Reviewerly/exHarmony huggingface dataset:
The Impact of High-Performance Work Practices and Job Involvement on the Project success Moderating Role of Islamic work Ethics

The research was conducted to evaluate the impact of High-performance work practices and Job Involvement on the Project success. The research was also intended to explore the moderating role of Islamic work ethics between the relationship of the high-performance work practices and job involvement and project success. The data was collected from the organizations which were involved in the projects, situated in Islamabad, Rawalpindi, and Multan. The 150 questioners were distributed and from them, 100 questionnaires were retrieved. The findings of the research suggested project success while the Islamic work ethics significantly moderates the relationship among the high-performance work practices and project success. This study is a significant contribution in the domain of project management and it has multiple implications at managerial level and academic level. The research also suggested the future directions for further research.
Signed Network Embedding with Dynamic Metric Learning

Network embedding is an important method to learn low-dimensional vector representations of nodes in networks, which has wide-ranging applications in network analysis such as link prediction. Most existing network embedding models focus on the unsigned networks with only positive links. However, networks should have both positive and negative links in practical applications such as the trust and distrust relationships in social networks. It is certain that there are different properties between positive links and negative links, which means the network embedding models designed for unsigned networks are not suitable for signed networks. In this paper, we propose SNE-DML, a signed network embedding model with dynamic metric learning. The model learns positive and negative distance metrics respectively in the training process. We conduct sign prediction experiments on three datasets and compare with seven baselines including three signed network embedding models and four state-of-the-art unsigned network embedding models. The experimental results show the effectiveness of our model.
Adaptive Transmission for Edge Learning via Training Loss Estimation

With the large-scale deployment of intelligent Internet of things (IoT) devices and the increasing need for computation support in wireless access networks, edge computing plays a vital role in satisfying these merging needs. The deployment of machine learning algorithms as one of the key applications at the network edge requires efficient training, in order to adapt themselves in the changing environment. However, the transmission of the training dataset collected by edge devices requires huge wireless communication resources. To address this issue, we exploit the fact that data samples have different importance for training, and use the training loss of the target machine learning model to represent the importance of data samples. Based on the importance metric, we propose a data upload scheme combining data compression that removes unimportant information and data filtering for data selection. As a result, the number of data samples as well as the size of every data sample to be transmitted can be substantially reduced while keeping the good training performance. Experiments show that training a machine learning model via the proposed scheme can enjoy faster convergence under limited wireless resources, specifically, we get almost the same training performance with only 2.5% of communication resources in the experiments training on MNIST dataset.
UIC Conference on Standardisation to be held on 6 November 2018 at Paris UIC headquarters – UIC Communications

Only a week left to register! https://events.uic.org/standardisation-conference Rail being the inherently systemic network that it is, needs to be able to work in seamless harmony with a wide…
An Insight on the Class Imbalance Problem and Its Solutions in Big Data

Expansion of data in the dimensions of volume, variety, or velocity is leading to big data. Learning from this big data is challenging and beyond capacity of conventional machine learning methods and techniques. Generally, big data getting generated from real-time scenarios is imbalance in nature with uneven distribution of classes. This imparts additional complexity in learning from big data since the class that is underrepresented is more influential and its correct classification becomes critical than that of overrepresented class. This chapter addresses the imbalance problem and its solutions in context of big data along with a detailed survey of work done in this area. Subsequently, it also presents an experimental view for solving imbalance classification problem and a comparative analysis between different methodologies afterwards.
Asking Questions to Provide a Causal Explanation – Do People Search for the Information Required by Cognitive Psychological Theories?

In this paper, we give a brief overview of current, cognitive-psychological theories, which provide an account for how people explain facts: causal model theories (the predominant type of dependence theory) and mechanistic theories. These theories differ in (i) what they assume people to explain and (ii) how they assume people to provide an explanation. In consequence, they require different types of knowledge in order to explain. We work out predictions from the theoretical accounts for the questions people may ask to fill in gaps in knowledge. Two empirical studies are presented looking at the questions people ask in order to get or give an explanation. The first observational study explored the causal questions people ask on the internet, including questions asking for an explanation. We also analyzed the facts that people want to have explained and found that people inquire about tokens and types of events as well as tokens and types of causal relations. The second experimental study directly investigated which information people ask for in order to provide an explanation. Several scenarios describing tokens and types of events were presented to participants. As a second factor, we manipulated whether the facts were familiar to participants or not. Questions were analyzed and coded with respect to the information inquired about. We found that both factors affected the types of questions participants asked. Surprisingly, participants asked only few questions about actual causation or about information, which would have allowed them to infer actual causation, when a token event had to be explained. Overall the findings neither fully supported causal model nor mechanistic theories. Hence, they are in contrast to many other studies, in which participants were provided with relevant information upfront and just asked for an explanation or judgment. We conclude that more empirical and theoretical work is needed to reconcile the findings from these two lines of research into causal explanations.
Sample complexity of classification with compressed input

One of the most studied problems in machine learning is finding reasonable constraints that guarantee the generalization of a learning algorithm. These constraints are usually expressed as some simplicity assumptions on the target. For instance, in the Vapnik–Chervonenkis (VC) theory the space of possible hypotheses is considered to have a limited VC dimension One way to formulate the simplicity assumption is via information theoretic concepts. In this paper, the constraint on the entropy H(X) of the input variable X is studied as a simplicity assumption. It is proven that the sample complexity to achieve an ∊-δ Probably Approximately Correct (PAC) hypothesis is bounded by 2cH(X)/∊+log1δa∊2 which is sharp up to the 1∊2 factor (a and c are constants). Moreover, it is shown that if a feature learning process is employed to learn the compressed representation from the dataset, this bound no longer exists. These findings have important implications on the Information Bottleneck (IB) theory which had been utilized to explain the generalization power of Deep Neural Networks (DNNs), but its applicability for this purpose is currently under debate by researchers. In particular, this is a rigorous proof for the previous heuristic that compressed representations are exponentially easier to be learned. However, our analysis pinpoints two factors preventing the IB, in its current form, to be applicable in studying neural networks. Firstly, the exponential dependence of sample complexity on 1/∊., which can lead to a dramatic effect on the bounds in practical applications when ∊ is small. Secondly, our analysis reveals that arguments based on input compression are inherently insufficient to explain generalization of methods like DNNs in which the features are also learned using available data.
Fast and Effective Median Filter Algorithms for Salt and Pepper Noise

For large data of the image processing in the fields of medication or communication, the salt and pepper noise is one of the key problems to affect the quality of the images. In order to obtain stable, fast, and effective filtering algorithms for salt and pepper noise, this paper proposes four filter algorithms, named fast and effective median filter1 (FEMF1), fast and effective median filter2(FEMF2), fast and effective median filter3(FEMF3), and fast and effective median filter4(FEMF4), respectively. FEMF1 uses a second-order differential method to convert the image to a binary matrix to determine the exact location of the broken pixels. It is stable and fast, however it has a sense of noise. FEMF2 and FEMF3 use different directions to search with changing windows. FEMF4 grows square bar window with unlimited growth and has search functions in all directions. FEMF4 costs the lowest time and presents the high quality performance. The running time of the FEMF2 is only slower than that of the FEMF4. Experiment show that FEMF2, FMEF3 and FEMF4 have stable, fast and effective features for low and high noises. These algorithms can be used in engineering applications.
African Languages in Information and Communications Technology

Abstract Africa is experiencing immense growth in the use of information technology (IT). Studies of this “Digital Revolution” have tended to focus on social and economic development issues, while lately also studies on the use of African languages on the Internet and in mobile telephony have emerged. Of particular interest is the use of African languages in written electronic communication: does IT increase the marginalization of African languages or does it create a space for their blossoming? This question will be examined in the chapter, considering the extension and the nature of the use of African languages in communication mediated by IT, like websites and electronic discussion forums, emails, instant messaging, social networking sites, and text messages. The technological dimensions related to writing systems, different efforts aiming at the standardization of keyboards, and the translation of software will also be discussed.
Object detection method on station logo with single shot multi‐box detector

In this work, the authors design an object detection method by the characteristics of the station with convolutional neural network, such as small scale-to-height ratio change and relatively fixed position. In order to realise the pre-processing and feature extraction of the station data, they collect the video samples and filter, frame, label and process to these samples. Also then, the training sample data and the test sample data are divided proportionally to train the station detection model. After that, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments proved its validity.
Chern–Simons quantum mechanics and fractional angular momentum in atom system

The model of a planar atom which possesses a nonvanishing electric dipole moment interacting with magnetic fields in a specific setting is studied. Energy spectra of this model and its reduced model, which is the limit of cooling down the atom to the negligible kinetic energy, are solved exactly. We show that energy spectra of the reduced model cannot be obtained directly from the full ones by taking the same limit. In order to get the energy spectra of the reduced model from the full model, we must regularize energy spectra of the full model properly when the limit of the negligible kinetic energy is taken. It is one of the characteristics of the Chern–Simons quantum mechanics. Besides this, the canonical angular momentum of the reduced model will take fractional values although the full model can only take integers. It means that it is possible to realize the Chern–Simons quantum mechanics and fractional angular momentum simultaneously by this model.
Advances in Describing and Managing Our Environment

This chapter examines links between the developments in selected technologies and our ways of teaching and learning. The focus is on some domains that are developing especially fast. A postulate follows that including knowledge about developments in science and current tools into the school curricula requires immediate attention. Enhancing the curricula with information about dynamically developing branches of science would likely exert a profound effect on making informed, successful decisions about future careers of students. The following text is about a novel approach to visual way of learning and instruction about processes and products. The visual approach relates to learning about natural processes and the current ways we capture their essence. Learning about advances in science requires application of graphical ways of presentation; the use of internet and digital media by instructors, professionals, and students; and making knowledge visualization an integral part of the learning process.
Review of Youdale (2020): Using Computers in the Translation of Literary Style: Challenges and Opportunities

This article reviews Using Computers in the Translation of Literary Style: Challenges and Opportunities
An Efficient Multi-scale Method for Single Image Super Resolution

Convolutional neural network has shown its superior performance in single image super resolution(SR) as in other computer vision applications. One of its disadvantage is that the computation needs will be significantly higher than that of other applications for the resolution of input is often high definition and it is growing rapidly. In this paper, the low-level vision attributed of super resolution is emphasized by using a multi-scale neural network. The proposed method do not take a very deep framework which is not suitable in ultra-high resolution or mobile terminals. To obtain the geometry structure, a deformable convolution layer is applied in shallow feature extraction which reduces the complexity of the network structure. The deformable convolution scheme constraints the explosion of parameter space and provides shallow features at different scales. The backbone structure is pyramid which offers more scale information for reconstruction. These ideas ensure the efficiency of the model which is also demonstrated on extensive experiments.
Motion Object Tracking for Thermal Imaging Using Particle Filter

Measurement monitoring has been applied in many applications such as scrutiny, the advancement in the system to assist the driver (ADAS), contumacious biometrics, environment generated by computer, etc. In the computer learning apparatus, video scrutiny has a decisive topic for doing research. Current research in this field includes the creation of a robust and reliable tracking system.
Adult-child 3D backward face aging model (3D B-FAM)

Face aging has been widely considered in many studies regarding all the potential applications. However, the de-aging known as the rejuvenation or backward modeling has recently received more attention. Previous studies mainly focused on rejuvenating faces from aged adults into young adults using two-dimensional (2D) models. In this work, we propose an extension of a previous 2D adult-child B-FAM into 3D model. This model allows a digital face appearance rejuvenation within a range of [75–3] years old. To evaluate the performances of the proposed approach, first, we proposed two performance evaluation modes, namely: Generic Perception Based and Biometric Verification Mode. Then, the performances have been evaluated over our own 3D database, called Face Time-Machine database constructed using 75 females and 70 males, leading to 500 textured surface meshes. Finally, results show that they are perceptually satisfying and system performance increases by using the faces obtained from our model.
Mammogram Denoising Using High Boost Filter and Vectorization Convolutional Neural Networks

Mammogram screening is the effective way to prevent breast cancer in the early detection stage. However, low dose radiation from x-ray machine causes a degradation of visuality of mammograms that make interpretation error to radiologists. In this paper, high boost filter and vectorization convolutional neural networks are combined to improve visual quality of mammograms. The experimental results illustrate that the proposed method can improve contrast and suppress noise in mammograms comparing to the traditional denoising methods.
Implementation of a 12-Million Hodgkin-Huxley Neuron Network on a Single Chip

Understanding the human brain is the biggest challenge for scientists in the twenty-first century. The Hodgkin-Huxley (HH) model is one of the most successful mathematical models for bio-realistic simulations of the brain. However, the simulation of HH neurons involves complex computation, which makes the implementation of large-scale brain networks difficult. In this paper, we propose a hardware architecture that efficiently computes a large-scale network of HH neurons. This architecture is based on the neuron machine hardware architecture, which has the limitation of speed as it has only one computation node. The proposed architecture is essentially a non-Von Neumann synchronous system with multiple computation nodes, called hardware neurons, to achieve linear speedup. In this paper, the design of a digital circuit that computes large-scale networks of HH neurons is presented as an example to provide a detailed description of the proposed architecture. This design supports axonal conduction delay of spikes and short- and long-term plasticity synapses, along with floating-point precision HH neurons. The design is implemented on a field-programmable gate array (FPGA) chip and computes a network of one million HH neurons in near real time. The implemented system can compute a network with up to 12 million HH neurons and 600 million synapses. The proposed design method can facilitate the design of systems supporting complex neuron models and their flexible implementation on reconfigurable FPGA chips.
E-Learning: Virtual Classroom in Moodle as a collaborative environment for the knowledge of the Institutional Educational Model in teachers of a university in Lima North

The knowledge of the Institutional Educational Model is a key piece to turn educational institutions into learning communities. Due to the community nature of this process,it is important that teachers achieve this objective through collaborative mechanisms. This research addresses the theme of the use of virtual collaborative environments to achieve knowledge of the Institutional Educational Model in university teachers. To this end, the study focuses on a sample of teachers from a university in North Lima belonging to the faculties of Engineering, Nursing, Education and Administration. The virtual classroom design was carried out using the Moodle platform and from the pedagogical point of view it was built considering collaborative and feedback tools. The study considers variables of gender, seniority and faculty of origin. The results are reported from the comparative analysis of evaluation rubrics and the degree of satisfaction of the participants.
Mapping Design Anthropology: Tracking the Development of an Emerging Transdisciplinary Field

The practice of design anthropology has continued to evolve since the publication of Design + Anthropology: Converging Pathways in Anthropology and Design in 2018. At that time, design anthropology was described as “an emerging transdisciplinary field.” ([1], [2]: 10, [3]). Working collaboratively with Ken Riopelle who provided analytical expertise in social network analysis, we approached this claim from the perspective of social network analysis “to investigate the human and nonhuman actors (i.e., people and institutions) that have contributed to design anthropological practice and theorizing.” [3]. Our initial goal was to determine if—and, if so, to what extent—design anthropology qualified as a disciplinary “field”. In our original analysis, we began by establishing a set of benchmarks that serve as indicators to identify a disciplinary field. In this paper, we revisit our initial analysis, updating it with new publications, contributors, blogs, groups, and other developments, to investigate if and how design anthropology has diffused.
A Feedback-Planning Scheme to Synthesize Control Functions

Limitations imposed on control functions, which are mostly caused by system properties, can significantly affect the performance of linear controllers. Such limitations can convert a linear controller to a nonlinear input signal that alters the convergence and stability of the system. In this article, we propose a systematic approach to construct controllers by cascading projected linear and piecewise constant function. The associated parameters of the piecewise functions are obtained by solving a planning problem in the state space of the system via Ariadne's clew framework. It must be noted that the proposed method is mostly suited as a "survival" strategy, than a typical controller, since it allows the system to recover from large deviations of the trajectory from the trim condition.
A Dynamic Updating Method for Release of Privacy Protected Data Based on Privacy Differences in Relational Data

To improve dynamic updating of privacy protected data release caused by multidimensional sensitivity attribute privacy differences in relational data, we propose a dynamic updating method for privacy protection data release based on the multidimensional privacy differences. By adopting the multi-sensitive bucketization technology (MSB), this method performs quantitative classification of the multidimensional sensitive privacy difference and the recorded value, provides the basic updating operation unit, and thereby realizes dynamic updating of privacy protection data release based on the privacy difference among relational data. The experiment confirms that the method can secure the data updating efficiency while ensuring the quality of data release.
Measurement Research on Influential Effect of Standardization on Technological Innovation

Abstract There is a close relationship between standardization and technological innovation. Based on the analysis of the function mechanism of standardization on technological innovation, this paper has established measurement indexes of the influential effect of standardization on technological innovation. As structural equation modeling (SEM) has its own advantages over statistical methods such as correlation analysis, regression analysis and path analysis, this study attempts to measure the influence degree of standardization on technological innovation by adopting SEM method, and has established a measurement model and a structural equation model for the standardized technological innovation effect. In addition, the study has discussed the fuzzy DEA method to analyze the standardized technological innovation effect of DMUs, established the fuzzy DEA-based index system for evaluating standardized technological innovation effect, constructed the fuzzy DEA model for evaluating standardized technological innovation effect, and provided the solution process of the model.