Reviewerly/exHarmony huggingface dataset:
Hateware and the Outsourcing of Responsibility

Section 230 of the Communications Decency Act of 1996 created a regulatory environment in which companies such as Facebook, Twitter, and Google can claim that they are not publishers of content and are therefore not responsible for harmful content posted by users. Abusers and harassers have exploited this environment and the hands-off policies of software companies in order to turn software into a weapon. In this chapter, we argue that the actions of regulators and corporations have helped foster the creation of something we call hateware, software that employs policies, algorithms, and designs that enable abuse, harassment, and other harmful behaviors. In this chapter, we examine Discord, a text and chat application, that was used to plan the “Unite the Right” rally in Charlottesville, Virginia. By analyzing the unique features of this platform and how it was used by members of the so-called “alt right,” we argue that design decisions, policies, and community norms can transform seemingly “neutral” software into hateware.
Dynamic Neural Network for Business and Market Analysis

The problem of predicting nonlinear and nonstationary signals is complex since the physical law that controls them is unknown and it is complicated to be considered. In these cases, it is necessary to devise nonlinear models that imitate or learn the rules of behavior of the problem and can be developed based on historical data. For this reason, neural networks are useful tools to deal with this type of problem due to their nonlinearly and their capacity of generalizing. This paper aims at exploring various types of neural network architectures and study their performance with time series predictions. Predictions on two sets of data (of a very different nature) will be made using three neural networks including multilayer perceptrons, recurrent neural network and long-short term memory varying some important parameters: input neurons, epochs and the anticipation with which the predictions are made. Then, all results will be compared using standard metrics. As a conclusion, the influence of the type of series under study is more important than the parameters considered in what concerns the performance. The management of the memory in the networks is a key to its success in the prediction of S&P 500 and electrical power time series.
Constructing a Knowledge Base of Coding Conventions from Online Resources

Coding conventions are a set of coding guidelines used by software developers to improve the readability of source code, increase software maintainability, and promote the reuse of coding patterns.In this paper, we introduce CCBase, a knowledge base of coding conventions, that was constructed from online resources.Specifically, CCBase was constructed as follows.We designed the ontology of the coding convention domain, crawled data related to coding conventions from a variety of online resources, and then extracted entities and relations using an NLP-enabled rule matching method.To uncover the latent relations, we further proposed a similarity metric to reveal the similar-to and relate-to relations, and developed a RCE algorithm to establish a unified type hierarchy of coding conventions.The resulting knowledge base contains 3139 coding conventions for Java and C++, with 3761 entities and 767 relations.Furthermore, we have extended the usability of CCBase by developing a question answering system on the base.We have conducted experiments to evaluate CCBase.The experimental results show that CCBase has a wide coverage on entities and relations in coding conventions domain, and the QA system achieves an F1 score of 84.5% on 214 questions raised in StackOverflow.
Evaluating portable Raman spectrometers for use in exploration of pegmatite dikes, Wekusko Lake, Manitoba

Research Article| September 30, 2019 Evaluating portable Raman spectrometers for use in exploration of pegmatite dikes, Wekusko Lake, Manitoba David Benn; David Benn § Department of Earth Sciences, Western University, London, Ontario N6A 5B7, Canada § Corresponding author e-mail address: DBenn2@uwo.ca Search for other works by this author on: GSW Google Scholar Robert Linnen; Robert Linnen Department of Earth Sciences, Western University, London, Ontario N6A 5B7, Canada Search for other works by this author on: GSW Google Scholar Tania Martins Tania Martins Manitoba Geological Survey, Winnipeg, Manitoba R3G 3P2, Canada Search for other works by this author on: GSW Google Scholar Author and Article Information David Benn § Department of Earth Sciences, Western University, London, Ontario N6A 5B7, Canada Robert Linnen Department of Earth Sciences, Western University, London, Ontario N6A 5B7, Canada Tania Martins Manitoba Geological Survey, Winnipeg, Manitoba R3G 3P2, Canada § Corresponding author e-mail address: DBenn2@uwo.ca Publisher: Mineralogical Association of Canada First Online: 07 Oct 2019 Online Issn: 1499-1276 Print Issn: 0008-4476 © 2019 Mineralogical Association of Canada The Canadian Mineralogist (2019) 57 (5): 711–713. https://doi.org/10.3749/canmin.AB00001 Article history First Online: 07 Oct 2019 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation David Benn, Robert Linnen, Tania Martins; Evaluating portable Raman spectrometers for use in exploration of pegmatite dikes, Wekusko Lake, Manitoba. The Canadian Mineralogist 2019;; 57 (5): 711–713. doi: https://doi.org/10.3749/canmin.AB00001 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyThe Canadian Mineralogist Search Advanced Search The increased focus on new battery technologies in electric cars and renewable energies has led to an increased use in lithium (Li)-ion batteries. Consequently, the demand for high grade Li has increased. There are two main sources for Li: pegmatites (e.g., Greenbushes, Australia) and brines (e.g., Salar de Atacama, Chile). Large battery companies, such as Tesla, are building battery factories in North America, offering new opportunities for North American deposits to compete with the major producers in Australia, South America and Asia. In Canada, the primary Li deposits are Li-Cs-Ta (LCT) pegmatites. This includes the Whabouchi... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
Cross-Modal Learning with Adversarial Samples

With the rapid developments of deep neural networks, numerous deep cross-modal analysis methods have been presented and are being applied in widespread real-world applications, including healthcare and safety-critical environments. However, the recent studies on robustness and stability of deep neural networks show that a microscopic modification, known as adversarial sample, which is even imperceptible to humans, can easily fool a well-performed deep neural network and brings a new obstacle to deep cross-modal correlation exploring. In this paper, we propose a novel Cross-Modal correlation Learning with Adversarial samples, namely CMLA, which for the first time presents the existence of adversarial samples in cross-modal data. Moreover, we provide a simple yet effective adversarial sample learning method, where inter- and intra- modality similarity regularizations across different modalities are simultaneously integrated into the learning of adversarial samples. Finally, our proposed CMLA is demonstrated to be highly effective in cross-modal hashing based retrieval. Extensive experiments on two cross-modal benchmark datasets show that the adversarial examples produced by our CMLA are efficient in fooling a target deep cross-modal hashing network. On the other hand, such adversarial examples can significantly strengthen the robustness of the target network by conducting an adversarial training.
Cryptanalysis of Enhanced More

Abstract Fully homomorphic encryption (FHE) has been among the most popular research topics of the last decade. While the bootstrapping-based, public key cryptosystems that follow Gentry’s original design are getting more and more efficient, their performance is still far from being practical. This leads to several attempts to construct symmetric FHE schemes that would not be as inefficient as their public key counterparts. Unfortunately, most such schemes were also based on (randomized) linear transformations, and shown to be completely insecure. One such broken scheme was the Matrix Operation for Randomization and Encryption (MORE). In a recent paper, Hariss, Noura and Samhat propose Enhanced MORE, which is supposed to improve over MORE’s weaknesses. We analyze Enhanced MORE, discuss why it does not improve over MORE, and show that it is even less secure by presenting a highly efficient ciphertext-only decryption attack. We implement the attack and confirm its correctness.
CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding

Spoken Language Understanding (SLU) mainly involves two tasks, intent detection and slot filling, which are generally modeled jointly in existing works. However, most existing models fail to fully utilize co-occurrence relations between slots and intents, which restricts their potential performance. To address this issue, in this paper we propose a novel Collaborative Memory Network (CM-Net) based on the well-designed block, named CM-block. The CM-block firstly captures slot-specific and intent-specific features from memories in a collaborative manner, and then uses these enriched features to enhance local context representations, based on which the sequential information flow leads to more specific (slot and intent) global utterance representations. Through stacking multiple CM-blocks, our CM-Net is able to alternately perform information exchange among specific memories, local contexts and the global utterance, and thus incrementally enriches each other. We evaluate the CM-Net on two standard benchmarks (ATIS and SNIPS) and a self-collected corpus (CAIS). Experimental results show that the CM-Net achieves the state-of-the-art results on the ATIS and SNIPS in most of criteria, and significantly outperforms the baseline models on the CAIS. Additionally, we make the CAIS dataset publicly available for the research community.
Virtual Reality Training Platform in Onshore Pipeline

This paper reviews the possibility to implement Virtual Reality (VR) training platform for maintenance in oil and gas industry. All major oil and gas companies in recent years have advocated design and develop Virtual Maintenance training system for the greater benefit of the humanity and the environment focusing on sustainable development. Oil and gas industry often requires people to work in hazardous environments, these environments are constantly increasing in size and complexity as companies look for new more cost effective ways of doing training and maintenance. Therefore the application of Virtual Reality may allow the new workers to practice and become familiar with the real work using Virtual Environment before performing the real tasks.
A framework to aggregate multiple ontology matchers

Purpose Although ontology matchers are annually proposed to address different aspects of the semantic heterogeneity problem, finding the most suitable alignment approach is still an issue. This study aims to propose a computational solution for ontology meta-matching (OMM) and a framework designed for developers to make use of alignment techniques in their applications. Design/methodology/approach The framework includes some similarity functions that can be chosen by developers and then, automatically, set weights for each function to obtain better alignments. To evaluate the framework, several simulations were performed with a data set from the Ontology Alignment Evaluation Initiative. Simple similarity functions were used, rather than aligners known in the literature, to demonstrate that the results would be more influenced by the proposed meta-alignment approach than the functions used. Findings The results showed that the framework is able to adapt to different test cases. The approach achieved better results when compared with existing ontology meta-matchers. Originality/value Although approaches for OMM have been proposed, it is not easy to use them during software development. On the other hand, this work presents a framework that can be used by developers to align ontologies. New ontology matchers can be added and the framework is extensible to new methods. Moreover, this work presents a novel OMM approach modeled as a linear equation system which can be easily computed.
Finding the Sweet Spot

With smartphones being a prime example, touchscreens became one of the most widely used interface to interact with computing systems. Compared to other touchscreen devices, smartphones pose additional challenges as the hand that interacts with the device is commonly used to also hold the device. Consequently, determining how fingers of the hand holding the device can interact with the screen is a non-trivial challenge. A body of recent work investigated the comfortable area in controlled lab studies. This poses limitations as it is based on the assumption that the grips used in the studies are representative for normal smartphone use. In this paper, we extend previous work by providing insights from in-the-wild studies using two different apps that were deployed in the Android App Store. Comparing our results with previous work we confirm that our data fits previously proposed models. Further analyzing the data, we highlight the sweet spot, the position that is touched if the input can be performed on the whole screen.
Hard Competencies Satisfaction Levels for Software Engineers: A Unified Framework

Software engineer’s/developer’s competency has long been established as a key pillar for the development of software. Nevertheless, the satisfaction levels derived from using a competency needs more investigation. The aim of this paper is to propose a framework for identifying hard competencies and their satisfaction levels. The paper contributes to the software engineering competency research by highlighting the satisfaction levels of hard competence for the benefit of the educators (academia), software engineers and users of software competence (practitioner).
A Data Analytics Framework for Decision-Making in Agriculture

Data Analytics and Information Communication Technologies have a key role to play in decision-making in Agriculture. It is important that these technologies are used strategically, during the process of policymaking, so that better and effective policies can be formulated. Policymaking should be carried out with a proper monitoring mechanism in place, duly supported by Data Analytics and the proactive involvement of all stakeholders. Agriculture constitutes a very important domain in India and hence decision making in this sector is very crucial for the Gross Domestic Product (GDP) of the country and for majority of the rural population. In this paper, we present a framework which uses three Data Analytics technologies for effective policymaking in the Agriculture sector. The Data Analytics technologies discussed are Simulation, Social Networking, and Statistical Analysis. The framework presents the application of each of the technologies mentioned, in the process of framing a policy for the agriculture domain. With the help of these technologies, this framework attempts to help both the policymaker and the farmer to achieve a more accurate and evidence-based decision-making.
Multi-watermarking Algorithm for Medical Image Based on NSCT-RDWT-DCT

With the rapid development of medical imaging technology, digital information systems have become more and more important. However, medical images are vulnerable to be attacked or malicious tampered during transmission. Medical image watermarking algorithm using wavelet transform can solve the above problems and has been widely used in recent years. But due to its poor resistance to geometric attacks, the security of medical images cannot be guaranteed. In this work, a medical image multi-watermarking algorithm based on NSCT-RDWT-DCT is proposed. First, a more robust feature vector of medical image is extracted by combining non-subsampled contour transform (NSCT), redundant discrete wavelet transform (RDWT) and discrete cosine transform (DCT) to resist geometric attacks. Then, zero watermarking technology is adopted to embed and extract watermarks considering the capacity and invisibility of the medical image. Moreover, Arnold Cat Map is applied to enhance the security of the watermarks information. The experimental results show that the proposed algorithm can effectively extract the watermarked information. Compared with the existing algorithms, it has good performance against both conventional attacks and geometric attacks.
Towards Capturing Sonographic Experience: Cognition-Inspired Ultrasound Video Saliency Prediction

For visual tasks like ultrasound (US) scanning, experts direct their gaze towards regions of task-relevant information. Therefore, learning to predict the gaze of sonographers on US videos captures the spatio-temporal patterns that are important for US scanning. The spatial distribution of gaze points on video frames can be represented through heat maps termed saliency maps. Here, we propose a temporally bidirectional model for video saliency prediction (BDS-Net), drawing inspiration from modern theories of human cognition. The model consists of a convolutional neural network (CNN) encoder followed by a bidirectional gated-recurrent-unit recurrent convolutional network (GRU-RCN) decoder. The temporal bidirectionality mimics human cognition, which simultaneously reacts to past and predicts future sensory inputs. We train the BDS-Net alongside spatial and temporally one-directional comparative models on the task of predicting saliency in videos of US abdominal circumference plane detection. The BDS-Net outperforms the comparative models on four out of five saliency metrics. We present a qualitative analysis on representative examples to explain the model’s superior performance.
Global Texture Enhancement for Fake Face Detection in the Wild

Generative Adversarial Networks (GANs) can generate realistic fake face images that can easily fool human beings.On the contrary, a common Convolutional Neural Network(CNN) discriminator can achieve more than 99.9% accuracyin discerning fake/real images. In this paper, we conduct an empirical study on fake/real faces, and have two important observations: firstly, the texture of fake faces is substantially different from real ones; secondly, global texture statistics are more robust to image editing and transferable to fake faces from different GANs and datasets. Motivated by the above observations, we propose a new architecture coined as Gram-Net, which leverages global image texture representations for robust fake image detection. Experimental results on several datasets demonstrate that our Gram-Net outperforms existing approaches. Especially, our Gram-Netis more robust to image editings, e.g. down-sampling, JPEG compression, blur, and noise. More importantly, our Gram-Net generalizes significantly better in detecting fake faces from GAN models not seen in the training phase and can perform decently in detecting fake natural images.
Gradient tracking and variance reduction for decentralized optimization and machine learning

Decentralized methods to solve finite-sum minimization problems are important in many signal processing and machine learning tasks where the data is distributed over a network of nodes and raw data sharing is not permitted due to privacy and/or resource constraints. In this article, we review decentralized stochastic first-order methods and provide a unified algorithmic framework that combines variance-reduction with gradient tracking to achieve both robust performance and fast convergence. We provide explicit theoretical guarantees of the corresponding methods when the objective functions are smooth and strongly-convex, and show their applicability to non-convex problems via numerical experiments. Throughout the article, we provide intuitive illustrations of the main technical ideas by casting appropriate tradeoffs and comparisons among the methods of interest and by highlighting applications to decentralized training of machine learning models.
A Transfer Learning Approach for Handwritten Numeral Digit Recognition

Handwritten numeral digit recognition is a classical problem in the field of computer vision, which has a wide range of applications in various fields including financial and post services. The accuracy of handwritten numeral digit recognition has been greatly improved by using deep learning in the past few years. However, deep learning relies on a large amount of training data and time-consuming calculation. In this paper, we adopt a transfer learning approach for handwritten numeral digit recognition and use both the multi-layer perceptron and convolutional neural network models to share the feature extraction process among five handwritten numerical datasets, namely, Tibetan, Arabic, Bangla, Devanagari, and Telugu. We compare the transfer learning scheme with the model based on a single dataset. We find that using the transfer learning method can significantly reduce the training time of the deep learning models, and slightly reduces the recognition accuracy.
DISIR: DEEP IMAGE SEGMENTATION WITH INTERACTIVE REFINEMENT

Abstract. This paper presents an interactive approach for multi-class segmentation of aerial images. Precisely, it is based on a deep neural network which exploits both RGB images and annotations. Starting from an initial output based on the image only, our network then interactively refines this segmentation map using a concatenation of the image and user annotations. Importantly, user annotations modify the inputs of the network – not its weights – enabling a fast and smooth process. Through experiments on two public aerial datasets, we show that user annotations are extremely rewarding: each click corrects roughly 5000 pixels. We analyze the impact of different aspects of our framework such as the representation of the annotations, the volume of training data or the network architecture. Code is available at this address.
Classification and Detection of Symbols in Ancient Papyri

The advent of powerful image processing and machine learning tools has generated opportunities for more automated and efficient paleography. In this work, methods were developed to automate aspects of the transcription process of ancient papyri. First, a technique is proposed that employs color thresholding and contouring for the automatic extraction of symbols from papyri photographs. Second, two symbol classifiers are considered: a support vector machine that intakes Histogram of Oriented Gradients features, and a convolutional neural network. Finally, a novel system is described based on the sliding window approach that limits the number of windows to be considered and uses a combination of a support vector machine and a convolutional neural network to benefit from both accurate and fast detections. The performance of these methods was evaluated on a set of papyri photographs from the Petrie Museum of Egyptian Archaeology.