Reviewerly/exHarmony huggingface dataset:
Geochemical background - what a complex meaning has such a simple expression!

The term background was introduced in geochemistry applied to the prospecting of mineral deposits to designate a value, below which the samples would represent normal contents and above which they would be deviations from normality and could represent anomalies related to mineral deposits. In the 1980’s, when the techniques applied in mineral exploration were absorbed by the nascent environmental sciences, the term background was also incorporated with a very similar meaning. However, since its first application, this term has been adopted arbitrarily, without considering the enormous diversity of underlying variables that can substantially alter its numerical meaning. In this article, the large number of components that make up this "complex system" known as geochemical background will be presented and discussed, and which cover virtually all concepts of geochemical exploration whether applied in geological-mining or environmental actions, such as: the type of sample collected, the sample preparation procedures, the fraction to be analyzed, the analytical technique used, the criterion for choosing the value, the sample density, the spatial variability and the variations over time.
LISA: A Case For Learned Index based Acceleration of Biological Sequence Analysis

ABSTRACT Next Generation Sequencing (NGS) is transforming fields like genomics, transcriptomics, and epigenetics with rapidly increasing throughput at reduced cost. This also demands overcoming performance bottlenecks in the downstream analysis of the sequencing data. A key performance bottleneck is searching for exact matches of entire or substrings of short DNA/RNA sequence queries in a long reference sequence database. This task is typically performed by using an index of the reference - such as FM-index, suffix arrays, suffix trees, hash tables, or lookup tables. In this paper, we propose accelerating this sequence search by substituting or enhancing the indexes with machine learning based indexes - called learned indexes - and present LISA (Learned Indexes for Sequence Analysis). We evaluate LISA through a number of case studies – that cover widely used software tools; short and long reads; human, animal, and plant genome datasets; DNA and RNA sequences; various traditional indexing techniques (FM-indexes, hash tables and suffix arrays) – and demonstrate significant performance benefits in a majority of them. For example, our experiments on real datasets show that LISA achieves speedups of up to 2.2 fold and 4.7 fold over the state-of-the-art FM-index based implementations for exact sequence search modules in popular tools bowtie2 and BWA-MEM2, respectively. Code availability LISA-based FM-index: https://github.com/IntelLabs/Trans-Omics-Acceleration-Library/tree/master/src/LISA-FMI LISA-based hash-table: https://github.com/IntelLabs/Trans-Omics-Acceleration-Library/tree/master/src/LISA-hash LISA applied to BWA-MEM2: https://github.com/bwa-mem2/bwa-mem2/tree/bwa-mem2-lisa .
TS-GAN with Policy Gradient for Text Summarization

Text summarization is a much evolving task, especially since neural networks were introduced. Similarly, generative adversarial networks (GANs) can be used to perform this task due to their ability to produce features or learn the whole sample distribution and produce correlated sample points. Thus, in paper, the authors exploited the characteristics of generative adversarial networks (GANs) for the abstractive text summarization task. The proposed generative adversarial model has three components: a generator which encodes the input sentences into much shorter representations; a discriminator which enforces generator to create understandable summaries; and a second discriminator which exerts upon generator to curb the output co-related to the input. The generator is optimized using policy gradient method, converting the problem into reinforcement learning. The ROUGE scores achieved by the model are as follows: R-1: 41.52, R-2: 16.20, R-L 37.21.
Attention Enhanced Single Stage Multimodal Reasoner

In this paper, we propose an Attention Enhanced Single Stage Multimodal Reasoner (ASSMR) to tackle the object referral task in the self-driving car scenario. We extract features from each modality and establish attention mechanisms to jointly process them. The Key Words Extractor (KWE) is used to extract the attribute and position/scale information of the target in the command, which are used to score the corresponding features through the Position/Scale Attention Module (P/SAM) and the Object Attention Module (OAM). Based on the attention mechanism, the effective part of the position/scale feature, the object attribute feature and the semantic feature of the command is enhanced. Finally, we map different features to a common embedding space to predict the final result. Our method is based on the simplified version of the Talk2Car dataset, and scored on 66.4 AP50 on the test set, while using the official region proposals.
Graph Neural Network Combined Knowledge Graph for Recommendation System

With a view to increase recommendation systems accuracy and practical applicability, using traditional methods which are namely interaction model between users and items, collaborative filtering and matrix factorization cannot achieve the supposed results. In fact, the properties between users or items always remains as social and knowledge relations. In this paper, we have proposed a new graph deep learning model associated with knowledge graph with the aim of modeling the latent feature of user and item. We exploit the relations of items based on knowledge graph as well as the relationships between users in social. Our model supplies the principle of organizing interactions as a graph, combines information from social network and all kind of relations in the heterogeneous knowledge graph. The model is evaluated on real world datasets to demonstrate this method’s effectiveness.
Machine Learning Based Text Summarization for Turkish News

In this paper, we propose an automatic text summarization model for Turkish news articles using machine learning models. Our proposed model uses sentence position, speech expression, presence of named entities and statements, term frequency and title similarity as features. We construct and share a new dataset for Turkish text summarization. In our experiments, we show that all our features we use have a positive impact on the performance of the system. In addition, we show that our model outperforms the latent semantic analysis based baseline method.
Deflated reputation using multiplicative long short-term memory neural networks

Current reputation systems are facing the inflation problem, which renders reputation systems to lose information and sometimes even cause misunderstandings. To address this problem, we propose a data-driven approach that combines natural language processing techniques with the conditional logit model for reputation deflation. We consider multiplicative long short-term memory neural networks (mLSTM) to predict sentiment scores from the feedback content. The mLSTM was pre-trained on 82.83 million unique reviews. We conduct experiments on one of the largest online labor marketplaces, Freelancer.com. We focus on comparing ratings and predicted sentiment scores in the online labor market. The results show that our proposed model can estimate deflated reputation information effectively. In addition, the estimated sentiment score is a quality disclosure signal, and has a better effect on the market outcome than the inflated reputation rating.
Dependency annotation of noun incorporation in polysynthetic languages

This paper describes an approach to annotating noun incorporation in Universal Dependencies. It motivates the need to annotate this particular morphosyntactic phenomenon and justifies it with respect to frequency of the construction. A case study is presented in which the proposed annotation scheme is applied to Chukchi, a language that exhibits noun incorporation. We compare argument encoding in Chukchi, English and Russian and find that while in English and Russian discourse elements are primarily tracked through noun phrases and pronouns, in Chukchi they are tracked through agreement marking and incorporation, with a lesser role for noun phrases.
Face Recognition Using Golden Ratio for Door Access Control System

The paper introduces a method-based correspondence scheme that integrates a permutation of Viola–Jones face detection method with characteristics of extracting golden ratio. The purpose of this article is to help users improve the security of sensitive places through facial recognition. We come to resolve the issue of low precision in the suggested technique. Here, we propose a new scheme for aligning the face using Viola–Jones and support vector machine face detection method followed by extraction technique of golden ratio function. The suggested technique is very effective, more realistic and accurate compared to other face detection techniques. The module contains a secure face identifier. The scheme is designed to satisfy the requirements of classification face to face in real scenario.
Persona 4.0

To compete for attention in today's online environment, webtexts must appeal not only to human audiences, but also to the search engines that rank them via a results page, driving traffic their way (or not). This article investigates the rhetorical dynamics of this fundamental dimension of contemporary online discourse, viewing search engines as a pivotal nexus between scholarly conversations regarding algorithmic rhetoric and attention management. Specifically, it positions the algorithm as an audience, one webtexts invoke via subtextual signals meant to elude human notice. It is animated by Charles E. Morris's formulation of the fourth persona, drawing on his framework to delineate a technologically oriented persona 4.0. In doing so, this paper broadens rhetoric's long-standing repertoire of constructs pertaining to persona to include algorithmic address. It then takes web searches for “public speaking,” and the top-ranking page by the motivational speaker Brian Tracy, as a site through which to explore the dynamics of persona 4.0, thereby also highlighting some of the disciplinary stakes.
Arabic dialect identification: An Arabic-BERT model with data augmentation and ensembling strategy

This paper presents the ArabicProcessors team’s deep learning system designed for the NADI 2020 Subtask 1 (country-level dialect identification) and Subtask 2 (province-level dialect identification). We used Arabic-Bert in combination with data augmentation and ensembling methods. Unlabeled data provided by task organizers (10 Million tweets) was split into multiple subparts, to which we applied semi-supervised learning method, and finally ran a specific ensembling process on the resulting models. This system ranked 3rd in Subtask 1 with 23.26% F1-score and 2nd in Subtask 2 with 5.75% F1-score.
The Enhanced Forensic Examination and Analysis for Mobile Cloud Platform by Applying Data Mining Methods

Investigating the mobile cloud environment is a challenging task due to the characteristics of voluminous data, dispersion of data, virtualization, and diverse data. Recent research works focus on applying the latest forensic methodologies to the mobile cloud investigation. This paper proposes an enhanced forensic examination and analysis model for the mobile cloud environment that incorporates timeline analysis, hash filtering, data carving, and data transformation sub-phases to improve the performance of the cloud evidence identification and overall forensic decision-making. It analyzes the timeline of events and filters the case-specific files based on the hash values and metadata using the data mining methods. The proposed forensic model performs the in-place carving on the filtered data to guide the investigation and integrates the heterogeneous file types and distributed pieces of evidence with the assistance of the data mining. Finally, the proposed approach employs LSTM based model that significantly improves the forensic decision making.
Importance Weighting of Diagnostic Trouble Codes for Anomaly Detection

Diagnostic Trouble Codes (DTCs) allow monitoring a wide range of fault conditions in heavy trucks. Ideally, a perfectly healthy vehicle should run without any active DTCs; in practice, vehicles often run with some active DTCs even though this does not pose a threat to their normal operation. When a DTC becomes active, it is therefore unclear whether it should be ignored or considered as a serious issue. Recent approaches in machine learning, such as training Variational Autoencoders (VAEs) for anomaly detection, do not help in this respect, for a number of reasons that we discuss based on actual experiments. In particular, a VAE tends to learn that a frequently active DTC is of no importance, when in fact it should not be dismissed completely; instead, such DTC should be assigned a relative weight that is smaller but still non-negligible when compared to other, more serious DTCs. In this work, we present an approach to measure the relative weights of active DTCs in a way that allows the analyst to prioritize them and focus on the most important ones. The approach is based on the concept of binary cross-entropy, and finds application not only in the analysis of DTCs from a single vehicle, but also in monitoring active DTCs across an entire fleet.
Control and Dynamics of a Distributed System with Variable Length

Dynamics problems of geometrically nonlinear distributed mechanical systems in space, air, and water environments with variable dimensions are among the least studied problems of mechanics. Specific features of mathematical models of cable tethering systems of telecontrolled unmanned submarines based on discrete and continuous models are considered. A number of specific features of their operation determines the expediency and sometimes the necessity of a purposeful change in cable length. In such cases, the cable is not only a component of the overall dynamic tethering system, but it acts as an independent controlled object. As a result of calculations, the earlier assumptions about the movement of the cable along its initial configuration of the towing line for the variable length of the cable were precised. It was found that this assumption is correct only for the initial transition section. It was also ascertained that at a given tachogram in the configuration of the towed line there is a point of inflection, which shifts from top to bottom when lifting it. This can be an additional factor promoting the cable system breakage and looping during towing.
Ads-Guard: Detecting Scammers in Online Classified Ads

Classified ad platforms have become a popular way for people to sell and purchase goods and services online. Unfortunately, while convenient, sites like Craigslist carry the risk of fraud because buyers do not meet sellers face to face. Many unsuspecting buyers fall victim to online scams and lose both time and money as a result. While there is literature available advising customers on how to avoid such scams, there is currently a lack of tools to automatically detect red flags of potential scams. One method that scammers use to trick buyers online is providing a contact phone number that belongs to a Voice over Internet Protocol (VoIP) service to hide their identities. Moreover, since scammers usually do not actually have the advertised product available to sell, they tend to use images copied from elsewhere on the Internet in their ads. Scammers also typically use certain common scam keywords to trick users. In this paper, we present a tool with intelligent components that automatically detects these indicators to determine the likelihood that a given classified ad is a scam. This tool aims to protect online shoppers by helping them make well-informed decisions about the legitimacy of classified ads.
Egyptian Universities Digital Certificate Verification Model Using Blockchain

The forgery of certificates is a long-term problem in the academic community of Egypt. It has become common to get a fake certificate, that claims an individual has graduated from a public/private university, and apply for jobs easily and be considered as qualified as someone who has a legit certificate. Blockchain, which is known for its reliability and trust, is used as a solution for that problem. The proposed system aims at creating digital certificates for Egyptian higher education as a solution for the forgery problem. This proposed system enables the tracking of a student's certificates; hence, the organizations and institutions can verify the obtained certificates. The proposed system is implemented using Linux Foundation's Hyperledger Fabric.
Conditional Disclosure of Secrets: Amplification, Closure, Amortization, Lower-bounds, and Separations

In the conditional disclosure of secrets (CDS) problem [Gertner et al., J. Comput. System Sci., 60 (2000), pp. 592--629] Alice and Bob, who hold inputs $x$ and $y$, respectively, wish to release a common secret $s$ to Carol (who knows both $x$ and $y$) if and only if the input $(x,y)$ satisfies some predefined predicate $f$. Alice and Bob are allowed to send a single message to Carol which may depend on their inputs and some joint randomness and the goal is to minimize the communication complexity while providing information-theoretic security. In this work, we initiate the study of CDS manipulation techniques and derive the following positive and negative results: (Closure) A CDS for $f$ can be turned into a CDS for its complement $\bar{f}$ with only a minor blow-up in complexity. More generally, for a (possibly nonmonotone) predicate $h$, we obtain a CDS for $h(f_1,\ldots,f_m)$ whose cost is essentially linear in the formula size of $h$ and polynomial in the CDS complexity of $f_i$. (Amplification) It is possible to reduce the privacy and correctness error of a CDS from constant to $2^{-k}$ with a multiplicative overhead of $O(k)$. Moreover, this overhead can be amortized over $k$-bit secrets. (Amortization) Every predicate $f$ over $n$-bit inputs admits a CDS for multibit secrets whose amortized communication complexity per secret bit grows linearly with the input length $n$ for sufficiently long secrets. In contrast, the best known upper-bound for single-bit secrets is exponential in $n$. (Lower-bounds) There exists a (nonexplicit) predicate $f$ over $n$-bit inputs for which any perfect (single-bit) CDS requires communication of at least $\Omega(n)$. This is an exponential improvement over the previously known $\Omega(\log n)$ lower-bound. (Separations) There exists an (explicit) predicate whose CDS complexity is exponentially smaller than its randomized communication complexity. This matches a lower-bound of Gay, Kerenidis, and Wee [Advances in Cryptology, Lecture Notes in Comput. Sci. 9216, Springer, New York, 2015, pp. 485--502] and, combined with another result of theirs, yields an exponential separation between the communication complexity of linear CDS and non-linear CDS. This is the first provable gap between the communication complexity of linear CDS (which captures most known protocols) and nonlinear CDS.
Improving the Gibbs sampler

Abstract The Gibbs sampler is a simple but very powerful algorithm used to simulate from a complex high‐dimensional distribution. It is particularly useful in Bayesian analysis when a complex Bayesian model involves a number of model parameters and the conditional posterior distribution of each component given the others can be derived as a standard distribution. In the presence of a strong correlation structure among components, however, the Gibbs sampler can be criticized for its slow convergence. Here we discuss several algorithmic strategies such as blocking, collapsing, and partial collapsing that are available for improving the convergence characteristics of the Gibbs sampler. This article is categorized under: Statistical and Graphical Methods of Data Analysis &gt; Markov Chain Monte Carlo Statistical and Graphical Methods of Data Analysis &gt; Bayesian Methods and Theory Statistical and Graphical Methods of Data Analysis &gt; Sampling
Unsupervised Approach for Zero-Shot Experiments: Bhojpuri–Hindi and Magahi–Hindi@LoResMT 2020

This paper reports a Machine Translation (MT) system submitted by the NLPRL team for the Bhojpuri–Hindi and Magahi–Hindi language pairs at LoResMT 2020 shared task. We used an unsupervised domain adaptation approach that gives promising results for zero or extremely low resource languages. Task organizers provide the development and the test sets for evaluation and the monolingual data for training. Our approach is a hybrid approach of domain adaptation and back-translation. Metrics used to evaluate the trained model are BLEU, RIBES, Precision, Recall and F-measure. Our approach gives relatively promising results, with a wide range, of 19.5, 13.71, 2.54, and 3.16 BLEU points for Bhojpuri to Hindi, Magahi to Hindi, Hindi to Bhojpuri and Hindi to Magahi language pairs, respectively.
Ensuring the security of youth in the online world: the potential of the digital art and steam education

The online world offers a huge number of opportunities and variety of information sources, which differ significantly from each other. To high extent the online world is the place, where youth spend most of their time. In such situation the creative and critical thinking are essential to help youth integrate and develop their individuality in the digital world. Furthermore, the interdisciplinary nature of digital art serves to develop creativity and critical thinking. In this way, they create new knowledge, which forms the necessary qualities for faster socialization in the virtual society. Thus, digital art merges with STEAM education, where science and art are integrated, namely Science, Technology, Engineering, Arts and Mathematics. The purpose of the current study is to identify the possible impact of STEAM education on the formation of the creative and critical thinking among young people. The authors use various methods such as analysis of the content, historical and comparative approach. The results show that the digital art and STEAM education can give the youth knowledge about how the technology is created and then express themselves through the art, which leads us to the conclusion that the indeed can support the creative and critical thinking.
Cross Domain Collaborative Filtering Recommender System for Academic Venue Personalization based on References

Finding the most appropriate venue to submit a scientific discovery plays a crucial rule in the acceptance results. However, an author is overwhelmed with the huge number of academic venues and often limits himself to known conferences where he already has publications. The use of recommender systems seems to be a good solution to filter out irrelevant items. Most recommender systems work on a single domain. Cross domain recommender systems allow to leverage ratings from multiple domains to improve recommendation results. In this paper, we propose a cross domain collaborative filtering based approach to suggest suitable academic venues. Our recommender system is able to deal with authors changing interests. It is also useful for young researchers who have not publications yet. We formalize the cross-domain problem for academic venues by investigating not only authors past publications, but also authors, from the reference list, past publications. Experimental results demonstrate the efficiency of our method.