Reviewerly/exHarmony huggingface dataset:
Land suitability analysis for solar farms exploitation using the GIS and Analytic Hierarchy Process (AHP) – a case study of Morocco

Given Morocco's geographical position and climatic conditions, solar energy will supply a large portion of the country's energy demand.In this paper, the suitability of Moroccan lands for hosting Solar Power Plants was studied using the combination of the Geographic Information System (GIS) and the Analytical Hierarchy Method (AHP).The multi-criteria decision framework integrates technical, socio-economic and environmental constraints.For this purpose, a GIS database was created using layers from various sources.In addition, since the potential of Global Horizontal Irradiation (GHI) is the most relevant criterion for the selection of solar farms, a high-quality solar satellite map with a spatial resolution of 0.27 km was used, covering a period from 1994 to 2018.Obtained results show a great potential for solar energy development in Morocco, represented by the availability of 90% of areas.In fact, the resulting map was classified into 6 different classes, namely: Very high suitability, High suitability, Moderate suitability, Low suitability, Very low suitability and Exclusion areas, which 53.88%, 24.08%, 0.15%, 0%, 0% and 21.89% are respectively the percentages of their area occupation.According to the performed investigations, the most significant criteria that should be considered include: The Global Horizontal Irradiation, Slope, Temperature and Slope orientation.The obtained map was then compared to the existing solar farms, and show that all the existing projects are located within areas classified as highly suitable.
A Deeper Look at the Hessian Eigenspectrum of Deep Neural Networks and its Applications to Regularization

Loss landscape analysis is extremely useful for a deeper understanding of the generalization ability of deep neural network models. In this work, we propose a layerwise loss landscape analysis where the loss surface at every layer is studied independently and also on how each correlates to the overall loss surface. We study the layerwise loss landscape by studying the eigenspectra of the Hessian at each layer. In particular, our results show that the layerwise Hessian geometry is largely similar to the entire Hessian. We also report an interesting phenomenon where the Hessian eigenspectrum of middle layers of the deep neural network are observed to most similar to the overall Hessian eigenspectrum. We also show that the maximum eigenvalue and the trace of the Hessian (both full network and layerwise) reduce as training of the network progresses. We leverage on these observations to propose a new regularizer based on the trace of the layerwise Hessian. Penalizing the trace of the Hessian at every layer indirectly forces Stochastic Gradient Descent to converge to flatter minima, which are shown to have better generalization performance. In particular, we show that such a layerwise regularizer can be leveraged to penalize the middlemost layers alone, which yields promising results. Our empirical studies on well-known deep nets across datasets support the claims of this work.
Measure and Evaluation of Semantic Divergence across Two Languages

Languages are dynamic systems: word usage may change over time, reflecting various societal factors.However, all languages do not evolve identically: the impact of an event, the influence of a trend or thinking, can differ between communities.In this paper, we propose to track these divergences by comparing the evolution of a word and its translation across two languages.We investigate several methods of building time-varying and bilingual word embeddings, using contextualised and non-contextualised embeddings.We propose a set of scenarios to characterize semantic divergence across two languages, along with a setup to differentiate them in a bilingual corpus.We evaluate the different methods by generating a corpus of synthetic semantic change across two languages, English and French, before applying them to newspaper corpora to detect bilingual semantic divergence and provide qualitative insight for the task.We conclude that BERT embeddings coupled with a clustering step lead to the best performance on synthetic corpora; however, the performance of CBOW embeddings is very competitive and more adapted to an exploratory analysis on a large corpus.
Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models

In this paper, we detail the relationship between convolutions and self-attention in natural language tasks.We show that relative position embeddings in self-attention layers are equivalent to recently-proposed dynamic lightweight convolutions, and we consider multiple new ways of integrating convolutions into Transformer self-attention.Specifically, we propose composite attention, which unites previous relative position embedding methods under a convolutional framework.We conduct experiments by training BERT with composite attention, finding that convolutions consistently improve performance on multiple downstream tasks, replacing absolute position embeddings.To inform future work, we present results comparing lightweight convolutions, dynamic convolutions, and depthwiseseparable convolutions in language model pretraining, considering multiple injection points for convolutions in self-attention layers.
MedNLI Is Not Immune: Natural Language Inference Artifacts in the Clinical Domain

Crowdworker-constructed natural language inference (NLI) datasets have been found to contain statistical artifacts associated with the annotation process that allow hypothesis-only classifiers to achieve better-than-random performance (Poliak et al., 2018;Gururangan et al., 2018;Tsuchiya, 2018).We investigate whether MedNLI, a physician-annotated dataset with premises extracted from clinical notes, contains such artifacts (Romanov and Shivade, 2018).We find that entailed hypotheses contain generic versions of specific concepts in the premise, as well as modifiers related to responsiveness, duration, and probability.Neutral hypotheses feature conditions and behaviors that co-occur with, or cause, the condition(s) in the premise.Contradiction hypotheses feature explicit negation of the premise and implicit negation via assertion of good health.Adversarial filtering demonstrates that performance degrades when evaluated on the difficult subset.We provide partition information and recommendations for alternative dataset construction strategies for knowledge-intensive domains.
Pre-training Universal Language Representation

Despite the well-developed cut-edge representation learning for language, most language representation models usually focus on specific levels of linguistic units.This work introduces universal language representation learning, i.e., embeddings of different levels of linguistic units or text with quite diverse lengths in a uniform vector space.We propose the training objective MiSAD that utilizes meaningful n-grams extracted from large unlabeled corpus by a simple but effective algorithm for pre-trained language models.Then we empirically verify that well designed pretraining scheme may effectively yield universal language representation, which will bring great convenience when handling multiple layers of linguistic objects in a unified way.Especially, our model achieves the highest accuracy on analogy tasks in different language levels and significantly improves the performance on downstream tasks in the GLUE benchmark and a question answering dataset.
Corporate management boards’ information security orientation: an analysis of cybersecurity incidents in DAX 30 companies

Our study analyzes the impact of cybersecurity incidents (CSIs) at executive level in German blue-chip companies between 2005 and 2018. Using Upper Echelons Theory, we examine the effect of CSIs on both the composition of management boards as well as their members’ profiles and related responsibilities, performing a qualitative in-depth analysis of the positive cases found. Our results show that while CSIs are a common problem for large German groups, only a few of them have addressed IT-related incidents directly through governance. Firms that reacted strongly went against the general trend, and either added a new functional unit to the management board or strengthened functions related to the issue. Our findings indicate that German blue-chip companies have not yet devised a common strategy to deal with CSIs. Firms that reshape their management boards tend instead to take a more compliance-oriented approach.
PlotCoder: Hierarchical Decoding for Synthesizing Visualization Code in Programmatic Context

Creating effective visualization is an important part of data analytics.While there are many libraries for creating visualizations, writing such code remains difficult given the myriad of parameters that users need to provide.In this paper, we propose the new task of synthesizing visualization programs from a combination of natural language utterances and code context.To tackle the learning problem, we introduce PLOTCODER, a new hierarchical encoder-decoder architecture that models both the code context and the input utterance.We use PLOTCODER to first determine the template of the visualization code, followed by predicting the data to be plotted.We use Jupyter notebooks containing visualization programs crawled from GitHub to train PLOTCODER.On a comprehensive set of test samples from those notebooks, we show that PLOTCODER correctly predicts the plot type of about 70% samples, and synthesizes the correct programs for 35% samples, performing 3-4.5% better than the baselines. 1
Information and communication technology in agribusiness: A study of mobile applications in perspective of India

Federation of Indian Chambers of Commerce and Industry in one of its finding in 2019 stated that about 58% Indians are dependent on agriculture and agriculture sector make about 15.96 % of India’s GDP. To get the best agriculture inputs and best harvest price is the big question for Indian farmers; thus, we can say that “Agriculture is the foundation of the Indian economy”. With the origin of Mobile Applications (m-apps) for agriculture and a huge dependency on Information and Communication Technology (ICT) in agribusiness, the scenario in rural India has been changing rapidly. Since India’s economy depends mainly on agriculture, there is a lot of potential for Information and Communication Technology and mobile applications for agribusiness and its marketing. With growing smartphones with m-apps penetration in rural India, the agribusiness in rural belts of India is set for extension and further digitalization to revolutionize the agriculture sector. In recent years, nearly all Indian farmers possess a mobile, and 50%are smartphones with internet connections. With Government's new legislative policy changes as the Digital India programme, mobile applications in India's rural belt cannot remain isolated. Digital India will connect rural Indians farmers worldwide through the internet and mobile applications and provide them with all necessary upliftment in agribusiness in India. This study has focused on the ICT and m-applications used in farming today and how they have changed agribusiness by providing a digital platform and with their impact on agribusiness.
Dialogue in the Wild: Learning from a Deployed Role-Playing Game with Humans and Bots

Much of NLP research has focused on crowdsourced static datasets and the supervised learning paradigm of training once and then evaluating test performance.As argued in de Vries et al. (2020), crowdsourced data has the issues of lack of naturalness and relevance to real-world use cases, while the static dataset paradigm does not allow for a model to learn from its experiences of using language (Silver et al., 2013).In contrast, one might hope for machine learning systems that become more useful as they interact with people.In this work, we build and deploy a role-playing game, whereby human players converse with learning agents situated in an open-domain fantasy world.We show that by training models on the conversations they have with humans in the game the models progressively improve, as measured by automatic metrics and online engagement scores.This learning is shown to be more efficient than crowdsourced data when applied to conversations with real users, as well as being far cheaper to collect.
Learning Hybrid Relationships for Person Re-identification

Recently, the relationship among individual pedestrian images and the relationship among pairwise pedestrian images have become attractive for person re-identification (re-ID) as they effectively improve the ability of feature representation. In this paper, we propose a novel method named Hybrid Relationship Network (HRNet) to learn the two types of relationships in a unified framework that makes use of their own advantages. Specifically, for the relationship among individual pedestrian images, we take the features of pedestrian images as the nodes to construct a locally-connected graph, so as to improve the discriminative ability of nodes. Meanwhile, we propose the consistent node constraint to inject the identity information into the graph learning process and guide the information to propagate accurately. As for the relationship among pairwise pedestrian images, we treat the feature differences of pedestrian images as the nodes to construct a fully-connected graph so as to estimate robust similarity of nodes. Furthermore, we propose the inter-graph propagation to alleviate the information loss for the fully-connected graph. Extensive experiments on Market-1501, DukeMTMCreID, CUHK03 and MSMT17 demonstrate that the proposed HRNet outperforms the state-of-the-art methods.
Social Engineering Attack Detection Using Machine Learning: Text Phishing Attack

The expansion of the internet leads to an increase in the number of cyber-attacks over the days.One of the most common cybersecurity attacks is social engineering, which depends on human physiology.The phishing attack is the most popular form of social engineering.The phishing attacks have many forms, but the traditional one from them is the messages.We need techniques to protect us from these attacks.Awareness, usage policies, and other procedures are not enough.Therefore, we proposed to use natural language processing (NLP) along with machine learning techniques for text phishing detection in this paper.We started with 6,224 emails from an existing dataset that contains both phishing and legitimate emails.NLP was used for preparing the data before extracting features from it and using the features for training the classification models by machine learning algorithm and for testing these models.The features were extracted using the Continuous Bag of Words (CBOW) in the Word2Vec algorithm.We are training four models using four different machine learning algorithms which are knearest neighbors (KNN), Multinomial Naive Bayes (MNB), Decision Tree and AdaBoost.The developed models had to classify the text messages into two categories, which are phishing and legitimate.While the dataset is unbalanced, we used performance measurements for unbalanced data in the evaluation process.Three of our models, which were trained by KNN, Decision Tree and AdaBoost algorithms, obtained considerable values while the MNB model obtained an insignificant value.
AREEBA: An Area Efficient Binary Huff-Curve Architecture

Elliptic curve cryptography is the most widely employed class of asymmetric cryptography algorithm. However, it is exposed to simple power analysis attacks due to the lack of unifiedness over point doubling and addition operations. The unified crypto systems such as Binary Edward, Hessian and Huff curves provide resistance against power analysis attacks. Furthermore, Huff curves are more secure than Edward and Hessian curves but require more computational resources. Therefore, this article has provided a low area hardware architecture for point multiplication computation of Binary Huff curves over GF(2163) and GF(2233). To achieve this, a segmented least significant digit multiplier for polynomial multiplications is proposed. In order to provide a realistic and reasonable comparison with state of the art solutions, the proposed architecture is modeled in Verilog and synthesized for different field programmable gate arrays. For Virtex-4, Virtex-5, Virtex-6, and Virtex-7 devices, the utilized hardware resources in terms of hardware slices over GF(2163) are 5302, 2412, 2982 and 3508, respectively. The corresponding achieved values over GF(2233) are 11,557, 10,065, 4370 and 4261, respectively. The reported low area values provide the acceptability of this work in area-constrained applications.
Residual visualization-guided explainable copy-relationship learning for image copy detection in social networks

In modern times, people are used to sharing individual works, giving opinions, and expressing emotions and sentiments by transmitting a variety of images such as personal photographs, pictures of purchased products, and emoticons on social networks. However, this results in a lot of illegal image copies being distributed, which causes the issues of misleading the public and copyright infringement. To identify such images, recent copy detection approaches learn similarity functions by directly exploring neural networks such as Siamese and pseudo-Siamese networks and thereby determine the copy relationships between images. Unfortunately, since such approaches directly use these networks as black boxes, without considering the essential differences between image copies and similar images, handling the prevalent need for copy detection, i.e., identifying image copies from similar images, remains a challenge. To address the above issue, first, a residual visualization scheme is proposed in this study to visualize the residual image by subtracting the original image from the geometrically aligned test image, thereby revealing the relationship between the two images. Then, guided by the observations on the residual visualization results, a newly designed explainable copy-relationship learning network (ECRLN) architecture is presented. In this network architecture, the characteristics of residual images, ranging from local details to global appearances and from low-level visual patterns to high-level semantics, are sufficiently utilized to learn a measurement function for computing the copy-relationship confidence between images. The extensive experiments conducted on the publicly available datasets as well as our own challenging dataset demonstrate that the proposed approach can accurately identify image copies from similar images with good interpretability and achieve state-of-the-art performance in detection accuracy and training efficiency.
Seatbelt detection in road surveillance images based on improved dense residual network with two-level attention mechanism

Seatbelt detection is an important topic in intelligent transport systems. The accuracy of seatbelt detection in traffic video surveillance is affected by many factors, such as complex road environments, lighting, weather, direction of the camera, etc., which bring difficulties for traditional image processing methods. We propose a seatbelt detection method based on classification using convolutional neural networks. Dense residual blocks are used to avoid gradient dispersion and information loss during the network training. A two-level attention mechanism is also introduced to further improve the network performance by simultaneously modeling the correlation among different feature channels using channel attention mechanism, and correlation among different pixels within the feature map using pixel attention mechanism. To make the whole system complete, we added the driver area localization module, which is accomplished using the YOLOV3 model. Small images concentrated on the driver location, which are obtained through the localization operation from larger images, are passed into the classification network for seatbelt detection. We compared the proposed method with several other methods. Comparative results show that the proposed method has higher accuracy and is more robust for seatbelt detection for complex environments.
Random neural networks in the infinite width limit as Gaussian processes

This article gives a new proof that fully connected neural networks with random weights and biases converge to Gaussian processes in the regime where the input dimension, output dimension, and depth are kept fixed, while the hidden layer widths tend to infinity. Unlike prior work, convergence is shown assuming only moment conditions for the distribution of weights and for quite general nonlinearities.
Systematic Mapping Study on the Machine Learning Lifecycle

The development of artificial intelligence (AI) has made various industries eager to explore the benefits of AI.There is an increasing amount of research surrounding AI, most of which is centred on the development of new AI algorithms and techniques.However, the advent of AI is bringing an increasing set of practical problems related to AI model lifecycle management that need to be investigated.We address this gap by conducting a systematic mapping study on the lifecycle of AI model.Through quantitative research, we provide an overview of the field, identify research opportunities, and provide suggestions for future research.Our study yields 405 publications published from 2005 to 2020, mapped in 5 different main research topics, and 31 sub-topics.We observe that only a minority of publications focus on data management and model production problems, and that more studies should address the AI lifecycle from a holistic perspective.
Speeding-Up Action Learning in a Social Robot With Dyna-Q+: A Bioinspired Probabilistic Model Approach

Robotic systems that are developed for social and dynamic environments require adaptive mechanisms to successfully operate. Consequently, learning from rewards has provided meaningful results in applications involving human-robot interaction. In those cases where the robot's state space and the number of actions is extensive, dimensionality becomes intractable and this drastically slows down the learning process. This effect is specially notorious in one-step temporal difference methods because just one update is performed per robot-environment interaction. In this paper, we prove how the action-based learning of a social robot can be improved by combining classical temporal difference reinforcement learning methods, such as Q-learning or Q( λ), with a probabilistic model of the environment. This architecture, which we have called Dyna, allows the robot to simultaneously act and plan using the experience obtained during real human-robot interactions. Principally, Dyna improves classical algorithms in terms of convergence speed and stability, which strengthens the learning process. Hence, in this work we have embedded a Dyna architecture in our social robot, Mini, to endow it with the ability to autonomously maintain an optimal internal state while living in a dynamic environment.
Secure Storage and Sharing of Visitor Images Generated by Smart Entrance on Public Cloud

Visitor validation at entrance generates a large number of image files that need to be transmitted over to cloud for future reference. The image data needs to be protected by active and passive adversaries from performing cryptographic attacks on these data. The image data also needs to be authenticated before giving it for future use. Focusing on reliable and secure image sharing, the proposed method involves building a novel cloud platform, which aims to provide a secure storage in the public cloud. The main objective of this paper is to provide a new way of secure image data storage and transmission on cloud using cryptographic algorithms. To overcome the flaws in current system, a novel method using BigchainDB, which has advantages of blockchain technology and traditional database, is proposed for storing attributes of image.