Reviewerly/exHarmony huggingface dataset:
A Novel Sampling Approach for Balancing The Data and Providing Health Care Management System by Government

Here in existing framework now days in shops they are keeping up old items and lapsed items if anyone utilized those items in a few circumstances will be harmed.What's more, a portion of the shop people are changing that all dates or more the cover and making it like a unique item in the wake of terminating time they are changing that all spreads everything.Fundamentally these issues are occurring in healing facility drug additionally their specialists are giving distinctive sorts of medication for various sickness.At whatever point they will understand that therapeutic shop they will give for specific sickness diverse prescription.Here to overcome each one of those issue first client needs to keep up every one of the items with id.presently after login the businessperson account they need to transfer every one of the insights regarding items and they need to keep up make item and terminate date all they need to keep up in the wake of transferring all that these all data will goes to administrator group (carefulness group ) now administrator group will deal with that all data and they can investigate and they will give all the data about the item lapsing date if the item will lapse they will send a notice to retailer before 15 days of item will terminate.At that point businessperson will make offer for that specific id items then just it won't be squander capable that items.It will demonstrate the fabricate date and terminate date if it was phony it won't demonstrate any outcome.if like that any client discover like that, they can send a mail.To administrator they can make a move on that specific shop.
Trial by Fire and Then Some for Electronic Resources: Connecting the Community through Customer Service

Electronic resource troubleshooting can feel like extinguishing a fire every day in a library. When a real fire comes along as Kansas State University (K-State) experienced at Hale Library, one learns not to take e-resources–and the fact that they are working–for granted. Regardless of the integrated library system and discovery layer, getting databases and online subscriptions back up is especially critical when physical collections are suddenly inaccessible. Returning to a sense of normalcy involves rethinking priorities, staffing, and the nature of the resources in given circumstances. Earlier in the fall of 2017, the acquisitions unit had begun a study of its Jane Hale ticketing system for e-resource issues and problems. Jane Hale is a not a real person, but rather a persona for a shared email account and workflow used in acquisitions and e-resources management troubleshooting. Prior to the fire, the Jane Hale e-resource ticketing system was staffed with the intention of delivering more timely access to e-resources. The work devoted to improving the e-resources ticketing system yielded many benefits following the fire, not the least being improved internal customer service and processes. The community of vendors, librarians, and patrons demonstrated an outpouring of support and kindness. From this experience, we learned the importance of creating good relationships and fostering a sense of community before access is disrupted. Communication, both internal and external, is also very significant. Disaster response for electronic resources became an effective continuity plan, underscoring the importance of deciding what is essential and how to use communication and other skills to meet the needs of the library community.
Adaptive Convolutional ReLUs

Rectified linear units (ReLUs) are currently the most popular activation function used in neural networks. Although ReLUs can solve the gradient vanishing problem and accelerate training convergence, it suffers from the dying ReLU problem in which some neurons are never activated if the weights are not updated properly. In this work, we propose a novel activation function, known as the adaptive convolutional ReLU (ConvReLU), that can better mimic brain neuron activation behaviors and overcome the dying ReLU problem. With our novel parameter sharing scheme, ConvReLUs can be applied to convolution layers that allow each input neuron to be activated by different trainable thresholds without involving a large number of extra parameters. We employ the zero initialization scheme in ConvReLU to encourage trainable thresholds to be close to zero. Finally, we develop a partial replacement strategy that only replaces the ReLUs in the early layers of the network. This resolves the dying ReLU problem and retains sparse representations for linear classifiers. Experimental results demonstrate that our proposed ConvReLU has consistently better performance compared to ReLU, LeakyReLU, and PReLU. In addition, the partial replacement strategy is shown to be effective not only for our ConvReLU but also for LeakyReLU and PReLU.
Fuzzy K-Nearest Neighbor for Restaurants Business Sentiment Analysis on TripAdvisor

Social media has grown so rapidly, so people easily to share their opinions, moments, etc. There are several types of research about social media, one of which is Sentiment Analysis (SA) that can also be referred to as opinions meaning (OM). Sentiment Analysis focuses on the classification of patterns that are derived from words that are positive words, negative words, and neutral words. In this paper, the researcher uses sentiment analysis with a machine learning approach and uses Fuzzy K-Nearest Neighbor (FK-NN) as the classification method. The dataset uses English text classification, to predicted sentiment of customer reviews about the positive or negative review. The predicted results show that Sentiment Analysis FK-NN is slightly close to the results of the previous research method, namely Probabilistic Latent Semantic Analysis (PLSA), which FK-NN is 72.05% and PLSA is 76%.
Application of New Multi-Scale Edge Fusion Algorithm in Structural Edge Extraction of Aluminum Foam

Accurate extraction of structural edge information of aluminum foam is an important method to study the complex structural properties of aluminum foam, but the conventional single-scale edge detection method is difficult to achieve complete extraction of structural edge information of aluminum foam. However, the multi-scale fusion edge detection method based on Gaussian smoothing also has some problems, such as strong edge diffusion, weak edge degradation, edge pixel movement and so on. In order to solve the shortcomings of the above methods, this paper proposes a multi-scale edge fusion algorithm based on texture suppression, which can extract the edge information of aluminum foam structure more accurately and completely. Firstly, preprocess the image. The illumination component of the image is extracted by the multi-scale fusion method, and the luminance of the image is corrected by the adaptive luminance correction method based on the two-dimensional gamma function. Secondly, construct multi-scale space. It is proposed to construct the guiding image of the guiding filtering by using the bilateral texture filtering and construct the multi-scale space by changing the scale factor of the guiding filtering. Both bilateral filtering and guided filtering have the function of suppressing the texture information of the image while maintaining the structural edge features of the image. Finally, extract edge seeds and fuse multi-scale edges. A new multi-scale image edge fusion algorithm is proposed, which uses seed edges as a medium to gradually merge multi-scale image edges. In order to extract the edge information of the foam aluminum cross-section structure more accurately and completely, the algorithm further optimizes the edge using gradient direction consistency and non-maximum suppression. In order to verify the feasibility of the proposed algorithm, this paper uses the dataset to test the proposed algorithm and a variety of existing algorithms, and compare the results of various algorithms by quantitative analysis. The experimental results show that the proposed algorithm is feasible and effective, and its performance is better than the comparison algorithm.
Identify the Object’s Shape using Augmented Reality Marker-based Technique.

At present, new technology affects daily life in both direct and indirect ways. Internet technology can connect people around the world through social networks. It can facilitate online shopping or e-commerce, which is the popular culture of today business. Contents provided in the online shopping must be in the form that customer can interact with, i.e., it must be converted from analog data to digital information. For apparel or clothing business, only picture and information of the dresses, such as size, color, etc., may not be enough, since the customer did not know whether it will fit their bodies or not. To make sure that the dress they wanted to buy fit their body, the body size of the customers must be known. With the known body size, generating the 3D model of the customer to try on the 3D virtual model of the dress is possible, and the decision to buy is possible. There are many ways to find the exact body size and generate a 3D model of the customers i.e. using 3D scanner, using Photogrammetry technique (merging many photographs of the customers’ bodies to create the 3d model) or generating 3D model with known information using 3d computer graphic software such as Autodesk Maya, 3D max. The techniques mentioned above have some drawback because it required either an expensive device or expert to create a 3D model which may take a long time. Therefore in this research, we present the technique using marker-based Augmented Reality to acquire the shape of the objects. By wrapping the markers around the surface of the object that we want to measure, each marker’s position can be identified, and when combined, the shape and sizes of the object can be created. This technique takes a shorter time than other method and does not require any sophisticated device but still give good results. We separate the experiment into three groups, group one, testing the concept with five objects with different sizes and shapes with one row markers and group two, testing cylindrical objects with four row markers, and group three, testing with a mannequin to find the shape of human’s body. we have found that the shape and size of the objects that we have created are very close to the real one with the maximum error of less than 5%. It possible to generate the whole 3D object which can be adjusted to support virtual fitting room.
Delay-Adaptive Distributed Stochastic Optimization

In large-scale optimization problems, distributed asynchronous stochastic gradient descent (DASGD) is a commonly used algorithm. In most applications, there are often a large number of computing nodes asynchronously computing gradient information. As such, the gradient information received at a given iteration is often stale. In the presence of such delays, which can be unbounded, the convergence of DASGD is uncertain. The contribution of this paper is twofold. First, we propose a delay-adaptive variant of DASGD where we adjust each iteration's step-size based on the size of the delay, and prove asymptotic convergence of the algorithm on variationally coherent stochastic problems, a class of functions which properly includes convex, quasi-convex and star-convex functions. Second, we extend the convergence results of standard DASGD, used usually for problems with bounded domains, to problems with unbounded domains. In this way, we extend the frontier of theoretical guarantees for distributed asynchronous optimization, and provide new insights for practitioners working on large-scale optimization problems.
Towards a Passages Extraction Method for Arabic Question Answering Systems

Question Answering Systems (QASs) aim to provide a precise answer to questions written in natural language. Passages extraction is a challenging task that affects directly the performance of any QAS. In this paper, we propose a passages extraction method for Arabic Question Answering Systems. It consists of two steps: (1) formulating the query from the Arabic questions user and (2) extracting candidate passages that contain most probably, the correct answers. First, we describe the querys formulation by using stemmed words and performing a Pos-tagging process. Then, we identify relevant passages from Arabic Wikipedia based on two levels of Information Retrieval (IR). In the first level, we extract relevant documents from Arabic Wikipedia based on both documents titles and Named Entities (NEs) contained in the formulated query. The second IR level extracts candidate passages from the pages extracted in the first level based on the similarity with the query. This allows to reduce the number of extracted passages and keep the N top-ranked ones. The obtained primary results are promising as they show a high level of similarity between a given question and the candidate passages.
Developing Information And Communicative Competence In Organizers Of School Educational Process

The paper considers possibilities of using information technologies to organize learning activities in a general education school and justifies the necessity of applying information technologies for implementation of managerial and organizational activities of schoolteacher. The article introduces the following levels of informational and communicative competence of schoolteachers. Low level of competence: Basic knowledge, skills and experience in application of ICT, skill of using ICT means in information analytical activities. Medium level of competence: Knowledge, skills and experience with ICT tools that allow using these tools independently in organizational, managerial, technological and information analytical activity by means of teaching materials that define the procedure and methods of using ICT means during the performance of an activity. High level of competence: Knowledge, skills and experience with ICT tools that allow analyzing, independently selecting and applying ICT means for organizational, managerial, technological and information analytical activities, including defining new methods for solution of professional tasks. Meanwhile, from analysis of the research results one may conclude that ICT competence of educational process organizers at schools has its specifics, while subject teachers, teaching staff, methodologists differ by ICT competence from other categories of educators. These differences are primarily related to functions exercised. It is known that the process of implementing ICT proceed at practically every level starting from municipal educational institutions to federal sector-wide programs. The developed recommendations for use of information technologies in organization and performance of managerial activities of a schoolteacher may be of use to general schoolteachers.
Assessing a 35mm Fixed-Lens Sony Alpha-5100 Intrinsic Parameters Prior to, During, and Post UAV Flight Mission

A fixed focal length lens (FFL) camera with on-adjustable focal length is common companions for conducting aerial photography using unmanned aerial vehicles (UAVs) due to its superiority on optical quality and wider maximum aperture, lighter weight and smaller sizes. A wide-angle 35mm FFL Sony a5100 camera had been used extensively in our recent aerial photography campaign using UAV. Since this off-the-self digital camera is categorized into a non-metric one, a stability performance issue in terms of intrinsic parameters raises a considerably attention, particularly on variations of the lens principal distance and principal point’s position relative to the camera’s CCD/CMOS sensor caused by the engine and other vibrations during flight data acquisitions. A series of calibration bundle adjustment was conducted to determine variations in the principal distances and principal point coordinates before commencing, during, and after accomplishment of the flight missions. This paper demonstrates the computation of the parameters and presents the resulting parameters for three different epochs. It reveals that there are distinct discrepancies of the principal distances and principal point coordinates prior to, during, and after the mission, that peaked around 1.2mm for the principal distance, as well as around 0.4mm and 1.3mm along the x-axis and the y-axis of the principal point coordinates respectively. In contrast, the lens distortions parameters show practically no perturbations in terms of radial, decentering, and affinity distortion terms during the experiments.
Mixpred: Video Prediction Beyond Optical Flow

Video prediction is a meaningful task for it has a wide range of application scenarios. And it is also a challenging task since it needs to learn the internal representation of a given video for both appearance and motion dynamics. The existing methods regard this problem as a spatiotemporal sequence forecasting problem and try to resolve it in a one-shot fashion, which causes the prediction result being blurry or inaccurate. So, a more intuitional thought is to split this problem into two parts: model the dynamic pattern of the given video and learn the appearance representation of given video frames. In this paper, we develop a novel network structure named MixPred based on this idea to address this issue. We divide the prediction problem into two parts as mentioned above and build two subnets to solve these two parts separately. Instead of fusing the results of subnets at the final layer, we put forward a parallel interaction style through the whole process to merge the dynamic information and content information in a more natural way. Besides, we propose three different connection methods for exploring the most effective connection structure. We trained the model on UCF-101 and KITTI, and testing our model on UCF-101, KITTI, and Caltech. The results demonstrate that our method achieves state-of-the-art both quantitatively and qualitatively.
Are Bank Employees Satisfied with Perceived Leadership and Empowerment?

One of the reasons affecting job satisfaction is the leadership style that engages the employees on a daily basis. Although perceived leadership has been studied well in the past, but there are fewer studies focusing on the mediating effect of employee empowerment. The key objective of this research is to study the relationship of perceived leadership, employee empowerment and job satisfaction of the employees, and also to study the mediating role of employee empowerment on the relationship between perceived leadership, and job satisfaction of the employee. A sample of 200 professionals from different banks was selected for the purpose of this study. Moreover, the Smart PLS 3.0 software was used to analyze whether the model was fit by referring to the PLS-SEM. The results of this study revealed that the independent variable (perceived Leadership) has a significant and positive influence on job satisfaction. All the hypotheses were accepted, and there were none that could be rejected after the results were revealed. The study proves that a partial mediating effect of employee empowerment exists between perceived leadership and job satisfaction. This study can help managers in designing the right strategies for retaining their employees by empowering them according to the boundaries set by their organizations. The results can help organizations to identify the potential reasons for job satisfaction, which will eventually lead to higher productivity and profitability.
Pengujian Black Box berbasis Equivalence Partitions pada Aplikasi Seleksi Promosi Kenaikan Jabatan

To guarantee the application (software) Selection Promotion Preferment is free from errors, testing is needed to test the quality of the application. This application has a problem when delete data, then the application will be tested with the Equivalence Partition-based Black Box Method thoroughly regarding the use, benefits, and results found from the use of the software. In testing the software Selection Promotion Preferment using Weight Product Method consists of 6 forms. Equivalence Partition-based Black Box method tests the quality of the application that will be carried out by testing software documentation with the discovery of errors on each form that is divided into three error models, namely errors in Function, Data Structure and Interface. The results of this test, to ensure the quality of the Selection Promotion Preferment application is free from errors by finding unintentional errors in the Selection Promotion Preferment application.
Introducing Probabilistic Bézier Curves for N-Step Sequence Prediction

Representations of sequential data are commonly based on the assumption that observed sequences are realizations of an unknown underlying stochastic process, where the learning problem includes determination of the model parameters. In this context, a model must be able to capture the multi-modal nature of the data, without blurring between single modes. This paper proposes probabilistic B&amp;apos;{e}zier curves (
Learning to Map Frequent Phrases to Sub-Structures of Meaning Representation for Neural Semantic Parsing

Neural semantic parsers usually generate meaning representation tokens from natural language tokens via an encoder-decoder model. However, there is often a vocabulary-mismatch problem between natural language utterances and logical forms. That is, one word maps to several atomic logical tokens, which need to be handled as a whole, rather than individual logical tokens at multiple steps. In this paper, we propose that the vocabulary-mismatch problem can be effectively resolved by leveraging appropriate logical tokens. Specifically, we exploit macro actions, which are of the same granularity of words/phrases, and allow the model to learn mappings from frequent phrases to corresponding sub-structures of meaning representation. Furthermore, macro actions are compact, and therefore utilizing them can significantly reduce the search space, which brings a great benefit to weakly supervised semantic parsing. Experiments show that our method leads to substantial performance improvement on three benchmarks, in both supervised and weakly supervised settings.
Instance Image Retrieval with Generative Adversarial Training

While generative adversarial training becomes promising technology for many computer vision tasks especially in image processing domain, it has few works so far on instance level image retrieval domain. In this paper, we propose an instance level image retrieval method with generative adversarial training (ILRGAN). In this proposal, adversarial training is adopted in the retrieval procedure. Both generator and discriminator are redesigned for retrieval task: the generator tries to retrieve similar images and passes them to the discriminator. And the discriminator tries to discriminate the dissimilar images from the images retrieved and then passes the decision to the generator. Generator and discriminator play min-max game until the generator retrieves images that the discriminator can not discriminate the dissimilar images. Experiments on four widely used databases show that adversarial training really works for instance level image retrieval and the proposed ILRGAN can get promising retrieval performances.