nils-herrmann/clean_scirepeval huggingface dataset:
A sparse-response deep belief network based on rate distortion theory

Deep belief networks (DBNs) are currently the dominant technique for modeling the architectural depth of brain, and can be trained efficiently in a greedy layer-wise unsupervised learning manner. However, DBNs without a narrow hidden bottleneck typically produce redundant, continuous-valued codes and unstructured weight patterns. Taking inspiration from rate distortion (RD) theory, which encodes original data using as few bits as possible, we introduce in this paper a variant of DBN, referred to as sparse-response DBN (SR-DBN). In this approach, Kullback–Leibler divergence between the distribution of data and the equilibrium distribution defined by the building block of DBN is considered as a distortion function, and the sparse response regularization induced by L1-norm of codes is used to achieve a small code rate. Several experiments by extracting features from different scale image datasets show that our approach SR-DBN learns codes with small rate, extracts features at multiple levels of abstraction mimicking computations in the cortical hierarchy, and obtains more discriminative representation than PCA and several basic algorithms of DBNs. & 2014 Elsevier Ltd. All rights reserved.
Image Caption with Global-Local Attention

Image caption is becoming important in the field of artificial intelligence. Most existing methods based on CNN-RNN framework suffer from the problems of object missing and misprediction due to the mere use of global representation at image-level. To address these problems, in this paper, we propose a global-local attention (GLA) method by integrating local representation at object-level with global representation at image-level through attention mechanism. Thus, our proposed method can pay more attention to how to predict the salient objects more precisely with high recall while keeping context information at image-level cocurrently. Therefore, our proposed GLA method can generate more relevant sentences, and achieve the state-of-the-art performance on the well-known Microsoft COCO caption dataset with several popular metrics.
Quantum clustering algorithms

By the term "quantization", we refer to the process of using quantum mechanics in order to improve a classical algorithm, usually by making it go faster. In this paper, we initiate the idea of quantizing clustering algorithms by using variations on a celebrated quantum algorithm due to Grover. After having introduced this novel approach to unsupervised learning, we illustrate it with a quantized version of three standard algorithms: divisive clustering, k-medians and an algorithm for the construction of a neighbourhood graph. We obtain a significant speedup compared to the classical approach.
Three-Dimensional Synthetic Aperture Radar Imaging Through Multilayered Walls

Most of existing through-the-wall radar imaging (TWRI) algorithms deal with a single layer homogeneous wall in two-dimensional scenario. In this communication we present a generalized three-dimensional (3-D) beamforming algorithm for the focused imaging of targets behind multilayered building walls. The far field layered medium Green's function is incorporated in the 3-D beamformer for the compensation of the wall effect. 3-D polarimetric imaging is exploited in TWRI for enhanced target identification and feature extraction as well as wall effect mitigation. Numerical results show that the proposed 3-D beamformer provides high quality focused images in various wall-target scenarios.
Investigating the correspondence between driver head position and glance location

The relationship between a driver’s glance orientation and corresponding head rotation is highly complex due to its nonlinear dependence on the individual, task, and driving context. This paper presents expanded analytic detail and findings from an effort that explored the ability of head pose to serve as an estimator for driver gaze by connecting head rotation data withmanually coded gaze region data using both a statistical analysis approach and a predictive (i.e.,machine learning) approach. For the latter, classification accuracy increased as visual angles between two glance locations increased. In other words, the greater the shift in gaze, the higher the accuracy of classification. This is an intuitive but important concept that wemake explicit through our analysis. The highest accuracy achieved was 83% using the method of Hidden Markov Models (HMM) for the binary gaze classification problem of (a) glances to the forward roadway versus (b) glances to the center stack. Results suggest that although there are individual differences in head-glance correspondence while driving, classifier models based on head-rotation data may be robust to these differences and therefore can serve as reasonable estimators for glance location. The results suggest that driver head pose can be used as a surrogate for eye gaze in several key conditions including the identification of high-eccentricity glances. Inexpensive driver head pose tracking may be a key element in detection systems developed to mitigate driver distraction and inattention. Subjects Human-Computer Interaction, Data Mining and Machine Learning
Automated Detection of Arbitrarily Shaped Buildings in Complex Environments From Monocular VHR Optical Satellite Imagery

This paper introduces a new approach for the automated detection of buildings from monocular very high resolution (VHR) optical satellite images. First, we investigate the shadow evidence to focus on building regions. To do that, we propose a new fuzzy landscape generation approach to model the directional spatial relationship between buildings and their shadows. Once all landscapes are collected, a pruning process is developed to eliminate the landscapes that may occur due to non-building objects. The final building regions are detected by GrabCut partitioning approach. In this paper, the input requirements of the GrabCut partitioning are automatically extracted from the previously determined shadow and landscape regions, so that the approach gained an efficient fully automated behavior for the detection of buildings. Extensive experiments performed on 20 test sites selected from a set of QuickBird and Geoeye-1 VHR images showed that the proposed approach accurately detects buildings with arbitrary shapes and sizes in complex environments. The tests also revealed that even under challenging environmental and illumination conditions, reasonable building detection performances could be achieved by the proposed approach.
SDN and OpenFlow for Dynamic Flex-Grid Optical Access and Aggregation Networks

We propose and discuss the extension of software-defined networking (SDN) and OpenFlow principles to optical access/aggregation networks for dynamic flex-grid wavelength circuit creation. The first experimental demonstration of an OpenFlow1.0-based flex-grid λ-flow architecture for dynamic 150 Mb/s per-cell 4 G Orthogonal Frequency Division Multiple Access (OFDMA) mobile backhaul (MBH) overlays onto 10 Gb/s passive optical networks (PON) without optical network unit (ONU)-side optical filtering, amplification, or coherent detection, over 20 km standard single mode fiber (SSMF) with a 1:64 passive split is also detailed. The proposed approach can be attractive for monetizing optical access/aggregation networks via on-demand support for high-speed, low latency, high quality of service (QoS) applications over legacy fiber infrastructure.
Development of prosthetic arm with pneumatic prosthetic hand and tendon-driven wrist

Recently, various prosthetic arms have been developed, but few are both attractive and functional. Considering human coexistence, prosthetic arms must be both safe and flexible. In this research, we developed a novel prosthetic arm with a five-fingered prosthetic hand using our original pneumatic actuators and a slender tendon-driven wrist using a wire drive and two small motors. Because the prosthetic hand’s driving source is comprised of small pneumatic actuators, the prosthetic hand is safe when it makes contact with people; it can also operate flexibly. In addition, the arm has a tendon-driven wrist to expand its motion space and to perform many operations. First, we explain the pneumatic hand’s drive mechanism and its tendon-driven wrist. Next, we identify the characteristics of the hand and the wrist and construct a control system for this arm and verify its control performance.
The long tail of recommender systems and how to leverage it

The paper studies the Long Tail problem of recommender systems when many items in the Long Tail have only few ratings, thus making it hard to use them in recommender systems. The approach presented in the paper splits the whole itemset into the head and the tail parts and clusters only the tail items. Then recommendations for the tail items are based on the ratings in these clusters and for the head items on the ratings of individual items. If such partition and clustering are done properly, we show that this reduces the recommendation error rates for the tail items, while maintaining reasonable computational performance.
Plantar fasciopathy: revisiting the risk factors.

Plantar fasciopathy is the most common cause of acquired sub-calcaneal heel pain in adults. To-date, research of this condition has mainly focused on management rather than causal mechanisms. The aetiology of plantar fasciopathy is likely to be multifactorial, as both intrinsic and extrinsic risk factors have been reported. The purpose of this review is to critically reevaluate risk factors for plantar fasciopathy.



A detailed literature review was undertaken using English language medical databases.



No clear consensus exists as to the relative strength of the risk factors reported.



To-date numerous studies have examined various intrinsic and extrinsic risk factors implicated in the aetiology of plantar fasciopathy. How these factors interact may provide useful data to establish an individuals' risk profile for plantar fasciopathy and their potential for response to treatment. Further research is indicated to rank the relative significance of these risk factors.
CSRR loaded Sierpinski gasket fractal antenna for multiband applications

The CSRR loaded Sierpinski fractal antenna for multiband applications are presented. This model fairs that the multiband presence of the Sierpinski fractal antenna is a fallout of its fractal nature. The antenna consists of iterations of sierpinski gasket with a complementary split ring resonator. In plan to provoke multi resonant frequency band aspect, these CSRR are implanted back side of the substrate. The simulated results offers a three operating bands, covering C band at 5.72 GHz, Ku band at 14.3 GHz and 16.06 GHz. The size of the CSRR loaded Gasket antenna is 14 mm × 12 mm × 1.6 mm3 printed on FR4 substrate. The parametric review and simulated radiation component are presented.
Towards HMD-based Immersive Analytics

Advances in 3D hardware and software have led to increasingly cheaper and simple-to-use immersive virtual reality systems that can provide real-time interactive 3D data representation. The immersive analytics field is developing as the newest avatar of 3D visual analytics, which may relaunch the long enduring 2D vs 3D visualization debate. However, the terms of the debate have changed: leveraging 3D human perception within virtual environments is now easier, and the immersive quality of today’s rendering is sufficient enough for researchers to concentrate on testing and designing immersive data representation and interaction rather than on technological problems. In this position paper we propose a short historical perspective on the use of immersive technologies for visual analytics and on the 2D vs 3D debate. We stress out five principles that we think should be followed to explore the HMD-based visual analytics design space, before introducing ongoing work within the IDEA project. CCS CONCEPTS • Human-Centered Computing→ Virtual Reality;Visual Analytics;Information visualisation
Blended Interaction : Toward a Framework for the Design of Interactive Spaces

In this paper, we propose Blended Interaction as a conceptual framework for the design of interactive spaces. We argue that for realizing a natural computer-supported collaboration in smart environments or interactive spaces, designers must achieve a holistic understanding and design of the users’ individual interactions, social interactions, workflows and their physical environment. To thoughtfully blend the power of the digital world with the users’ pre-existing skills and practices, we propose and explain conceptual blending as a potential design methodology. We illustrate our framework by discussing related theoretical and conceptual work and by explaining the design decisions we made in recent projects. In particular, we highlight how Blended Interaction introduces a new and more accurate description of users’ cognition and interaction in interactive spaces that can serve as a tool for HCI researchers and interaction designers.
Sentiment analysis and classification based on textual reviews

Mining is used to help people to extract valuable information from large amount of data. Sentiment analysis focuses on the analysis and understanding of the emotions from the text patterns. It identifies the opinion or attitude that a person has towards a topic or an object and it seeks to identify the viewpoint underlying a text span. Sentiment analysis is useful in social media monitoring to automatically characterize the overall feeling or mood of consumers as reflected in social media toward a specific brand or company and determine whether they are viewed positively or negatively on the web. This new form of analysis has been widely adopted in customer relation management especially in the context of complaint management. For automating the task of classifying a single topic textual review, document-level sentiment classification is used for expressing a positive or negative sentiment. So analyzing sentiment using Multi-theme document is very difficult and the accuracy in the classification is less. The document level classification approximately classifies the sentiment using Bag of words in Support Vector Machine (SVM) algorithm. In proposed work, a new algorithm called Sentiment Fuzzy Classification algorithm with parts of speech tags is used to improve the classification accuracy on the benchmark dataset of Movies reviews dataset.
Single carrier FDMA for uplink wireless transmission

Single carrier frequency division multiple access (SC FDMA), a modified form of orthogonal FDMA (OFDMA), is a promising technique for high data rate uplink communications in future cellular systems. SC-FDMA has similar throughput performance and essentially the same overall complexity as OFDMA. A principal advantage of SC-FDMA is the peak-to-average power ratio (PARR), which is lower than that of OFDMA. SC FDMA is currently a strong candidate for the uplink multiple access scheme in the long term evolution of cellular systems under consideration by the third generation partnership project (3GPP). In this paper, we give an overview of SC-FDMA. We also analyze the effects of subcarrier mapping on throughput and PARR. Among the possible subcarrier mapping approaches, we find that localized FDMA (LFDMA) with channel-dependent scheduling (CDS) results in higher throughput than interleaved FDMA (JFDMA). However, the PARR performance of IFDMA is better than that of LFDMA. As in other communications systems there are complex tradeoffs between design parameters and performance in an SC-FDMA system
Robust Sensing of Distal Pulse Waveforms on a Modified Weighing Scale for Ubiquitous Pulse Transit Time Measurement

The measurement of aortic pulse transit time (PTT), the time for the arterial pulse wave to travel from the carotid to the femoral artery, can provide valuable insight into cardiovascular health, specifically regarding arterial stiffness and blood pressure (BP). To measure aortic PTT, both proximal and distal arterial pulse timings are required. Recently, our group has demonstrated that the ballistocardiogram signal measured on a modified weighing scale can provide an unobtrusive, yet accurate, means of obtaining a proximal timing reference; however, there are no convenient, reliable methods to extract the distal timing from a subject standing on the modified weighing scale. It is common to use a photoplethysmograph (PPG) attached to a toe to measure this distal pulse, but we discovered that this signal is greatly deteriorated as the subject stands on the scale. In this paper, we propose a novel method to measure the distal pulse using a custom reflective PPG array attached to the dorsum side of the foot (D-PPG). A total of 12 subjects of varying skin tones were recruited to assess the preliminary validation of this approach. Pulse measurements using the D-PPG were taken from seated and standing subjects, and the commercially available PPG were measured for facilitating comparison of timing measurements. We show that the D-PPG was the only sensor to retain the high detection rate of feasible timing values. To further test and optimize the system, various factors such as applied pressure, measurement location, and LED/photodiode configuration were tested.
Robust Abandoned Object Detection Using Dual Foregrounds

As an alternative to the tracking-based approaches that heavily depend on accurate detection of moving objects, which often fail for crowded scenarios, we present a pixelwise method that employs dual foregrounds to extract temporally static image regions. Depending on the application, these regions indicate objects that do not constitute the original background but were brought into the scene at a subsequent time, such as abandoned and removed items, illegally parked vehicles. We construct separate longand short-term backgrounds that are implemented as pixelwise multivariate Gaussian models. Background parameters are adapted online using a Bayesian update mechanism imposed at different learning rates. By comparing each frame with these models, we estimate two foregrounds. We infer an evidence score at each pixel by applying a set of hypotheses on the foreground responses, and then aggregate the evidence in time to provide temporal consistency. Unlike optical flow-based approaches that smear boundaries, our method can accurately segment out objects even if they are fully occluded. It does not require on-site training to compensate for particular imaging conditions. While having a low-computational load, it readily lends itself to parallelization if further speed improvement is necessary.
Tunneling Transistors Based on Graphene and 2-D Crystals

As conventional transistors become smaller and thinner in the quest for higher performance, a number of hurdles are encountered. The discovery of electronic-grade 2-D crystals has added a new “layer” to the list of conventional semiconductors used for transistors. This paper discusses the properties of 2-D crystals by comparing them with their 3-D counterparts. Their suitability for electronic devices is discussed. In particular, the use of graphene and other 2-D crystals for interband tunneling transistors is discussed for low-power logic applications. Since tunneling phenomenon in reduced dimensions is not conventionally covered in texts, the physics is developed explicitly before applying it to transistors. Though we are in an early stage of learning to design devices with 2-D crystals, they have already been the motivation behind a list of truly novel ideas. This paper reviews a number of such ideas.
An Ontology-Based Approach to Model-Driven Software Product Lines

Software development in highly variable domains constrained by tight regulations and with many business concepts involved results in hard to deliver and maintain applications, due to the complexity of dealing with the large number of concepts provided by the different parties and system involved in the process. One way to tackle these problems is thru combining software product lines and model-driven software development supported by ontologies. Software product lines and model-driven approaches would promote reuse on the software artifacts and, if supported by an ontological layer, those artifacts would be domain-validated. We intend to create a new conceptual framework for software development with domain validated models in highly variable domains. To define such a framework we will propose a model that relates several dimensions and areas of software development thru time and abstraction levels. This model would guarantee to the software house traceability of components, domain validated artifacts, easy to maintain and reusable components, due to the relations and mappings we propose to establish in the conceptual framework, between the software artifacts and the ontology.
Vision-based intelligent vehicles: State of the art and perspectives

Recently, a large emphasis has been devoted to Automatic Vehicle Guidance since the automation of driving tasks carries a large number of benefits, such as the optimization of the use of transport infrastructures, the improvement of mobility, the minimization of risks, travel time, and energy consumption. This paper surveys the most common approaches to the challenging task of Autonomous Road Following reviewing the most promising experimental solutions and prototypes developed worldwide using AI techniques to perceive the environmental situation by means of artificial vision. The most interesting results and trends in this field as well as the perspectives on the evolution of intelligent vehicles in the next decades are also sketched out.
A brief review: factors affecting the length of the rest interval between resistance exercise sets.

Research has indicated that multiple sets are superior to single sets for maximal strength development. However, whether maximal strength gains are achieved may depend on the ability to sustain a consistent number of repetitions over consecutive sets. A key factor that determines the ability to sustain repetitions is the length of rest interval between sets. The length of the rest interval is commonly prescribed based on the training goal, but may vary based on several other factors. The purpose of this review was to discuss these factors in the context of different training goals. When training for muscular strength, the magnitude of the load lifted is a key determinant of the rest interval prescribed between sets. For loads less than 90% of 1 repetition maximum, 3-5 minutes rest between sets allows for greater strength increases through the maintenance of training intensity. However, when testing for maximal strength, 1-2 minutes rest between sets might be sufficient between repeated attempts. When training for muscular power, a minimum of 3 minutes rest should be prescribed between sets of repeated maximal effort movements (e.g., plyometric jumps). When training for muscular hypertrophy, consecutive sets should be performed prior to when full recovery has taken place. Shorter rest intervals of 30-60 seconds between sets have been associated with higher acute increases in growth hormone, which may contribute to the hypertrophic effect. When training for muscular endurance, an ideal strategy might be to perform resistance exercises in a circuit, with shorter rest intervals (e.g., 30 seconds) between exercises that involve dissimilar muscle groups, and longer rest intervals (e.g., 3 minutes) between exercises that involve similar muscle groups. In summary, the length of the rest interval between sets is only 1 component of a resistance exercise program directed toward different training goals. Prescribing the appropriate rest interval does not ensure a desired outcome if other components such as intensity and volume are not prescribed appropriately.