nils-herrmann/clean_scirepeval huggingface dataset:
DASCo: dynamic adaptive streaming over CoAP

This paper presents the Dynamic Adaptive Streaming over CoAP (DASCo), a solution for adaptive media streaming in the Internet of Things (IoT) environment. DASCo combines DASH (Dynamic Adaptive Streaming over HTTP), the widespread open standard for HTTP-compliant streaming, with Constrained Application Protocol (CoAP), the vendor-independent web transfer protocol designed for resource-constrained devices. The proposed solution uses DASH formats and mechanisms to make media segments available for consumers, and exploits CoAP to deliver media segments to consumers’ applications. Consequently, the DASCo player offers native interoperability with IoT devices that are accessed via CoAP, thus it allows easy access to data collected by different sensors in order to enrich the multimedia services. In the paper we present an overview of constraints of default CoAP implementation with respect to media streaming, and propose guidelines for development of adaptive streaming service over CoAP. Moreover, we discuss the features of CoAP that can be investigated when designing an efficient adaptive algorithm for DASCo. Presented experimental results show poor performance of DASCo when default values of CoAP transmission parameters have been used. However, adjusting the parameters according to the network conditions considerably improves DASCo efficiency. Furthermore, in bad network conditions the enhanced DASCo is characterized by a more stable download rate compared to DASH. This feature is important in the context of dynamic media adaptation, since it allows an adaptation algorithm to better fit media bit rate with download conditions.
Autonomous Approach and Landing for a Low-Cost Quadrotor Using Monocular Cameras

In this paper, we propose a monocular vision system for approach and landing using a low-cost micro aerial vehicle (MAV). The system enables an off-the-shelf Parrot AR.Drone 2.0 quadrotor MAV to autonomously detect a landpad, approach it, and land on it. Particularly, we exploit geometric properties of a circular landpad marker in order to estimate the exact flight distance between the quadrotor and the landing spot. We then employ monocular simultaneous localization and mapping (SLAM) to fly towards the landpad while accurately following a trajectory. Notably, our system does not require the landpad to be located directly underneath the MAV.
Classification of Attributes in a Natural Language Query into Different SQL Clauses

Attribute information in a natural language query is one of the key features for converting a natural language query into a Structured Query Language1 (SQL) in Natural Language Interface to Database systems. In this paper, we explore the task of classifying the attributes present in a natural language query into different SQL clauses in a SQL query. In particular, we investigate the effectiveness of various features and Conditional Random Fields for this task. Our system uses a statistical classifier trained on manually prepared data. We report our results on three different domains and also show how our system can be used for generating a complete SQL query.
The Koch Monopole : A Small Fractal Antenna

Fractal objects have some unique geometrical properties. One of them is the possibility to enclose in a finite area an infinitely long curve. The resulting curve is highly convoluted being nowhere differentiable. One such curve is the Koch curve. In this paper, the behavior the Koch monopole is numerically and experimentally analyzed. The results show that as the number of iterations on the small fractal Koch monopole are increased, the of the antenna approaches the fundamental limit for small antennas.
OHSUMED: An Interactive Retrieval Evaluation and New Large Test Collection for Research

A series of information retrieval experiments was earned out with a computer installed in a medical practice setting for relatively inexperienced physician end-users. Using a commercial MEDLINE product based on the vector space model, these physicians searched just as effectively as more experienced searchers using Boolean searching. The results of this experiment were subsequently used to create anew large medical test collection, which was used in experiments with the SMART ~trieval system to obtain baseline performance data as well as compare SMART with the other searchers.
High-Selectivity Dual-Band Bandpass Filter Using a Quad-Mode Resonator With Source-Load Coupling

This letter presents a novel dual-band bandpass filter with controllable frequencies and bandwidths as well as a high out-of-band rejection level. The proposed filter utilizes a novel stub-loaded quad-mode resonator. Every two modes, which can be flexibly controlled, are utilized to form a passband with controllable frequency and bandwidth. Source-load coupling and hook-shape feed lines are introduced and high selectivity is achieved. An experimental filter is implemented and the experimental results are presented for validation.
Developing an Assessment Method of Active Aging: University of Jyvaskyla Active Aging Scale.

To develop an assessment method of active aging for research on older people.



A multiphase process that included drafting by an expert panel, a pilot study for item analysis and scale validity, a feedback study with focus groups and questionnaire respondents, and a test-retest study. Altogether 235 people aged 60 to 94 years provided responses and/or feedback.



We developed a 17-item University of Jyvaskyla Active Aging Scale with four aspects in each item (goals, ability, opportunity, and activity; range 0-272). The psychometric and item properties are good and the scale assesses a unidimensional latent construct of active aging.



Our scale assesses older people's striving for well-being through activities pertaining to their goals, abilities, and opportunities. The University of Jyvaskyla Active Aging Scale provides a quantifiable measure of active aging that may be used in postal questionnaires or interviews in research and practice.
Human action recognition using multi-view image sequences

Recognizing human action from image sequences is an active area of research in computer vision. In this paper, we present a novel method for human action recognition from image sequences in different viewing angles that uses the Cartesian component of optical flow velocity and human body shape feature vector information. We use principal component analysis to reduce the higher dimensional shape feature space into low dimensional shape feature space. We represent each action using a set of multidimensional discrete hidden Markov model and model each action for any viewing direction. We performed experiments of the proposed method by using KU gesture database. Experimental results based on this database of different actions show that our method is robust
A 2.4GHz CMOS Gilbert Mixer in 180nm Technology

This paper presents the design of an analog CMOS Gilbert cell Mixer for RF Receiver front end application. Input RF frequency of the mixer is 2.4GHz and operated at 1.8V power supply. The schematic has been implemented using UMC-180nm CMOS technology and simulated on spectre-RF simulator of Cadence. The input RF and LO frequency of the proposed mixer is 2.4GHz and 2.25GHz respectively, resulting in an output IF frequency of 250MHz. It achieves a conversion Gain of around 13dB and considerable NF. IIP3 and 1-db Compression point are also measured. Layout design and extracted view of the mixer is also presented in this paper.
An improved Q-PLL to overcome the speed reversal problems in sensorless PMSM drive

Many existing sensorless control methods are based on flux observers, where phase-locked loops (PLLs) were used to extract the position information from the estimated rotor flux. This paper proposes an improved quadrature-component-based phase locked loop (Q-PLL) scheme for sensorless control of permanent magnet synchronous motor (PMSM) drives. To show the superiority of the proposed method in solving the problem of speed reversal, other three kinds of PLLs are also presented for the aim of comparison. This improved PLL is integrated with SMO-based sensorless control of PMSM drives. Compared to conventional Q-PLL, the main advantage of the proposed PLL is that it can work well without a gain scheduling remedial strategy or a judgment of the sign of speed in terms of speed reversal. Furthermore, it has a better performance in the transient and steady state response. Both Simulation and experimental results are presented to demonstrate the good performances of the proposed Q-PLL.
Linear codes over Z4+uZ4: MacWilliams identities, projections, and formally self-dual codes

Linear codes are considered over the ring Z 4 + uZ 4 , a non-chain extension of Z 4. Lee weights, Gray maps for these codes are defined and MacWilliams identities for the complete, symmetrized and Lee weight enumer-ators are proved. Two projections from Z 4 + uZ 4 to the rings Z 4 and F 2 + uF 2 are considered and self-dual codes over Z 4 +uZ 4 are studied in connection with these projections. Finally three constructions are given for formally self-dual codes over Z 4 + uZ 4 and their Z 4-images together with some good examples of formally self-dual Z 4-codes obtained through these constructions.
Simgrid: A Toolkit for the Simulation of Application Scheduling

Advances in hardware and software technologies have made it possible to deploy parallel applications over increasingly large sets of distributed resources. Consequently, the study of scheduling algorithms for such applications has been an active area of research. Given the nature of most scheduling problems one must resort to simulation to effectively evaluate and compare their efficacy over a wide range of scenarios. It has thus become necessary to simulate those algorithms for increasingly complex distributed, dynamic, heterogeneous environments. In this pa per we present Simgrid, a simulation toolkit for the study of scheduling algorithms for distributed application. Thi s paper gives the main concepts and models behind Simgrid, describes its API and highlights current implementation is sues. We also give some experimental results and describe work that builds on Simgrid’s functionalities.
New graduate nurses' experiences in their first year of practice.

This study aimed to explore new graduates' experiences of entering the nursing workforce in NSW, Australia, and to identify factors that impact on their transition to the workforce, satisfaction and likelihood of retention.



The nature of new graduates' experiences in their first year of employment has been shown to have a significant impact on their future career directions. It is well reported that often these experiences are stressful and unsatisfying.



A mixed method cross sectional design was used combining quantitative and qualitative approaches. Data was gathered by online survey and focus groups.



A total of 282 new graduates, aged 21 to 54, responded to the online survey (response rate 24%). Overall, respondents were satisfied with their recruitment process (mean 3.54) and support for professional development (mean 3.37) but job satisfaction was rated lower (mean 2.91). Qualitative findings from focus groups and survey comments revealed a number of key factors impacting on the experience of transition for new graduates. These are; the nature of the workplace environment, the level and nature of support available to new graduates, together with their propensity to learn and adapt to workplace cultures and to accommodate their own expectations and the expectations of others, and to a lesser degree, the amount of prior experience.



There is an urgent need to develop and test a range of evidence based approaches that will both empower nurses and embed systematic approaches that enable equitable and contextually relevant stewardship of new graduate nurses into the future.
Privacy-Preserving Photo Sharing based on a Public Key Infrastructure

A significant number of pictures are posted to social media sites or exchanged through instant messaging and cloud-based sharing services. Most social media services offer a range of access control mechanisms to protect users privacy. As it is not in the best interest of many such services if their users restrict access to their shared pictures, most services keep users’ photos unprotected which makes them available to all insiders. This paper presents an architecture for a privacy-preserving photo sharing based on an image scrambling scheme and a public key infrastructure. A secure JPEG scrambling is applied to protect regional visual information in photos. Protected images are still compatible with JPEG coding and therefore can be viewed by any one on any device. However, only those who are granted secret keys will be able to descramble the photos and view their original versions. The proposed architecture applies an attribute-based encryption along with conventional public key cryptography, to achieve secure transmission of secret keys and a fine-grained control over who may view shared photos. In addition, we demonstrate the practical feasibility of the proposed photo sharing architecture with a prototype mobile application, ProShare, which is built based on iOS platform.
UO_UPV: Deep Linguistic Humor Detection in Spanish Social Media

Natural Language Understanding becomes very hard when creativity and figurative language are used in social communication. Humor constitutes an illustrative example of how humans use creative language to produce funny content. Therefore, create new methods and resources for analyzing properly humorous texts is an important issue in Natural Language Processing (NLP) and even more in Human Computer Interaction (HCI). In this sense, this paper introduces our UO UPV system developed for the Humor Analysis based on Human Annotation (HAHA) track proposed in IberEval 2018 Workshop. The task focuses on classifying tweets in Spanish as humorous or not, and predicting how funny they are. To solve this task, our proposal combines both linguistic features and an Attention-based Recurrent Neural Network, where the attention layer helps to calculate the contribution of each term towards targeted humorous classes. Experimental results show that our model achieves encourage results.
A Framework for Validating Models of Evasion Attacks on Machine Learning, with Application to PDF Malware Detection

Machine learning (ML) techniques are increasingly common in security applications, such as malware and intrusion detection. However, there is increasing evidence that machine learning models are susceptible to evasion attacks, in which an adversary makes small changes to the input (such as malware) in order to cause erroneous predictions (for example, to avoid being detected). Evasion attacks on ML fall into two broad categories: 1) those which generate actual malicious instances and demonstrate both evasion of ML and efficacy of attack (we call these problem space attacks), and 2) attacks which directly manipulate features used by ML, abstracting efficacy of attack into a mathematical cost function (we call these feature space attacks). Central to our inquiry is the following fundamental question: are feature space models of attacks useful proxies for real attacks? In the process of answering this question, we make two major contributions: 1) a general methodology for evaluating validity of mathematical models of ML evasion attacks, and 2) an application of this methodology as a systematic hypothesis-driven evaluation of feature space evasion attacks on ML-based PDF malware detectors. Specific to our case study, we find that a) feature space evasion models are in general not adequate in representing real attacks, b) such models can be significantly improved by identifying conserved features (features that are invariant in real attacks) whenever these exist, and c) ML hardened using the improved feature space models remains robust to alternative attacks, in contrast to ML hardened using a very powerful class of problem space attacks, which does not.
Neural embedding-based indices for semantic search

Traditional information retrieval techniques that primarily rely on keyword-based linking of the query and document spaces face challenges such as the vocabulary mismatch problem where relevant documents to a given query might not be retrieved simply due to the use of different terminology for describing the same concepts. As such, semantic search techniques aim to address such limitations of keyword-based retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic information might not lead to improved retrieval performance over keyword-based search, their consideration enables the retrieval of a set of relevant documents that cannot be retrieved by keyword-based methods. As such, building indices that store and provide access to semantic information during the retrieval process is important. While the process for building and querying keyword-based indices is quite well understood, the incorporation of semantic information within search indices is still an open challenge. Existing work have proposed to build one unified index encompassing both textual and semantic information or to build separate yet integrated indices for each information type but they face limitations such as increased query process time. In this paper, we propose to use neural embeddings-based representations of term, semantic entity, semantic type and documents within the same embedding space to facilitate the development of a unified search index that would consist of these four information types. We perform experiments on standard and widely used document collections including Clueweb09-B and Robust04 to evaluate our proposed indexing strategy from both effectiveness and efficiency perspectives. Based on our experiments, we find that when neural embeddings are used to build inverted indices; hence relaxing the requirement to explicitly observe the posting list key in the indexed document: (a) retrieval efficiency will increase compared to a standard inverted index, hence reduces the index size and query processing time, and (b) while retrieval efficiency, which is the main objective of an efficient indexing mechanism improves using our proposed method, retrieval effectiveness also retains competitive performance compared to the baseline in terms of retrieving a reasonable number of relevant documents from the indexed corpus. Email addresses: fatemeh.lashkari@unb.ca (Fatemeh Lashkari), bagheri@ryerson.ca (Ebrahim Bagheri), ghorbani@unb.ca (Ali A. Ghorbani)  2018
An Empirical Investigation of the Impact of Individual and Work Characteristics on Telecommuting Success

Individual and work characteristics are used in telecommuting plans; however, their impact on telecommuting success is not well known. We studied how employee tenure, work experience, communication skills, task interdependence, work output measurability, and task variety impact telecommuter productivity, performance, and satisfaction after taking into account the impact of communication technologies. Data collected from 89 North American telecommuters suggest that in addition to the richness of the media, work experience, communication skills, and task interdependence impact telecommuting success. These characteristics are practically identifiable and measurable; therefore, we expect our findings to help managers convert increasing telecommuting adoption rates to well-defined and measurable gains.
Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks

Efforts to reduce the numerical precision of computations in deep learning training have yielded systems that aggressively quantize weights and activations, yet employ wide high-precision accumulators for partial sums in inner-product operations to preserve the quality of convergence. The absence of any framework to analyze the precision requirements of partial sum accumulations results in conservative design choices. This imposes an upper-bound on the reduction of complexity of multiply-accumulate units. We present a statistical approach to analyze the impact of reduced accumulation precision on deep learning training. Observing that a bad choice for accumulation precision results in loss of information that manifests itself as a reduction in variance in an ensemble of partial sums, we derive a set of equations that relate this variance to the length of accumulation and the minimum number of bits needed for accumulation. We apply our analysis to three benchmark networks: CIFAR-10 ResNet 32, ImageNet ResNet 18 and ImageNet AlexNet. In each case, with accumulation precision set in accordance with our proposed equations, the networks successfully converge to the single precision floating-point baseline. We also show that reducing accumulation precision further degrades the quality of the trained network, proving that our equations produce tight bounds. Overall this analysis enables precise tailoring of computation hardware to the application, yielding areaand power-optimal systems.
ISCEV Standard for full-field clinical electroretinography (2015 update)

This document, from the International Society for Clinical Electrophysiology of Vision (ISCEV), presents an updated and revised ISCEV Standard for full-field clinical electroretinography (ffERG or simply ERG). The parameters for Standard flash stimuli have been revised to accommodate a variety of light sources including gas discharge lamps and light emitting diodes. This ISCEV Standard for clinical ERGs specifies six responses based on the adaptation state of the eye and the flash strength: (1) Dark-adapted 0.01 ERG (rod ERG); (2) Dark-adapted 3 ERG (combined rod-cone standard flash ERG); (3) Dark-adapted 3 oscillatory potentials; (4) Dark-adapted 10 ERG (strong flash ERG); (5) Light-adapted 3 ERG (standard flash “cone” ERG); and (6) Light-adapted 30 Hz flicker ERG. ISCEV encourages the use of additional ERG protocols for testing beyond this minimum standard for clinical ERGs.
Design on the low-leakage diode string for using in the power-rail ESD clamp circuits in a 0.35-/spl mu/m silicide CMOS process

A new design of the diode string with very low leakage current is proposed for use in the ESD clamp circuits across the power rails. By adding an NMOS-controlled lateral SCR (NCLSCR) device into the stacked diode string, the leakage current of this new diode string with six stacked diodes at 5 V (3.3 V) forward bias can be reduced to only 2.1 (1.07) nA at a temperature of 125/spl deg/C in a 0.35 /spl mu/m silicide CMOS process, whereas the previous designs have a leakage current in the order of mA. The total blocking voltage of this new design with NCLSCR can be linearly adjusted by changing the number of the stacked diodes in the diode string without causing latch-up danger across the power rails. From the experimental results, the human-body-model ESD level of the ESD clamp circuit with the proposed low-leakage diode string is greater than 8 kV in a 0.35 /spl mu/m silicide CMOS process by using neither ESD implantation nor the silicide-blocking process modifications.
Emotion classification using minimal EEG channels and frequency bands

In this research we propose to use EEG signal to classify two emotions (i.e., positive and negative) elicited by pictures. With power spectrum features, the accuracy rate of SVM classifier is about 85.41%. Considering each pair of channels and different frequency bands, it shows that frontal pairs of channels give a better result than the other area and high frequency bands give a better result than low frequency bands. Furthermore, we can reduce number of pairs of channels from 7 to 5 with almost the same accuracy and can cut low frequency bands in order to save computation time. All of these are beneficial to the development of emotion classification system using minimal EEG channels in real-time.