Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis for Turkish Unstructured Data by Machine Translation

Recent online popular platforms such as social media, blogs, and newspapers generate a vast amount of unstructured data per second. Sentiment Analysis (SA) is an efficient technique to identify and extract subjective information in unstructured data to enable businesses to understand the emotional tendency of the interactive users towards its products or services. However, analyzing unstructured data can be more difficult than structural data. In particular, the performance of SA techniques decreases due to the structural complexity of the language. SA techniques are widely used in English since it is universal and structurally more suitable for SA. On the other hand, structural difficulties and complexities in Turkish cause performance degradation of SA studies compared to English. This study aims to overcome this difficulty by first translating Turkish texts into English texts by machine translation, and then realizing sentiment analysis on English texts. To demonstrate the success of machine translation, the experiments are conducted on two different data sets and results are given in a comparative manner for both on Turkish as the original language and English as the translated language. Data sets in both languages are classified by six different machine learning methods which are Logistic Regression, Naive Bayes, Decision Tree, Random Forest, Support Vector Machine, and Artificial Neural Network. When the success rates of machine learning methods are examined, a significant increase is observed by machine translation for most of the methods.
Teachers’ Strategies for a Multimodal Teaching Model in Higher Education

This article presents a case of a four-year experience of implementation of a higher education program for students in their first year of studies in the social sciences and humanities, one of the many programs offered by Universidad de la República (UDELAR) in its decentralization process. The program has been designed and implemented as a regional project, with a multimodal teaching model, which has developed on the basis of three components: face to face classes, videoconferencing and a Moodle platform. This paper provides an account of the action research cycle of CIOS proposal development, its implementation strategy and the process by which teachers got involved in the digital competency building endeavor required. It intends to provide a systematic account of this process experienced by this particular community of teachers, as they have gone through a technology appropriation process and they have engaged in ongoing course redesign.
Simpler proof for nonlinearity of majority function

Given a Boolean function f, the (Hamming) weight wt(f) and the nonlinearity N(f) are well known to be important in designing functions that are useful in cryptography. The nonlinearity is expensive to compute, in general, so any formulas giving the nonlinearity for particular functions f are significant. The well known majority function has been extensively studied in a cryptographic context for the last dozen years or so, and there is a formula for its nonlinearity. The known proofs for this formula rely on many detailed results for the Krawtchouk polynomials. This paper gives a much simpler proof.
Saliency Model Based on Discriminative Feature and Bi-directional Message Interaction for Fabric Defect Detection

Due to the complexity of the texture background and the diversity of the defect types of the fabric image, the traditional method of fabric defect detection shows poor detection performance. Recent advances on salient object detection benefit from Fully Convolutional Neural Network (FCN) and achieve the good performance. Meanwhile, the deficiencies in a fabric image are salient compared to the texture background, so the saliency model is very feasible for fabric defect detection. In this paper, we propose a novel saliency model based on discriminative feature and bi-directional message interaction for fabric defect detection. Firstly, we design a multi-scale attention-guided feature extraction module in which the multi-scale context-aware feature extraction block and channel attention block are respectively used to capture multi-scale contextual information and assign greater weight to more discriminative features corresponding to the right fabric defect scale. Then a bi-directional message interaction module is designed to promote the effectiveness of feature with specific resolution by interacting message along both directions, which further improves the availability of the feature extraction. After the bi-directional message interaction module, we use a cross-level contrast feature extraction module, which elevates features with locally strong contrast along each resolution axis, to predict saliency maps. Finally, the predicted saliency maps are efficiently merged to produce the final prediction result. We conduct extensive experiments to evaluate our net and experiment results demonstrate that the proposed method outperforms the state-of-the-art approaches.
AI Turning Points and the Road Ahead

Perez Cruz, Lito Treisman, DavidToday Artificial Intelligence (AI) is enjoying a revival of interest. A decade ago, computer product makers avoided the term, for fear of being branded wide-eyed dreamers. Now seen in a positive light, AI is enjoying frenzied attention from the public. This is a marked change. In this paper we track and examine what led to the present day preoccupation of the public to things AI, a significant deviation from some thirty years ago. In this study, we extend the work Making AI Great Again where we will provide more arguments for the factors that led to this turn of events and evaluate if what we are seeing is “real” AI. Along the way, we will offer ways in keeping the AI hope real despite the sometimes exaggerated hype that could cloud the AI achievements of the past. It is our aim that this work helps in someway to prevent AI from experiencing another Winter.
A Prototype of Portable Digital Forensics Imaging Tools using Raspberry Device

Abstract One of the digital forensics activities has the goal to prove a cybercrime. There are several stages in digital forensics when doing an investigation. Each stage has its suitable hardware and software that is used while investigating a case. Standalone forensics hardware is a suitable media in the process of investigation. It can do an acquisition and imaging process at the same time as the investigation process. Nowadays, standalone forensics hardware for imaging devices has a very high price. This paper gives a solution to develop low budget portable imaging forensics devices using a raspberry device. The device enables us to do an acquisition to a hard disk or flash drive. The output has a raw format file type i.e.dd. This device includes a logging file consisting of detailed information related to the digital evidence also hashing to provide file integrity. The result of this paper describes how to construct a prototype low budget portable device for digital forensics acquisition using a raspberry device and how to operate it in GUI. The prototype was successfully created and tested in several scenarios. The performance test of this device has a result of that transfer rate of 1,85 MB/s.
Speculator and Influencer Evaluation in Stock Market by Using Social Media

Social media platforms are places where people post their feelings and thoughts about a topic. The institutions, organizations, individuals, or companies that are the subject of these ideas are affected by these posts. As discussed in different studies, companies in stock exchange markets are affected by the posts made on these social media platforms. At the same time, individuals who are aware of this fact, namely speculators and influencers, may make profit by manipulating the truth. In this study, possible speculators or influencers using the Twitter social media platform are investigated. As the target companies, Google, Amazon, Apple, Tesla, and Microsoft were chosen, which are among the largest companies on the NASDAQ stock exchange market. In the study, asentiment analysis using the Loughran and McDonald sentiment analysis dictionary was utilized. The sentiment analysis results were used to model different machine learning algorithms. With the models, individuals who had too many positive or negative effects as possible speculators or influencers were identified. The study was performed for 5 years of data. The results indicates that (1) without noise reduction, it is not possible to establish a correlation on individual tweets and their effects on the stock market; (2) it is not possible to establish a correlation between the number of tweets and the volume of companies; (3) the effect of threshold on the accuracy, which has been done and proven in different studies, has also been proven in this study; (4) RBF Kernel SVM method gives better result than other machine learning methods.
Accumulated Generalized Mean Value - a New Approach to Flow-Based Feature Generation for Encrypted Traffic Characterization

In this paper, we concentrate on encrypted traffic detection and characterization and analyze the popular ISCXVPN2016 dataset. The detection of encrypted traffic and further characterization of its content is crucial both for securing the required Quality of Service and for Network Intrusion Detection. We introduce Accumulated Generalized Mean Value (AGMV), a novel preprocessing method for flow-based features. We apply this method to features from ISCXVPN2016 dataset and compare the performance of basic Machine Learning classifiers on the preprocessed and the original datasets. Furthermore, we compare our results with those obtained in recent state-of-the-art studies. We demonstrate the proposed simple preprocessing technique dramatically improves the performance of basic classifiers e.g. Random Forest. Moreover, combination of AGMV and Random Forest outperforms the existing state-of-the-art models. AGMV can be combined with more sophisticated classifiers for performance improvement in a variety of tasks where flow-based parameters play an important role. The new dataset is published online for further analysis. The proposed method, source code and the results are available online at our GitHub repository: https://github.com/AleksandrIvchenko/agm.
Assessing Understanding of Maintainability using Code Review

Maintainability is an important quality attribute of code, and so should be a key learning outcome for software engineering programmes. This raises the question of how to assess this learning outcome. In this practical report we describe how we exploited the code review mechanism provided by GitHub, the “pull request”, to assess students’ understanding of maintainability. It requires a slightly non-standard workflow by the students and a reporting tool to assemble the code review comments in a form suitable for assessment. We give the details of what we learned to make it work that should allow others to conduct similar kinds of assessment.
Decentralized Exchanges: The 'Wild West' of Cryptocurrency Trading

Cryptocurrencies are traded on two types of exchanges – centralized and decentralized. Although trading in the largest cryptocurrencies primarily occurs on centralized exchanges, most newly issued tokens can only be exchanged using decentralized platforms. Volumes in these decentralized exchanges (including automated market makers) has recently increased exponentially. We examine the role of this new and unmonitored market, utilizing an extensive sample of tokens exclusively traded on decentralized platforms. We show significant differences in the listing and trading characteristics of these tokens relative to their centralized equivalents. A small selection of these tokens obtain listing on a centralized exchange during the sample period, which is accompanied by a significant increase in trading activity, consistent with market segmentation. A centralized listing results in a migration of volume away from decentralized platforms, revealing a strong preference by token holders for deeper and more liquid markets over the increased security and anonymity offered by decentralized exchanges.
A Computer-Aided Diagnosis System of Breast Lesion Classification Based on Multi Angle Fusion Strategy in Ultrasound Images

Ultrasound is one of the most widely applied imaging modalities for breast lesion assessment. The accurate breast lesion diagnosis can improve patients’ survival rate. The purpose of this study is to develop a computer-aided diagnosis (CAD) system that can acquire information from grayscale and elastic ultrasound images to classify benign and malignant breast tumors. The U-Net was used for automatic segmentation of grayscale ultrasound images. After reconstructing elastic ultrasound images, 218 breast features (morphological, gray, calcification, texture and elastic features) were extracted. We proposed a feature selection method based on multi-angle fusion strategy (MAFS) to select important breast features. In this method, least absolute shrinkage and selection operator (LASSO), mutual information (MI) and random forest (RF) feature selectors were fused to establish a robust and accurate feature selector. A support vector machine (SVM) classifier was used for breast lesion classification. The ultrasound images in our study contained 77 malignant cases out of 199 breast lesion cases, in which the results of core biopsy or fine-needle aspiration were regarded as the golden standard. The area under the receiver operating characteristic curve (AUC) of the combined breast set based on MAFS, LASSO, MI and RF were 0.897, 0.871, 0.880 and 0.869, respectively. The accuracy, sensitivity and specificity of the breast lesion classification performance based on MAFS were 91.0%, 84.0% and 95.4%, respectively. Our results demonstrate the feasibility that the CAD system based on MAFS can be applied in breast lesion diagnosis. Also, the CAD system based on MAFS performed well in grayscale and elastic ultrasound image sets, showing it had robust and generalization ability in ultrasound images.
Scalable Behavioral Authentication

A Behavioral Authentication (BA) system constructs a behavioral profile for a user and uses it to verify their identity claims. It is primarily used as a second factor in user authentication. A BA system starts with an initial database of user profiles, and uses a verification algorithm to accept or reject a verification request, that consists of a claimed identity and behavioral data by measuring the distance between the profile of the claimed identity and the presented behavioral data. As new users join the system, the size of profile database grows and for a user u, the probability of other users with “close” profiles increases, and this results in an increase in the error probability of verification algorithm. We analyze this problem in distance-based verification systems, and introduce the notion of scalability of BA systems that requires the system error probability to remain (almost) unchanged as the size of the profile database grows. To achieve scalability, we propose personalization of verification algorithm. More specifically, for a user u the verification algorithm considers a set of doppelgangers that consists of users that have “close” profiles to u, and uses the set to form additional verification checks for verification requests against u. We use extensive experiments to validate the above, including the increase in the verification error with the increase in the database size, and implement and evaluate our proposed scalable verification algorithm in reducing this error. To our knowledge, this is the first systematic treatment of scalability of BA systems. Our notion of scalability and the approach to achieving it are general and applicable to other distance-based authentication systems.
Indexing Arabic names: the basics

Some of the rules for indexing Arabic names are different for premodern and modern names, while other rules hold for both premodern and modern. This article helps the indexer to recognize those names that cannot be split, and to recognize honorifics, titles and, in premodern names, certain types of nisbas (descriptors) that should not be used as the entry point. Indexers should follow the author’s lead in finding the entry point for premodern names. Rules for inverting modern names and tips for correct sorting are also given.
Curation of Historical Arabic Handwritten Digit Datasets from Ottoman Population Registers: A Deep Transfer Learning Case Study

With the increasing number of digitization efforts of historical manuscripts and archives, automatical information retrieval systems need to extract meaning fast and reliably. Historical archives bring more challenges for these systems when compared to modern manuscripts. More advanced algorithms, archive specific methods, preprocessing techniques are needed to retrieve information. Cutting-edge machine learning algorithms should also be applied to retrieve meaning from these documents. One of the most important research issues of historical document analysis is the lack of public datasets. Although there are plenty of public datasets for modern document analysis, the number of public annotated historical archives is limited. Researchers can test novel algorithms on these modern datasets and infer some results, but their performance is unknown without testing them on historical datasets. In this study, we created a historical Arabic handwritten digit dataset by combining manual annotation and automatic document analysis techniques. The dataset is open for researchers and contained more than 6000 digits. We then tested deep transfer learning algorithms and various machine learning techniques to recognize these digits and achieved promising results.
Experimental Comparison and Scientometric Inspection of Research for Word Embeddings

Word embedding or universal embeddings are the representation of textual data into vectors of real numbers. It acts as a link between the human understanding of a text to that of the machine. It maps the high-dimensional textual data into a vector space of low dimension, which represents the complex relationships existing in the data. Thus, it boosts the performance of tasks involving natural language processing. To know about the popularity gain and the impact of this field, research literature between 2000 and 2019 is analyzed with the help of a scientometric mapping for research done in word embeddings. The paper visualizes year-wise analysis, demographic analysis, category-wise distribution, and document type-wise distribution of the publications indexed in Web of Science (WoS). The paper also comprises a comparative study of Word2Vec, FastText, global vector representation of words, and bidirectional encoder representations for transformers. Pre-trained models are used for experimental comparison. Performance is measured by calculating the deviation of the similarity score of two words given by the models from manually assigned similarity scores by experts, repeated over a list of words on various datasets. The least deviation is shown by FastText due to the usage of morphological information in the skip-gram model and n-gram architecture.
Fuzzy logic based techniques for early software defect assessment

Assessing the defects of the software in the pre phases of the software development life cycle (SDLC), will improve the quality of the software. Defect density indicators of the software identified in the pre and mid phases of SDLC provide a chance for the early identification of the problem which affects the software development and unexpected incurred cost. Early identification of the software metrics which affects reliability is important. However, there are numerous software metrics available in pre phases. Consequently we have selected only reliability relevant software metrics. Thinking about the uncertainty present in the introductory phases and the degree of reliability expected by the customer, in this article we arranged a model based on fuzzy logic techniques which empowers the scholars and industry experts to assess the number of defects before testing phase.
Rate–distortion optimization of multi-exposure image coding for high dynamic range image coding

High dynamic range (HDR) images have many practical applications because they offer an extended dynamic range and a more realistic visual experience. A HDR image is usually stored in floating-point format, so pre-processing is required to make the HDR image compatible with coding standards. A transfer function is also used to achieve better coding efficiency. Typically, HDR images are generated using several low dynamic range (LDR) images with different exposures. Instead of compressing the HDR image when it is generated from images with multiple exposures, this study proposes a technique to compress the multi-exposure images. The HDR image generation, as well as the multi-exposure images fusion, can be realized in the decoder. The proposed framework encodes the multi-exposure images using MV-HEVC where the inter-view redundancy is well exploited when an accurate intensity-mapping function between the multi-exposure images has been established. Multi-exposure image coding is used to produce a high-quality HDR image so the rate–distortion optimization (RDO) is modified by considering both the reconstruction quality of the current block and its effect on the multi-exposure fused image. A Lagrange multiplier is modified to maintain a balance between the rate and the modified distortion during the RDO process. Compared to encoding the generated HDR image using HEVC range extension, the experimental results show that the proposed technique achieves significant bitrate savings for equivalent quality in terms of HDR-VDP-2.
Learning from mistakes: Improving spelling correction performance with automatic generation of realistic misspellings

Abstract Sequence to sequence models (seq2seq) require a large amount of labelled training data to learn the mapping between the input and output. A large set of misspelled words together with their corrections is needed to train a seq2seq spelling correction system. Low‐resource languages such as Turkish usually lack such large annotated datasets. Although misspelling‐reference pairs can be synthesized with a random procedure, the generated dataset may not well match to genuine human‐made misspellings. This might degrade the performance in realistic test scenarios. In this paper, we propose a novel procedure to automatically introduce human‐like misspellings to legitimate words in Turkish language. Generated human‐like misspellings are used to improve the performance of a seq2seq spelling correction system. The proposed system consists of two separate models; a misspelling generator and a spelling corrector. The generator is trained using a relatively small number of human‐made misspellings and their manual corrections. Reference words and their misspellings are used as inputs and outputs of the generator, respectively. As a result, it is trained to add realistic spelling errors to the valid words. Training data of the spelling corrector is augmented by the generator's human‐like misspellings. In the experiments, we observe that the data augmentation significantly improves the spelling correction performance. Our proposed method yields 5% absolute improvement over the state‐of‐the‐art Turkish spelling correction systems in a test set which contains human‐made misspellings from Twitter messages.