Reviewerly/exHarmony huggingface dataset:
A Case Study on the Relevance of the Journal Impact Factor

Highlights Publication profiles of seven highly published authors were analyzed from Google Scholar profiles. Journal impact factor (JIF) not strongly correlated to citations in the first two years after publication. Significant correlation between the number of citations in the first two years and total citations to an article. More important to submit manuscripts to journals with appropriate readership than the highest JIF. Abstract. In today’s world of research publishing, authors are typically encouraged to submit manuscripts to journals with the highest possible journal impact factor (JIF). This approach inherently assumes that the higher the JIF, the more likely the article will be cited. However, calls to move away from the JIF are becoming more common. This study analyzes the publication profiles of seven authors who are members of the Natural Resources and Environmental Systems (NRES) community of the American Society of Agricultural and Biological Engineers (ASABE) and who frequently publish in ASABE journals. This study hypothesized that statistically significant correlations existed between the JIF and the (1) total number of citations, (2) number of citations per year, and/or (3) number of citations in the first two years after publication. Based on 999 articles published by these seven authors from 1982 to 2018, statistically significant but fairly low to moderate correlations were observed between the JIF and the total citations, citations per year, and citations in the first two years after publication. The greatest correlation was observed between the number of citations that an article received in the first two years after publication and the number of citations per year (or the total number of citations). Therefore, ensuring an appropriate readership to generate short-term citations was more important than the JIF. When compared to high-JIF journals, there were statistically fewer citations per year and fewer citations in the first two years after publication for articles published in ASABE journals. While ASABE journal articles possess high citation longevity (i.e., cited half-life), efforts to immediately improve short-term metrics should focus on attracting high-quality research and improving article visibility. To extrapolate these findings to a wider community, future research should investigate these correlations for researchers in other ASABE technical communities and at various career stages. Keywords: Citations, Journal citation factor, Journal impact factor, Peer review, Research impact.
SemEval-2020 Task 5: Detecting Counterfactuals by Disambiguation.

In this paper, we explore strategies to detect and evaluate counterfactual sentences. Since causal insight is an inherent characteristic of a counterfactual, is it possible to use this information in order to locate antecedent and consequent fragments in counterfactual statements? We thus propose to compare and evaluate models to correctly identify and chunk counterfactual sentences. In our experiments, we attempt to answer the following questions: First, can a learned model discern counterfactual statements reasonably well? Second, is it possible to clearly identify antecedent and consequent parts of counterfactual sentences?
[Establishment of a deep feature-based classification model for distinguishing benign and malignant breast tumors on full-filed digital mammography].

To develop a deep features-based model to classify benign and malignant breast lesions on full- filed digital mammography.The data of full-filed digital mammography in both craniocaudal view and mediolateral oblique view from 106 patients with breast neoplasms were analyzed. Twenty-three handcrafted features (HCF) were extracted from the images of the breast tumors and a suitable feature set of HCF was selected using t-test. The deep features (DF) were extracted from the 3 pre-trained deep learning models, namely AlexNet, VGG16 and GoogLeNet. With abundant breast tumor information from the craniocaudal view and mediolateral oblique view, we combined the two extracted features (DF and HCF) as the two-view features. A multi-classifier model was finally constructed based on the combined HCF and DF sets. The classification ability of different deep learning networks was evaluated.Quantitative evaluation results showed that the proposed HCF+DF model outperformed HCF model, and AlexNet produced the best performances among the 3 deep learning models.The proposed model that combines DF and HCF sets of breast tumors can effectively distinguish benign and malignant breast lesions on full-filed digital mammography.
The Impact of COVID-19 on Bitcoin Trading Activity: A Preliminary Assessment

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Modelling Virtual Machine Workload in Heterogeneous Cloud Computing Platforms

Cloud computing, as a trend technology, has stemmed from the concept of virtualization. Virtualization makes the resources available to the public to use without any concern for ownership or maintenance cost. In addition, the hosted applications in cloud computing platforms are highly interactive and require intensive resources. The new trend is to duplicate these applications in multiple virtual machines based on demand. Such a scheme needs an efficient resource provisioning to manage the resource assignment to multiple virtual machines properly. One of the issues in the current resource provisioning technique is assigning the resources proactively without predicting the workload of hosted applications, which cause load imbalance and resource wasting. Thus, this paper proposes a new model to predict the application workload. The experimental results show the ability of the proposed model to allocate more virtual machines and to balance the workload among the physical machines.
INFORMATION TECHNOLOGIES AND THE FUTURE OF EDUCATIONIN THE REPUBLIC OF KAZAKHSTAN

The aim of the article is a comprehensive approach to addressing the digitalization of education in the Republic of Kazakhstan based on identifying problems in this area, forming priority tasks and possible ways to solve them. Implementation of IT-programs in the learning process making serious additions to the pedagogical requirements, ethics, and discipline of students also the content of the disciplines. All these aspects are becoming especially relevant today when the quality of education is becoming a strategic task and the key to the successful development of the countrys economy in the digital era. The article touches on the problems of the modern digital generation, its dependence on communication in social networks and instant messengers, the impact of this dependence on health and the level of knowledge. The algorithm for educational technologies will be the main educational goals: the ability to think outside the box, be creative in solving problems, and develop logic and intelligence. Therefore, to formulate requirements for training ICTs, it is necessary to implement measures to assess the effectiveness of implemented programs based on experimental data. Such studies are being conducted today to improve the quality of the educational process, which means that they can be used for ICT. In conclusion, if to base on the results of the survey, the main problems were identified and a number of tasks were set for a deeper study, a general idea was formed on the scope of the forthcoming work and the goals for its achievement.
Estimating the compressed breast-shape using deep learning

Knowledge of the compressed breast shape can be valuable information to improve tomosynthesis reconstructions. The goal of this work was to use a convolutional neural network to refine the shape as estimated from tomosynthesis projection data. Training data was created by generating random three-dimensional breast shapes and simulating the limited angle projections. A rough approximation of the breast shape was made by segmenting and then back-projecting the projection data. Following this, a 3-layer u-net was trained on 900 pairs of simulated breast shapes and the corresponding shape estimates. The resulting network was applied to 100 test cases, where it significantly reduced the average distance between the surfaces of the true and estimated breast shapes from 2.3 mm to 0.5 mm (p &lt; 0.001). If these results can be confirmed using patient data, it is likely that advanced image processing techniques that rely on precise knowledge of the compressed breast shape will become feasible since our work now provides a method to obtain such an accurate estimate of the breast shape without the need for any additional imaging hardware.
Identification and feature extraction of drainage pipeline cracks based on SVD and edge recognition method

In order to solve the limitations and accuracy of current closed circuit television system (CCTV) for underground drainage pipeline robots, this paper uses singular value decomposition (SVD) method to preprocess images, which can binarize images to separate cracks features. On this basis, the sliding window segmentation and edge recognition method are used to realize the automatic identification and feature extraction of the cracks in the drainage pipe. The experimental results show that the proposed crack defect identification and feature extraction method can effectively identify the pipeline crack defects and feature extraction, and this method improve the efficiency and reliability of CCTV video detection in drainage pipelines.
A Workflow Manager for Complex NLP and Content Curation Workflows

We present a workflow manager for the flexible creation and customisation of NLP processing pipelines. The workflow manager addresses challenges in interoperability across various different NLP tasks and hardware-based resource usage. Based on the four key principles of generality, flexibility, scalability and efficiency, we present the first version of the workflow manager by providing details on its custom definition language, explaining the communication components and the general system architecture and setup. We currently implement the system, which is grounded and motivated by real-world industry use cases in several innovation and transfer projects.
HEAX: High-Performance Architecture for Computation on Homomorphically Encrypted Data in the Cloud

With the rapid increase in cloud computing, concerns surrounding dataprivacy, security, and confidentiality also have been increased significantly.Not only cloud providers are susceptible to internal and external hacks, butalso in some scenarios, data owners cannot outsource the computation due toprivacy laws such as GDPR, HIPAA, or CCPA. Fully Homomorphic Encryption (FHE)is a groundbreaking invention in cryptography that, unlike traditionalcryptosystems, enables computation on encrypted data without ever decryptingit. However, the most critical obstacle in deploying FHE at large-scale is theenormous computation overhead. In this paper, we present HEAX, a novel hardware architecture for FHE thatachieves unprecedented performance improvement. HEAX leverages multiple levelsof parallelism, ranging from ciphertext-level to fine-grained modulararithmetic level. Our first contribution is a new highly-parallelizablearchitecture for number-theoretic transform (NTT) which can be of independentinterest as NTT is frequently used in many lattice-based cryptography systems.Building on top of NTT engine, we design a novel architecture for computationon homomorphically encrypted data. We also introduce several techniques toenable an end-to-end, fully pipelined design as well as reducing on-chip memoryconsumption. Our implementation on reconfigurable hardware demonstrates164-268x performance improvement for a wide range of FHE parameters.
Spyder: Aggression Detection on Multilingual Tweets

In the last few years, hate speech and aggressive comments have covered almost all the social media platforms like facebook, twitter etc. As a result hatred is increasing. This paper describes our (Team name: Spyder) participation in the Shared Task on Aggression Detection organised by TRAC-2, Second Workshop on Trolling, Aggression and Cyberbullying. The Organizers provided datasets in three languages – English, Hindi and Bengali. The task was to classify each instance of the test sets into three categories – “Overtly Aggressive” (OAG), “Covertly Aggressive” (CAG) and “Non-Aggressive” (NAG). In this paper, we propose three different models using Tf-Idf, sentiment polarity and machine learning based classifiers. We obtained f1 score of 43.10%, 59.45% and 44.84% respectively for English, Hindi and Bengali.
Reformulating Information Retrieval from Speech and Text as a Detection Problem

In the IARPA MATERIAL program, information retrieval (IR) is treated as a hard detection problem; the system has to output a single global ranking over all queries, and apply a hard threshold on this global list to come up with all the hypothesized relevant documents. This means that how queries are ranked relative to each other can have a dramatic impact on performance. In this paper, we study such a performance measure, the Average Query Weighted Value (AQWV), which is a combination of miss and false alarm rates. AQWV requires that the same detection threshold is applied to all queries. Hence, detection scores of different queries should be comparable, and, to do that, a score normalization technique (commonly used in keyword spotting from speech) should be used. We describe unsupervised methods for score normalization, which are borrowed from the speech field and adapted accordingly for IR, and demonstrate that they greatly improve AQWV on the task of cross-language information retrieval (CLIR), on three low-resource languages used in MATERIAL. We also present a novel supervised score normalization approach which gives additional gains.
Assessment of the behavior uncertainty level of the short time series

Abstract The article considers the problem of assessing the level of uncertainty in the behavior of the short time series. The approach to solving this problem, based on the use of the fuzzy linear regression equation with the asymmetric parameters has been proposed. The methods for assessing the level of uncertainty in the behavior of the short time series using the fuzzy similarity measures have been considered. A herewith, the time series which describes the equation of the classical linear regression is used as the standard time series. The presented results of experimental studies confirm the effectiveness of using the proposed approach to assess the level of uncertainty in the behavior of the short time series.
TALN/LS2N Participation at the BUCC Shared Task: Bilingual Dictionary Induction from Comparable Corpora

This paper describes the TALN/LS2N system participation at the Building and Using Comparable Corpora (BUCC) shared task. We first introduce three strategies: (i) a word embedding approach based on fastText embeddings; (ii) a concatenation approach using both character Skip-Gram and character CBOW models, and finally (iii) a cognates matching approach based on an exact match string similarity. Then, we present the applied strategy for the shared task which consists in the combination of the embeddings concatenation and the cognates matching approaches. The covered languages are French, English, German, Russian and Spanish. Overall, our system mixing embeddings concatenation and perfect cognates matching obtained the best results while compared to individual strategies, except for English-Russian and Russian-English language pairs for which the concatenation approach was preferred.
Portable Network Graphics Approach to the Authentication of Halftone Images with Henon Map Encryption

A new method of blind image authentication based on secret sharing and data repair capability via the use of Portable Network Graphics (PNG) is proposed. The method manipulates the properties of alpha plane to embed the authentication signals as well as the data required for repairing. Care must be taken to maintain the transparency property. The image is halftoned and the authentication signals are generated from the halftoned image. Shamir secret sharing technique is used for the creation of data for repairing. The parameters are selected such that as many shares as possible should be generated for good quality reconstruction. The new scheme guarantees, no matter what type of attack, at least some shares must survive it and hence the actual data can be reconstructed. The authentication is done at the block level, but repairing is done at the pixel level. The embedded authentication signals are extracted from the alpha plane and compared with that computed from the received image. If there is a mismatch, then that block is marked as tampered. Then, repairing is done to the tampered blocks by reverse Shamir algorithm. To enhance the security of the grayscale document images, an encryption based on chaos theory is incorporated.
Itemset Mining Based Episode Profiling of Terrorist Attacks Using Weighted Ontology

Itemset mining is a prominent research problem of data mining domain. It is extensively utilized for knowledge discovery in domains dealing with multi-component records or itemsets. Terrorism is a similar domain where every terrorist attack carries attack attributes such as location, target and attack type as components. Treatment of terrorist attack episodes as itemsets can facilitate effective pattern analysis and forecast of future attack episodes. This paper introduces a novel approach of mapping three major attributes of terrorist attacks taken place in a region in a single weighted ontology. The weighted ontology is later employed to discover and forecast useful information about possible attack episodes in the future.
Weak instances of SIDH variants under improved torsion-point attacks

SIDH is a post-quantum key exchange algorithm based on the presumed difficulty of computing isogenies between supersingular elliptic curves. However, the exact hardness assumption SIDH relies on is not the pure isogeny problem; attackers are also provided with the action of the secret isogeny restricted to a subgroup of the curve. Petit (2017) leverages this information to break variants of SIDH in polynomial time, thus demonstrating that exploiting torsion-point information can lead to an attack in some cases. The contribution of this paper is twofold: First, we revisit and improve the techniques of Petit to span a broader range of parameters. Second, we construct SIDH variants designed to be weak against the resulting attacks; this includes weak choices of starting curve under moderately imbalanced parameters as well as weak choices of base field under balanced parameters. We stress that our results do not reveal any weakness in the NIST submission SIKE. However, they do get closer than previous attacks in several ways and may have an impact on the security of SIDH-based group key exchange (Azarderakhsh et al., 2019) and certain instantiations of B-SIDH (Costello, 2019).
Transductive Data-Selection Algorithms for Fine-Tuning Neural Machine Translation

Machine Translation models are trained to translate a variety of documents from one language into another. However, models specifically trained for a particular characteristics of the documents tend to perform better. Fine-tuning is a technique for adapting an NMT model to some domain. In this work, we want to use this technique to adapt the model to a given test set. In particular, we are using transductive data selection algorithms which take advantage the information of the test set to retrieve sentences from a larger parallel set. In cases where the model is available at translation time (when the test set is provided), it can be adapted with a small subset of data, thereby achieving better performance than a generic model or a domain-adapted model.
Interpol review of imaging and video 2016–2019

This review paper covers the forensic-relevant literature in imaging and video analysis from 2016 to 2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.int/content/download/14458/file/Interpol%20Review%20Papers%202019.pdf.
How do software architects perceive technical debt in Colombian industry? An analysis of technical debt causes

Abstract Technical debt is a metaphor used to describe technical decisions that can give the company a benefit in the short term but possibly hurting the overall quality of the software in the long term. Architectural decisions are considered one of the most common sources of technical debt, therefore, it becomes relevant to understand what causes lead to technical debt from the point of view of software architects. To accomplish this task, we used a survey research method to collect and analyze a corpus of 28 software architects from Colombia, as a part of the InsighTD project. Results showed that inappropriate planning is the most cited technical debt cause by software architects. However, results differ when comparison against engineers and manager are performed. Innacurate time estimate and producing more without quality were the most selected causes of technical debt according to engineers and managers. To improve this comparison, the rank-biased overlapping technique was used. As more elements were compared, more similar were these lists of causes among all three roles.
The 2019 BBN Cross-lingual Information Retrieval System.

In this paper, we describe a cross-lingual information retrieval (CLIR) system that, given a query in English, and a set of audio and text documents in a foreign language, can return a scored list of relevant documents, and present findings in a summary form in English. Foreign audio documents are first transcribed by a state-of-the-art pretrained multilingual speech recognition model that is finetuned to the target language. For text documents, we use multiple multilingual neural machine translation (MT) models to achieve good translation results, especially for low/medium resource languages. The processed documents and queries are then scored using a probabilistic CLIR model that makes use of the probability of translation from GIZA translation tables and scores from a Neural Network Lexical Translation Model (NNLTM). Additionally, advanced score normalization, combination, and thresholding schemes are employed to maximize the Average Query Weighted Value (AQWV) scores. The CLIR output, together with multiple translation renderings, are selected and translated into English snippets via a summarization model. Our turnkey system is language agnostic and can be quickly trained for a new low-resource language in few days.