nils-herrmann/clean_scirepeval huggingface dataset:
Multiclass Object Recognition with Sparse, Localized Features

We apply a biologically inspired model of visual object recognition to the multiclass object categorization problem. Our model modifies that of Serre, Wolf, and Poggio. As in that work, we first apply Gabor filters at all positions and scales; feature complexity and position/scale invariance are then built up by alternating template matching and max pooling operations. We refine the approach in several biologically plausible ways, using simple versions of sparsification and lateral inhibition. We demonstrate the value of retaining some position and scale information above the intermediate feature level. Using feature selection we arrive at a model that performs better with fewer features. Our final model is tested on the Caltech 101 object categories and the UIUC car localization task, in both cases achieving state-of-the-art performance. The results strengthen the case for using this class of model in computer vision.
Cyber Physical System for Stroke Detection

Stroke is one of the fatal diseases that affect the brain and causes death within 3 to 10 h. However, most of the deaths caused by a stroke can be avoided with the identification of the nature of stroke and react to it in a timely manner by intelligent health systems. The state-of-the-art cyber-physical systems (CPS) enable interaction between physical and computational world to identify any anomaly in the physical world and respond to it. The response of CPS may vary depending upon the context of the physical world. Extensive research has been done in this area from the perspective of wireless sensor networks, body area networks, and wearable smart devices. This paper proposes a CPS for detecting the occurrence of stroke in patients, who have a high risk of stroke or have survived a stroke before. The developed CPS sends recorded data to the doctor and alerts him when the stroke occurs. The proposed system is operating on data acquired from electroencephalography sensors from patients’ brain. This article aimed at decreasing human mortality rate due to stroke and will bridge the gaps in CPS due to interdisciplinary isolation. The disciplines involved in the development of a CPS include communication networks, pattern recognition, software engineering, mathematics, and biomedical.
Enhancing Self-Efficacy and Performance: An Experimental Comparison of Psychological Techniques.

We assessed how 6 psychological performance enhancement techniques (PETs) differentially improved self-efficacy (SE) and skill performance. We also assessed whether vicarious experiences and verbal persuasion as posited sources of SE (Bandura, 1982 ) were supported and, further, if the effects of the 6 PETs remained after controlling for achievement motivation traits and self-esteem.



A within-subject design assessed each individual across 2 trials for 3 disparate PETs. A between-groups design assessed differences between PETs paired against each other for 3 similar novel tasks. Participants (N = 96) performed 2 trials of 10 attempts at each of the tasks (kick, throw, golf putt) in a counterbalanced sequence using their nondominant limb. Participants completed the Sport Orientation Questionnaire, Rosenberg Self-Esteem Scale, and General Self-Efficacy Scale and were randomly allocated to either the modeling or imagery, goal-setting or instructional self-statement, or knowledge-of-results or motivational feedback conditions aligned with each task.



An instructional self-statement improved performance better than imagery, modeling, goal setting, and motivational and knowledge-of-results augmented feedback. Motivational auditory feedback most improved SE. Increased SE change scores were related to increased performance difference scores on all tasks after controlling for age, sex, achievement motivation, and self-esteem.



Some sources of SE may be more influential than others on both SE and performance improvements. We provide partial support for the sources of SE proposed by Bandura's social-cognitive theory with verbal persuasion but not vicarious experiences improving SE.
Manifold alignment using Procrustes analysis

In this paper we introduce a novel approach to manifold alignment, based on Procrustes analysis. Our approach differs from "semi-supervised alignment" in that it results in a mapping that is defined everywhere - when used with a suitable dimensionality reduction method - rather than just on the training data points. We describe and evaluate our approach both theoretically and experimentally, providing results showing useful knowledge transfer from one domain to another. Novel applications of our method including cross-lingual information retrieval and transfer learning in Markov decision processes are presented.
Mechanical stability of the in vivo lumbar spine: implications for injury and chronic low back pain.

One important mechanical function of the lumbar spine is to support the upper body by transmitting compressive and shearing forces to the lower body during the performance of everyday activities. To enable the successful transmission of these forces, mechanical stability of the spinal system must be assured. The purpose of this study was to develop a method and to quantify the mechanical stability of the lumbar spine in vivo during various three-dimensional dynamic tasks. A lumbar spine model, one that is sensitive to the various ways that individuals utilize their muscles and ligaments, was used to estimate the lumbar spine stability index three times per second throughout the duration of each trial. Anatomically, this model included a rigid pelvis, ribcage, five vertebrae, 90 muscle fascicles and lumped parameter discs, ligaments and facets. The method consisted of three sub-models: a cross-bridge bond distribution-moment muscle model for estimating muscle force and stiffness from the electromyogram, a rigid link segment body model for estimating external forces and moments acting on the lumbar vertebrae, and an 18 degrees of freedom lumbar spine model for estimating moments produced by 90 muscle fascicles and lumped passive tissues. Individual muscle forces and their associated stiffness estimated from the EMG-assisted optimization algorithm, along with external forces were used for calculating the relative stability index of the lumbar spine for three subjects. It appears that there is an ample stability safety margin during tasks that demand a high muscular effort. However, lighter tasks present a potential hazard of spine buckling, especially if some reduction in passive joint stiffness is present. Several hypotheses on the mechanism of injury associated with low loads and aetiology of chronic back pain are presented in the context of lumbar spine stability.
Manifold surface reconstruction of an environment from sparse Structure-from-Motion data

The majority of methods for the automatic surface reconstruction of an environment from an image sequence have two steps: Structure-from-Motion and dense stereo. From the computational standpoint, it would be interesting to avoid dense stereo and to generate a surface directly from the sparse cloud of 3D points and their visibility information provided by Structure-fromMotion. The previous attempts to solve this problem are currently very limited: the surface is non-manifold or has zero genus, the experiments are done on small scenes or objects using a few dozens of images. Our solution does not have these limitations. Furthermore, we experiment with hand-held or helmet-held catadioptric cameras moving in a city and generate 3D models such that the camera trajectory can be longer than one kilometer.
A Rule-Based Approach For Effective Sentiment Analysis

The success of Web 2.0 applications has made online social media websites tremendous assets for supporting critical business intelligence applications. The knowledge gained from social media can potentially lead to the development of novel services that are better tailored to users’ needs and at the same time meet the objectives of businesses offering them. Online consumer reviews are one of the critical social media contents. Proper analysis of consumer reviews not only provides valuable information to facilitate the purchase decisions of customers but also helps merchants or product manufacturers better understand general responses of customers on their products for marketing campaign improvement. This study aims at designing an approach for supporting the effective analysis of the huge volume of online consumer reviews and, at the same time, settling the major limitations of existing approaches. Specifically, the proposed rule-based sentiment analysis (R-SA) technique employs the class association rule mining algorithm to automatically discover interesting and effective rules capable of extracting product features or opinion sentences for a specific product feature interested. According to our preliminary evaluation results, the R-SA technique performs well in comparison with its benchmark technique.
Stopping GAN Violence: Generative Unadversarial Networks

While the costs of human violence have attracted a great deal of attention from the research community, the effects of the network-on-network (NoN) violence popularised by Generative Adversarial Networks have yet to be addressed. In this work, we quantify the financial, social, spiritual, cultural, grammatical and dermatological impact of this aggression and address the issue by proposing a more peaceful approach which we term Generative Unadversarial Networks (GUNs). Under this framework, we simultaneously train two models: a generator G that does its best to capture whichever data distribution it feels it can manage, and a motivator M that helps G to achieve its dream. Fighting is strictly verboten and both models evolve by learning to respect their differences. The framework is both theoretically and electrically grounded in game theory, and can be viewed as a winner-shares-all two-player game in which both players work as a team to achieve the best score. Experiments show that by working in harmony, the proposed model is able to claim both the moral and log-likelihood high ground. Our work builds on a rich history of carefully argued position-papers, published as anonymous YouTube comments, which prove that the optimal solution to NoN violence is more GUNs. Takes skill to be real, time to heal each other Tupac Shakur, Changes, 1998
20.7 A 13.8µW binaural dual-microphone digital ANSI S1.11 filter bank for hearing aids with zero-short-circuit-current logic in 65nm CMOS

This paper presents an ANSI S1.11 1/3-octave filter-bank chip for binaural hearing aids with two microphones per ear. Binaural multimicrophone systems significantly suppress noise interference and preserve interaural time cues at the cost of significantly higher computational and power requirements than monophonic single-microphone systems. With clock rates around the 1MHz mark, these systems are ideal candidates for low-power implementation through charge-recovery design. At such low clock frequencies, however, charge-recovery logic suffers from short-circuit currents that limit its theoretical energy efficiency . The chip described in this paper is designed in 65nm CMOS using a new charge-recovery logic, called zero-short-circuit-current (ZSCC) logic, that drastically reduces short-circuit current. It processes 4 input streams at 1.75MHz with a charge recovery rate of 92%, achieving 9.7× lower power per input compared with the 40nm monophonic single-input chip that represents the published state of the art .
Can We Predict a Riot? Disruptive Event Detection Using Twitter

In recent years, there has been increased interest in real-world event detection using publicly accessible data made available through Internet technology such as Twitter, Facebook, and YouTube. In these highly interactive systems, the general public are able to post real-time reactions to “real world” events, thereby acting as social sensors of terrestrial activity. Automatically detecting and categorizing events, particularly small-scale incidents, using streamed data is a non-trivial task but would be of high value to public safety organisations such as local police, who need to respond accordingly. To address this challenge, we present an end-to-end integrated event detection framework that comprises five main components: data collection, pre-processing, classification, online clustering, and summarization. The integration between classification and clustering enables events to be detected, as well as related smaller-scale “disruptive events,” smaller incidents that threaten social safety and security or could disrupt social order. We present an evaluation of the effectiveness of detecting events using a variety of features derived from Twitter posts, namely temporal, spatial, and textual content. We evaluate our framework on a large-scale, real-world dataset from Twitter. Furthermore, we apply our event detection system to a large corpus of tweets posted during the August 2011 riots in England. We use ground-truth data based on intelligence gathered by the London Metropolitan Police Service, which provides a record of actual terrestrial events and incidents during the riots, and show that our system can perform as well as terrestrial sources, and even better in some cases.
Cujo: efficient detection and prevention of drive-by-download attacks

The JavaScript language is a core component of active and dynamic web content in the Internet today. Besides its great success in enhancing web applications, however, JavaScript provides the basis for so-called drive-by downloads---attacks exploiting vulnerabilities in web browsers and their extensions for unnoticeably downloading malicious software. Due to the diversity and frequent use of obfuscation in these attacks, static code analysis is largely ineffective in practice. While dynamic analysis and honeypots provide means to identify drive-by-download attacks, current approaches induce a significant overhead which renders immediate prevention of attacks intractable.
 In this paper, we present Cujo, a system for automatic detection and prevention of drive-by-download attacks. Embedded in a web proxy, Cujo transparently inspects web pages and blocks delivery of malicious JavaScript code. Static and dynamic code features are extracted on-the-fly and analysed for malicious patterns using efficient techniques of machine learning. We demonstrate the efficacy of Cujo in different experiments, where it detects 94% of the drive-by downloads with few false alarms and a median run-time of 500 ms per web page---a quality that, to the best of our knowledge, has not been attained in previous work on detection of drive-by-download attacks.
Using Distributed Scrum for Supporting an Online Community-A Qualitative Descriptive Study of Students ’ Perceptions

One purpose of higher education is to prepare students for a modern and ever-changing global society characterized by increasing complexity and collaborative environments. Scrum is an agile, widely used framework for project management dealing with the development of complex products. Scrum projects are conducted in small, empowered teams with intense communication, interaction and collaboration between the team members, facilitated by a servant-leader Scrum master. Scrum has been commonly used in professional software development and is also now being adopted in other areas, including education. There have been few studies of the application of Scrum in higher education and very few of them have studied distributed Scrum in an online context. An online learning community has several positive effects for students such as increased learning, engagement, retention and lower risks for isolation and dropouts. Participating in and contributing to a team is dependent on a sense of community, which can be difficult to build up in a distributed environment where members are geographically dispersed and do not have the possibility to meet and communicate face to face. This study examines to what extent and how distributed Scrum can support building an online learning community, from a student perspective. Twenty students, enrolled in an online course in distributed software development, participated in four Scrum projects as members of distributed Scrum teams, each team consisting of five students. Students’ perceptions were investigated by conducting semi-structured interviews. The interview transcripts were analyzed according to Rovai’s four dimensions of a classroom community. The results indicate that students were very satisfied with their distributed Scrum projects and that they experienced a high degree of flexibility during the projects. The Scrum process promoted and initiated communication and interaction among students and they learned how to communicate and collaborate effectively in an online environment. The transparency in Scrum was perceived as a key factor to open communication and effective collaboration and also contributed to increasing their motivation and engagement in the projects. Another interesting outcome of this study was understanding the importance of creating a team with members who are similar regarding competence level, ambition and preferences in working schedule.
A Review of Image Denoising Algorithms, with a New One

The search for efficient image denoising methods still is a valid challenge, at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. All show an outstanding performance when the image model corresponds to the algorithm assumptions, but fail in general and create artifacts or remove image fine structures. The main focus of this paper is, first, to define a general mathematical and experimental methodology to compare and classify classical image denoising algorithms, second, to propose an algorithm (Non Local Means) addressing the preservation of structure in a digital image. The mathematical analysis is based on the analysis of the “method noise”, defined as the difference between a digital image and its denoised version. The NL-means algorithm is proven to be asymptotically optimal under a generic statistical image model. The denoising performance of all considered methods are compared in four ways; mathematical: asymptotic order of magnitude of the method noise under regularity assumptions; perceptual-mathematical: the algorithms artifacts and their explanation as a violation of the image model; quantitative experimental: by tables of L distances of the denoised version to the original image. The most powerful evaluation method seems, however, to be the visualization of the method noise on natural images. The more this method noise looks like a real white noise, the better the method.
An overview of 3GPP device-to-device proximity services

Device-to-device communication is likely to be added to LTE in 3GPP Release 12. In principle, exploiting direct communication between nearby mobile devices will improve spectrum utilization, overall throughput, and energy consumption, while enabling new peer-to-peer and location-based applications and services. D2D-enabled LTE devices can also become competitive for fallback public safety networks, which must function when cellular networks are not available or fail. Introducing D2D poses many challenges and risks to the long-standing cellular architecture, which is centered around the base station. We provide an overview of D2D standardization activities in 3GPP, identify outstanding technical challenges, draw lessons from initial evaluation studies, and summarize "best practices" in the design of a D2D-enabled air interface for LTE-based cellular networks.
Dispositional Factors in Internet Use: Personality Versus Cognitive Style

This study directly tests the effect of personality and cognitive style on three measures of Internet use. The results support the use of personality—but not cognitive style—as an antecedent variable. After controlling for computer anxiety, selfefficacy, and gender, including the “Big Five” personality factors in the analysis significantly adds to the predictive capabilities of the dependent variables. Including cognitive style does not. The results are discussed in terms of the role of personality and cognitive style in models of technology adoption and use.
The Timeboxing process model for iterative software development

In today’s business where speed is of essence, an iterative development approach that allows the functionality to be delivered in parts has become a necessity and an effective way to manage risks. In an iterative process, the development of a software system is done in increments, each increment forming of an iteration and resulting in a working system. A common iterative approach is to decide what should be developed in an iteration and then plan the iteration accordingly. A somewhat different iterative is approach is to time box different iterations. In this approach, the length of an iteration is fixed and what should be developed in an iteration is adjusted to fit the time box. Generally, the time boxed iterations are executed in sequence, with some overlap where feasible. In this paper we propose the timeboxing process model that takes the concept of time boxed iterations further by adding pipelining concepts to it for permitting overlapped execution of different iterations. In the timeboxing process model, each time boxed iteration is divided into equal length stages, each stage having a defined function and resulting in a clear work product that is handed over to the next stage. With this division into stages, pipelining concepts are employed to have multiple time boxes executing concurrently, leading to a reduction in the delivery time for product releases. We illustrate the use of this process model through an example of a commercial project that was successfully executed using the proposed model.
WCP-RNN: a novel RNN-based approach for Bio-NER in Chinese EMRs

Deep learning has achieved remarkable success in a wide range of domains. However, it has not been comprehensively evaluated as a solution for the task of Chinese biomedical named entity recognition (Bio-NER). The traditional deep-learning approach for the Bio-NER task is usually based on the structure of recurrent neural networks (RNN) and only takes word embeddings into consideration, ignoring the value of character-level embeddings to encode the morphological and shape information. We propose an RNN-based approach, WCP-RNN, for the Chinese Bio-NER problem. Our method combines word embeddings and character embeddings to capture orthographic and lexicosemantic features. In addition, POS tags are involved as a priori word information to improve the final performance. The experimental results show our proposed approach outperforms the baseline method; the highest F-scores for subject and lesion detection tasks reach 90.36 and 90.48% with an increase of 3.10 and 2.60% compared with the baseline methods, respectively.
Lane detection and tracking system based on the MSER algorithm, hough transform and kalman filter

We present a novel lane detection and tracking system using a fusion of Maximally Stable Extremal Regions (MSER) and Progressive Probabilistic Hough Transform (PPHT). First, MSER is applied to obtain a set of blobs including noisy pixels (e.g., trees, cars and traffic signs) and the candidate lane markings. A scanning refinement algorithm is then introduced to enhance the results of MSER and filter out noisy data. After that, to achieve the requirements of real-time systems, the PPHT is applied. Compared to Hough transform which returns the parameters ρ and Θ, PPHT returns two end-points of the detected line markings. To track lane markings, two kalman trackers are used to track both end-points. Several experiments are conducted in Ottawa roads to test the performance of our framework. The detection rate of the proposed system averages 92.7% and exceeds 84.9% in poor conditions.