nils-herrmann/clean_scirepeval huggingface dataset:
A Kinect based gesture recognition algorithm using GMM and HMM

Gesture recognition is a quite promising field in robotics and many Human-Computer Interaction (HCI) related areas. This research uses Microsoft® Kinect to capture the 3D position data of joints, and uses Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM) to model full-body gestures. We propose a gesture recognition algorithm to segment gestures from real-time data flow, and finally achieved to recognize predefined full-body gestures in real-time. This proposed method gives a high recognition rate of 94.36%, indicating the capability of the new method.
DW-AES: A Domain-Wall Nanowire-Based AES for High Throughput and Energy-Efficient Data Encryption in Non-Volatile Memory

Big-data storage poses significant challenges to anonymization of sensitive information against data sniffing. Not only will the encryption bandwidth be limited by the I/O traffic, the transfer of data between the processor and the memory will also expose the input-output mapping of intermediate computations on I/O channels that are susceptible to semi-invasive and non-invasive attacks. Limited by the simplistic cell-level logic, existing logic-in-memory computing architectures are incapable of performing the complete encryption process within the memory at reasonable throughput and energy efficiency. In this paper, a block-level in-memory architecture for advanced encryption standard (AES) is proposed. The proposed technique, called DW-AES, maps all AES operations directly to the domain-wall nanowires. The entire encryption process can be completed within a homogeneous, high-density, and standby-power-free non-volatile spintronic-based memory array without exposing the intermediate results to external I/O interface. Domain-wall nanowire-based pipelining and multi-issue pipelining methods are also proposed to increase the throughput of the baseline DW-AES with an insignificant area overhead and negligible difference on leakage power and energy consumption. The experimental results show that DW-AES can reduce the leakage power and area by the orders of magnitude compared with existing CMOS ASIC accelerators. It has an energy efficiency of 22 pJ/b, which is 5× and 3× better than the CMOS ASIC and memristive CMOL-based implementations, respectively. Under the same area budget, the proposed DW-AES achieves 4.6× higher throughput than the latest CMOS ASIC AES with similar power consumption. The throughput improvement increases to 11× for pipelined DW-AES at the expense of doubling the power consumption.
Plate-laminated corporate-feed slotted waveguide array antenna at 350-GHz band by silicon process

A corporate feed slotted waveguide array antenna with broadband characteristics in term of gain in the 350 GHz band is achieved by measurement for the first time. The etching accuracy for thin laminated plates of the diffusion bonding process with conventional chemical etching is limited to ±20μm. This limits the use of this process for antenna fabrication in the submillimeter wave band where the fabrication tolerances are very severe. To improve the etching accuracy of the thin laminated plates, a new fabrication process has been developed. Each silicon wafer is etched by DRIE (deep reactive ion etcher) and is plated by gold on the surface. This new fabrication process provides better fabrication tolerances about ±5 μm using wafer bond aligner. The thin laminated wafers are then bonded with the diffusion bonding process under high temperature and high pressure. To validate the proposed antenna concepts, an antenna prototype has been designed and fabricated in the 350 GHz band. The 3dB-down gain bandwidth is about 44.6 GHz by this silicon process while it was about 15GHz by the conventional process using metal plates in measurement.
Automatic Generation of Typographic Font from a Small Font Subset

This paper addresses the automatic generation of a typographic font from a subset of characters. Specifically, we us e a subset of a typographic font to extrapolate additional characters. Consequently, we obtain a complete font containing a number of characters suf ficient for daily use.The automated generation of Japanese fonts is in high demand because a Japanese font requires over 1,000 characters. Unf ortunately, professional typographers create most fonts, resulting insignificant financial and time investments for font generation. The proposed method can be a great aid for font creation because designers do not n eed to create the majority of the characters for a new font. The proposed method uses strokes from given samples for font generation. The str okes, from which we construct characters, are extracted by exploitinga character skeleton dataset. This study makes three main contribution s: a novel method of extracting strokes from characters, which is appl icable to both standard fonts and their variations; a fully automated approach for constructing characters; and a selection method for samplecharacters. We demonstrate our proposed method by generating 2,965 char cters in 47 fonts. Objective and subjective evaluations verify thatthe generated characters are similar to handmade characters.
Food Recommender Systems: Important Contributions, Challenges and Future Research Directions

The recommendation of food items is important for many reasons. Attaining cooking inspiration via digital sources is becoming evermore popular; as are systems, which recommend other types of food, such as meals in restaurants or products in supermarkets. Researchers have been studying these kinds of systems for many years, suggesting not only that can they be a means to help people find food they might want to eat, but also help them nourish themselves more healthily. This paper provides a summary of the state-of-the-art of so-called food recommender systems, highlighting both seminal and most recent approaches to the problem, as well as important specializations, such as food recommendation systems for groups of users or systems which promote healthy eating. We moreover discuss the diverse challenges involved in designing recsys for food, summarise the lessons learned from past research and outline what we believe to be important future directions and open questions for the field. In providing these contributions we hope to provide a useful resource for researchers and practitioners alike.
Visuo-acoustic stimulation that helps you to relax: A virtual reality setup for patients in the intensive care unit

After prolonged stay in an intensive care unit (ICU) patients often complain about cognitive impairments that affect health-related quality of life after discharge. The aim of this proof-of-concept study was to test the feasibility and effects of controlled visual and acoustic stimulation in a virtual reality (VR) setup in the ICU. The VR setup consisted of a head-mounted display in combination with an eye tracker and sensors to assess vital signs. The stimulation consisted of videos featuring natural scenes and was tested in 37 healthy participants in the ICU. The VR stimulation led to a reduction of heart rate (p = 0. 049) and blood pressure (p = 0.044). Fixation/saccade ratio (p < 0.001) was increased when a visual target was presented superimposed on the videos (reduced search activity), reflecting enhanced visual processing. Overall, the VR stimulation had a relaxing effect as shown in vital markers of physical stress and participants explored less when attending the target. Our study indicates that VR stimulation in ICU settings is feasible and beneficial for critically ill patients.
How Can Automatic Feedback Help Students Construct Automata?

In computer-aided education, the goal of automatic feedback is to provide a meaningful explanation of students' mistakes. We focus on providing feedback for constructing a deterministic finite automaton that accepts strings that match a described pattern. Natural choices for feedback are binary feedback (correct/wrong) and a counterexample of a string that is processed incorrectly. Such feedback is easy to compute but might not provide the student enough help. Our first contribution is a novel way to automatically compute alternative conceptual hints. Our second contribution is a rigorous evaluation of feedback with 377 students. We find that providing either counterexamples or hints is judged as helpful, increases student perseverance, and can improve problem completion time. However, both strategies have particular strengths and weaknesses. Since our feedback is completely automatic, it can be deployed at scale and integrated into existing massive open online courses.
Robust ECG signal classification for detection of atrial fibrillation using a novel neural network

Electrocardiograms (ECG) provide a non-invasive approach for clinical diagnosis in patients with cardiac problems, particularly atrial fibrillation (AF). Robust, automatic AF detection in clinics remains challenging. Deep learning has emerged as an effective tool for handling complex data analysis with minimal pre- and post-processing. A 16-layer 1D Convolutional Neural Network (CNN) was designed to classify the ECGs including AF. One of the key advances of the proposed CNN was that skip connections were employed to enhance the rate of information transfer throughout the network by connecting layers earlier in the network with layers later in the network. Skip connections led to a significant increase in the feature learning capabilities of the CNN as well as speeding up the training time. For comparisons, we also have implemented recurrent neural networks (RNN) and spectrogram learning. The CNN was trained on 8,528 ECGs and tested on 3,685 ECGs ranging from 9 to 60 seconds in length. The proposed 16-layer CNN outperformed RNNs and spectrogram learning. The training of the CNN took 2 hours on a Titan XPascal GPU (NVidia) with 3840 cores. The testing accuracy for the CNN was 82% and the runtime was ∼0.01 seconds for each signal classification. Particularly, the proposed CNN identified normal rhythm, AF and other rhythms with an accuracy of 90%, 82% and 75% respectively. We have demonstrated a novel CNN with skip connections to perform efficient, automatic ECG signal classification that could potentially aid robust patient diagnosis in real time.
Machine Learning and Social Robotics for Detecting Early Signs of Dementia

This paper presents the EACare project, an ambitious multidisciplinary collaboration with the aim to develop an embodied system, capable of carrying out neuropsychological tests to detect early signs of dementia, e.g., due to Alzheimer’s disease. The system will use methods from Machine Learning and Social Robotics, and be trained with examples of recorded clinician-patient interactions. The interaction will be developed using a participatory design approach. We describe the scope and method of the project, and report on a first Wizard of Oz prototype.
Putting Objects in Perspective

Image understanding requires not only individually estimating elements of the visual world but also capturing the interplay among them. In this paper, we provide a framework for placing local object detection in the context of the overall 3D scene by modeling the interdependence of objects, surface orientations, and camera viewpoint. Most object detection methods consider all scales and locations in the image as equally likely. We show that with probabilistic estimates of 3D geometry, both in terms of surfaces and world coordinates, we can put objects into perspective and model the scale and location variance in the image. Our approach reflects the cyclical nature of the problem by allowing probabilistic object hypotheses to refine geometry and vice-versa. Our framework allows painless substitution of almost any object detector and is easily extended to include other aspects of image understanding. Our results confirm the benefits of our integrated approach.
A novel wavelet transform technique for on-line partial discharge measurements. 1. WT de-noising algorithm

Medium and high voltage power cables are widely used in the electrical industry with substantial growth over the last 20-30 years ago, particular in the use of XLPE insulated systems. Ageing of the cable insulation is becoming an increasing problem that requires development of reliable methods for on-line condition assessment. For insulation condition assessment of MV and HV cables, partial discharge (PD) monitoring is one of the most effective techniques. However on-site and on-line PD measurements are affected by electromagnetic interference (EMI) that makes sensitive PD detection very difficult, if not impossible. This paper describes implementation of wavelet transform techniques to reject noise from on-line partial discharge measurements on cables. A new wavelet threshold determination method is proposed with the technique. With implementation of this novel de-noising method, PD measurement sensitivity has been greatly improved. In addition, a full AC cycle data recovery can be achieved instead of focusing only on recovering individual PD pulses. Other wavelet threshold de-noising methods are discussed and examined under a noisy environment to compare their performance with the new method proposed here. The method described here has been found to be superior to the other wavelet-based methods
Interference by huntingtin and atrophin-1 with cbp-mediated transcription leading to cellular toxicity.

Expanded polyglutamine repeats have been proposed to cause neuronal degeneration in Huntington's disease (HD) and related disorders, through abnormal interactions with other proteins containing short polyglutamine tracts such as the transcriptional coactivator CREB binding protein, CBP. We found that CBP was depleted from its normal nuclear location and was present in polyglutamine aggregates in HD cell culture models, HD transgenic mice, and human HD postmortem brain. Expanded polyglutamine repeats specifically interfere with CBP-activated gene transcription, and overexpression of CBP rescued polyglutamine-induced neuronal toxicity. Thus, polyglutamine-mediated interference with CBP-regulated gene transcription may constitute a genetic gain of function, underlying the pathogenesis of polyglutamine disorders.
Experiences of stigma and access to HAART in children and adolescents living with HIV/AIDS in Brazil.

This study describes and conceptualizes the experiences of stigma in a group of children living with HIV in São Paulo, Brazil, and evaluates the impact of access to highly active antiretroviral therapy (HAART) over the social course of AIDS and over the children's experiences of stigma. Through ethnographic research in São Paulo from 1999 to 2001, the life trajectories of 50 children ages 1-15 living with or affected by HIV were studied. Data were collected via participant observation and semi-structured informal interviews and analyzed using social theories on illness experience and social inequality. Our results demonstrate that AIDS-related stigma occurs within complex discrimination processes that change as children reach adolescence. We found that structural violence in the forms of poverty, racism, and inequalities in social status, gender, and age fuels children's experiences of stigma. We also describe how access to HAART changes the lived experience of children, reduces stigma, and brings new challenges in AIDS care such as adolescents' sexuality and treatment adherence. Based on these results, we propose structural violence as the framework to study stigma and argue that interventions to reduce stigma that solely target the perception and attitudes toward people living with HIV are limited. In contrast universal access to HAART in Brazil is a powerful intervention that reduces stigma, in that it transforms AIDS from a debilitating and fatal disease to a chronic and manageable one, belongs to a broader mechanism to assure citizens' rights, and reduces social inequalities in access to health care.
An Overview Of Document Image Analysis Systems

This paper presents an overview of Document Image Analysis Systems, their composing modules, the approaches these modules use, as well as uses for these applications. One of the main goals is to present some of the most important technologies and methods behind the Document Image Analysis domain in order to evaluate the best approach when dealing with real-world documents. The other main goal is to ensure a foundation for those starting to build such complex software systems and to give an elaborate technical answer to the question: “How to make physical documents available to a large number of people?”
The social structure of free and open source software development

Metaphors, such as the Cathedral and Bazaar, used to describe the organization of FLOSS projects typically place them in sharp contrast to proprietary development by emphasizing FLOSS’s distinctive social and communications structures. But what do we really know about the communication patterns of FLOSS projects? How generalizable are the projects that have been studied? Is there consistency across FLOSS projects? Questioning the assumption of distinctiveness is important because practitioner-advocates from within the FLOSS community rely on features of social structure to describe and account for some of the advantages of FLOSS production. To address this question, we examined 120 project teams from SourceForge, representing a wide range of FLOSS project types, for their communications centralization as revealed in the interactions in the bug tracking system. We found that FLOSS development teams vary widely in their communications centralization, from projects completely centered on one developer to projects that are highly decentralized and exhibit a distributed pattern of conversation between developers and active users. We suggest, therefore, that it is wrong to assume that FLOSS projects are distinguished by a particular social structure merely because they are FLOSS. Our findings suggest that FLOSS projects might have to work hard to achieve the expected development advantages which have been assumed to flow from “going open.” In addition, the variation in communications structure across projects means that communications centralization is useful for comparisons between FLOSS teams.
Mitigation of DDoS attacks in cloud computing

Cloud computing provided its users with the more convenient way to use the resources and with a model where the users are charged as per the usage of their resources. The model is known as “pay-as-per-usage”. Users can access the cloud services from anywhere at any time. They only need a working internet connection. Despite of all these benefits, cloud comes with some drawbacks also. The issue related to security of cloud is a biggest concern today. The attackers and hackers flood the network with attack packets and these are hard to identify as everything present on cloud is in virtual form. DDoS (Distributed Denial of Service) is a cloud-specific attack in which attack source is always more than one; multiple machines attacks on a user by sending packets with large data overhead. Such attacks make the resources unavailable to the user by overwhelming the network with unwanted traffic. In this research paper the main aim is to filter out the data packets with large data overhead to avoid DDoS attack and the Mean Time to Security Failure (MTSF) will be calculated so that an alternative dynamic plan can be adopted.
The Tale of e-Government: A Review of the Stories that Have Been Told So Far and What is Yet to Come

Since its first appearance, the concept of eGovernment has evolved into a recognized means that has helped the public sector to increase its efficiency and effectiveness. A lot of research has therefore been done in this area to elaborate on the different aspects encompassing this concept. However, when looking at the existing e-Government literature, research mostly focuses on one specific aspect of e-Government and there are few generic publications that provide an overview of the diversity of this interdisciplinary research field over a longer term period. This study analyzes the abstracts of eight e-Government journals from 2000 to 2016 by means of a quantitative text mining analysis, backed by a qualitative Delphi approach. The article concludes with a discussion on the findings and implications as well as directions for future research.
Feature-preserving mesh denoising via bilateral normal filtering

In this paper, we propose a feature-preserving mesh denoising algorithm which is effective, simple and easy to implement. The proposed method is a two-stage procedure with a bilateral surface normal filtering followed by integration of the normals for least squares error (LSE) vertex position updates. It is well-known that normal variations offer more intuitive geometric meaning than vertex position variations. A smooth surface can be described as one having smoothly varying normals whereas features such as edges and corners appear as discontinuities in the normals. Thus we cast feature-preserving mesh denoising as a robust surface normal estimation using bilateral filtering. Our definition of "intensity difference" used in the influence weighting function of the bilateral filter robustly prevents features such as sharp edges and corners from being washed out. We will demonstrate this capability by comparing the results from smoothing CAD-like models with other smoothing algorithms.
A Locality Aware Convolutional Neural Networks Accelerator

The advantages of Convolutional Neural Networks (CNNs) with respect to traditional methods for visual pattern recognition have changed the field of machine vision. The main issue that hinders broad adoption of this technique is the massive computing workload in CNN that prevents real-time implementation on low-power embedded platforms. Recently, several dedicated solutions have been proposed to improve the energy efficiency and throughput, nevertheless the huge amount of data transfer involved in the processing is still a challenging issue. This work proposes a new CNN accelerator exploiting a novel memory access scheme which significantly improves data locality in CNN related processing. With this scheme, external memory access is reduced by 50% while achieving similar or even better throughput. The accelerator is implemented using 28nm CMOS technology. Implementation results show that the accelerator achieves a performance of 102GOp/s @800MHz while consuming 0.303mm2 in silicon area. Power simulation shows that the dynamic power of the accelerator is 68mW. Its flexibility is demonstrated by running various different CNN benchmarks.