nils-herrmann/clean_scirepeval huggingface dataset:
Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations

In recent years, deep neural networks (DNN) have demonstrated significant business impact in large scale analysis and classification tasks such as speech recognition, visual object detection, pattern extraction, etc. Training of large DNNs, however, is universally considered as time consuming and computationally intensive task that demands datacenter-scale computational resources recruited for many days. Here we propose a concept of resistive processing unit (RPU) devices that can potentially accelerate DNN training by orders of magnitude while using much less power. The proposed RPU device can store and update the weight values locally thus minimizing data movement during training and allowing to fully exploit the locality and the parallelism of the training algorithm. We evaluate the effect of various RPU device features/non-idealities and system parameters on performance in order to derive the device and system level specifications for implementation of an accelerator chip for DNN training in a realistic CMOS-compatible technology. For large DNNs with about 1 billion weights this massively parallel RPU architecture can achieve acceleration factors of 30, 000 × compared to state-of-the-art microprocessors while providing power efficiency of 84, 000 GigaOps∕s∕W. Problems that currently require days of training on a datacenter-size cluster with thousands of machines can be addressed within hours on a single RPU accelerator. A system consisting of a cluster of RPU accelerators will be able to tackle Big Data problems with trillions of parameters that is impossible to address today like, for example, natural speech recognition and translation between all world languages, real-time analytics on large streams of business and scientific data, integration, and analysis of multimodal sensory data flows from a massive number of IoT (Internet of Things) sensors.
Facial expression recognition with PCA and LBP features extracting from active facial patches

Facial expression recognition is an important part of Natural User Interface (NUI). Feature extraction is one important step which could contribute to fast and accurate expression recognition. In order to extract more effective features from the static images, this paper proposes an algorithm based on the combination of gray pixel value and Local Binary Patterns (LBP) features. Principal component analysis (PCA) is used to reduce dimensions of the features which are combined by the gray pixel value and Local Binary Patterns (LBP) features. All the features are extracted from the active facial patches. The active facial patches are these face regions which undergo a major change during different expressions. Softmax regression classifier is used to classify the six basic facial expressions, the experimental results on extended Cohn-Kanade (CK+) database gain an average recognition rate of 96.3% under leave-one-out cross validation method which validates every subject in the database.
Multimodal Classification of Remote Sensing Images: A Review and Future Directions

Earth observation through remote sensing images allows the accurate characterization and identification of materials on the surface from space and airborne platforms. Multiple and heterogeneous image sources can be available for the same geographical region: multispectral, hyperspectral, radar, multitemporal, and multiangular images can today be acquired over a given scene. These sources can be combined/fused to improve classification of the materials on the surface. Even if this type of systems is generally accurate, the field is about to face new challenges: the upcoming constellations of satellite sensors will acquire large amounts of images of different spatial, spectral, angular, and temporal resolutions. In this scenario, multimodal image fusion stands out as the appropriate framework to address these problems. In this paper, we provide a taxonomical view of the field and review the current methodologies for multimodal classification of remote sensing images. We also highlight the most recent advances, which exploit synergies with machine learning and signal processing: sparse methods, kernel-based fusion, Markov modeling, and manifold alignment. Then, we illustrate the different approaches in seven challenging remote sensing applications: 1) multiresolution fusion for multispectral image classification; 2) image downscaling as a form of multitemporal image fusion and multidimensional interpolation among sensors of different spatial, spectral, and temporal resolutions; 3) multiangular image classification; 4) multisensor image fusion exploiting physically-based feature extractions; 5) multitemporal image classification of land covers in incomplete, inconsistent, and vague image sources; 6) spatiospectral multisensor fusion of optical and radar images for change detection; and 7) cross-sensor adaptation of classifiers. The adoption of these techniques in operational settings will help to monitor our planet from space in the very near future.
Mention-anomaly-based Event Detection and tracking in Twitter

The ever-growing number of people using Twitter makes it a valuable source of timely information. However, detecting events in Twitter is a difficult task, because tweets that report interesting events are overwhelmed by a large volume of tweets on unrelated topics. Existing methods focus on the textual content of tweets and ignore the social aspect of Twitter. In this paper we propose MABED (Mention-Anomaly-Based Event Detection), a novel method that leverages the creation frequency of dynamic links (i.e. mentions) that users insert in tweets to detect important events and estimate the magnitude of their impact over the crowd. The main advantages of MABED over prior works are that (i) it relies solely on tweets, meaning no external knowledge is required, and that (ii) it dynamically estimates the period of time during which each event is discussed rather than assuming a predefined fixed duration. The experiments we conducted on both English and French Twitter data show that the mention-anomaly-based approach leads to more accurate event detection and improved robustness in presence of noisy Twitter content. Last, we show that MABED helps with the interpretation of detected events by providing clear and precise descriptions.
Voronoi treemaps for the visualization of software metrics

In this paper we present a hierarchy-based visualization approach for software metrics using Treemaps. Contrary to existing rectangle-based Treemap layout algorithms, we introduce layouts based on arbitrary polygons that are advantageous with respect to the aspect ratio between width and height of the objects and the identification of boundaries between and within the hierarchy levels in the Treemap. The layouts are computed by the iterative relaxation of Voronoi tessellations. Additionally, we describe techniques that allow the user to investigate software metric data of complex systems by utilizing transparencies in combination with interactive zooming.
AoT: Authentication and Access Control for the Entire IoT Device Life-Cycle

The consumer electronics industry is witnessing a surge in Internet of Things (IoT) devices, ranging from mundane artifacts to complex biosensors connected across disparate networks. As the demand for IoT devices grows, the need for stronger authentication and access control mechanisms is greater than ever. Legacy authentication and access control mechanisms do not meet the growing needs of IoT. In particular, there is a dire need for a holistic authentication mechanism throughout the IoT device life-cycle, namely from the manufacturing to the retirement of the device. As a plausible solution, we present Authentication of Things (AoT), a suite of protocols that incorporate authentication and access control during the entire IoT device life span. Primarily, AoT relies on Identity- and Attribute-Based Cryptography to cryptographically enforce Attribute-Based Access Control (ABAC). Additionally, AoT facilitates secure (in terms of stronger authentication) wireless interoperability of new and guest devices in a seamless manner. To validate our solution, we have developed AoT for Android smartphones like the LG G4 and evaluated all the cryptographic primitives over more constrained devices like the Intel Edison and the Arduino Due. This included the implementation of an Attribute-Based Signature (ABS) scheme. Our results indicate AoT ranges from highly efficient on resource-rich devices to affordable on resource-constrained IoT-like devices. Typically, an ABS generation takes around 27 ms on the LG G4, 282 ms on the Intel Edison, and 1.5 s on the Arduino Due.
Finki at SemEval-2016 Task 4: Deep Learning Architecture for Twitter Sentiment Analysis

In this paper, we present a novel deep learning architecture for sentiment analysis in Twitter messages. Our system finki, employs both convolutional and gated recurrent neural networks to obtain a more diverse tweet representation. The network is trained on top of GloVe word embeddings pre-trained on the Common Crawl dataset. Both neural networks are used to obtain a fixed length representation of variable sized tweets, and the concatenation of these vectors is supplied to a fully connected softmax layer with dropout regularization. The system is evaluated on benchmark datasets from the Sentiment Analysis in Twitter task of the SemEval 2016 challenge where our model achieves best and second highest results on the 2-point and 5-point quantification subtasks respectively. Despite not relying on any hand-crafted features, our system manages the second highest average rank on the considered subtasks.
Performance of grid-connected PV system based on SAPF for power quality improvement

This paper presents the design of a shunt Active Power Filter (SAPF) for grid-connected photovoltaic systems. The proposed system injects PV power into the grid, by feeding the SAPF; to eliminate harmonics currents and compensate reactive power produced by nonlinear loads. To inject the photovoltaic power to the grid we use a boost converter controlled by a Fuzzy logic (FLC) algorithm for maximum power point tracking (MPPT). The SAPF system is based on a two-level voltage source inverter (VSI); P-Q theory algorithm is used for references harmonic currents extraction. The overall system is designed and developed using MATLAB/Simulink software. Simulation results confirm the performance of the grid-connected photovoltaic system based on SAPF. For the MPPT controller, the results show that the proposed FLC algorithm is fast in finding the MPPT than conventional techniques used for MPPT like perturbed and observed (P&O). The simulated compensation system shows its effectiveness such as the sinusoidal form of the currents and the reactive power compensation. The proposed solution has achieved a low Total Harmonic Distortion (THD), demonstrating the efficiency of the presented method. Also, the results determine the performances of the proposed system and offer future perspectives of renewable energy for power quality improvement.
Tactical differences in coping with rejection sensitivity: the role of prevention pride.

Prevention pride reflects a person's subjective history of success in preventing negative outcomes, leading to a strategic avoidance of errors of commission (e.g., explicit mistakes) in new situations. Two studies examined the impact of prevention pride on the strategies that highly rejection sensitive (HRS) people use to cope with the anxiety of anticipated rejection and the negative feelings elicited by perceived rejection. It was hypothesized that prevention pride orientation would lead HRS people toward covert and passive rather than overt and active forms of negative coping. Results indicated that HRS individuals who were also high in prevention pride reported increased use of self-silencing, presumably to prevent rejection. When rejection was perceived, however, they expressed hostility passively, by reducing positive behavior (e.g., withdrawing love and support) while inhibiting direct, active acts of hostility (e.g., yelling).
A study of the uniqueness of source code

This paper presents the results of the first study of the uniqueness of source code. We define the uniqueness of a unit of source code with respect to the entire body of written software, which we approximate with a corpus of 420 million lines of source code. Our high-level methodology consists of examining a collection of 6,000 software projects and measuring the degree to which each project can be `assembled' solely from portions of this corpus, thus providing a precise measure of `uniqueness' that we call syntactic redundancy. We parameterized our study over a variety of variables, the most important of which being the level of granularity at which we view source code. Our suite of experiments together consumed approximately four months of CPU time, providing quantitative answers to the following questions: at what levels of granularity is software unique, and at a given level of granularity, how unique is software? While we believe these questions to be of intrinsic interest, we discuss possible applications to genetic programming and developer productivity tools.
Which NoSQL Database? A Performance Overview

NoSQL data stores are widely used to store and retrieve possibly large amounts of data, typically in a key-value format. There are many NoSQL types with different performances, and thus it is important to compare them in terms of performance and verify how the performance is related to the database type. In this paper, we evaluate five most popular NoSQL databases: Cassandra, HBase, MongoDB, OrientDB and Redis. We compare those databases in terms of query performance, based on reads and updates, taking into consideration the typical workloads, as represented by the Yahoo! Cloud Serving Benchmark. This comparison allows users to choose the most appropriate database according to the specific mechanisms and application needs. TYPE OF PAPER AND
The INTERSPEECH 2017 Computational Paralinguistics Challenge: Addressee, Cold & Snoring

The INTERSPEECH 2017 Computational Paralinguistics Challenge addresses three different problems for the first time in research competition under well-defined conditions: In the Addressee sub-challenge, it has to be determined whether speech produced by an adult is directed towards another adult or towards a child; in the Cold sub-challenge, speech under cold has to be told apart from ‘healthy’ speech; and in the Snoring subchallenge, four different types of snoring have to be classified. In this paper, we describe these sub-challenges, their conditions, and the baseline feature extraction and classifiers, which include data-learnt feature representations by end-to-end learning with convolutional and recurrent neural networks, and bag-of-audiowords for the first time in the challenge series.
Compressive Document Summarization via Sparse Optimization

In this paper, we formulate a sparse optimization framework for extractive document summarization. The proposed framework has a decomposable convex objective function. We derive an efficient ADMM algorithm to solve it. To encourage diversity in the summaries, we explicitly introduce an additional sentence dissimilarity term in the optimization framework. We achieve significant improvement over previous related work under similar data reconstruction framework. We then generalize our formulation to the case of compressive summarization and derive a block coordinate descent algorithm to optimize the objective function. Performance on DUC 2006 and DUC 2007 datasets shows that our compressive summarization results are competitive against the state-of-the-art results while maintaining reasonable readability.
Multiview Clustering via Adaptively Weighted Procrustes

In this paper, we make a multiview extension of the spectral rotation technique raised in single view spectral clustering research. Since spectral rotation is closely related to the Procrustes Analysis for points matching, we point out that classical Procrustes Average approach can be used for multiview clustering. Besides, we show that direct applying Procrustes Average (PA) in multiview tasks may not be optimal theoretically and empirically, since it does not take the clustering capacity differences of different views into consideration. Other than that, we propose an Adaptively Weighted Procrustes (AWP) approach to overcome the aforementioned deficiency. Our new AWP weights views with their clustering capacities and forms a weighted Procrustes Average problem accordingly. The optimization algorithm to solve the new model is computational complexity analyzed and convergence guaranteed. Experiments on five real-world datasets demonstrate the effectiveness and efficiency of the new models.
Scholarly paper recommendation via user's recent research interests

We examine the effect of modeling a researcher's past works in recommending scholarly papers to the researcher. Our hypothesis is that an author's published works constitute a clean signal of the latent interests of a researcher. A key part of our model is to enhance the profile derived directly from past works with information coming from the past works' referenced papers as well as papers that cite the work. In our experiments, we differentiate between junior researchers that have only published one paper and senior researchers that have multiple publications. We show that filtering these sources of information is advantageous -- when we additionally prune noisy citations, referenced papers and publication history, we achieve statistically significant higher levels of recommendation accuracy.
Corporate residence fraud detection

With the globalisation of the world's economies and ever-evolving financial structures, fraud has become one of the main dissipaters of government wealth and perhaps even a major contributor in the slowing down of economies in general. Although corporate residence fraud is known to be a major factor, data availability and high sensitivity have caused this domain to be largely untouched by academia. The current Belgian government has pledged to tackle this issue at large by using a variety of in-house approaches and cooperations with institutions such as academia, the ultimate goal being a fair and efficient taxation system. This is the first data mining application specifically aimed at finding corporate residence fraud, where we show the predictive value of using both structured and fine-grained invoicing data. We further describe the problems involved in building such a fraud detection system, which are mainly data-related (e.g. data asymmetry, quality, volume, variety and velocity) and deployment-related (e.g. the need for explanations of the predictions made).
Customer Churn Prediction Modelling Based on Behavioural Patterns Analysis using Deep Learning

Customer churn refers to when a customer ceases their relationship with a company. A churn rate, used to estimate growth, is now considered as important a metric as financial profit. With growing competition in the market, companies are desperate to keep the churn rate as low as possible. Thus, churn prediction has gained critical importance, not just for existing customers, but also for predicting trends of future customers. This paper demonstrates prediction of churn on a Telco dataset using a Deep Learning Approach. A multilayered Neural Network was designed to build a non-linear classification model. The churn prediction model works on customer features, support features, usage features and contextual features. The possibility of churn as well as the determining factors are predicted. The trained model then applies the final weights on these features and predict the possibility of churn for that customer. An accuracy of 80.03% was achieved. Since the model also provides the churn factors, it can be used by companies to analyze the reasons for these factors and take steps to eliminate them.
Eigentaste: A Constant Time Collaborative Filtering Algorithm

Eigentaste is a collaborative filtering algorithm that uses universal queries to elicit real-valued user ratings on a common set of items and applies principal component analysis (PCA) to the resulting dense subset of the ratings matrix. PCA facilitates dimensionality reduction for offline clustering of users and rapid computation of recommendations. For a database of n users, standard nearest-neighbor techniques require O(n) processing time to compute recommendations, whereas Eigentaste requires O(1) (constant) time. We compare Eigentaste to alternative algorithms using data from Jester, an online joke recommending system. Jester has collected approximately 2,500,000 ratings from 57,000 users. We use the Normalized Mean Absolute Error (NMAE) measure to compare performance of different algorithms. In the Appendix we use Uniform and Normal distribution models to derive analytic estimates of NMAE when predictions are random. On the Jester dataset, Eigentaste computes recommendations two orders of magnitude faster with no loss of accuracy. Jester is online at: http://eigentaste.berkeley.edu
Neural Networks and the Bias/Variance Dilemma

Feedforward neural networks trained by error backpropagation are examples of nonparametric regression estimators. We present a tutorial on nonparametric inference and its relation to neural networks, and we use the statistical viewpoint to highlight strengths and weaknesses of neural models. We illustrate the main points with some recognition experiments involving artificial data as well as handwritten numerals. In way of conclusion, we suggest that current-generation feedforward neural networks are largely inadequate for difficult problems in machine perception and machine learning, regardless of parallel-versus-serial hardware or other implementation issues. Furthermore, we suggest that the fundamental challenges in neural modeling are about representation rather than learning per se. This last point is supported by additional experiments with handwritten numerals.
Head pose estimation based on face symmetry analysis

This paper addresses the problem of head pose estimation in order to infer non-intrusive feedback from users about gaze attention. The proposed approach exploits the bilateral symmetry of the face. Size and orientation of the symmetrical area of the face is used to estimate roll and yaw poses by the mean of decision tree model. The approach does not need the location of interest points on face and presents robustness to partial occlusions. Tests were performed on different datasets (FacePix, CMU PIE, Boston University) and our approach coped with variability in illumination and expressions. Results demonstrate that the changes in the size of the regions that contain a bilateral symmetry provide accurate pose estimation.
Mobile peer-to-peer video streaming over information-centric networks

Information Centric Networking (ICN) is a network paradigm alternative to the classic host-centric communication model: it provides users with content exposed as names, instead of providing communication channels between hosts. In this paper, we present a peer-to-peer application for live streaming of video content encoded at multiple bit rates. The application enables a small set of neighbouring cellular/Wi-Fi devices to increase the quality of video playback by using the Wi-Fi network to share the portion of the live stream downloaded by each peer via the cellular network. The application exploits the main functionalities of ICN: routing by name, in network caching and multicast delivery. Our work includes the implementation of a Java prototype of the application on a test-bed composed by Linux machines running the CCNx tool and streaming MPEG-DASH videos. We measured the performance of our solution and verified on the field that ICN simplifies the development of applications, as it provides built-in functions, which would be much more difficult to implement by relying on classical TCP/IP tools only.