Reviewerly/exHarmony huggingface dataset:
The Impact of Dynamic Capabilities and Information System on Organizational Effectiveness in Cellular Communication Companies in Jordon: Mediating Role of Organizational Ambidexterity

The goal of the ongoing research is to examine the impact of dynamic capabilities such as exploitation capability, exploration capability and information system on the organizational effectiveness of cellular communication firms in Jordan. The examination of the mediating role of organizational ambidexterity among the nexus of dynamic capabilities such as exploitation capability and exploration capability and organizational effectiveness is also included in the goals of the ongoing research. The quantitative method of data gathering has been utilized by the researchers to collect the data from the employees of the cellular communication firms currently operating in Jordan while AMOS has been used for the purpose of analysis. The results exposed that the positive nexus has been found among the nexus of dynamic capabilities, organizational ambidexterity, and organizational effectiveness. The results also revealed that the organizational ambidexterity has positive mediating among the links of dynamic capabilities such as exploitation capability and exploration capability on organizational effectiveness. These findings are suitable for the regulation developing authorities that they should develop and implement the effective regulation related to the dynamic capabilities and organizational ambidexterity that enhance the organizational effectiveness of the firm.
Asymmetric image encryption and authentication in interference-based scheme using random modulus decomposition

A scheme for asymmetric image encryption and authentication based on interference is proposed here by using sparse representation and random modulus decomposition. An original image is encoded into a complex value image in the Fresnel domain and then sparsely sampled to acquire the sparse spectrum. The sparse spectrum generates four phase-only masks using random modulus decomposition and the interference principle. No information concerning the original image, including its silhouette, can be extracted even if one, two, or three of the four phase-only masks are used for decryption. The parameters of random modulus decomposition provide additional keys for further strengthening the security of our scheme, and the four phase-only masks are all employed for decryption and authentication. Numerical simulations are used to verify the feasibility and security of this scheme.
tPAKE: Typo-Tolerant Password-Authenticated Key Exchange

Password-authenticated key exchange (PAKE) enables a user to authenticate to a server by proving the knowledge of the password without actually revealing their password to the server. PAKE protects user passwords from being revealed to an adversary who compromises the server (or a disgruntled employee). Existing PAKE protocols, however, do not allow even a small typographical mistake in the submitted password, such as accidentally adding a character at the beginning or at the end of the password. Logins are rejected for such password submissions; the user has to retype their password and reengage in the PAKE protocol with the server. Prior works have shown that users often make typographical mistakes while typing their passwords. Allowing users to log in with small typographical mistakes would improve the usability of passwords and help users log in faster. Towards this, we introduce tPAKE: a typo-tolerant PAKE, that allows users to authenticate (or exchange high-entropy keys) using a password while tolerating small typographical mistakes. tPAKEallows edit-distance-based errors, but only those that are frequently made by users. This benefits security, while still improving usability. We discuss the security considerations and challenges in designing tPAKE. We implement tPAKE and show that it is computationally feasible to be used in place of traditional PAKEs while providing improved usability. We also provide an extension to tPAKE, called adaptive-tPAKE, that will enable the server to allow a user to log in with their frequent mistakes (without ever learning those mistakes).
End to End Chinese Lexical Fusion Recognition with Sememe Knowledge

In this paper, we present Chinese lexical fusion recognition, a new task which could be regarded as one kind of coreference recognition.First, we introduce the task in detail, showing the relationship with coreference recognition and differences from the existing tasks.Second, we propose an end-to-end model for the task, handling mentions as well as coreference relationship jointly.The model exploits the state-of-the-art contextualized BERT representations as the encoder, and is further enhanced with the sememe knowledge from HowNet by graph attention networks.We manually annotate a benchmark dataset for the task and then conduct experiments on it.Results demonstrate that our final model is effective and competitive for the task.Detailed analysis is offered for comprehensively understanding the new task and our proposed model.
Auto-Agent-Distiller: Towards Efficient Deep Reinforcement Learning Agents via Neural Architecture Search

AlphaGo's astonishing performance has ignited an explosive interest in developing deep reinforcement learning (DRL) for numerous real-world applications, such as intelligent robotics. However, the often prohibitive complexity of DRL stands at the odds with the required real-time control and constrained resources in many DRL applications, limiting the great potential of DRL powered intelligent devices. While substantial efforts have been devoted to compressing other deep learning models, existing works barely touch the surface of compressing DRL. In this work, we first identify that there exists an optimal model size of DRL that can maximize both the test scores and efficiency, motivating the need for task-specific DRL agents. We therefore propose an Auto-Agent-Distiller (A2D) framework, which to our best knowledge is the first neural architecture search (NAS) applied to DRL to automatically search for the optimal DRL agents for various tasks that optimize both the test scores and efficiency. Specifically, we demonstrate that vanilla NAS can easily fail in searching for the optimal agents, due to its resulting high variance in DRL training stability, and then develop a novel distillation mechanism to distill the knowledge from both the teacher agent's actor and critic to stabilize the searching process and improve the searched agents' optimality. Extensive experiments and ablation studies consistently validate our findings and the advantages and general applicability of our A2D, outperforming manually designed DRL in both the test scores and efficiency. All the codes will be released upon acceptance.
Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data

We present the first sublinear memory sketch that can be queried to find the nearest neighbors in a dataset. Our online sketching algorithm compresses an N element dataset to a sketch of size $O(N^b \log^3 N)$ in $O(N^{(b+1)} \log^3 N)$ time, where $b &lt; 1$. This sketch can correctly report the nearest neighbors of any query that satisfies a stability condition parameterized by $b$. We achieve sublinear memory performance on stable queries by combining recent advances in locality sensitive hash (LSH)-based estimators, online kernel density estimation, and compressed sensing. Our theoretical results shed new light on the memory-accuracy tradeoff for nearest neighbor search, and our sketch, which consists entirely of short integer arrays, has a variety of attractive features in practice. We evaluate the memory-recall tradeoff of our method on a friend recommendation task in the Google Plus social media network. We obtain orders of magnitude better compression than the random projection based alternative while retaining the ability to report the nearest neighbors of practical queries.
nlpUP at SemEval-2020 Task 12 : A Blazing Fast System for Offensive Language Detection

In this paper, we introduce our submission for the SemEval Task 12, sub-tasks A and B for offensive language identification and categorization in English tweets.This year the dataset for Task A is significantly larger than in the previous year.Therefore, we have adapted the BlazingText algorithm to extract embedding representation and classify texts after filtering and sanitizing the dataset according to the conventional text patterns on social media.We have gained both advantages of a speedy training process and obtained a good F1 score of 90.88% on the test set.For sub-task B, we opted to fine-tune a Bidirectional Encoder Representation from a Transformer (BERT) to accommodate the limited data for categorizing offensive tweets.We have achieved an F1 score of only 56.86%, but after experimenting with various label assignment thresholds in the pre-processing steps, the F1 score improved to 64%.
SSN-NLP at SemEval-2020 Task 4: Text Classification and Generation on Common Sense Context Using Neural Networks

Common sense validation deals with testing whether a system can differentiate natural language statements that make sense from those that do not make sense.This paper describes the our approach to solve this challenge.For common sense validation with multi choice, we propose a stacking based approach to classify sentences that are more favourable in terms of common sense to the particular statement.We have used majority voting classifier methodology amongst three models such as Bidirectional Encoder Representations from Transformers (BERT), Micro Text Classification (Micro TC) and XLNet.For sentence generation, we used Neural Machine Translation (NMT) model to generate explanatory sentences.
On the Usability of Object-Oriented Design Patterns for a Better Software Quality

Abstract Software design patterns incarnate expert knowledge distilled from the practical experience in object-oriented design, in a compact and reusable form. The article presents a quantitative study of the usability of the object-oriented software design patterns (known as Gang of Four patterns) applied for improving the testability, maintainability, extendibility, readability, reliability, and performance efficiency of software applications. We received 82 usable responses from software professionals in Bulgaria, with 65 of them addressing both the usability and recognition of each one of the Gang of Four patterns, together with their impact on important software quality characteristics. As well, we studied the approach of each software developer in choosing a particular design pattern to use in order to solve a problem. We found statistically significant differences between the most recognized and most useful patterns and between the most unrecognized and most useless patterns, split into creational, structural, and behavioral groups.
SIMULATION OF THE PROCESS OF PROFESSIONAL DEVELOPMENT OF THE FUTURE TEACHER OF LABOR EDUCATION AND TECHNOLOGY BY MEANS OF INDEPENDENT WORK

In the article reveals the content of the structural and functional model of professional development of future teachers of labor training and technology. To visualize the specific goals, ways and results of professional development of future teachers of labor training and technology in the process of independent work, a modeling method was used, which is based on the principle of analogy and provides the opportunity to study a real object not directly but through consideration an object similar to it, that is, its model. The method of experimental research «pedagogical modeling» is revealed as the initial stage of process design, which involves creating conditions to achieve the goal, determining the stages and components of this process, developing strategies, forms, methods, tools, technologies of professional development and more. The structural-functional model is based primarily on the social order based on the expected results of the future teacher of labor training and technology for continuous professional development by means of independent work throughout life. Given the specifics of the process of professional development of future teachers of labor education and technology in the process of independent work, in our opinion, we must take into account the model of future professional activity, the structural elements of which we include taking into account the social order theoretical and methodological block (purpose, conceptual principles, methodological approaches, principles); procedural-semantic block (structural components, strategies, forms, methods, means) with an interactive development program and diagnostic-effective block (criteria, indicators, levels), focused on the preparation of future teachers of labor training and technology for continuous professional development by means of independent work during life. Also, in the article defines and theoretically substantiates the pedagogical conditions for ensuring the success and efficiency of the studied phenomenon, which ensured the continuity, efficiency and reliability of the process. Key words: model, modeling, professional development, future teachers of labor training and technologies.
A RULE-BASED STEMMER FOR PUNJABI ADJECTIVES

This research work is concerned with the development of a rule-based stemmer for stemming of adjectives in the Punjabi language. Stemming is a method of deriving the root word from the inflected word. The proposed Punjabi Adjective Stemmer (PAS) uses a rule-based approach for converting the inflected Punjabi adjectives to their root forms. A database containing valid root adjectives occurring in the Punjabi language has been created. This database stores 1,762 Punjabi root adjectives. When an adjective word is fed to PAS as an input, first it compares the input word with the root database to determine whether the input adjective is a root adjective or an inflected one. If the input adjective is a root adjective, then no stemming is required and the input adjective is returned as the output. Otherwise, the inflected input adjective is sent to the suffix-stripping algorithm to get the corresponding root adjective. The suffix-stripping algorithm uses a set of predefined rules. India is a linguistically rich country with 22 languages recognized officially. But the computational resources developed for these languages are very scarce. Most of the stemmers developed for Punjabi language so far concentrated on nouns and proper names. PAS is the only stemmer developed so far for specifically addressing the problem of stemming of Punjabi adjectives. PAS has an overall accuracy of 88.76%.
Residual Energy-Based Models for Text

Current large-scale auto-regressive language models display impressive fluency and can generate convincing text. In this work we start by asking the question: Can the generations of these models be reliably distinguished from real text by statistical discriminators? We find experimentally that the answer is affirmative when we have access to the training data for the model, and guardedly affirmative even if we do not. This suggests that the auto-regressive models can be improved by incorporating the (globally normalized) discriminators into the generative process. We give a formalism for this using the Energy-Based Model framework, and show that it indeed improves the results of the generative models, measured both in terms of perplexity and in terms of human evaluation.
An Unsupervised Method for Learning Representations of Multi-word Expressions for Semantic Classification

This paper explores an unsupervised approach to learning a compositional representation function for multi-word expressions (MWEs), and evaluates it on the Tratz dataset, which associates two-word expressions with the semantic relation between the compound constituents (e.g. the label employer is associated with the noun compound government agency) (Tratz, 2011).The composition function is based on recurrent neural networks, and is trained using the Skip-Gram objective to predict the words in the context of MWEs.Thus our approach can naturally leverage large unlabeled text sources.Further, our method can make use of provided MWEs when available, but can also function as a completely unsupervised algorithm, using MWE boundaries predicted by a single, domain-agnostic part-of-speech pattern.With pre-defined MWE boundaries, our method outperforms the previous state-of-the-art performance on the coarse-grained evaluation of the Tratz dataset (Tratz, 2011), with an F1 score of 50.4%.The unsupervised version of our method approaches the performance of the supervised one, and even outperforms it in some configurations.
Forecasting unemployment rate in the time of COVID-19 pandemic using Google trends data (case of Indonesia)

The outbreak of COVID-19 is having a significant impact on the contraction of Indonesia`s economy, which is accompanied by an increase in unemployment. This study aims to predict the unemployment rate during the COVID-19 pandemic by making use of Google Trends data query share for the keyword “phk” (work termination) and former series from official labor force survey conducted by Badan Pusat Statistik (Statistics Indonesia). The method used is ARIMAX. The results of this study show that the ARIMAX model has good forecasting capabilities. This is indicated by the MAPE value of 13.46%. The forecast results show that during the COVID-19 pandemic period (March to June 2020) the open unemployment rate is expected to increase, with a range of 5.46% to 5.70%. The results of forecasting the open unemployment rate using ARIMAX during the COVID-19 period produce forecast values are consistent and close to reality, as an implication of using the Google Trends index query as an exogenous variable can capture the current conditions of a phenomenon that is happening. This implies that the time series model which is built based on the causal relationship between variables reflects current phenomenon if the required data is available and real-time, not only past historical data.
TIMBERT: Toponym Identifier For The Medical Domain Based on BERT

In this paper, we propose an approach to automate the process of place name detection in the medical domain to enable epidemiologists to better study and model the spread of viruses.We created a family of Toponym Identification Models based on BERT (TIMBERT), in order to learn in an end-to-end fashion the mapping from an input sentence to the associated sentence labeled with toponyms.When evaluated with the SemEval 2019 task 12 test set (Weissenbacher et al., 2019), our best TIMBERT model achieves an F1 score of 90.85%, a significant improvement compared to the state-of-the-art of 89.13% (Wang et al.,
Comparison of Type-2 Fuzzy Inference Method and Deep Neural Networks for Mass Detection from Breast Ultrasonography Images

In this study, mass detection from breast ultrasonography images was realized using deep neural networks. Dataset is a collection of publicly available ultrasonography images which were classified by their biopsy results. A total of 153 breast ultrasonography images that contain 89 malign and 64 benign tumours were used. Image augmentation and deep neural network software was developed using Python 3,5 environment on Visual Studio Community 2017 IDE. A hybrid method including Keras ImageDataGenerator Class and image preprocessing techniques was introduced. Twenty images from both classes were randomly split from the dataset for testing after the network was designed. The network had a success rate of 100% at an epoch value of 70. The result of this study was compared with the result of another study that implemented type-2 fuzzy inference system with a success rate of 99,34%. &#x0D; As a conclusion, it can be expressed that the deep neural networks are more successful than fuzzy inference systems in tumour detection from breast ultrasonography images. Therefore, it can be more convenient to use deep neural network technology in computer aided detection systems for mass detection from breast ultrasonography images.
Implementation of Service Oriented Architecture in Cloud Computing for Periodic Salary Increment

Technological advances are used to help improve government performance in matters relating to the use of technology in the process of Periodic Salary Increment, which is the task of the sub-division of staffing at the Regional Government Forestry Service Office of West Java Province. Judging from the document for Standard Operating Procedures (SOP) for the subdivision of the West Java Provincial Government Forestry Service office in 2018, in the work process section of periodic salary increases with stages that require time and energy so that the performance process appears less effective. So that it implements a system that can complete the periodic salary work process using the Cloud Computing-based Service-Oriented Architecture (SOA) method. The purpose of using cloud computing is to increase reliability and flexibility without increasing computing costs. Cloud computing has become a solution and opportunity for the development of the Information and Communication Technology (ICT) industry to get better advantages over other technologies. On the other hand, the user does not need to see or have knowledge of the physical location and system configuration of the service. This research resulted in the performance of the sub-division of the staffing of the West Java Provincial Government Forestry Service in the process of making regular salary increases more effective. And from the test can prove that 74.74% of the questions answered "Yes" are successful.
A survey of steganalysis methods in the papers of foreign authors

The paper provides a survey of foreign studies regarding steganalysis, aimed to detect hidden message insertion made by applying least significant bit replacement and discrete cosine transform algorithms. It is noted the further steganalysis methodology development splits in two directions: a decrease of the complexity and cost of processing and detection while maintaining a high level of classification rate, which is quite justified in the case of the presence of insertions with a large payload, i.e. up to 100%; or an increase of the insert recognition efficiency when dealing with images of a low payload. Besides, during the last five years, steganalisys methods based on machine learning and deep learning began to play a dominant role in steganalysis
Analysis of the Countermeasures for Training Talents of Advanced Mathematics Teaching in Application-oriented Undergraduate Colleges

With the transformation and development of local undergraduate colleges and universities in China, there are many problems in the training mode and teaching content of Advanced Mathematics professionals, which can no longer meet the demand for talents in the new era. In regard to the Advanced Mathematics Teaching major of application-oriented undergraduate colleges and universities, it should actively change teaching concepts and innovate teaching methods, so as to continuously improve the quality and level of teaching, and cultivate high-quality talents that meet the needs of social development. This article focuses on the cultivation of Advanced Mathematics Teaching professionals in application-oriented undergraduate colleges.