nils-herrmann/clean_scirepeval huggingface dataset:
An empirical examination of factors contributing to the creation of successful e-learning environments

Although existing models of e-learning effectiveness in information systems (IS) have increased our understanding of how technology can support and enhance learning, most of our models do not take into account the importance of social presence. Thus, this study extends previous research by developing a model of e-learning effectiveness which adds social presence to other oft studied variables including application-specific computer self-efficacy (AS-CSE), perceived usefulness, course interaction, and e-learning effectiveness. Using data from 345 individuals, this model was validated through a field study in an introductory IS survey course. Results indicate that AS-CSE and perceived usefulness were related to course performance, course satisfaction, and course instrumentality. In addition, course interaction was related to course performance and satisfaction. Finally, social presence was related to course satisfaction and course instrumentality. Implications for research and practice are discussed.
Breakfast reduces declines in attention and memory over the morning in schoolchildren

Twenty-nine schoolchildren were tested throughout the morning on 4 successive days, having a different breakfast each day (either of the cereals Cheerios or Shreddies, glucose drink or No breakfast). A series of computerised tests of attention, working memory and episodic secondary memory was conducted prior to breakfast and again 30, 90, 150 and 210 min later. The glucose drink and No breakfast conditions were followed by declines in attention and memory, but the declines were significantly reduced in the two cereal conditions. This study provides objective evidence that a typical breakfast of cereal rich in complex carbohydrates can help maintain mental performance over the morning.
Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly

Wireless-sensor-network-based home monitoring system for elderly activity behavior involves functional assessment of daily activities. In this paper, we reported a mechanism for estimation of elderly well-being condition based on usage of house-hold appliances connected through various sensing units. We defined two new wellness functions to determine the status of the elderly on performing essential daily activities. The developed system for monitoring and evaluation of essential daily activities was tested at the homes of four different elderly persons living alone and the results are encouraging in determining wellness of the elderly.
A low noise readout circuit for integrated electrochemical biosensor arrays

This paper presents a low noise electrochemical interface circuit that is tuned to the needs of protein-based biosensor arrays and compatible with the formation of fully integrated biochemical microsystems. The circuit includes an integrated potentiostat and highly sensitive amperometric readout amplifier. It achieves good noise performance while supporting biosensor output currents from 10 pA to 10 /spl mu/A to suit a wide range of sensitivities and electrode areas. Implemented in 0.5 /spl mu/m CMOS process, it is compatible with a post-CMOS process to realize an integrated biosensor array.
Action Recognition with the Augmented MoCap Data using Neural Data Translation

This study aims at generating reliable augmented training data to learn a robust deep model for action recognition. The prior knowledge inferred from few training data is not sufficient to well represent the real data distribution, which makes action recognition quite challenging. Inspired by the recent advances in neural machine translation, we propose a neural data translation (NDT) to tackle the aforementioned issue by directly learning the mapping between paired data of the same action class in an end-to-end fashion. The proposed NDT is a sequence-to-sequence generative model. It can be trained with few paired training data, and generates an abundant set of augmented actions with diverse appearance. Specifically, we adopt stochastic pair selection to compile a set of paired training data. Each pair consists of two actions of the same class. One action serves as the input to NDT, while the other acts as the desired output. By learning the mapping between data of the same class, NDT implicitly encodes the intra-class variations so that it can synthesize high-quality actions for augmentation. We evaluated our method on two public datasets, including the Florence3D-action and UCI HAR datasets. The promising results demonstrate that the actions generated by our method effectively improve the performance of action recognition with few examples.
Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images

Malaria is a blood disease caused by the Plasmodium parasites transmitted through the bite of female Anopheles mosquito. Microscopists commonly examine thick and thin blood smears to diagnose disease and compute parasitemia. However, their accuracy depends on smear quality and expertise in classifying and counting parasitized and uninfected cells. Such an examination could be arduous for large-scale diagnoses resulting in poor quality. State-of-the-art image-analysis based computer-aided diagnosis (CADx) methods using machine learning (ML) techniques, applied to microscopic images of the smears using hand-engineered features demand expertise in analyzing morphological, textural, and positional variations of the region of interest (ROI). In contrast, Convolutional Neural Networks (CNN), a class of deep learning (DL) models promise highly scalable and superior results with end-to-end feature extraction and classification. Automated malaria screening using DL techniques could, therefore, serve as an effective diagnostic aid. In this study, we evaluate the performance of pre-trained CNN based DL models as feature extractors toward classifying parasitized and uninfected cells to aid in improved disease screening. We experimentally determine the optimal model layers for feature extraction from the underlying data. Statistical validation of the results demonstrates the use of pre-trained CNNs as a promising tool for feature extraction for this purpose.
A Genetic Algorithm Approach to Dynamic Job Shop Scheduling Problems

This paper describes a genetic algorithm approach to the dynamic job shop scheduling problem with jobs arriving continually. Both deterministic and stochastic models of the dynamic problem were investigated. The objective functions examined were weighted flow time, maximum tardiness, weighted tardiness, weighted lateness, weighted number of tardy jobs, and weighted earliness plus weighted tardi-ness. In the stochastic model, we further tested the approach under various manufacturing environments with respect to the machine workload, imbalance of machine workload, and due date tightness. The results indicate that the approach performs well and is robust with regard to the objective function and the manufacturing environment in comparison with priority rule approaches.
A 2.6GHz band 537W peak power GaN HEMT asymmetric Doherty amplifier with 48% drain efficiency at 7dB

A 537W saturation output power (Psat) asymmetric Doherty amplifier for 2.6GHz band was successfully developed. The main and peak amplifiers were implemented with Psat of 210W and 320W GaN HEMTs. The newly developed 320W GaN HEMT consists of a single GaN die, both input and output partial match networks and a compact package. Its output matching network was tuned to inverse class F and a single-ended 320W GaN HEMT achieved higher than 61.8% drain efficiency from 2.4GHz to 2.7GHz. The 210W and 320W GaN HEMTs asymmetric Doherty amplifier exhibited 57.3dBm (537W) Psat and 48% drain efficiency with −50.6dBc ACLR at 50.3dBm (107W) average output power using a 4-carrier W-CDMA signal and commercially available digital pre-distortion system. These excellent performances show the good suitability for 2.6GHz band basestations.
Persistent monitoring in discrete environments: Minimizing the maximum weighted latency between observations

In this paper, we consider the problem of planning a path for a robot to monitor a known set of features of interest in an environment. We represent the environment as a graph with vertex weights and edge lengths. The vertices represent regions of interest, edge lengths give travel times between regions, and the vertex weights give the importance of each region. As the robot repeatedly performs a closed walk on the graph, we define the weighted latency of a vertex to be the maximum time between visits to that vertex, weighted by the importance (vertex weight) of that vertex. Our goal is to find a closed walk that minimizes the maximum weighted latency of any vertex. We show that there does not exist a polynomial time algorithm for the problem. We then provide two approximation algorithms; an O(logn)-approximation algorithm and an O(logρG)-approximation algorithm, where ρG is the ratio between the maximum and minimum vertex weights. We provide simulation results which demonstrate that our algorithms can be applied to problems consisting of thousands of vertices, and a case study for patrolling a city for crime.
A Handwritten Character Recognition System Using Directional Element Feature and Asymmetric Mahalanobis Distance

This paper presents a precise system for handwritten Chinese and Japanese character recognition. Before extracting directional element feature (DEF) from each character image, transformation based on partial inclination detection (TPID) is used to reduce undesired effects of degraded images. In the recognition process, city block distance with deviation (CBDD) and asymmetric Mahalanobis distance (AMD) are proposed for rough classification and fine classification. With this recognition system, the experimental result of the database ETL9B reaches to 99.42%.
iHadoop: Asynchronous Iterations for MapReduce

MapReduce is a distributed programming framework designed to ease the development of scalable data-intensive applications for large clusters of commodity machines. Most machine learning and data mining applications involve iterative computations over large datasets, such as the Web hyperlink structures and social network graphs. Yet, the MapReduce model does not efficiently support this important class of applications. The architecture of MapReduce, most critically its dataflow techniques and task scheduling, is completely unaware of the nature of iterative applications, tasks are scheduled according to a policy that optimizes the execution for a single iteration which wastes bandwidth, I/O, and CPU cycles when compared with an optimal execution for a consecutive set of iterations. This work presents iHadoop, a modified MapReduce model, and an associated implementation, optimized for iterative computations. The iHadoop model schedules iterations asynchronously. It connects the output of one iteration to the next, allowing both to process their data concurrently. iHadoop's task scheduler exploits inter-iteration data locality by scheduling tasks that exhibit a producer/consumer relation on the same physical machine allowing a fast local data transfer. For those iterative applications that require satisfying certain criteria before termination, iHadoop runs the check concurrently during the execution of the subsequent iteration to further reduce the application's latency. This paper also describes our implementation of the iHadoop model, and evaluates its performance against Hadoop, the widely used open source implementation of MapReduce. Experiments using different data analysis applications over real-world and synthetic datasets show that iHadoop performs better than Hadoop for iterative algorithms, reducing execution time of iterative applications by 25% on average. Furthermore, integrating iHadoop with HaLoop, a variant Hadoop implementation that caches invariant data between iterations, reduces execution time by 38% on average.
Fast Training of Convolutional Networks through FFTs

Convolutional networks are one of the most widely employed architectures in computer vision and machine learning. In order to leverage their ability to learn complex functions, large amounts of data are required for training. Training a large convolutional network to produce state-of-the-art results can take weeks, even when using modern GPUs. Producing labels using a trained network can also be costly when dealing with web-scale datasets. In this work, we present a simple algorithm which accelerates training and inference by a significant factor, and can yield improvements of over an order of magnitude compared to existing state-of-the-art implementations. This is done by computing convolutions as pointwise products in the Fourier domain while reusing the same transformed feature map many times. The algorithm is implemented on a GPU architecture and addresses a number of related challenges.
nTorrent: Peer-to-Peer File Sharing in Named Data Networking

BitTorrent is a popular application for peer-to-peer file sharing in today's Internet. To achieve robust and efficient data dissemination as an application overlay, BitTorrent implements a data-centric paradigm on top of TCP/IP's point-to-point packet delivery, which requires each peer to obtain network layer connectivity information (e.g., peer IP address, distance to each peer, routing policies) that is exclusively available at the network layer in order to select the best peers for data retrieval. This paper presents the design of nTorrent, which provides BitTorrent-like functions natively in Named Data Networking (NDN). We use simulations to examine how well the NDN's data-centric communication model can natively support such an application. Our work exposes the differences between the IP- based BitTorrent and nTorrent, and the issues and impact of moving IP-based applications to NDN-enabled networks.
Experience with SCRAM, a SCenario Requirements Analysis Method

A method of scenario based requirements engineering is described that uses a combination of early prototypes, scenario scripts and design rationale to elicit and validate user requirements. Experience in using the method on an EU project, Multimedia Broker, is reported. Quantitative data on requirements sessions is analysed to assess user participation and quality of requirements captured. The method worked well but there were problems in the use of design rationale and control of turn taking in RE sessions. Lessons learned in using the method are summarised and future improvements for the method are discussed.
Gavello's Procedure: An Old Earlobe Reconstruction Method, Revisited and Touched Up

Earlobe reconstruction following traumatic damage is a challenge for most of the plastic and reconstructive surgeons. Several methods developed for reconstruction of this deformity have shown to bear various advantages and disadvantages. Our modified Gavello's procedure, without using skin grafting in resurfacing the flap donor site, has shown to be satisfactory. This paper describes our modified operative procedure as well as a case resulting in a good looking earlobe and fine scars in flap donor site.
Application of Metamorphic Testing to Supervised Classifiers

Many applications in the field of scientific computing - such as computational biology, computational linguistics, and others - depend on Machine Learning algorithms to provide important core functionality to support solutions in the particular problem domains. However, it is difficult to test such applications because often there is no "test oracle" to indicate what the correct output should be for arbitrary input. To help address the quality of such software, in this paper we present a technique for testing the implementations of supervised machine learning classification algorithms on which such scientific computing software depends. Our technique is based on an approach called "metamorphic testing", which has been shown to be effective in such cases. More importantly, we demonstrate that our technique not only serves the purpose of verification, but also can be applied in validation. In addition to presenting our technique, we describe a case study we performed on a real-world machine learning application framework, and discuss how programmers implementing machine learning algorithms can avoid the common pitfalls discovered in our study. We also discuss how our findings can be of use to other areas outside scientific computing, as well.
Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-to-End Learning from Demonstration

We propose a technique for multi-task learning from demonstration that trains the controller of a low-cost robotic arm to accomplish several complex picking and placing tasks, as well as non-prehensile manipulation. The controller is a recurrent neural network using raw images as input and generating robot arm trajectories, with the parameters shared across the tasks. The controller also combines VAE-GAN-based reconstruction with autoregressive multimodal action prediction. Our results demonstrate that it is possible to learn complex manipulation tasks, such as picking up a towel, wiping an object, and depositing the towel to its previous position, entirely from raw images with direct behavior cloning. We show that weight sharing and reconstruction-based regularization substantially improve generalization and robustness, and training on multiple tasks simultaneously increases the success rate on all tasks.
Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks

Deep learning classifiers are known to be inherently vulnerable to manipulation by intentionally perturbed inputs, named adversarial examples. In this work, we establish that reinforcement learning techniques based on Deep Q-Networks (DQNs) are also vulnerable to adversarial input perturbations, and verify the transferability of adversarial examples across different DQN models. Furthermore, we present a novel class of attacks based on this vulnerability that enable policy manipulation and induction in the learning process of DQNs. We propose an attack mechanism that exploits the transferability of adversarial examples to implement policy induction attacks on DQNs, and demonstrate its efficacy and impact through experimental study of a game-learning scenario.
The Effects of Intervention Using a Robot on the Levels of Compliance of Four Children with Autism to Requests Given by T heir Mothers

The Effects of Intervention Using a Robot on the Levels of Compliance of Four Children with Autism to Requests Given by Their Mothers Holly J. Nelson Department of Communication Disorders Master of Science The current study presents the use of a humanoid robot to facilitate compliant behaviors to two types of directives in four children with autism. The children participated in a three-month intervention program that incorporated core components of the SCERTS model in order to facilitate social communication (Prizant, Wetherby, Rubin, & Laurent, 2003). Treatment sessions were comprised of 40 minutes of traditional treatment and 10 minutes of interaction with a humanoid robot. Preand post-intervention assessment were conducted, each involving a 5 minute interaction with the child’s mother, in which they were presented with directives in the form of physical manipulation and verbal requests accompanied by a gesture or model. These preand post-intervention sessions were recorded, analyzed, and coded for compliant and noncompliant behavior to the directives, as well as any eye contact, language, or reciprocal action that accompanied their behavior. The overall results were variable, revealing that two participants made notable gains, one child remained consistent, and another participant showed a decrease in compliant behavior in the post-intervention sessions. Further research should be conducted to include a longer period of baseline and intervention, more systematic identification of the most effective probes for the child, and documentation of the child’s physical and emotional state.
Pseudo test collections for training and tuning microblog rankers

Recent years have witnessed a persistent interest in generating pseudo test collections, both for training and evaluation purposes. We describe a method for generating queries and relevance judgments for microblog search in an unsupervised way. Our starting point is this intuition: tweets with a hashtag are relevant to the topic covered by the hashtag and hence to a suitable query derived from the hashtag. Our baseline method selects all commonly used hashtags, and all associated tweets as relevance judgments; we then generate a query from these tweets. Next, we generate a timestamp for each query, allowing us to use temporal information in the training process. We then enrich the generation process with knowledge derived from an editorial test collection for microblog search.
 We use our pseudo test collections in two ways. First, we tune parameters of a variety of well known retrieval methods on them. Correlations with parameter sweeps on an editorial test collection are high on average, with a large variance over retrieval algorithms. Second, we use the pseudo test collections as training sets in a learning to rank scenario. Performance close to training on an editorial test collection is achieved in many cases. Our results demonstrate the utility of tuning and training microblog search algorithms on automatically generated training material.
Electromagnetic Lens-Focusing Antenna Enabled Massive MIMO: Performance Improvement and Cost Reduction

Massive multiple-input-multiple-output (MIMO) techniques have been recently advanced to tremendously improve the performance of wireless communication networks. However, the use of very large antenna arrays at the base stations brings new issues, such as the significantly increased hardware and signal processing costs. In order to reap the performance gains of massive MIMO and yet reduce its cost, this paper proposes a novel system design by integrating an electromagnetic (EM) lens with the large antenna array, termed the EM-lens enabled MIMO. The EM lens has the capability of focusing the power of an incident wave to a small area of the antenna array, whereas the location of the focal area varies with the angle of arrival (AoA) of the wave. Hence, in scenarios where the arriving signals from geographically separated users have different AoAs, the EM-lens enabled receiver provides two new benefits, namely, energy focusing and spatial interference rejection. By taking into account the effects of imperfect channel estimation via pilot-assisted training, in this paper, we analytically show that the average received signal-to-noise ratio in both the single-user and multiuser uplink transmissions can be improved by the EM-lens enabled system. Furthermore, we demonstrate that the proposed design makes it possible to considerably reduce the hardware and signal processing costs with only slight degradations in performance. To this end, two complexity/cost reduction schemes are proposed, which are small-MIMO processing with parallel receiver filtering applied over subgroups of antennas to reduce the computational complexity, and channel covariance based antenna selection to reduce the required number of radio frequency chains. Numerical results are provided to corroborate our analysis and show the great potential advantages of our proposed EM-lens enabled MIMO system for next generation cellular networks.