nils-herrmann/clean_scirepeval huggingface dataset:
High Speed FFT based Pulse Detection for a Digital ESM Receiver for Airborne Applications

This paper brings out a unique FFT based Pulse detection approach for a Two channel Digital ESM (Electronic Support Measure) Receiver targeted for Airborne EW applications. The proposed approach uses a high speed ADC (1.5 GHz) and FPGA based architecture for sampling and Digital processing of the received signals. A high speed 256 point FFT engine is realised in FPGA to separate up to 4 overlapped pulses in 500 MHz instantaneous BW. Pulses with PW as low as 100 ns can be detected in presence of CW signals in the 750-1250 MHz input band with this approach. The hardware realised to verify the algorithm and the FPGA implementation for Pulse Detection Engine are discussed in the paper. The simulated and measured results for the Pulse Detection algorithm are presented. This pulse detection approach gives up to 45 dB single signal dynamic range.
Incidental haptic sensations influence social judgments and decisions.

Touch is both the first sense to develop and a critical means of information acquisition and environmental manipulation. Physical touch experiences may create an ontological scaffold for the development of intrapersonal and interpersonal conceptual and metaphorical knowledge, as well as a springboard for the application of this knowledge. In six experiments, holding heavy or light clipboards, solving rough or smooth puzzles, and touching hard or soft objects nonconsciously influenced impressions and decisions formed about unrelated people and situations. Among other effects, heavy objects made job candidates appear more important, rough objects made social interactions appear more difficult, and hard objects increased rigidity in negotiations. Basic tactile sensations are thus shown to influence higher social cognitive processing in dimension-specific and metaphor-specific ways.
"Roles for the Boys?": Mining Cast Lists for Gender and Role Distributions over Time

Film and television play an important role in popular culture. However studies that require watching and annotating video are time-consuming and expensive to run at scale. We explore information mined from media database cast lists to explore the evolution of different roles over time. We focus on the gender distribution of those roles and how this changes over time. Finally, we compare real-life census gender distributions to our web-mediated onscreen gender data. We propose these methodologies are a useful adjunct to traditional analysis that allow researchers to explore the relationship between online and onscreen gender depictions.
Pilot Contamination Reduction in Massive MIMO System

This paper proposed two efficient pilot contamination reduction schemes based on the TDD massive MIMO system model: the directional pilot scheme and the multicell processing (MCP) scheme. Closed expressions of the user throughput are also derived. According to the simulation results obtained, we can know that the capability of the massive MIMO system can be significantly improved and the robustness is also achieved through the proposed scheme. Due to a lack of good strategies for pilot contamination reduction, the proposed schemes in this paper provide some new solutions for this problem with strong innovation.
QoE-Oriented Rate Adaptation for DASH With Enhanced Deep Q-Learning

With the popularity of handheld devices, the development of wireless communication technology and the proliferation of multimedia resources, mobile video has become the main business in LTE networks with explosive traffic demands. How to improve the quality of experience (QoE) of mobile video in the dynamic and complex network environment has become a research focus. Dynamic adaptive streaming over HTTP technology introduces adaptive bitrate (ABR) requests at the client side to improve video QoE and various rate adaptation algorithms are also constantly proposed. In view of the limitations of the existing heuristic or learning-based ABR methods, we propose redirecting enhanced Deep Q-learning toward DASH video QoE (RDQ), a QoE-oriented rate adaptation framework based on enhanced deep Q-learning. First, we establish a chunkwise subjective QoE model and utilize it as the reward function in reinforcement learning so that the strategy can converge toward the direction of maximizing the subjective QoE score. Then, we apply several effective improvements of deep Q-learning to the RDQ agent’s neural network architecture and learning mechanism to achieve faster convergence and higher average reward than other learning-based methods. The proposed RDQ agent has been thoroughly evaluated using trace-based simulation on the real-time LTE network data. For disparate network scenarios and different video contents, the RDQ agent can outperform the existing methods in terms of the QoE score. The breakdown analysis shows that RDQ can suppress the number and the duration of the stalling events to the minimum while maintaining high video bitrate, thus achieving better QoE performance than other methods.
Effects of Video Game Training on Behavioral and Electrophysiological Measures of Attention and Memory: Protocol for a Randomized Controlled Trial

Neuroplasticity-based approaches seem to offer promising ways of maintaining cognitive health in older adults and postponing the onset of cognitive decline symptoms. Although previous research suggests that training can produce transfer effects, this study was designed to overcome some limitations of previous studies by incorporating an active control group and the assessment of training expectations.



The main objectives of this study are (1) to evaluate the effects of a randomized computer-based intervention consisting of training older adults with nonaction video games on brain and cognitive functions that decline with age, including attention and spatial working memory, using behavioral measures and electrophysiological recordings (event-related potentials [ERPs]) just after training and after a 6-month no-contact period; (2) to explore whether motivation, engagement, or expectations might account for possible training-related improvements; and (3) to examine whether inflammatory mechanisms assessed with noninvasive measurement of C-reactive protein in saliva impair cognitive training-induced effects. A better understanding of these mechanisms could elucidate pathways that could be targeted in the future by either behavioral or neuropsychological interventions.



A single-blinded randomized controlled trial with an experimental group and an active control group, pretest, posttest, and 6-month follow-up repeated measures design is used in this study. A total of 75 cognitively healthy older adults were randomly distributed into experimental and active control groups. Participants in the experimental group received 16 1-hour training sessions with cognitive nonaction video games selected from Lumosity, a commercial brain training package. The active control group received the same number of training sessions with The Sims and SimCity, a simulation strategy game.



We have recruited participants, have conducted the training protocol and pretest assessments, and are currently conducting posttest evaluations. The study will conclude in the first semester of 2017. Data analysis will take place during 2017. The primary outcome is transfer of benefit from training to attention and working memory functions and the neural mechanisms underlying possible cognitive improvements.



We expect that mental stimulation with video games will improve attention and memory both at the behavioral level and in ERP components promoting brain and mental health and extending independence among elderly people by avoiding the negative personal and economic consequences of long-term care.
The ROSE Source-to-Source Compiler Infrastructure

The development of future software for proposed new computer architectures is expected to make significant demands on compiler technologies. Significant rewriting of applications will be likely to support the use of new hardware features and preserve the current software investment. Existing compilers provide little support for the broad range of research efforts addressing exascale challenges, such as parallelism, locality, resiliency, power efficiency, etc. The economics of how new machines will change an existing code base of software, that is too expensive to manually rewrite, may well drive automated mechanisms to transform existing software to take advantage of future machine features. This approach will lessen the cost and delay of the move to new, and possibly radically different, future architectures. Source-to-source compilers provides a pragmatic vehicle to support research, development, and deployment of novel compiler technologies by compiler experts or even advanced application developers. Within a source-to-source approach the input source code is read by the compiler, an internal representation (IR) is constructed, the IR is the basis of analysis that is used to guide transformations, the transformations occur on the IR, the IR is used to regenerate new source code, which is then compiled by a backend compiler. Our source-to-source compiler, ROSE, is a project to support the requirements of DOE. Work on ROSE has focused on the development of a community based project to define source-to-source compilation for a broad range of languages especially targeted at DOE applications (addressing robustness and large scale codes as required for DOE applications). Novel research areas are most easily supported when they can leverage significant tool chains that interact and use source code while allowing the hardware vendor’s own compiler for low level optimizations. In fact, high level optimization are rarely feasible for existing low level compilers for common languages such as C, C++, and Fortran. ROSE addresses the economics of how compiler research can be moved closer to the audience with significant technical performance problems and for whom the hardware is likely to be changing significantly in the next decade. Within ROSE it is less the goal to solve all problems than to permit domain experts to better solve their own problems. This talk will focus on the design and motivation for ROSE as an open community source-to-source compiler infrastructure to support performance optimization, tools for analysis, verification and software assurance, and general cus[Copyright notice will appear here once ’’ option is removed.] tom analysis and transformations needs directly on software using the languages common within DOE High Performance Computing.
Morphological Operations and Projection Profiles based Segmentation of Handwritten Kannada Document

Segmentation is an important task of any Optical Character Recognition (OCR) system. It separates the image text documents into lines, words and characters. The accuracy of OCR system mainly depends on the segmentation algorithm being used. Segmentation of handwritten text of some Indian languages like Kannada, Telugu, Assamese is difficult when compared with Latin based languages because of its structural complexity and increased character set. It contains vowels, consonants and compound characters. Some of the characters may overlap together. Despite several successful works in OCR all over the world, development of OCR tools in Indian languages is still an ongoing process. Character segmentation plays an important role in character recognition because incorrectly segmented characters are unlikely to be recognized correctly. In this paper, a segmentation scheme for segmenting handwritten Kannada scripts into lines, words and characters using morphological operations and projection profiles is proposed. The method was tested on totally unconstrained handwritten Kannada scripts, which pays more challenge and difficulty due to the complexity involved in the script. Usage of the morphology made extracting text lines efficient by an average extraction rate of 94.5% .Because of the varying inter and intra word gaps an average segmentation rate of 82.35% and 73.08% for words and characters respectively is obtained.
Improving Transferability of Adversarial Examples with Input Diversity

Though convolutional neural networks have achieved stateof-the-art performance on various vision tasks, they are extremely vulnerable to adversarial examples, which are obtained by adding humanimperceptible perturbations to the original images. Adversarial examples can thus be used as an useful tool to evaluate and select the most robust models in safety-critical applications. However, most of the existing adversarial attacks only achieve relatively low success rates under the challenging black-box setting, where the attackers have no knowledge of the model structure and parameters. To this end, we propose to improve the transferability of adversarial examples by creating diverse input patterns. Instead of only using the original images to generate adversarial examples, our method applies random transformations to the input images at each iteration. Extensive experiments on ImageNet show that the proposed attack method can generate adversarial examples that transfer much better to different networks than existing baselines. To further improve the transferability, we (1) integrate the recently proposed momentum method into the attack process; and (2) attack an ensemble of networks simultaneously. By evaluating our method against top defense submissions and official baselines from NIPS 2017 adversarial competition, this enhanced attack reaches an average success rate of 73.0%, which outperforms the top 1 attack submission in the NIPS competition by a large margin of 6.6%. We hope that our proposed attack strategy can serve as a benchmark for evaluating the robustness of networks to adversaries and the effectiveness of different defense methods in future. The code is public available at https://github.com/cihangxie/DI-2-FGSM.
Sparse Additive Subspace Clustering

In this paper, we introduce and investigate a sparse additive model for subspace clustering problems. Our approach, named SASC (Sparse Additive Subspace Clustering), is essentially a functional extension of the Sparse Subspace Clustering (SSC) of Elhamifar & Vidal [7] to the additive nonparametric setting. To make our model computationally tractable, we express SASC in terms of a finite set of basis functions, and thus the formulated model can be estimated via solving a sequence of grouped Lasso optimization problems. We provide theoretical guarantees on the subspace recovery performance of our model. Empirical results on synthetic and real data demonstrate the effectiveness of SASC for clustering noisy data points into their original subspaces.
Ultra wideband L-loop antenna

The antenna design for ultra wideband (UWB) signal radiation is one of the main challenges of the UWB system, especially when low-cost, geometrically small and radio efficient structures are required for typical applications. This study presents a novel printed loop antenna with introducing an L shape portion to its arms. The antenna offers excellent performance for lower-band frequency of UWB system, ranging from 3.1 GHz to 5.1 GHz. The antenna exhibits a 10 dB return loss bandwidth over the entire frequency band. The antenna is designed on FR4 substrate and fed with 50 ohms coupled tapered transmission line. It is found that the lower frequency band depends on the L portion of the loop antenna; however the upper frequency limit was decided by the taper transmission line. Though with very simple geometry, the results are satisfactory.
Traffic Flow Prediction using Adaboost Algorithm with Random Forests as a Weak Learner

Traffic Management and Information Systems, which rely on a system of sensors, aim to describe in real-time traffic in urban areas using a set of parameters and estimating them. Though the state of the art focuses on data analysis, little is done in the sense of prediction. In this paper, we describe a machine learning system for traffic flow management and control for a prediction of traffic flow problem. This new algorithm is obtained by combining Random Forests algorithm into Adaboost algorithm as a weak learner. We show that our algorithm performs relatively well on real data, and enables, according to the Traffic Flow Evaluation model, to estimate and predict whether there is congestion or not at a given time on road intersections. Machine Learning, Boosting, Classification, Traffic Congestion, Data Collecting, Magnetic Loop Detectors, Signalized Intersections, Traffic Signal Timing Optimization.
A Hybrid Intrusion Detection System of Cluster-based Wireless Sensor Networks

(WSNs) have made them extremely useful in various applications. WSNs are susceptible to attack, because they are cheap, small devices and are deployed in open and unprotected environments. In this paper, we propose an Intrusion Detection System (IDS) created in Cluster-based Wireless Sensor Networks (CWSNs). According to the capability of Cluster Head (CH) is better than other Sensor Nodes (SNs) in CWSN. Therefore, a Hybrid Intrusion Detection System (HIDS) is designed in this research. The CH is used to detect intruders that not only decreases the consumption of energy, but also efficiently reduces the amount of information in the entire network. However, the lifetime of network can be prolonged by the proposed HIDS.
Methods for approximating value functions for the Dominion card game

Artificial neural networks have been successfully used to approximate value functions for tasks involving decision making. In domains where decisions require a shift in judgment as the overall state changes, it is hypothesized here that methods utilizing multiple artificial neural networks are likely to provide a benefit as an approximation of a value function over those that employ a single network. The card game Dominion was chosen as the domain to examine this. This paper compares artificial neural networks generated by multiple machine learning methods successfully applied to other games (such as in TD-Gammon) to a genetic algorithm method for generating two neural networks for different phases of the game along with evolving the transition point. The results demonstrate a greater success ratio with the genetic algorithm applied to two neural networks. This suggests that future work examining more complex neural network configurations and richer evolutionary exploration could apply to Dominion as well as other domains necessitating shifts in strategy.
Usability of Forensics Tools: A User Study

Digital forensics has become a critical part of almost every investigation, and users of digital forensics tools are becoming more diverse in their backgrounds and interests. As a result, usability is an important aspect of these tools. This paper examines the usability aspect of forensics tools through interviews and surveys designed to obtain feedback from professionals using these tools as part of their regularly assigned duties. The study results highlight a number of usability issues that need to be taken into consideration when designing and implementing digital forensics tools.
Adding structure to top-k: from items to expansions

Keyword based search interfaces are extremely popular as a means for efficiently discovering items of interest from a huge collection, as evidenced by the success of search engines like Google and Bing. However, most of the current search services still return results as a flat ranked list of items. Considering the huge number of items which can match a query, this list based interface can be very difficult for the user to explore and find important items relevant to their search needs. In this work, we consider a search scenario in which each item is annotated with a set of keywords. E.g., in Web 2.0 enabled systems such as flickr and del.icio.us, it is common for users to tag items with keywords. Based on this annotation information, we can automatically group query result items into different expansions of the query corresponding to subsets of keywords. We formulate and motivate this problem within a top-k query processing framework, but as that of finding the top-k most important expansions. Then we study additional desirable properties for the set of expansions returned, and formulate the problem as an optimization problem of finding the best k expansions satisfying all the desirable properties. We propose several efficient algorithms for this problem. Our problem is similar in spirit to recent works on automatic facets generation, but has the important difference and advantage that we don't need to assume the existence of pre-defined categorical hierarchy which is critical for these works. Through extensive experiments on both real and synthetic datasets, we show our proposed algorithms are both effective and efficient.
Facebook use and adolescents ’ emotional states of depression , anxiety , and stress

Facebook (FB) is becoming an increasingly salient feature in peoples’ lives and has grown into a bastion in our current society with over 1 billion users worldwide –the majority of which are college students. However, recent studies conducted suggest that the use of Facebook may impacts individuals’ well being. Thus, this paper aimed to explore the effects of Facebook usage on adolescents’ emotional states of depression, anxiety, and stress.  A cross sectional design was utilized in this investigation. The study population included 76 students enrolled in the Bachelor of Science in Nursing program from a government university in Samar, Philippines. Facebook Intensity Scale (FIS) and the Depression Anxiety and Stress Scale (DASS) were the primary instruments used in this study.  Findings indicated correlation coefficients of 0.11 (p=0.336), 0.07 (p=0.536), and 0.10 (p=0.377) between Facebook Intensity Scale (FIS) and Depression, Anxiety, and Stress scales in the DASS. Time spent on FBcorrelated significantly with depression (r=0.233, p=0.041) and anxiety (r=0.259, p=0.023). Similarly, the three emotional states (depression, anxiety, and stress) correlated significantly.  Intensity of Facebook use is not directly related to negative emotional states. However, time spent on Facebooking increases depression and anxiety scores. Implications of the findings to the fields of counseling and psychology are discussed.
Prosthetic Hand Finger Control Using Fuzzy Sliding Modes

In order to improve the life quality of amputees, providing approximate manipulation ability of a human hand to that of a prosthetic hand is considered by many researchers. In this study, a biomechanical model of the index finger of the human hand is developed based on the human anatomy. Since the activation of finger bones are carried out by tendons, a tendon configuration of the index finger is introduced and used in the model to imitate the human hand characteristics and functionality. Then, fuzzy sliding mode control where the slope of the sliding surface is tuned by a fuzzy logic unit is proposed and applied to have the finger model to follow a certain trajectory. The trajectory of the finger model, which mimics the motion characteristics of the human hand, is pre-determined from the camera images of a real hand during closing and opening motion. Also, in order to check the robust behaviour of the controller, an unexpected joint friction is induced on the prosthetic finger on its way. Finally, the resultant prosthetic finger motion and the tendon forces produced are given and results are discussed.