Reviewerly/exHarmony huggingface dataset:
Leveraging User Embeddings and Text to Improve CTR Predictions With Deep Recommender Systems

Predicting user engagement, often framed as a CTR prediction problem, is important to maximize user satisfaction in social networks. The 2020 Recsys Challenge was sponsored by Twitter and set the goal of predicting four types of user engagement using a dataset with 160 million tweets. Our approach extracted information from the tweet’s text tokens and built optimized user embeddings. We designed our model based on ideas from recommender systems and deep learning that had been successful in CTR prediction tasks. We show that our modifications to existing state-of-the-art architectures and feature engineering improved the model’s ability to predict user engagement. Factored’s team was called Los Trinadores and had the 6th best submission of the challenge with an overall score of 22. The code for our solution is available at https://github.com/factoredai/recsys20-challenge/.
Sulfur dioxide adsorption by Iron Oxide Nanoparticles@Clinoptilolite/HCl

Introduction: The purpose of this study was to investigate and compare the effect of iron oxide nanoparticles on the adsorption of sulfur dioxide by modified zeolite with hydrochloric acid. In this investigation was used modi- fied zeolite with HCl with and without iron oxide nanoparticles (Iron Oxide Nanoparticles@Clinoptilolite/HCl) as adsorbent.&#x0D; Materials and methods: Structural characteristics, chemical composition and specific surface area of adsorbent were determined using the FTIR, FE- SEM, EDX, Mapping, XRD, XRF and BET techniques. Glass cylinder filled with zeolite seeds and SO2 cylinder balanced with N2 gas was used for experi- ments. It was evaluated factors affecting SO2 uptake process including tem- perature and contact time, also thermodynamics and kinetics of adsorption. Sulfur dioxide adsorption of real sample was taken with both adsorbents.&#x0D; Results: Adsorption efficiency of SO2 in the synthetic and actual sample were %82.8±5.5 and %67.2±7.21 respectively, by modified zeolite with HCl and iron oxide nanoparticles in the optimum conditions of temperature of 25 °C and duration 28.5 min. As well as, removal percentage average was obtained in the synthetic and actual sample %46.1±4.34 and %35.8±5.85 respectively, by modified zeolite with HCl without nanoparticles in optimum condition of temperature of 25 °C and contact time of 20.5 min.The results showed that SO2 adsorption is an exothermic and spontaneous process and adsorption ki- netics of sulfur dioxide by both adsorbent is more consistent Pseudo-second order kinetics model.&#x0D; Conclusion: The use of iron oxide nanoparticles on the zeolite can increase SO2 removal efficiency from the gas phase.
Research and Application of Jiugu Coal Industry Drilling Video Network System

The high risk of the entire coal industry and the characteristics of every coal mine in Jiugu Coal Industry: large water and gas, and complex geological conditions are considered in this paper. With the consideration of Jiugu Coal Industry's network environment and the superiority's network requirements of underground video surveillance system, a full needs analysis is conducted from the perspective of information technology and information technology. And security management mechanisms are effectively combined to open up initiatives and new measures of innovative production safety management. The paper created a mine drilling video networking system with good fusion. With the help of information technology, the management level of security risks and management purposes are improved so as to provide a strong technical support for enterprise safety production.
Image Debanding Using Iterative Adaptive Sparse Filtering

Due to a limited bit-depth representation of pixel values and lossy compression methods, an image may exhibit annoying banding or false-contouring artifact. Existing banding-removal approaches mainly include low pass filtering, dithering and requantization. In this paper, we propose an image debanding method that iteratively improves the image using a contentadaptive spatial filter. The filter uses adaptive span based on local width of the band and operates on 1D sparse pixel-window. The input image is filtered in each direction i.e. vertical, horizontal and two diagonals sequentially while offering parallel processing in each direction. The iterative framework gradually improves the quality of filtered image until it converges to the final output. Experimental results show that our proposed method is effective in debanding images.
Task-based usability testing and performance measurements over the internet

User-centered design and usability engineering are two concepts that bothroughly date back to the 1990s, and both of which have had a large impacton the practices of software-engineering and the field of software-design.How much effort is required to take these concepts and apply them, withoutmuch prior experience, to today's interconnected and geographicallydistributed working environment? Not much actually, by leveraging Python and Flask, this report demonstratesthat it is possible for a software-developer with little prior knowledge, tobuild and deploy the functional foundation of an web-basedusability-testing-platform, accessible through any browser, that allowed 81participants to run a total of 698 task-based test-scenarios over the span offive days. Additionally, by conducting some initial interviews about current usabilityissues. Using this information as the basis for the aforementionedtest-scenarios, you now have the building blocks for a online-accessibleusability-testing-platform providing insight into real-world usabilityproblems, ready for use in any modern software industry, such as gamedevelopment.
Privacy Preserving K-Means Clustering: A Secure Multi-Party Computation Approach

Knowledge discovery is one of the main goals of Artificial Intelligence. This Knowledge is usually stored in databases spread in different environments, being a tedious (or impossible) task to access and extract data from them. To this difficulty we must add that these datasources may contain private data, therefore the information can never leave the source. Privacy Preserving Machine Learning (PPML) helps to overcome this difficulty, employing cryptographic techniques, allowing knowledge discovery while ensuring data privacy. K-means is one of the data mining techniques used in order to discover knowledge, grouping data points in clusters that contain similar features. This paper focuses in Privacy Preserving Machine Learning applied to K-means using recent protocols from the field of criptography. The algorithm is applied to different scenarios where data may be distributed either horizontally or vertically.
Automatic Invoice Data Extraction as a Constraint Satisfaction Problem

Invoice processing has traditionally been heavily dependent onmanual labor, where the task is to identify and move certaininformation from an origin to a destination. A time demandingtask with a hi ...
CoFF: Cooperative Spatial Feature Fusion for 3D Object Detection on Autonomous Vehicles

To reduce the amount of transmitted data, feature map based fusion is recently proposed as a practical solution to cooperative 3D object detection by autonomous vehicles. The precision of object detection, however, may require significant improvement, especially for objects that are far away or occluded. To address this critical issue for the safety of autonomous vehicles and human beings, we propose a cooperative spatial feature fusion (CoFF) method for autonomous vehicles to effectively fuse feature maps for achieving a higher 3D object detection performance. Specially, CoFF differentiates weights among feature maps for a more guided fusion, based on how much new semantic information is provided by the received feature maps. It also enhances the inconspicuous features corresponding to far/occluded objects to improve their detection precision. Experimental results show that CoFF achieves a significant improvement in terms of both detection precision and effective detection range for autonomous vehicles, compared to previous feature fusion solutions.
SISTEM INFORMASI MANAJEMEN PENGOLAHAN DATA ASET MATERIAL PROMOSI MENGGUNAKAN QR CODE

The development of smartphones today is very rapid and can not be separated in everyday life in finding information or making information and in all fields of work requires communication tools to access data and information, it is no wonder in terms of smartphone work being an inseparable part separate, this research I use smartphone especially Android which is currently one of the operational systems that are commonly used by the public especially Indonesia so that it can be used to help team performance in the field in this case Merchandiser in conducting Data Processing Asset Material Promotion Using Qr Code to monitor and control the allocation of promotional material assets in the field to facilitate reporting of promotional material assets.&#x0D; QR Code is a two-dimensional matrix code or barcode that comes from the word "Quick Response", where the contents of the code can be deciphered quickly and precisely, Optimizing the use of applications in this case QR code to facilitate monitoring and control of promotional materials in the field which will help merchandiser to help make it easier to do work in the field and directly connected to the admin computer at the head office, so it will be easy, fast, and accurate.
Classifying White Blood Cells from Peripheral Blood Smear Image Using Histogram of Oriented Gradient Feature of Nuclei Shape

Researchers developed various methods and algorithms to classify white blood cells (WBC) from a blood smear image to assist the hematologist or for an automatic system to analyze pathological and hematological conditions related to disease quickly, efficiently and accurately. In this work, we proposed a simple technique for the WBC classification from peripheral blood smear image based on the types of nucleus cell. The developed algorithms utilized Histogram of oriented gradient feature (HOG) typically known use for human shape recognition or classification. The Segmentation of WBC nuclei is based on a simple YCbCr color space and K-means clustering. The HOG feature contains information of the nucleus cell shape which then is classified using support vector machine (SVM) and back propagation artificial neural network (ANN). The experimental results establish that the proposed HOG feature is effectively and suitable for the WBC classification based on the shape of nucleus. We are able to categorize the type of WBC based on the nuclei shape with more than 95% of average accuracy.
Diagnose Expert System Dental Disease In Humans Method Using Dempster Shafer

Dental disease is one of the many health problems Complained of by the people of Indonesia. Dental health is a reflection of human health. Lack of knowledge and limited sources of information on oral health have the caused public awareness to maintain oral and dental health is still low .. The development of one of the fields of information technology namely artificial intelligence has been Widely applied in various fields of life can be used as a solution to Overcome this problem. In this study, the dental and oral disease expert system uses the Dempster Shafer method to control inferences that Contain thought patterns and reasoning mechanisms used by experts in solving problems.
Application of Data Mining Method Using Association Rules Apriori To Shopping Cart Analysis On Sale Transactions (Case Study Alfamidi Burnt Stone)

Also Often data mining is called knowledge discovery in databases (KDD), ie activities include the collection, historical use of data to find regularities, patterns or relationships in data sets with a large size. The company may be interested to know if some groups consistently goods items purchased together. This study analyzes the transaction of data information retrieval from the sale of skin care and hair care using data mining algorithms priori Alfamidi Burnt Stones with the highest support value is 8% and the highest value is 5% confidance
DETERMINATION OF VIDEO IMAGES DELAY TIME

Video camera is the source of the image of the object selected for observation or protection.Video cameras are used to collect information, being sometimes indispensable device.When analyzing images, it is important that the condition for the accurate operation of the video frame transmission system is fulfilled.All cameras have a slight delay in image transmission.For image analysis, it is important to know the delay time
The Formation of Entrepreneurship Competence of Students in Household and Industrial Arts Classes at General Secondary Schools

The article substantiates the necessity of forming entrepreneurial competence of students` at general secondary education institutions. Different approaches of scientists to the definition of the concept «entrepreneurial competence» have been analyzed. The basic directions of forming initiative and entrepreneurship of students in household and industrial arts classes at general secondary schools have been considered; various forms and methods of this work have been characterized. It has been noted that these forms and methods should reflect a new activity approach to the educational process. The author has emphasized that it`s important for a household and industrial arts teacher to create an atmosphere of trust, comfortable conditions for cooperation, the development of leadership skills and responsibility for the group activities. In addition, the motivational atmosphere in class is an important factor for the formation of entrepreneurship. Excursions, game technologies, project development, discussions and trade fairs have been identified as the most effective forms and methods of forming entrepreneurial competence. The qualities that students should develop during the formation of entrepreneurial competence have been specified. The article has explored the experience of forming the entrepreneurial competence of students at general education institutions in the USA.
A little goes a long way: Improving toxic language classification despite data scarcity

Detection of some types of toxic language is hampered by extreme scarcity of labeled training data. Data augmentation - generating new synthetic data from a labeled seed dataset - can help. The efficacy of data augmentation on toxic language classification has not been fully explored. We present the first systematic study on how data augmentation techniques impact performance across toxic language classifiers, ranging from shallow logistic regression architectures to BERT - a state-of-the-art pre-trained Transformer network. We compare the performance of eight techniques on very scarce seed datasets. We show that while BERT performed the best, shallow classifiers performed comparably when trained on data augmented with a combination of three techniques, including GPT-2-generated sentences. We discuss the interplay of performance and computational overhead, which can inform the choice of techniques under different constraints.
Generating Commonsense Explanation by Extracting Bridge Concepts from Reasoning Paths

Commonsense explanation generation aims to empower the machine's sense-making capability by generating plausible explanations to statements against commonsense. While this task is easy to human, the machine still struggles to generate reasonable and informative explanations. In this work, we propose a method that first extracts the underlying concepts which are served as \textit{bridges} in the reasoning chain and then integrates these concepts to generate the final explanation. To facilitate the reasoning process, we utilize external commonsense knowledge to build the connection between a statement and the bridge concepts by extracting and pruning multi-hop paths to build a subgraph. We design a bridge concept extraction model that first scores the triples, routes the paths in the subgraph, and further selects bridge concepts with weak supervision at both the triple level and the concept level. We conduct experiments on the commonsense explanation generation task and our model outperforms the state-of-the-art baselines in both automatic and human evaluation.
SURVEY ON STOCK MARKET PREDICTION TECHNIQUES IN DATA MINING PROCESS

In present day financial market, the most critical issue is to find basic way to deal with outline and visualizing the predictions in stock-markets to be made by individuals in request to attain greatest benefit by investments. The stock market is a groundbreaking, non-straight dynamical and complex framework. Long term investment is one of the significant investment decisions. However, evaluating shares and calculating rudimentary qualities for organizations for long term investment is troublesome. In this paper surveyed on stock market prediction techniques in data mining process.
Dynamic Multi-Agent Path Finding based on Conflict Resolution using Answer Set Programming

We study a dynamic version of multi-agent path finding problem (called D-MAPF) where existing agents may leave and new agents may join the team at different times.We introduce a new method to solve D-MAPF based on conflict-resolution.The idea is, when a set of new agents joins the team and there are conflicts, instead of replanning for the whole team, to replan only for a minimal subset of agents whose plans conflict with each other.We utilize answer set programming as part of our method for planning, replanning and identifying minimal set of conflicts.
Data-Driven Neuron Allocation for Scale Aggregation Networks

Successful visual recognition networks benefit from aggregating information spanning from a wide range of scales. Previous research has investigated information fusion of connected layers or multiple branches in a block, seeking to strengthen the power of multi-scale representations. Despite their great successes, existing practices often allocate the neurons for each scale manually, and keep the same ratio in all aggregation blocks of an entire network, rendering suboptimal performance. In this paper, we propose to learn the neuron allocation for aggregating multi-scale information in different building blocks of a deep network. The most informative output neurons in each block are preserved while others are discarded, and thus neurons for multiple scales are competitively and adaptively allocated. Our scale aggregation network (ScaleNet) is constructed by repeating a scale aggregation (SA) block that concatenates feature maps at a wide range of scales. Feature maps for each scale are generated by a stack of downsampling, convolution and upsampling operations. The data-driven neuron allocation and SA block achieve strong representational power at the cost of considerably low computational complexity. The proposed ScaleNet, by replacing all 3x3 convolutions in ResNet with our SA blocks, achieves better performance than ResNet and its outstanding variants like ResNeXt and SE-ResNet, in the same computational complexity. On ImageNet classification, ScaleNets absolutely reduce the top-1 error rate of ResNets by 1.12 (101 layers) and 1.82 (50 layers). On COCO object detection, ScaleNets absolutely improve the mmAP with backbone of ResNets by 3.6 (101 layers) and 4.6 (50 layers) on Faster RCNN, respectively. Code and models are released at https://github.com/Eli-YiLi/ScaleNet.
AN EMPIRICAL ANALYSIS OF SIMILARITY MEASURES FOR UNSTRUCTURED DATA

With fast growth in size of digital text documents over internet and digital repositories, the pools of digital document is piling up day by day. Due to this digital revolution and growth, an efficient and effective technique is required to handle such an enormous amount of data. It is extremely important to understand the documents properly to mine them. To find coherence among documents text similarity measurement pays a humongous role.Â  The goal of similarity computation is to identify cohesion among text documents and to make the text ready for the required applications such as document organization, plagiarism detection, query matching etc. This task is one of the most fundamental task in the area of information retrieval, information extraction, document organization, plagiarism detection and text mining problems. But effectiveness of document clustering is highly dependent on this task.Â  In this paper four similarity measures are implemented and their descriptive statistics is compared. The results are found to be satisfactory. Graphs are drawn for visualization of results.
Quality of library staff training in the context of labor market re-quirements

The article is presented in English: DOI: 10.33186/1027-3689-2020-8-27-38
Exploring Mesozoic Climates - Modeling and Evaluation of Proxy Distributions

&amp;lt;p align=&amp;quot;justify&amp;quot;&amp;gt;The Mesozoic Era (~252-66 Ma) is a decisive period in Earth&amp;amp;#8217;s history. It is marked by a tectonic transition from the Pangea supercontinent towards a modern continental configuration as well as the ecological success of the dinosaurs and the evolution of mammals, flowering plants, stony corals and important groups of planktic calcifiers. The Mesozoic is generally considered as a greenhouse climate period, with especially high global temperatures during the Triassic and the Late Cretaceous. Here, we present novel modeling results on the evolution of global climatic conditions through the Mesozoic.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;An ensemble of equilibrium climate states for 40 geological timeslices between 255 and 60 Ma is simulated with the CLIMBER-3&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;&amp;amp;#945; &amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;Earth System Model of Intermediate Complexity. The influence of changing paleogeography, sea level, vegetation cover, solar luminosity, orbital configuration and atmospheric CO&amp;lt;/span&amp;gt;&amp;lt;sub&amp;gt;&amp;lt;span&amp;gt;2 &amp;lt;/span&amp;gt;&amp;lt;/sub&amp;gt;&amp;lt;span&amp;gt;concentration is systematically tested based on constraints from published geological proxy reconstructions and &amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;previous &amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;modeling &amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;work&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;.&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;Atmospheric pCO&amp;lt;/span&amp;gt;&amp;lt;sub&amp;gt;&amp;lt;span&amp;gt;2&amp;lt;/span&amp;gt;&amp;lt;/sub&amp;gt;&amp;lt;span&amp;gt; is found to be the strongest driver of global mean temperatures, which are generally elevated above the present and reach &amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;&amp;amp;#8805;&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;20&amp;amp;#176;C in the Late Triassic to Early Jurassic and the mid-Cretaceous if a recently published pCO&amp;lt;/span&amp;gt;&amp;lt;sub&amp;gt;&amp;lt;span&amp;gt;2&amp;lt;/span&amp;gt;&amp;lt;/sub&amp;gt;&amp;lt;span&amp;gt; proxy compilation is employed. The simulated seasonal latitudinal shift of high precipitation zones exhibits a maximum during the mid-Triassic to Early Jurassic and therefore supports the notion of a &amp;amp;#8220;Megamonsoon&amp;amp;#8221; during this time. Simulated humid and arid climate zones generally agree well with spatial distributions of geologic climate indicators like coal and evaporites, although some discrepancies exist. The same applies to the correlation of fossil stony coral reef distributions with regions where seawater temperatures would have been suitable for (modern) coral reefs. We will discuss which changes of Earth System parameters throughout the Mesozoic can best explain shifts in these distributions.&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;