Reviewerly/exHarmony huggingface dataset:
Text and metadata extraction from scanned Arabic documents using support vector machines

Text information in scanned documents becomes accessible only when extracted and interpreted by a text recognizer. For a recognizer to work successfully, it must have detailed location information about the regions of the document images that it is asked to analyse. It will need focus on page regions with text skipping non-text regions that include illustrations or photographs. However, text recognizers do not work as logical analyzers. Logical layout analysis automatically determines the function of a document text region, that is, it labels each region as a title, paragraph, or caption, and so on, and thus is an essential part of a document understanding system. In the past, rule-based algorithms have been used to conduct logical layout analysis, using limited size data sets. We here instead focus on supervised learning methods for logical layout analysis. We describe LABA, a system based on multiple support vector machines to perform logical Layout Analysis of scanned Books pages in Arabic. The system detects the function of a text region based on the analysis of various images features and a voting mechanism. For a baseline comparison, we implemented an older but state-of-the-art neural network method. We evaluated LABA using a data set of scanned pages from illustrated Arabic books and obtained high recall and precision values. We also found that the F-measure of LABA is higher for five of the tested six classes compared to the state-of-the-art method.
A Secure Code Review Retrospective

We discuss in this paper a process used at The MITRE Corporation for reviewing source code to identify security weaknesses. A key tenant of our process is that secure code review should not be done by the developers of the code, but by a separate code review team of seasoned individuals that leverage a combination of analytical methods. This paper describes MITRE’s recommended practices for secure code review and provides some lessons learned from reflecting on over 300 such engagements since 2009.
Query-to-Session Matching: Do NOT Forget History and Future during Response Selection for Multi-Turn Dialogue Systems

Given a user query, traditional multi-turn retrieval-based dialogue systems first retrieve a set of candidate responses from the historical dialogue sessions. Then the response selection models select the most appropriate response to the given query. However, previous work only considers the matching between the query and the response but ignores the informative dialogue session in which the response is located. Nevertheless, this session, composed of the response, the response's history and the response's future, always contains valuable contextual information which can help the response selection task. More specifically, if the current query and a response's history both refer to the same question, we can conclude that this response is quite likely to answer this query. As for the response's future, it can always provide contextual hints and supplementary information that might be omitted in the response. Inspired by such motivation, we propose a query-to-session matching (QSM) framework to make full use of the session information: matching the query with the candidate session instead of the response only. Different from the previous work which ranks response directly, the response in the session with the highest query-to-session matching score will be selected as the desired response. In our proposed framework, the query, history, and future are all sequences of utterances, which makes it necessary to model the relationships among the utterances. So we propose a novel dialogue flow aware query-to-session matching (DF-QSM) model. The dialogue flows model the relationships among the utterances through a memory network. To our best knowledge, our paper is the first work to utilize both the response's history and future in the response selection task. The experimental results on three multi-turn response selection benchmarks show that our proposed model outperforms existing state-of-the-art methods by a large margin.
Web Recommended System Library Book Selection Using Item Based Collaborative Filtering Method

The recommendation system is one feature that is widely used by software today. The recommendation system is Beneficial for users to make it easier for users to make a book selection by providing book recommendations that may be following the desired book preferences. The recommendation system in this research uses the Item Based Collaborative Filtering method, where this method is the result of combining Collaborative Filtering and Item Based. Collaborative Filtering uses the rating matrix to calculate ratings, and Item Based uses book attributes to calculate similarity attributes between books.The collaborative filtering method has been used successfully in several applications. The Collaborative Filtering method predicts user preferences for items in a word-of-month manner, i.e. user preferences are determined by considering opinions in the form of preference ratings.Based on the results of tests conducted, the minimum MAE generated by the system is 0.018 with a maximum accuracy of 99.63% contained in the first test, meaning that the more variable data rating data, the higher the MAE value generated.
Data Mining applied to the navigation task in autonomous robots

This paper reports the results of the classifications algorithms used, in data mining, for the creation of a navigation model applied to an autonomous robot to perform the task of wall-following. The goal is to make the robotic vehicle capable of moving, guiding and making decisions autonomously, avoiding collisions, using the smallest resources set as possible. A simulator was used to develop a test environment, the robot vehicle and simulate the proposed navigation system. The results suggest that the J48 (C4.5) classification algorithm obtained the most satisfactory results, generating a model with high degree confidence in decision making.
Improvement and Design of Genetic Algorithm in Personalized Test Paper Composition System

Based on the question of test papers in personalized learning, this paper makes special improvements and designs for the individual genotype, selection, crossover, and mutation processes in traditional genetic algorithms. At the same time, in the design of the fitness function, based on the disadvantages that the dimension cannot be unified when calculating the fitness function by linear weighting method in the traditional literature, a vector distance calculation method was selected to calculate the objective function, which solved the unification of different constraints Questions that differ between dimensions. In addition, based on the problem that duplicate questions may appear in one test paper, this paper designs a deduplication operator and adds it to the step of genetic algorithm.
A Dataset of Journalists' Interactions with Their Readership

The comment sections of online news platforms are an important space to indulge in political conversations and to discuss opinions. Although primarily meant as forums where readers discuss amongst each other, they can also spark a dialog with the journalists who authored the article. A small but important fraction of comments address the journalists directly, e.g., with questions, recommendations for future topics, thanks and appreciation, or article corrections. However, the sheer number of comments makes it infeasible for journalists to follow discussions around their articles in extenso. A better understanding of this data could support journalists in gaining insights into their audience and fostering engaging and respectful discussions. To this end, we present a dataset of dialogs in which journalists of The Guardian replied to reader comments and identify the reasons why. Based on this data, we formulate the novel task of recommending reader comments to journalists that are worth reading or replying to, i.e., ranking comments in such a way that the top comments are most likely to require the journalists' reaction. As a baseline, we trained a neural network model with the help of a pair-wise comment ranking task. Our experiment reveals the challenges of this task and we outline promising paths for future work. The data and our code are available for research purposes from: https://hpi.de/naumann/projects/repeatability/text-mining.html
A cluster analysis of data mining studies in library and information science from 2006 to 2018

Abstract Analyzing content from articles published in major LIS journals during the years 2006, 2012, and 2018, this study examines the use of data mining strategies in LIS research. A total of 301 articles were identified that utilized data mining strategies across the 3 years studied, with the JASIST having the most such articles with 42, with Information Processing and Management following close behind with 32. A cluster analysis performed based on word frequency in these articles abstracts identified three unique clusters associated with the topics of Publications/Citations, Consumer Behavior, and Information/Media Use. A cluster analysis of the proportion of articles indicates a shift away from Publications/Citations towards more Consumer Behavior‐based data mining studies.
Deep Learning-based Hourly Temperature Prediction

Accurate prediction of temperature is an important part of fine weather forecast services (such as heating energy consumption in winter, Winter Olympic Games, etc.). Therefore, the accurate prediction of hourly temperature is very significant in the management of human health and the decision-making of government. In this study, a long short term (LSTM) memory model was proposed and used to predict the next hour's temperature in mega-cites in North China. It was fully considered for the historic temperature and meteorological condition. As a result, the predictor secured a fast and accurate prediction performance by fully reflecting the long-term historic process of input time series data through LSTM. The meteorological data from Beijing, Tianjin, Shijiazhuang and Taiyuan which represents the mega-cites of North China during October 1 to December 31 in 2016-2018 were used to verify the validity of the proposed method. In conclusion, the proposed method was proved to have a good prediction performance in cooling and turning warming processes, making up for the poor performance of turning weather prediction in the previous research. It confirmed that the forward supplement LSTM model has the best prediction ability for hourly temperature prediction in Beijing among mega-cites in North China. The results also indicate great potential of the machine learning method in improving local weather forecast and the potential to serve the 2022 Winter Olympics.
CryptoWills: How to Bequeath Cryptoassets

In this paper, we put forth the problem of bequeathing cryptoassets. In this problem, a testator wishes to bequeath cryptoassets - e.g. secrets, static keys or cryptocurrency - to their heirs. Crucially, the testator should retain control of their assets before their passing. Additionally testator needs to maintain privacy, i.e. beneficiaries must not learn the bequest. Moreover, beneficiaries must not be able to determine whether they will inherit at all before testator's decease. We formally define the security goals of a cryptographic will (cryptowill) protocol and subsequently present schemes fulfilling the required security properties.
Performance and Security Tradeoffs in Cryptographic Hash Functions

A cryptographic hash function is an important component used in many applications, such as blockchain, authentication, data integrity, and digital signature. With the rapid increase in usage of mobile devices, more attention goes towards the tradeoffs between performance and security of cryptographic hash functions on mobile devices due to their limited computational power. The researchers in this paper study the most common cryptographic hash functions and highlights the tradeoffs between their performance and security. The hash functions considered in this study are MD4, MD5, Whirlpool, and the hash functions in the SHA family. The security of these hash functions is compared based on recent attacks in terms of collision resistance, preimage attacks, and sensitivity analysis. While the performance is tested on different input block sizes, useful observations and recommendations are made based on the results of this study.
Pairwise Learning to Rank for Image Quality Assessment

Because the pairwise comparison is a natural and effective way to obtain subjective image quality scores, we propose an objective full-reference image quality assessment (FR-IQA) index based on pairwise learning to rank (PLR). We first compose a large number of pairs of images, extract their features, and compute their preference labels as training labels. We then obtain a pairwise preference model by training a binary classifier using the features and labels. Because image quality is affected by the masking effect, we propose extracting frequency-aware quality features by adapting state-of-the-art IQA metrics. The learned pairwise preference model is then used to predict the preference between pairs of images in the testing dataset. The quality of each image is computed as the number of preferences. Experimental results on four IQA databases validate that the proposed PLR-based IQA index achieves higher consistency with human subjective evaluation than the state-of-the-art IQA metrics.
FPGA Implementations for Real-Time Processing of High-Frame-Rate and High-Resolution Image Streams

This paper presents FPGA implementations of two standard image preprocessing algorithms, namely image filtering and image averaging. The implementations allow processing of high-frame-rate and high-resolution image streams in real-time. The developed implementations are evaluated in terms of resource usage, power consumption, and achievable frame rates. The performance of the developed implementation of image filtering algorithm is compared with implementation provided by MATLAB's Vision HDL Toolbox. The algorithms are evaluated on Microsemi's Smartfusion2 Advanced Development Kit. The development board includes a SmartFusion2 M2S150 SoC FPGA. For verification, the board is connected to an industrial camera which uses the Camera Link interface. Limitations of processing image streams with FPGA platforms are discussed.
Learning Formatting Style Transfer and Structure Extraction for Spreadsheet Tables with a Hybrid Neural Network Architecture

Table formatting is a typical task for spreadsheet users to better exhibit table structures and data relationships. But quickly and effectively formatting tables is a challenge for users. Lots of manual operations are needed, especially for complex tables. In this paper, we propose techniques for table formatting style transfer, i.e., to automatically format a target table according to the style of a reference table. Considering the latent many-to-many mappings between table structures and formats, we propose CellNet, which is a novel end-to-end, multi-task model leveraging conditional Generative Adversarial Networks (cGANs) with three key components to (1) model and recognize table structures; (2) encode formatting styles; (3) learn and apply the latent mapping based on recognized table structure and encoded style, respectively. Moreover, we build up a spreadsheet table corpus containing 5,226 tables with high-quality formats and 784 tables with human-labeled structures. Our evaluation shows that CellNet is highly effective according to both quantitative metrics and human perception studies by comparing with heuristic-based and other learning-based methods.
From Lightweight Cybersecurity Assessment to SME Certification Scheme in Belgium

Companies are increasingly exposed to cybersecurity threats. Among them, SMEs are especially vulnerable due to their lack of maturity and resilience. They are also increasingly targeted by cyberattacks. In order to help SMEs to secure their business against such threats, various programs are developing all over Europe. We report here about the evolution of a Belgian program first anchored in good practices and self-assessment to a lightweight certification scheme fitting the needs and capabilities of SMEs. We present how the various means interconnect to form a maturity raising scale. We also detail some supporting tools for the current assessment and the future certification scheme.
Software Engineering For Automated Game Design

As we develop more assistive and automated game design systems, the question of how these systems should be integrated into game development workflows, and how much adaptation may be required, becomes increasingly important. In this paper we explore the impact of software engineering decisions on the ability of an automated game design system to understand a game's codebase, generate new game code, and evaluate its work. We argue that a new approach to software engineering may be required in order for game developers to fully benefit from automated game designers.
Co-Embedding Additional Security Data and Obfuscating Low-Level FPGA Program Code

The paper is devoted to the issues of equivalent transformation of the FPGA program code in order to protect the integrity of this code. The paper considers an approach in which a digital watermark is covertly embedding into the FPGA program code. This digital watermark contains the monitoring data needed to implement the code integrity monitoring procedure. As a result, the digital watermark is hidden in the program code and forms a single whole with the program code. The embedded digital watermark does not change the characteristics of the device and does not make changes to its operation. Due to this approach, the fact of performing integrity monitoring in relation to program code is not obvious to an external observer. At the same time, monitoring data is also hidden and inaccessible. The paper proposes a method that improves integrity monitoring by jointly perform two processes: embedding secret additional data into the FPGA program code and obfuscating this program code. Moreover, both of these processes are proposed to be performed using one common system of equivalent transformations for the elementary units of the FPGA program code. Improved integrity monitoring is seen in the fact that the probability of detecting a digital watermark in FPGA program code is reduced. The paper describes an experiment showing the advantage of the proposed method over existing methods. The paper also provides recommendations on the possibility of using the method in areas that are related to the task of monitoring the integrity for FPGA program code.
MR Image Enhancement using Stationary Wavelet Transform based Approach

Magnetic resonance (MR) imaging is a widely used modality in clinical image diagnosis. Usually, these images contain anatomical structures, tissue regions along with the artefacts. Low contrast is a challenging issue in the diagnosis of medical images. It may occur in MR images due to poor imaging environment and instrument errors. Further, the quality of an image may degrade during processing and transmission. The spatial domain techniques used for enhancement fails to retain the complex edge/structural features of the MR images. So to address this problem, we suggest a stationary wavelet transform based approach for enhancing the MR image quality. The enhancement is done in the transform domain. Only the detail component of the transformed image is enhanced using adaptive histogram equalization technique. The proposed technique is experimented using synthetic and real MR images of different anatomical structures of the human body. The method is validated using evaluation indices: absolute mean brightness error, edge based contrast measure and relative contrast measure. The evaluation parameters show the superiority of the suggested technique in comparison to the state-of-the-art methods.
Vege Application! Using Mobile Application to Buy Vegetarian Food

Vegetarian food is one solution for people to start living healthy. Today there are still many unhealthy foods circulating and consumed continuously by the Indonesian people, especially the city of Batam. This is due to the difficulty of people getting healthy food without chemicals that harm the body. People really want a healthy life in an easy and inexpensive way. The development of android technology on smartphones is very possible to realize healthy people's lives easily and cheaply. Android technology will also be an important point in helping to get healthy vegetarian food without having to shop out of the house. Public trust is also a concern in this study. With the COD (Cash On Delivery) method, it is expected that vege applications can increase public confidence in buying vegetarian food. This method has been widely used by other major sales applications in Indonesia and has been proven successful in adding application users. The growth in the number of very fast couriers in Indonesia can help the system used by the Vege application. Users can anytime order vegetarian food on an Android smartphone without having to get out of the house just to find healthy food. Couriers will deliver directly to the user's residence and pay as many orders to the courier. This system is expected to help reduce the number of undernourished communities that continue to increase in Indonesia.
How China Detects and Blocks Shadowsocks

Shadowsocks is one of the most popular circumvention tools in China. Since May 2019, there have been numerous anecdotal reports of the blocking of Shadowsocks from Chinese users. In this study, we reveal how the Great Firewall of China (GFW) detects and blocks Shadowsocks and its variants. Using measurement experiments, we find that the GFW uses the length and entropy of the first data packet in each connection to identify probable Shadowsocks traffic, then sends seven different types of active probes, in different stages, to the corresponding servers to test whether its guess is correct.
Neural road semantic segmentation in driving scenarios

The purpose of this paper is to review the best semantic segmentation algorithms which involve deep neural networks and to compare one of the most used ones on a specific task for autonomous driving - road segmentation, regarding the accuracy and the time. We analyze the state of the art, then we propose a new dataset for road semantic segmentation consisting of images from our university campus, annotated by our team and taking into account the time of the day. We analyze the results of the road segmentation task obtained from the best segmentation models and we describe some metrics (accuracy, IoU) regarding the time of the day (day, dusk, night).
Syntax Analysis of Serbian Language using Context-free Grammars

Syntax analysis is one of the major phases in natural language processing. This paper presents a method for syntax analysis of Serbian language based on the context-free grammars. First, it describes building a POS tagger for the Serbian language. Secondly, it defines a context-free grammar for Serbian language. The syntax analyzer is implemented by using NLTK tool. Experiments showed that proposed analyzer creates correct syntax trees for 74% of tested sentences.