Reviewerly/exHarmony huggingface dataset:
Impact of Employees’ Performance on Achieving Competitive Advantage: A Field Study in the Mobile Phone-Run Companies in Sana'a

This study aimed to identify the impact of the employees’ performance on the competitive advantage in the mobile phone-run companies in Sana’a. The population of this study was (1648) employees (male and female) from different administrative levels. The required sample for the study, according to Krejcie and Morgan, is (313) employees and the actual sample of the present study was (302) employees with a rate of (96.5%) of the required sample. The descriptive analytical approach was adopted and a questionnaire was used to collect the data. The findings revealed that the employees’ performance has an effect on achieving the competitive advantage, but it still does not meet the aspirations of the companies under consideration. Furthermore, the study found out that there are statistically significant differences in the average answers of the sample on achieving competitive advantage due to organizational variables represented in the number of employees and the name of company while these companies have not achieved differences in achieving the competitive advantage in terms of excellence, flexibility, innovation and responsiveness, according to the age variable. The study recommended that more attention should be paid to the reinforcement of the employees’ performance which can lead to the achievement of the competitive advantage in the companies instead of seeking external resources that are difficult to keep pace with.&#x0D; Keywords: employees’ performance, mobile run companies, competitive advantage.
Dissemination of professional experience in development students&amp;apos; readiness for research activities

Conceptual approaches to developing the readiness of future specialists in the field of advertising and public relations for research activities are considered. The question of the need for developing experience, skills, research activities, considering the dissemination of professional experience, the main motives and incentives for its acquisition is actualized.
The potential of a family in forming mix-aged educational community

The article presents the main trends of interaction between family and educational organization. The article concludes that the upbringing potential of mix-aged community, that on the need for research this area in order to enrich the educational space with new form of interaction, new form of non-formal educational practices.
Design work as a method of training specialists Working with substitude families

This article discusses the experience of using the project method used in the training of teachers working with substitute families. The authors carry out a review and analytical study of the existing experience of using the project method and offer a project technology for training specialists to work with substitute families.
Reducing the Effort for Systematic Reviews in Software Engineering

Context. Systematic Reviews (SRs) are means for collecting and synthesizing evidence from the identification and analysis of relevant studies from multiple sources. To this aim, they use a well-defined methodology meant to mitigate the risks of biases and ensure repeatability for later updates. SRs, however, involve significant effort. Goal. The goal of this paper is to introduce a novel methodology that reduces the amount of manual tedious tasks involved in SRs while taking advantage of the value provided by human expertise. Method. Starting from current methodologies for SRs, we replaced the steps of keywording and data extraction with an automatic methodology for generating a domain ontology and classifying the primary studies. This methodology has been applied in the Software Engineering sub-area of Software Architecture and evaluated by human annotators. Results. The result is a novel Expert-Driven Automatic Methodology, EDAM, for assisting researchers in performing SRs. EDAM combines ontology-learning techniques and semantic technologies with the human-in-the-loop. The first (thanks to automation) fosters scalability, objectivity, reproducibility and granularity of the studies; the second allows tailoring to the specific focus of the study at hand and knowledge reuse from domain experts. We evaluated EDAM on the field of Software Architecture against six senior researchers. As a result, we found that the performance of the senior researchers in classifying papers was not statistically significantly different from EDAM. Conclusions. Thanks to automation of the less-creative steps in SRs, our methodology allows researchers to skip the tedious tasks of keywording and manually classifying primary studies, thus freeing effort for the analysis and the discussion.
Discrete Optimization of Ray Potentials for Semantic 3D Reconstruction

Dense semantic 3D reconstruction is typically formulated as a discrete or continuous problem over label assignments in a voxel grid, combining semantic and depth likelihoods in a Markov Random Field framework. The depth and semantic information is incorporated as a unary potential, smoothed by a pairwise regularizer. However, modelling likelihoods as a unary potential does not model the problem correctly leading to various undesirable visibility artifacts. We propose to formulate an optimization problem that directly optimizes the reprojection error of the 3D model with respect to the image estimates, which corresponds to the optimization over rays, where the cost function depends on the semantic class and depth of the first occupied voxel along the ray. The 2-label formulation is made feasible by transforming it into a graph-representable form under QPBO relaxation, solvable using graph cut. The multi-label problem is solved by applying alpha-expansion using the same relaxation in each expansion move. Our method was indeed shown to be feasible in practice, running comparably fast to the competing methods, while not suffering from ray potential approximation artifacts.
Constructing Antidictionaries in Output-Sensitive Space

A word $x$ that is absent from a word $y$ is called minimal if all its proper factors occur in $y$. Given a collection of $k$ words $y_1,y_2,\ldots,y_k$ over an alphabet $\Sigma$, we are asked to compute the set $\mathrm{M}^{\ell}_{y_{1}\#\ldots\#y_{k}}$ of minimal absent words of length at most $\ell$ of word $y=y_1\#y_2\#\ldots\#y_k$, $\#\notin\Sigma$. In data compression, this corresponds to computing the antidictionary of $k$ documents. In bioinformatics, it corresponds to computing words that are absent from a genome of $k$ chromosomes. This computation generally requires $\Omega(n)$ space for $n=|y|$ using any of the plenty available $\mathcal{O}(n)$-time algorithms. This is because an $\Omega(n)$-sized text index is constructed over $y$ which can be impractical for large $n$. We do the identical computation incrementally using output-sensitive space. This goal is reasonable when $||\mathrm{M}^{\ell}_{y_{1}\#\ldots\#y_{N}}||=o(n)$, for all $N\in[1,k]$. For instance, in the human genome, $n \approx 3\times 10^9$ but $||\mathrm{M}^{12}_{y_{1}\#\ldots\#y_{k}}|| \approx 10^6$. We consider a constant-sized alphabet for stating our results. We show that all $\mathrm{M}^{\ell}_{y_{1}},\ldots,\mathrm{M}^{\ell}_{y_{1}\#\ldots\#y_{k}}$ can be computed in $\mathcal{O}(kn+\sum^{k}_{N=1}||\mathrm{M}^{\ell}_{y_{1}\#\ldots\#y_{N}}||)$ total time using $\mathcal{O}(\mathrm{MaxIn}+\mathrm{MaxOut})$ space, where $\mathrm{MaxIn}$ is the length of the longest word in $\{y_1,\ldots,y_{k}\}$ and $\mathrm{MaxOut}=\max\{||\mathrm{M}^{\ell}_{y_{1}\#\ldots\#y_{N}}||:N\in[1,k]\}$. Proof-of-concept experimental results are also provided confirming our theoretical findings and justifying our contribution.
Modeling Data Reuse in Deep Neural Networks by Taking Data-Types into Cognizance

In recent years, researchers have focused on reducing the model size and number of computations (measured as "multiply-accumulate" or MAC operations) of DNNs. The energy consumption of a DNN depends on both the number of MAC operations and the energy efficiency of each MAC operation. The former can be estimated at design time; however, the latter depends on the intricate data reuse patterns and underlying hardware architecture. Hence, estimating it at design time is challenging. This work shows that the conventional approach to estimate the data reuse, viz. arithmetic intensity, does not always correctly estimate the degree of data reuse in DNNs since it gives equal importance to all the data types. We propose a novel model, termed "data type aware weighted arithmetic intensity" ($DI$), which accounts for the unequal importance of different data types in DNNs. We evaluate our model on 25 state-of-the-art DNNs on two GPUs. We show that our model accurately models data-reuse for all possible data reuse patterns for different types of convolution and different types of layers. We show that our model is a better indicator of the energy efficiency of DNNs. We also show its generality using the central limit theorem.
A Smartphone enabled Approach to Manage COVID-19 Lockdown and Economic Crisis

The emergence of novel COVID-19 causing an overload in health system and high mortality rate. The key priority is to contain the epidemic and prevent the infection rate. In this context, many countries are now in some degree of lockdown to ensure extreme social distancing of entire population and hence slowing down the epidemic spread. Further, authorities use case quarantine strategy and manual second/third contact-tracing to contain the COVID-19 disease. However, manual contact tracing is time consuming and labor-intensive task which tremendously overload public health systems. In this paper, we developed a smartphone-based approach to automatically and widely trace the contacts for confirmed COVID-19 cases. Particularly, contact-tracing approach creates a list of individuals in the vicinity and notifying contacts or officials of confirmed COVID-19 cases. This approach is not only providing awareness to individuals they are in the proximity to the infected area, but also tracks the incidental contacts that the COVID-19 carrier might not recall. Thereafter, we developed a dashboard to provide a plan for government officials on how lockdown/mass quarantine can be safely lifted, and hence tackling the economic crisis. The dashboard used to predict the level of lockdown area based on collected positions and distance measurements of the registered users in the vicinity. The prediction model uses K-means algorithm as an unsupervised machine learning technique for lockdown management.
DEVDAN: Deep Evolving Denoising Autoencoder

The Denoising Autoencoder (DAE) enhances the flexibility of the data stream method in exploiting unlabeled samples. Nonetheless, the feasibility of DAE for data stream analytic deserves an in-depth study because it characterizes a fixed network capacity that cannot adapt to rapidly changing environments. Deep evolving denoising autoencoder (DEVDAN), is proposed in this paper. It features an open structure in the generative phase and the discriminative phase where the hidden units can be automatically added and discarded on the fly. The generative phase refines the predictive performance of the discriminative model exploiting unlabeled data. Furthermore, DEVDAN is free of the problem-specific threshold and works fully in the single-pass learning fashion. We show that DEVDAN can find competitive network architecture compared with state-of-the-art methods on the classification task using ten prominent datasets simulated under the prequential test-then-train protocol.
ParaFIS:A new online fuzzy inference system based on parallel drift anticipation

This paper proposes a new architecture of incremen-tal fuzzy inference system (also called Evolving Fuzzy System-EFS). In the context of classifying data stream in non stationary environment, concept drifts problems must be addressed. Several studies have shown that EFS can deal with such environment thanks to their high structural flexibility. These EFS perform well with smooth drift (or incremental drift). The new architecture we propose is focused on improving the processing of brutal changes in the data distribution (often called brutal concept drift). More precisely, a generalized EFS is paired with a module of anticipation to improve the adaptation of new rules after a brutal drift. The proposed architecture is evaluated on three datasets from UCI repository where artificial brutal drifts have been applied. A fit model is also proposed to get a "reactivity time" needed to converge to the steady-state and the score at end. Both characteristics are compared between the same system with and without anticipation and with a similar EFS from state-of-the-art. The experiments demonstrates improvements in both cases.
Studying Attitudes and Social Norms in Agile Software Development

The purpose of this paper is to review research on attitudes and social norms and connect it to the agile software development context. Furthermore, I propose additional theories from social psychology (mainly the theory of planned behavior and using the degree of internalization of social norms) that would most certainly be useful for further sense-making of human factors-related research on agile teams.
Continuous-time quantum search and time-dependent two-level quantum systems

It was recently emphasized by Byrnes, Forster, and Tessler [Phys. Rev. Lett. 120, 060501 (2018)] that the continuous-time formulation of Grover's quantum search algorithm can be intuitively understood in terms of Rabi oscillations between the source and the target subspaces. In this work, motivated by this insightful remark and starting from the consideration of a time-independent generalized quantum search Hamiltonian as originally introduced by Bae and Kwon [Phys. Rev. A 66, 012314 (2002)], we present a detailed investigation concerning the physical connection between quantum search Hamiltonians and exactly solvable time-dependent two-level quantum systems. Specifically, we compute in an exact analytical manner the transition probabilities from a source state to a target state in a number of physical scenarios specified by a spin-1/2 particle immersed in an external time-dependent magnetic field. In particular, we analyze both the periodic oscillatory as well as the monotonic temporal behaviors of such transition probabilities and, moreover, explore their analogy with characteristic features of Grover-like and fixed-point quantum search algorithms, respectively. Finally, we discuss from a physics standpoint the connection between the schedule of a search algorithm, in both adiabatic and nonadiabatic quantum mechanical evolutions, and the control fields in a time-dependent driving Hamiltonian.
Large-scale mammography CAD with Deformable Conv-Nets

State-of-the-art deep learning methods for image processing are evolving into increasingly complex meta-architectures with a growing number of modules. Among them, region-based fully convolutional networks (R-FCN) and deformable convolutional nets (DCN) can improve CAD for mammography: R-FCN optimizes for speed and low consumption of memory, which is crucial for processing the high resolutions of to 50 micrometers used by radiologists. Deformable convolution and pooling can model a wide range of mammographic findings of different morphology and scales, thanks to their versatility. In this study, we present a neural net architecture based on R-FCN / DCN, that we have adapted from the natural image domain to suit mammograms -- particularly their larger image size -- without compromising resolution. We trained the network on a large, recently released dataset (Optimam) including 6,500 cancerous mammograms. By combining our modern architecture with such a rich dataset, we achieved an area under the ROC curve of 0.879 for breast-wise detection in the DREAMS challenge (130,000 withheld images), which surpassed all other submissions in the competitive phase.
Comparative Analysis of deep learning based object detection model’s for their application in autonomous vehicles

In this work, we compare the detection accuracy and speed of several state of the art models for the task of detecting red and green traffic lights. We compare detection performance and speed of YOLOv4, ScaledYOLOv4 and YOLOR. All of these are single stage object detection models. Two stage models have good detection accuracy but are slower than single stage detectors, single stage detectors are faster and also have good detection accuracy which makes them reliable in real time object detection. We discuss about the object detection models and the evaluation metric that we used to score our models. Than we discuss about the results of our work.
A Textual Entailment Recognition Method Fused with Language Knowledge

Recognizing Textual Entailment (RTE) is a challenging task in natural language processing (NLP). Current mainstream RTE models mainly learn text features and inference knowledge from training data, which leads to weak generalization ability. In response to the problem mentioned above, this paper proposes a Chinese RTE model fused with language knowledge that uses the semantic information of the Ci Lin and the word vector representation based on the sememe. The accuracy of our model achieved 80.60% on the CNLI textual entailment test set and 80.11% on the Chinese test set of XNLI. The experimental results show that the method fused with language knowledge can effectively improve Chinese textual entailment recognition accuracy, and using the larger training corpus will produce better results.
Corpus Construction for Aviation Speech Recognition

In the aviation field, safety is always the top priority. Human error is one of the important factors affecting flight safety, which may cause serious consequences. Since voice conversation is the primary way of communication in the cockpit, speech recognition technology can be applied to detect possible human error, and this technology requires carefully annotated speech text for model training. This paper proposes a small-scale Chinese civil aviation professional corpus, which is constructed from civil aviation flight manuals and speech data in real flight scenarios with manual audio filtering and text annotation, thorough preprocessing and cleansing procedures. Besides, we identify keywords to extract the corpus, so that we can make the topics of our corpus more focused on the aviation domain, thus allowing the model to better learn the unique features of aviation speech such as aviation terms, quantity words, etc. Moreover, we contrast the WER of the speech recognition results before and after using our corpus. The experimental results have shown that our proposed corpus can improve the effect of aviation speech recognition.
The PRIMPing Routine -- Tiling through Proximal Alternating Linearized Minimization

Mining and exploring databases should provide users with knowledge and new insights. Tiles of data strive to unveil true underlying structure and distinguish valuable information from various kinds of noise. We propose a novel Boolean matrix factorization algorithm to solve the tiling problem, based on recent results from optimization theory. In contrast to existing work, the new algorithm minimizes the description length of the resulting factorization. This approach is well known for model selection and data compression, but not for finding suitable factorizations via numerical optimization. We demonstrate the superior robustness of the new approach in the presence of several kinds of noise and types of underlying structure. Moreover, our general framework can work with any cost measure having a suitable real-valued relaxation. Thereby, no convexity assumptions have to be met. The experimental results on synthetic data and image data show that the new method identifies interpretable patterns which explain the data almost always better than the competing algorithms.
The Construction of Chinese Culture in English Teaching from the Perspective of the Internet of Things

At present, the country pays more and more attention to English teaching(ET). In the process of ET, Chinese culture(CC) is the basis for students to learn English, and it is also an important way to cultivate cross-cultural communication skills and comprehensively use modern language to conduct thinking activities and solve practical problems. However, the reality is that ET is facing more and more serious cultural deficiencies. English learners are constantly exposed to Western culture and gradually weaken their Chinese cultural identity. Therefore, it is very necessary and urgent to carry out the construction of CC in ET. At the same time, the Internet of Things is a product of the emergence and development of the information society. It can not only realize the sharing and utilization of information resources, but also meet the learning needs of students through various intelligent terminal devices, thereby further expanding the scope of application in ET and improving the level of educational informatization. Therefore, it becomes more and more meaningful to study the construction of CC in ET in the context of the development of the Internet of Things. This article uses questionnaire surveys and data analysis methods to understand people's views on the introduction of CC into ET through questionnaire surveys, so as to further complete the work of constructing CC in it. According to the survey results, among the 120 interviewees, 46 people think it is necessary to infiltrate CC in English learning, while only 7 people think it is unnecessary. It can be seen that, in the eyes of most of the interviewees, it is necessary to understand and learn CC in ET, and it will also be of great help to English learning.
Research on recognizing required items based on opencv and machine learning

Starting from the background of the outbreak of New Coronavirus, in order to realize the function of automatically identifying the required items by machine, the support vector machine algorithm in the neural network and the traditional computer vision algorithm opencv were used. The software developed by pycharm and python programming language was used to compile automatically a software to identify whether the required items were filled out. And on the basis of completing the software, it is connected to the embedded device high-speed clapper. It is applied to Fuzhou Customs to help the customs staff review the health form and declaration card of inbound and outbound passengers, which not only saves the time of staff and passengers, but also contributes to the prevention and control of epidemic situation to a certain extent.
Integration Practice of Modern Information Technology in English Teaching

By integrating modern information technology into English teaching, we can improve the efficiency of English classroom teaching in an all-round way, and at the same time create a life scene which is easy for students to understand, to stimulate students' interest and enthusiasm to participate in English learning, and to help English teaching activities to achieve a comprehensive breakthrough. Therefore, we must realize the important position of modern information technology in English teaching, give full play to the advantage of information technology application, and promote English teaching to realize the intelligent reform. Based on the advantages of integration and application of modern information technology in English teaching, this paper systematically explores the practical approach of integration of modern information technology in English teaching.