Reviewerly/exHarmony huggingface dataset:
The Leadership Behavior of Hotel Managers and the Job Satisfaction of Staff in Five-Star Hotels in the Aqaba Special Economic Zone Authority (ASEZA)

The research examines the leadership actions of hotel managers and the job satisfaction of staff in five-star hotels in the Aqaba Special Economic Zone Authority (ASEZA) and the connection of these behaviors to gender and expertise variables and their effects on the activation of leadership positions, the promotion of successful trends and the diffusion of experience. The research used the descriptive methodology in its survey type to fit the essence of the analysis, as a sample of its objective was selected from personnel in five-star hotels in Jordan, consisting of (300) staff members and their employees. The research suggested that the conduct of hotel managers was of a negative nature, since the analysis revealed that there was a negative relationship with each dimension of the relationship between the manager and the worker and the aspects of job and authority, although there was no relationship between the dimension of the relationship between the manager and the worker and the legal dimension.The study proposed a set of recommendations which, in their entirety, centered on the significance of human relations within the hotel and provided them with sufficient treatment to achieve a better level of job satisfaction for all workers and at their different administrative levels, as well as broadening the basis for involvement in decision-making.
A class of APcN power functions over finite fields of even characteristic

In this paper, we investigate the power functions $F(x)=x^d$ over the finite field $\mathbb{F}_{2^{4n}}$, where $n$ is a positive integer and $d=2^{3n}+2^{2n}+2^{n}-1$. It is proved that $F(x)=x^d$ is APcN at certain $c$'s in $\mathbb{F}_{2^{4n}}$, and it is the second class of APcN power functions over finite fields of even characteristic. Further, the $c$-differential spectrum of these power functions is also determined.
NccFlow: Unsupervised Learning of Optical Flow With Non-occlusion from Geometry

Optical flow estimation is a fundamental problem of computer vision and has many applications in the fields of robot learning and autonomous driving. This paper reveals novel geometric laws of optical flow based on the insight and detailed definition of non-occlusion. Then, two novel loss functions are proposed for the unsupervised learning of optical flow based on the geometric laws of non-occlusion. Specifically, after the occlusion part of the images are masked, the flowing process of pixels is carefully considered and geometric constraints are conducted based on the geometric laws of optical flow. First, neighboring pixels in the first frame will not intersect during the pixel displacement to the second frame. Secondly, when the cluster containing adjacent four pixels in the first frame moves to the second frame, no other pixels will flow into the quadrilateral formed by them. According to the two geometrical constraints, the optical flow non-intersection loss and the optical flow non-blocking loss in the non-occlusion regions are proposed. Two loss functions punish the irregular and inexact optical flows in the non-occlusion regions. The experiments on datasets demonstrated that the proposed unsupervised losses of optical flow based on the geometric laws in non-occlusion regions make the estimated optical flow more refined in detail, and improve the performance of unsupervised learning of optical flow. In addition, the experiments training on synthetic data and evaluating on real data show that the generalization ability of optical flow network is improved by our proposed unsupervised approach.
Deep Gaussian Process Emulation using Stochastic Imputation

Deep Gaussian processes (DGPs) provide a rich class of models that can better represent functions with varying regimes or sharp changes, compared to conventional GPs. In this work, we propose a novel inference method for DGPs for computer model emulation. By stochastically imputing the latent layers, our approach transforms a DGP into a linked GP: a novel emulator developed for systems of linked computer models. This transformation permits an efficient DGP training procedure that only involves optimizations of conventional GPs. In addition, predictions from DGP emulators can be made in a fast and analytically tractable manner by naturally utilizing the closed form predictive means and variances of linked GP emulators. We demonstrate the method in a series of synthetic examples and empirical applications, and show that it is a competitive candidate for DGP surrogate inference, combining efficiency that is comparable to doubly stochastic variational inference and uncertainty quantification that is comparable to the fully-Bayesian approach. A $\texttt{Python}$ package $\texttt{dgpsi}$ implementing the method is also produced and available at https://github.com/mingdeyu/DGP.
BumpChat: A Secure Mobile Communication System

BumpChat is a secure messaging application for Android devices. The app makes use of the Near Field Communication (NFC) hardware on devices to securely transmit cryptographic keys. Communication between devices after pairing is handled securely by a web server, through a simple PHP-driven API. Data is encrypted at all stages: at rest on end devices, in transit to the server, and at rest on the server. A user-defined password is used to protect data on the Android device and is required to be entered on every start-up. There are two primary encryption algorithms used for message security (AES-256) and inbox verification with the server (RSA-2048).
Evaluation of Heuristic Portal Lecturer at XYZ University Bandung Indonesia

The lecturer portal at XYZ University is used to support lecturer activities at a university. The purpose of this paper is to test the usability of the information system on the XYZ University lecturer portal whether it is in accordance with the acceptability criteria of usability testing. The test method uses Nielsen's heuristic evaluation with Nielsen's ten principles. The high level of popularity and use of the system by users to help them is related to the high level of usability. The test involved 5 evaluators who acted as idea givers, critics and evaluators from 10 aspects of usability as material for analysis. The results of the analysis show that there are independent variables that have a dominant influence on the usability of the application, namely the visibility of the system variable, the recognition rather than recall variable, the error prevention variable, the flexibility and efficiency of use variable and the help and documentation variable. The results showed that the questionnaire index value of the XYZ University lecturer portal information system has a severity rating of 0.67, which means that the problem does not have to be fixed directly, but can be fixed when there is development.
Identifying Gender from Images of Face

The main objective of this project is to classify the gender based on different facial features such as eyes, nose, mouth, overall features such as face contour, head shape, hair line etc. The gender classification algorithm uses machine learning technique (supervised learning). In this case the algorithm is trained on a set of male and female faces and then used to classify new data. In this project, face detection and gender classification methods are combined. The face detection acts as a pre-processing operation to the gender classifier that determines the gender.There are multiple methods in which facial recognition systems work, but in general, they work by comparing selected facial features from a given image with faces within a database. It is also described as a Biometric Artificial Intelligence based application that can uniquely identify a person by analyzing patterns based on the person's facial textures and shape.Automated gender recognition plays an important role in many application areas such as human computer interaction,biometric, surveillance, demographic statistics etc.
P-wave tomographic model from local bulletin data for improved seismic location in and around Israel

&amp;lt;p&amp;gt;For the past 40 years, the Geophysical Institute of Israel has been in charge of the recording, monitoring and relocating of local earthquakes. Due to the variety of data analysts and data sources, as well as several network upgrades, the resulting bulletin data has to be completed and homogenised, and station metadata needs to be tracked down, and sometimes corrected. For those reasons, as well as because of the lack of consensus on an accurate model for seismic velocities in the area, published source locations are often poorly constrained. We present a homogenised Israeli bulletin, including natural and man-made explosion data. We extract sets of seismic sources with location accuracy greater than 5 km (GT5), as well as GT0 explosions.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We select a set of events with the highest network coverage, comprising (1) natural earthquakes, (2) man-made quarry or mine blasts, (3) GT5 earthquakes or explosions, and (4) GT0 explosions. We relocate them altogether using the &amp;lt;em&amp;gt;BayesLoc&amp;lt;/em&amp;gt; package, a Bayesian, hierarchical, multi-event locator which produces, after source relocation, event-, station- and phase-specific correction terms. We put different a priori constraints on the different categories of seismic events, allowing poorly constrained origin parameters to improve thanks to the more accurate GT locations. &amp;lt;em&amp;gt;BayesLoc&amp;lt;/em&amp;gt; also produces traveltime correction terms that can be used to correct systematic errors in the dataset, as well as error estimates.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Eventually, we invert this homogenised local traveltime dataset in order to invert for a &amp;lt;em&amp;gt;P&amp;lt;/em&amp;gt;-wave crustal velocity model of Israel and its surroundings. To do so, we use the &amp;lt;em&amp;gt;Fast Marching Tomography&amp;lt;/em&amp;gt; package, which allows the representation of a wide variety of input structures (starting model and geometry of layer boundaries) and can take many different types of input data. We show preliminary inversion tests and results that are in good agreement with past local studies.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This crustal model of Israel is ultimately to be used as a starting model in a larger tomographic study of the Eastern Mediterranean and Middle East region, where the &amp;lt;em&amp;gt;Regional Seismic Travel Time&amp;lt;/em&amp;gt; approach is to be expanded, in order to improve the CTBT&amp;amp;#8217;s capabilities in monitoring the regional seismicity. Eventually, such a velocity model could also be used to relocate the whole earthquake catalogue more accurately, and improve the Earthquake Early Warning System currently in development in Israel.&amp;lt;/p&amp;gt;
Depth Image Resolution Enhancement Using Discrete Wavelet Transform and Convolution Neural Networks

The depth image plays an increasingly important role in fundamental research and daily applications, with the reducing the price and increasing the number of affordable and portable depth cameras. Infrared sensors or depth sensors are widely used to control dynamic and static 3D scenes. However, the depth image quality is limited to low-quality images, as the infrared sensor does not have high resolution. Therefore, given the problems and the importance of using 3-D images, the quality of these images should be improved in order to provide accurate images from depth cameras. In this paper a resolution enhancement method of depth images using convolutional neural networks is considered. A convolutional neural network with a depth of 20 and three layers and a pre-trained neural network is used. We developed the system and tested its performance for two datasets, Middlebury and EURECOM Kinect Face. Results show for EURECOM Kinect Face images, PSNR improvement is approximately 7 to 16 dB and for Middlebury images the PSNR improvement is about 6 to 12 dB.
On the 4-Adic Complexity of Quaternary Sequences with Ideal Autocorrelation

In this paper, we determine the 4-adic complexity of the balanced quaternary sequences of period $2p$ and $2(2^n-1)$ with ideal autocorrelation defined by Kim et al. (ISIT, pp. 282-285, 2009) and Jang et al. (ISIT, pp. 278-281, 2009), respectively. Our results show that the 4-adic complexity of the quaternary sequences defined in these two papers is large enough to resist the attack of the rational approximation algorithm.
DON'T SELFI AS A STRATEGY TO EDUCATE CHARACTERS BUILDING OF ENGLISH SUBJECT

ABSTRAK This study was conducted due to the degradation of character value of the students. This condition causes the teacher’s concern and encourages her to find the best solusion in order to overcome the problem. One of the solusion is by applying a strategy called Don’t Selfi ( The combination of Class Regulation and Students’ Explaining and Facilitating Strategy). The purpose of the study are ; 1).to investigate whether Don’t SELFI affect the students’ achievement, 2). To know the effect of the application of Don’t SELFI towards the students’ partiucipation, and 3) to know the effect of Don’t SELFI strategy towards the students’ character especially Nationalism value. The study is a Classroom Action Research which was conducted at SMA Negeri 8 Banda Aceh. The subject of the study was the students of class XII-IPA 2 of SMA Negeri 8 Banda Aceh. The result of the study shows that 1). The application of Don’t SELFI strategy improve the students’ achievement, 2). The application of Don;t SELFI strategy increase the students’ participation, and 3). The application of Don;t SELFI strategy increase the students’ character aspect. KeywordS: Don’t Selfi, a, strategy, character building
Learning from Partially Overlapping Labels: Image Segmentation under Annotation Shift

Scarcity of high quality annotated images remains a limiting factor for training accurate image segmentation models. While more and more annotated datasets become publicly available, the number of samples in each individual database is often small. Combining different databases to create larger amounts of training data is appealing yet challenging due to the heterogeneity as a result of differences in data acquisition and annotation processes, often yielding incompatible or even conflicting information. In this paper, we investigate and propose several strategies for learning from partially overlapping labels in the context of abdominal organ segmentation. We find that combining a semi-supervised approach with an adaptive cross entropy loss can successfully exploit heterogeneously annotated data and substantially improve segmentation accuracy compared to baseline and alternative approaches.
Self-adaptive Perception Model for Action Segment Detection

Action segment detection is an important yet challenging problem, since we need to localize the proposals which contain an action instance in a long untrimmed video with arbitrary length and random position. This task requires us not only to find the precise moment of starting and ending of an action instance, but also to detect action instances as many as possible. We propose a new model Self-Adaptive Perception to address this problem. We predict the action boundaries by classifying start and end of each action as separate components, allowing our model to predict the starting and ending boundaries roughly and generate candidate proposals. We evaluate each candidate proposals by a novel and flexible architecture called Discriminator. It can extract enough semantic information and generate precise confidence score of whether a proposal contains an action within its region, which benefit from the self-adaptive architecture. We conduct solid and rich experiments on large dataset Activity-Net, the result shows that our method achieves a competitive performance, outperforming most published state-of-the-art method in the field. And further experiments demonstrate the effect of each module of our model.
Hybrid Bayesian Neural Networks with Functional Probabilistic Layers

Bayesian neural networks provide a direct and natural way to extend standard deep neural networks to support probabilistic deep learning through the use of probabilistic layers that, traditionally, encode weight (and bias) uncertainty. In particular, hybrid Bayesian neural networks utilize standard deterministic layers together with few probabilistic layers judicially positioned in the networks for uncertainty estimation. A major aspect and benefit of Bayesian inference is that priors, in principle, provide the means to encode prior knowledge for use in inference and prediction. However, it is difficult to specify priors on weights since the weights have no intuitive interpretation. Further, the relationships of priors on weights to the functions computed by networks are difficult to characterize. In contrast, functions are intuitive to interpret and are direct since they map inputs to outputs. Therefore, it is natural to specify priors on functions to encode prior knowledge, and to use them in inference and prediction based on functions. To support this, we propose hybrid Bayesian neural networks with functional probabilistic layers that encode function (and activation) uncertainty. We discuss their foundations in functional Bayesian inference, functional variational inference, sparse Gaussian processes, and sparse variational Gaussian processes. We further perform few proof-of-concept experiments using GPflus, a new library that provides Gaussian process layers and supports their use with deterministic Keras layers to form hybrid neural network and Gaussian process models.
THE EFFECTIVENESS OF PICTURE AND PICTURE LEARNING MODELS ON THE SKILLS OF WRITING NARRATIVE PAPERS

Based on the result of interview and observation it was known that the Indonesian language learning outcomes on essay writing the topic were low because the teachers still used the lecture method in learning process. This research aimed to test the effectiveness of the picture and picture learning model on essay writing skills of the students in Mijen District Elementary School, Semarang City. This research used quantitative method with the type of experimental research. This research design was quasi experimental design in the form of nonequivalent pretest-posttest design. The sample technique used was cluster random sampling in the number of 57 students. The data collection techniques used were interviews, observation, documentation, and performance tests. The results showed that the experimental class posttest results were higher than the control class and included in good categories. That means the picture and picture learning model was effective to be used on essay writing skills topic. The n-gain test results showed that the experimental class obtained a higher increase in the amount of 0.32 in the medium category and the control class was in the amount of 0.12 in the low category. The conclusion of this research is a picture and picture learning model was effective to be used on essay writing skills topic in Mijen District Elementary School, Semarang City. The n-gain test results showed that the experimental class obtained a higher increase in the amount of 0.32 in the medium category and the control class was in the amount of 0.12 in the low category. The conclusion of this research is a picture and picture learning model was effective to be used on essay writing skills topic in Mijen District Elementary School, Semarang City. The n-gain test results showed that the experimental class obtained a higher increase in the amount of 0.32 in the medium category and the control class was in the amount of 0.12 in the low category. The conclusion of this research is a picture and picture learning model was effective to be used on essay writing skills topic in Mijen District Elementary School, Semarang City.
Reflections on the Cultivation of Non-normal Students in Local Normal Universities under the Background of Normal Professional Certification

With the development of non-normal majors in local normal universities, the problems in the process of training non normal majors are gradually prominent. Under the background of normal professional certification, by sorting out and analyzing the problems encountered in the cultivation process of non-normal students majoring in vacuum technology in local normal universities, and combining with the characteristics of non-normal majors, this paper puts forward some countermeasures to improve the cultivation quality of non-normal students, such as low professional awareness of students, unreasonable curriculum and weak teachers.
Development of an Information Network for Affiliating Universities in Kerala

Library and information networks play a vital role in the sharing of information resources and building cooperation among libraries. Library cooperative initiatives like consortium and resource sharing services can seize the pricing of e-resources and ensure uniform availability of information resources. Regional level library networks can satisfy the information requirements of the user community in the micro-level. Kerala state is bestowed with sufficient infrastructure and human resources for the development of a network for library cooperation. This study enquires the possibility of the development of an information network for college libraries under four prominent affiliating universities in Kerala state to facilitate the avenues for cooperation and resource sharing.
Modeling Time-Dependent Systems using Dynamic Quantum Bayesian Networks

Advances in data collection using inexpensive sensors have enabled monitoring the performance of dynamic systems, and to implement appropriate control actions to improve their performance. Moreover, engineering systems often operate under uncertain conditions; therefore, the real-time decision-making framework should not only consider real-time sensor data processing but also several uncertainty sources that may impact the performance of dynamic systems. In this paper, we investigate the modeling of such time-dependent system behavior using a dynamic quantum Bayesian network (DQBN), which is the quantum version of a classical dynamic Bayesian network (DBN). The DBN framework has been extensively used in various domains for its ability to model stochastic relationships between random variables across time. The use of the quantum amplitude amplification algorithm provides quadratic speedup for inference and prediction in Bayesian networks. In this paper, we combine the modeling capabilities of DBN with the computational advantage of quantum amplitude amplification for efficient modeling and control of time-dependent systems. We implement the proposed DQBN framework on IBM Q hardware, and compare its performance with classical DBN implementation and the IBM Qiskit simulator.