nils-herrmann/clean_scirepeval huggingface dataset:
Kernel Region ( a ) Top Level ( b ) Compute Unit Level ( c ) Processing Element Level

OpenCL FPGA has recently gained great popularity with emerging needs for workload acceleration such as Convolutional Neural Network (CNN), which is the most popular deep learning architecture in the domain of computer vision. While OpenCL enhances the code portability and programmability of FPGA, it comes at the expense of performance. The key challenge is to optimize the OpenCL kernels to efficiently utilize the flexible hardware resources in FPGA. Simply optimizing the OpenCL kernel code through various compiler options turns out insufficient to achieve desirable performance for both compute-intensive and data-intensive workloads such as convolutional neural networks . In this paper, we first propose an analytical performance model and apply it to perform an in-depth analysis on the resource requirement of CNN classifier kernels and available resources on modern FPGAs. We identify that the key performance bottleneck is the onchip memory bandwidth. We propose a new kernel design to effectively address such bandwidth limitation and to provide an optimal balance between computation, on-chip, and off-chip memory access. As a case study, we further apply these techniques to design a CNN accelerator based on the VGG model. Finally, we evaluate the performance of our CNN accelerator using an Altera Arria 10 GX1150 board. We achieve 866 Gop/s floating point performance at 370MHz working frequency and 1.79 Top/s 16-bit fixed-point performance at 385MHz. To the best of our knowledge, our implementation achieves the best power efficiency and performance density compared to existing work.
Requirements and architecture design principles for a smart city experiment with sensor and social networks integration

Smart city infrastructures offer unique testbeds ground for innovative experimentation and services offering. Sensors networks in cities with integrated social networks activities can improve people-centric services, while improving infrastructures setting. This work summarizes the principles and priorities chosen in a smart city experiment, entitled SEN2SOC which bridges sensor measurements and social networks interactions for supporting smart city services. SEN2SOC prioritizes requirements along particular categories which cover data collection, users sensing along with applications implementation and architectural concerns. These requirements are correlated with the suggested components in an architecture which is flexible enough in order to permit various activities control flow in terms of data preprocessing, conditions detection, statistical analysis as well as applications development and social data mining.
Generative Adversarial Networks in Estimation of Distribution Algorithms for Combinatorial Optimization

Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Generative Ad-versarial Networks (GAN) are generative neural networks which can be trained to implicitly model the probability distribution of given data, and it is possible to sample this distribution. We integrate a GAN into an EDA and evaluate the performance of this system when solving combinatorial optimization problems with a single objective. We use several standard benchmark problems and compare the results to state-of-the-art multivariate EDAs. GAN-EDA doe not yield competitive results – the GAN lacks the ability to quickly learn a good approximation of the probability distribution. A key reason seems to be the large amount of noise present in the first EDA generations.
Depth-disparity calibration for augmented reality on binocular optical see-through displays

We present a study of depth-disparity calibration for augmented reality applications using binocular optical see-through displays. Two techniques were proposed and compared. The "paired-eyes" technique leverages the Panum's fusional area to help viewer find alignment between the virtual and physical objects. The "separate-eyes" technique eliminates the need of binocular fusion and involves using both eyes sequentially to check the virtual-physical object alignment on retinal images. We conducted a user study to measure the calibration results and assess the subjective experience of users with the proposed techniques.
The Role of Deliberate Practice in the Acquisition of Expert Performance

The theoretical framework presented in this article explains expert performance as the end result of individuals' prolonged efforts to improve performance while negotiating motivational and external constraints. In most domains of expertise, individuals begin in their childhood a regimen of effortful activities (deliberate practice) designed to optimize improvement. Individual differences, even among elite performers, are closely related to assessed amounts of deliberate practice. Many characteristics once believed to reflect innate talent are actually the result of intense practice extended for a minimum of 10 years. Analysis of expert performance provides unique evidence on the potential and limits of extreme environmental adaptation and learning.
What and Where: 3D Object Recognition with Accurate Pose

Many applications of 3D object recognition, such as augmented reality or robotic manipulation, require an accurate solution for the 3D pose of the recognized objects. This is best accomplished by building a metrically accurate 3D model of the object and all its feature locations, and then fitting this model to features detected in new images. In this chapter, we describe a system for constructing 3D metric models from multiple images taken with an uncalibrated handheld camera, recognizing these models in new images, and precisely solving for object pose. This is demonstrated in an augmented reality application where objects must be recognized, tracked, and superimposed on new images taken from arbitrary viewpoints without perceptible jitter. This approach not only provides for accurate pose, but also allows for integration of features from multiple training images into a single model that provides for more reliable recognition.
Computational augmented reality eyeglasses

In this paper we discuss the design of an optical see-through head-worn display supporting a wide field of view, selective occlusion, and multiple simultaneous focal depths that can be constructed in a compact eyeglasses-like form factor. Building on recent developments in multilayer desktop 3D displays, our approach requires no reflective, refractive, or diffractive components, but instead relies on a set of optimized patterns to produce a focused image when displayed on a stack of spatial light modulators positioned closer than the eye accommodation distance. We extend existing multilayer display ray constraint and optimization formulations while also purposing the spatial light modulators both as a display and as a selective occlusion mask. We verify the design on an experimental prototype and discuss challenges to building a practical display.
Evaluating data storage structures of MapReduce

MapReduce framework and its open-source implementation Hadoop, a scalable and fault-tolerant infrastructure for big data analysis on large clusters, can achieve different performance with different data storage structures. This paper evaluates the performance about three kinds of data storage structures of MapReduce, namely row-store, column-store, and RCFile. The evaluating experiments are designed to test three data storage structures in terms of data loading time, data storage space, and query execution time. The experimental results show that RCFile data storage structure can achieve better performance in most cases.
Efficient elastic burst detection in data streams

Burst detection is the activity of finding abnormal aggregates in data streams. Such aggregates are based on sliding windows over data streams. In some applications, we want to monitor many sliding window sizes simultaneously and to report those windows with aggregates significantly different from other periods. We will present a general data structure for detecting interesting aggregates over such elastic windows in near linear time. We present applications of the algorithm for detecting Gamma Ray Bursts in large-scale astrophysical data. Detection of periods with high volumes of trading activities and high stock price volatility is also demonstrated using real time Trade and Quote (TAQ) data from the New York Stock Exchange (NYSE). Our algorithm beats the direct computation approach by several orders of magnitude.
>1.3-Tb/s VCSEL-Based On-Board Parallel-Optical Transceiver Module for High-Density Optical Interconnects

This paper gives a detailed description of a >1.3-Tb/s VCSEL-based on-board parallel-optical transceiver module for high-density optical interconnects. The optical module integrates a 28-Gb/s × 24-channel transmitter and receiver into one package with a 1-in2 footprint, thereby yielding a data rate density as high as 1.34 Tb/s/in2. A unique module design is developed to utilize the whole top surface for thermal dissipation and the bottom surface for optical and electrical interfaces. The heat dissipation characteristics are studied in simulations and experiments. A test system of 24-channel optical loop-back link is built to evaluate the performance when operating all 24 channels by 28.05-Gb/s 231–1 PRBS bit streams simultaneously. With a total power consumption of 9.1 W, the optical module can be operated within the operating case temperature under a practical air cooling condition. When operating all 24 channels simultaneously, a total jitter margin at BER of 10–12 is larger than 0.4 U.I. at a monitor channel. The measured jitter margin is consistent regardless of any channel operation.
Are Accuracy and Robustness Correlated

Machine learning models are vulnerable to adversarial examples formed by applying small carefully chosen perturbations to inputs that cause unexpected classification errors. In this paper, we perform experiments on various adversarial example generation approaches with multiple deep convolutional neural networks including Residual Networks, the best performing models on ImageNet Large-Scale Visual Recognition Challenge 2015. We compare the adversarial example generation techniques with respect to the quality of the produced images, and measure the robustness of the tested machine learning models to adversarial examples. Finally, we conduct large-scale experiments on cross-model adversarial portability. We find that adversarial examples are mostly transferable across similar network topologies, and we demonstrate that better machine learning models are less vulnerable to adversarial examples.
BabelNet: Building a Very Large Multilingual Semantic Network

In this paper we present BabelNet – a very large, wide-coverage multilingual semantic network. The resource is automatically constructed by means of a methodology that integrates lexicographic and encyclopedic knowledge from WordNet and Wikipedia. In addition Machine Translation is also applied to enrich the resource with lexical information for all languages. We conduct experiments on new and existing gold-standard datasets to show the high quality and coverage of the resource.
Development of knee power assist using backdrivable electro-hydrostatic actuator

Backdrivability is a keyword with rising importance, not only in humanoid robots, but also to wearable robots. Power assist robots, namely exoskeletons are expected to provide mobility and independence to elderly and people with disability. In such robots, without backdrivability, error between human and robot motion can be painful; sometimes dangerous. In this research, we apply a type of hydraulic actuator, specially designed to realize backdrivability, to exoskeletal robot to improve backdrivability. We present the basic methodology to apply such hydraulic actuator to wearable robots. We developed prototype of knee joint power assist exoskeleton and performed fundamental tests to verify the design method validity.
A first simulation on optimizing EDS for cabin baggage screening regarding throughput

Airport security screening is vital for secure air transportation. Screening of cabin baggage heavily relies on human operators reviewing X-ray images. Explosive detection systems (EDS) developed for cabin baggage screening can be a very valuable addition security-wise. Depending on the EDS machine and settings, false alarm rates increase, which could reduce throughput. A discrete event simulation was used to investigate how different machine settings of EDS, different groups of X-ray screeners, and different durations of alarm resolution with explosives trace detection (ETD) influence throughput of a specific cabin baggage screening process. For the modelling of screening behavior in the context of EDS and for the estimation of model parameters, data was borrowed from a human-machine interaction experiment and a work analysis. In a second step, certain adaptations were tested for their potential to reduce the impact of EDS on throughput. The results imply that moderate increases in the false alarm rate by EDS can be buffered by employing more experienced and trained X-ray screeners. Larger increases of the false alarm rate require a fast alarm resolution and additional resources for the manual search task.
Femto Clouds: Leveraging Mobile Devices to Provide Cloud Service at the Edge

Mobile devices are becoming increasingly capable computing platforms with significant processor power and memory. However, mobile compute capabilities are often underutilized. In this paper we consider how a collection of co-located devices can be orchestrated to provide a cloud service at the edge. Scenarios with co-located devices include, but are not limited to, passengers with mobile devices using public transit services, students in classrooms and groups of people sitting in a coffee shop. To this end, we propose the femtocloud system which provides a dynamic, self-configuring and multi-device mobile cloud out of a cluster of mobile devices. We present the femtocloud system architecture designed to enable multiple mobile devices to be configured into a coordinated cloud computing service despite churn in mobile device participation. We develop a prototype of our femtocloud system and use it in addition to simulations to evaluate the performance of the system showing its efficiency and ability to leverage the available devices' compute capacity. We contribute to a line of research on small, local and possibly private clouds.
Development and Validation of the User Version of the Mobile Application Rating Scale (uMARS)

The Mobile Application Rating Scale (MARS) provides a reliable method to assess the quality of mobile health (mHealth) apps. However, training and expertise in mHealth and the relevant health field is required to administer it.



This study describes the development and reliability testing of an end-user version of the MARS (uMARS).



The MARS was simplified and piloted with 13 young people to create the uMARS. The internal consistency and test-retest reliability of the uMARS was then examined in a second sample of 164 young people participating in a randomized controlled trial of a mHealth app. App ratings were collected using the uMARS at 1-, 3,- and 6-month follow up.



The uMARS had excellent internal consistency (alpha = .90), with high individual alphas for all subscales. The total score and subscales had good test-retest reliability over both 1-2 months and 3 months.



The uMARS is a simple tool that can be reliably used by end-users to assess the quality of mHealth apps.
The slot-coupled hemispherical dielectric resonator antenna with a parasitic patch: applications to the circularly polarized antenna and wide-band antenna

The aperture-coupled hemispherical dielectric resonator antenna (DRA) with a parasitic patch is studied rigorously. Using the Green's function approach, integral equations for the unknown patch and slot currents are formulated and solved using the method of moments. The theory is utilized to design a circularly polarized (CP) DRA and a wide-band linearly polarized (LP) DRA. In the former, the CP frequency and axial ratio (AR) can easily be controlled by the patch location and patch size, respectively, with the impedance matched by varying the slot length and microstrip stub length. It is important that the AR will not be affected when the input impedance is tuned, and the CP design is therefore greatly facilitated. For the wide-band LP antenna, a maximum bandwidth of 22% can be obtained, which is much wider than the previous bandwidth of 7.5% with no parasitic patches. Finally, the frequency-tuning characteristics of the proposed antenna are discussed. Since the parasitic patch can be applied to any DRAs, the method will find applications in practical DRA designs.
Procedural Game Adaptation : Framing Experience Management as Changing an MDP

In this paper, we present the Procedural Game Adaptation (PGA) framework, a designer-controlled way to change a game’s dynamics during end-user play. We formalize a video game as a Markov Decision Process, and frame the problem as maximizing the reward of a given player by modifying the game’s transition function. By learning a model of each player to estimate her rewards, PGA managers can change the game’s dynamics in a player-informed way. Following a formal definition of the components of the framework, we illustrate its versatility by using it to represent two existing adaptive systems: PaSSAGE, and Left 4 Dead’s AI Director.
Query-Dependent Contextualization of Streaming Data

We propose a method for linking entities in a stream of short textual documents that takes into account context both inside a document and inside the history of documents seen so far. Our method uses a generic optimization framework for combining several entity ranking functions, and we introduce a global control function to control optimization. Our results demonstrate the effectiveness of combining entity ranking functions that take into account context, which is further boosted by 6% when we use an informed global control function.
A Guide to Conducting a Systematic Literature Review of Information Systems Research

Levy and Ellis (2006) and Webster and Watson (2002) lament the fact that information systems (IS) scholars tend to be unaware of the need for structure in literature reviews. Even today, the rigorous, standardized methodology for conducting a systematic literature review (SLR) that has developed from the health sciences and other fields is virtually unknown in IS research. In this paper, we adapt Fink's (2005, p. 3) definition of a research literature review as our operative definition of a systematic literature review: "a systematic, explicit, [comprehensive, (p. 17)] and reproducible method for identifying, evaluating, and synthesizing the existing body of completed and recorded work produced by researchers, scholars, and practitioners." Although there exists an abundance of guides to conducting such reviews in other research fields, none entirely meet the unique needs of IS researchers. In response to this shortage of guides, we present here the features and value of a systematic literature review, and adapt the methodology to the particular context of IS research.
Interactive holographic application using augmented reality EduCard and 3D holographic pyramid for interactive and immersive learning

The advancement of augmented reality (AR) and holographic display technologies have a great potential to support and improve teaching and learning process, because the 3D images give new perspectives to the students to understand certain topic easily and intuitively. In this paper, an Interactive Holographic Display is introduced, which is aimed to ease the teachers to deliver the knowledge to the students as well as to provide self-learning for the students. This proposed solution is implemented using the AR tracking technique and fused with 3D holographic pyramid display. This makes the virtual objects can display in thin air like a real object and makes the holographic effect more realistic and interactive, as the user can interact with the virtual objects using an image target. This paper explains how the system is physically realized in term of hardware configuration and software design.
A Low-Profile Reconfigurable Cavity-Backed Slot Antenna With Frequency, Polarization, and Radiation Pattern Agility

A novel cavity-backed slot antenna, with the ability of reconfiguring the frequency, polarization, and radiation pattern, is presented. The reconfigurability is realized by electronically controlling the state of switches between two crossed slots etched on the surfaces of a substrate integrated waveguide cavity. The antenna is capable of simultaneously changing the radiation patterns between forward and backward directions, switching the polarization among two orthogonal linearly polarized (LP) and two orthogonal circularly polarized (CP) states, tuning between three frequency bands for LP states and between two frequency bands for CP states. A fully functional prototype is developed and tested, demonstrating the antenna with measured gains of approximately 4 dBi and stable unidirectional radiation patterns for all 20 states. In addition, the proposed design possesses a low profile of 0.01 free-space wavelength.
Using parse tree validation to prevent SQL injection attacks

An SQL injection attack targets interactive web applications that employ database services. Such applications accept user input, such as form fields, and then include this input in database requests, typically SQL statements. In SQL injection, the attacker provides user input that results in a different database request than was intended by the application programmer. That is, the interpretation of the user input as part of a larger SQL statement, results in an SQL statement of a different form than originally intended. We describe a technique to prevent this kind of manipulation and hence eliminate SQL injection vulnerabilities. The technique is based on comparing, at run time, the parse tree of the SQL statement before inclusion of user input with that resulting after inclusion of input. Our solution is efficient, adding about 3 ms overhead to database query costs. In addition, it is easily adopted by application programmers, having the same syntactic structure as current popular record set retrieval methods. For empirical analysis, we provide a case study of our solution in J2EE. We implement our solution in a simple static Java class, and show its effectiveness and scalability.
PaperVis: Literature Review Made Easy

Reviewing literatures for a certain research field is always important for academics. One could use Google-like information seeking tools, but oftentimes he/she would end up obtaining too many possibly related papers, as well as the papers in the associated citation network. During such a process, a user may easily get lost after following a few links for searching or cross-referencing. It is also difficult for the user to identify relevant/important papers from the resulting huge collection of papers. Our work, called PaperVis, endeavors to provide a user-friendly interface to help users quickly grasp the intrinsic complex citation-reference structures among a specific group of papers. We modify the existing Radial Space Filling (RSF) and Bullseye View techniques to arrange involved papers as a node-link graph that better depicts the relationships among them while saving the screen space at the same time. PaperVis applies visual cues to present node attributes and their transitions among interactions, and it categorizes papers into semantically meaningful hierarchies to facilitate ensuing literature exploration. We conduct experiments on the InfoVis 2004 Contest Dataset to demonstrate the effectiveness of PaperVis.