nils-herrmann/clean_scirepeval huggingface dataset:
Deep Learning in Mobile and Wireless Networking: A Survey

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.
A survey of literature on the teaching of introductory programming

Three decades of active research on the teaching of introductory programming has had limited effect on classroom practice. Although relevant research exists across several disciplines including education and cognitive science, disciplinary differences have made this material inaccessible to many computing educators. Furthermore, computer science instructors have not had access to a comprehensive survey of research in this area. This paper collects and classifies this literature, identifies important work and mediates it to computing educators and professional bodies.
 We identify research that gives well-supported advice to computing academics teaching introductory programming. Limitations and areas of incomplete coverage of existing research efforts are also identified. The analysis applies publication and research quality metrics developed by a previous ITiCSE working group [74].
Predicting elections from social media : a three-country , three-method comparative study

This study introduces and evaluates the robustness of different volumetric, sentiment, and social network approaches to predict the elections in three Asian countries – Malaysia, India, and Pakistan from Twitter posts. We find that predictive power of social media performs well for India and Pakistan but is not effective for Malaysia. Overall, we find that it is useful to consider the recency of Twitter posts while using it to predict a real outcome, such as an election result. Sentiment information mined using machine learning models was the most accurate predictor of election outcomes. Social network information is stable despite sudden surges in political discussions, for e.g. around electionsrelated news events.  combining sentiment and volume information, or sentiment and social network information, are effective at predicting smaller vote shares, for e.g. vote shares in the case of independent candidates and regional parties. We conclude with a detailed discussion on the caveats of social media analysis for predicting real-world outcomes and recommendations for future work. ARTICLE HISTORY Received 1 August 2017 Revised 12 February 2018
Logic Tensor Networks for Semantic Image Interpretation

Semantic Image Interpretation (SII) is the task of extracting structured semantic descriptions from images. It is widely agreed that the combined use of visual data and background knowledge is of great importance for SII. Recently, Statistical Relational Learning (SRL) approaches have been developed for reasoning under uncertainty and learning in the presence of data and rich knowledge. Logic Tensor Networks (LTNs) are an SRL framework which integrates neural networks with first-order fuzzy logic to allow (i) efficient learning from noisy data in the presence of logical constraints, and (ii) reasoning with logical formulas describing general properties of the data. In this paper, we develop and apply LTNs to two of the main tasks of SII, namely, the classification of an image’s bounding boxes and the detection of the relevant part-of relations between objects. To the best of our knowledge, this is the first successful application of SRL to such SII tasks. The proposed approach is evaluated on a standard image processing benchmark. Experiments show that the use of background knowledge in the form of logical constraints can improve the performance of purely data-driven approaches, including the state-of-the-art Fast Region-based Convolutional Neural Networks (Fast R-CNN). Moreover, we show that the use of logical background knowledge adds robustness to the learning system when errors are present in the labels of the
"Alexa is my new BFF": Social Roles, User Satisfaction, and Personification of the Amazon Echo

Amazon's Echo and its conversational agent Alexa open exciting opportunities for understanding how people perceive and interact with virtual agents. Drawing from user reviews of the Echo posted to Amazon.com, this case study explores the degree to which user reviews indicate personification of the device, sociability level of interactions, factors linked with personification, and influences on user satisfaction. Results indicate marked variance in how people refer to the device, with over half using the personified name Alexa but most referencing the device with object pronouns. Degree of device personification is linked with sociability of interactions: greater personification co-occurs with more social interactions with the Echo. Reviewers mentioning multiple member households are more likely to personify the device than reviewers mentioning living alone. Even after controlling for technical issues, personification predicts user satisfaction with the Echo.
Introduction: Aspects of Artificial General Intelligence

This book contains materials that come out of the Artificial General Intelligence Research Institute (AGIRI) Workshop, held in May 20-21, 2006 at Washington DC. The theme of the workshop is “Transitioning from Narrow AI to Artificial General Intelligence.” In this introductory chapter, we will clarify the notion of “Artificial General Intelligence”, briefly survey the past and present situation of the field, analyze and refute some common objections and doubts regarding this area of research, and discuss what we believe needs to be addressed by the field as a whole in the near future. Finally, we will briefly summarize the contents of the other chapters in this collection.
Analyzing Newspaper Crime Reports for Identification of Safe Transit Paths

In this paper, we propose a method to find the safest path between two locations, based on the geographical model of crime intensities. We consider the police records and news articles for finding crime density of different areas of the city. It is essential to consider news articles as there is a significant delay in updating police crime records. We address this problem by updating the crime intensities based on current news feeds. Based on the updated crime intensities, we identify the safest path. It is this real time updation of crime intensities which makes our model way better than the models that are presently in use. Our model would also inform the user of crime sprees in a particular area thereby ensuring that user avoids these crime hot spots.
Design considerations for a 10 GHz CMOS transmit-receive switch

A transmit-receive (T/R) switch is designed to operate at 10 GHz in a triple-well CMOS process. The transmit switch includes multiple parallel resonant networks to improve loss and isolation characteristics and the receive switch includes an impedance matching network and DC bias circuitry to increase linearity. A simple simulation model is proposed for linearity analysis of the receive switch. Finally, a figure-of-merit for T/R switches is proposed and used to compare previous published results.
Model-Based Simulation at Runtime for Self-Adaptive Systems

Modern software systems are subject to uncertainties, such as dynamics in the availability of resources or changes of system goals. Self-adaptation enables a system to reason about runtime models to adapt itself and realises its goals under uncertainties. Our focus is on providing guarantees for adaption goals. A prominent approach to provide such guarantees is automated verification of a stochastic model that encodes up-to-date knowledge of the system and relevant qualities. The verification results allow selecting an adaption option that satisfies the goals. There are two issues with this state of the art approach: i) changing goals at runtime (a challenging type of uncertainty) is difficult, and ii) exhaustive verification suffers from the state space explosion problem. In this paper, we propose a novel modular approach for decision making in self-adaptive systems that combines distinct models for each relevant quality with runtime simulation of the models. Distinct models support on the fly changes of goals. Simulation enables efficient decision making to select an adaptation option that satisfies the system goals. The tradeoff is that simulation results can only provide guarantees with a certain level of accuracy. We demonstrate the benefits and tradeoffs of the approach for a service-based telecare system.
Uncertainty in Neural Network Word Embedding: Exploration of Threshold for Similarity

Word embedding, specially with its recent developments, promises a quantification of the similarity between terms. However, it is not clear to which extent this similarity value can be genuinely meaningful and useful for subsequent tasks. We explore how the similarity score obtained from the models is really indicative of term relatedness. We first observe and quantify the uncertainty factor of the word embedding models regarding to the similarity value. Based on this factor, we introduce a general threshold on various dimensions which effectively filters the highly related terms. Our evaluation on four information retrieval collections supports the effectiveness of our approach as the results of the introduced threshold are significantly better than the baseline while being equal to or statistically indistinguishable from the optimal results.
Smart traffic light control system

Traffic light control systems are widely used to monitor and control the flow of automobiles through the junction of many roads. They aim to realize smooth motion of cars in the transportation routes. However, the synchronization of multiple traffic light systems at adjacent intersections is a complicated problem given the various parameters involved. Conventional systems do not handle variable flows approaching the junctions. In addition, the mutual interference between adjacent traffic light systems, the disparity of cars flow with time, the accidents, the passage of emergency vehicles, and the pedestrian crossing are not implemented in the existing traffic system. This leads to traffic jam and congestion. We propose a system based on PIC microcontroller that evaluates the traffic density using IR sensors and accomplishes dynamic timing slots with different levels. Moreover, a portable controller device is designed to solve the problem of emergency vehicles stuck in the overcrowded roads.
26-42 GHz SOI CMOS low noise amplifier

A complementary metal-oxide semiconductor (CMOS) single-stage cascode low-noise amplifier (LNA) is presented in this paper. The microwave monolithic integrated circuit (MMIC) is fabricated using digital 90-nm silicon-on-insulator (SOI) technology. All impedance matching and bias elements are implemented on the compact chip, which has a size of 0.6 mm /spl times/ 0.3 mm. The supply voltage and supply current are 2.4 V and 17 mA, respectively. At 35 GHz and 50 /spl Omega/ source/load impedances, a gain of 11.9 dB, a noise figure of 3.6 dB, an output compression point of 4 dBm, an input return loss of 6 dB, and an output return loss of 18 dB are measured. The -3-dB frequency bandwidth ranges from 26 to 42 GHz. All results include the pad parasitics. To the knowledge of the author, the results are by far the best for a silicon-based millimeter-wave LNA reported to date. The LNA is well suited for systems operating in accordance to the local multipoint distribution service (LMDS) standards at 28 and 38 GHz and the multipoint video distribution system (MVDS) standard at 42 GHz.
Fast Constrained Non-negative Matrix Factorization for Whole-Brain Calcium Imaging Data

Advances in optical recording technologies allow whole-brain recordings with single cell resolution of small animals, such as larval zebrafish. A crucial step for further neural analysis is the move from voxel-space to neural-space, i.e. the detection of all neurons and extraction/demixing of how each neurons activity evolves in time. The sheer amount of data has led experimenters towards simple but fast methods such as averaging voxels in a small neighborhood around identified neuron centers. More statistically-principled methods based on non-negative matrix factorization (NMF) better conserve information, yielding higher signal-to-noise ratio and more recovered neurons, and allow the demixing of overlapping neurons. However, previous implementations of these methods have been too slow for the large data sizes obtained with calcium imaging and have not yet been employed routinely in practice. Here we exploit advances in NMF paired with downsampling schemes for the specific data at hand to obtain a fast NMF method. Given access to a small cluster, processing of the data takes the same time as the experiment. Though we focus on batch processing performed after the experiment, our method opens the way to online real-time processing.
Google Scholar as a new source for citation analysis

Traditionally, the most commonly used source of bibliometric data is Thomson ISI Web of Knowledge, in particular the Web of Science and the Journal Citation Reports (JCR), which provide the yearly Journal Impact Factors (JIF). This paper presents an alternative source of data (Google Scholar, GS) as well as 3 alternatives to the JIF to assess journal impact (h-index, g-index and the number of citations per paper). Because of its broader range of data sources, the use of GS generally results in more comprehensive citation coverage in the area of management and international business. The use of GS particularly benefits academics publishing in sources that are not (well) covered in ISI. Among these are books, conference papers, non-US journals, and in general journals in the field of strategy and international business. The 3 alternative GS-based metrics showed strong correlations with the traditional JIF. As such, they provide academics and universities committed to JIFs with a good alternative for journals that are not ISI-indexed. However, we argue that these metrics provide additional advantages over the JIF and that the free availability of GS allows for a democratization of citation analysis as it provides every academic access to citation data regardless of their institution’s financial means.
Silicon CMOS devices beyond scaling

devices beyond scaling W. Haensch E. J. Nowak R. H. Dennard P. M. Solomon A. Bryant O. H. Dokumaci A. Kumar X. Wang J. B. Johnson M. V. Fischetti To a large extent, scaling was not seriously challenged in the past. However, a closer look reveals that early signs of scaling limits were seen in high-performance devices in recent technology nodes. To obtain the projected performance gain of 30% per generation, device designers have been forced to relax the device subthreshold leakage continuously from one to several nA/lm for the 250-nm node to hundreds of nA/lm for the 65-nm node. Consequently, passive power density is now a significant portion of the power budget of a high-speed microprocessor. In this paper we discuss device and material options to improve device performance when conventional scaling is power-constrained. These options can be separated into three categories: improved short-channel behavior, improved current drive, and improved switching behavior. In the first category fall advanced dielectrics and multi-gate devices. The second category comprises mobility-enhancing measures through stress and substrate material alternatives. The third category focuses mainly on scaling of SOI body thickness to reduce capacitance. We do not provide details of the fabrication of these different device options or the manufacturing challenges that must be met. Rather, we discuss the fundamental scaling issues related to the various device options. We conclude with a brief discussion of the ultimate FET close to the fundamental silicon device limit.
Integrated pre-processing for Bayesian nonlinear system identification with Gaussian processes

We introduce GP-FNARX: a new model for nonlinear system identification based on a nonlinear autoregressive exogenous model (NARX) with filtered regressors (F) where the nonlinear regression problem is tackled using sparse Gaussian processes (GP). We integrate data pre-processing with system identification into a fully automated procedure that goes from raw data to an identified model. Both pre-processing parameters and GP hyper-parameters are tuned by maximizing the marginal likelihood of the probabilistic model. We obtain a Bayesian model of the system's dynamics which is able to report its uncertainty in regions where the data is scarce. The automated approach, the modeling of uncertainty and its relatively low computational cost make of GP-FNARX a good candidate for applications in robotics and adaptive control.
Optimal rates of convergence for persistence diagrams in Topological Data Analysis

Computational topology has recently known an important development toward data analysis, giving birth to the field of topological data analysis. Topological persistence, or persistent homology, appears as a fundamental tool in this field. In this paper, we study topological persistence in general metric spaces, with a statistical approach. We show that the use of persistent homology can be naturally considered in general statistical frameworks and persistence diagrams can be used as statistics with interesting convergence properties. Some numerical experiments are performed in various contexts to illustrate our results.
A secure camouflaged threshold voltage defined logic family

A myriad of security vulnerabilites can be exposed via the reverse engineering of the integrated circuits contained in electronics systems. The goal of IC reverse engineering is to uncover the functionality and internal structure of the chip via techniques such as depackaging/delayering, high-resolution imaging, probing, and side-channel examination. With this knowledge, an attacker can more efficiently mount various attacks, clone/-counterfeit the design possibly with hardware Trojans inserted, and discover trade secrets. We propose a gate camouflaging technique that relies on the usage of different threshold voltage transistors, but with identical layouts, to determine the logic gate function. In our threshold voltage defined (TVD) camouflaging technique, every TVD logic gate has the same physical structure and is one time mask programmed with different threshold implants for different boolean functionality. We design and implement TVD logic gates in an industrial 65nm bulk CMOS process. Using post-layout extracted simulation, we evaluate the logic style for VLSI overheads (area, power, delay) versus conventional logic, for process variablity robustness, and for various security metrics. Further, we evaluate the macro block overheads for ISCAS benchmark designs under various levels of TVD gate replacement upto and including 100% replacement. TVD logic gates are found to be CMOS process compatible, low overhead, and to increase security against various forms of attacks.
Clustering Experiments on Big Transaction Data for Market Segmentation

This paper addresses the Volume dimension of Big Data. It presents a preliminary work on finding segments of retailers from a large amount of Electronic Funds Transfer at Point Of Sale (EFTPOS) transaction data. To the best of our knowledge, this is the first time a work on Big EFTPOS Data problem has been reported. A data reduction technique using the RFM (Recency, Frequency, Monetary) analysis as applied to a large data set is presented. Ways to optimise clustering techniques used to segment the big data set through data partitioning and parallelization are explained. Preliminary analysis on the segments of the retailers output from the clustering experiments demonstrates that further drilling down into the retailer segments to find more insights into their business behaviours is warranted.
Recessed-gate structure approach toward normally off high-Voltage AlGaN/GaN HEMT for power electronics applications

A recessed-gate structure has been studied with a view to realizing normally off operation of high-voltage AlGaN/GaN high-electron mobility transistors (HEMTs) for power electronics applications. The recessed-gate structure is very attractive for realizing normally off high-voltage AlGaN/GaN HEMTs because the gate threshold voltage can be controlled by the etching depth of the recess without significant increase in on-resistance characteristics. With this structure the threshold voltage can be increased with the reduction of two-dimensional electron gas (2DEG) density only under the gate electrode without reduction of 2DEG density in the other channel regions such as the channel between drain and gate. The threshold-voltage increase was experimentally demonstrated. The threshold voltage of fabricated recessed-gate device increased to -0.14 V while the threshold voltage without the recessed-gate structure was about -4 V. The specific on-resistance of the device was maintained as low as 4 m/spl Omega//spl middot/cm/sup 2/ and the breakdown voltage was 435 V. The on-resistance and the breakdown voltage tradeoff characteristics were the same as those of normally on devices. From the viewpoint of device design, the on-resistance for the normally off device was modeled using the relationship between the AlGaN layer thickness under the gate electrode and the 2DEG density. It is found that the MIS gate structure and the recess etching without the offset region between recess edge and gate electrode will further improve the on-resistance. The simulation results show the possibility of the on-resistance below 1 m/spl Omega//spl middot/cm/sup 2/ for normally off AlGaN/GaN HEMTs operating at several hundred volts with threshold voltage up to +1 V.
A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection

It is known that head gesture and brain activity can reflect some human behaviors related to a risk of accident when using machine-tools. The research presented in this paper aims at reducing the risk of injury and thus increase worker safety. Instead of using camera, this paper presents a Smart Safety Helmet (SSH) in order to track the head gestures and the brain activity of the worker to recognize anomalous behavior. Information extracted from SSH is used for computing risk of an accident (a safety level) for preventing and reducing injuries or accidents. The SSH system is an inexpensive, non-intrusive, non-invasive, and non-vision-based system, which consists of an Inertial Measurement Unit (IMU) and dry EEG electrodes. A haptic device, such as vibrotactile motor, is integrated to the helmet in order to alert the operator when computed risk level (fatigue, high stress or error) reaches a threshold. Once the risk level of accident breaks the threshold, a signal will be sent wirelessly to stop the relevant machine tool or process.