nils-herrmann/clean_scirepeval huggingface dataset:
Multi-Modal Bayesian Embeddings for Learning Social Knowledge Graphs

We study the extent to which online social networks can be connected to knowledge bases. The problem is referred to as learning social knowledge graphs. We propose a multi-modal Bayesian embedding model, GenVector, to learn latent topics that generate word embeddings and network embeddings simultaneously. GenVector leverages large-scale unlabeled data with embeddings and represents data of two modalities—i.e., social network users and knowledge concepts—in a shared latent topic space. Experiments on three datasets show that the proposed method clearly outperforms state-of-the-art methods. We then deploy the method on AMiner, an online academic search system to connect with a network of 38,049,189 researchers with a knowledge base with 35,415,011 concepts. Our method significantly decreases the error rate of learning social knowledge graphs in an online A/B test with live users.
A framework for end-user instruction of a robot assistant for manufacturing

Small Manufacturing Entities (SMEs) have not incorporated robotic automation as readily as large companies due to rapidly changing product lines, complex and dexterous tasks, and the high cost of start-up. While recent low-cost robots such as the Universal Robots UR5 and Rethink Robotics Baxter are more economical and feature improved programming interfaces, based on our discussions with manufacturers further incorporation of robots into the manufacturing work flow is limited by the ability of these systems to generalize across tasks and handle environmental variation. Our goal is to create a system designed for small manufacturers that contains a set of capabilities useful for a wide range of tasks, is both powerful and easy to use, allows for perceptually grounded actions, and is able to accumulate, abstract, and reuse plans that have been taught. We present an extension to Behavior Trees that allows for representing the system capabilities of a robot as a set of generalizable operations that are exposed to an end-user for creating task plans. We implement this framework in CoSTAR, the Collaborative System for Task Automation and Recognition, and demonstrate its effectiveness with two case studies. We first perform a complex tool-based object manipulation task in a laboratory setting. We then show the deployment of our system in an SME where we automate a machine tending task that was not possible with current off the shelf robots.
“Told you i didn't like it”: Exploiting uninteresting items for effective collaborative filtering

We study how to improve the accuracy and running time of top-N recommendation with collaborative filtering (CF). Unlike existing works that use mostly rated items (which is only a small fraction in a rating matrix), we propose the notion of pre-use preferences of users toward a vast amount of unrated items. Using this novel notion, we effectively identify uninteresting items that were not rated yet but are likely to receive very low ratings from users, and impute them as zero. This simple-yet-novel zero-injection method applied to a set of carefully-chosen uninteresting items not only addresses the sparsity problem by enriching a rating matrix but also completely prevents uninteresting items from being recommended as top-N items, thereby improving accuracy greatly. As our proposed idea is method-agnostic, it can be easily applied to a wide variety of popular CF methods. Through comprehensive experiments using the Movielens dataset and MyMediaLite implementation, we successfully demonstrate that our solution consistently and universally improves the accuracies of popular CF methods (e.g., item-based CF, SVD-based CF, and SVD++) by two to five orders of magnitude on average. Furthermore, our approach reduces the running time of those CF methods by 1.2 to 2.3 times when its setting produces the best accuracy. The datasets and codes that we used in experiments are available at: https://goo.gl/KUrmip.
A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenarios

Class imbalance and class overlap are two of the major problems in data mining and machine learning. Several studies have shown that these data complexities may affect the performance or behavior of artificial neural networks. Strategies proposed to face with both challenges have been separately applied. In this paper, we introduce a hybrid method for handling both class imbalance and class overlap simultaneously in multi-class learning problems. Experimental results on five remote sensing data show that the combined approach is a promising method.
Nonlinear Residual Echo Suppression using a Power Filter Model of the Acoustic Echo Path

Loudspeakers and amplifiers of mobile communication receivers may cause significant nonlinear distortion in the acoustic echo path, resulting in a limitation of the performance of linear echo cancelers. In this contribution, we present a nonlinear acoustic echo suppressor in order to increase the attenuation of the nonlinearly distorted residual echo. The proposed approach is based on a power filter model of the acoustic echo path which is applied to the estimation of the power spectral density of the nonlinear residual echo. These time-variant estimates are used to appropriately adjust the frequency-dependent gain values of the echo suppressor. The performance of the proposed approach is evaluated in realistic experimental set-ups.
Colour tuning in human visual cortex measured with functional magnetic resonance imaging

The primate retina contains three classes of cones, the L, M and S cones, which respond preferentially to long-, middle- and short-wavelength visible light, respectively. Colour appearance results from neural processing of these cone signals within the retina and the brain. Perceptual experiments have identified three types of neural pathways that represent colour: a red–green pathway that signals differences between L- and M-cone responses; a blue–yellow pathway that signals differences between S-cone responses and a sum of L- and M-cone responses; and a luminance pathway that signals a sum of L- and M-cone responses. It might be expected that there are neurons in the primary visual cortex with response properties that resemble these three perceptual pathways, but attempts to find them have led to inconsistent results. We have therefore used functional magnetic resonance imaging (fMRI) to examine responses in the human brain to a large number of colours. In visual cortical areas V1 and V2, the strongest response is to red–green stimuli, and much of this activity is from neurons receiving opposing inputs from L and M cones. A strong response is also seen with blue–yellow stimuli, and this response declines rapidly as the temporal frequency of the stimulus is increased. These responses resemble psychophysical measurements, suggesting that colour signals relevant for perception are encoded in a large population of neurons in areas V1 and V2.
A New Intelligence-Based Approach for Computer-Aided Diagnosis of Dengue Fever

Identification of the influential clinical symptoms and laboratory features that help in the diagnosis of dengue fever (DF) in early phase of the illness would aid in designing effective public health management and virological surveillance strategies. Keeping this as our main objective, we develop in this paper a new computational intelligence-based methodology that predicts the diagnosis in real time, minimizing the number of false positives and false negatives. Our methodology consists of three major components: 1) a novel missing value imputation procedure that can be applied on any dataset consisting of categorical (nominal) and/or numeric (real or integer); 2) a wrapper-based feature selection method with genetic search for extracting a subset of most influential symptoms that can diagnose the illness; and 3) an alternating decision tree method that employs boosting for generating highly accurate decision rules. The predictive models developed using our methodology are found to be more accurate than the state-of-the-art methodologies used in the diagnosis of the DF.
Scrybe : A Blockchain Ledger for Clinical Trials

The recent popularity of cryptocurrencies has highlighted the versatility and applications of a decentralized, public blockchain. Blockchains provide a data structure that can guarantee both the integrity and non-repudiation of data, as well as providing provenance pertaining to such data. Our novel Lightweight Mining (LWM) algorithm provides these guarantees with minimal resource requirements. Our approach to blockchain-based data provenance, paired with the LWM algorithm, provides the legal and ethical framework for auditors to validate clinical trials, expediting the research process, and saving lives and money in the process. Contributions of this paper include the following: we explain how to adapt and apply a novel, blockchain-based provenance system to enhance clinical trial data integrity and nonrepudiation. We explain the key features of the Scrybe system that enable this outcome, and we describe resilience of the system to denial of service attacks and repudiation. We conclude that Scrybe can provide a system and method for secure data provenance for clinical trials, consistent with the legal and ethical requirements for the lifecycle management of such data.
Plug-ins for GNU Radio Companion

This paper gives an insight on how to develop plug-ins (signal processing blocks) for GNU Radio Companion. GRC is on the monitoring computer and does bulk of the signal processing before transmission and after reception. The coding done in order to develop any block is discussed. A block that performs Huffman coding has been built. Huffman coding is a coding technique that gives a prefix code. A block that performs convolution coding at any desired rate using any generator polynomial has also been built. Both Huffman and Convolution coding are done on data stored in file sources by these blocks. This paper thus describes the ease of signal processing that can be attained by developing blocks in demand by changing the C++ and PYTHON codes of the HOWTO package. Being an open source it is available to all, is highly cost effective and is a field with great potential.
From machine learning to machine reasoning

A plausible definition of “reasoning” could be “algebraically manipulating previously acquired knowledge in order to answer a new question”. This definition covers first-order logical inference or probabilistic inference. It also includes much simpler manipulations commonly used to build large learning systems. For instance, we can build an optical character recognition system by first training a character segmenter, an isolated character recognizer, and a language model, using appropriate labelled training sets. Adequately concatenating these modules and fine tuning the resulting system can be viewed as an algebraic operation in a space of models. The resulting model answers a new question, that is, converting the image of a text page into a computer readable text. This observation suggests a conceptual continuity between algebraically rich inference systems, such as logical or probabilistic inference, and simple manipulations, such as the mere concatenation of trainable learning systems. Therefore, instead of trying to bridge the gap between machine learning systems and sophisticated “all-purpose” inference mechanisms, we can instead algebraically enrich the set of manipulations applicable to training systems, and build reasoning capabilities from the ground up.
Emerging Security Mechanisms for Medical Cyber Physical Systems

The following decade will witness a surge in remote health-monitoring systems that are based on body-worn monitoring devices. These Medical Cyber Physical Systems (MCPS) will be capable of transmitting the acquired data to a private or public cloud for storage and processing. Machine learning algorithms running in the cloud and processing this data can provide decision support to healthcare professionals. There is no doubt that the security and privacy of the medical data is one of the most important concerns in designing an MCPS. In this paper, we depict the general architecture of an MCPS consisting of four layers: data acquisition, data aggregation, cloud processing, and action. Due to the differences in hardware and communication capabilities of each layer, different encryption schemes must be used to guarantee data privacy within that layer. We survey conventional and emerging encryption schemes based on their ability to provide secure storage, data sharing, and secure computation. Our detailed experimental evaluation of each scheme shows that while the emerging encryption schemes enable exciting new features such as secure sharing and secure computation, they introduce several orders-of-magnitude computational and storage overhead. We conclude our paper by outlining future research directions to improve the usability of the emerging encryption schemes in an MCPS.
Learning classifiers from only positive and unlabeled data

The input to an algorithm that learns a binary classifier normally consists of two sets of examples, where one set consists of positive examples of the concept to be learned, and the other set consists of negative examples. However, it is often the case that the available training data are an incomplete set of positive examples, and a set of unlabeled examples, some of which are positive and some of which are negative. The problem solved in this paper is how to learn a standard binary classifier given a nontraditional training set of this nature.
 Under the assumption that the labeled examples are selected randomly from the positive examples, we show that a classifier trained on positive and unlabeled examples predicts probabilities that differ by only a constant factor from the true conditional probabilities of being positive. We show how to use this result in two different ways to learn a classifier from a nontraditional training set. We then apply these two new methods to solve a real-world problem: identifying protein records that should be included in an incomplete specialized molecular biology database. Our experiments in this domain show that models trained using the new methods perform better than the current state-of-the-art biased SVM method for learning from positive and unlabeled examples.
Adaptive shadow maps

Shadow maps provide a fast and convenient method of identifying shadows in scenes but can introduce aliasing. This paper introduces the Adaptive Shadow Map (ASM) as a solution to this problem. An ASM removes aliasing by resolving pixel size mismatches between the eye view and the light source view. It achieves this goal by storing the light source view (i.e., the shadow map for the light source) as a hierarchical grid structure as opposed to the conventional flat structure. As pixels are transformed from the eye view to the light source view, the ASM is refined to create higher-resolution pieces of the shadow map when needed. This is done by evaluating the contributions of shadow map pixels to the overall image quality. The improvement process is view-driven, progressive, and confined to a user-specifiable memory footprint. We show that ASMs enable dramatic improvements in shadow quality while maintaining interactive rates.
Toward constructing evidence-based legal arguments using legal decision documents and machine learning

This paper explores how to extract argumentation-relevant information automatically from a corpus of legal decision documents, and how to build new arguments using that information. For decision texts, we use the Vaccine/Injury Project (V/IP) Corpus, which contains default-logic annotations of argument structure. We supplement this with presuppositional annotations about entities, events, and relations that play important roles in argumentation, and about the level of confidence that arguments would be successful. We then propose how to integrate these semantic-pragmatic annotations with syntactic and domain-general semantic annotations, such as those generated in the DeepQA architecture, and outline how to apply machine learning and scoring techniques similar to those used in the IBM Watson system for playing the Jeopardy! question-answer game. We replace this game-playing goal, however, with the goal of learning to construct legal arguments.
Novel trends in automotive networks: A perspective on Ethernet and the IEEE Audio Video Bridging

Ethernet is going to play a major role in automotive communications, thus representing a significant paradigm shift in automotive networking. Ethernet technology will allow for multiple in-vehicle systems (such as, multimedia/infotainment, camera-based advanced driver assistance and on-board diagnostics) to simultaneously access information over a single unshielded twisted pair cable. The leading technology for automotive applications is the IEEE Audio Video Bridging (AVB), which offers several advantages, such as open specification, multiple sources of electronic components, high bandwidth, the compliance with the challenging EMC/EMI automotive requirements, and significant savings on cabling costs, thickness and weight. This paper surveys the state of the art on Ethernet-based automotive communications and especially on the IEEE AVB, with a particular focus on the way to provide support to the so-called scheduled traffic, that is a class of time-sensitive traffic (e.g., control traffic) that is transmitted according to a time schedule.
Robust Feature Selection Using Ensemble Feature Selection Techniques

Robustness or stability of feature selection techniques is a topic of recent interest, and is an important issue when selected feature subsets are subsequently analysed by domain experts to gain more insight into the problem modelled. In this work, we investigate the use of ensemble feature selection techniques, where multiple feature selection methods are combined to yield more robust results. We show that these techniques show great promise for high-dimensional domains with small sample sizes, and provide more robust feature subsets than a single feature selection technique. In addition, we also investigate the effect of ensemble feature selection techniques on classification performance, giving rise to a new model selection strategy.
Multi-sensor fusion method using kalman filter to improve localization accuracy based on android smart phone

Android smart phone can be used in ITS (Intelligent Transportation Systems) to obtain people and vehicle's location since it is integrated with GPS, direction sensor and acceleration sensor. Because the GPS built in smart phone always has an error of dozens of meters, improving the positioning accuracy is necessary before introducing it into ITS. This paper proposed an approach to improve the accuracy to the street level and get a smooth trajectory without jump points. It is convenient for everyone to use it because almost everyone has a smart phone. Firstly, road-matching algorithm is used to improve the localization accuracy to street level; secondly, speed and direction information are introduced to better reflect the real trajectory; thirdly, Kalman filter is used to eliminate the jump points and make the trajectory smooth; finally, the optimal result obtained from the process of Kalman filter is interpolated to reflect more details. The experiment result shows that the approach is effective.
Advancing students' computational thinking skills through educational robotics: A study on age and gender relevant differences

This work investigates the development of students’ computational thinking (CT) skills in the context of educational robotics (ER) learning activity. The study employs an appropriate CT model for operationalising and exploring students’ CT skills development in two different age groups (15 and 18 years old) and across gender. 164 students of different education levels (Junior high: 89; High vocational: 75) engaged in ER learning activities (2 hours per week, 11 weeks totally) and their CT skills were evaluated at different phases during the activity, using different modality (written and oral) assessment tools. The results suggest that: (a) students reach eventually the same level of CT skills development independent of their age and gender, (b) CT skills inmost cases need time to fully develop (students’ scores improve significantly towards the end of the activity), (c) age and gender relevant differences appear when analysing students’ score in the various specific dimensions of the CT skills model, (d) the modality of the skill assessment instrumentmay have an impact on students’ performance, (e) girls appear inmany situations to need more training time to reach the same skill level compared to boys.
Hardware Trojans – Prevention , Detection , Countermeasures ( A Literature Review )

A Hardware Trojan is a malicious, undesired, intentional modification of an electronic circuit or design, resulting in the incorrect behaviour of an electronic device when in operation – a back-door that can be inserted into hardware. A Hardware Trojan may be able to defeat any and all security mechanisms (software or hardware-based) and subvert or augment the normal operation of an infected device. This may result in modifications to the functionality or specification of the hardware, the leaking of sensitive information, or a Denial of Service (DoS) attack. Understanding Hardware Trojans is vital when developing next generation defensive mechanisms for the development and deployment of electronics in the presence of the Hardware Trojan threat. Research over the past five years has primarily focussed on detecting the presence of Hardware Trojans in infected devices. This report reviews the state-of-the-art in Hardware Trojans, from the threats they pose through to modern prevention, detection and countermeasure techniques. APPROVED FOR PUBLIC RELEASE
Modeling of failure probability and statistical design of Spin-Torque Transfer Magnetic Random Access Memory (STT MRAM) array for yield enhancement

Spin-Torque Transfer Magnetic RAM (STT MRAM) is a promising candidate for future universal memory. It combines the desirable attributes of current memory technologies such as SRAM, DRAM and flash memories. It also solves the key drawbacks of conventional MRAM technology: poor scalability and high write current. In this paper, we analyzed and modeled the failure probabilities of STT MRAM cells due to parameter variations. Based on the model, we developed an efficient simulation tool to capture the coupled electro/magnetic dynamics of spintronic device, leading to effective prediction for memory yield. We also developed a statistical optimization methodology to minimize the memory failure probability. The proposed methodology can be used at an early stage of the design cycle to enhance memory yield.
Hardware Trojan detection using path delay fingerprint

Trusted IC design is a recently emerged topic since fabrication factories are moving worldwide in order to reduce cost. In order to get a low-cost but effective hardware trojan detection method to complement traditional testing methods, a new behavior-oriented category method is proposed to divide trojans into two categories: explicit payload trojan and implicit payload trojan. This categorization method makes it possible to construct trojan models and then lower the cost of testing. Path delays of nominal chips are collected to construct a series of fingerprints, each one representing one aspect of the total characteristics of a genuine design. Chips are validated by comparing their delay parameters to the fingerprints. The comparison of path delays makes small trojan circuits significant from a delay point of view. The experimentpsilas results show that the detection rate on explicit payload trojans is 100%, while this method should be developed further if used to detect implicit payload trojans.