nils-herrmann/clean_scirepeval huggingface dataset:
Gated Fusion Network for Joint Image Deblurring and Super-Resolution

Single-image super-resolution is a fundamental task for vision applications to enhance the image quality with respect to spatial resolution. If the input image contains degraded pixels, the artifacts caused by the degradation could be amplified by superresolution methods. Image blur is a common degradation source. Images captured by moving or still cameras are inevitably affected by motion blur due to relative movements between sensors and objects. In this work, we focus on the super-resolution task with the presence of motion blur. We propose a deep gated fusion convolution neural network to generate a clear high-resolution frame from a single natural image with severe blur. By decomposing the feature extraction step into two task-independent streams, the dualbranch design can facilitate the training process by avoiding learning the mixed degradation all-in-one and thus enhance the final high-resolution prediction results. Extensive experiments demonstrate that our method generates sharper super-resolved images from low-resolution inputs with high computational efficiency.
Heart rate responses during small-sided games and short intermittent running training in elite soccer players: a comparative study.

The purpose of this study was to compare heart rate (HR) responses within and between physical controlled (short-duration intermittent running) and physical integrated (sided games) training methods in elite soccer players. Ten adult male elite soccer players (age, 26 +/- 2.9 years; body mass, 78.3 +/- 4.4 kg; maximum HR [HRmax], 195.4 +/- 4.9 b x min(-1) and velocity at maximal aerobic speed (MAS), 17.1 +/- 0.8 km x h(-1)) performed different short-duration intermittent runs, e.g., 30-30 (30 seconds of exercise interspersed with 30 seconds of recovery) with active recovery, and 30-30, 15-15, 10-10, and 5-20 seconds with passive recovery, and different sided games (1 versus 1, 2 versus 2, 4 versus 4, 8 versus 8 with and without a goalkeeper, and 10 versus 10). In both training methods, HR was measured and expressed as a mean percentage of HR reserve (%HRres). The %HRres in the 30-30-second intermittent run at 100% MAS with active recovery (at 9 km.h with corresponding distance) was significantly higher than that with passive recovery (85.7% versus 77.2% HRres, respectively, p < 0.001) but also higher than the 1 versus 1 (p < 0.01), 4 versus 4 (p <or= 0.05), 8 versus 8 (p < 0.001), and 10 versus 10 (p < 0.01) small-sided games. The %HRres was 2-fold less homogeneous during the different small-sided games than during the short-duration intermittent running (intersubjects coefficient of variation [CV] = 11.8% versus 5.9%, respectively). During the 8 versus 8 sided game, the presence of goalkeepers induced an approximately 11% increase in %HRres and reduced homogeneity when compared to games without goalkeepers (intersubject CV = 15.6% versus 8.8%). In conclusion, these findings showed that some small-sided games allow the HR to increase to the same level as that in short-duration intermittent running. The sided game method can be used to bring more variety during training, mixing physical, technical, and tactical training approaching the intensity of short-duration intermittent running but with higher intersubject variability.
Attention Mechanism in Speaker Recognition: What Does it Learn in Deep Speaker Embedding?

This paper presents an experimental study on deep speaker embedding with an attention mechanism that has been found to be a powerful representation learning technique in speaker recognition. In this framework, an attention model works as a frame selector that computes an attention weight for each frame-level feature vector, in accord with which an utterance-level representation is produced at the pooling layer in a speaker embedding network. In general, an attention model is trained together with the speaker embedding network on a single objective function, and thus those two components are tightly bound to one another. In this paper, we consider the possibility that the attention model might be decoupled from its parent network and assist other speaker embedding networks and even conventional i-vector extractors. This possibility is demonstrated through a series of experiments on a NIST Speaker Recognition Evaluation (SRE) task, with 9.0% EER reduction and 3.8% minCprimary reduction when the attention weights are applied to i-vector extraction. Another experiment shows that DNN-based soft voice activity detection (VAD) can be effectively combined with the attention mechanism to yield further reduction of minCprimary by 6.6% and 1.6% in deep speaker embedding and i-vector systems, respectively.
CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms

We propose a new system for generating art. The system generates art by looking at art and learning about style; and becomes creative by increasing the arousal potential of the generated art by deviating from the learned styles. We build over Generative Adversarial Networks (GAN), which have shown the ability to learn to generate novel images simulating a given distribution. We argue that such networks are limited in their ability to generate creative products in their original design. We propose modifications to its objective to make it capable of generating creative art by maximizing deviation from established styles and minimizing deviation from art distribution. We conducted experiments to compare the response of human subjects to the generated art with their response to art created by artists. The results show that human subjects could not distinguish art generated by the proposed system from art generated by contemporary artists and shown in top art
Assessment of suicidal intention: the Scale for Suicide Ideation.

This article describes the rationale, development, and validation of the Scale for Suicide Ideation (SSI), a 19-item clinical research instrument designed to quantify and assess suicidal intention. The scale was found to have high internal consistency and moderately high correlations with clinical ratings of suicidal risk and self-administered measures of self-harm. Furthermore, it was sensitive to changes in levels of depression and hopelessness over time. Its construct validity was supported by two studies by different investigators testing the relationship between hopelessness, depression, and suicidal ideation and by a study demonstrating a significant relationship between high level of suicidal ideation and "dichotomous" attitudes about life and related concepts on a semantic differential test. Factor analysis yielded three meaningful factors: active suicidal desire, specific plans for suicide, and passive suicidal desire.
Role of Prefrontal Persistent Activity in Working Memory

The prefrontal cortex is activated during working memory, as evidenced by fMRI results in human studies and neurophysiological recordings in animal models. Persistent activity during the delay period of working memory tasks, after the offset of stimuli that subjects are required to remember, has traditionally been thought of as the neural correlate of working memory. In the last few years several findings have cast doubt on the role of this activity. By some accounts, activity in other brain areas, such as the primary visual and posterior parietal cortex, is a better predictor of information maintained in visual working memory and working memory performance; dynamic patterns of activity may convey information without requiring persistent activity at all; and prefrontal neurons may be ill-suited to represent non-spatial information about the features and identity of remembered stimuli. Alternative interpretations about the role of the prefrontal cortex have thus been suggested, such as that it provides a top-down control of information represented in other brain areas, rather than maintaining a working memory trace itself. Here we review evidence for and against the role of prefrontal persistent activity, with a focus on visual neurophysiology. We show that persistent activity predicts behavioral parameters precisely in working memory tasks. We illustrate that prefrontal cortex represents features of stimuli other than their spatial location, and that this information is largely absent from early cortical areas during working memory. We examine memory models not dependent on persistent activity, and conclude that each of those models could mediate only a limited range of memory-dependent behaviors. We review activity decoded from brain areas other than the prefrontal cortex during working memory and demonstrate that these areas alone cannot mediate working memory maintenance, particularly in the presence of distractors. We finally discuss the discrepancy between BOLD activation and spiking activity findings, and point out that fMRI methods do not currently have the spatial resolution necessary to decode information within the prefrontal cortex, which is likely organized at the micrometer scale. Therefore, we make the case that prefrontal persistent activity is both necessary and sufficient for the maintenance of information in working memory.
Knowledge Base Completion: Baselines Strike Back

Many papers have been published on the knowledge base completion task in the past few years. Most of these introduce novel architectures for relation learning that are evaluated on standard datasets such as FB15k and WN18. This paper shows that the accuracy of almost all models published on the FB15k can be outperformed by an appropriately tuned baseline — our reimplementation of the DistMult model. Our findings cast doubt on the claim that the performance improvements of recent models are due to architectural changes as opposed to hyperparameter tuning or different training objectives. This should prompt future research to re-consider how the performance of models is evaluated and reported.
Interpretative case studies on agile team productivity and management

The management of software development productivity is a key issue in software organizations, where the major drivers are lower cost and shorter time-to-market. Agile methods, including Extreme Programming and Scrum, have evolved as “light” approaches that simplify the software development process, potentially leading to increased team productivity. However, little empirical research has examined which factors do have an impact on productivity and in what way, when using agile methods.  Our objective is to provide a better understanding of the factors and mediators that impact agile team productivity.  We have conducted a multiple-case study for six months in three large Brazilian companies that have been using agile methods for over two years. We have focused on the main productivity factors perceived by team members through interviews, documentation from retrospectives, and non-participant observation.  We developed a novel conceptual framework, using thematic analysis to understand the possible mechanisms behind such productivity factors. Agile team management was found to be the most influential factor in achieving agile team productivity. At the intra-team level, the main productivity factors were team design (structure and work allocation) and member turnover. At the inter-team level, the main productivity factors were how well teams could be effectively coordinated by proper interfaces and other dependencies and avoiding delays in providing promised software to dependent teams.  Teams should be aware of the influence and magnitude of turnover, which has been shown negative for agile team productivity. Team design choices remain an important factor impacting team productivity, even more pronounced on agile teams that rely on teamwork and people factors. The intra-team coordination processes must be adjusted to enable productive work by considering priorities and pace between teams. Finally, the revised conceptual framework for agile team productivity supports further tests through confirmatory studies.
Person-centered therapy and solution-focused brief therapy: An integration of present and future awareness.

The authors propose an integration of person-centered therapy, with its focus on the here and now of client awareness of self, and solution-focused therapy, with its future-oriented techniques that also raise awareness of client potentials. Although the two theories hold different assumptions regarding the therapist's role in facilitating client change, it is suggested that solution-focused techniques are often compatible for use within a person-centered approach. Further, solution-focused activities may facilitate the journey of becoming self-aware within the person-centered tradition. This article reviews the two theories, clarifying the similarities and differences. To illustrate the potential integration of the approaches, several types of solution-focused strategies are offered through a clinical example.
Factors Affecting Effective use of Safety Wears among Construction Site Operatives : Lessons from Indigenous Firms in South Western Nigeria *

Work related hazard is not a new phenomenon but its frequent occurrence on sites remain a major setback towards successful projects delivery. The study assessed factors affecting effective use of safety wears on construction sites. Based on snowball and random sampling technique, 128 copies of questionnaire were administered to participants with years of experience on construction management. The outcome of the study revealed that the top aspect of safety practices currently explored by the indigenous construction firms on sites include: provision of temporary fence, provision of accidents prevention procedure and development and frequently review of Safety Policy for building projects. The result of Independent Samples T-test showed the top three factors preventing effective use of safety wears on construction sites are: inadequate engagement of Safety Managers on sites, lack of proper training on effective use of safety wears and workers failure to adapt with safety practices as it was against their traditional training. This paper concluded that effective use of audio and visual displaying gadgets on site, conducting in-house safety training for the workers and ensure one Safety Manager is employ on every construction sites will go a long way in improving workers safety practices on sites.
Large Margin Neural Language Models

Neural language models (NLMs) are generative, and they model the distribution of grammatical sentences. Trained on huge corpus, NLMs are pushing the limit of modeling accuracy. Besides, they have also been applied to supervised learning tasks that decode text, e.g., automatic speech recognition (ASR). By re-scoring the n-best list, NLM can select grammatically more correct candidate among the list, and significantly reduce word/char error rate. However, the generative nature of NLM may not guarantee a discrimination between “good” and “bad” (in a task-specific sense) sentences, resulting in suboptimal performance. This work proposes an approach to adapt a generative NLM to a discriminative one. Different from the commonly used maximum likelihood objective, the proposed method aims at enlarging the margin between the “good” and “bad” sentences. It is trained end-to-end and can be widely applied to tasks that involve the re-scoring of the decoded text. Significant gains are observed in both ASR and statistical machine translation (SMT) tasks.
Aspect, Lexical Semantic Representation, and Argument Expression

Over the last twenty years aspectual notions have been increasingly appealed to in structuring verbal lexical semantic representations and, concomitantly, in formulating principles of argument expression. This move has been further fueled by the significant insights that have emerged from this line of research. Yet, despite the enthusiasm for aspectual notions that their proliferation demonstrates, I propose that such notions are not the panacea that their considerable use would suggest. Although I also have adopted them in my work, my continuing research into lexical semantic representation and argument expression has suggested to me that the links between aspect, lexical semantic representation, and argument expression are not so simple and transparent as they are made out to be. I use this study to reassess the contributions of aspect to lexical semantic representation and argument expression. The striking acceptance of aspectual notions as a means of structuring lexical semantic representations may have its roots in some well-known drawbacks of lexical semantic representations that take the form of semantic role lists. As often pointed out, semantic role lists are not grounded in a theory of events, leaving them unconstrained and vulnerable to criticism. Aspectual classifications, proposed at least as early as Aristotle and taken up more recently by Vendler (1957), Kenny (1963), and many others, offer a ready-made theory of the ontological types of events, which grounds them in their temporal contours. Furthermore, aspectual classifications have proved their usefulness in accounts of temporal entailments and temporal adverbial distribution. With this incentive, aspectual classes have been increasingly adopted as the appropriate event types for the twin purposes of structuring lexical semantic representations and formulating a theory of argument expression, as I now review. I then consider how well such attempts succeed. I suggest that it is right to
Rising Food Prices , Food Price Volatility , and Political Unrest

Do food prices cause political unrest? Throughout history, riots appear to have frequently broken out as a consequence of high food prices. This paper studies the impact of food prices on political unrest using monthly data on food prices at the international level. Because food prices and political unrest are jointly determined, the incidence of natural disasters in a given month is used in an attempt to identify the causal relationship between food prices and political unrest. Empirical results indicate that between January 1990 and January 2011, food price increases have led to increased political unrest, whereas food price volatility has been associated with decreases in political unrest. These ndings are consistent with those of the applied microeconomics literature on the welfare impacts of food prices.
AHP-based approaches for supplier evaluation : Problems and perspectives

Supplier evaluation has assumed a strategic role in determining competitiveness of large manufacturing companies. An increasing number of researches have been devoted to the development of different kind of methodologies to cope with this problem. Nevertheless, while the number of applications is growing, there is little empirical evidence of the practical usefulness of such tools with a dichotomy between theoretical approaches and empirical applications. Considering this evidence, the goal of this paper is to contribute to understand the above dichotomy by implementing, in a corporate environment, a model for supplier evaluation based on the Analytical Hierarchical Process (AHP), one of the most prominent methodologies used to address the problem. The analysis of the implementation process of the methodology allows the identification of strengths and weaknesses of using formalized supplier selection models to tackle the supplier evaluation problem, also highlighting potential barriers preventing firms to adopt such methods. Relevant issues arising from the application and managerial implications for both customer and suppliers are discussed. & 2012 Elsevier Ltd. All rights reserved.
Smart Irrigation Using Internet of Things

Automation in agriculture system is very important these days. This paper proposes an automated system for irrigating the fields. ESP-8266 WIFI module chip is used to connect the system to the internet. Various types of sensors are used to check the content of moisture in the soil, and the water is supplied to the soil through the motor pump. IOT is used to inform the farmers of the supply of water to the soil through an android application. Every time water is given to the soil, the farmer will get to know about that.
Ecological Adaptive Cruise Controller for Plug-In Hybrid Electric Vehicles Using Nonlinear Model Predictive Control

Plug-in hybrid electric vehicles (PHEVs) are promising options for future transportation. Having two sources of energy enables them to offer better fuel economy and fewer emissions. Significant research has been done to take advantage of future route information to enhance vehicle performance. In this paper, an ecological adaptive cruise controller (Eco-ACC) is used to improve both fuel economy and safety of the Toyota Prius Plug-in Hybrid. Recently, an emerging trend in the research has been to improve the adaptive cruise controller. However, the majority of research to date has focused on driving safety, and only rare reports in the literature substantiate the applicability of such systems for PHEVs. Here, we demonstrate that using an Eco-ACC system can simultaneously improve total energy costs and vehicle safety. The developed controller is equipped with an onboard sensor that captures upcoming trip data to optimally adjust the speed of PHEVs. The nonlinear model predictive control technique (NMPC) is used to optimally control vehicle speed. To prepare the NMPC controller for real-time applications, a fast and efficient control-oriented model is developed. The authenticity of the model is validated using a high-fidelity Autonomie-based model. To evaluate the designed controller, the global optimum solution for cruise control problem is found using Pontryagin's minimum principle (PMP). To explore the efficacy of the controller, PID and linear MPC controllers are also applied to the same problem. Simulations are conducted for different driving scenarios such as driving over a hill and car following. These simulations demonstrate that NMPC improves the total energy cost up to 19%.
A signed binary multiplication technique pdf

Bit is added to the left of the partial product using sign extension.products required to half that required by a simple add and shift method. A signed binary multiplication technique. Quarterly Journal of.Booths multiplication algorithm is a multiplication algorithm that multiplies two signed binary numbers in twos complement notation. The above mentioned technique is inadequate when the multiplicand is most negative number that can be represented e.g. Create a book Download as PDF Printable version. Binary Multiplication Division. Sign-and-magnitude: the most significant bit represents.BINARY MULTIPLICATION.  This method adds the multiplicand X to itself Y times, where Y de. In the case of binary multiplication, since the digits are 0 and 1, each step of.implemented a Signed-Unsigned Booths Multiplier and a. defined as the multiplication performed on signed binary numbers and. While the second method.accept unsigned binary inputs, one bit at a time, least significant bit first, and produce. Method for the multiplication of signed numbers, based on their earlier.will also describe how to apply this new method to the familiar multipliers such as Booth and.