Reviewerly/exHarmony huggingface dataset:
A proof of concept for machine learning-based virtual knapping using neural networks

Prehistoric stone tools are an important source of evidence for the study of human behavioural and cognitive evolution. Archaeologists use insights from the experimental replication of lithics to understand phenomena such as the behaviours and cognitive capacities required to manufacture them. However, such experiments can require large amounts of time and raw materials, and achieving sufficient control of key variables can be difficult. A computer program able to accurately simulate stone tool production would make lithic experimentation faster, more accessible, reproducible, less biased, and may lead to reliable insights into the factors that structure the archaeological record. We present here a proof of concept for a machine learning-based virtual knapping framework capable of quickly and accurately predicting flake removals from 3D cores using a conditional adversarial neural network (CGAN). We programmatically generated a testing dataset of standardised 3D cores with flakes knapped from them. After training, the CGAN accurately predicted the length, volume, width, and shape of these flake removals using the intact core surface information alone. This demonstrates the feasibility of machine learning for investigating lithic production virtually. With a larger training sample and validation against archaeological data, virtual knapping could enable fast, cheap, and highly-reproducible virtual lithic experimentation.
Can artificial intelligence replace ultrasound as a complementary tool to mammogram for the diagnosis of the breast cancer?

To study the impact of artificial intelligence (AI) on the performance of mammogram with regard to the classification of the detected breast lesions in correlation to ultrasound-aided mammograms.Ethics committee approval was obtained in this prospective analysis. The study included 2000 mammograms. The mammograms were interpreted by the radiologists and breast ultrasound was performed for all cases. The Breast Imaging Reporting and Data System (BI-RADS) score was applied regarding the combined evaluation of the mammogram and the ultrasound modalities. Each breast side was individually assessed with the aid of AI scanning in the form of targeted heat-map and then, a probability of malignancy (abnormality scoring percentage) was obtained. Operative and the histopathology data were the standard of reference.Normal assigned cases (BI-RADS 1) with no lesions were excluded from the statistical evaluation. The study included 538 benign and 642 malignant breast lesions (n = 1180, 59%). BI-RADS categories for the breast lesions with regard to the combined evaluation of the digital mammogram and ultrasound were assigned BI-RADS 2 (Benign) in 385 lesions with AI median value of the abnormality scoring percentage of 10 (n = 385/1180, 32.6%), and BI-RADS 5 (malignant) in 471, that had showed median percentage AI value of 88 (n = 471/1180, 39.9%). AI abnormality scoring of 59% yielded a sensitivity of 96.8% and specificity of 90.1% in the discrimination of the breast lesions detected on the included mammograms.AI could be considered as an optional primary reliable complementary tool to the digital mammogram for the evaluation of the breast lesions. The color hue and the abnormality scoring percentage presented a credible method for the detection and discrimination of breast cancer of near accuracy to the breast ultrasound. So consequently, AI- mammogram combination could be used as a one setting method to discriminate between cases that require further imaging or biopsy from those that need only time interval follows up.Recently, the indulgence of AI in the work-up of breast cancer was concerned. AI noted as a screening strategy for the detection of breast cancer. In the current work, the performance of AI was studied with regard to the diagnosis not just the detection of breast cancer in the mammographic-detected breast lesions. The evaluation was concerned with AI as a possible complementary reading tool to mammogram and included the qualitative assessment of the color hue and the quantitative integration of the abnormality scoring percentage.
Characterizing Buffer Overflow Vulnerabilities in Large C/C++ Projects

Security vulnerabilities are present in most software systems, especially in projects with a large codebase, with several versions over the years, developed by many developers.Issues with memory management, in particular buffer overflow, are among the most frequently exploited vulnerabilities in software systems developed in C/C++.Nevertheless, most buffer overflow vulnerabilities are not detectable by vulnerability detection tools and static analysis tools (SATs).To improve vulnerability detection, we need to better understand the characteristics of such vulnerabilities and their root causes.In this study, we analyze 159 vulnerable code units from three representative projects (i.e., Linux Kernel, Mozilla, and Xen).First, the vulnerable code is characterized using the Orthogonal Defect Classification (ODC), showing that most buffer overflow vulnerabilities are related to missing or incorrect checking (e.g., missing if construct around statement or incorrect logical expression used as branch condition).Then, we run two widely used C/C++ Static Analysis Tools (SATs) (i.e., CppCheck and Flawfinder) on the vulnerable and neutral (after the vulnerability fix) versions of each code unit, showing the low effectiveness of this type of tool in detecting buffer overflow vulnerabilities.Finally, we characterize the vulnerable and neutral versions of each code unit using software metrics, demonstrating that, although such metrics are frequently used as indicators of software quality, there is no clear correlation between them and the existence of buffer overflow in the code.As a result, we highlight a set of observations that should be considered to improve the detection of buffer overflow vulnerabilities.
Accuracy of mammography and ultrasonography and their BI-RADS in detection of breast malignancy.

We aimed to compare the diagnostic accuracy of mammography and ultrasonography and their breast imaging-reporting and data system (BI-RADS) classification versus breast core needle biopsy (CNB) findings in distinguishing the breast masses.This cross-sectional study was conducted during 2016-2018 on female patients who were referred to a radiology center in Babol, northern Iran, for routine screening and/or for CNB. Patients underwent sonography and mammography by a senior radiologist. The breast lesions were also evaluated according to BI-RADS classification. CNB was performed on the breast masses by the same radiologist and pathological procedures were performed by an expert pathologist. Descriptive statistics were used to analyze the data.In total, 213 breast masses were finally assessed, of which 107 (50.2 %) masses were benign and 106 (49.8 %) masses were malignant. The sensitivity for mammography and ultrasound alone was 72.6% and 68.9%, respectively. This rate for combined mammography and ultrasound was 84.9%. About BI-RADS classification, 28 masses were classified as BI-RADS 3, 99 as BI-RADS 4A, 4 as BI-RADS 4B, 18 as BI-RADS 4C, and 64 as BI-RADS 5. BI-RADS 4A had the highest sensitivity (70.1%) among BI-RADS categories. The highest specificity pertained to BI-RADS 3 and 5 (100%) among BI-RADS categories. Also, the highest accuracy was related to BI-RADS 5 (80.3%).The results of the present study showed that combined mammography and ultrasound had a higher rate of accuracy than mammography or ultrasound alone. Furthermore, the imaging methods BI-RADS classification had an acceptable positive predictive value.
LogoNet: A Robust Layer-Aggregated Dual-Attention Anchorfree Logo Detection Framework with an Adversarial Domain Adaptation Approach

The task of logo detection is desirable and important for various fields. However, it is challenging and difficult to identify logos in complex scenarios as a logo can appear in different styles and platforms. Logo images include diverse contexts, sizes, projective transformation, resolution, illumination and fonts, which make it more difficult to detect a logo. To address these issues, we presented a deep learning-based algorithm for logo detection called LogoNet. It includes an hourglass like top-down bottom-up feature extraction network, a spatial attention module and an anchorfree detection head similar to CenterNet. In order to improve performance, in this paper, an extended version of LogoNet is proposed, called—Dual-Attention LogoNet, that exploits different attention mechanisms more efficiently. The incorporated channel-wise and spatial attention modules refine and generate robust and balanced feature maps to predict visual and semantic information more accurately. In addition, we propose a lightweight architecture for both LogoNet and Dual-Attention LogoNet for practical applications. The proposed lightweight architecture significantly reduces the number of network parameters and improves the inference time to address the real-time performance while maintaining accuracy. Furthermore, to address the domain shift problem in practical applications, we also propose an adversarial-learning-based domain adaptation approach, which is easily adaptable to any anchorfree detectors. Our attention-based method shows a 1.8% improvement in accuracy compared to the state-of-the-art detection network on the FlickrLogos-32 dataset. Our proposed domain adaptation approach significantly improves performance by 1.3% mAP compared to direct transfer on the target domain without increasing any labeling cost and network parameters.
BERT-KG: A Short Text Classification Model Based on Knowledge Graph and Deep Semantics

Chinese short text classification is one of the increasingly significant tasks in Natural Language Processing (NLP). Different from documents and paragraphs, short text faces the problems of shortness, sparseness, non-standardization, etc., which brings enormous challenges for traditional classification methods. In this paper, we propose a novel model named BERT-KG, which can classify Chinese short text promptly and accurately and overcome the difficulty of short text classification. BERT-KG enriches short text features by obtaining background knowledge from the knowledge graph and further embeds the three-tuple information of the target entity into a BERT-based model. Then we fuse the dynamic word vector with the knowledge of the short text to form a feature vector for short text. And finally, the learned feature vector is input into the Softmax classifier to obtain a target label for short text. Extensive experiments conducted on two real-world datasets demonstrate that BERT-KG significantly improves the classification performance compared with state-of-the-art baselines.
Joint Cryptography and Channel-Coding Based on Low-Density Parity-Check Codes and Advanced Encryption Standard for 5G Systems

Security and reliability issues have always been a big challenge in digital communications. With the emerging 5G technologies, new requirements are introduced where latency reduction is one of the most important. In this paper, a joint security and reliability method is proposed to fulfil both requirements in a single step with reduced computational complexity that leads to reduced latency for 5G communication systems. Based on Advanced Encryption Standard (AES) cryptosystem and Low-Density Parity-Check (LDPC) codes, the proposed method processes encryption and encoding at the same time. MATLAB simulation results show that the proposed method performs better compared to the previous conventional methods where encoding is done after encryption.
Reinforcement Learning with Videos: Combining Offline Observations with Interaction.

Reinforcement learning is a powerful framework for robots to acquire skills from experience, but often requires a substantial amount of online data collection. As a result, it is difficult to collect sufficiently diverse experiences that are needed for robots to generalize broadly. Videos of humans, on the other hand, are a readily available source of broad and interesting experiences. In this paper, we consider the question: can we perform reinforcement learning directly on experience collected by humans? This problem is particularly difficult, as such videos are not annotated with actions and exhibit substantial visual domain shift relative to the robot's embodiment. To address these challenges, we propose a framework for reinforcement learning with videos (RLV). RLV learns a policy and value function using experience collected by humans in combination with data collected by robots. In our experiments, we find that RLV is able to leverage such videos to learn challenging vision-based skills with less than half as many samples as RL methods that learn from scratch.
Impact of Social Engineering Attacks: A Literature Review

Social engineeringFuertes, Walter is the practice, which allowsArévalo, Diana attackers to obtainCastro, Joyce Denisse sensitive or confidential information fromRon, Marioa user of a systemEstrada, Carlos Andrés or organization, exploiting specificAndrade, Roberto characteristics of the human being. This is considered to be stillPeña, Felix Fernández one of the most threateningBenavides, Eduardo attacks within the digital world. The current study aims to explore social engineering attacks with significant impact. We conducted a systematic literature review from 2011 to 2020, applying the Barbara Kitchenham Methodological Guide. The main findings are concentrated in companies, financial institutions, and even vehicle vulnerabilities, which has caused economic losses and a decrease in the image and reputation loss damage of individuals and companies. Most of the causes are related to human behavior, such as innocence, unconsciousness, and lack of training or capacity. The primary victims are newly contracted workers, people with a certain lack of knowledge, celebrities, politicians, and middle and senior managers. Furthermore, social networks and e-mail are the primary sources from which attacks occur. Finally, we identified that Phishing and Ransomware are the most significant attacks on companies and individuals.
A Deep Learning-Based Approach to Single/Mixed Script-Type Identification

Script identification acts as the precursor in recognizing the scene text by optical character recognition (OCR). But this is not the fundamental issue to the OCR engine. Before script identification, script-type classification is necessary since nowadays the scene texts in natural images are not comprised of a single script but also multi-script words at character level are found very frequently in different places like posters, T-shirts graffiti, hoardings, banners, etc. In this work, a deep learning-based framework has been designed to classify single/mixed script images. To assess the effectiveness of the structure presented, experiments were also performed with an outlier class consisting of a wide variety of single scripts. Experiments were performed with over 4 K images and The best precision of 98.30% was reached. This method was compared with a standard deep learning and handcrafted feature-based technique where the proposed technique produced a better result.
Towards Making Fusion Data FAIR

The concept of FAIR data is being increasingly adopted at national and global levels as a way of maximising the impact and transparency of publicly-funded research outcomes and data. In this paper we introduce the FAIR requirements of the Fusion community as well as technological directions proposed by the Fair4Fusion project aiming at increasing the accessibility to fusion data.
What do hashtags afford in digital fashion communication? An exploratory study on Gucci-related hashtags on Twitter and Instagram

Abstract Being enmeshed in a digital environment, we daily produce internet-mediated texts – encompassing several different semiotic codes – accessible on a global scale. Posts on different networks usually contain hashtags, which can be understood as affordances or behavior opportunities. These affordances allow specific actions both from the part of the writer and the reader. They can also be “behavior triggers,” which invite certain behavior online (e.g., to like, share, comment) or offline (e.g., to buy items of a specific brand). Digital fashion communication experts should take into consideration these affordances to pursue their goals in the activities related to marketing, promoting, creating engagement and communicating either about a brand or a brand item. While there is general research on reasons to hashtag and on the functions they perform, there is a gap in what hashtags “mean” and “do” in the fashion domain. The objectives of this paper are threefold: 1) to give an overview of the history of hashtags; 2) to present the affordances of hashtags, while creating a general table of affordances from the addresser’s perspective, based on the literature overview; 3) to perform a qualitative analysis of hashtags on Gucci official Twitter and Instagram accounts during a given time span and on other Gucci-related hashtags posted by common users. The choice of the brand is due to Gucci’s focus on active digital communication practices. Results can be valuable for both researchers and industry professionals involved in digital fashion communication.
TGHop: an explainable, efficient, and lightweight method for texture generation

An explainable, efficient, and lightweight method for texture generation, called TGHop (an acronym of Texture Generation PixelHop), is proposed in this work.Although synthesis of visually pleasant texture can be achieved by deep neural networks, the associated models are large in size, difficult to explain in theory, and computationally expensive in training.In contrast, TGHop is small in its model size, mathematically transparent, efficient in training and inference, and able to generate highquality texture.Given an exemplary texture, TGHop first crops many sample patches out of it to form a collection of sample patches called the source.Then, it analyzes pixel statistics of samples from the source and obtains a sequence of fine-to-coarse subspaces for these patches by using the PixelHop++ framework.To generate texture patches with TGHop, we begin with the coarsest subspace, which is called the core, and attempt to generate samples in each subspace by following the distribution of real samples.Finally, texture patches are stitched to form texture images of a large size.It is demonstrated by experimental results that TGHop can generate texture images of superior quality with a small model size and at a fast speed.
Exploring Cortex-M Microarchitectural Side Channel Information Leakage

The growing Internet of Things (IoT) market demands side-channel attack resistant, efficient, cryptographic implementations.Such implementations, however, are microarchitecture-specific, and cannot be implemented without an in-depth structural knowledge of the CPU and memory information leakage patterns; a description of such information leakages is presently not disclosed by any processor design company.In this work we propose the first Instruction Set Architecture (ISA) level framework for microarchitectural leakage characterization.Our framework allows to extract a microarchitectural leakage profile from a superscalar in-order processor; we infer detailed pipeline characteristics through the observation of instruction execution timings, and provide an identification of the datapath registers via a side-channel measuring setup.The extracted model can serve as a foundation for building solid countermeasures against side-channel attacks on software cryptographic implementations.We validate the extracted models on the ARM Cortex-M4 and ARM Cortex-M7 CPUs, two of the most widespread ARM microcontroller cores.Finally, as a further validation of the effectiveness of our derived model, we mount a successful attack on unprotected AES implementations for each of the examined platforms.
Valuation of Significant Difference Between Various Agile Methods Using One Way ANOVA

ANOVA stands for analysis of variance it is a statistical test used to research the difference among the methods of greater than two agencies or groups. The study shows a statistical data test for different groups of various data sets from IEEE and ACM journals and conferences which represent in terms of interval data to perform one-way analysis of variance (ANOVA) test with group data. The data was selected from IEEE and ACM journals and conferences from 2014 to 2019 of different agile users for knowing which type of methods are most preferable by users using various agile methodology. After which we apply a one-way ANOVA test on the sample data. The results show that each data collected from conferences and journals depicts a significant difference between each compared sample so, user preferences are changing in selecting a various methodology. Thus, the conclusion shows that because of differences that occur by applying one-way ANOVA the users mostly preferred to change their methodology to XP (Extreme Programming) in agile methodology.
GupShup: Summarizing Open-Domain Code-Switched Conversations

Code-switching is the communication phenomenon where the speakers switch between different languages during a conversation.With the widespread adoption of conversational agents and chat platforms, codeswitching has become an integral part of written conversations in many multi-lingual communities worldwide.Therefore, it is essential to develop techniques for understanding and summarizing these conversations.Towards this objective, we introduce the task of abstractive summarization of Hindi-English (Hi-En) code-switched conversations.We also develop the first code-switched conversation summarization dataset -GupShup, which contains over 6,800 Hi-En conversations and their corresponding human-annotated summaries in English (En) and Hi-En.We present a detailed account of the entire data collection and annotation process.We analyze the dataset using various code-switching statistics.We train state-of-the-art abstractive summarization models and report their performances using both automated metrics and human evaluation.Our results show that multi-lingual mBART and multi-view seq2seq models obtain the best performances on this new dataset.We also conduct an extensive qualitative analysis to provide insight into the models and some of their shortcomings.
Image Retrieval for Arguments Using Stance-Aware Query Expansion

Many forms of argumentation employ images as persuasive means, but research in argument mining has been focused on verbal argumentation so far.This paper shows how to integrate images into argument mining research, specifically into argument retrieval.By exploiting the sophisticated image representations of keyword-based image search, we propose to use semantic query expansion for both the pro and the con stance to retrieve "argumentative images" for the respective stance.Our results indicate that even simple expansions provide a strong baseline, reaching a precision@10 of 0.49 for images being (1) on-topic, (2) argumentative, and (3) on-stance.An in-depth analysis reveals a high topic dependence of the retrieval performance and shows the need to further investigate on images providing contextual information.