nils-herrmann/clean_scirepeval huggingface dataset:
Identifying Ghanaian pre-service teachers ’ readiness for computer use : A Technology Acceptance Model approach

This study extends the technology acceptance model to identify factors that influence technology acceptance among pre-service teachers in Ghana. Data from 380 usable questionnaires were tested against the research model. Utilising the extended technology acceptance model (TAM) as a research framework, the study found that: pre-service teachers’ pedagogical beliefs, perceived ease of use, perceived usefulness of computer technology and attitude towards computer use to be significant determinants of actual use of computer technology. Results obtained employing multiple stepwise regression analysis revealed that: (1) pre-service teachers’ pedagogical beliefs significantly influenced both perceived ease of use and perceived usefulness, (2) both perceived ease of use and perceived usefulness influence attitude towards computer use and attitude towards computer use significantly influences pre-service teachers’ actual use of computers. However, statistically, perceived ease of use did not significantly influence perceived usefulness. The findings contribute to the literature by validating the TAM in the Ghanaian context and provide several prominent implications for the research and practice of technology integration development.
INVESTIGATING THE INFLUENCE OF PERCEIVED ORGANIZATIONAL SUPPORT , PSYCHOLOGICAL EMPOWERMENT AND ORGANIZATIONAL LEARNING ON JOB PERFORMANCE : AN EMPIRICAL INVESTIGATION

Original scientific paper The current study explores the influence of perceived organizational support, psychological empowerment, and organizational learning on job performance in the bank industry. A sample of 280 employees was randomly selected from a pool of Iranian banks’ employees. The data were collected by the questionnaire and analysed using the structural equation model. The results showed that perceived organizational support influenced positively psychological empowerment, organizational learning, and job performance. In addition, psychological empowerment and organizational learning had a positive influence on job performance. In general, the results of the study suggested that perceived organizational support would play an important role and significantly contribute to psychological empowerment and organizational learning which would in turn improve job performance.
A Machine Learning-based Forensic Discriminator of Pornographic and Bikini Images

The increased use of microcomputers and smart-phones has contributed to social progress, but it has also facilitated the exchange of illegal files, such as child pornography photographs, increasing the demand for digital forensic examination in these devices, which can store more than 300,000 images. Based on the large number of files to be analyzed, it is necessary to use capable algorithms to perform this detection, especially in the most challenging scenarios, such as the distinction between pornographic and bikini images. In this work, we present an approach that improves the ``Algorithm for detection of Nudity proposed by Ap-Apid. Our method improved the performance of this algorithm by using machine learning in extracted features from detected skin regions instead of using static rules to classify this kind of images. In addition, we used detected faces as features in order to mitigate false positives in portrait photos. For conducting the training, validation and testing phases, we used the AIIA-PID4 pornographic data set. Furthermore, we also present a statistical analysis by comparing our approach to two algorithms, one based on the “Algorithm for detection of Nudity” and another proposed by the AIIA-PID4 pornographic data set author. The experimental results showed that we achieved an accuracy of 96.96% and 94.94 in the F1-score metric, increasing the accuracy by 79.19% and 18.21% compared to the referred works, respectively.
Identifying Different Transportation Modes from Trajectory Data Using Tree-Based Ensemble Classifiers

Recognition of transportation modes can be used in different applications including human behavior research, transport management and traffic control. Previous work on transportation mode recognition has often relied on using multiple sensors or matching Geographic Information System (GIS) information, which is not possible in many cases. In this paper, an approach based on ensemble learning is proposed to infer hybrid transportation modes using only Global Position System (GPS) data. First, in order to distinguish between different transportation modes, we used a statistical method to generate global features and extract several local features from sub-trajectories after trajectory segmentation, before these features were combined in the classification stage. Second, to obtain a better performance, we used tree-based ensemble models (Random Forest, Gradient Boosting Decision Tree, and XGBoost) instead of traditional methods (K-Nearest Neighbor, Decision Tree, and Support Vector Machines) to classify the different transportation modes. The experiment results on the later have shown the efficacy of our proposed approach. Among them, the XGBoost model produced the best performance with a classification accuracy of 90.77% obtained on the GEOLIFE dataset, and we used a tree-based ensemble method to ensure accurate feature selection to reduce the model complexity.
New Deep Neural Nets for Fine-Grained Diabetic Retinopathy Recognition on Hybrid Color Space

Automatic diabetes retinopathy (DR) recognition can help DR carriers to receive treatment in early stages and avoid the risk of vision loss. In this paper, we emphasize the role of multiple filter sizes in learning fine-grained discriminant features and propose: (i) two deep convolutional neural networks - Combined Kernels with Multiple Losses Network (CKML Net) and VGGNet with Extra Kernel (VNXK), which are an improvement upon GoogLeNet and VGGNet in context of DR tasks. Learning from existing research, (ii) we propose a hybrid color space, LGI, for DR recognition via proposed nets. (iii) Transfer learning is applied to solve the challenge of imbalanced dataset. The effectiveness of proposed new nets and color space is evaluated using two grand challenge retina datasets: EyePACS and Messidor. Our experimental results show: (iv) CKML Net improves upon GoogLeNet and VNXK improves upon VGGNet on both datasets using the LGI color space. Additionally, proposed methodology improves upon other state of the art results on Messidor dataset for referable/non-referable screening.
Development of Computer-Aided Diagnostic (CADx) System for Distinguishing Neoplastic from Nonneoplastic Lesions in CT Colonography (CTC): Toward CTC beyond Detection

Half of the polyps surgically removed during conventional colonoscopy are benign with no malignant potential. Our purpose was to develop a CADx system for distinction between neoplastic and non-neoplastic lesions in CTC to reduce "unnecessary" colonoscopic polypectomy. Although computer aided detection (CADe) systems have been developed, less attention was given to the development of CADx systems. Our CADx system consists of shape-index-based coarse segmentation of lesions, 3D volume growing and sub-voxel refinement for fine segmentation of lesions, morphologic and texture feature analysis, Wilks' lambda-based stepwise feature selection, linear discriminant analysis for providing an integrated imaging biomarker for diagnosis of neoplastic lesions. Our database contained biopsy-confirmed 54 neoplastic lesions in 29 patients and 14 non-neoplastic lesions in 10 patients. Our CADx system integrating the selected features was able to determine an accurate likelihood of being a neoplasm and distinguish 87% (47/54) neoplastic lesions from 57% (8/14) non-neoplastic lesions correctly only using computed tomography (CT) images, achieving an area under the receiver operating characteristic curve (AUC) of 0.82. This study showed the potential of the use of CTC as a diagnostic tool beyond already accepted detection, thus, CTC with CADx would be potentially useful for reducing "unnecessary" polypectomy.
A Noise Reduction and Linearity Improvement Technique for a Differential Cascode LNA

A typical common source cascode low-noise amplifier (CS-LNA) can be treated as a CS-CG two stage amplifier. In the published literature, an inductor is added at the drain of the main transistor to reduce the noise contribution of the cascode transistors. In this work, an inductor connected at the gate of the cascode transistor and capacitive cross-coupling are strategically combined to reduce the noise and the nonlinearity influences of the cascode transistors in a differential cascode CS-LNA. It uses a smaller noise reduction inductor compared with the conventional inductor based technique. It can reduce the noise, improve the linearity and also increase the voltage gain of the LNA. The proposed technique is theoretically formulated. Furthermore, as a proof of concept, a 2.2 GHz inductively degenerated CS-LNA was fabricated using TSMC 0.35 mum CMOS technology. The resulting LNA achieves 1.92 dB noise figure, 8.4 dB power gain, better than 13 dB S11, more than 30 dB isolation (S12), and -2.55 dBm IIP3, with the core fully differential LNA consuming 9 mA from a 1.8 V power supply.
Learning from Ambiguously Labeled Face Images

Learning a classifier from ambiguously labeled face images is challenging since training images are not always explicitly-labeled. For instance, face images of two persons in a news photo are not explicitly labeled by their names in the caption. We propose a Matrix Completion for Ambiguity Resolution (MCar) method for predicting the actual labels from ambiguously labeled images. This step is followed by learning a standard supervised classifier from the disambiguated labels to classify new images. To prevent the majority labels from dominating the result of MCar, we generalize MCar to a weighted MCar (WMCar) that handles label imbalance. Since WMCar outputs a soft labeling vector of reduced ambiguity for each instance, we can iteratively refine it by feeding it as the input to WMCar. Nevertheless, such an iterative implementation can be affected by the noisy soft labeling vectors, and thus the performance may degrade. Our proposed Iterative Candidate Elimination (ICE) procedure makes the iterative ambiguity resolution possible by gradually eliminating a portion of least likely candidates in ambiguously labeled faces. We further extend MCar to incorporate the labeling constraints among instances when such prior knowledge is available. Compared to existing methods, our approach demonstrates improvements on several ambiguously labeled datasets.
Supervised Learning Based Algorithm Selection for Deep Neural Networks

Many recent deep learning platforms rely on thirdparty libraries (such as cuBLAS) to utilize the computing power of modern hardware accelerators (such as GPUs). However, we observe that they may achieve suboptimal performance because the library functions are not used appropriately. In this paper, we target at optimizing the operations of multiplying a matrix with the transpose of another matrix (referred to as NT operation hereafter), which contribute half of the training time of fully connected deep neural networks. Rather than directly calling the library function, we propose a supervised learning based algorithm selection approach named MTNN, which uses a gradient boosted decision tree to select one from two alternative NT implementations intelligently: (1) calling the cuBLAS library function; (2) calling our proposed algorithm TNN that uses an efficient out-of-place matrix transpose. We evaluate the performance of MTNN on two modern GPUs: NVIDIA GTX 1080 and NVIDIA Titan X Pascal. MTNN can achieve 96% of prediction accuracy with very low computational overhead, which results in an average of 54% performance improvement on a range of NT operations. To further evaluate the impact of MTNN on the training process of deep neural networks, we have integrated MTNN into a popular deep learning platform Caffe. Our experimental results show that the revised Caffe can outperform the original one by an average of 28%. Both MTNN and the revised Caffe are open-source.
High Switching Performance of 1700-V, 50-A SiC Power MOSFET Over Si IGBT/BiMOSFET for Advanced Power Conversion Applications

Due to wider band gap of silicon carbide (SiC) compared to silicon (Si), MOSFET made in SiC has considerably lower drift region resistance, which is a significant resistive component in high-voltage power devices. With low on-state resistance and its inherently low switching loss, SiC MOSFETs can offer much improved efficiency and compact size for the converter compared to those using Si devices. In this paper, we report switching performance of a new 1700-V, 50-A SiC MOSFET designed and developed by Cree, Inc. Hard-switching losses of the SiC MOSFETs with different circuit parameters and operating conditions are measured and compared with the 1700-V Si BiMOSFET and 1700-V Si IGBT, using same test set-up. Based on switching and conduction losses, the operating boundary of output power and switching frequency of these devices are found out in a dc-dc boost converter and compared. The switching dv/dts and di/dts of SiC MOSFET are captured and discussed in the perspective of converter design. To validate the continuous operation, three dc-dc boost converters using these devices, are designed and tested at 10 kW of power with 1 kV of output voltage and 10 kHz of switching frequency. 1700V SiC Schottky diode is used as the blocking diode in each case. Corresponding converter efficiencies are evaluated and the junction temperature of each device is estimated. To demonstrate high switching frequency operation, the SiC MOSFET is switched upto 150 kHz within permissible junction temperature rise. A switch combination of the 1700-V SiC MOSFET and 1700-V SiC Schottky diode connected in series is also evaluated for zero voltage switching turn-ON behavior and compared with those of bipolar Si devices. Results show substantial power loss saving with the use of SiC MOSFET.
Telemade: A Testing Framework for Learning-Based Malware Detection Systems

Learning-based malware detectors may be errorneous due to two inherent limitations. First, there is a lack of differentiability: selected features may not reflect essential differences between malware and benign apps. Second, there is a lack of comprehensiveness: the machine learning (ML) models are usually based on prior knowledge of existing malware (i.e., training dataset) so malware can evolve to evade the detection. There is a strong need for an automated framework to help security analysts to detect errors in learning-based malware detection systems. Existing techniques to generate adversarial samples for learning-based systems (that take images as inputs) employ feature mutations based on feature vectors. Such techniques are infeasible to generate adversarial samples (e.g., evasive malware) for malware detection system because the synthesized mutations may break the inherent constraints posed by code structures of the malware, causing either crashes or malfunctioning of malicious payloads. To address the challenge, we propose Telemade, a testing framework for learning-based malware detectors.
Which robot am I thinking about? The impact of action and appearance on people's evaluations of a moral robot

In three studies we found further evidence for a previously discovered Human-Robot (HR) asymmetry in moral judgments: that people blame robots more for inaction than action in a moral dilemma but blame humans more for action than inaction in the identical dilemma (where inaction allows four persons to die and action sacrifices one to save the four). Importantly, we found that people's representation of the “robot” making these moral decisions appears to be one of a mechanical robot. For when we manipulated the pictorial display of a verbally described robot, people showed the HR asymmetry only when making judgments about a mechanical-looking robot, not a humanoid robot. This is the first demonstration that robot appearance affects people's moral judgments about robots.
agriGO: a GO analysis toolkit for the agricultural community

Gene Ontology (GO), the de facto standard in gene functionality description, is used widely in functional annotation and enrichment analysis. Here, we introduce agriGO, an integrated web-based GO analysis toolkit for the agricultural community, using the advantages of our previous GO enrichment tool (EasyGO), to meet analysis demands from new technologies and research objectives. EasyGO is valuable for its proficiency, and has proved useful in uncovering biological knowledge in massive data sets from high-throughput experiments. For agriGO, the system architecture and website interface were redesigned to improve performance and accessibility. The supported organisms and gene identifiers were substantially expanded (including 38 agricultural species composed of 274 data types). The requirement on user input is more flexible, in that user-defined reference and annotation are accepted. Moreover, a new analysis approach using Gene Set Enrichment Analysis strategy and customizable features is provided. Four tools, SEA (Singular enrichment analysis), PAGE (Parametric Analysis of Gene set Enrichment), BLAST4ID (Transfer IDs by BLAST) and SEACOMPARE (Cross comparison of SEA), are integrated as a toolkit to meet different demands. We also provide a cross-comparison service so that different data sets can be compared and explored in a visualized way. Lastly, agriGO functions as a GO data repository with search and download functions; agriGO is publicly accessible at
Early detection of diabetic retinopathy from digital retinal fundus images

Diabetic retinopathy is the impairment of the retinal blood vessels due to complications of diabetes, which can subsequently lead to loss of vision. The only solution for this problem is through the use of a retinal screening system that would diagnose the retinal damage at an early stage. This paper proposes the use of morphological operations and segmentation techniques for the detection of blood vessels, exudates and microaneurysms. The retinal fundus image is partitioned into four sub images. Various features are extracted from the retinal fundus image. Haar wavelet transformations are applied on the features extracted. Principal component analysis technique is then applied for better feature selection. Back propagation neural network and one rule classifier techniques are used for the classifying the images as diabetic or non-diabetic. Experiments are performed on a publically available diabetic retinopathy data set DIARETDB1. Performance is evaluated with metrics like sensitivity, specificity and accuracy, the results obtained are encouraging.
Virtual Reality und Augmented Reality (VR/AR)

Preise, Perspektiven, Potenziale Unter 300 € soll sie kosten. Ende 2014 soll sie als Produkt erscheinen. Die Oculus Rift, eine Brille, die ihre Träger in eine Virtuelle Realität (VR) versetzen kann. Bisherige VR-Brillen kosten oft mehr als das Zehnfache und vermitteln aufgrund eines eingeschränkteren Sichtfelds keinen derart guten Eindruck einer virtuellen 3D Welt. Bessere Hardware für einen Bruchteil des Preises? Kein Einzelfall. Ein neues Anwendungsfeld macht es möglich: Entertainment. Statt wie bisherige VR Hardware eine kleine Zielgruppe z. B. für industrielle Anwendungen zu adressieren, zielen neue Geräte auf den Computerspielemarkt, einen Massenmarkt. Nach einer vom Bundesverband Interaktive Unterhaltungssoftware in Auftrag gegebene Studie der GfK wird das Marktvolumen allein in Deutschland mit 1,82 Milliarden Euro beziffert. Die Firma Oculus VR brauchte vor dem Hintergrund derartiger Marktperspektiven nur vier Stunden, um über Crowdfunding mittels der Online-Plattform Kickstarter 250.000 US$ als Startkapital zu sammeln. Das Unternehmen wurde inzwischen für ca. zwei Milliarden US$ aufgekauft. Insgesamt bieten derart erschwingliche Hardware und derart hohe Investitionen für die Anwendbarkeit und die Verbreitung von VR neue Perspektiven. Wird VR also massentauglich werden können? VR verfolgt das Ziel, Nutzer in eine scheinbare Welt zu versetzen, in der sie sich präsent fühlen. Dazu werden Technologien eingesetzt, die das Eintauchen, die Immersion, in diese virtuelle Welt erleichtern sollen, indem künstliche Reize für die visuelle und auditive Wahrnehmung erzeugt werden, manchmal auch für weitere Sinne wie den haptischen Sinn oder den Gleichgewichtssinn. Spezielle VR-Brillen spielen über ein Display dem rechten und linken Auge Bilder einer virtuellen 3D Welt ein. Außerdem ermittelt ein Sensor die aktuelle Kopfposition und Blickrichtung, sodass man sich in der virtuellen Welt durch Drehen des Kopfes einfach umschauen kann, genauso wie man es auch aus der Realität gewohnt ist. Im Extremfall einer perfekten VR könnte man virtuelle Welt und Realität nicht mehr unterscheiden. Dies wird in einigen Science Fiction Filmen, z. B. ,,Die Matrix“ dargestellt, in denen die künstlichen Reize über eine Art Steckdose direkt in das Gehirn eingespielt werden. Soweit muss man aber nicht gehen, auch heute schafft man es schon, überzeugende virtuelle Umgebungen zu realisieren. Bei Menschen, die man an die Dachkante eines virtuellen Wolkenkratzers stellt, kann man erhöhten Pulsschlag und feuchte Hände feststellen. Und das obwohl diese Menschen wissen, dass sie nicht an einem gefährlichen Abgrund, sondern in einer sicheren VR Umgebung stehen. Hier kommt eine Eigenschaft von Menschen zum Tragen, die der Philosoph Samuel T. Coleridge ,,willing
An Intrusion Detection System , ( IDS ) with Machine Learning ( ML ) Model Combining Hybrid Classifiers

An Intrusion Detection System (IDS) with Machine Learning (ML) model Combining Hybrid Classifiers i.e. Naïve Byes classifier and C 4.5 classifier is proposed for intrusion detection. In the proposed model, a multi-layer Hybrid Classifier is adopted to estimate whether the action is an attack or normal data. First, a misuse detection model is built based on the C4.5 decision tree algorithm and then the normal training data is decomposed into smaller subsets using the model. Next, multiple one-class Naïve Byes algorithm models are created for the decomposed subsets Hybrid Classifier is used as a preprocessor of Intrusion Detection System to reduce the dimension of feature vectors and shorten training time. In order to reduce or minimize the noise caused by feature differences and improve the performance of Intrusion Detection System. The proposed hybrid intrusion detection method was evaluated by conducting experiments with the NSL-KDD data set, which is a modified version of well-known KDD Cup 99 data set. Intusion Detectio, Machine Learning, Security, Hybrid Classifiers
Battery-Free Connected Machine Vision with WISPCam

Sustained exponential improvements in the energy efficiency of microelectronics has recently enabled us to build battery-free camera systems that are powered entirely by propagating radio waves. This paper describes primitive machine vision applications built using this highly constrained, battery-free camera system. After describing the WISPCam system and its constraints, we show how to use it to capture (relatively) high-resolution images of faces, without ever capturing a full frame at high resolution. This example application illustrates the issues that arise in partitioning a demanding vision application across mobile hardware that is highly constrained in power, storage, computation and communication.