nils-herrmann/clean_scirepeval huggingface dataset:
Survey of Reasoning using Neural networks

Reason and inference require process as well as memory skills by humans. Neural networks are able to process tasks like image recognition (better than humans) but in memory aspects are still limited (by attention mechanism, size). Recurrent Neural Network (RNN) and it’s modified version LSTM are able to solve small memory contexts, but as context becomes larger than a threshold, it is difficult to use them. The Solution is to use large external memory. Still, it poses many challenges like, how to train neural networks for discrete memory representation, how to describe long term dependencies in sequential data etc. Most prominent neural architectures for such tasks are Memory networks: inference components combined with long term memory and Neural Turing Machines: neural networks using external memory resources. Also, additional techniques like attention mechanism, end to end gradient descent on discrete memory representation are needed to support these solutions. Preliminary results of above neural architectures on simple algorithms (sorting, copying) and Question Answering (based on story, dialogs) application are comparable with the state of the art. In this paper, I explain these architectures (in general), the additional techniques used and the results of their application.
A MapReduce Implementation of C 4 . 5 Decision Tree Algorithm

Recent years have witness the development of cloud computing and the big data era, which brings up challenges to traditional decision tree algorithms. First, as the size of dataset becomes extremely big, the process of building a decision tree can be quite time consuming. Second, because the data cannot fit in memory any more, some computation must be moved to the external storage and therefore increases the I/O cost. To this end, we propose to implement a typical decision tree algorithm, C4.5, using MapReduce programming model. Specifically, we transform the traditional algorithm into a series of Map and Reduce procedures. Besides, we design some data structures to minimize the communication cost. We also conduct extensive experiments on a massive dataset. The results indicate that our algorithm exhibits both time efficiency and scalability.
AUGMENTED REALITY SYSTEM USING LIDAR POINT CLOUD DATA FOR DISPLAYING DIMENSIONAL INFORMATION OF OBJECTS ON MOBILE PHONES

Mobile augmented reality system is the next generation technology to visualise 3D real world intelligently. The technology is expanding at a fast pace to upgrade the status of a smart phone to an intelligent device. The research problem identified and presented in the current work is to view actual dimensions of various objects that are captured by a smart phone in real time. The methodology proposed first establishes correspondence between LiDAR point cloud, that are stored in a server, and the image t hat is captured by a mobile. This correspondence is established using the exterior and interior orientation parameters of the mobile camera and the coordinates of LiDAR data points which lie in the viewshed of the mobile camera. A pseudo intensity image is generated using LiDAR points and their intensity. Mobile image and pseudo intensity image are then registered using image registration method SIFT thereby generating a pipeline to locate a point in point cloud corresponding to a point (pixel) on the mobile image. The second part of the method uses point cloud data for computing dimensional information corresponding to the pairs of points selected on mobile image and fetch the dimensions on top of the image. This paper describes all steps of the proposed method. The paper uses an experimental setup to mimic the mobile phone and server system and presents some initial but encouraging results.  Corresponding author.
Automatic Three-Dimensional Cephalometric Annotation System Using Three-Dimensional Convolutional Neural Networks

Three-dimensional (3D) cephalometric analysis using computerized tomography data has been rapidly adopted for dysmorphosis and anthropometry. Several different approaches to automatic 3D annotation have been proposed to overcome the limitations of traditional cephalometry. The purpose of this study was to evaluate the accuracy of our newly-developed system using a deep learning algorithm for automatic 3D cephalometric annotation.  To overcome current technical limitations, some measures were developed to directly annotate 3D human skull data. Our deep learning-based model system mainly consisted of a 3D convolutional neural network and image data resampling.  The discrepancies between the referenced and predicted coordinate values in three axes and in 3D distance were calculated to evaluate system accuracy. Our new model system yielded prediction errors of 3.26, 3.18, and 4.81 mm (for three axes) and 7.61 mm (for 3D). Moreover, there was no difference among the landmarks of the three groups, including the midsagittal plane, horizontal plane, and mandible (p>0.05).  A new 3D convolutional neural network-based automatic annotation system for 3D cephalometry was developed. The strategies used to implement the system were detailed and measurement results were evaluated for accuracy. Further development of this system is planned for full clinical application of automatic 3D cephalometric annotation.
FlowTime: Dynamic Scheduling of Deadline-Aware Workflows and Ad-Hoc Jobs

With rapidly increasing volumes of data to be processed in modern data analytics, it is commonplace to run multiple data processing jobs with inter-job dependencies in a datacenter cluster, typically as recurring data processing workloads. Such a group of inter-dependent data analytic jobs is referred to as a workflow, and may have a deadline due to its mission-critical nature. In contrast, non-recurring ad-hoc jobs are typically best-effort in nature, and rather than meeting deadlines, it is desirable to minimize their average job turnaround time. The state-of-the-art scheduling mechanisms focused on meeting deadlines for individual jobs only, and are oblivious to workflow deadlines. In this paper, we present FlowTime, a new system framework designed to make scheduling decisions for workflows so that their deadlines are met, while simultaneously optimizing the performance of ad-hoc jobs. To achieve this objective, we first adopt a divide-and-conquer strategy to transform the problem of workflow scheduling to a deadline-aware job scheduling problem, and then design an efficient algorithm that tackles the scheduling problem with both deadline-aware jobs and ad-hoc jobs by solving its corresponding optimization problem directly using a linear program solver. Our experimental results have clearly demonstrated that FlowTime achieves the lowest deadline-miss rates for deadline-aware workflows and 2-10 times shorter average job turnaround time, as compared to the state-of-the-art scheduling algorithms.
Local Higher-Order Graph Clustering

Local graph clustering methods aim to find a cluster of nodes by exploring a small region of the graph. These methods are attractive because they enable targeted clustering around a given seed node and are faster than traditional global graph clustering methods because their runtime does not depend on the size of the input graph. However, current local graph partitioning methods are not designed to account for the higher-order structures crucial to the network, nor can they effectively handle directed networks. Here we introduce a new class of local graph clustering methods that address these issues by incorporating higher-order network information captured by small subgraphs, also called network motifs. We develop the Motif-based Approximate Personalized PageRank (MAPPR) algorithm that finds clusters containing a seed node with minimal \emph{motif conductance}, a generalization of the conductance metric for network motifs. We generalize existing theory to prove the fast running time (independent of the size of the graph) and obtain theoretical guarantees on the cluster quality (in terms of motif conductance). We also develop a theory of node neighborhoods for finding sets that have small motif conductance, and apply these results to the case of finding good seed nodes to use as input to the MAPPR algorithm. Experimental validation on community detection tasks in both synthetic and real-world networks, shows that our new framework MAPPR outperforms the current edge-based personalized PageRank methodology.
Mobile application security: malware threats and defenses

Due to the quantum leap in functionality, the rate of upgrading traditional mobile phones to smartphones is tremendous. One of the most attractive features of smartphones is the availability of a large number of apps for users to download and install. However, it also means hackers can easily distribute malware to smartphones, launching various attacks. This issue should be addressed by both preventive approaches and effective detection techniques. This article first discusses why smartphones are vulnerable to security attacks. Then it presents malicious behavior and threats of malware. Next, it reviews the existing malware prevention and detection techniques. Besides more research in these directions, it points out efforts from app developers, app store administrators, and users, who are also required to defend against such malware.
Bandwidth Improvement of Microstrip Patch Antenna Using H- Shaped Patch

Despite the many advantages of microstrip patch antennas, they do have some considerable drawbacks. One of the main limitations with patch antennas is their inherently narrowband performance due to its resonant nature. With bandwidth as low as a few percent; broadband applications using conventional patch designs are limited. So for the antenna miniaturization and bandwidth improvement H-shaped microstrip patch antenna used. In this paper, authors cover two aspects of microstrip antenna designs. The first is the analysis of single element narrowband rectangular microstrip antenna which operates at the central frequency of 3.3 GHz. The second aspect is the analysis and design of slot cut H-shaped microstrip antenna. The simulation process has been done through high frequency structure simulator (HFSS). The properties of antenna such as bandwidth, S parameter, VSWR has been investigated and compared between a single element rectangular and H-shaped microstrip antenna.
A Survey of Emerging M2M Systems: Context, Task, and Objective

Machine-to-machine (M2M) systems enable machines or devices to collect data, exchange information, and act on the environment without direct human intervention. A device in an M2M system not only collects data for its own usage but also shares the data with other devices automatically to achieve certain goals. Therefore, emerging M2M systems for civil transportation, electric power grid, medical treatment, industrial automation, etc., can be set up based on the networking of devices. In this survey paper, we first introduce the general architecture and communication networks for M2M systems. Then, we categorize emerging M2M systems according to the types of M2M context, M2M task, and M2M objective. We further survey recent solutions for M2M systems from both academia and industry with the insights of the M2M system categorization. Finally, we summarize challenges in developing M2M system solutions.
Fuzzy controlled fast charging system for lithium-ion batteries

A DSP is adopted to construct a fuzzy controlled lithium-ion battery charging system. By using this intelligent charging system, the data collection, calculation and peripheral circuit control are performed for the battery charging status. According to the lithium-ion battery charging specifications, two hours are required for battery charging. A fuzzy logic controller (FLC) is constructed by using the battery protection cell voltage and voltage difference among the batteries. They are used as the input variable to shorten the charging and equalizing time and assure that the battery will be operated within the safety voltage range.
Xsens MVN : Full 6 DOF Human Motion Tracking Using Miniature Inertial Sensors

The Xsens MVN motion capture suit is an easy-touse, cost efficient system for full-body human motion capture. MVN is based on unique, state-of-the-art miniature inertial sensors, biomechanical models and sensor fusion algorithms. MVN does not need external cameras, emitters or markers. It can thus be used outdoors as well as indoors, there are no restrictions for lighting, it does not suffer from problems of occlusion or missing markers. In addition, unique for inertial motion capture technology: the sensor-suit captures any type of movement, including running, jumping, crawling and cartwheels.
An improved power quality induction heater using zeta converter

This paper presents an induction heater (IH) with power factor correction (PFC) for domestic induction heating applications. A half bridge voltage fed series resonant inverter (VFSRI) based IH is fed from the front end PFC corrected zeta converter. The PFC-zeta converter operating in continuous inductor conduction mode (CICM) is used to regulate the DC link voltage. The switch current stress of PFC-zeta converter is controlled by operating zeta converter in CICM mode using a well-established internal current loop and outer voltage loop approach. The PFC-zeta converter based voltage fed IH converts intermediate DC bus voltage to high frequency (30 kHz) voltage suitable for domestic induction heating. A 2 kW PFC-zeta converter based IH is designed and its performance is simulated in MATLAB with power quality indices within an IEC 61000-3-2 standard.
D-AMINO ACID OXIDASE: A REVIEW

Over many years, D-serine and glycine were found to be the endogenous ligands for glycine-binding site for N-methyl-D-aspartate receptors. D-serine before being used up by the cells undergoes oxidative deamination by the enzyme D-amino acid oxidase (DAAO) and makes D-serine levels reduced in the brain, thereby affecting brain functions. In this review, we will discuss about the synthesis, location, therapeutic potential of DAAO function, role in cognition, and neuropathic pain.
Behavioral Cloning from Observation

Humans often learn how to perform tasks via imitation: they observe others perform a task, and then very quickly infer the appropriate actions to take based on their observations. While extending this paradigm to autonomous agents is a wellstudied problem in general, there are two particular aspects that have largely been overlooked: (1) that the learning is done from observation only (i.e., without explicit action information), and (2) that the learning is typically done very quickly. In this work, we propose a two-phase, autonomous imitation learning technique called behavioral cloning from observation (BCO), that aims to provide improved performance with respect to both of these aspects. First, we allow the agent to acquire experience in a self-supervised fashion. This experience is used to develop a model which is then utilized to learn a particular task by observing an expert perform that task without the knowledge of the specific actions taken. We experimentally compare BCO to imitation learning methods, including the state-of-the-art, generative adversarial imitation learning (GAIL) technique, and we show comparable task performance in several different simulation domains while exhibiting increased learning speed after expert trajectories become available.
The PLEX Cards and its techniques as sources of inspiration when designing for playfulness

Playfulness can be observed in all areas of human activity. It is an attitude of making activities more enjoyable. Designing for playfulness involves creating objects that elicit a playful approach and provide enjoyable experiences. In this paper, we introduce the design and evaluation of the PLEX Cards and its two related idea generation techniques. The cards were created to communicate the 22 categories of a playful experiences framework to designers and other stakeholders who wish to design for playfulness. We have evaluated the helpfulness of both the cards and their associated techniques in two studies. The results show that the PLEX Cards and its associated techniques are valuable sources of inspiration when designing for playfulness.
Microservices: A Systematic Mapping Study

Microservices have recently emerged as an architectural style, addressing how to build, manage, and evolve architectures out of small, self-contained units. Particularly in the cloud, the microservices architecture approach seems to be an ideal complementation of container technology at the PaaS level However, there is currently no secondary study to consolidate this research. We aim here to identify, taxonomically classify and systematically compare the existing research body on microservices and their application in the cloud. We have conducted a systematic mapping study of 21 selected studies, published over the last two years until end of 2015 since the emergence of the microservices pattern. We classified and compared the selected studies based on a characterization framework. This results in a discussion of the agreed and emerging concerns within the microservices architectural style, positioning it within a continuous development context, but also moving it closer to cloud and container technology.
Self-produced Guidance for Weakly-Supervised Object Localization

Weakly supervised methods usually generate localization results based on attention maps produced by classification networks. However, the attention maps exhibit the most discriminative parts of the object which are small and sparse. We propose to generate Self-produced Guidance (SPG) masks which separate the foreground i.e., the object of interest, from the background to provide the classification networks with spatial correlation information of pixels. A stagewise approach is proposed to incorporate high confident object regions to learn the SPG masks. The high confident regions within attention maps are utilized to progressively learn the SPG masks. The masks are then used as an auxiliary pixel-level supervision to facilitate the training of classification networks. Extensive experiments on ILSVRC demonstrate that SPG is effective in producing high-quality object localizations maps. Particularly, the proposed SPG achieves the Top-1 localization error rate of 43.83% on the ILSVRC validation set, which is a new state-of-the-art error rate.
A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments

Current progress in wearable and implanted health monitoring technologies has strong potential to alter the future of healthcare services by enabling ubiquitous monitoring of patients. A typical health monitoring system consists of a network of wearable or implanted sensors that constantly monitor physiological parameters. Collected data are relayed using existing wireless communication protocols to a base station for additional processing. This article provides researchers with information to compare the existing low-power communication technologies that can potentially support the rapid development and deployment of WBAN systems, and mainly focuses on remote monitoring of elderly or chronically ill patients in residential environments.
Human-Computer Interaction. Interaction Technologies

One character is entered with two steps: firstly, selecting a group of five characters, and secondly selecting one character from the group. For Japanese Hiragana letters, each row of a table of the Japanese syllabary, is used as the group. For alphabets, every five letters in alphabetical order are gathered to one group. Each of the left edge, the upper edge, and the right edge of the screen is divided into two segments. A pair of groups is assigned to each of the segment. The group is selected by slide-in. The slide-in is the operation that first touch a finger to the outside of the screen then move it inside the screen as it is. Since the fingertip definitely passes through the edge of the screen during the slide-in, the crossing can be detected with the area which width is only 2 mm. Therefore, 73.6% of the screen can be used for displaying text if the size of the screen is 1.63 in. When the group is selected, the screen is divided to 3 × 3 blocks and 5 characters of the group are displayed there. By tapping on one character, it is entered. In the experiment, beginners entered characters at a speed of 28.7 [CPM] in average, after 5.3 min usage in total. The input speed of an expert is 67.7 [CPM]. In another experiment entering 10 words a day, the input speed exceeded 50 [CPM] after 30 days, when total time of use is 39.5 min.
Multiframe Motion Coupling via Infimal Convolution Regularization for Video Super Resolution

The idea of video super resolution is to use the temporal information of seeing a scene from many slightly different viewpoints in the successive frames of a video to enhance the overall resolution and quality of each frame. Classical energy minimization approaches first establish a correspondence of the current video frame to several of its neighbors and then use this temporal information to enhance it. In this paper we propose the first variational super resolution approach that computes several super resolved frames in one joint optimization procedure by incorporating motion information between the high resolution image frames themselves. As a consequence, the number of motion estimation problems grows linearly in the number of frames, opposed to a quadratic growth of classical methods. In addition, we use infimal convolution regularization to automatically determine the reliability of the motion information and reweight the regularization locally. We demonstrate that our approach yields state-of-the-art results and even is competitive with learning based approaches that require a significant amount of training data.
Reputation and e-commerce : eBay auctions and the asymmetrical impact of positive and negative ratings

This analysis explores the impact and nature of reputation as related to e-commerce by looking at the importance of a seller’s reputational rating on the final bid price associated with eBay auctions. Positive reputational ratings emerged as mildly influential in determining final bid price. However, negative reputational ratings emerged as highly influential and detrimental. Thus, we find strong evidence for the importance of reputation when engaging in e-commerce and equally strong evidence concerning the exaggerated influence of negative reputation.
Today ’ s State-Owned Enterprises of China : Are They Dying Dinosaurs or Dynamic Dynamos ?

The authors thank Bianca Bain for her assistance on an earlier draft of the paper and the University of Macao for providing financial support.  This paper raises the question and provides empirical evidence regarding the status of the evolution of the state-owned enterprises (SOEs) in China today. In this study, we compare the SOEs to domestic private-owned enterprises (POEs) and foreign-controlled businesses (FCBs) in the context of their organizational cultures. While a new ownership form, many of the POEs, evolved from former collectives that reflect the traditional values of Chinese business. Conversely, the FCBs are much more indicative of the large global MNCs. Therefore, we look at the SOEs in the context of these two reference points. We conclude that the SOEs of today have substantially transformed to approximate a configuration desired by the Chinese government when it began the SOE transformation a couple of decades ago to make them globally competitive. The SOEs of today appear to be appropriately described as China's economic dynamic dynamo for the future.