nils-herrmann/clean_scirepeval huggingface dataset:
Caffeine: Towards uniformed representation and acceleration for deep convolutional neural networks

With the recent advancement of multilayer convolutional neural networks (CNN), deep learning has achieved amazing success in many areas, especially in visual content understanding and classification. To improve the performance and energy-efficiency of the computation-demanding CNN, the FPGA-based acceleration emerges as one of the most attractive alternatives.
 In this paper we design and implement Caffeine, a hardware/software co-designed library to efficiently accelerate the entire CNN on FPGAs. First, we propose a uniformed convolutional matrix-multiplication representation for both computation-intensive convolutional layers and communication-intensive fully connected (FCN) layers. Second, we design Caffeine with the goal to maximize the underlying FPGA computing and bandwidth resource utilization, with a key focus on the bandwidth optimization by the memory access reorganization not studied in prior work. Moreover, we implement Caffeine in the portable high-level synthesis and provide various hardware/software definable parameters for user configurations. Finally, we also integrate Caffeine into the industry-standard software deep learning framework Caffe. We evaluate Caffeine and its integration with Caffe by implementing VGG16 and AlexNet network on multiple FPGA platforms. Caffeine achieves a peak performance of 365 GOPS on Xilinx KU060 FPGA and 636 GOPS on Virtex7 690t FPGA. This is the best published result to our best knowledge. We achieve more than 100x speedup on FCN layers over previous FPGA accelerators. An end-to-end evaluation with Caffe integration shows up to 7.3x and 43.5x performance and energy gains over Caffe on a 12-core Xeon server, and 1.5x better energy-efficiency over the GPU implementation on a medium-sized FPGA (KU060). Performance projections to a system with a high-end FPGA (Virtex7 690t) shows even higher gains.
Generic summarization and keyphrase extraction using mutual reinforcement principle and sentence clustering

A novel method for simultaneous keyphrase extraction and generic text summarization is proposed by modeling text documents as weighted undirected and weighted bipartite graphs. Spectral graph clustering algorithms are useed for partitioning sentences of the documents into topical groups with sentence link priors being exploited to enhance clustering quality. Within each topical group, saliency scores for keyphrases and sentences are generated based on a mutual reinforcement principle. The keyphrases and sentences are then ranked according to their saliency scores and selected for inclusion in the top keyphrase list and summaries of the document. The idea of building a hierarchy of summaries for documents capturing different levels of granularity is also briefly discussed. Our method is illustrated using several examples from news articles, news broadcast transcripts and web documents.
Personality Traits , Emotional Intelligence and Academic Achievements of University Students

This research investigated the relationships between personality traits, emotional intelligence and academic achievements among 160 university students in Malaysia. Big Five Inventory (BFI) was used to measure the five dimensions of personality traits extraversion, agreeableness, conscientiousness, neuroticism, openness; Schutte Emotional Intelligence Scale (SEIS) was used to measure emotional intelligence and students’ academic achievement was measured by Cumulative Grade Point Average (CGPA). Bivariate analysis using Pearson Correlation method indicated that extraversion (r=.311, p<.05), agreeableness (r=.378, p<.05), conscientiousness (r=.315, p<.05) and openness (r=.497, p<.05) were positively and significantly correlated with emotional intelligence. Neuroticism (r= -.303, p<.05) was found negatively and significantly associated with emotional intelligence. However, emotional intelligence (r=.002, p>.05) was insignificantly associated with academic achievement. Future researches are recommended to employ Structural Equation Modeling analysis to determine how both personality traits and emotional intelligence have an impact on academic achievements.
Vitamin D deficiency and depression in adults: systematic review and meta-analysis.

There is conflicting evidence about the relationship between vitamin D deficiency and depression, and a systematic assessment of the literature has not been available.



To determine the relationship, if any, between vitamin D deficiency and depression.



A systematic review and meta-analysis of observational studies and randomised controlled trials was conducted.



One case-control study, ten cross-sectional studies and three cohort studies with a total of 31 424 participants were analysed. Lower vitamin D levels were found in people with depression compared with controls (SMD = 0.60, 95% CI 0.23-0.97) and there was an increased odds ratio of depression for the lowest v. highest vitamin D categories in the cross-sectional studies (OR = 1.31, 95% CI 1.0-1.71). The cohort studies showed a significantly increased hazard ratio of depression for the lowest v. highest vitamin D categories (HR = 2.21, 95% CI 1.40-3.49).



Our analyses are consistent with the hypothesis that low vitamin D concentration is associated with depression, and highlight the need for randomised controlled trials of vitamin D for the prevention and treatment of depression to determine whether this association is causal.
The Self-Compassion Scale is a Valid and Theoretically Coherent Measure of Self-Compassion

Recently, the Self-Compassion Scale (SCS) has been criticized for problems with psychometric validity. Further, the use of an overall self-compassion score that includes items representing the lack of self-compassion has been called into question. I argue that the SCS is consistent with my definition of self-compassion, which I see as a dynamic balance between the compassionate versus uncompassionate ways that individuals emotionally respond to pain and failure (with kindness or judgment), cognitively understand their predicament (as part of the human experience or as isolating), and pay attention to suffering (in a mindful or over-identified manner). A summary of new empirical evidence is provided using a bi-factor analysis, which indicates that at least 90 % of the reliable variance in SCS scores can be explained by an overall self-compassion factor in five different populations, justifying the use of a total scale score. Support for a six-factor structure to the SCS was also found; however, suggesting the scale can be used in a flexible manner depending on the interests of researchers. I also discuss the issue of whether a two-factor model of the SCS—which collapses self-kindness, common humanity, and mindfulness items into a Bself-compassion^ factor and self-judgment, isolation, and over-identification items into a Bself-criticism^ factor—makes theoretical sense. Finally, I present new data showing that self-compassion training increases scores on the positive SCS subscales and decreases scores on the negative subscales, supporting the idea that self-compassion represents more compassionate and fewer uncompassionate responses to suffering.
CuriousDroid: Automated User Interface Interaction for Android Application Analysis Sandboxes

Mobile computing has experienced enormous growth in market share and computational power in recent years. As a result, mobile malware is becoming more sophisticated and more prevalent, leading to research into dynamic sandboxes as a widespread approach for detecting malicious applications. However, the event-driven nature of Android applications renders critical the capability to automatically generate deterministic and intelligent user interactions to drive analysis subjects and improve code coverage. In this paper, we present CuriousDroid, an automated system for exercising Android application user interfaces in an intelligent, user-like manner. CuriousDroid operates by decomposing application user interfaces on-the-fly and creating a context-based model for interactions that is tailored to the current user layout. We integrated CuriousDroid with Andrubis, a well-known Android sandbox, and conducted a large-scale evaluation of 38,872 applications taken from different data sets. Our evaluation demonstrates significant improvements in both end-to-end sample classification as well as increases in the raw number of elicited behaviors at runtime.
Autonomous Flight in Unstructured and Unknown Indoor Environments by Abraham Galton

This thesis presents the design, implementation, and valid ation of a system that enables a micro air vehicle to autonomously explore and map unstruct u ed and unknown indoor environments. Such a vehicle would be of considerable use in many real-world applications such as search and rescue, civil engineering inspection, an d a host of military tasks where it is dangerous or difficult to send people. While mapping and exploration capabilities are common for ground vehicles today, air vehicles seeking t o achieve these capabilities face unique challenges. While there has been recent progres s toward sensing, control, and navigation suites for GPS-denied flight, there have been few demonstrations of stable, goal-directed flight in real environments. The main focus of this research is the development of real-ti me state estimation techniques that allow our quadrotor helicopter to fly autonomous ly in indoor, GPS-denied environments. Accomplishing this feat required the developm ent of a large integrated system that brought together many components into a cohesive packa ge. As such, the primary contribution is the development of the complete working sys tem. I show experimental results that illustrate the MAV’s ability to navigate accurat ely in unknown environments, and demonstrate that our algorithms enable the MAV to operate au tonomously in a variety of indoor environments.   Associate Professor of Aeronautics and Astronautic s
Monitoring body temperature of newborn infants at neonatal intensive care units using wearable sensors

Body temperature is one of the key parameters for health monitoring of premature infants at the neonatal intensive care unit (NICU). In this paper, we propose and demonstrate a design of non-invasive neonatal temperature monitoring with wearable sensors. A negative temperature coefficient (NTC) resistor is applied as the temperature sensor due to its accuracy and small size. Conductive textile wires are used to make the sensor integration compatible for a wearable non-invasive monitoring platform, such as a neonatal smart jacket. Location of the sensor, materials and appearance are designed to optimize the functionality, patient comfort and the possibilities for aesthetic features. A prototype belt is built of soft bamboo fabrics with NTC sensor integrated to demonstrate the temperature monitoring. Experimental results from the testing on neonates at NICU of Máxima Medical Center (MMC), Veldhoven, the Netherlands, show the accurate temperature monitoring by the prototype belt comparing with the standard patient monitor.
Convolutional Neural Networks Arise From Ising Models and Restricted Boltzmann Machines

Convolutional neural net-like structures arise from training an unstructured deep belief network (DBN) using structured simulation data of 2-D Ising Models at criticality. The convolutional structure arises not just because such a structure is optimal for the task, but also because the belief network automatically engages in block renormalization procedures to “rescale” or “encode” the input, a fundamental approach in statistical mechanics. This work primarily reviews the work of Mehta et al. , the group that first made the discovery that such a phenomenon occurs, and replicates their results training a DBN on Ising models, confirming that weights in the DBN become spatially concentrated during training on critical Ising samples.
Energy aware virtual machine placement scheduling in cloud computing based on ant colony optimization approach

Cloud computing provides resources as services in pay-as-you-go mode to customers by using virtualization technology. As virtual machine (VM) is hosted on physical server, great energy is consumed by maintaining the servers in data center. More physical servers means more energy consumption and more money cost. Therefore, the VM placement (VMP) problem is significant in cloud computing. This paper proposes an approach based on ant colony optimization (ACO) to solve the VMP problem, named as ACO-VMP, so as to effectively use the physical resources and to reduce the number of running physical servers. The number of physical servers is the same as the number of the VMs at the beginning. Then the ACO approach tries to reduce the physical server one by one. We evaluate the performance of the proposed ACO-VMP approach in solving VMP with the number of VMs being up to 600. Experimental results compared with the ones obtained by the first-fit decreasing (FFD) algorithm show that ACO-VMP can solve VMP more efficiently to reduce the number of physical servers significantly, especially when the number of VMs is large.
Georeferenced Point Clouds: A Survey of Features and Point Cloud Management

This paper presents a survey of georeferenced point clouds. Concentration is, on the one hand, put on features, which originate in the measurement process themselves, and features derived by processing the point cloud. On the other hand, approaches for the processing of georeferenced point clouds are reviewed. This includes the data structures, but also spatial processing concepts. We suggest a categorization of features into levels that reflect the amount of processing. Point clouds are found across many disciplines, which is reflected in the versatility of the literature suggesting specific features.
A time-based collective factorization for topic discovery and monitoring in news

Discovering and tracking topic shifts in news constitutes a new challenge for applications nowadays. Topics evolve,emerge and fade, making it more difficult for the journalist -or the press consumer- to decrypt the news. For instance, the current Syrian chemical crisis has been the starting point of the UN Russian initiative and also the revival of the US France alliance. A topical mapping representing how the topics evolve in time would be helpful to contextualize information. As far as we know, few topic tracking systems can provide such temporal topic connections. In this paper, we introduce a novel framework inspired from Collective Factorization for online topic discovery able to connect topics between different time-slots. The framework learns jointly the topics evolution and their time dependencies. It offers the user the ability to control, through one unique hyper-parameter, the tradeoff between the past accumulated knowledge and the current observed data. We show, on semi-synthetic datasets and on Yahoo News articles, that our method is competitive with state-of-the-art techniques while providing a simple way to monitor topics evolution (including emerging and disappearing topics).
A review of the tripartite structure of subjective well-being: implications for conceptualization, operationalization, analysis, and synthesis.

Subjective well-being (SWB) comprises a global evaluation of life satisfaction and positive and negative affective reactions to one's life. Despite the apparent simplicity of this tripartite model, the structure of SWB remains in question. In the present review, the authors identify five prominent structural conceptualizations in which SWB is cast variously as three separate components, a hierarchical construct, a causal system, a composite, and as configurations of components. Supporting evidence for each of these models is reviewed, strengths and weaknesses are evaluated, and commonalities and discrepancies among approaches are described. The authors demonstrate how current ambiguities concerning the tripartite structure of SWB have fundamental implications for conceptualization, measurement, analysis, and synthesis. Given these ambiguities, it is premature to propose a definitive structure of SWB. Rather, the authors outline a research agenda comprising both short-term and longer-term steps toward resolving these foundational, yet largely unaddressed, issues concerning SWB.
Eyes-Free Art: Exploring Proxemic Audio Interfaces For Blind and Low Vision Art Engagement

Engagement in the arts1 is an important component of participation in cultural activities, but remains a largely unaddressed challenge for people with sensory disabilities. Visual arts are generally inaccessible to people with visual impairments due to their inherently visual nature. To address this, we present Eyes-Free Art, a design probe to explore the use of proxemic audio for interactive sonic experiences with 2D art work. The proxemic audio interface allows a user to move closer and further away from a painting to experience background music, a novel sonification, sound effects, and a detailed verbal description. We conducted a lab study by creating interpretations of five paintings with 13 people with visual impairments and found that participants enjoyed interacting with the artwork. We then created a live installation with a visually impaired artist to iterate on this concept to account for multiple users and paintings. We learned that a proxemic audio interface allows for people to feel immersed in the artwork. Proxemic audio interfaces are similar to visual because they increase in detail with closer proximity, but are different because they need a descriptive verbal overview to give context. We present future research directions in the space of proxemic audio interactions.
Large-Scale Image Clustering Based on Camera Fingerprints

Practical applications of digital forensics are often faced with the challenge of grouping large-scale suspicious images into a vast number of clusters, each containing images taken by the same camera. This task can be approached by resorting to the use of <italic>sensor pattern noise</italic> (SPN), which serves as the fingerprint of the camera. The challenges of large-scale image clustering come from the sheer volume of the image set and the high dimensionality of each image. The difficulties can be further aggravated when the number of classes (i.e., the number of cameras) is much higher than the average size of class (i.e., the number of images acquired by each camera). We refer to this as the <inline-formula> <tex-math notation="LaTeX">$NC\gg SC$ </tex-math></inline-formula> problem, which is not uncommon in many practical scenarios. In this paper, we propose a novel clustering framework that is capable of addressing the <inline-formula> <tex-math notation="LaTeX">$NC\gg SC$ </tex-math></inline-formula> problem without a training process. The proposed clustering framework was evaluated on the Dresden image database and compared with the state-of-the-art SPN-based image clustering algorithms. Experimental results show that the proposed clustering framework is much faster than the state-of-the-art algorithms while maintaining a high level of clustering quality.
A Machine Learning Approach For Classifying Sentiments in Arabic tweets

Nowadays, sentiment analysis methods become more and more popular especially with the proliferation of social media platform users number. In the same context, this paper presents a sentiment analysis approach which can faithfully translate the sentimental orientation of Arabic Twitter posts, based on a novel data representation and machine learning techniques. The proposed approach applied a wide range of features: lexical, surface-form, syntactic, etc. We also made use of lexicon features inferred from two Arabic sentiment words lexicons. To build our supervised sentiment analysis system, we use several standard classification methods (Support Vector Machines, K-Nearest Neighbour, Naïve Bayes, Decision Trees, Random Forest) known by their effectiveness over such classification issues.
 In our study, Support Vector Machines classifier outperforms other supervised algorithms in Arabic Twitter sentiment analysis. Via an ablation experiments, we show the positive impact of lexicon based features on providing higher prediction performance.
GANosaic: Mosaic Creation with Generative Texture Manifolds

This paper presents a novel framework for generating texture mosaics with convolutional neural networks. Our method is called GANosaic and performs optimization in the latent noise space of a generative texture model, which allows the transformation of a content image into a mosaic exhibiting the visual properties of the underlying texture manifold. To represent that manifold, we use a state-of-the-art generative adversarial method for texture synthesis , which can learn expressive texture representations from data and produce mosaic images with very high resolution. This fully convolutional model generates smooth (without any visible borders) mosaic images which morph and blend different textures locally. In addition, we develop a new type of differentiable statistical regularization appropriate for optimization over the prior noise space of the PSGAN model.
Single-chip microprocessor that communicates directly using light

Data transport across short electrical wires is limited by both bandwidth and power density, which creates a performance bottleneck for semiconductor microchips in modern computer systems—from mobile phones to large-scale data centres. These limitations can be overcome by using optical communications based on chip-scale electronic–photonic systems enabled by silicon-based nanophotonic devices8. However, combining electronics and photonics on the same chip has proved challenging, owing to microchip manufacturing conflicts between electronics and photonics. Consequently, current electronic–photonic chips are limited to niche manufacturing processes and include only a few optical devices alongside simple circuits. Here we report an electronic–photonic system on a single chip integrating over 70 million transistors and 850 photonic components that work together to provide logic, memory, and interconnect functions. This system is a realization of a microprocessor that uses on-chip photonic devices to directly communicate with other chips using light. To integrate electronics and photonics at the scale of a microprocessor chip, we adopt a ‘zero-change’ approach to the integration of photonics. Instead of developing a custom process to enable the fabrication of photonics, which would complicate or eliminate the possibility of integration with state-of-the-art transistors at large scale and at high yield, we design optical devices using a standard microelectronics foundry process that is used for modern microprocessors. This demonstration could represent the beginning of an era of chip-scale electronic–photonic systems with the potential to transform computing system architectures, enabling more powerful computers, from network infrastructure to data centres and supercomputers.
Natural language processing

A procedure is described whereby a computer can determine whether two fingerpring impressions were made by the same finger. The procedure used the X and Y coordinates and the individual directions of the minutiae (ridge endings and bifurcations). The identity of two impressions is established by computing the density of clusters of points in AX and AY space where AX and AY are the differences in coordinates that are found in going from one of the fingerpring impressions to the other. Single fingerpring classification is discussed and experimental results using machine-read minutiae data are given.