nils-herrmann/clean_scirepeval huggingface dataset:
Entorhinal cortex of the rat: topographic organization of the cells of origin of the perforant path projection to the dentate gyrus.

By using three-dimensional computer reconstruction techniques and the production of two-dimensional unfolded maps, we analyzed the topographic organization of projections from the entorhinal cortex of the rat to the dentate gyrus. The retrograde tracers, Fast blue and Diamidino yellow, were injected at all septotemporal levels of the dentate gyrus, and the distribution of retrogradely labeled layer II cells in the entorhinal cortex was plotted by using computer-aided microscopy systems. Discrete injections of fluorescent dyes into the dentate gyrus labeled bands of layer II neurons in the entorhinal cortex that covered approximately 45% of its surface area. Injections confined to the septal half of the dentate gyrus resulted in a band that occupied the most lateral and caudomedial portions of the entorhinal cortex. Although there were subtle changes in the density of labeled cells in this region, essentially the same region of cells was labeled after any injection into the septal half of the dentate gyrus. Injections into mid-septotemporal levels of the dentate gyrus (50-75% of the distance from the septal pole) led to a distinctly different pattern of retrograde labeling. A more medial portion of the lateral entorhinal cortex and a more rostral portion of the medial entorhinal area were labeled in these cases. Another change in entorhinal labeling occurred when the injection involved the most temporal quarter of the dentate gyrus. Injections into this area led to a constrained region of entorhinal labeling that included the most medial portion of the lateral entorhinal area and the most rostral portion of the medial entorhinal area. Although the domains of cells projecting to septal, mid-septotemporal, and temporal levels of the dentate gyrus were not entirely segregated, there was relatively little overlap of the three populations of neurons. These data raise the possibility that different portions of the entorhinal-hippocampal circuit are capable of semiautonomous information processing, at least at the stage of input to the dentate gyrus.
More Accurate Entity Ranking Using Knowledge Graph and Web Corpus

Recent years have witnessed some convergence in the architecture of entity search systems driven by a knowledge graph (KG) and a corpus with annotated entity mentions. However, each specific system has some limitations. We present AQQUCN, an entity search system that combines the best design principles into a public reference implementation. AQQUCN does not depend on well-formed question syntax, but works equally well with syntax-poor keyword queries. It uses several convolutional networks (convnets) to extract subtle, overlapping roles of query words. Instead of ranking structured query interpretations, which are then executed on the KG to return unranked sets, AQQUCN directly ranks response entities, by closely integrating coarse-grained predicates from the KG with fine-grained scoring from the corpus, into a single ranking model. Over and above competitive F1 score, AQQUCN gets the best entity ranking accuracy on two syntax-rich and two syntaxpoor public query workloads amounting to over 8,000 queries, with 16– 18% absolute improvement in mean average precision (MAP), compared to recent systems.
Modality-convolutions: Multi-modal gesture recognition based on convolutional neural network

We proposed a novel method of feature extraction for multi-modal images called modality-convolution. It extracts both the intra- and inter-modality information. Whats more, it completes the data fusion at pixel-level so that the complementarity of information contained in multi-modal data is fully utilized. Based on the modality-convolution, we describe a modality-CNN for multi-modal gesture recognition. For extracting the features in RGB-D images, the modality-CNN is adopted in the gesture recognition framework. The framework use DBN to present the skeleton data. Then, the probability obtained by the two networks are fused and put into the HMM to carry out dynamic gestures classification. We use the Jaccar Index to calculate the accuracy of gesture recognition. A comparative experiment on ChaLearn LAP 2014 gesture datasets shows that the modality-convolution is able to extract the inter- and intra-modality information effectively, which is helpful to improve the accuracy.
Nutritional Recommendations for Cardiovascular Disease Prevention

Lifestyle factors, including nutrition, play an important role in the etiology of Cardiovascular Disease (CVD). This position paper, written by collaboration between the Israel Heart Association and the Israel Dietetic Association, summarizes the current, preferably latest, literature on the association of nutrition and CVD with emphasis on the level of evidence and practical recommendations. The nutritional information is divided into three main sections: dietary patterns, individual food items, and nutritional supplements. The dietary patterns reviewed include low carbohydrate diet, low-fat diet, Mediterranean diet, and the DASH diet. Foods reviewed in the second section include: whole grains and dietary fiber, vegetables and fruits, nuts, soy, dairy products, alcoholic drinks, coffee and caffeine, tea, chocolate, garlic, and eggs. Supplements reviewed in the third section include salt and sodium, omega-3 and fish oil, phytosterols, antioxidants, vitamin D, magnesium, homocysteine-reducing agents, and coenzyme Q10.
A Review on Fault Diagnosis of Induction Motor using Artificial Neural Networks

Different alternatives to detect and diagnose faults in induction machines have been proposed and implemented in the last years. The technology of artificial neural networks has been successfully used to solve the motor incipient fault detection problem. The characteristics, obtained by this technique, distinguish them from the traditional ones, which, in most cases, need that the machine which is being analyzed is not working to do the diagnosis. This paper reviews an artificial neural network (ANN) based technique to identify rotor faults in a three-phase induction motor. The main types of faults considered are broken bar and dynamic eccentricity. At light load, it is difficult to distinguish between healthy and faulty rotors because the characteristic broken rotor bar fault frequencies are very close to the fundamental component and their amplitudes are small in comparison. As a result, detection of the fault and classification of the fault severity under light load is almost impossible. In order to overcome this problem, the detection of rotor faults in induction machines is done by analysing the starting current using a newly developed quantification technique based on artificial neural networks.
A community-based approach to identify the most influential nodes in social networks

One of the important issues concerning the spreading process in social networks is the influence maximization. This is the problem of identifying the set of the most influential nodes in order to begin the spreading process based on an information diffusion model in the social networks. In this study, two new methods considering the community structure of the social networks and influence-based closeness centrality measure of the nodes are presented to maximize the spread of influence on the multiplication threshold, minimum threshold and linear threshold information diffusion models. The main objective of this study is to improve the efficiency with respect to the run time while maintaining the accuracy of the final influence spread. Efficiency improvement is obtained by reducing the number of candidate nodes subject to evaluation in order to find the most influential. Experiments consist of two parts: first, the effectiveness of the proposed influence-based closeness centrality measure is established by comparing it with available centrality measures; second, the evaluations are conducted to compare the two proposed community-based methods with well-known benchmarks in the literature on the real datasets, leading to the results demonstrate the efficiency and effectiveness of these methods in maximizing the influence spread in social networks.
Position-based rigid-body dynamics

We propose a position-based approach for largescale simulations of rigid bodies at interactive frame-rates. Our method solves positional constraints between rigid bodies and therefore integrates nicely with other position-based methods. Interaction of particles and rigid bodies through common constraints enables two-way coupling with deformables. The method exhibits exceptional performance and stability while being user-controllable and easy to implement. Various results demonstrate the practicability of our method for the resolution of collisions, contacts, stacking and joint constraints.
VELOCITY DEPENDENCE OF JOINT FRICTION IN ROBOTIC MANIPULATORS WITH GEAR TRANSMISSIONS

This paper analyses the problem of modelling joint friction in robotic manipulators with gear transmissions at joint velocities varying from close to zero until their maximum appearing values. It is shown that commonly used friction models that incorporate Coulomb, (linear) viscous and Stribeck components are inadequate to describe the friction behaviour for the full velocity range. A new friction model is proposed that relies on insights from tribological models. The basic friction model of two lubricated discs in rolling-sliding contact is used to analyse viscous friction and friction caused by asperity contacts inside gears and roller bearings of robot joint transmissions. The analysis shows different viscous friction behaviour for gears and pre-stressed bearings. The sub-models describing the viscous friction and the friction due to the asperity contacts are combined into two friction models; one for gears and one for the pre-stressed roller bearings. In this way, a new friction model  is developed that accurately describes the friction behaviour in the sliding regime with a minimal and physically sound parametrisation. The model is linear in the parameters that are temperature dependent, which allows to estimate these parameters during the inertia parameter identification experiments. The model, in which the Coulomb friction effect has disappeared, has the same number of parameters as the commonly used Stribeck model . The model parameters are identified experimentally on a Stäubli RX90 industrial robot.
Mirage: Toward a Stealthier and Modular Malware Analysis Sandbox for Android

Nowadays, malware is affecting not only PCs but also mobile devices, which became pervasive in everyday life. Mobile devices can access and store personal information (e.g., location, photos, and messages) and thus are appealing to malware authors. One of the most promising approach to analyze malware is by monitoring its execution in a sandbox (i.e., via dynamic analysis). In particular, most malware sandboxing solutions for Android rely on an emulator, rather than a real device. This motivates malware authors to include runtime checks in order to detect whether the malware is running in a virtualized environment. In that case, the malicious app does not trigger the malicious payload. The presence of differences between real devices and Android emulators started an arms race between security researchers and malware authors, where the former want to hide these differences and the latter try to seek them out. In this paper we present Mirage, a malware sandbox architecture for Android focused on dynamic analysis evasion attacks. We designed the components of Mirage to be extensible via software modules, in order to build specific countermeasures against such attacks. To the best of our knowledge, Mirage is the first modular sandbox architecture that is robust against sandbox detection techniques. As a representative case study, we present a proof of concept implementation of Mirage with a module that tackles evasion attacks based on sensors API return values.
A Model-Based Method for Remaining Useful Life Prediction of Machinery

Remaining useful life (RUL) prediction allows for predictive maintenance of machinery, thus reducing costly unscheduled maintenance. Therefore, RUL prediction of machinery appears to be a hot issue attracting more and more attention as well as being of great challenge. This paper proposes a model-based method for predicting RUL of machinery. The method includes two modules, i.e., indicator construction and RUL prediction. In the first module, a new health indicator named weighted minimum quantization error is constructed, which fuses mutual information from multiple features and properly correlates to the degradation processes of machinery. In the second module, model parameters are initialized using the maximum-likelihood estimation algorithm and RUL is predicted using a particle filtering-based algorithm. The proposed method is demonstrated using vibration signals from accelerated degradation tests of rolling element bearings. The prediction result identifies the effectiveness of the proposed method in predicting RUL of machinery.
Advances in Wafer Level Packaging (WLP)

Wafer level packages (WLPs) with various design configurations are rapidly gaining tremendous applications throughout semiconductor industry due to small-form factor, low-cost, and high performance. Because of the innovative production processes utilized in WLP manufacturing and the accompanying rise in the price of gold, the traditional wire bonding packages are no longer as attractive as they used to be. In addition, WLPs provide the smallest form factor to satisfy multifunctional device requirements along with improved signal integrity for today’s handheld electronics. Existing wire bonding devices can be easily converted to WLPs by adding a redistribution layer (RDL) during backend wafer level processing. Since the input/output (I/O) pads do not have to be routed to the perimeter of the die, the WLP die can be designed to have a much smaller footprint as compared to its wire bonding counterpart, which means more area-array dies can be packed onto a single wafer to reduce overall processing costs per die. Conventional (fan-in) WLPs are formed on the dies while they are still on the uncut wafer. The result is that the final packaged product is the same size as the die itself. Recently, fan-out WLPs have emerged. Fan-out WLP starts with the reconstitution or reconfiguration of individual dies to an artificial molded wafer. Fan-out WLPs eliminate the need of expensive substrate as in flip-chip packages, while expanding the WLP size with molding compound for higher I/O applications without compromising on the board level reliability. Essentially, WLP enables the next generation of portable electronics at a competitive price. Many future products using through-silicon-via (TSV) technology will be packaged as WLPs. There have been relatively few publications focused on the latest results of WLP development and research. Many design guidelines, such as material selection and geometry dimensions of under bump metallurgy (UBM), RDL, passivation and solder alloy, for optimum board level reliability performance of WLPs, are still based on technical know-how gained from flip-chip or wire bonding BGA reliability studies published in the past two decades. However, WLPs have their unique product requirements for design guidelines, process conditions, material selection, reliability tests, and failure analysis. In addition, WLP is also an enabling technology for 3D package and system-in-package (SIP), justifying significant research attention. The timing is therefore ripe for this edition to summarize the state-of-the-art research advances in wafer level packaging in various fields of interest. Integration of WLP in 3D packages with TSV or wireless proximity communication (PxC), as well as applications in Microelectromechanical Systems (MEMS) packaging and power packaging, will be highlighted in this issue. In addition, the stateof-the-art simulation is applied to design for enhanced package and board level reliability of WLPs, including thermal cycling test,
Design and development of wireless sensor node for anti-poaching

Smuggling/Theft of Trees has led to an increase risk of Natural Resource getting extinguished. Animals are losing their Natural Habitat, thus causing a total imbalance in  The theft of trees causes a huge economic loss to people who grow trees for a livelihood. This paper proposes a microcontroller based anti-poaching system employing a WSN protocol, which is capable of detecting theft by monitoring vibrations produced while cutting trees using a MEMS accelerometer. A low power msp430f5528 microcontroller is used along with 2.4 GHz CC2500 transreceiver to communicate to a central Base Station from a unknown location where activity is detected. WSN is widely used technology for monitoring application to reduce the limitation of transmitting capacity of a communication module and also to reduce battery consumtion of device to extend Battery life. The embedded system architecture and the hardware/software designs are described in detail.
Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey

Deep learning has recently achieved very promising results in a wide range of areas such as computer vision, speech recognition and natural language processing. It aims to learn hierarchical representations of data by using deep architecture models. In a smart city, a lot of data (e.g. videos captured from many distributed sensors) need to be automatically processed and analyzed. In this paper, we review the deep learning algorithms applied to video analytics of smart city in terms of different research topics: object detection, object tracking, face recognition, image classification and scene labeling.
Dynamic Zoom-in Network for Fast Object Detection in Large Images

We introduce a generic framework that reduces the computational cost of object detection while retaining accuracy for scenarios where objects with varied sizes appear in high resolution images. Detection progresses in a coarse-to-fine manner, first on a down-sampled version of the image and then on a sequence of higher resolution regions identified as likely to improve the detection accuracy. Built upon reinforcement learning, our approach consists of a model (R-net) that uses coarse detection results to predict the potential accuracy gain for analyzing a region at a higher resolution and another model (Q-net) that sequentially selects regions to zoom in. Experiments on the Caltech Pedestrians dataset show that our approach reduces the number of processed pixels by over 50% without a drop in detection accuracy. The merits of our approach become more significant on a high resolution test set collected from YFCC100M dataset, where our approach maintains high detection performance while reducing the number of processed pixels by about 70% and the detection time by over 50%.
Real-time traffic classification with Twitter data mining

The growth of vehicles in Yogyakarta Province, Indonesia is not proportional to the growth of roads. This problem causes severe traffic jam in many main roads. Common traffic anomalies detection using surveillance camera requires manpower and costly, while traffic anomalies detection with crowdsourcing mobile applications are mostly owned by private. This research aims to develop a real-time traffic classification by harnessing the power of social network data, Twitter. In this study, Twitter data are processed to the stages of preprocessing, feature extraction, and tweet classification. This study compares classification performance of three machine learning algorithms, namely Naive Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT). Experimental results show that SVM algorithm produced the best performance among the other algorithms with 99.77% and 99.87% of classification accuracy in balanced and imbalanced data, respectively. This research implies that social network service may be used as an alternative source for traffic anomalies detection by providing information of traffic flow condition in real-time.
Marble MLFQ: An educational visualization tool for the multilevel feedback queue algorithm

Understanding the behaviour of algorithms is a key element of computer science. However, this learning objective is not always easy to achieve, as the behaviour of some algorithms is complicated or not readily observable, or affected by the values of their input parameters. To assist students in learning the multilevel feedback queue scheduling algorithm (MLFQ), we designed and developed an interactive visualization tool, Marble MLFQ, that illustrates how the algorithm works under various conditions. The tool is intended to supplement course material and instructions in an undergraduate operating systems course. The main features of Marble MLFQ are threefold: (1) It animates the steps of the scheduling algorithm graphically to allow users to observe its behaviour; (2) It provides a series of lessons to help users understand various aspects of the algorithm; and (3) It enables users to customize input values to the algorithm to support exploratory learning.
Compact, Dual-Polarized UWB-Antenna, Embedded in a Dielectric

A compact dual-polarized antenna is described for ultrawideband (UWB) applications. The main features of the antenna, besides an ultrawidebandwidth are low cross-polarization and small dimensions. The feed of the antenna is based on the tapered slot antennas, enclosed in a dielectric. The new antenna concept, including the feed of the antenna for dual-polarization and its integration, is described in detail. Prototypes are shown and their performances are demonstrated, based on simulation and measurement results in frequency and time domain. The antenna prototype input match is better than -10 dB, a maximal gain of 10.5 dBi and a mean polarization decoupling of approximately 17 dB in the main beam direction in the 3.1-10.6 GHz UWB band are achieved. In the time domain a peak value of 0.4 m/ns, a full width at half maximum of 100 ps and a ringing of 145 ps are measured. With a maximum antenna diameter of 35 mm and 53 mm length this new antenna is also suited for UWB antenna arrays.
Intel virtualization technology

A virtualized system includes a new layer of software, the virtual machine monitor. The VMM's principal role is to arbitrate accesses to the underlying physical host platform's resources so that multiple operating systems (which are guests of the VMM) can share them. The VMM presents to each guest OS a set of virtual platform interfaces that constitute a virtual machine (VM). Once confined to specialized, proprietary, high-end server and mainframe systems, virtualization is now becoming more broadly available and is supported in off-the-shelf systems based on Intel architecture (IA) hardware. This development is due in part to the steady performance improvements of IA-based systems, which mitigates traditional virtualization performance overheads. Intel virtualization technology provides hardware support for processor virtualization, enabling simplifications of virtual machine monitor software. Resulting VMMs can support a wider range of legacy and future operating systems while maintaining high performance.