nils-herrmann/clean_scirepeval huggingface dataset:
Security and Privacy Challenges in Smart Cities

The construction of smart cities will bring about a higher quality of life to the masses through digital interconnectivity, leading to increased efficiency and accessibility in cities. Smart cities must ensure individual privacy and security in order to ensure that its citizens will participate. If citizens are reluctant to participate, the core advantages of a smart city will dissolve. This article will identify and offer possible solutions to five smart city challenges, in hopes of anticipating destabilizing and costly disruptions. The challenges include privacy preservation with high dimensional data, securing a network with a large attack surface, establishing trustworthy data sharing practices, properly utilizing artificial intelligence, and mitigating failures cascading through the smart network. Finally, further research directions are provided to encourage further exploration of smart city challenges before their construction.
Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling

Many real-world applications of reinforcement learning require an agent to select optimal actions from continuous spaces. Recently, deep neural networks have successfully been applied to games with discrete actions spaces. However, deep neural networks for discrete actions are not suitable for devising strategies for games where a very small change in an action can dramatically affect the outcome. In this paper, we present a new selfplay reinforcement learning framework which equips a continuous search algorithm which enables to search in continuous action spaces with a kernel regression method. Without any handcrafted features, our network is trained by supervised learning followed by self-play reinforcement learning with a high-fidelity simulator for the Olympic sport of curling. The program trained under our framework outperforms existing programs equipped with several hand-crafted features and won an international digital curling competition.
A model of saliency-based auditory attention to environmental sound

A computational model of auditory attention to environmental sound, inspired by the structure of the human auditory system, is presented. The model simulates how listeners switch their attention over time between different auditory streams, based on bottom-up and top-down cues. The bottom-up cues are determined by the time-dependent saliency of each stream. The latter is calculated on the basis of an auditory saliency map, which encodes the intensity and the amount of spectral and temporal irregularities of the sound, and binary spectro-temporal masks for the different streams. The top-down cues are determined by the amount of volitional focusing on particular auditory streams. A competitive winner-takes-all mechanism, which balances bottom-up and top-down attention for each stream, determines which stream is selected for entry into the working memory. Consequently, the model is able to delimit the time periods during which particular streams are paid attention to. Although the main ideas could be applied to all types of sound, the implementation of the model was targeted at environmental sound in particular. As an illustration, the model is used to reproduce results from a detailed field experiment on the perception of transportation noise. Finally, it is shown how this model could be a valuable tool, complementing auralization, in the design of outdoor soundscapes.
Object Captioning and Retrieval with Natural Language

We address the problem of jointly learning vision and language to understand the object in a fine-grained manner. The key idea of our approach is the use of object descriptions to provide the detailed understanding of an object. Based on this idea, we propose two new architectures to solve two related problems: object captioning and natural language-based object retrieval. The goal of the object captioning task is to simultaneously detect the object and generate its associated description, while in the object retrieval task, the goal is to localize an object given an input query. We demonstrate that both problems can be solved effectively using hybrid end-toend CNN-LSTM networks. The experimental results on our new challenging dataset show that our methods outperform recent methods by a fair margin, while providing a detailed understanding of the object and having fast inference time. The source code will be made available.
Trust-Based Requirements Traceability

Information retrieval (IR) approaches have proven useful in recovering traceability links between free-text documentation and source code. IR-based traceability recovery approaches produce ranked lists of traceability links between pieces of documentation and source code. These traceability links are then pruned using various strategies and, finally, validated by human experts. In this paper we propose two contributions to improve the precision and recall of traceability links and, thus, reduces the required human experts' manual validation effort. First, we propose a novel approach, Trust race, inspired by Web trust models to improve the precision and recall of traceability links: Trust race uses intractability recovery approach to obtain a set of traceability links, which rankings are then re-evaluated using a set of other traceability recovery approaches. Second, we propose a novel traceability recovery approach, His trace, to identify traceability links between requirements and source code through CVS/SVN change logs using a Vector Space Model (VSM). We combine a traditional recovery traceability approach with His trace to build Trust race in which we use Histraceas one expert adding knowledge to the traceability links extracttedfrom CVS/SVN  change logs. We apply Trustrace on two case studies to compare its traceability links with those recovered using only the VSM-based approach, in terms of precision and recall. We show that Trustrace improves with statistical significance the precision of the traceability links while also improving recall but without statistical significance.
Autonomous racing using model predictive control

Autonomous vehicles are expected to have a significant impact on our societies by freeing humans from the driving task, and thus eliminating the human factor in one of the most dangerous places: roads. As a matter of facts, road kills are one of the largest source of human deaths and many countries put the decrease of these casualties as one of their top priorities. It is expected that autonomous vehicles will dramatically help in achieving that. Moreover, using controllers to optimize both the car behaviour on the road and higher level traffic management could reduce traffic jams and increase the commuting speed overall. To minimize road kills, an approach is to design controllers that would handle the car at its limits of handling, by integrating complex dynamics such as adherence loss it is possible to prevent the car from leaving the road. A convenient setup to evaluate this type of controllers is a racing context: a controller is steering a car to complete a track as fast as possible without leaving the road and by brining the car to its limits of handling. In this thesis, we design a controller for an autonomous vehicle with the goal of driving it from A to B as fast as possible. This is the main motivation in racing applications. The controller should steer the car with the goal to minimize the racing time. This controller was designed within the model predictive controller (MPC) framework, where we used the concept of road-aligned model. In contrast with the standard mpc techniques, we use the objective function to maximize the progress along the reference path, by integrating a linear model of the vehicle progression along the centerline. Combined with linear vehicle model and constraints, a optimization problem providing the vehicle with the future steering and throttle values to apply is formulated and solved with linear programming in an on-line fashion during the race. We show the effectiveness of our controller in simulation, where the designed controller exhibits typical race drivers behaviours and strategies when steering a vehicle along a given track. We ultimately confront it with similar controllers from the literature, and derive its strength and weaknesses compared to them.
Maximum Margin Reward Networks for Learning from Explicit and Implicit Supervision

Neural networks have achieved state-ofthe-art performance on several structuredoutput prediction tasks, trained in a fully supervised fashion. However, annotated examples in structured domains are often costly to obtain, which thus limits the applications of neural networks. In this work, we propose Maximum Margin Reward Networks, a neural networkbased framework that aims to learn from both explicit (full structures) and implicit supervision signals (delayed feedback on the correctness of the predicted structure). On named entity recognition and semantic parsing, our model outperforms previous systems on the benchmark datasets, CoNLL-2003 and WebQuestionsSP.
Double-Rotor Flux-Switching Permanent Magnet Machine With Yokeless Stator

In this paper, novel design of a flux-switching permanent magnet machine (FSPMM) with a yokeless stator and two rotors has been proposed. The proposed machine has been found to perform on par with a 6-slot/13-pole C-core conventional FSPMM in terms of torque output. Additionally, the proposed machine has no significant unbalanced magnetic forces, which is a major challenge in conventional FSPMM with odd number of rotor poles. Step-by-step parametric optimization has been performed using finite-element models of the machine in order to achieve the highest torque with the given magnetic and electrical constraints.
Developing criteria for establishing interrater reliability of specific items: applications to assessment of adaptive behavior.

A set of criteria based upon biostatistical considerations for determining the interrater reliability of specific adaptive behavior items in a given setting was presented. The advantages and limitations of extant statistical assessment procedures were discussed. Also, a set of guidelines for differentiating type of adaptive behavior that are statistically reliable from those that are reliable in a clinical or practical sense was delineated. Data sets were presented throughout in order to illustrate the advantages of recommended statistical procedures over other available ones.
Collaborative problem solving: a study of MathOverflow

The Internet has the potential to accelerate scientific problem solving by engaging a global pool of contributors. Existing approaches focus on broadcasting problems to many independent solvers. We investigate other approaches that may be advantageous by examining a community for mathematical problem solving -- MathOverflow -- in which contributors communicate and collaborate to solve new mathematical 'micro-problems' online. We contribute a simple taxonomy of collaborative acts derived from a process-level examination of collaborations and a quantitative analysis relating collaborative acts to solution quality. Our results indicate a diversity of ways in which mathematicians are reaching a solution, including by iteratively advancing a solution. A better understanding of such collaborative strategies can inform the design of tools to support distributed collaboration on complex problems.
An Enhanced Tibia Fracture Detection Tool Using Image Processing and Classification Fusion Techniques in X-Ray Images By

Automatic detection of fractures from x-ray images is considered as an important process in medical image analysis by both orthopaedic and radiologic point of view. This paper proposes a fusion-classification technique for automatic fracture detection from long bones, in particular the leg bones (Tibia bones). The proposed system has four steps, namely, preprocessing, segmentation, feature extraction and bone detect ion, which uses an amalgamation of image processing techniques for successful detection of fractures. Three classifiers, Feed Forward Back Propagation Neural Networks (BPNN), Support Vector Machine Classifiers (SVM) and Naïve Bayes Classifiers (NB) are used during fusion classification. The results from various experiments prove that the proposed system is shows significant improvement in terms of detection rate and speed of classification.
A better approximation ratio for the vertex cover problem

We reduce the approximation factor for the vertex cover to 2 − Θ (1/&sqrt;logn) (instead of the previous 2 − Θ ln ln n/2ln n obtained by Bar-Yehuda and Even [1985] and Monien and Speckenmeyer [1985]). The improvement of the vanishing factor comes as an application of the recent results of Arora et al. [2004] that improved the approximation factor of the sparsest cut and balanced cut problems. In particular, we use the existence of two big and well-separated sets of nodes in the solution of the semidefinite relaxation for balanced cut, proven by Arora et al. [2004]. We observe that a solution of the semidefinite relaxation for vertex cover, when strengthened with the triangle inequalities, can be transformed into a solution of a balanced cut problem, and therefore the existence of big well-separated sets in the sense of Arora et al. [2004] translates into the existence of a big independent set.
Malware Detection by HTTPS Traffic Analysis

In order to evade detection by network-traffic analysis, a growing proportion of malware uses the encrypted HTTPS protocol. We explore the problem of detecting malware on client computers based on HTTPS traffic analysis. In this setting, malware has to be detected based on the host IP address, ports, timestamp, and data volume information of TCP/IP packets that are sent and received by all the applications on the client. We develop a scalable protocol that allows us to collect network flows of known malicious and benign applications as training data and derive a malware-detection method based on a neural networks and sequence classification. We study the method’s ability to detect known and new, unknown malware in a largescale empirical study.
Choice of intra-articular injection in treatment of knee osteoarthritis: platelet-rich plasma, hyaluronic acid or ozone options

This study was performed to compare the efficacy of treatment in three groups of patients with knee osteoarthritis (OA) given an intra-articular injection of platelet-rich plasma (PRP), hyaluronic acid (HA) or ozone gas. A total of 102 patients with mild–moderate and moderate knee OA who presented at the polyclinic with at least a 1-year history of knee pain and VAS score ≥4 were randomly separated into three groups. Group 1 (PRP group) received intra-articular injection of PRP × 2 doses, Group 2 (HA group) received a single dose of HA, and Group 3 (Ozone group) received ozone × four doses. Weight-bearing anteroposterior–lateral and Merchant’s radiographs of both knees were evaluated. WOMAC and VAS scores were applied to all patients on first presentation and at 1, 3, 6 and 12 months. At the end of the 1st month after injection, significant improvements were seen in all groups. In the 3rd month, the improvements in WOMAC and VAS scores were similar in Groups 1 and 2, while those in Group 3 were lower (p < 0.001). At the 6th month, while the clinical efficacies of PRP and HA were similar and continued, the clinical effect of ozone had disappeared (p < 0.001). At the end of the 12th month, PRP was determined to be both statistically and clinically superior to HA (p < 0.001). In the treatment of mild–moderate knee OA, PRP was more successful than HA and ozone injections, as the application alone was sufficient to provide at least 12 months of pain-free daily living activities. Therapeutic study,
Optimal Online Cyberbullying Detection

Cyberbullying has emerged as a serious societal and public health problem that demands accurate methods for the detection of cyber-bullying instances in an effort to mitigate the consequences. While techniques to automatically detect cyberbullying incidents have been developed, the scalability and timeliness of existing cyberbullying detection approaches have largely been ignored. We address this gap by formulating cyberbullying detection as a sequential hypothesis testing problem. Based on this formulation, we propose a novel algorithm designed to reduce the time to raise a cyberbullying alert by drastically reducing the number of feature evaluations necessary for a decision to be made. We demonstrate the effectiveness of our approach using a real-world dataset from Twitter, one of the top five networks with the highest percentage of users reporting cyberbullying instances. We show that our approach is highly scalable while not sacrificing accuracy for scalability.
Why Deep Learning Is Changing the Way to Approach NGS Data Processing: A Review

Nowadays, big data analytics in genomics is an emerging research topic. In fact, the large amount of genomics data originated by emerging next-generation sequencing (NGS) techniques requires more and more fast and sophisticated algorithms. In this context, deep learning is re-emerging as a possible approach to speed up the DNA sequencing process. In this review, we specifically discuss such a trend. In particular, starting from an analysis of the interest of the Internet community in both NGS and deep learning, we present a taxonomic analysis highlighting the major software solutions based on deep learning algorithms available for each specific NGS application field. We discuss future challenges in the perspective of cloud computing services aimed at deep learning based solutions for NGS.
CannyLines: A parameter-free line segment detector

In this paper, we present a robust line segment detection algorithm to efficiently detect the line segments from an input image. Firstly a parameter-free Canny operator, named as CannyPF, is proposed to robustly extract the edge map from an input image by adaptively setting the low and high thresholds for the traditional Canny operator. Secondly, both efficient edge linking and splitting techniques are proposed to collect collinear point clusters directly from the edge map, which are used to fit the initial line segments based on the least-square fitting method. Thirdly, longer and more complete line segments are produced via efficient extending and merging. Finally, all the detected line segments are validated due to the Helmholtz principle [1, 2] in which both the gradient orientation and magnitude information are considered. Experimental results on a set of representative images illustrate that our proposed line segment detector, named as CannyLines, can extract more meaningful line segments than two popularly used line segment detectors, LSD [3] and ED-Lines , especially on the man-made scenes.
The Brain on Stress: Vulnerability and Plasticity of the Prefrontal Cortex over the Life Course

The prefrontal cortex (PFC) is involved in working memory and self-regulatory and goal-directed behaviors and displays remarkable structural and functional plasticity over the life course. Neural circuitry, molecular profiles, and neurochemistry can be changed by experiences, which influence behavior as well as neuroendocrine and autonomic function. Such effects have a particular impact during infancy and in adolescence. Behavioral stress affects both the structure and function of PFC, though such effects are not necessarily permanent, as young animals show remarkable neuronal resilience if the stress is discontinued. During aging, neurons within the PFC become less resilient to stress. There are also sex differences in the PFC response to stressors. While such stress and sex hormone-related alterations occur in regions mediating the highest levels of cognitive function and self-regulatory control, the fact that they are not necessarily permanent has implications for future behavior-based therapies that harness neural plasticity for recovery.
Business ecosystem analysis framework

The concept of business ecosystems has gained popularity from researchers and practitioners since first introduced in Moore's article in Harvard Business Review in 1993 “Predators and prey: a new ecology of competition”. Inspired by biology, the concept of business ecosystems provides a metaphor to comprehend the inter-wined nature of industries . The reason behind the popularity of this concept and its differentiation from other business theories like Osterwalder's Business Model Canvas  and Porter's Value Chain Approach  is its comprehensiveness and inclusion of several other components surrounding any business. In this paper, the necessary methods, components and tools for creating, analyzing and managing a business ecosystem successfully will be introduced. The aim of the paper is to propose a Business Ecosystem Analysis Framework that covers all the steps needed to analyze, manage and ensure the feasibility and sustainability of any ecosystem. This framework can be used to locate strengths and weaknesses of an ecosystem, identify its main actors and define how their maturity, interconnectedness and continuity can be improved. The results of the research can be applied to any industry sector.
AdGraph: A Machine Learning Approach to Automatic and Effective Adblocking

Filter lists are widely deployed by adblockers to block ads and other forms of undesirable content in web browsers. However, these filter lists are manually curated based on informal crowdsourced feedback, which brings with it a significant number of maintenance challenges. To address these challenges, we propose a machine learning approach for automatic and effective adblocking called AdGraph. Our approach relies on information obtained from multiple layers of the web stack (HTML, HTTP, and JavaScript) to train a machine learning classifier to block ads and trackers. Our evaluation on Alexa top-10K websites shows that AdGraph automatically and effectively blocks ads and trackers with 97.7% accuracy. Our manual analysis shows that AdGraph has better recall than filter lists, it blocks 16%more ads and trackers with 65% accuracy. We also show that AdGraph is fairly robust against adversarial obfuscation by publishers and advertisers that bypass filter lists.
100 W GaN HEMT power amplifier module with > 60% efficiency over 100–1000 MHz bandwidth

We have demonstrated a decade bandwidth 100 W GaN HEMT power amplifier module with 15.5–18.6 dB gain, 104–121 W CW output power and 61.4–76.6 % drain efficiency over the 100–1000 MHz band. The 2 × 2 inch compact power amplifier module combines four 30 W lossy matched broadband GaN HEMT PAs packaged in a ceramic SO8 package. Each of the 4 devices is fully matched to 50 Ω and obtains 30.8–35.7 W with 68.6–79.6 % drain efficiency over the band. The packaged amplifiers contain a GaN on SiC device operating at 48V drain voltage, alongside GaAs integrated passive matching circuitry. The four devices are combined using a broadband low loss coaxial balun. We believe this combination of output power, bandwidth and efficiency is the best reported to date. These amplifiers are targeted for use in multi-band public mobile radios and for instrumentation applications.