Reviewerly/exHarmony huggingface dataset:
A Facial Expression Recognition Algorithm based on CNN and LBP Feature

In recent years, facial expression recognition technology has been widely used in computer vision, security monitoring and image classification. However, in practical application, it is difficult to solve the rotation problem of facial expression image, which leads to the decrease of expression recognition rate and is difficult to meet the actual demand. Although the convolutional neural network (CNN) can extract the high-dimensional features of the image and has the invariance of gray scale, it does not have the invariance of rotation. Local binary model (LBP) is a feature extraction algorithm with rotation invariance, which can solve the rotation problem to some extent. To solve the above problems, this paper proposes a face expression recognition algorithm based on CNN and LBP, and compares this algorithm with other algorithms. The simulation results show that this algorithm can improve the expression recognition rate under rotation to some extent.
Design of secure cryptosystem based on chaotic components and AES S-Box

In this paper, we design, realize and evaluate a new secure cryptosystem based on a Pseudo-Chaotic Number Generator(PCNG), a global diffusion and a block cipher in Cipher Block Chaining(CBC) mode. The proposed PCNG, defined over a finite field, is based on a discrete Chebyshev map coupled with a Pseudo-Random Number Generator(PRNG) and a discrete skew tent map. Our PCNG can remove a hidden danger of deteriorated security caused by the dynamical degradation encountered in numerical implementation of chaotic maps defined on real numbers. The global diffusion uses a Horizontal Addition Diffusion(HAD) and a Vertical Addition Diffusion(VAD) followed by a modified 2-D cat map. The block cipher in CBC mode is based on a confusion layer using the AES S-Box, followed by a diffusion layer built on the modified 2-D cat map and a key addition operation. The global diffusion enhances the diffusion process of the block cipher. Conducted security analysis and the obtained results have proven that the proposed cryptosystem design is robust with high security and that it can be applied to practical implementations.
Deep-Aligned Convolutional Neural Network for Skeleton-Based Action Recognition and Segmentation

Abstract Convolutional neural networks (CNNs) are deep learning frameworks which are well known for their notable performance in classification tasks. Hence, many skeleton-based action recognition and segmentation (SBARS) algorithms benefit from them in their designs. However, a shortcoming of such applications is the general lack of spatial relationships between the input features in such data types. Besides, non-uniform temporal scalings are a common issue in skeleton-based data streams which leads to having different input sizes even within one specific action category. In this work, we propose a novel deep-aligned convolutional neural network (DACNN) to tackle the above challenges for the particular problem of SBARS. Our network is designed by introducing a new type of filters in the context of CNNs which are trained based on their alignments to the local subsequences in the inputs. These filters result in efficient predictions as well as learning interpretable patterns in the data. Also, our DACNN framework can incrementally expand its deep structure based on the learning progress, which makes it flexible regarding different SBARS datasets. We empirically evaluate our framework on real-world benchmarks showing that the proposed DACNN algorithm obtains a competitive performance compared to the state of the art while benefiting from a less complicated yet more interpretable model.
Arabic Sentiment Analysis (ASA) Using Deep Learning Approach

Sentiment analysis is one of the major fields in natural language processing whose main task is to extract sentiments, opinions, attitudes, and emotions from a subjective text. And for its importance in decision making and in people's trust with reviews on web sites, there are many academic researches to address sentiment analysis problems. Deep Learning (DL) is a powerful Machine Learning (ML) technique that has emerged with its ability of feature representation and differentiating data, leading to state-of-the-art prediction results. In recent years, DL has been widely used in sentiment analysis, however, there is scarce in its implementation in the Arabic language field. Most of the previous researches address other languages like English. The proposed model tackles Arabic Sentiment Analysis (ASA) by using a DL approach. ASA is a challenging field where Arabic language has a rich morphological structure more than other languages. In this work, Long Short-Term Memory (LSTM) as a deep neural network has been used for training the model combined with word embedding as a first hidden layer for features extracting. The results show an accuracy of about 82% is achievable using DL method.
From Firm Solar Power Forecasts to Firm Solar Power Generation an Effective Path to Ultra-High Renewable Penetration a New York Case Study

We introduce firm solar forecasts as a strategy to operate optimally overbuilt solar power plants in conjunction with optimally sized storage systems so as to make up for any power prediction errors, and hence entirely remove load balancing uncertainty emanating from grid-connected solar fleets. A central part of this strategy is the plant overbuilding that we term implicit storage. We show that strategy, while economically justifiable on its own account, is an effective entry step to achieving least-cost ultra-high solar penetration where firm power generation will be a prerequisite. We demonstrate that in the absence of an implicit storage strategy, ultra-high solar penetration would be vastly more expensive. Using the New York Independent System Operator (NYISO) as a case study, we determine current and future costs of firm forecasts for a comprehensive set of scenarios in each ISO electrical region, comparing centralized vs. decentralized production and assessing load flexibility’s impact. We simulate the growth of the strategy from firm forecast to firm power generation. We conclude that ultra-high solar penetration enabled by the present strategy, whereby solar would firmly supply the entire NYISO load, could be achieved locally at electricity production costs comparable to current NYISO wholesale market prices.
Co-purchaser Recommendation for Online Group Buying

Abstract Online group buying is a burgeoning business model of Internet shopping, in which people with the same merchandise interests form a group and co-purchase goods with favorable prices. The buyer who launches the co-purchase is called the initiator, and other buyers are called the co-purchasers. Although recommending co-purchasers for a target buyer (co-purchase initiator) on the group buying is an interesting problem, existing studies have paid few attention to this topic. Different from the collaborator recommendation that only considers users with high similarity to the target user, co-purchaser recommendation takes both users with high and weak similarity into account, and the recommendation results can achieve high recall and diversity. However, the task turns out to be a challenging problem since it is hard to make a precise recommendation for buyers with weak similarity. To address the problem, we propose the following two methods. In the first one, we directly impose a penalty to the weak similar co-purchasers in the embedding space. To further improve the recommendation performance, in the second one, we smoothly increase the co-occurrence probability of the weak similar co-purchasers by truncated bias walk. Our experimental results on real datasets show that the proposed methods, particularly the latter, can effectively complete the co-purchaser recommendation and has high recommendation performance. In addition, considering that co-purchase may last longer, the total recommendation result can be generated in multiple stages and adjust the current recommendation list based on the feedback from the recommendation of previous stages. It is a trick for all co-purchaser recommendation methods to make the total result better.
An efficient apriori algorithm for frequent pattern mining using mapreduce in healthcare data

The development for data mining technology in healthcare is growing today as knowledge and data mining are a must for the medical sector. Healthcare organizations generate and gather large quantities of daily information. Use of IT allows for the automation of data mining and information that help to provide some interesting patterns which remove manual tasks and simple data extraction from electronic records, a process of electronic data transfer which secures medical records, saves lives and cuts the cost of medical care and enables early detection of infectious diseases. In this research paper an improved Apriori algorithm names Enhanced Parallel and Distributed Apriori (EPDA) is presented for the health care industry, based on the scalable environment known as Hadoop MapReduce. The main aim of the work proposed is to reduce the huge demands for resources and to reduce overhead communication when frequent data are extracted, through split-frequent data generated locally and the early removal of unusual data. The paper shows test results, whereby the EPDA performs in terms of the time and number of rules generated with a database of healthcare and different minimum support values.
Overview of OSACT4 Arabic Offensive Language Detection Shared Task

This paper provides an overview of the offensive language detection shared task at the 4th workshop on Open-Source Arabic Corpora and Processing Tools (OSACT4). There were two subtasks, namely: Subtask A, involving the detection of offensive language, which contains unacceptable or vulgar content in addition to any kind of explicit or implicit insults or attacks against individuals or groups; and Subtask B, involving the detection of hate speech, which contains insults or threats targeting a group based on their nationality, ethnicity, race, gender, political or sport affiliation, religious belief, or other common characteristics. In total, 40 teams signed up to participate in Subtask A, and 14 of them submitted test runs. For Subtask B, 33 teams signed up to participate and 13 of them submitted runs. We present and analyze all submissions in this paper.
Code smell detection and identification in imbalanced environments

Code smells are sub-optimal design choices that could lower software maintainability. Previous literature did not consider an important characteristic of the smell detection problem, namely data imbalance. When considering a high number of code smell types, the number of smelly classes is likely to largely exceed the number of non-smelly ones, and vice versa. Moreover, most studies did address the smell identification problem, which is more likely to present a higher imbalance as the number of smelly classes is relatively much less than the number of non-smelly ones. Furthermore, an additional research gap in the literature consists in the fact that the number of smell type identification methods is very small compared to the detection ones. The main challenges in smell detection and identification in an imbalanced environment are: (1) the structuring of the smell detector that should be able to deal with complex splitting boundaries and small disjuncts, (2) the design of the detector quality evaluation function that should take into account data imbalance, and (3) the efficient search for effective software metrics’ thresholds that should well characterize the different smells. Furthermore, the number of smell type identification methods is very small compared to the detection ones. We propose ADIODE, an effective search-based engine that is able to deal with all the above-described challenges not only for the smell detection case but also for the identification one. Indeed, ADIODE is an EA (Evolutionary Algorithm) that evolves a population of detectors encoded as ODTs (Oblique Decision Trees) using the F-measure as a fitness function. This allows ADIODE to efficiently approximate globally-optimal detectors with effective oblique splitting hyper-planes and metrics’ thresholds. We note that to build the BE, each software class is parsed using a particular tool with the aim to extract its metrics’ values, based on which the considered class is labeled by means of a set of existing advisors; which could be seen as a two-step construction process. A comparative experimental study on six open-source software systems demonstrates the merits and the outperformance of our approach compared to four of the most representative and prominent baseline techniques available in literature. The detection results show that the F-measure of ADIODE ranges between 91.23 % and 95.24 %, and its AUC lies between 0.9273 and 0.9573. Similarly, the identification results indicate that the F-measure of ADIODE varies between 86.26 % and 94.5 %, and its AUC is between 0.8653 and 0.9531.
CryptoPIM: In-memory Acceleration for Lattice-based Cryptographic Hardware

Quantum computers promise to solve hard mathematical problems such as integer factorization and discrete logarithms in polynomial time, making standardized public-key cryptosystems insecure. Lattice-Based Cryptography (LBC) is a promising post-quantum public key cryptographic protocol that could replace standardized public key cryptography, thanks to the inherent post-quantum resistant properties, efficiency, and versatility. A key mathematical tool in LBC is the Number Theoretic Transform (NTT), a common method to compute polynomial multiplication. It is the most compute-intensive routine and requires acceleration for practical deployment of LBC protocols. In this paper, we propose CryptoPIM, a high-throughput Processing In-Memory (PIM) accelerator for NTT-based polynomial multiplier with the support of polynomials with degrees up to 32k. Compared to the fastest FPGA implementation of an NTT-based multiplier, CryptoPIM achieves on average 31x throughput improvement with the same energy and only 28% performance reduction, thereby showing promise for practical deployment of LBC.
AI-Based Yoga Pose Estimation for Android Application

The importance of yoga is renowned worldwide and its health benefits, which were preached by ancient sages, have stood the test of time. Even though yoga is becoming preeminent, there are important challenges faced while doing yoga such as performing it with incorrect form, the classes being expensive and the shortage of time in our busy lives. Computer vision techniques exhibit promising solutions for human pose estimation. However, these techniques are seldom applied in the domain of health or exercise, with no literature or projects cited specifically for yoga. This paper surveys the various technologies that can be used for pose estimation and concludes the best method based on the usability for an android application. The paper then discusses the methodology that will be used to deploy the yoga pose estimation on an android application, how the app is modeled and the working of each component is explained.
Artificial Intelligence Improves the Accuracy in Histologic Classification of Breast Lesions

Abstract Objectives This study evaluated the usefulness of artificial intelligence (AI) algorithms as tools in improving the accuracy of histologic classification of breast tissue. Methods Overall, 100 microscopic photographs (test A) and 152 regions of interest in whole-slide images (test B) of breast tissue were classified into 4 classes: normal, benign, carcinoma in situ (CIS), and invasive carcinoma. The accuracy of 4 pathologists and 3 pathology residents were evaluated without and with the assistance of algorithms. Results In test A, algorithm A had accuracy of 0.87, with the lowest accuracy in the benign class (0.72). The observers had average accuracy of 0.80, and most clinically relevant discordances occurred in distinguishing benign from CIS (7.1% of classifications). With the assistance of algorithm A, the observers significantly increased their average accuracy to 0.88. In test B, algorithm B had accuracy of 0.49, with the lowest accuracy in the CIS class (0.06). The observers had average accuracy of 0.86, and most clinically relevant discordances occurred in distinguishing benign from CIS (6.3% of classifications). With the assistance of algorithm B, the observers maintained their average accuracy. Conclusions AI tools can increase the classification accuracy of pathologists in the setting of breast lesions.
A Preliminary Exploration of GANs for Keyphrase Generation

We introduce a new keyphrase generation approach using Generative Adversarial Networks (GANs). For a given document, the generator produces a sequence of keyphrases, and the discriminator distinguishes between human-curated and machine-generated keyphrases. We evaluated this approach on standard benchmark datasets. We observed that our model achieves state-of-the-art performance in the generation of abstractive keyphrases and is comparable to the best performing extractive techniques. Although we achieve promising results using GANs, they are not significantly better than the state-of-the-art generative models. To our knowledge, this is one of the first works that use GANs for keyphrase generation. We present a detailed analysis of our observations and expect that these findings would help other researchers to further study the use of GANs for the task of keyphrase generation.
An Attack Simulation Language for the IT Domain

Cyber-attacks on IT infrastructures can have disastrous consequences for individuals, regions, as well as whole nations. In order to respond to these threats, the cyber security assessment of IT infrastructures can foster a higher degree of security and resilience against cyber-attacks. Therefore, the use of attack simulations based on system architecture models is proposed. To reduce the effort of creating new attack graphs for each system under assessment, domain-specific languages (DSLs) can be employed. DSLs codify the common attack logics of the considered domain. Previously, MAL (the Meta Attack Language) was proposed, which serves as a framework to develop DSLs and generate attack graphs for modeled infrastructures. In this article, we propose coreLang as a MAL-based DSL for modeling IT infrastructures and analyzing weaknesses related to known attacks. To model domain-specific attributes, we studied existing cyber-attacks to develop a comprehensive language, which was iteratively verified through a series of brainstorming sessions with domain modelers. Finally, this first version of the language was validated against known cyber-attack scenarios.
Learning to Represent Image and Text with Denotation Graph

Learning to fuse vision and language information and representing them is an important research problem with many applications. Recent progresses have leveraged the ideas of pre-training (from language modeling) and attention layers in Transformers to learn representation from datasets containing images aligned with linguistic expressions that describe the images. In this paper, we propose learning representations from a set of implied, visually grounded expressions between image and text, automatically mined from those datasets. In particular, we use denotation graphs to represent how specific concepts (such as sentences describing images) can be linked to abstract and generic concepts (such as short phrases) that are also visually grounded. This type of generic-to-specific relations can be discovered using linguistic analysis tools. We propose methods to incorporate such relations into learning representation. We show that state-of-the-art multimodal learning models can be further improved by leveraging automatically harvested structural relations. The representations lead to stronger empirical results on downstream tasks of cross-modal image retrieval, referring expression, and compositional attribute-object recognition. Both our codes and the extracted denotation graphs on the Flickr30K and the COCO datasets are publically available on https://sha-lab.github.io/DG.
ShExML: improving the usability of heterogeneous data mapping languages for first-time users

Integration of heterogeneous data sources in a single representation is an active field with many different tools and techniques. In the case of text-based approaches—those that base the definition of the mappings and the integration on a DSL—there is a lack of usability studies. In this work we have conducted a usability experiment ( n = 17) on three different languages: ShExML (our own language), YARRRML and SPARQL-Generate. Results show that ShExML users tend to perform better than those of YARRRML and SPARQL-Generate. This study sheds light on usability aspects of these languages design and remarks some aspects of improvement.
Validation of Bifacial Photovoltaic Simulation Software against Monitoring Data from Large-Scale Single-Axis Trackers and Fixed Tilt Systems in Denmark

The size and number of utility-scale bifacial photovoltaic (PV) installations has proliferated in recent years but concerns over modeling accuracy remain. The aim of this work is to provide the PV community with a validation study of eight tools used to simulate bifacial PV performance. We simulate real 26 kilowatt-peak (kWp) bifacial arrays within a 420-kWp site located in northern Europe (55.6° N, 12.1° E). The substructures investigated include horizontal single-axis trackers (HSATs) and fixed tilt racks that have dimensions analogous to those found in utility-scale PV installations. Each bifacial system has a monofacial reference system with similar front side power. We use on-site solar radiation (global, diffuse, and beam) and albedo measurements from spectrally flat class A sensors as inputs to the simulation tools, and compare the modeled values to field measurements of string level power, rear and front plane of array irradiance, and module temperature. Our results show that state-of-the-art bifacial performance models add ~0.5% uncertainty to the PV modeling chain. For the site investigated, 2-D view factor fixed tilt simulations are within ±1% of the measured monthly bifacial gain. However, simulations of single-axis tracker systems are less accurate, wherein 2-D view factor and 3-D ray tracing are within approximately 2% and 1% of the measured bifacial gain, respectively.