Reviewerly/exHarmony huggingface dataset:
A Tag-Based Transformer Community Question Answering Learning-to-Rank Model in the Home Improvement Domain

Community Question Answering (CQA) is an Information Retrieval (IR) task that allows matching complex subjective questions and candidate answers based on user posts in community web forums. User questions and comment-based answers deal with many problems, such as redundancy or ambiguity of linguistic information. In this paper, we propose a pairwise learning-to-rank model community QA model in the home improvement domain. For a user question, this model must rank candidate answers in order of relevance. Our main contribution consists of transformer-based language models using user tags to accurate the model generalisation. To train our model, we also propose a proper CQA dataset in home improvement domain that consists of information extracted from community forums. We evaluate our approach by comparing the performance based on analysis with the state-of-the-art method on text or document similarity.
Raw Paper Material Stock Forecasting with Long Short-Term Memory

The manufacturing business is one of the businesses in Indonesia that continues to show its development from year to year. Like a manufacturing business in general, one of the important efforts made in the printing business is the supply of raw paper materials to produce finished goods. The purpose of this research is making a forecasting of the raw paper material for printing company on 7 different types of 269 historical data with weekly intervals from January 2015 to February 2020 before the Covid19 pandemic season. Forecasting is done using the Long Short Term Memory method with Python language. The model architecture for training and testing is carried out using vanilla LSTM with single input, hidden and output layer with the configuration of 64 neurons in the hidden layer, 150 epoch, 12 batch size and Adam Optimizer (lr = 0.0001) which was repeated 10 times for best result. The test results show the best window size length in the model for each paper raw material differently from 4 to 16. All models was successfully forecasting the test data with an average MAPE of the overall forecast of 21.48%.
Minimalist Fitted Bayesian Classifier-Based on Likelihood Estimations and Bag-of-Words

The expansion of institutional repositories involves new challenges for autonomous agents that control the quality of semantic annotations in large amounts of scholarly knowledge. While evaluating metadata integrity in documents was already widely tackled in the literature, a majority of the frameworks are intractable when confronted with a big data environment. In this paper, we propose an optimal strategy based on feature engineering to identify spurious objects in large academic repositories. Through an application case dealing with a Brazilian institutional repository containing objects like PhD theses and MSc dissertations, we use maximum likelihood estimations and bag-of-words techniques to fit a minimalist Bayesian classifier that can quickly detect inconsistencies in class assertions guaranteeing approximately 94% of accuracy.
LERANet: Low-light Enhancement Network based on Retinex and Attention

The performance of vision based applications is often limited by low-light imaging environments. While various methods have been proposed to enhance image contrast, noise is inevitably amplified. In addition, most methods result in over-enhancement in bright regions. In this paper, we consider a convolutional neural network, named as LERANet, to decouple a dark image into reflectance and illumination, which can thus enhance contrast and reduce noise. An attention module is also integrated in the network to avoid over-enhancement. Experimental results demonstrate the effectiveness of the proposed LERANet on noise suppression and detail preservation. In addition, both subjective and objective comparisons with state-of-the-art algorithms indicate the superiority of the proposed method.
Estimating Properties of Dynamic Graphical Models

We study the problem of estimating properties of dynamic graphical models in the non-asymptotic regime. Instead of characterizing the behavior of estimation rules with asymptotic consistency, we study sharp bounds on the sample size required to estimate the properties. We show that for certain spatio-temporal Markov random fields governed by an underlying graph, one can estimate certain natural properties with logarithmic (w.r.t. the size of the underlying graph) sample complexity. Matching lower bounds are also established for such estimation problems. We highlight our results with a “bit river” abstraction, where a Bernoulli source at one node flows along the edges of an underlying graph, and the task is to obtain the flow trajectory. If we do not have any restriction on the model class under consideration, we also show that an exponential sample size is required for even very simple properties.
Sensor-Based Motion Analysis of Paralympic Boccia Athletes

In this study, using sensors, we analyzed the movements of Japan’s top-level boccia athletes, which is one of the official Paralympic sports. Motion analysis in sports has been widely studied, and a gradual approach is commonly available to help amateurs improve their skills. However, in two specific cases, personal observations are necessary. One is how to improve the skills of the top athletes. As the other, specific handicaps also require individualized support. Because the top boccia players fit into both cases, we measured the movements of each top player using a sensor-based system. In the actual training camp, we took the approach of summarizing and discussing the results of the daytime measurements at night and applying them to the practice the following day.
MMArt-ACM'21: International Joint Workshop on Multimedia Artworks Analysis and Attractiveness Computing in Multimedia 2021

The International Joint Workshop on Multimedia Artworks Analysis and Attractiveness Computing in Multimedia (MMArt-ACM) solicits contributions on methodology advancement and novel applications of multimedia artworks and attractiveness computing that emerge in the era of big data and social media. The topics of the accepted papers cover an analytic topic on comic contents understanding to generative topics on image synthesis and conversion. The actual MMArt-ACM'21 Proceedings are available at: https://dl.acm.org/doi/proceedings/10.1145/3460426.
Architectural Approaches for Emerging Technologies-based Mobile Apps – a Systematic Mapping Study

The development of mobile applications has been strongly boosted in recent years in diverse environments with the inclusion of emerging technologies such as augmented reality (AR), virtual reality (VR), smart things (STs), etc. In spite of hardware improvements for enhancing the computation and storage capabilities of mobile devices, limitations remain for applications demanding high computation and resources for provided better interactive experiences. Therefore, in this context, appropriate decisions in software architectural designs are a key factor in achieving proper quality parameters for mobile applications. The objective of this paper is to carry out a systematic mapping study to present the different structural design approaches applied in current mobile applications enriched with some emerging technology. With this research, we contribute by describing the current technologies aimed at mobile apps, the composition and use of the software architectural elements, and its relevance through the analysis of quality aspects.
Low-light Image Enhancement Based on Weighted Adaptive Guided Filter

It is known that filtering process always yields blurring and loss of details in image processing, especially in the case of noisy. In this paper, a new weighted adaptive guided filter (WAGIF) is proposed to estimate illumination component. Specifically, the proposed WAGIF consists of three steps: 1) Local skewness is employed to distinguish the flat areas and texture areas of the underlying image. 2) An adaptive regularization parameter is designed to filter the image adaptively according to the skewness. 3) The filtered results are weighted and aggregated to output the final image. After obtaining the illumination component, the Retinex model is used to obtain the reflection component. Furthermore, the illumination component is processed by sigmoid function and the reflection component is enhanced by gamma correction. Finally, the illumination component and the reflection component are reconstructed to obtain an image with high contrast and rich details. The experimental results show that the proposed method is better than the existing methods in terms of denoising performance and structure preservation for low-light image enhancement.
Automated Real-time 3D Visual Servoing Control of Single Cell Surgery Processes

In this article, we propose an automated real-time 3D visual servoing control of single cell surgery processes, which integrates the automation of micromanipulator calibration process, cell initial setup process, and the visual serving control surgery operation. During the cell initial setup process, a deep learning-based object detection algorithm is developed to compute the cell location in real-time for cell surgery. These coordinates are used as feedback derived two-dimensional (2D) images acquired under low microscope magnification for the motion control of a motorized microscope stage. Visual serving control of cell surgery process is achieved through a cell blastomere centroid three-dimensional (3D) localization algorithm. This algorithm computes in real-time centroid position feedback of the blastomere of interest in the 3D image coordinate from 3D Z-stack images. As the most important function within the overall 3D localization algorithm, a 3D edge detection algorithm is used to extract the edges inside the blastomere. The extracted edges are converted into 3D point cloud which is used to determine the centroid location through 3D K-mean clustering algorithm. A position based visual servoing (PBVS) controller is then used to maneuver the micropipette under closed-loop control enabling various cell surgery procedures to be carried out.
A note on cyclotomic polynomials and Linear Feedback Shift Registers

Linear Feedback Shift Registers (LFSR) are tools commonly used in cryptography in many contexts, for example as pseudo-random numbers generators. In this paper we characterize LFSR with certain symmetry properties. Related to this question we also classify polynomials f satisfying the property that if α is a root of f then f (αdeg f) = 0. The classification heavily depends on the choice of the fields of coefficients of the polynomial; we consider the cases and K = ℚ.
Multi-resolution Video Steganography Technique Based on Stationary Wavelet Transform (SWT) and Singular Value Decomposition (SVD)

The digital data nowadays is always in increase. This data may be secret data that should be kept and saved. Therefore, steganography algorithms are proposed to hide the important data text, image or video. This paper is proposing a steganography algorithm to hide a secret image in a video using the multi-resolution stationary wavelet transform (SWT) and the singular value decomposition (SVD). Three-resolution 3D SWT is performed to the cover video frames. Then, SVD is applied to one of the transform sub-bands. SVD is applied also to the secret image. Both singular matrices are embedded. At last, inverse transform is performed to get the stego-video. The results of image hiding in each resolution are compared, and hiding in the third resolution outperformed in peak signal-to-noise ratio (PSNR) value. The algorithm was tested using different color and grayscale images and showed promising qualitative and quantitative results.
5.1: <i>Invited Paper:</i> Preferred LCD and OLED Luminance In Daily Tasks Is Affected By Ambient Lighting and Age

Preferred display luminance varies in relation to visual demand, image contrast, and ambient illumination. OLED is known to have a higher contrast ratio at a lower luminance output, and is more energy efficient at a lower luminance compared to LCD. To assess the preferred luminance in OLED and LCD in varied ambient lighting conditions, 48 young and older subjects were asked to perform reading, spreadsheet search, and videos viewing. They pre‐selected a preferred luminance level and then reassessed it after a 2‐min session for these tasks to arrive at a preferred luminance. Results show OLED has a lower preferred luminance compared to LCD, especially in bright viewing. Older viewers maintained a similarly higher luminance preference in dark and bright conditions, whereas young viewers preferred a lower luminance in dark viewing but not bright viewing. Conversely, pupil size was smaller in older viewers and in the bright condition. Image qualities were also better for the OLED, except brightness range and color vividness. These outcomes suggest lower OLED luminance is beneficial across age groups, ambient lighting, and task types, and can reduce energy consumption.
Research on English Word Intelligent Learning System Based on Mobile Education Concept

Because traditional education is easily restricted by learning place and time, it is difficult to meet people's learning needs. With the rapid development of information technology, information technology has been widely applied to the education industry, resulting in a new mobile education model. Mobile education is an educational way in which users use mobile devices such as mobile phones to educate and learn. At present, the government vigorously implements education reform, and mobile education has become the main method to improve the level of education for all, and it is also the focus of future education development. Mobile education is a brand-new form of distance learning. Users can get rid of the constraints of time and space and receive education anytime and anywhere. Based on the advantages of the development mode system in the field of English instructional design, this paper puts forward the development mode of intelligent ubiquitous learning system for English words, so that English word learners are not limited by time and space, and provide reference for the design and development of mobile learning software system.
Investigation on Vulnerabilities Location in Solidity Smart Contracts

Smart contracts had a very fast increasing development in the last years. Once a smart contract is deployed on a blockchain due to code immutability, its residual vulnerabilities cannot be patched. Reducing the number of residual vulnerabilities becomes thus very important and normally is achieved through static analyzers. This paper investigates the physical position (location) of vulnerabilities in Solidity smart contracts. To this purpose, we use a language-independent systematization of vulnerabilities and we consider the outputs of a set of static analyzers processing a representative set of smart contracts. We analyze the distributions of the locations where tools find positive outcomes. We create the ground truth of vulnerabilities for a subset S of smart contracts through manual inspection and we first perform a comparison of the distributions within this set. Then we generalize our findings by comparing the distributions between the manually inspected subset and the full set. Such comparison allows us to identify where certain classes of vulnerabilities are located, suggesting specific areas in Solidity smart contracts where the search for vulnerabilities should focus.
Fraud Detection in Online Market Research

The key distinguishing approach in this paper is to utilize random sampling on the training set for optimal solution to maximize the fraud coverage as well as minimize the false positive rate. We have experimented with the variety of optimal solutions to discover a different bad actors segment. We have adapted a partial labeled data in the industry setting together with self developed set of SQL based rules in order to compensate in timely manner for just enough labelled data available for supervised learning model to detect fraud users before they negatively impact our business. Here, at DISQO, market research firm that provides raw data to our partners and clients as well as is a reputable panel for consumers to share their feedback on variety of brands and products, we were facing challenges related to noisy labelled data. Thus, set of rules were developed to assess every user against fraud in the following grade, A (red, very suspicious), B (yellow) and C (green). We started with a simple grading system. Then, after the optimal problem was formulated to maximize the fraud detection on the random sampled training set we were solving for optimal solution, and collected all of these solutions to average out and design our final solution in order to detect Fraud with better precision and improved recall from \(26\%\) to \(52\%\). Lastly we have developed a methodology to combine these optimal coefficient solutions in order to have a well generalized fraud detection model as averaging the coefficients next to the dynamic labels via Logistic Regression. However, we have achieved the best results when we solved for the optimal fraud coverage segment and trained on the hand picked number of classifiers to learn the separation in the data between bad and good actors. Then we have created a fraud vector of 5-dimensions, that consisted of the probabilities retrieved from hand picked classifiers based on the optimal solutions (we had 3 fraud segments retrieved from optimal solutions), one of the fraud vector’s dimension contained the CNN probability, other two were XGBoost and Logistic based probability, and kept the auto-encoder reconstruction error as another fraud vector dimension. At the end, we compare fraud vector magnitude on every users to assess quickly the fraud overall risk, we use every classifier probability and auto-encoder reconstruction error as fraud dimensions.
Interpreting uninterpretable predictors: kernel methods, Shtarkov solutions, and random forests

Many of the best predictors for complex problems are typically regarded as hard to interpret physically. These include kernel methods, Shtarkov solutions, and random forests. We show that, despite the inability to interpret these three predictors to infinite precision, they can be asymptotically approximated and admit conceptual interpretations in terms of their mathematical/statistical properties. The resulting expressions can be in terms of polynomials, basis elements, or other functions that an analyst may regard as interpretable.
Path Optimization of Intelligent Wheelchair Based on an Improved Ant Colony Algorithm

This paper presents an improved ant colony optimization algorithm for solving intelligent wheelchair path planning problem. The existing ant colony algorithm has many problems in solving intelligent wheelchair path planning problem, such as the extra corners of raster maps and the irregularity of corners. In this paper, the improved ant colony optimization algorithm which can effectively remove the extra corners and smooth the corners with three Bezier curves, combining the constraints of wheelchair physical conditions and environmental conditions was proposed. MATLAB software is used to verify the feasibility and effectiveness of algorithm. Meanwhile, we use the virtual reality technology provided by ROS system to establish GAZEBO physical simulation experiment platform to verify the practicability and feasibility of the algorithm in the mobile robot system. Finally, the effectiveness of the improved algorithm is verified by the movement of the mobile robot in the actual test environment. The results of the proposed algorithm are found to be satisfactory.
The Knowledge Level and Behavior of Patients with HBV Regarding HBV Infection and Prevention

Objectives: To determine the knowledge of hepatitis B infected patients and their behaviors regarding hepatitis B virus (HBV) infection and vaccination.Materials and Methods: This study was conducted in Ankara Atatürk Training and Research Hospital, Department of Infectious Diseases.A total of 402 patients followed for one year or more were included in the study.Results: Seventy-seven percent of the patients knew that HBV affects the liver, 64.2% of the patients were aware that HBV was an infectious disease and the possibility of inactive carriage was known by 91% of the patients.Although almost all patients were aware of the transmission routes, they were found to assume that transmission rates could be high even in circumstances where the risk of transmission was actually very low.Vaccination and its effects were recognized by all of the patients, as well as the possibility for progression to cirrhosis.Seventy-six percent of the patients were aware of treatments for HBV infection. Conclusion:In this study, the knowledge and awareness level of the patients was considered to be good overall, despite the fact that there were some gaps in patient knowledge.We believe that these positive findings were associated with the educational activities in our clinic.
Cybercrime, cybersecurity and water utilities

A total of 20 known cases of cybercrime attacks on water utilities have been seen in five countries between 2000 and 2021, with a steady increase in their frequency. The actual number of cyberattacks is higher, with 40 recorded in the United States in 2014–15. It is chiefly driven by organized crime and hostile state actors, along with disgruntled former employees. Vulnerabilities occur because of utilities adopting supervisory control and data acquisition (SCADA) systems without appropriate cybersecurity measures along with staff who have properly trained to be aware of phishing attacks. Threats such as these highlight the need for water utilities to have a comprehensive risk management system in operation.
TagNN: A Code Tag Generation Technology for Resource Retrieval from Open-Source Big Data

With the vigorous development of open-source software, a huge number of open-source projects and open-source codes have been accumulated in open-source big data, which contains a wealth of code resources. However, effectively and efficiently retrieving the relevant code snippets in such a large amount of open-source big data is an extremely difficult problem. There are usually large gaps between the user’s natural language description and the open-source code snippets. In this paper, we propose a novel code tag generation and code retrieval approach named TagNN, which combines software engineering empirical knowledge and a deep learning algorithm. The experimental results show that our method has good effects on code tag generation and code snippet retrieval.
Passage Similarity and Diversification in Non-factoid Question Answering

The rise in popularity of mobile and voice search has led to a shift in focus from document retrieval to short answer passage retrieval for non-factoid questions. Some of the questions have multiple answers, and the aim is to retrieve a set of relevant answer passages, which covers all these alternatives. Compared to documents, answers are more specific and typically form more defined types or groups. Grouping answer passages based on strong similarity measures may provide a means of identifying these types. Typically, kNN clustering in combination with term-based representations have been used in Information Retrieval (IR) scenarios. An alternate method is to use pre-trained distributional representations such as GloVe and BERT, which capture additional semantic relationships. The recent success of trained neural models for various tasks provides the motivation for generating more task-specific representations. However, due to the absence of large datasets for incorporating passage level similarity information, a more feasible alternative is to use weak supervision based training. This information can then be used to generate a final ranked list of diversified answers using standard diversification algorithms.