nils-herrmann/clean_scirepeval huggingface dataset:
OCA: Opinion corpus for Arabic

Sentiment analysis is a challenging new task related to text mining and natural language processing. Although there are, at present, several studies related to this theme, most of these focus mainly on English texts. The resources available for opinion mining (OM) in other languages are still limited. In this article, we present a new Arabic corpus for the OM task that has been made available to the scientific community for research purposes. The corpus contains 500 movie reviews collected from different web pages and blogs in Arabic, 250 of them considered as positive reviews, and the other 250 as negative opinions. Furthermore, different experiments have been carried out on this corpus, using machine learning algorithms such as support vector machines and Naïve Bayes. The results obtained are very promising and we are encouraged to continue this line of research.
Mux-Kmeans: multiplex kmeans for clustering large-scale data set

Kmeans clustering algorithm is widely used in a number of scientific applications due to its simple iterative nature and ease of implementation. The quality of clustering result highly depends on the selection of initial centroids. Different selections of initial centroids result in different clustering results. In practice, people run a series of Kmeans processes with multiple initial centroid groups serially and return the best clustering result among them. However, in the era of big data, a Kmeans process is implemented on MapReduce to scale to large data sets. Even a single Kmeans process on MapReduce requires considerable long runtime.  Rather than executing multiple Kmeans processes serially, Mux-Kmeans launches these Kmeans processes concurrently with multiple centroid groups. In each iteration, Mux-Kmeans (i) evaluates these Kmeans processes, (ii) prunes the low-quality Kmeans processes, and (iii) incubates new Kmeans processes. After a certain number of iterations, it finally obtains the best among these local optimal results. We implement Mux-Kmeans on MapReduce and evaluate it on Amazon EC2. The experimental results show that starting from the same initial centroid groups, the clustering result of Mux-Kmeans is always non-worse than the best of a series of Kmeans processes. Mux-Kmeans also saves elapsed time than serial multiple Kmeans processes.
Characterizing Storage Workloads with Counter Stacks

Existing techniques for identifying working set sizes based on miss ratio curves (MRCs) have large memory overheads which make them impractical for storage workloads. We present a novel data structure, the counter stack, which can produce approximate MRCs while using sublinear space. We show how counter stacks can be checkpointed to produce workload representations that are many orders of magnitude smaller than full traces, and we describe techniques for estimating MRCs of arbitrary workload combinations over arbitrary windows in time. Finally, we show how online analysis using counter stacks can provide valuable insight into live workloads.
Exploring Overlapping Functional Units with Various Structure in Protein Interaction Networks

Revealing functional units in protein-protein interaction (PPI) networks are important for understanding cellular functional organization. Current algorithms for identifying functional units mainly focus on cohesive protein complexes which have more internal interactions than external interactions. Most of these approaches do not handle overlaps among complexes since they usually allow a protein to belong to only one complex. Moreover, recent studies have shown that other non-cohesive structural functional units beyond complexes also exist in PPI networks. Thus previous algorithms that just focus on non-overlapping cohesive complexes are not able to present the biological reality fully. Here, we develop a new regularized sparse random graph model (RSRGM) to explore overlapping and various structural functional units in PPI networks. RSRGM is principally dominated by two model parameters. One is used to define the functional units as groups of proteins that have similar patterns of connections to others, which allows RSRGM to detect non-cohesive structural functional units. The other one is used to represent the degree of proteins belonging to the units, which supports a protein belonging to more than one revealed unit. We also propose a regularizer to control the smoothness between the estimators of these two parameters. Experimental results on four S. cerevisiae PPI networks show that the performance of RSRGM on detecting cohesive complexes and overlapping complexes is superior to that of previous competing algorithms. Moreover, RSRGM has the ability to discover biological significant functional units besides complexes.
Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena

The individual properties of visual objects, like form or color, are represented in different areas in our visual cortex. In order to perceive one coherent object, its features have to be bound together. This was found to be achieved in cat and monkey brains by temporal correlation of the firing rates of neurons which code the same object. This firing rate is predominantly observed in the gamma frequency range (approx. 30–80 Hz, mainly around 40 Hz). In addition, it has been shown in humans that stimuli which flicker at gamma frequencies are processed faster by our brains than when they flicker at different frequencies. These effects could be due to neural oscillators, which preferably oscillate at certain frequencies, so-called resonance frequencies. It is also known that neurons in visual cortex respond to flickering stimuli at the frequency of the flickering light. If neural oscillators exist with resonance frequencies, they should respond more strongly to stimulation with their resonance frequency. We performed an experiment, where ten human subjects were presented flickering light at frequencies from 1 to 100 Hz in 1-Hz steps. The event-related potentials exhibited steady-state oscillations at all frequencies up to at least 90 Hz. Interestingly, the steady-state potentials exhibited clear resonance phenomena around 10, 20, 40 and 80 Hz. This could be a potential neural basis for gamma oscillations in binding experiments. The pattern of results resembles that of multiunit activity and local field potentials in cat visual cortex.
Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree

We seek to improve deep neural networks by generalizing the pooling operations that play a central role in current architectures. We pursue a careful exploration of approaches to allow pooling to learn and to adapt to complex and variable patterns. The two primary directions lie in (1) learning a pooling function via (two strategies of) combining of max and average pooling, and (2) learning a pooling function in the form of a tree-structured fusion of pooling filters that are themselves learned. In our experiments every generalized pooling operation we explore improves performance when used in place of average or max pooling. We experimentally demonstrate that the proposed pooling operations provide a boost in invariance properties relative to conventional pooling and set the state of the art on several widely adopted benchmark datasets; they are also easy to implement, and can be applied within various deep neural network architectures. These benefits come with only a light increase in computational overhead during training (ranging from 5% to 15% in timing experiments) and a very modest increase in the number of model parameters. For example, using 45 additional parameters, we improve AlexNet performance on ImageNet by 6% relative (top-5, single-view).
Negative eigenvalues of the Hessian in deep neural networks

The loss function of deep networks is known to be non-convex but the precise nature of this nonconvexity is still an active area of research. In this work, we study the loss landscape of deep networks through the eigendecompositions of their Hessian matrix. In particular, we examine how important the negative eigenvalues are and the benefits one can observe in handling them appropriately.
Quantitative analysis of the effects of polarization and pattern reconfiguration for mmWave 5G mobile antenna prototypes

Real-time beam-steering of highly directive antennas at mmWave spectrum under unpredictable propagation environments remains a highly elusive challenge for the upcoming 5G era. Reconfigurable and multifunctional mobile 5G antenna and RF architectures which are desired to achieve sufficient QoS introduce additional technical and economic costs which may outweigh the benefits. This paper investigates the effects and relative significance of key antenna design parameters with QoS benchmarks based on real-life mmWave antennas integrated in fully operable 5G mobile device prototypes.
Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking

is paper proposes a new neural architecture for collaborative ranking with implicit feedback. Our model, LRML (Latent Relational Metric Learning) is a novel metric learning approach for recommendation. More specically, instead of simple push-pull mechanisms between user and item pairs, we propose to learn latent relations that describe each user item interaction. is helps to alleviate the potential geometric inexibility of existing metric learing approaches. is enables not only beer performance but also a greater extent of modeling capability, allowing our model to scale to a larger number of interactions. In order to do so, we employ a augmented memory module and learn to aend over these memory blocks to construct latent relations. e memory-based aention module is controlled by the user-item interaction, making the learned relation vector specic to each user-item pair. Hence, this can be interpreted as learning an exclusive and optimal relational translation for each user-item interaction. e proposed architecture demonstrates the state-of-the-art performance across multiple recommendation benchmarks. LRML outperforms other metric learning models by 6% − 7.5% in terms of Hits@10 and nDCG@10 on large datasets such as Netix and MovieLens20M. Moreover, qualitative studies also demonstrate evidence that our proposed model is able to infer and encode explicit sentiment, temporal and aribute information despite being only trained on implicit feedback. As such, this ascertains the ability of LRML to uncover hidden relational structure within implicit datasets.
Location Detection and Tracking of Moving Targets by a 2D IR-UWB Radar System

In indoor environments, the Global Positioning System (GPS) and long-range tracking radar systems are not optimal, because of signal propagation limitations in the indoor environment. In recent years, the use of ultra-wide band (UWB) technology has become a possible solution for object detection, localization and tracking in indoor environments, because of its high range resolution, compact size and low cost. This paper presents improved target detection and tracking techniques for moving objects with impulse-radio UWB (IR-UWB) radar in a short-range indoor area. This is achieved through signal-processing steps, such as clutter reduction, target detection, target localization and tracking. In this paper, we introduce a new combination consisting of our proposed signal-processing procedures. In the clutter-reduction step, a filtering method that uses a Kalman filter (KF) is proposed. Then, in the target detection step, a modification of the conventional CLEAN algorithm which is used to estimate the impulse response from observation region is applied for the advanced elimination of false alarms. Then, the output is fed into the target localization and tracking step, in which the target location and trajectory are determined and tracked by using unscented KF in two-dimensional coordinates. In each step, the proposed methods are compared to conventional methods to demonstrate the differences in performance. The experiments are carried out using actual IR-UWB radar under different scenarios. The results verify that the proposed methods can improve the probability and efficiency of target detection and tracking.
A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations

We have developed a robust qualitative method for robot expl oration, mapping, and navigation in large-scale spatial environments. An environment is large-scaleif its spatial structure is at a significantly larger scale than the sensory horizon of the observer. Experiments with a simulated robot in a variety of 2-D enviro nments have demonstrated that our method can build an accurate map of an unknown environment in spite o f substantial random and systematic sensorimotor error. Most current approaches to robot exploration and mapping an alyze sensor input to build a geometric map of the environment, then extract topological structure from the geometric description. sensors! geometry ! topology:
Abstraction and reformulation in artificial intelligence.

This paper contributes in two ways to the aims of this special issue on abstraction. The first is to show that there are compelling reasons motivating the use of abstraction in the purely computational realm of artificial intelligence. The second is to contribute to the overall discussion of the nature of abstraction by providing examples of the abstraction processes currently used in artificial intelligence. Although each type of abstraction is specific to a somewhat narrow context, it is hoped that collectively they illustrate the richness and variety of abstraction in its fullest sense.
Deep Visual Analogy-Making

In addition to identifying the content within a single image, relating images and generating related images are critical tasks for image understanding. Recently, deep convolutional networks have yielded breakthroughs in predicting image labels, annotations and captions, but have only just begun to be used for generating high-quality images. In this paper we develop a novel deep network trained end-to-end to perform visual analogy making, which is the task of transforming a query image according to an example pair of related images. Solving this problem requires both accurately recognizing a visual relationship and generating a transformed query image accordingly. Inspired by recent advances in language modeling, we propose to solve visual analogies by learning to map images to a neural embedding in which analogical reasoning is simple, such as by vector subtraction and addition. In experiments, our model effectively models visual analogies on several datasets: 2D shapes, animated video game sprites, and 3D car models.
Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition

Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved promising performance in sequential data modeling. The hidden layers in RNNs can be regarded as the memory units, which are helpful in storing information in sequential contexts. However, when dealing with high dimensional input data, such as video and text, the input-to-hidden linear transformation in RNNs brings high memory usage and huge computational cost. This makes the training of RNNs very difficult. To address this challenge, we propose a novel compact LSTM model, named as TR-LSTM, by utilizing the low-rank tensor ring decomposition (TRD) to reformulate the input-to-hidden transformation. Compared with other tensor decomposition methods, TRLSTM is more stable. In addition, TR-LSTM can complete an end-to-end training and also provide a fundamental building block for RNNs in handling large input data. Experiments on real-world action recognition datasets have demonstrated the promising performance of the proposed TR-LSTM compared with the tensortrain LSTM and other state-of-the-art competitors.
Reconnaissance d'événements et d'actions à partir de la profondeur thermique 3D. (Event and action recognition from thermal and 3D depth Sensing)

Modern computer vision algorithms try to understand the human activity using 3D visible sensors. However, there are inherent problems using 2D visible sensors as a data source. First, visible light images are sensitive to illumination changes and background clutter. Second, the 3D structural information of the scene is degraded when mapping the 3D scene to 2D images. Recently, the easy access to the RGBD data at real-time frame rate is leading to a revolution in perception and inspired many new research. Time of Flight (ToF) and multi-view sensors have been used to model the 3D structure of the scene. Otherwise, infrared thermography (IRT), also known as thermal imaging, is an ideal technology to investigate thermal anomalie under different circumstances because it provides complete thermal images of an object with no physical attachments (nonintrusive). IRT is now being introduced to a wide range of different applications, such as medical diagnostic and surveillance. However, finding meaningful features from a time series data from thermal video is still a challenging problem, especially for event detection. This problem is particularly hard due to enormous variations in visual and motion appearance of object, moving background, occlusions and thermal noise. In this thesis, we propose a framework for the detection of visual events in thermal video and 3d human actions in RGBD data. Despite differences in the applications, the associated fundamental problems share numerous properties, for instance the necessity of handling vision-based approach for the automatic recognition of events. The first part of the thesis deals with the recognition of events in thermal video. In this context, the use of time series is challenging due to the graphical nature which exposes hidden patterns and structural changes in data. In this study, we investigated the use of visual texture patterns for time series classification. Our principal aim was to develop a general framework for time series data mining based on event analysis with an application to the medical domain. In particular, we are interested to pain/no-pain detection using parametric statistics and shape descriptors in order to analyze and to classify time 2D distribution data sets. We first extracted automatically thermal-visual facial features from each face considered as the region of interest (ROI) of the image.
CGPA: Coarse-Grained Pipelined Accelerators

High-level synthesis (HLS) tools dramatically reduce the nonrecurring engineering cost of creating specialized hardware accelerators. Existing HLS tools are successful in synthesizing efficient accelerators for program kernels with regular memory accesses and simple control flows. For other programs, however, these tools yield poor performance because they invoke computation units for instructions sequentially, without exploiting parallelism. To address this problem, this paper proposes Coarse-Grained Pipelined Accelerators (CGPA), an HLS framework that utilizes coarsegrained pipeline parallelism techniques to synthesize efficient specialized accelerator modules from irregular C/C++ programs without requiring any annotations. Compared to the sequential method, CGPA shows speedups of 3.0x--3.8x for 5 kernels from programs in different domains.
SCL: Simplifying Distributed SDN Control Planes

We consider the following question: what consistency model is appropriate for coordinating the actions of a replicated set of SDN controllers? We first argue that the conventional requirement of strong consistency, typically achieved through the use of Paxos or other consensus algorithms, is conceptually unnecessary to handle unplanned network updates. We present an alternate approach, based on the weaker notion of eventual correctness, and describe the design of a simple coordination layer (SCL) that can seamlessly turn a set of single-image SDN controllers (that obey certain properties) into a distributed SDN system that achieves this goal (whereas traditional consensus mechanisms do not). We then show through analysis and simulation that our approach provides faster responses to network events. While our primary focus is on handling unplanned network updates, our coordination layer also handles policy updates and other situations where consistency is warranted. Thus, contrary to the prevailing wisdom, we argue that distributed SDN control planes need only be slightly more complicated than single-image controllers.
Decision-making and Management for Economic Development ” PU for

relationship between personality and job performance has been a frequently studied topic in industrial psychology in the past century (Barrick, Mount & Judge, 2001). Job performance is a multi-dimensional construct which indicates how well employees perform their tasks, the initiative they take and the resourcefulness they show in solving problems. Furthermore, it indicates the extent to which they complete tasks, the way they utilise their available resources and the time and energy they, and by dispositional factors. Dispositional variables can be described as personality characteristics, needs, attitudes, preferences and motives that result in a tendency to react to situations in a predetermined (predisposed) manner (House, Shane & Herrold, 1996). Job performance is influenced by aptitude, need for achievement, self-regard, locus of control, affective temperament and the interaction between these Traditionally industrial psychologists have questioned the usefulness of personality measures in predicting job-related criteria (such as job performance), because of pessimistic conclusions of early reviews of the topic (e.g. Guion & Gottier, 1965) and concerns that most personality measures are faked (Reilly & Warech, 1993). However, evidence has suggested that personality measures are valid predictors of diverse job-related criteria (Goldberg, 1993). Unlike many measures of cognitive ability, personality measures typically do not have an adverse impact on disadvantaged employees (Hogan, Hogan & Roberts, 1996) and thus can enhance fairness in personnel decisions. Recent research showed that personality dimensions are related In this research the relationship between personality dispositions and job performance is studied from a trait perspective, and more specifically the five-factor model of personality dimensions as conceptualised by Costa and McCrae (1992). The five-factor model of personality represents a structure of traits, developed and elaborated over the last five decades. Factors are defined by groups of intercorrelated traits, which are referred to as facets (McCrae & Costa, 1997). The five-factor model of personality as measured by the Neo-Personality Inventory Revised (NEO-PI-R) includes Neuroticism, Extraversion, Openness, Agreeableness and Conscientiousness (McCrae & Costa, 1997). The reason for deciding on this conceptualisation is because the validity of broad personality dimensions is superior too narrowly defined dimensions (Ashton, 1998). showed that various big five personality dimensions are related to job performance. concluded that Extraversion and Conscientiousness predict job performance in various occupations. However, these studies have all been carried out elsewhere in the world and in other contexts. In South Africa, the use of psychometric tests in studies of job performance is still a …