nils-herrmann/clean_scirepeval huggingface dataset:
High-performance work systems and occupational safety.

Two studies were conducted investigating the relationship between high-performance work systems (HPWS) and occupational safety. In Study 1, data were obtained from company human resource and safety directors across 138 organizations. LISREL VIII results showed that an HPWS was positively related to occupational safety at the organizational level. Study 2 used data from 189 front-line employees in 2 organizations. Trust in management and perceived safety climate were found to mediate the relationship between an HPWS and safety performance measured in terms of personal-safety orientation (i.e., safety knowledge, safety motivation, safety compliance, and safety initiative) and safety incidents (i.e., injuries requiring first aid and near misses). These 2 studies provide confirmation of the important role organizational factors play in ensuring worker safety.
Sparse Recovery of Hyperspectral Signal from Natural RGB Images

Hyperspectral imaging is an important visual modality with growing interest and range of applications. The latter, however, is hindered by the fact that existing devices are limited in either spatial, spectral, and/or temporal resolution, while yet being both complicated and expensive. We present a low cost and fast method to recover high quality hyperspectral images directly from RGB. Our approach first leverages hyperspectral prior in order to create a sparse dictionary of hyperspectral signatures and their corresponding RGB projections. Describing novel RGB images via the latter then facilitates reconstruction of the hyperspectral image via the former. A novel, larger-than-ever database of hyperspectral images serves as a hyperspectral prior. This database further allows for evaluation of our methodology at an unprecedented scale, and is provided for the benefit of the research community. Our approach is fast, accurate, and provides high resolution hyperspectral cubes despite using RGB-only input.
A framework for designing cloud forensic-enabled services (CFeS)

Cloud computing is used by consumers to access cloud services. Malicious actors exploit vulnerabilities of cloud services to attack consumers. The link between these two assumptions is the cloud service. Although cloud forensics assists in the direction of investigating and solving cloud-based cyber-crimes, in many cases the design and implementation of cloud services fall back. Software designers and engineers should focus their attention on the design and implementation of cloud services that can be investigated in a forensic sound manner. This paper presents a methodology that aims on assisting designers to design cloud forensic-enabled services. The methodology supports the design of cloud services by implementing a number of steps to make the services cloud forensic enabled. It consists of a set of cloud forensic constraints, a modeling language expressed through a conceptual model and a process based on the concepts identified and presented in the model. The main advantage of the proposed methodology is the correlation of cloud services’ characteristics with the cloud investigation while providing software engineers the ability to design and implement cloud forensic-enabled services via the use of a set of predefined forensic-related tasks.
Me , myself , and lie : The role of self-awareness in deception

Deception has been studied extensively but still little is known about individual differences in deception ability. We investigated the relationship between self-awareness and deception ability. We enlisted novice actors to portray varying levels of deception. Forty-two undergraduates viewed the videotaped portrayals and rated the actors believability. Actors with high private self-awareness were more effective deceivers, suggesting that high self-monitors are more effective at deceiving. Self-awareness may lead to knowledge of another s mental state (i.e., Theory of Mind), which may improve an individual s deception ability.
The Devil is in the Tails: Fine-grained Classification in the Wild

The world is long-tailed. What does this mean for computer vision and visual recognition? The main two implications are (1) the number of categories we need to consider in applications can be very large, and (2) the number of training examples for most categories can be very small. Current visual recognition algorithms have achieved excellent classification accuracy. However, they require many training examples to reach peak performance, which suggests that long-tailed distributions will not be dealt with well. We analyze this question in the context of eBird, a large fine-grained classification dataset, and a state-ofthe-art deep network classification algorithm. We find that (a) peak classification performance on well-represented categories is excellent, (b) given enough data, classification performance suffers only minimally from an increase in the number of classes, (c) classification performance decays precipitously as the number of training examples decreases, (d) surprisingly, transfer learning is virtually absent in current methods. Our findings suggest that our community should come to grips with the question of long tails.
A 180-mV subthreshold FFT processor using a minimum energy design methodology

In emerging embedded applications such as wireless sensor networks, the key metric is minimizing energy dissipation rather than processor speed. Minimum energy analysis of CMOS circuits estimates the optimal operating point of clock frequencies, supply voltage, and threshold voltage according to A. Chandrakasan et al. (see ibid., vol.27, no.4, p.473-84, Apr. 1992). The minimum energy analysis shows that the optimal power supply typically occurs in subthreshold (e.g., supply voltages that are below device thresholds). New subthreshold logic and memory design methodologies are developed and demonstrated on a fast Fourier transform (FFT) processor. The FFT processor uses an energy-aware architecture that allows for variable FFT length (128-1024 point), variable bit-precision (8 b and 16 b) and is designed to investigate the estimated minimum energy point. The FFT processor is fabricated using a standard 0.18-/spl mu/m CMOS logic process and operates down to 180 mV. The minimum energy point for the 16-b 1024-point FFT processor occurs at 350-mV supply voltage where it dissipates 155 nJ/FFT at a clock frequency of 10 kHz.
The Cost of Syntax and How to Avoid It: Text versus Frame-Based Editing

Plain text has always been the predominant medium for writing and editing programs for expert users. Text is powerful and flexible, but requires more careful manipulation than structural editors, such as those found in block-based environments. In addition, in textual editors programmers are responsible for managing detailed orthography and layout - when beginners work with text, significant time is spent managing syntax problems, indentation and spacing. Frame-based editing is a new editing paradigm that combines the structural editing of block-based systems with the flexibility and keyboard-focus of text editing. In this paper, we empirically examine how much time and effort is spent by beginners on managing syntax errors and indentation, which can be automatically saved by switching to frame-based editing. The data is obtained using the Blackbox dataset, the results predict a clear advantage of frame-based editing over traditional text editors.
Learning with Taxonomies: Classifying Documents and Words

Automatically extracting semantic information about word meaning and document topic from text typically involves an extensive number of classes. Such classes may represent predefined word senses, topics or document categories and are often organized in a taxonomy. The latter encodes important information, which should be exploited in learning classifiers from labeled training data. To that extent, this paper presents an extension of multiclass Support Vector Machine learning which can incorporate prior knowledge about class relationships. The latter can be encoded in the form of class attributes, similarities between classes or even a kernel function defined over the set of classes. The paper also discusses how to specify and optimize meaningful loss functions based on the relative position of classes in the taxonomy. We include experimental results for text categorization and for word sense classification.
A Survey on Quality Assurance Techniques for Big Data Applications

With the rapid advance of big data and cloud computing, building high quality big data systems in different application fields has gradually became a popular research topic in academia and industry as well as government agencies. However, more quality problems lead to application errors. Although the current research work has discussed how to ensure the quality of big data applications from several aspects, there is no systematic discussion on how to ensure the quality of large data applications. Therefore, a systematic study on big data application quality assurance is very necessary and critical. This paper focuses on the survey of quality assurance techniques of big data applications, and it introduces big data properties and quality attributes. It mainly discusses the key approaches to ensure the quality of big data applications and they are testing, model-driven architecture (MDA), monitoring, fault tolerance, verification and also prediction techniques. In addition, this paper also discusses the impact of big data characteristics on big data applications.
ReGAN: A pipelined ReRAM-based accelerator for generative adversarial networks

Generative Adversarial Networks (GANs) have recently drawn tremendous attention in many artificial intelligence (AI) applications including computer vision, speech recognition, and natural language processing. While GANs deliver state-of-the-art performance on these AI tasks, it comes at the cost of high computational complexity. Although recent progress demonstrated the promise of using ReRMA-based Process-In-Memory for acceleration of convolutional neural networks (CNNs) with low energy cost, the unique training process required by GANs makes them difficult to run on existing neural network acceleration platforms: two competing networks are simultaneously cotrained in GANs, and hence, significantly increasing the need of memory and computation resources. In this work, we propose ReGAN — a novel ReRAM-based Process-In-Memory accelerator that can efficiently reduce off-chip memory accesses. Moreover, ReGAN greatly increases system throughput by pipelining the layer-wise computation. Two techniques, namely, Spatial Parallelism and Computation Sharing are particularly proposed to further enhance training efficiency of GANs. Our experimental results show that ReGAN can achieve 240× performance speedup compared to GPU platform averagely, with an average energy saving of 94×.
The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10.

The Mini-International Neuropsychiatric Interview (M.I.N.I.) is a short structured diagnostic interview, developed jointly by psychiatrists and clinicians in the United States and Europe, for DSM-IV and ICD-10 psychiatric disorders. With an administration time of approximately 15 minutes, it was designed to meet the need for a short but accurate structured psychiatric interview for multicenter clinical trials and epidemiology studies and to be used as a first step in outcome tracking in nonresearch clinical settings. The authors describe the development of the M.I.N.I. and its family of interviews: the M.I.N.I.-Screen, the M.I.N.I.-Plus, and the M.I.N.I.-Kid. They report on validation of the M.I.N.I. in relation to the Structured Clinical Interview for DSM-III-R, Patient Version, the Composite International Diagnostic Interview, and expert professional opinion, and they comment on potential applications for this interview.
The role of multichannel integration in customer relationship management

This paper reviews the strategic role of multichannel integration in customer relationship management (CRM) with the objective proposing a structured approach to the development of an integrated multichannel strategy. Alternative perspectives of CRM are reviewed and it is concluded that adoption of a strategic perspective is essential for success. Multichannel integration is posited as one of the key crossfunctional processes in CRM strategy development. The nature of industry channel structure and channel participants, channel options, and alternative channel strategies are reviewed. The customer experience is explored both within and across channels. Analytical tools, such as market structure maps, the customer relationship life cycle, and demand chain analysis, are described. Key steps in building an integrated multichannel strategy are examined. Major challenges faced by enterprises in their adoption of an integrated multichannel approach and areas for future research are discussed. D 2004 Elsevier Inc. All rights reserved.
Complementary Split-Ring Resonator for Crack Detection in Metallic Surfaces

A new sensor based on complementary split-ring resonators is presented to detect sub-millimeter surface cracks. The sensing mechanism is based on perturbing the electromagnetic field around an electrically small resonator, thus initiating a shift in the resonance frequency. The sensor is simple to fabricate and inexpensive as it is etched out in the ground plane of a microstrip-line using printed circuit board technology. The sensor exhibits a frequency shift of more than 240 MHz for a 100 μm crack.
A soft-switching synchronous buck converter for Zero Voltage Switching (ZVS) in light and full load conditions

This paper proposes a new soft switching synchronous Buck converter to provide complete ZVS conditions for both high and low side MOSFETs under the light- or no-load, as well as full load conditions. In the continuous conduction mode (CCM) with a heavy load current condition, the high side MOSFET is generally turned ON while the body diode of low side MOSFET is in the conducting condition in a dead-time region, which causes switching loss in the high side MOSFET. A proposed soft-switching Buck converter consists of the conventional synchronous Buck converter with additional L and C. By using a resonance with the additional L and C, the inductor current in CCM can be effectively commutated from the low side body diode to the high side body diode of the two MOSFETs during the dead time region. The detailed modes of operation with experimental results are provided to verify performance.
Weakly supervised object detection using pseudo-strong labels

Object detection is an import task of computer vision. A variety of methods have been proposed, but methods using the weak labels still do not have a satisfactory result. In this paper, we propose a new framework that using the weakly supervised method’s output as the pseudo-strong labels to train a strongly supervised model. One weakly supervised method is treated as black-box to generate class-specific bounding boxes on train dataset. A de-noise method is then applied to the noisy bounding boxes. Then the de-noised pseudo-strong labels are used to train a strongly object detection network. The whole framework is still weakly supervised because the entire process only uses the image-level labels. The experiment results on PASCAL VOC 2007 prove the validity of our framework, and we get result 43.4% on mean average precision compared to 39.5% of the previous best result and 34.5% of the initial method, respectively. And this frame work is simple and distinct, and is promising to be applied to other method easily.
Generalized Bloom Filters

In this paper, we propose the Generalized Bloom Filter (GBF), a space-efficient data structure to securely represent a set. Different from the standard Bloom Filter, the GBF imposes an upper bound on the false-positive probability. The key idea of the GBF is to reset bits of the filter. For that purpose, the GBF uses both hash functions that set and hash functions that reset bits. This procedure limits the false positives at the expense of introducing false negatives in membership queries. We derive expressions for the false-positive and false-negative probabilities and show that they are both upper-bounded in the GBF. We also show that these upper bounds depend only on the number of hash functions used, k0 and k1, and on the relative size of the filter, m/n. As a result, a saturated filter cannot yield high falsepositive ratios anymore. Simulations are conducted to validate the expressions. We show that the GBF is robust and fits well for security purposes, such as securing distributed applications that transmit the filter over the network.
Comparison of 2.3-kV Medium-Voltage Multilevel Converters for Industrial Medium-Voltage Drives

This paper compares the expense of power semiconductors and passive components of a (2.3 kV, 2.4 MVA) two-level, three-level neutral-point-clamped, three-level flying-capacitor, four-level flying-capacitor, and five-level series-connected H-bridge voltage source converter on the basis of the state-of-the-art 6.5-, 3.3-, 2.5-, and 1.7-kV insulated gate bipolar transistors for industrial medium-voltage drives. The power semiconductor losses, the loss distribution, the installed switch power, the design of flying capacitors, and the components of an sine filter for retrofit applications are considered.
Implementation of network intrusion detection system using variant of decision tree algorithm

As the need of internet is increasing day by day, the significance of security is also increasing. The enormous usage of internet has greatly affected the security of the system. Hackers do monitor the system minutely or keenly, therefore the security of the network is under observation. A conventional intrusion detection technology indicates more limitation like low detection rate, high false alarm rate and so on. Performance of the classifier is an essential concern in terms of its effectiveness; also number of feature to be examined by the IDS should be improved. In our work, we have proposed two techniques, C4.5 Decision tree algorithm and C4.5 Decision tree with Pruning, using feature selection. In C4.5 Decision tree with pruning we have considered only discrete value attributes for classification. We have used KDDCup'99 and NSL_KDD dataset to train and test the classifier. The Experimental Result shows that, C4.5 decision tree with pruning approach is giving better results with all most 98% of accuracy.
A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

http://dx.doi.org/10.1016/j.aei.2015.01.008 1474-0346/ 2015  q Handled by W.O. O’Brien ⇑ Corresponding author at: The University of Nottingham, Faculty of Engineering, Department of Civil Engineering, Room B27 Coates Building, University Park, Nottingham NG7 2RD, UK. Tel.: +44 (0) 115 84 68933. E-mail address: christian.koch@nottingham.ac.uk (C. Koch). URL: http://www.nottingham.ac.uk/civeng (C. Koch). Christian Koch a,⇑, Kristina Georgieva , Varun Kasireddy , Burcu Akinci , Paul Fieguth d
Learning Visual Servoing with Deep Features and Fitted Q-Iteration

Visual servoing involves choosing actions that move a robot in response to observations from a camera, in order to reach a goal configuration in the world. Standard visual servoing approaches typically rely on manually designed features and analytical dynamics models, which limits their generalization capability and often requires extensive application-specific feature and model engineering. In this work, we study how learned visual features, learned predictive dynamics models, and reinforcement learning can be combined to learn visual servoing mechanisms. We focus on target following, with the goal of designing algorithms that can learn a visual servo using low amounts of data of the target in question, to enable quick adaptation to new targets. Our approach is based on servoing the camera in the space of learned visual features, rather than image pixels or manually-designed keypoints. We demonstrate that standard deep features, in our case taken from a model trained for object classification, can be used together with a bilinear predictive model to learn an effective visual servo that is robust to visual variation, changes in viewing angle and appearance, and occlusions. A key component of our approach is to use a sample-efficient fitted Q-iteration algorithm to learn which features are best suited for the task at hand. We show that we can learn an effective visual servo on a complex synthetic car following benchmark using just 20 training trajectory samples for reinforcement learning. We demonstrate substantial improvement over a conventional approach based on image pixels or hand-designed keypoints, and we show an improvement in sample-efficiency of more than two orders of magnitude over standard model-free deep reinforcement learning algorithms. Videos are available at http://rll.berkeley.edu/visual_servoing.
Off-the-line primary-side regulation LED lamp driver with single-stage PFC and TRIAC dimming using LED forward-voltage and duty-variation tracking control

Non-isolation LED drivers  are more attractive than isolated drivers in terms of efficiency and lower fabrication cost. However, some areas such as bar-typed fluorescent lamp retrofit applications still require a solution with isolation. Even for replacement of the incandescent and compact fluorescent lamps, some LED lamp makers prefer isolated driver solutions due to safety issues. One of the popular solutions is the flyback converter which is widely used in low-to-moderate power applications . However, generally, it requires secondary voltage or current-sense circuit, and opto-coupler isolator to form a feedback loop. Thus, primary-side regulation (PSR) flyback converters have been steadily used in LED applications since they do not need secondary circuits and isolator . This paper introduces a PSR method for LED lamp which satisfies low price, good efficiency, high power factor (PF) and triode AC switch (TRIAC) dimming control.
Exploring Entity-centric Networks in Entangled News Streams

The increasing number of news outlets and the frequency of the news cycle have made it all but impossible to obtain the full picture from online news. Consolidating news from different sources has thus become a necessity in online news processing. Despite the amount of research that has been devoted to different aspects of new event detection and tracking in news streams, solid solutions for such entangled streams of full news articles are still lacking. Many existing works focus on streams of microblogs since the analysis of news articles raises the additional problem of summarizing or extracting the relevant sections of articles. For the consolidation of identified news snippets, schemes along numerous different dimensions have been proposed, including publication time, temporal expressions, geo-spatial references, named entities, and topics. The granularity of aggregated news snippets then includes such diverse aspects as events, incidents, threads, or topics for various subdivisions of news articles. To support this variety of granularity levels, we propose a comprehensive network model for the representation of multiple entangled streams of news documents. Unlike previous methods, the model is geared towards entity-centric explorations and enables the consolidation of news along all dimensions, including the context of entity mentions. Since the model also serves as a reverse index, it supports explorations along the dimensions of sentences or documents for an encompassing view on news events. We evaluate the performance of our model on a large collection of entangled news streams from major news outlets of English speaking countries and a ground truth that we generate from event summaries in the Wikipedia Current Events portal.