nils-herrmann/clean_scirepeval huggingface dataset:
Pose Information Assisted 60 GHz Networks: Towards Seamless Coverage and Mobility Support

60 GHz millimeter-wave networking has emerged as the next frontier technology to provide multi-Gbps wireless connectivity. However, the intrinsic directionality and limited field-of-view of 60 GHz antennas make the links extremely sensitive to user mobility and orientation change. Hence, seamless coverage, even at room level, becomes challenging. In this paper, we propose Pia, a robust 60 GHz network architecture that can provide seamless coverage and mobility support at multi-Gbps bitrate. Pia comprises multiple cooperating access points (APs). It leverages the pose information on mobile clients to proactively select the AP and manage multi-link spatial reuse. These decisions require a model of the pose/location of the APs and ambient reflectors. We address these challenges through a set of AP-pose sensing and compressive angle estimation algorithms that fuse the pose measurement with link quality measurement on the client. We have implemented Pia using commodity 60 GHz platforms. Our experiments show that Pia reduces the occurrence of link outage by 6.3x and improves the spatial sharing capacity by 76%, compared to conventional schemes that only use in-band information for adaptation.
Analyzing the State of Static Analysis: A Large-Scale Evaluation in Open Source Software

The use of automatic static analysis has been a software engineering best practice for decades. However, we still do not know a lot about its use in real-world software projects: How prevalent is the use of Automated Static Analysis Tools (ASATs) such as FindBugs and JSHint? How do developers use these tools, and how does their use evolve over time? We research these questions in two studies on nine different ASATs for Java, JavaScript, Ruby, and Python with a population of 122 and 168,214 open-source projects. To compare warnings across the ASATs, we introduce the General Defect Classification (GDC) and provide a grounded-theory-derived mapping of 1,825 ASAT-specific warnings to 16 top-level GDC classes. Our results show that ASAT use is widespread, but not ubiquitous, and that projects typically do not enforce a strict policy on ASAT use. Most ASAT configurations deviate slightly from the default, but hardly any introduce new custom analyses. Only a very small set of default ASAT analyses is widely changed. Finally, most ASAT configurations, once introduced, never change. If they do, the changes are small and have a tendency to occur within one day of the configuration's initial introduction.
A Data Mining Approach to Inquiry Based Inductive Learning Practice in Engineering Education

Inquiry based Inductive learning methodology is one of the best technique especially for the engineering students who are expected to solve real world problems. But it is very difficult to standardize a particular learning methodology for an institution with diverse attitude, diverse characteristics, diverse languages, diverse financial back grounds, and diverse cultural scenario with variable educational standard of the students. As the teaching methodology depends on the content and time as well as learners' varied style of learning, the same learner may prefer inductive teaching for one topic and deductive approach for the other topic. This paper initiates a sample study that is taken for a particular institution, in the particular environment, for the particular batch and particular set of students. The sample data are collected from a classroom by distributing the questionnaire attempted by two different batches of student having questions pertaining to Inquiry based and deductive learning. The system is developed and tested twice after teaching the content using inductive method and implemented using attribute relevance, discriminant rules of class discrimination mining. The results are visualized through bar charts and shows that the two batches of learners of different years have different learning characteristics.
Everything you always wanted to know about SEM ( structural equations modeling ) but were afraid to ask

This article is intended to serve as a primer for structural equations models for the behavioral researcher. The technique is not mysterious—it is a natural extension of factor analysis and regression. The measurement part of a structural equations model is essentially a confirmatory factor analysis, and the structural part of the model is like a regression but vastly more flexible in the types of theoretical models that may be tested. The models and notation are introduced and the syntax is provided to replicate the analyses in the paper. Part II of this article will appear in the next issue of the Journal of Consumer Psychology, and it covers advanced issues, including fit indices, sample size, moderators, longitudinal data, mediation, and so forth.  Structural equations modeling (SEM) is an important tool for marketing researchers. Our top journals contain numerous articles with SEM analyses of data as varied as B2B relationship marketing studies (e.g., Anderson and Narus, 1990; Selnes and Sallis, 2003; Steenkamp and Baumgartner, 2000) or B2C customer satisfaction surveys (e.g., Luo and Bhattacharya, 2006). Survey data from studies such as these are a natural fit for SEM, but when SEM is considered as a natural extension of regression (to be demonstrated shortly), it should be clear that SEM is a statistical tool, orthogonal to the substantive domain of data on which it is implemented. Thus, SEM may also be applied to secondary data bases (e.g., behavioral CRM or loyalty program data) or data collected in experimental settings (e.g., measures of memory, attitudes, and intentions), etc. For example, it is not unusual for an experimental consumer psychologist to inquire about mediation in an attempt to clarify the theoretical processes by which some phenomenon occurs (e.g., Iacobucci, 2008). If mediation clarifies the conceptual picture somewhat, with the insertion of just one new construct— the mediator—imagine howmuch richer the theorizing might be if researchers tried to formulate and test even more complex nomological networks. The Journal of Consumer Psychology presents this article to encourage more frequent and knowledgeable use of SEMs. We begin with an overview of the SEM approach: (1) we discuss the E-mail address: dawn.iacobucci@owen.vanderbilt.edu. 1057-7408/$ see front matter  we review the source models of factor analysis and path analysis, and (3) we combine these to develop the full SEM model, and describe the entirety of parameters that may be estimated. Software syntax is introduced to enhance the likelihood of readers' trial and adoption.
Applications of the solid state transformer concept in the electrical power system

Nowadays the electrical main grid is managed in AC due to several reasons derived from the past. Recent years have experimented a rapid evolution of power electronic components able to dramatically enhance the effectiveness of the active and reactive power flow management. The inverter diffusion makes possible the intelligent integration between systems with different characteristics (i.e. in terms of voltage magnitude and frequency) and the exploitation of generation sources and storage systems typically operating in DC. One of the most interesting components able to interconnect different portions of electrical networks is the Solid State Transformer. The Solid State transformer is formed by few static power converters that operate sinergically and are connected in series with a high frequency transformer. Different voltage levels are made available to achieve high results in terms of system integration, efficiency and flexibility. Taking advantages from the described component structure and main features, in this paper the authors aim to evaluate the main potentials of this technology if widely introduced in the main power system. Starting from the single component description, a couple of possible applications are presented and discussed in terms of assets and weak points.
Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions

This paper presents a drowsiness on-line detection system for monitoring driver fatigue level under real driving conditions, based on the data of steering wheel angles (SWA) collected from sensors mounted on the steering lever. The proposed system ﬁrstly extracts approximate entropy (ApEn)featuresfromﬁxedslidingwindowsonreal-timesteeringwheelanglestimeseries. Afterthat, this system linearizes the ApEn features series through an adaptive piecewise linear ﬁtting using a given deviation. Then, the detection system calculates the warping distance between the linear features series of the sample data. Finally, this system uses the warping distance to determine the drowsiness state of the driver according to a designed binary decision classiﬁer. The experimental data were collected from 14.68 h driving under real road conditions, including two fatigue levels: "wake" and "drowsy". The results show that the proposed system is capable of working online with an average 78.01% accuracy, 29.35% false detections of the "awake" state, and 15.15% false detections of the "drowsy" state. The results also conﬁrm that the proposed method based on SWA signal is valuable for applications in preventing trafﬁc accidents caused by driver fatigue.
Appliance-specific power usage classification and disaggregation

Energy disaggregation (also referred to as nonintrusive load monitoring) is the task of inferring individual loads of the system from an aggregated signal. Appliance-specific energy usage feedback provides consumers with a better understanding of the impact of their consumption behavior and may lead to behavioral changes that improve energy efficiency. Studies have shown consumers improved efficiency by as much as 15% after getting direct feedback of this type . Once a signal is disaggregated, the signals need to be classified according to the appropriate appliance. With the increasing interest in energy efficiency and recent relevance of machine learning, there is a lot of potential for both predicting and classifying appliance-specific load signals using a wide range of machine learning algorithms. In this study, we utilize a publically available dataset of power signals from multiple households to disaggregate, then classify appliance-specific energy loads.
Hardware implementation of a quasi-resonant bidirectional flyback converter for capacitive loads

This paper presents hardware implementation of a quasi-resonant bi-directional flyback converter (QR-BFC). The design of power stage, control stage and feedback sensing stage is presented specifically for a low power high voltage capacitive load charging and discharging application. The design objective is to achieve faster charge and discharge process with high efficiency, small size and low cost. The converter operates in three modes — charge, discharge and burst mode. Power stage design presents guidelinies for selection of power stage elements of the converter. The control stage design presents implementation of a minimum voltage point (MVP) switching logic of primary and secondary MOSFETs by use of basic logic gates in a low cost CPLD. The isolated feedback sensing design stage presents a resistive capacitive divider circuit for implementation of burst mode voltage regulation. Experimental results are presented with a hardware prototype design.
A MAV that flies like an airplane and hovers like a helicopter

Near-earth environments, such as forests, caves, tunnels, and urban structures make reconnaissance, surveillance and search-and-rescue missions difficult and dangerous to accomplish. Micro-air-vehicles (MAVs), equipped with wireless cameras, can assist in such missions by providing real-time situational awareness. This paper describes an additional flight modality enabling fixed-wing MAVs to supplement existing endurance superiority with hovering capabilities. This secondary flight mode can also be used to avoid imminent collisions by quickly transitioning from cruise to hover flight. A sensor suite which allows for autonomous hovering by regulating the aircraft's yaw. pitch and roll angles is also described
The effect of different types of corrective feedback on ESL student writing

Debate about the value of providing corrective feedback on L2 writing has been prominent in recent years as a result of Truscott’s [Truscott, J. (1996). The case against grammar correction in L2 writing classes. Language Learning, 46, 327–369] claim that it is both ineffective and harmful and should therefore be abandoned. A growing body of empirical research is now investigating the agenda proposed by Ferris [Ferris, D.R. (1999). The case for grammar correction in L2 writing classes. A response to Truscott (1996). Journal of Second Language Writing, 8, 1–10, Ferris, D.R. (2004). The ‘‘Grammar Correction’’ debate in L2 writing: Where are we, and where do we go from here? (and what do we do in the meantime. . .?). Journal of Second Language Writing, 13, 49–62.]. Contributing to this research base, the study reported in this article investigated whether the type of feedback (direct, explicit written feedback and student–researcher 5 minute individual conferences; direct, explicit written feedback only; no corrective feedback) given to 53 adult migrant students on three types of error (prepositions, the past simple tense, and the definite article) resulted in improved accuracy in new pieces of writing over a 12 week period. The study found a significant effect for the combination of written and conference feedback on accuracy levels in the use of the past simple tense and the definite article in new pieces of writing but no overall effect on accuracy improvement for feedback types when the three error categories were considered as a single group. Significant variations in accuracy across the four pieces of writing support earlier SLA discoveries that L2 learners, in the process of acquiring new linguistic forms, may perform them with accuracy on one occasion but fail to do so on other similar occasions.
Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach

Knowledge bases are employed in a variety of applications from natural language processing to semantic web search; alas, in practice their usefulness is hurt by their incompleteness. Embedding models attain state-of-the-art accuracy in knowledge base completion, but their predictions are notoriously hard to interpret. In this paper, we adapt “pedagogical approaches” (from the literature on neural networks) so as to interpret embedding models by extracting weighted Horn rules from them. We show how pedagogical approaches have to be adapted to take upon the large-scale relational aspects of knowledge bases and show experimentally their strengths and weaknesses.
Deep learning approaches to problems in speech recognition, computational chemistry, and natural language text processing

Deep learning approaches to problems in speech recognition, computational chemistry, and natural language text processing George Edward Dahl Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2015 The deep learning approach to machine learning emphasizes high-capacity, scalable models that learn distributed representations of their input. This dissertation demonstrates the efficacy and generality of this approach in a series of diverse case studies in speech recognition, computational chemistry, and natural language processing. Throughout these studies, I extend and modify the neural network models as needed to be more effective for each task. In the area of speech recognition, I develop a more accurate acoustic model using a deep neural network. This model, which uses rectified linear units and dropout, improves word error rates on a 50 hour broadcast news task. A similar neural network results in a model for molecular activity prediction substantially more effective than production systems used in the pharmaceutical industry. Even though training assays in drug discovery are not typically very large, it is still possible to train very large models by leveraging data from multiple assays in the same model and by using effective regularization schemes. In the area of natural language processing, I first describe a new restricted Boltzmann machine training algorithm suitable for text data. Then, I introduce a new neural network generative model of parsed sentences capable of generating reasonable samples and demonstrate a performance advantage for deeper variants of the model.
Semantic audio content-based music recommendation and visualization based on user preference examples

Preference elicitation is a challenging fundamental problem when designing recommender systems. In the present work we propose a content-based technique to automatically generate a semantic representation of the user’s musical preferences directly from audio. Starting from an explicit set of music tracks provided by the user as evidence of his/her preferences, we infer high-level semantic descriptors for each track obtaining a user model. To prove the benefits of our proposal, we present two applications of our technique. In the first one, we consider three approaches to music recommendation, two of them based on a semantic music similarity measure, and one based on a semantic probabilistic model. In the second application, we address the visualization of the user’s musical preferences by creating a humanoid cartoon-like character – the Musical Avatar – automatically inferred from the semantic representation. We conducted a preliminary evaluation of the proposed technique in the context of these applications with 12 subjects. The results are promising: the recommendations were positively evaluated and close to those coming from state-ofthe-art metadata-based systems, and the subjects judged the generated visualizations to capture their core preferences. Finally, we highlight the advantages of the proposed semantic user model for enhancing the user interfaces of information filtering systems.
Executive Involvement and Participation in the Management of Information Technology

Executive support is often prescribed as critical for fully tapping the benefits of information technology (IT). However, few investigations have attempted to determine what type of executive support is likely or organizationally appropriate. This article puts forward alternative models of executive support. The models are tested by examining chief executive officers’ behaviors in and perceptions of IT activities. CEOs and information systems executives are surveyed and further data collected from industry handbooks and from chairmen’s annual letters to shareholders. The results suggest that executive involvement (a psychological state) is more strongly associated with the firm’s progressive use of IT than executive participation (actual behaviors) in IT activities. Executive involvement is influenced by a CEO’s participation, prevailing organizational conditions, and the executive’s functional background. CEO’s perceptions about the importance of IT in their firms were generally positive, although they participated in IT activities rather infrequently.
The Use of Case-Based Reasoning for the Monitoring of Financial Fraud Transactions

Financial transaction fraud constitutes an acute problem domain for detection and early warning systems. Despite that different branches of AI have been addressing the problem since the late 90s, CBR approaches have seldom been applied. This paper provides a proof of concept approach and experimental investigation for the use of a CBR Intelligent Monitoring System for detecting abnormal patterns in financial transaction flows. The representation of workflow related knowledge in this research using graphs is explained. The workflow process is orchestrated by a software system using BPEL technologies within a service-oriented architecture. Workflow cases are represented in terms of events and their corresponding temporal relationships. The matching and CBR retrieval mechanisms used in this research are explained and a simple evaluation of the approach is provided using simulation data. Further work on the system and the extension to a full intelligent monitoring and process optimisation system is finally presented.
Through-package-via formation and metallization of glass interposers

Interposer technology has evolved from ceramic to organic materials and most recently to silicon. Organic substrates exhibit poor dimensional stability, thus requiring large capture pads which make them unsuitable for very high I/Os with fine pitch interconnections. Therefore, there has been a trend to develop silicon interposers. Silicon interposers however, suffer in two ways; 1) they are expensive to process due to the need for electrical insulation around via walls, and 2) they are limited in size by the silicon wafer from which they originate. In this paper, glass is proposed as a superior alternative interposer technology to address the limitations of both silicon and organic interposers. The inherent electrical properties of glass, together with large area panel size availability, make it superior compared to organic and silicon-based interposers. Glass however, is not without its challenges. It suffers in two ways: 1) formation of vias at low cost, and 2) its lower thermal conductivity compared to silicon. This research explores glass as an interposer material, and addresses the above key challenges in through package via (TPV) formation and subsequent low cost and large area metallization to achieve very high I/Os at fine pitch.
A low-power 20-GHz 52-dB/spl Omega/ transimpedance amplifier in 80-nm CMOS

This paper describes the design of a transimpedance amplifier (TIA) for a low-power, short-distance, high-density fiber-optic interconnect communication system. The single-ended circuit has been designed in an 80-nm digital CMOS process and consumes only 2.2 mW from a 1-V supply. The measured results show a transimpedance gain of 52 dB/spl Omega/ and a large bandwidth of 20 GHz. This work presents the highest bandwidth at the lowest power consumption for CMOS transimpedance amplifiers reported to date.
Personality-based refinement for sentiment classification in microblog

Microblog has become one of the most widely used social media for people to share information and express opinions. As information propagates fast in social network, understanding and analyzing public sentiment implied in user-generated content is beneficial for many fields and has been applied to applications such as social management, business and public security. Most previous work on sentiment analysis makes no distinctions of the tweets by different users and ignores the diverse word use of people. As some sentiment expressions are used by specific groups of people, the corresponding textual sentiment features are often neglected in the analysis process. On the other hand, previous psychological findings have shown that personality influences the ways people write and talk, suggesting that people with same personality traits tend to choose similar sentiment expressions. Inspired by this, in this paper we propose a method to facilitate sentiment classification in microblog based on personality traits. To this end, we first develop a rule-based method to predict users’ personality traits based on the most well-studied personality model, the Big Five model. In order to leverage more effective but not widely used sentiment features, we then extract those features grouped by different personality traits and construct personality-based sentiment classifiers. Moreover, we adopt an ensemble learning strategy to integrate traditional textual feature based and our personality-based sentiment classification. Experimental studies on Chinese microblog dataset show the effectiveness of our method in refining the performance of both the traditional and state-of-the-art sentiment classifiers. Our work is among the first to explicitly explore the role of user’s personality in social media analytics and its application in sentiment classification.