nils-herrmann/clean_scirepeval huggingface dataset:
Investigating the global trend of RF power amplifiers with the arrival of 5G

To satisfy the continuously increasing demand for high data rates and mobility required by new wireless applications, the 5G has gained much attention recently. Radio frequency power amplifiers (RF-PAs), as one of critical components of 5G transmitter system, are becoming a hot issue. In this paper, the statistical analysis on RF PA papers shows the research on RF-PAs in Asia-Pacific and cooperation between different affiliations and countries are gradually becoming more prominent, showing the globalization trend of RF PA research and the 5G technologies. The decreased research cycle of RF PA shows the processes of research on PA and 5G technologies are speeding up. Some promising RF-PA technologies for 5G wireless communication system are also been discussed.
Didier Dubey Suarez Medina Assessment of Web-based Information Security Awareness Courses

Information security awareness web-based courses are commonly recommended in cyber security strategies to help build a security culture capable of addressing information systems breaches caused by user mistakes whose negligence or ignorance of policies may endanger information systems assets. A research gap exists on the impact of Information Security Awareness Web-Based Courses: these are failing in changing to a significant degree the behavior of participants regarding compliance and diligence, which translates into continuous vulnerabilities. The aim of this work is to contribute with a theoretical and empirical analysis on the potential strengths and weaknesses of Information Security Awareness Web-Based Courses and with two practical tools readily applicable for designers and reviewers of web-based or mediatized courses on information security awareness and education. The research design seeks to respond two research questions. The first on the formulation of a minimum set of criteria that could be applied to Information Security Awareness Web-Based Courses, to support their real impact on employee’s diligence and compliance, resulting in eleven criteria for courses’ assessment and a checklist. The second, about a controlled experiment to explore the actual impact of an existing course, in respect to diligence and compliance using phishing emails as educational tools, that reaffirms the theoretical assumptions arrived to earlier. The development of minimum criteria and their systematic implementation pursue behavioral change, emphasizes the importance of disciplinary integration in cyber security research, and advocates for the development of a solid security culture of diligence and compliance, capable of supporting the protection of organizations from information system threats. The results gathered in this study suggest that achieving positive results in the existing information security tests that follow security awareness courses does not necessarily imply that diligence or information security policies compliance are affected. These preliminary findings accumulate evidence on the importance of implementing the recommendations formulated in this work.
Lean, six sigma and lean sigma: fads or real process improvement methods?

The purpose of this paper is to explore if six sigma and lean are new methods, or if they are repackaged versions of previously popular methods – total quality management (TQM) and just-in-time (JIT).  The study is based on a critical comparison of lean with JIT and six sigma with TQM, a study of the measure of the publication frequency – the number of academic articles published every year of the previous 30 years – for each topic, and a review of critical success factors (CSF) for change efforts.  The more recent concepts of lean and six sigma have mainly replaced – but not necessarily added to – the concepts of JIT and TQM. lean and six sigma are essentially repackaged versions of the former, and the methods seem to follow the fad (product) life cycle. The literature offers fairly similar and rather general CSF for these methods, e.g. top management support and the importance of communication and information. What seems to be missing, however, is the need for a systemic approach to organizational change and improvement.  A prediction is, given the fad or product life cycle phenomenon, that there will be a new method promoted soon, something perhaps already experienced with the borderline preposterous concept of lean six sigma. On the other hand, based on the gap in time between both JIT and lean, and TQM and six sigma – a gap filled by BRP/reengineering – the next method will be process oriented. This paper concludes with the discussion of the need for a process-based approach to organizational improvement efforts.  This paper is of value in that it analyzes what lessons can be learnt from organizational change and improvement efforts. The analysis includes a comparison of CSF for any change project before discussing the need for a process (systems) perspective for successful organizational improvement efforts.
Is a Positive Review Always Effective? Advertising Appeal Effect in the Persuasion of Online Customer Reviews

Despite the expected value of online customer reviews as an emerging advertising medium, the manner of enforcing its effectiveness on customers’ purchase behavior is still a question not well answered. To address the question, we propose a new central route cue called “advertising appeal” to examine attitude and intention to purchase a product or service based on the reasoning of the elaboration likelihood model. We also propose that consumers’ consumption goal and attitude certainty separately moderate the advertising appeal effect on purchase intention through a degree of favorable attitude. We test these hypotheses by conducting a laboratory experiment with 50 participants. In this experiment, we manipulate two kinds of advertising appeals and consumption goals. This study demonstrates that attribute-based appeal reviews are more influential than emotion-based appeal reviews in the persuasion process, regardless of the individuals’ consumption goals. However, the advertising appeal effect on purchase intention is more pronounced for hedonic consumers than for utilitarian consumers.
A Novel Planar Slot Array Antenna With Omnidirectional Pattern

A novel planar slot array antenna with omnidirectional radiation pattern in the horizontal plane is proposed. The antenna has a simple structure based on a stripline. Its fabrication employs a low-cost and accurate planar printed technique. A series of rectangular-shaped loop slots are etched on two parallel ground planes, and act as radiators. The basic principle of operation of the antenna is discussed in this study. An antenna with eight back-to-back slots is designed by employing the genetic algorithm (GA) implemented on a cluster system to achieve good omnidirectional radiation characteristics. A prototype antenna is fabricated and measured. The measurement results agree with the simulation data and show that this antenna possesses encouraging features. For example, at the working frequency of 5.8 GHz, it has a well-formed omnidirectional pattern with maximum gain variation less than 0.5 dB in the horizontal plane, a high gain level of 10 dBi, side lobes 10 dB below the main lobe and no beam squint. Meanwhile, this antenna achieves an impedance bandwidth of 4.6% of the center frequency (5.68-5.95 GHz) for S11 <; -10 dB and exhibits stable omnidirectional performance over the impedance bandwidth.
`Putting the Face to the Voice' Matching Identity across Modality

Speech perception provides compelling examples of a strong link between auditory and visual modalities. This link originates in the mechanics of speech production, which, in shaping the vocal tract, determine the movement of the face as well as the sound of the voice. In this paper, we present evidence that equivalent information about identity is available cross-modally from both the face and voice. Using a delayed matching to sample task, XAB, we show that people can match the video of an unfamiliar face, X, to an unfamiliar voice, A or B, and vice versa, but only when stimuli are moving and are played forward. The critical role of time-varying information is underlined by the ability to match faces to voices containing only the coarse spatial and temporal information provided by sine wave speech . The effect of varying sentence content across modalities was small, showing that identity-specific information is not closely tied to particular utterances. We conclude that the physical constraints linking faces to voices result in bimodally available dynamic information, not only about what is being said, but also about who is saying it.
An Introduction to Temporal Graphs: An Algorithmic Perspective

A temporal graph is, informally speaking, a graph that changes with time. When time is discrete and only the relationships between the participating entities may change and not the entities themselves, a temporal graph may be viewed as a sequence G1, G2 . . . , Gl of static graphs over the same (static) set of nodes V . Though static graphs have been extensively studied, for their temporal generalization we are still far from having a concrete set of structural and algorithmic principles. Recent research shows that many graph properties and problems become radically different and usually substantially more difficult when an extra time dimension is added to them. Moreover, there is already a rich and rapidly growing set of modern systems and applications that can be naturally modeled and studied via temporal graphs. This, further motivates the need for the development of a temporal extension of graph theory. We survey here recent results on temporal graphs and temporal graph problems that have appeared in the Computer Science community.
Regions, Periods, Activities: Uncovering Urban Dynamics via Cross-Modal Representation Learning

With the ever-increasing urbanization process, systematically modeling people’s activities in the urban space is being recognized as a crucial socioeconomic task. This task was nearly impossible years ago due to the lack of reliable data sources, yet the emergence of geo-tagged social media (GTSM) data sheds new light on it. Recently, there have been fruitful studies on discovering geographical topics from GTSM data. However, their high computational costs and strong distributional assumptions about the latent topics hinder them from fully unleashing the power of GTSM. To bridge the gap, we present CrossMap, a novel crossmodal representation learning method that uncovers urban dynamics with massive GTSM data. CrossMap first employs an accelerated mode seeking procedure to detect spatiotemporal hotspots underlying people’s activities. Those detected hotspots not only address spatiotemporal variations, but also largely alleviate the sparsity of the GTSM data. With the detected hotspots, CrossMap then jointly embeds all spatial, temporal, and textual units into the same space using two different strategies: one is reconstructionbased and the other is graph-based. Both strategies capture the correlations among the units by encoding their cooccurrence and neighborhood relationships, and learn lowdimensional representations to preserve such correlations. Our experiments demonstrate that CrossMap not only significantly outperforms state-of-the-art methods for activity recovery and classification, but also achieves much better efficiency.
Adapting instance weights for unsupervised domain adaptation using quadratic mutual information and subspace learning

Domain adaptation (DA) algorithms utilize a label-rich old dataset (domain) to build a machine learning model (classification, detection etc.) in a label-scarce new dataset with different data distribution. Recent approaches transform cross-domain data into a shared subspace by minimizing the shift between their marginal distributions. In this paper, we propose a novel iterative method to learn a common subspace based on non-parametric quadratic mutual information (QMI) between data and corresponding class labels. We extend a prior work of discriminative subspace learning based on maximization of QMI and integrate instance weighting into the QMI formulation. We propose an adaptive weighting model to identify relevant samples that share underlying similarity across domains and ignore irrelevant ones. Due to difficulty of applying cross-validation, an alternative strategy is integrated with the proposed algorithm to setup model parameters. A set of comprehensive experiments on benchmark datasets is conducted to prove the efficacy of our proposed framework over state-of-the-art approaches.
Photometric stereo on carbon fiber surfaces ∗

The production of carbon fiber-reinforced plastic (CFRP) is currently changing from a highly manual and expensive to an automated process. However for automated production of CFRP parts new sensor systems for quality control are required. In this article we present a photometric stereo inspection system that is able to automatically evaluate critical quality criteria of carbon fiber fabrics. Based on the sensor output we propose a specific segmentation method, tailored towards the typical properties of woven carbon fiber fabrics that partitions the fabric into single segments for feature calculation and classification. Finally we show that the proposed system is able to detect a multitude of defects in a real-time system.
MOSFET switched 20 kV, 500 A, 100 ns pulse generator with series connected pulse transformers

This paper describes a MOSFET switched pulser, which is composed of 30 series connections of pulse generating modules (PGMs) that can produce a 700 V/500 A/100 ns pulse with the rising time of 60 ns or less. All PGMs are connected in series through the specially designed pulse transformers to obtain 20 kV/500 A/100 ns pulses at repetition rates up to 20 kHz. To obtain the fast rising time pulse, it is essential to minimize the leakage inductance of the pulse transformer and the wiring inductance between pulse transformers. In this paper, two kinds of layout for the series connection of pulse transformers are introduced and compared to the conventional methods. Also, the authors minimized the losses by adopting an energy recovery circuit that returns to the source the stored energy in the clamp circuit of the primary windings of the pulse transformers. Experimental results are given for the proposed pulser under the resistor load and the wire-cylinder plasma reactor load, respectively.
Applying Machine Learning Techniques to Analysis of Gene Expression Data: Cancer Diagnosis

Classification of patient samples is a crucial aspect of cancer diagnosis. DNA hybridization arrays simultaneously measure the expression levels of thousands of genes and it has been suggested that gene expression may provide the additional information needed to improve cancer classification and diagnosis. This paper presents methods for analyzing gene expression data to classify cancer types. Machine learning techniques, such as Bayesian networks, neural trees, and radial basis function (RBF) networks, are used for the analysis of CAMDA Data Set 2. These techniques have their own properties including the ability of finding important genes for cancer classification, revealing relationships among genes, and classifying cancer. This paper reports on comparative evaluation of the experimental results of these methods.
How data science can advance mental health research

Accessibility of powerful computers and availability of so-called big data from a variety of sources means that data science approaches are becoming pervasive. However, their application in mental health research is often considered to be at an earlier stage than in other areas despite the complexity of mental health and illness making such a sophisticated approach particularly suitable. In this Perspective, we discuss current and potential applications of data science in mental health research using the UK Clinical Research Collaboration classification: underpinning research; aetiology; detection and diagnosis; treatment development; treatment evaluation; disease management; and health services research. We demonstrate that data science is already being widely applied in mental health research, but there is much more to be done now and in the future. The possibilities for data science in mental health research are substantial. Russ et al. discuss the broad applications of data science to mental health research and consider future ways that big data can improve detection, diagnosis, treatment, healthcare provision and disease management.
SDLB: A Scalable and Dynamic Software Load Balancer for Fog and Mobile Edge Computing

Mobile Edge Computing (MEC) provides computing/storage offloading and resource virtualization to mobile devices at the network edge. A load balancer is a necessary network function to determine the destination MEC host of each packet from a mobile device, for such virtualization. Due to the new characteristics of MEC, such as resource limitation and high dynamics, existing solutions of cloud load balancer cannot be directly applied to MEC. This paper presents a new design of a Scalable and Dynamic Load Balancer, called SDLB, that satisfies the requirements of MEC. The core algorithm of SDLB is minimal perfect hashing, which provides two perfect features as a load balancer. Evaluation results show that SDLB is faster by 4x to 10x and uses much less (< 50%) memory, than a widely-used load balancer design for cloud.
Towards Machine Learning on the Semantic Web

In this paper we explore some of the opportunities and challenges for machine learning on the Semantic Web. The Semantic Web provides standardized formats for the representation of both data and ontological background knowledge. Semantic Web standards are used to describe meta data but also have great potential as a general data format for data communication and data integration. Within a broad range of possible applications machine learning will play an increasingly important role: Machine learning solutions have been developed to support the management of ontologies, for the semi-automatic annotation of unstructured data, and to integrate semantic information into web mining. Machine learning will increasingly be employed to analyze distributed data sources described in Semantic Web formats and to support approximate Semantic Web reasoning and querying. In this paper we discuss existing and future applications of machine
Real-Time Inter-Frame Histogram Builder for SPAD Image Sensors

CMOS image sensors based on single-photon avalanche-diodes (SPAD) are suitable for 2D and 3D vision. Limited by uncorrelated noise and/or low illumination conditions, image capturing becomes nearly impossible in a single-shot exposure time. Moreover, the depth accuracy is affected by jitter. Therefore, many frames need to be taken to reconstruct the final accurate image. The proposed reconstruction algorithm is based on pixel-wise histogram building. Specifically, a histogram is built on the fly for each pixel of the array from the ongoing acquired frames. This paper presents the design and implementation on FPGA of a real-time pixel-wise inter-frame histogram builder at 1 kfps. The design has been proven with a  $64\times 64$ -pixels SPAD camera. Its remarkable robustness has been demonstrated in harsh conditions, such as 42 kHz of dark count rate (DCR) and high background illumination up to 20 times larger than the DCR. The system has a graphic user interface for 2D/3D imager configuration, image streaming, and pixel-wise histogram streaming.
High Performance Ternary Multiplier Using CNTFET

Ternary logic is a promising alternative to the conventional binary logic in VLSI design as it provides the advantages of reduced interconnects, higher operating speeds and smaller chip area. This work presents a ternary multiplier using carbon nanotube field effect transistors (CNTFETs). The proposed designs use in-depth analysis of addition required for designing a two trit multiplier. Based on this analysis two efficient adders are proposed. These adders are used to optimize the multiplier design. The proposed circuits are extensively simulated using HSPICE to obtain power, delay and power delay product. The circuit performances are compared with designs reported in recent literature. This circuit demonstrates a power delay product improvement up to 16.8%, with lesser transistor count of 16%. So, the use of these circuits in complex arithmetic circuits will be advantageous.
The kNN-TD Reinforcement Learning Algorithm

A reinforcement learning algorithm called kNN-TD is introduced. This algorithm has been developed using the classical formulation of temporal difference methods and a k-nearest neighbors scheme as its expectations memory. By means of this kind of memory the algorithm is able to generalize properly over continuous state spaces and also take benefits from collective action selection and learning processes. Furthermore, with the addition of probability traces, we obtain the kNN-TD(λ) algorithm which exhibits a state of the art performance. Finally the proposed algorithm has been tested on a series of well known reinforcement learning problems and also at the Second Annual RL Competition with excellent results.
PTE: Enumerating Trillion Triangles On Distributed Systems

How can we enumerate triangles from an enormous graph with billions of vertices and edges? Triangle enumeration is an important task for graph data analysis with many applications including identifying suspicious users in social networks, detecting web spams, finding communities, etc. However, recent networks are so large that most of the previous algorithms fail to process them. Recently, several MapReduce algorithms have been proposed to address such large networks; however, they suffer from the massive shuffled data resulting in a very long processing time. In this paper, we propose PTE (Pre-partitioned Triangle Enumeration), a new distributed algorithm for enumerating triangles in enormous graphs by resolving the structural inefficiency of the previous MapReduce algorithms. PTE enumerates trillions of triangles in a billion scale graph by decreasing three factors: the amount of shuffled data, total work, and network read.
 Experimental results show that PTE provides up to 47 times faster performance than recent distributed algorithms on real world graphs, and succeeds in enumerating more than 3 trillion triangles on the ClueWeb12 graph with 6.3 billion vertices and 72 billion edges, which any previous triangle computation algorithm fail to process.
A Review of Face Recognition against Longitudinal Child Faces

It is an established fact that the face-based biometric system performance is affected by the variation that is caused as a result of aging; however, the question has not been adequately investigated for non-adults, i.e. children from birth to adulthood. The majority of research and development in automated face recognition has been focused on adults. The objective of this paper is to establish an understanding of face recognition against non-adults. This work develops a publicly available longitudinal child face database of child celebrities from images in the wild (ITWCC). This work explores the challenges of biological changes due to maturation, i.e. the face grows longer and wider, the nose expands, the lips widen, etc, i.e. craniofacial morphology, and examines the impact on face recognition. The systems chosen are: Cognitec’s FaceVacs 8.3, Open Source Biometric Recognition (SF4), principal component analysis (PCA), linear discriminant analysis (LDA), local region principal component analysis (LRPCA), and cohort linear discriminant analysis. Face matchers recorded low performance: top performance in verification is 37% TAR at 1% FAR and best rank-1 identification reached 25% recognition rate on a gallery of 301 subjects.