 22.23% for the hybrid system (lowercase format without punctuation). Major limitation for Wav2Letter has been a high training computational demand (between 6 hours and 1 day/epoch, depending on the training set). This forced us to stop the training process to meet the Challenge deadline. But we believe that with more training time it will provide competitive results with the hybrid system.
Power Waves and the Scattering Matrix

This paper discusses the physical meaning and properties of the waves defined by v%+ z%Ib ~ = V. – Z,*Ii a%= ,, 2u/Re Z,] 2<1 ReZtl where V, and 1, are the voltage at and the current flowing into the ith port of a junction and Z, is the impedance of the circuit connected to the ith port. The square of the magnitude of these waves is directly related to the exchangeable power of a source and the reflected power. For this reason, in this paper, they are called the power waves. For certain applications where the power relations are of main concern, the power waves are more suitable quantities than the conventional traveling waves. The lossless and reciprocal conditions as well as the frequency characteristics of the scattering matrix are presented. Then, the formula is given for a new scattering matrix when the 2,’s are changed. As an application, the condition under which an amplifier can be matched simultaneously at both input and output ports as well as the condition for the network to be unconditionally stable are given in terms of the scattering matrix components. Also a brief comparison is made between the traveling waves and the power waves.
Ancient Kaṭapayādi System Sanskrit Encryption Technique Unified

Computers today, generate enormous amount of data/information with each moment passing by. With the production of such huge amount of information comes its indispensable part of information security. Encryption Algorithms today drastically increase the file size. Hence the secure transmission of data requires extra bandwidth. Here in this paper we propose a system AKS - SETU, which is also the abbreviation to the title of this paper. Using the ancient technique of encryption from Sanskrit, AKS - SETU not only encrypts the information but also attempts on decreasing of the file size. AKS - SETU performs Sanskrit encryption, which we propose to be termed as Sanscryption.
Short-Circuit Current of Wind Turbines With Doubly Fed Induction Generator

The short-circuit current contribution of wind turbines has not received much attention so far. This paper considers the short-circuit behavior, especially the short-circuit current of wind turbines with a doubly fed induction generator. Mostly, these wind turbines have a crowbar to protect the power electronic converter that is connected to the rotor windings of the induction generator. First, the maximum value of the short-circuit current of a conventional induction machine is determined. The differences between a crowbar-protected doubly fed induction generator and a conventional induction generator are highlighted and approximate equations for the maximum short-circuit current of a doubly fed induction generator are determined. The values obtained in this way are compared to the values obtained from time domain simulations. The differences are less then 15%
Real-time anomaly-based distributed intrusion detection systems for advanced Metering Infrastructure utilizing stream data mining

The advanced Metering Infrastructure (AMI) is one of the core components of smart grids' architecture. As AMI components are connected through mesh networks in a distributed mechanism, new vulnerabilities will be exploited by grid's attackers who intentionally interfere with network's communication system and steal customer data. As a result, identifying distributed security solutions to maintain the confidentiality, integrity, and availability of AMI devices' traffic is an essential requirement that needs to be taken into account. This paper proposes a real-time distributed intrusion detection system (DIDS) for the AMI infrastructure that utilizes stream data mining techniques and a multi-layer implementation approach. Using unsupervised online clustering techniques, the anomaly-based DIDS monitors the data flow in the AMI and distinguish if there are anomalous traffics. By comparing between online and offline clustering techniques, the experimental results showed that online clustering “Mini-Batch K-means” were successfully able to suit the architecture requirements by giving high detection rate and low false positive rates.
A Survey of Mobile Cloud Computing Applications: Perspectives and Challenges

As mobile computing has been developed for decades, a new model for mobile computing, namely, mobile cloud computing, emerges resulting from the marriage of powerful yet affordable mobile devices and cloud computing. In this paper we survey existing mobile cloud computing applications, as well as speculate future generation mobile cloud computing applications. We provide insights for the enabling technologies and challenges that lie ahead for us to move forward from mobile computing to mobile cloud computing for building the next generation mobile cloud applications. For each of the challenges, we provide a survey of existing solutions, identify research gaps, and suggest future research areas.
Voice recognition based wireless home automation system

Home Automation industry is growing rapidly; this is fuelled by the need to provide supporting systems for the elderly and the disabled, especially those who live alone. Coupled with this, the world population is confirmed to be getting older. Home automation systems must comply with the household standards and convenience of usage. This paper details the overall design of a wireless home automation system (WHAS) which has been built and implemented. The automation centres on recognition of voice commands and uses low-power RF ZigBee wireless communication modules which are relatively cheap. The home automation system is intended to control all lights and electrical appliances in a home or office using voice commands. The system has been tested and verified. The verification tests included voice recognition response test, indoor ZigBee communication test, and the compression and decompression tests of DPCM (Differential Pulse Code Modulation) speech signals. The tests involved a mix of 35 male and female subjects with different English accents. 35 different voice commands were sent by each person. Thus the test involved sending a total of 1225 commands and 79.8% of these commands were recognised correctly.
It's More than Just Sharing Game Play Videos! Understanding User Motives in Mobile Game Social Media

As mobile gaming has become increasingly popular in recent years, new forms of mobile game social media such as GameDuck that share mobile gameplay videos have emerged. In this work, we set out to understand the user motives of GameDuck by leveraging the Uses and Gratification Theory. We first explore the major motive themes from users' responses (n=138) and generate motivation survey items. We then identify the key motivators by conducting exploratory factor analysis of the survey results (n=354). Finally, we discuss how this new social media relates to existing systems such as Twitch.
Image Mining: A New Approach for Data Mining

We introduce a new focus for data mining, which is concerned with knowledge discovery in image databases. We expect all aspects of data mining to be relevant to image mining but in this first work we concentrate on the problem of finding associations. To that end, we present a data mining algorithm to find association rules in 2-dimensional color images. The algorithm has four major steps: feature extraction, object identification, auxiliary image creation and object mining. Our algorithm is general in that it does not rely on any type of domain knowledge. A synthetic image set containing geometric shapes was generated to test our initial algorithm implementation. Our experimental results show that image mining is feasible. We also suggest several directions for future work in this area.
Scalable and Secure Logistic Regression via Homomorphic Encryption

Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive data such as private or medical information, cares are necessary. In this paper, we propose a secure system for protecting the training data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training in logistic regression, we show that only additively homomorphic encryption is needed to build our system. Our system is secure and scalable with the dataset size.
A Convolutional Recurrent Neural Network for Real-Time Speech Enhancement

Many real-world applications of speech enhancement, such as hearing aids and cochlear implants, desire real-time processing, with no or low latency. In this paper, we propose a novel convolutional recurrent network (CRN) to address real-time monaural speech enhancement. We incorporate a convolutional encoderdecoder (CED) and long short-term memory (LSTM) into the CRN architecture, which leads to a causal system that is naturally suitable for real-time processing. Moreover, the proposed model is noiseand speaker-independent, i.e. noise types and speakers can be different between training and test. Our experiments suggest that the CRN leads to consistently better objective intelligibility and perceptual quality than an existing LSTM based model. Moreover, the CRN has much fewer trainable parameters.
Extended Tracking Powers: Measuring the Privacy Diffusion Enabled by Browser Extensions

Users have come to rely on browser extensions to realize features that are not implemented by browser vendors. Extensions offer users the ability to, among others, block ads, de-clutter websites, enrich pages with third-party content, and take screenshots. At the same time, because of their privileged position inside a user’s browser, extensions have access to content and functionality that is not available to webpages, such as, the ability to conduct and read crossorigin requests, as well as get access to a browser’s history and cookie jar. In this paper, we report on the first large-scale study of privacy leakage enabled by extensions. By using dynamic analysis and simulated user interactions, we investigate the leaking happening by the 10,000 most popular browser extensions of Google Chrome and find that a non-negligible fraction leaks sensitive information about the user’s browsing habits, such as, their browsing history and search-engine queries. We identify common ways that extensions use to obfuscate this leakage and discover that, while some leakage happens on purpose, a large fraction of it is accidental because of the way that extensions attempt to introduce thirdparty content to a page’s DOM. To counter the inference of a user’s interests and private information enabled by this leakage, we design, implement, and evaluate BrowsingFog, a browser extension that automatically browses the web in a way that conceals a user’s true interests, from a vantage point of history-stealing, third-party trackers.
An Advanced Current Control Strategy for Three-Phase Shunt Active Power Filters

This paper proposes an advanced control strategy to enhance performance of shunt active power filter (APF). The proposed control scheme requires only two current sensors at the supply side and does not need a harmonic detector. In order to make the supply currents sinusoidal, an effective harmonic compensation method is developed with the aid of a conventional proportional-integral (PI) and vector PI controllers. The absence of the harmonic detector not only simplifies the control scheme but also significantly improves the accuracy of the APF, since the control performance is no longer affected by the performance of the harmonic tracking process. Furthermore, the total cost to implement the proposed APF becomes lower, owing to the minimized current sensors and the use of a four-switch three-phase inverter. Despite the simplified hardware, the performance of the APF is improved significantly compared to the traditional control scheme, thanks to the effectiveness of the proposed compensation scheme. The proposed control scheme is theoretically analyzed, and a 1.5-kVA APF is built in the laboratory to validate the feasibility of the proposed control strategy.
Self-Supervised Generalisation with Meta Auxiliary Learning

Learning with auxiliary tasks has been shown to improve the generalisation of a primary task. However, this comes at the cost of manuallylabelling additional tasks which may, or may not, be useful for the primary task. We propose a new method which automatically learns labels for an auxiliary task, such that any supervised learning task can be improved without requiring access to additional data. The approach is to train two neural networks: a label-generation network to predict the auxiliary labels, and a multi-task network to train the primary task alongside the auxiliary task. The loss for the label-generation network incorporates the multi-task network’s performance, and so this interaction between the two networks can be seen as a form of meta learning. We show that our proposed method, Meta AuXiliary Learning (MAXL), outperforms single-task learning on 7 image datasets by a significant margin, without requiring additional auxiliary labels. We also show that MAXL outperforms several other baselines for generating auxiliary labels, and is even competitive when compared with human-defined auxiliary labels. The self-supervised nature of our method leads to a promising new direction towards automated generalisation. The source code is available at https://github.com/lorenmt/maxl.