nils-herrmann/clean_scirepeval huggingface dataset:
Short text classification based on Wikipedia and Word2vec

Different from long texts, the features of Chinese short texts is much sparse, which is the primary cause of the low accuracy in the classification of short texts by using traditional classification methods. In this paper, a novel method was proposed to tackle the problem by expanding the features of short text based on Wikipedia and Word2vec. Firstly, build the semantic relevant concept sets of Wikipedia. We get the articles that have high relevancy with Wikipedia concepts and use the word2vec tools to measure the semantic relatedness between target concepts and related concepts. And then we use the relevant concept sets to extend the short texts. Compared to traditional similarity measurement between concepts using statistical method, this method can get more accurate semantic relatedness. The experimental results show that by expanding the features of short texts, the classification accuracy can be improved. Specifically, our method appeared to be more effective.
Being overweight has limited effect on SCARF osteotomy outcome for hallux valgus correction

The purpose of this study was to investigate the association between body mass index (BMI) and the results of SCARF osteotomy of the first metatarsal for hallux valgus (HV) correction, as the literature on this is scant. This prospective study was carried out between 2011 and 2015. One hundred and thirty-three patients diagnosed with moderate to severe HV underwent a SCARF corrective osteotomy. We divided the patients into two groups according to their BMI: normal and overweight. Postoperative follow-up was two years. All patients were examined twice by two medical doctors simultaneously: pre-operatively and post-operatively at two years’ follow-up. Data collected included biometrical records, X-rays [HV angle (HVA), intermetatarsal angle (IMA), American Orthopaedic Foot and Ankle Society Hallux Metatarsophalangeal Index (AOFAS-HMI) and visual analogue scale (VAS) for pain and satisfaction]. There was a significant difference between patient age (p = 0.001), age at onset (p < 0.001) and AOFAS-HMI (p = 0.035) at follow-up. Other parameters were similar in both groups. Regardless of BMI, the radiological outcome was comparable. Despite a significant difference in AOFAS-HMI results, pain and satisfaction level were similar. The authors agreed that high BMI has protective role in the prevalence of HV.
A moving target DDoS defense mechanism

In this paper, we introduce a moving target defense mechanism that defends authenticated clients against Internet service DDoS attacks. Our mechanism employs a group of dynamic, hidden proxies to relay traffic between authenticated clients and servers. By continuously replacing attacked proxies with backup proxies and reassigning (shuffling) the attacked clients onto the new proxies, innocent clients are segregated from malicious insiders through a series of shuffles. To accelerate the process of insider segregation, we designed an efficient greedy algorithm which is proven to have near optimal empirical performance. In addition, the insider quarantine capability of this greedy algorithm is studied and quantified to enable defenders to estimate the resource required to defend against DDoS attacks and meet defined QoS levels under various attack scenarios. Simulations were then performed which confirmed the theoretical results and showed that our mechanism is effective in mitigating the effects of a DDoS attack. The simulations also demonstrated that the overhead introduced by the shuffling procedure is low.
Standard-Compliant Multiview Video Coding and Streaming for Virtual Reality Applications

Virtual reality (VR) systems employ multiview cameras or camera rigs to capture a scene from the entire 360-degree perspective. Due to computational or latency constraints, it might not be possible to stitch multiview videos into a single video sequence prior to encoding. In this paper we investigate the coding and streaming of multiview VR video content. We present a standard-compliant method where we first divide the camera views into two types: Primary views represent a subset of camera views with lower resolution and non-overlapping (minimally overlapping) content which cover the entire 360-degree field-of-view to guarantee immediate monoscopic viewing during very rapid head movements. Auxiliary views consist of remaining camera views with higher resolution which produce overlapping content with the primary views and are additionally used for stereoscopic viewing. Based on this categorization, we propose a coding arrangement in which, the primary views are independently coded in the base layer and the additional auxiliary views are coded as an enhancement layer, using inter-layer prediction from primary views. The proposed system not only meets the low latency requirements of VR systems, but also conforms to the existing multilayer extensions of the High Efficiency Video Coding standard. Simulation results show that the coding and streaming performance of the proposed scheme is significantly improved compared to earlier methods.
Blockchain Platform for Industrial Internet of Things

Internet of Things (IoT) are being adopted for industrial and manufacturing applications such as manufacturing automation, remote machine diagnostics, prognostic health management of industrial machines and supply chain management. CloudBased Manufacturing is a recent on-demand model of manufacturing that is leveraging IoT technologies. While Cloud-Based Manufacturing enables on-demand access to manufacturing resources, a trusted intermediary is required for transactions between the users who wish to avail manufacturing services. We present a decentralized, peer-to-peer platform called BPIIoT for Industrial Internet of Things based on the Block chain technology. With the use of Blockchain technology, the BPIIoT platform enables peers in a decentralized, trustless, peer-to-peer network to interact with each other without the need for a trusted intermediary.
Study of wheel slip and traction forces in differential drive robots and slip avoidance control strategy

The effect of wheel slip in differential drive robots is investigated in this paper. We consider differential drive robots with two driven wheels and ball-type caster wheels that are used to provide balance and support to the mobile robot. The limiting values of traction forces for slip and no slip conditions are dependent on wheel-ground kinetic and static friction coefficients. The traction forces are used to determine the fraction of input torque that provides robot motion and this is used to calculate the actual position of the robot under slip conditions. The traction forces under no slip conditions are used to determine the limiting value of the wheel torque above which the wheel slips. This limiting torque value is used to set a saturation limit for the input torque to avoid slip. Simulations are conducted to evaluate the behavior of the robot during slip and no slip conditions. Experiments are conducted under similar slip and no slip conditions using a custom built differential drive mobile robot with one caster wheel to validate the simulations. Experiments are also conducted with the torque limiting strategy. Results from model simulations and experiments are presented and discussed.
Exact low-rank matrix completion via convex optimization

Suppose that one observes an incomplete subset of entries selected uniformly at random from a low-rank matrix. When is it possible to complete the matrix and recover the entries that have not been seen? We show that in very general settings, one can perfectly recover all of the missing entries from a sufficiently large random subset by solving a convex programming problem. This program finds the matrix with the minimum nuclear norm agreeing with the observed entries. The techniques used in this analysis draw upon parallels in the field of compressed sensing, demonstrating that objects other than signals and images can be perfectly reconstructed from very limited information.
Collabode: collaborative coding in the browser

Collaborating programmers should use a development environment designed specifically for collaboration, not the same one designed for solo programmers with a few collaborative processes and tools tacked on. This paper describes Collabode, a web-based Java integrated development environment built to support close, synchronous collaboration between programmers. We discuss three collaboration models in which participants take on distinct roles: micro-outsourcing to combine small contributions from many assistants; test-driven pair programming for effective pairwise development; and a mobile instructor connected to the work of many students. In particular, we report very promising preliminary results using Collabode to support micro-outsourcing.
A Multi-Modal Agent Based Mobile Route Advisory System for Public Transport Network

In a metropolis such as Hong Kong, the transport network is massive, dynamic, and complicated, and therefore route finding is not an easy task, especially when routes comprising several modes of transport vehicles (such as bus, train, and ferry). This problem is even more important for e-tourism tourists and moving workforces, who may need to visit an unfamiliar part of the metropolis. To find a route that is the most cost-effective is even harder and time-consuming. In this paper, we present a conceptual model for a multi-modal public transport network and a multi-agent information system (MAIS) implementation architecture. We propose several intelligent approaches in the search agents to enhance the performance of route finding, in particular a knowledge-basket approach. Backend information agents gather updates periodically from transportation companies. We also consider hired transportation services, such as taxis and vans, which might offer similar costs for group commuters. We further support an agent-based auction sub-system for reservation of these mobile vehicles. We have built a route advisory system prototype with such features. Our system supports a flexible multiple user interface views on different mobile platforms. Our paper also includes some benchmark results to demonstrate the effectiveness our approach.
A Characterization of the Utility of Using Artificial Intelligence to Test Two Artificial Intelligence Systems

An artificial intelligence system, designed for operations in a real-world environment faces a nearly infinite set of possible performance scenarios. Designers and developers, thus, face the challenge of validating proper performance across both foreseen and unforeseen conditions, particularly when the artificial intelligence is controlling a robot that will be operating in close proximity, or may represent a danger, to humans. While the manual creation of test cases allows limited testing (perhaps ensuring that a set of foreseeable conditions trigger an appropriate response), this may be insufficient to fully characterize and validate safe system performance. An approach to validating the performance of an artificial intelligence system using a simple artificial intelligence test case producer (AITCP) is presented. The AITCP allows the creation and simulation of prospective operating scenarios at a rate far exceeding that possible by human testers. Four scenarios for testing an autonomous navigation control system are presented: single actor in two-dimensional space, multiple actors in two-dimensional space, single actor in three-dimensional space, and multiple actors in three-dimensional space. The utility of using the AITCP is compared to that of human testers in each of these scenarios.
Interactive volume rendering using multi-dimensional transfer functions and direct manipulation widgets

Most direct volume renderings produced today employ one-dimensional transfer functions, which assign color and opacity to the volume based solely on the single scalar quantity which comprises the dataset. Though they have not received widespread attention, multi-dimensional transfer functions are a very effective way to extract specific material boundaries and convey subtle surface properties. However, identifying good transfer functions is difficult enough in one dimension, let alone two or three dimensions. This paper demonstrates an important class of three-dimensional transfer functions for scalar data (based on data value, gradient magnitude, and a second directional derivative), and describes a set of direct manipulation widgets which make specifying such transfer functions intuitive and convenient. We also describe how to use modern graphics hardware to interactively render with multi-dimensional transfer functions. The transfer functions, widgets, and hardware combine to form a powerful system for interactive volume exploration.
Sensorless Direct Field-Oriented Control of Three-Phase Induction Motor Drives for Low-Cost Applications

A sensorless direct rotor field-oriented control (SDRFOC) scheme of three-phase induction motors for low-cost applications is presented in this paper. The SDRFOC algorithm is based on a sensorless closed-loop rotor flux observer whose main advantages are simplicity and robustness to motor parameter detuning. The whole algorithm has been developed and implemented on a low-cost fixed-point digital signal processor controller. Experimental results are presented for a 0.5-kW induction motor drive for a primary vacuum pump used in industry applications.
Fresh-Phish: A Framework for Auto-Detection of Phishing Websites

Denizens of the Internet are coming under a barrage of phishing attacks of increasing frequency and sophistication. Emails accompanied by authentic looking websites are ensnaring users who, unwittingly, hand over their credentials compromising both their privacy and security. Methods such as the blacklisting of these phishing websites become untenable and cannot keep pace with the explosion of fake sites. Detection of nefarious websites must become automated and be able to adapt to this ever evolving form of social engineering. We develop a framework, called ""Fresh-Phish"", for creating current machine learning data for phishing websites. Using 30 different website features that we query using python, we build a large labeled dataset and analyze several machine learning classifiers against this dataset to determine which is the most accurate. We analyze not just the accuracy of the technique, but also how long it takes to train the model.
Simpler is better? Lexicon-based ensemble sentiment classification beats supervised methods

It has been shown in this paper that simplistic Bag of Words (BoW) lexicon methods for sentiment polarity assignment with ensemble classifiers are much faster than a supervised approach to sentiment classification while yielding similar accuracy. BoW methods also proved to be efficient and fast across all examined datasets. Moreover, a new approach to lexicon extraction that can be successfully used for sentiment polarity assignment is presented in the paper. It has been shown that accuracy obtained from such lexicons outperforms other lexicon based approaches.
Higher-Order Syntactic Attention Network for Longer Sentence Compression

Sentence compression methods based on LSTM can generate fluent compressed sentences. However, the performance of these methods is significantly degraded when compressing long sentences since it does not explicitly handle syntactic features. To solve this problem, we propose a higher-order syntactic attention network (HiSAN) that can handle higher-order dependency features as an attention distribution on LSTM hidden states. Furthermore, to avoid the influence of incorrect parse results, we train HiSAN by maximizing the probability of a correct output together with the attention distribution. Experiments on the Google sentence compression dataset show that our method achieved the best performance in terms of F1 as well as ROUGE-1,2 and L scores, 83.2, 82.9, 75.8 and 82.7, respectively. In subjective evaluations, HiSAN outperformed baseline methods in both readability and informativeness.
Aggregates Caching in Columnar In-Memory Databases

The mixed database workloads found in enterprise applications are comprised of short-running transactional as well as analytical queries with resource-intensive data aggregations. In this context, caching the query results of long-running queries is desirable as it increases the overall performance. However, traditional caching approaches are inefficient in a way that changes in the base data result in invalidation or recalculation of cached results. Columnar in-memory databases with a main-delta architecture are optimized for a novel caching mechanism for aggregate queries that is the main contribution of this paper. With the separation into a readoptimized main storage and write-optimized delta storage, we do not invalidate cached query results when new data is inserted to the delta storage. Instead, we use the cached query result and combine it with the newly added records in the delta storage. We evaluate this caching mechanism with mixed database workloads and show how it compares to existing work in this area.
Big data analytics on traditional HPC infrastructure using two-level storage

Data-intensive computing has become one of the major workloads on traditional high-performance computing (HPC) clusters. Currently, deploying data-intensive computing software framework on HPC clusters still faces performance and scalability issues. In this paper, we develop a new two-level storage system by integrating Tachyon, an in-memory file system with OrangeFS, a parallel file system. We model the I/O throughputs of four storage structures: HDFS, OrangeFS, Tachyon and two-level storage. We conduct computational experiments to characterize I/O throughput behavior of two-level storage and compare its performance to that of HDFS and OrangeFS, using TeraSort benchmark. Theoretical models and experimental tests both show that the two-level storage system can increase the aggregate I/O throughputs. This work lays a solid foundation for future work in designing and building HPC systems that can provide a better support on I/O intensive workloads with preserving existing computing resources.
A Database for Handwritten Text Recognition Research

We propose in this correspondence a new method to perform two-class clustering of 2-D data in a quick and automatic way by preserving certain features of the input data. The method is analytical, deterministic, unsupervised, automatic, and noniterative. The computation time is of order n if the data size is n, and hence much faster than any other method which requires the computation of an n-by-n dissimilarity matrix. Furthermore, the proposed method does not have the trouble of guessing initial values. This new approach is thus more suitable for fast automatic hierarchical clustering or any other fields requiring fast automatic two-class clustering of 2-D data. The method can be extended to cluster data in higher dimensional space. A 3-D example is included.
Real-time object classification in 3D point clouds using point feature histograms

This paper describes a LIDAR-based perception system for ground robot mobility, consisting of 3D object detection, classification and tracking. The presented system was demonstrated on-board our autonomous ground vehicle MuCAR-3, enabling it to safely navigate in urban traffic-like scenarios as well as in off-road convoy scenarios. The efficiency of our approach stems from the unique combination of 2D and 3D data processing techniques. Whereas fast segmentation of point clouds into objects is done in a 2 D occupancy grid, classifying the objects is done on raw 3D point clouds. For fast object feature extraction, we advocate the use of statistics of local point cloud properties, captured by histograms over point features. In contrast to most existing work on 3D point cloud classification, where real-time operation is often impossible, this combination allows our system to perform in real-time at 0.1s frame-rate.
A Developmental Model of Critical Thinking

The critical thinking movement, it is suggested, has much to gain from conceptualizing its subject matter in a developmental framework. Most instructional programs designed to teach critical thinking do not draw on contemporary empirical research in cognitive development as a potential resource. The developmental model of critical thinking outlined here derives from contemporary empirical research on directions and processes of intellectual development in children and adolescents. It identifies three forms of second-order cognition (meta-knowing)--metacognitive, metastrategic, and epistemological--that constitute an essential part of what develops cognitively to make critical thinking possible.
Accelerating computer vision algorithms using OpenCL framework on the mobile GPU - A case study

Recently, general-purpose computing on graphics processing units (GPGPU) has been enabled on mobile devices thanks to the emerging heterogeneous programming models such as OpenCL. The capability of GPGPU on mobile devices opens a new era for mobile computing and can enable many computationally demanding computer vision algorithms on mobile devices. As a case study, this paper proposes to accelerate an exemplar-based inpainting algorithm for object removal on a mobile GPU using OpenCL. We discuss the methodology of exploring the parallelism in the algorithm as well as several optimization techniques. Experimental results demonstrate that our optimization strategies for mobile GPUs have significantly reduced the processing time and make computationally intensive computer vision algorithms feasible for a mobile device. To the best of the authors' knowledge, this work is the first published implementation of general-purpose computing using OpenCL on mobile GPUs.
The past, present and future of social neuroscience: A European perspective

This review provides an overview of the field of social neuroscience from a European perspective and focuses mainly on outlining research topics which originated in European laboratories. After a brief historical synopsis of the emergence of this young field, the most relevant findings related to the investigation of the neural networks underlying our capacity to understand the minds of others are summarized. More specifically, three routes of social cognition are distinguished: (1) our capacity to mentalize, or to infer intentions and beliefs of others, (2) our capacity to mimic and understand other's motor actions, and (3) our capacity to empathize, or to share and understand the feelings of others. More recent studies focusing on social emotions such as love, compassion, revenge or our sense of fairness will be discussed linking the field of social neuroscience to the even younger field of neuroeconomics, with the focus on the study of human social interactions using game theoretical paradigms. Finally, the use of a multi-method and multi-disciplinary research approach combining genetic, pharmacological, computational and developmental aspects is advocated and future directions for the study of interactive minds are discussed.