nils-herrmann/clean_scirepeval huggingface dataset:
Mindless computing: designing technologies to subtly influence behavior

Persuasive technologies aim to influence user's behaviors. In order to be effective, many of the persuasive technologies developed so far relies on user's motivation and ability, which is highly variable and often the reason behind the failure of such technology. In this paper, we present the concept of Mindless Computing, which is a new approach to persuasive technology design. Mindless Computing leverages theories and concepts from psychology and behavioral economics into the design of technologies for behavior change. We show through a systematic review that most of the current persuasive technologies do not utilize the fast and automatic mental processes for behavioral change and there is an opportunity for persuasive technology designers to develop systems that are less reliant on user's motivation and ability. We describe two examples of mindless technologies and present pilot studies with encouraging results. Finally, we discuss design guidelines and considerations for developing this type of persuasive technology.
Graph-based Neural Multi-Document Summarization

We propose a neural multi-document summarization (MDS) system that incorporates sentence relation graphs. We employ a Graph Convolutional Network (GCN) on the relation graphs, with sentence embeddings obtained from Recurrent Neural Networks as input node features. Through multiple layer-wise propagation, the GCN generates high-level hidden sentence features for salience estimation. We then use a greedy heuristic to extract salient sentences while avoiding redundancy. In our experiments on DUC 2004, we consider three types of sentence relation graphs and demonstrate the advantage of combining sentence relations in graphs with the representation power of deep neural networks. Our model improves upon traditional graph-based extractive approaches and the vanilla GRU sequence model with no graph, and it achieves competitive results against other state-of-the-art multidocument summarization systems.
A Through-Dielectric Ultrawideband (UWB) Switched-Antenna-Array Radar Imaging System

A through-dielectric switched-antenna-array radar imaging system is shown that produces near real-time imagery of targets on the opposite side of a lossy dielectric slab. This system operates at S-band, provides a frame rate of 0.5 Hz, and operates at a stand-off range of 6 m or greater. The antenna array synthesizes 44 effective phase centers in a linear array providing $\lambda/2$ element-to-element spacing by time division multiplexing the radar's transmit and receive ports between 8 receive elements and 13 transmit elements, producing 2D (range vs. cross-range) imagery of what is behind a slab. Laboratory measurements agree with simulations, the air-slab interface is range gated out of the image, and target scenes consisting of cylinders and soda cans are imaged through the slab. A 2D model of a slab, a cylinder, and phase centers shows that blurring due to the slab and bistatic phase centers on the array is negligible when the radar sensor is located at stand-off ranges of 6 m or greater.
A systematic literature review on the barriers faced by newcomers to open source software projects

Numerous open source software projects are based on volunteers’ collaboration and require a continuous influx of newcomers for their continuity. Newcomers face barriers that can lead them to give up. These barriers hinder both developers willing to make a single contribution and those willing to become a project member.  This study aims to identify and classify the barriers that newcomers face when contributing to Open Source Software projects.  We conducted a systematic literature review of papers reporting empirical evidence regarding the barriers that newcomers face when contributing to Open Source Software (OSS) projects. We retrieved 291 studies *Corresponding author [Address: Rua das Cerejeiras, 60 CEP 87301-350 Campo Mourao–PR–Brazil – Phone +55(44)88383380 ] Email addresses: igorfs@utfpr.edu.br (Igor Steinmacher), magsilva@utfpr.edu.br (Marco Aurelio Graciotto Silva), gerosa@ime.usp.br (Marco Aurelio Gerosa), redmiles@ics.uci.edu (David F. Redmiles) Preprint submitted to Information and Software Technology November 5, 2014 by querying 4 digital libraries. Twenty studies were identified as primary. We performed a backward snowballing approach, and searched for other papers published by the authors of the selected papers to identify potential studies. Then, we used a coding approach inspired by open coding and axial coding procedures from Grounded Theory to categorize the barriers reported by the selected studies.  We identified 20 studies providing empirical evidence of barriers faced by newcomers to OSS projects while making a contribution. From the analysis, we identified 15 different barriers, which we grouped into five categories: social interaction, newcomers’ previous knowledge, finding a way to start, documentation, and technical hurdles. We also classified the problems with regard to their origin: newcomers, community, or product.  The results are useful to researchers and OSS practitioners willing to investigate or to implement tools to support newcomers. We mapped technical and non-technical barriers that hinder newcomers’ first contributions. The most evidenced barriers are related to socialization, appearing in 75% (15 out of 20) of the studies analyzed, with a high focus on interactions in mailing lists (receiving answers and socialization with other members). There is a lack of in-depth studies on technical issues, such as code issues. We also noticed that the majority of the studies relied on historical data gathered from software repositories and that there was a lack of experiments and qualitative studies in this area.
14.5 Envision: A 0.26-to-10TOPS/W subword-parallel dynamic-voltage-accuracy-frequency-scalable Convolutional Neural Network processor in 28nm FDSOI

ConvNets, or Convolutional Neural Networks (CNN), are state-of-the-art classification algorithms, achieving near-human performance in visual recognition . New trends such as augmented reality demand always-on visual processing in wearable devices. Yet, advanced ConvNets achieving high recognition rates are too expensive in terms of energy as they require substantial data movement and billions of convolution computations. Today, state-of-the-art mobile GPU's and ConvNet accelerator ASICs [2][3] only demonstrate energy-efficiencies of 10's to several 100's GOPS/W, which is one order of magnitude below requirements for always-on applications. This paper introduces the concept of hierarchical recognition processing, combined with the Envision platform: an energy-scalable ConvNet processor achieving efficiencies up to 10TOPS/W, while maintaining recognition rate and throughput. Envision hereby enables always-on visual recognition in wearable devices.
Palmprint verification using binary orientation co-occurrence vector

The development of accurate and robust palmprint verification algorithms is a critical issue in automatic palmprint authentication systems. Among various palmprint verification approaches, the orientation based coding methods, such as competitive code (CompCode), palmprint orientation code (POC) and robust line orientation code (RLOC), are state-of-the-art ones. They extract and code the locally dominant orientation as features and could match the input palmprint in real-time and with high accuracy. However, using only one dominant orientation to represent a local region may lose some valuable information because there are cross lines in the palmprint. In this paper, we propose a novel feature extraction algorithm, namely binary orientation co-occurrence vector (BOCV), to represent multiple orientations for a local region. The BOCV can better describe the local orientation features and it is more robust to image rotation. Our experimental results on the public palmprint database show that the proposed BOCV outperforms the CompCode, POC and RLOC by reducing the equal error rate (EER) significantly.
Three-dimensional SIW and high-performance air-filled SIW for millimeter-wave substrate integrated circuits and systems

Emerging millimeter-wave frequency applications require high performance, low-cost and compact devices and circuits. This is the reason why the Substrate Integrated Waveguide (SIW) technology, which combines some advantages of planar circuits and metallic waveguides, has focused a lot of attention in recent years. However, not all three-dimensional metallic waveguide devices and circuit are integrable in planar form. In its first section, this paper reviews recently proposed three-dimensional SIW devices that are taking advantages of the third-dimension to achieve either more compact or multidimensional circuits at millimeter wave frequencies. Also, in a second section, special interest is oriented to recent development of air-filled SIW based on low-cost multilayer printed circuit board (PCB) for high performance millimeter-wave substrate integrated circuits and systems.
Multilingual sentiment analysis: from formal to informal and scarce resource languages

The ability to analyse online user-generated content related to sentiments (e.g., thoughts and opinions) on products or policies has become a de-facto skillset for many companies and organisations. Besides the challenge of understanding formal textual content, it is also necessary to take into consideration the informal and mixed linguistic nature of online social media languages, which are often coupled with localised slang as a way to express ‘true’ feelings. Due to the multilingual nature of social media data, analysis based on a single official language may carry the risk of not capturing the overall sentiment of online content. While efforts have been made to understand multilingual sentiment analysis based on a range of informal languages, no significant electronic resource has been built for these localised languages. This paper reviews the various current approaches and tools used for multilingual sentiment analysis, identifies challenges along this line of research, and provides several recommendations including a framework that is particularly applicable for dealing with scarce resource languages.
The NIDES Statistical Component Description and Justification

Preface SRI International has prepared this document as a full and complete disclosure of the Next-Generation Intrusion-Detection Expert System (NIDES)  statistical algorithm , including how it works, what decisions influenced the form of the algorithm, and the rationale behind those decisions. We have divided this document into four sections:  Chapter 1 is a description of the NIDES statistical algorithm. It describes what the algorithm is and how it functions.  Chapter 2 is a broad justification for the NIDES statistical algorithm. This section also includes a comparison to other statistical approaches to intrusion detection.  Chapter 3 is a set of statistical criteria that can be used to evaluate the ap-propriateness of any statistical approach to intrusion detection. Although we did not formally use these criteria in the development of the NIDES statistical algorithm, they nevertheless had an important influence on the development of the algorithm. They may also be used to evaluate the suggestions of other statistical algorithm developers.  Chapter 4 is a set of specific questions and answers that can be posed about the NIDES statistical algorithm. In this section we explore in more depth the specific choices we made in developing the NIDES statistical algorithm. We have found it convenient to use the question-and-answer format to address the relationship of the NIDES statistical algorithm to the work of Helman et al.
Developing a Computer Science Concept Inventory for Introductory Programming

A Concept Inventory (CI) is a set of multiple choice questions used to reveal student's misconceptions related to some topic. Each available choice (besides the correct choice) is a distractor that is carefully developed to address a specific misunderstanding, a student wrong thought. In computer science introductory programming courses, the development of CIs is still beginning, with many topics requiring further study and analysis. We identify, through analysis of open-ended exams and instructor interviews, introductory programming course misconceptions related to function parameter use and scope, variables, recursion, iteration, structures, pointers and boolean expressions. We categorize these misconceptions and define high-quality distractors founded in words used by students in their responses to exam questions. We discuss the difficulty of assessing introductory programming misconceptions independent of the syntax of a language and we present a detailed discussion of two pilot CIs related to parameters: an open-ended question (to help identify new misunderstandings) and a multiple choice question with suggested distractors that we identified.
Research on leg-wheel hybrid stair-climbing robot, Zero Carrier

It is greatly demanded that to develop a kind of stably stair climbing mobile vehicle to assist the physically handicapped in moving outdoors. In this paper, we first propose a novel leg-wheel hybrid stair-climbing vehicle, "Zero Carrier", which consists of eight unified prismatic-joint legs, four of which attached active wheels and other four attached passive casters. Zero Carrier can be designed lightweight, compact, powerful, together with its significant stability on stair climbing motion, since its mechanism is mostly concentrated in its eight simplified legs. We discuss the leg mechanism and control method of the first trial model, Zero Carrier I, and verify its performance based on the experiments of stair climbing and moving over obstacles performed by Zero Carrier I
Point-Wise Approach for Yandex Personalized Web Search Challenge

The paper describes a solution for the Yandex Personalized Web Search Challenge. The goal of the challenge is to rerank top ten web search query results to bring most personally relevant results on the top, thereby improving the search quality. The paper focuses on feature engineering for learning to rank in web search, including a novel pair-wise feature, shortand long-term personal navigation features. The paper demonstrates that point-wise logistic regression can achieve the stat-of-the-art performance in terms of normalized discounted cumulative gain with capability to scale up.
A recursive algorithm for the forced alignment of very long audio segments

In this paper we address the problem of aligning very long (often more than one hour) audio files to their corresponding textual transcripts in an effective manner. We present an efficient recursive technique to solve this problem that works well even on noisy speech signals. The key idea of this algorithm is to turn the forced alignment problem into a recursive speech recognition problem with a gradually restricting dictionary and language model. The algorithm is tolerant to acoustic noise and errors or gaps in the text transcript or audio tracks. We report experimental results on a 3 hour audio file containing TV and radio broadcasts. We will show accurate alignments on speech under a variety of real acoustic conditions such as speech over music and speech over telephone lines. We also report results when the same audio stream has been corrupted with white additive noise or compressed using a popular web encoding format such as RealAudio. This algorithm has been used in our internal multimedia indexing project. It has processed more than 200 hours of audio from varied sources, such as WGBH NOVA documentaries and NPR web audio files. The system aligns speech media content in about one to five times realtime, depending on the acoustic conditions of the audio signal.
Atlantis: robust, extensible execution environments for web applications

Today's web applications run inside a complex browser environment that is buggy, ill-specified, and implemented in different ways by different browsers. Thus, web applications that desire robustness must use a variety of conditional code paths and ugly hacks to deal with the vagaries of their runtime. Our new exokernel browser, called Atlantis, solves this problem by providing pages with an extensible execution environment. Atlantis defines a narrow API for basic services like collecting user input, exchanging network data, and rendering images. By composing these primitives, web pages can define custom, high-level execution environments. Thus, an application which does not want a dependence on Atlantis'predefined web stack can selectively redefine components of that stack, or define markup formats and scripting languages that look nothing like the current browser runtime. Unlike prior microkernel browsers like OP, and unlike compile-to-JavaScript frameworks like GWT, Atlantis is the first browsing system to truly minimize a web page's dependence on black box browser code. This makes it much easier to develop robust, secure web applications.
Moral Complexity: The Fatal Attraction of Truthiness and the Importance of Mature Moral Functioning.

Recently, intuitionist theories have been effective in capturing the academic discourse about morality. Intuitionist theories, like rationalist theories, offer important but only partial understanding of moral functioning. Both can be fallacious and succumb to truthiness: the attachment to one's opinions because they "feel right," potentially leading to harmful action or inaction. Both intuition and reasoning are involved in deliberation and expertise. Both are malleable from environmental and educational influence, making questions of normativity-which intuitions and reasoning skills to foster-of utmost importance. Good intuition and reasoning inform mature moral functioning, which needs to include capacities that promote sustainable human well-being. Individual capacities for habituated empathic concern and moral metacognition-moral locus of control, moral self-regulation, and moral self-reflection-comprise mature moral functioning, which also requires collective capacities for moral dialogue and moral institutions. These capacities underlie moral innovation and are necessary for solving the complex challenges humanity faces.
A Low-Power 26-GHz Transformer-Based Regulated Cascode SiGe BiCMOS Transimpedance Amplifier

Low-power high-speed optical receivers are required to meet the explosive growth in data communication systems. This paper presents a 26 GHz transimpedance amplifier (TIA) that employs a transformer-based regulated cascode (RGC) input stage which provides passive negative-feedback gain that enhances the effective transconductance of the TIA's input common-base transistor; reducing the input resistance and isolating the parasitic photodiode capacitance. This allows for considerable bandwidth extension without significant noise degradation or power consumption. Further bandwidth extension is achieved through series inductive peaking to isolate the photodetector capacitance from the TIA input. The optimum choice of series inductive peaking value and key transformer parameters for bandwidth extension and jitter minimization is analyzed. Fabricated in a 0.25-µm SiGe BiCMOS technology and tested with an on-chip 150 fF capacitor to emulate a photodiode, the TIA achieves a 53 dBΩ single-ended transimpedance gain with a 26 GHz bandwidth and 21.3 pA/$\sqrt{\rm Hz}$ average input-referred noise current spectral density. Total chip power including output buffering is 28.2 mW from a 2.5 V supply, with the core TIA consuming 8.2 mW, and the chip area including pads is 960 µm x 780 µm.
Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces

This paper presents the machine learning architecture of the Snips Voice Platform, a software solution to perform Spoken Language Understanding on microprocessors typical of IoT devices. The embedded inference is fast and accurate while enforcing privacy by design, as no personal user data is ever collected. Focusing on Automatic Speech Recognition and Natural Language Understanding, we detail our approach to training high-performance Machine Learning models that are small enough to run in real-time on small devices. Additionally, we describe a data generation procedure that provides sufficient, high-quality training data without compromising user privacy.
Parental monitoring and the prevention of child and adolescent problem behavior: a conceptual and empirical formulation.

The present report accomplishes three goals. First, to provide an empirical rationale for placing parental monitoring of children's adaptations as a key construct in development and prevention research. Second, to stimulate more research on parental monitoring and provide an integrative framework for various research traditions as well as developmental periods of interest. Third, to discuss current methodological issues that are developmentally and culturally sensitive and based on sound measurement. Possible intervention and prevention strategies that specifically target parental monitoring are discussed.
Fuzzy optimization for supply chain planning under supply, demand and process uncertainties

In today’s global marketplace, individual firms do not compete as independent entities rather as an integral part of a supply chain. This paper proposes a fuzzy mathematical programming model for supply chain planning which considers supply, demand and process uncertainties. The model has been formulated as a fuzzy mixed-integer linear programming model where data are ill-known andmodelled by triangular fuzzy numbers. The fuzzy model provides the decision maker with alternative decision plans for different degrees of satisfaction. This proposal is tested by using data from a real automobile supply chain.
Vision-based Real-time Driver Fatigue Detection System for Efficient Vehicle Control

In modern days, a large no of automobile accidents are caused due to driver fatigue. To address the problem we propose a vision-based real-time driver fatigue detection system based on eye-tracking, which is an active safety system. Eye tracking is one of the key technologies, for, future driver assistance systems since human eyes contain much information about the driver's condition such as gaze, attention level, and fatigue level. Face and eyes of the driver are first localized and then marked in every frame obtained from the video source. The eyes are tracked in real time using correlation function with an automatically generated online template. Additionally, driver’s distraction and conversations with passengers during driving can lead to serious results. A real-time vision-based model for monitoring driver’s unsafe states, including fatigue state is proposed. A time-based eye glance to mitigate driver distraction is proposed.  Driver fatigue, Eye-Tracking, Template matching,
Fog Computing for the Internet of Things: Security and Privacy Issues

The inherent characteristics of Internet of Things (IoT) devices, such as limited storage and computational power, require a new platform to efficiently process data. The concept of fog computing has been introduced as a technology to bridge the gap between remote data centers and IoT devices. Fog computing enables a wide range of benefits, including enhanced security, decreased bandwidth, and reduced latency. These benefits make the fog an appropriate paradigm for many IoT services in various applications such as connected vehicles and smart grids. Nevertheless, fog devices (located at the edge of the Internet) obviously face many security and privacy threats, much the same as those faced by traditional data centers. In this article, the authors discuss the security and privacy issues in IoT environments and propose a mechanism that employs fog to improve the distribution of certificate revocation information among IoT devices for security enhancement. They also present potential research directions aimed at using fog computing to enhance the security and privacy issues in IoT environments.