Reviewerly/exHarmony huggingface dataset:
A Probabilistic Approach to Personalize Type-Based Facet Ranking for POI Suggestion

Faceted Search Systems (FSS) have become one of the main search interfaces used in vertical search systems, offering users meaningful facets to refine their search query and narrow down the results quickly to find the intended search target. This work focuses on the problem of ranking type-based facets. In a structured information space, type-based facets (t-facets) indicate the category to which each object belongs. When they belong to a large multi-level taxonomy, it is desirable to rank them separately before ranking other facet groups. This helps the searcher in filtering the results according to their type first. This also makes it easier to rank the rest of the facets once the type of the intended search target is selected. Existing research employs the same ranking methods for different facet groups. In this research, we propose a two-step approach to personalize t-facet ranking. The first step assigns a relevance score to each individual leaf-node t-facet. The score is generated using probabilistic models and it reflects t-facet relevance to the query and the user profile. In the second step, this score is used to re-order and select the sub-tree to present to the user. We investigate the usefulness of the proposed method to a Point Of Interest (POI) suggestion task. Our evaluation aims at capturing the user effort required to fulfil her search needs by using the ranked facets. The proposed approach achieved better results than other existing personalized baselines.
Deconfounding Representation Learning Based on User Interactions in Recommendation Systems

Representation learning provides an attractive solution to capture users' real intents by modeling user interactions in recommendation systems. However, there exist influencing factors called confounders in the process of user interactions. Most traditional methods might ignore these confounders, resulting in learning inaccurate users' intents. To address the issue, we take a new perspective to develop a deconfounding representation learning model named DRL. Concretely, we infer the unobserved confounders existing in the user-item interactions with an inference network. Then we leverage a generative network to generate users' personalized intents that contain no unobserved confounders. In order to learn comprehensive users' intents, we model the user-user interactions by adopting state-of-the-art GNN with a new aggregating strategy. Thus, the users' real intents we learn not only have their own personalized information but also imply the influence of their friends. The results of two real-world experiments demonstrate that our model can learn accurate and comprehensive representations.
Research on Collaborative Learning of College English Based on the Internet Mixed Online and Offline Teaching

Collaborative learning is a creative and effective teaching theory and strategy system with students' cooperation as the basic power and group activities as the essential teaching methods. Collaborative learning is a kind of all-round learning in which teachers assign learning tasks and control the teaching process, and let students cooperate to carry out English practice activities within or between groups under the guidance of teachers, so as to complete the learning tasks and achieve the teaching goals. This thesis will discuss the advantages of collaborative learning and its application in the mixed teaching of college English online and offline, aiming at exploring the effective ways and methods of implementing collaborative teaching mode in blended teaching of college English online and offline. Hopefully, through this paper, a teaching mode suitable for the characteristics of students in this class can be found, further promoting the reform of college English teaching mode, guiding the reform of college English teaching in the same grade and the whole university.
Automated analysis of the educational-professional program "Informatics", implemented at the faculty of computer science and cybernetics, with the programs of other institutions of higher education in the specialty 122 "Computer Science"

The article presents a comparative analysis of the educational and professional program "Informatics" of the first (bachelor's) level of higher education in the field of knowledge 12 "Information Technology", specialty 122 "Computer Science", which is implemented at the Faculty of Computer Science and Cybernetics Taras Shevchenko National University of Kyiv with educational and professional programs of the same level and specialties of other institutions of higher education in Ukraine. During the analysis, they were compared with the approved standard of the first (bachelor's) level of higher education in the specialty 122 "Computer Science". In order to conduct a comparative analysis, the authors developed and completed a educational program database. The result of the study is checking the educational program for completeness, that is lack of competencies that are not provided by any discipline and sufficiency, that is the lack of disciplines that do not provide any competence. The ratio of competencies and disciplines in different programs in a common specialty is analyzed.
Principles and Key Applications: Principles and Applications of Synthetic Solar Irradiance

In this chapter we first describe the basic concepts of synthetic generation of time series data. We examine several of the realms where it is needed. One area is downscaling from a coarse temporal resolution to a higher frequency. For example, when estimating the performance of solar cells, one-minute data is more useful than hourly, but it may not be available. Also, infilling missing data is crucial if one is forecasting output from a solar farm. On the other hand, generating any number of years of data from a limited number of measured years—known as bootstrapping—is useful for estimating risk for farms and other solar installations. In energy markets, there is scope for generating possible future trajectories for either solar radiation time series or financial instruments that depend on them. In describing these situations and others, we briefly explain the mechanisms for these computations. We end by describing in detail some specific example applications of synthetic solar irradiance tools.
Nested Error Map Generation Network for No-Reference Image Quality Assessment

We propose a multi-task learning neural network for No-Reference image quality assessment (NR-IQA). The pro-posed architecture consists of a backbone feature extractor, a nested multi-task generative module and a quality regression module. We adopt a coarse-to-fine strategy to predict objective error maps in two subtasks optimized with different loss functions. The network is designed to be nested such that discriminative features learned from subtasks are efficiently shared by the primary task. Perceptual distortion maps are achieved by applying masking mechanism between reconstructed error maps and the learned distortion sensitivity map. At last, a quality regression module is adopted to nonlinearly map masked distortions to the subjective score. Experimental results demonstrate the superior performances of the proposed model over state-of-the-art models. The implementation of our method is released at https://github.com/R-JunmingChen/NEMG-IQA.
Critical Success Factors of Software Projects: The Perspective A Software Company from Indonesia

The successful implementation of software projects is the desire of every software project developer, but according to the data from Standish Group in 2018, the success of developing software projects is still very small at 23%. Therefore, this study conducted action research on ABC Ltd., which has a core business in developing software projects. In developing ABC project there were still projects that have failed such as exceeding the predetermined time target. This research determines the priorities of critical success factors (CSF) that ABC Ltd. must consider in improving the project development process. The data were collected using questionnaires with a pairwise comparison scale and the research respondents were ABC Ltd. employees who were directly involved in developing projects were then analyzed using the Analytic Hierarchy Process (AHP). Based on the results of the analysis obtained clear project goal is alternatives with the highest rank then followed by communication, project planning, clear team composition, top management support, appropriate resource allocation, changes in project requirements, project funding, team competency, project work methodology and project time. Based on the ratings that were obtained, interviews were conducted to obtain stakeholder responses that will become as recommendations in making improvements to each of the success factors of the project, hoping that it could increase the success of software project development at ABC, Ltd.
Verifying phishmon

Phishing attacks are the scourge of the network security manager's job. Looking for a solution to counter this trend, this paper examines and verifies the efficacy of Phishmon, a machine learning framework for scrutinizing webpages that relies on technical attributes of the webpage's structure for classification. More specifically, each of the four machine learning algorithms mentioned in the original paper are applied to a portion of the data set used by Phishmon's creators in order to verify and confirm their results.
Efficient Virtualization in Cloud Computing

In the era of Internet technology, Cloud Computing plays a vital role in research. The main purpose of cloud computing is to transport the resources and services on the user's demand. To run an organization successfully, it provides flexible infrastructure and easy method of handling a massive data. To improve the efficiency of cloud computing virtualization role is crucial. Firstly, this survey has studied the introduction and cloud computing evolution with fundamental properties. Section three describes the service and deployment-based models and section four shows virtualization and its approaches. Section five focuses on advantages and disadvantages of cloud computing. After all section six concludes the article.
Exploring Smartphone Relationships through Roland Barthes using an Instrumented Pillow Technology Probe

We examine a vocabulary of affective language, borrowed from Roland Barthes' "A Lover's Discourse: Fragments," and its applicability to discourse describing smartphone attachment. This vocabulary, adopted from four of Barthes' terms, waiting, dependency, anxiety, and absence, is used as a discursive lens to illustrate some of the many ways people understand and engage with their relationships to their smartphones. Based on this, a survey is conducted, and a speculative technology probe is created in the form of an instrumented pillow for people to lock away their smartphones during the night. The pillow is deployed in a diary study in which five people sleep with their phone locked away for multiple nights. The self-reported and observed behaviours are presented in a selection of vignettes. The results support the proposed discursive lens and suggest future interdisciplinary strategies to investigate how people relate to interactive technology, using a combined approach of literary theory and a technology probe supported by survey and study data.
Crowd Counting Via Multi-Level Regression With Latent Gaussian Maps

Crowd counting still confronts two primary challenges: limited ability to deal with cross density levels caused by fixed density maps and lack of fine-grained or coarse-grained guidance for density estimation. In this paper, a novel end-to-end crowd counting framework via multi-level regression with latent Gaussian maps is proposed, which is consisted of GaussianNet, EstimateNet and Discriminator. GaussianNet is composed of masked Gaussian convolutional blocks and vanillia convolutional layers, to generate latent Gaussian maps adaptively for various density levels. The latent Gaussian maps are then treated as the ground truth density maps for EstimateNet, which outputs density estimations and follows the principle of adversarial learning with Discriminator. Moreover, multi-level losses are combined for density map regression guidance. Extensive experiments on the major public datasets outperform state-of-the-art ones, illustrating the superior validity of the proposed framework.
Numerical Solution of Stochastic Differential Equations in Stiefel Manifolds via Tangent Space Parametrization

Stochastic differential equations (SDEs) evolving in Stiefel manifold have numerous applications in Science and Engineering. While numerical schemes for ordinary differential equations (ODEs) in Stiefel manifolds are reasonably well established, much less has been done for numerical SDEs schemes in Stiefel manifolds. A crucial challenge is to ensure that the trajectory remains on the manifold. But many existing SDE numerical schemes fail to do this. Here we achieve this by extending the so-called 'tangent space parameterization’ (TaSP) for ODEs to SDEs. In so doing we discover a previously missed constraint. We give simulations to illustrate the new numerical scheme.
Learning Integrodifferential Models for Image Denoising

We introduce an integrodifferential extension of the edge-enhancing anisotropic diffusion model for image denoising. By accumulating weighted structural information on multiple scales, our model is the first to create anisotropy through multiscale integration. It follows the philosophy of combining the advantages of model-based and data-driven approaches within compact, insightful, and mathematically well-founded models with improved performance. We explore trained results of scale-adaptive weighting and contrast parameters to obtain an explicit modelling by smooth functions. This leads to a transparent model with only three parameters, without significantly decreasing its denoising performance. Experiments demonstrate that it outperforms its diffusion-based predecessors. We show that both multiscale information and anisotropy are crucial for its success.
Impossible Staircase: Vertically Real Walking in an Infinite Virtual Tower

We present Impossible Staircase, a real-walking virtual reality system that allows users to climb an infinite virtual tower. Our set-up consists of an one-level scaffold and a lifter. A user climbs up the scaffold by real walking on a stairway while wearing a head-mounted display, and gets reset to the ground level by a lifter imperceptibly. By repeating this process, the user perceives an illusion of climbing an infinite number of levels. Our system achieves the illusion by (1) controlling the movement of the lifter to generate reverse and imperceptible motion, (2) guiding the user through the scaffold with delay mechanisms to reset the lifter in time, and (3) procedural generating overlapping structures to enlarge perceived height of each level. We built a working system and demonstrated it with a 15-min experience. With the working system, we conducted user studies to gain deeper insights into vertical motion simulation and vertical real walking in virtual reality.
Is Functionality All That Matters? Examining Everyday User Opinions of Augmented Reality Devices

How users feel towards Augmented Reality (AR) is often shaped by the context of use. This paper reports from a study with 112 participants on how users experience AR in everyday situations. The respondents in an online survey were queried about comfort, aesthetics, functionality, privacy, and physical limitations. The responses revealed a range of opinions, from general enjoyment to key issues and features that users are concerned with, such as pain and tiredness, device size, social acceptance of AR devices, and accessibility. Considerations for future work regarding these issues are discussed. These include balancing functionality with appearance, involving a variety of users in the design process, and focusing on devices that the everyday person is likely to use. Overall users appear keen to see AR device improvements resulting in more practical and accessible devices.
A Comparative Study Between PCA and Sift Algorithm for Static Face Recognition

With the rapid growth in the field of information technology, peoples depend on the technology for solving many kinds of issues that can help in running on daily bases activities, as one of information security features, face recognition became one of the business intelligence methods to identify and recognize peoples in different domains, this paper compares the well-known and reputed state of arts algorithms for static face recognition such as PCA and Sift. The two algorithms start by reading image from camera and then Pre-Process the image, extracting the features, and recognize the image. Based on the experiment the results outlined that Sift algorithm achieved better performance of face recognition compared to the PCA.
A delayed Elastic-Net approach for performing adversarial attacks

With the rise of the so-called Adversarial Attacks, there is an increased concern on model security. In this paper we present two different contributions: novel measures of robustness (based on adversarial attacks) and a novel adversarial attack. The key idea behind these metrics is to obtain a measure that could compare different architectures, with independence of how the input is preprocessed (robustness against different input sizes and value ranges). To do so, a novel adversarial attack is presented, performing a delayed elastic-net adversarial attack (constraints are only used whenever a successful adversarial attack is obtained). Experimental results show that our approach obtains state-of-the-art adversarial samples, in terms of minimal perturbation distance. Finally, a benchmark of ImageNet pretrained models is used to conduct experiments aiming to shed some light about which model should be selected whenever security is a role factor.
Optimized S-Box Architectures of PRESENT Cipher for Resource Constrained Applications

This paper presents the design of three different types of S-box architectures for the PRESENT cipher with an aim of optimizing the design parameters for resource constrained applications. Three variants of S-box design are proposed here. The first includes minimization of the existing Boolean function using logic minimization and K-maps, while the other two designs focus on MUX-based design optimizations. The proposed designs give us an idea about the trade-offs involved in the design and performance metrics. FPGA implementations of the proposed design which includes modifying the substitution layer, reduces the slices used by around 27% and also achieves an enhancement of throughput when compared with related state-of-the-art architectures. These results are tailor made to meet the design specifications for IoT enabled devices which to the best of our knowledge has not been investigated earlier.
Depth Map Refinement Using Super-Pixel Segmentation in Multi-View Systems

Depth information is one of the most crucial components to provide the immersive sense in multi-view video system. We propose a post-processing algorithm to refine the depth map using super-pixel segmentation and considering the relation between multiple views. The proposed algorithm divides the depth map of the reference view into multiple segments by using super pixel technique and projects each patch to the neighbor views. And then, the depth values of the neighbor views are enhanced by extrapolation along a plane model. The enhanced depth values of the neighbor views and the initial value of the reference view are weight summed to update the initial value. The proposed algorithm overcomes the problems related to occlusions and holes by using the enhance depth maps. Simulation results show that the proposed algorithm outperforms the conventional algorithms subjectively and objectively.
Haze removal with channel-wise scattering coefficient awareness based on grey pixels

Before being captured by observers, the information carried by light may be attenuated by the transmission medium. According to the atmospheric scattering model, this attenuation is wavelength-dependent and increases with distance. However, most existing haze removal methods ignore this wavelength dependency and therefore cannot handle well the color distortions caused by it. To solve this problem, we propose a scattering coefficient awareness method based on the image formation model. The proposed method first makes an initial transmission estimation by the dark channel prior and then calculates the scattering coefficient ratios based on the initial transmission map and the grey pixels in the image. After that, fine transmission maps in RGB channels are calculated from these ratios and compensated for in sky areas. A global correction is also applied to eliminate the color bias induced by the light source before the final output. Qualitatively and quantitatively compared on synthetic and real images against state-of-the-art methods, the proposed method provides better results for the scenes with either white fog or colorized haze.
Kernel Orthogonal Nonnegative Matrix Factorization: Application to Multispectral Document Image Decomposition

As a nonlinear extension of the standard nonnegative matrix factorization (NMF), kernel-based variants have demonstrated to be more effective for discovering meaningful latent features from raw data. However, many existing kernel methods allow only obtaining the basis matrix in the projected feature space, which prevents its inverse mapping back to the original space as requested in many applications. In this work, we propose a new kernel orthogonal NMF method that does not suffer from the pre-image issue. We incorporate the orthogonality constraint as an optimization problem over the Stiefel manifold to improve the sparsity and the model’s clustering properties. We solve the proposed model with an efficient optimization approach based on the alternating direction method of multipliers (ADMM) scheme and the projected gradients method. We validate our model on the task of blind decomposition of real-world Multispectral (MS) document images. Our experiments demonstrate the competitiveness of our proposed model in comparison to the state-of-the-art techniques.
Status of energy in Syria - Study on how to meet the energy shortage by means of renewable resources

In this paper, a shedding light on the energy status in Syria before, during and after the war, a case statement of Syria's previous and current production of oil and gas, which are the main sources of fuel for power plants in Syria is presented, in the previous decades, Syria used to cover its need of oil and gas, and export the surplus. Electricity is one of the major consumers of primary energy. Since 2011, the electricity sector has been facing serious challenges including the difficulties in providing the fuel necessary to operate power plants and acts of sabotage and looting for various components of the electrical network, which affected the electricity generation as it decreased more than 60% during the war. On the other hand, Syria enjoys abundant renewable resources due to its geographical location, where the number of sunny hours is between 2830 to 3280 annually and the effective wind potential that can be exploited reaches up to 7000 MW, which is nearly equals to the installed capacity of the conventional power plants that used to meet the demand before the war. Later in this paper, a study was presented to the savings achieved by the use of renewable energies in meeting the expected energy deficit in coming years, which is practically the period of rebuilding Syria, as it shows a considerable savings of about 1.8 billion dollars annually, which helps along with implementing smart grid techniques in rehabilitating the electricity network and achieving the plan of the Syrian Ministry of Electricity regarding renewable energies strategy until 2030.