Reviewerly/exHarmony huggingface dataset:
Modelling and Designing of NLP Interface to Database for Afaan Oromoo

Database management system has been widely used for storing and retrieving data. However, database is often hard to access the data since their interface is rigid in cooperating with user, due to that analysis of natural language query interface to relational databases has gained much interest in research community. This can be termed as structured free query interface as it allows the users to retrieve the data from the database without knowing the underlying schema. Structured free query interface should address majorly two problems. Querying the system with natural language interfaces is comfortable for the naive users but it is difficult for the machine to understand. The other problem is that the users can query the system with different expressions to retrieve the same information. The different words used in the query can have same meaning and the same word can have multiple meanings. Hence; it is the responsibilities of the NLI to understand the exact meaning of the word in the particular context to handle such problem the researcher develops an algorithm that is from manually crafted rule that extracted from user queries for proper query translation and SQL query generation. The proposed, pattern matching approach to natural language interface to database system has been passes through various phases. The idea behind the approach is to overcome the problem bottleneck luck of tools in the language, while pattern-matching method can fill the gap of those resources. This makes our approach suitable for AONIDB since Afaan Oromoo is under resourced language. The exact meaning of the word used in the query in particular context obtained using domain-mapping dictionary and database-mapping table constructed form student database that contains grade, department and course table for mapping database information with the help of an algorithm developed by researcher based on the rule that extracted from collected user query. The system has been developed in which user can input query in Afaan Oromoo language and can see result in the same language. The proposed system can handle list query, single and multiple conditional queries, aggregate function queries and join queries. The proposed system evaluated on 100 Afaan Oromoo queries using student database with precision, recall and F-measure. The result of testing is very encouraging and an average performance of 94.2% that implies that the systems validity and reliability is very high, an indicator of its strong and successfully features use and operation. Yet; further experiments using different approaches that extend this work are needed for a better performance.
Nexus between financial innovation and economic growth in Lesotho: evidence from ARDL and nonlinear ARDL approaches

This paper sets out to explore the relationship between financial innovation and economic growth in Lesotho using time series data spanning 1980-2016. The autoregressive distributed lag (ARDL) bounds testing approach is applied to gauge long-run cointegration while the nonlinear ARDL (NARDL) model is used to test validity of short and long-run symmetric effects of financial innovation on economic growth. Results of the bounds tests revealed existence of long-run cointegration between financial innovation and economic growth in Lesotho. Positive changes in financial innovation related positively with economic growth in the long-run but had a statistically insignificant impact in the short-run. Furthermore, negative changes in financial innovation were found to have no significant impact on Lesotho's economic growth in the short and long-run. The Government of Lesotho is recommended to foster greater financial innovation in the financial system to assist financial services expansion and efficient financial intermediation to support sustainable economic growth.
Evaluating information criteria in latent class analysis: application to identify classes of breast cancer dataset

In recent studies, latent class analysis (LCA) modelling has been proposed as a convenient alternative to standard classification methods. It has become a popular tool for clustering respondents into homogeneous subgroups based on their responses on a set of categorical variables. The absence of a common accepted statistical indicator for deciding the number of classes in the study of population represents one of the major unresolved issues in the application of the LCA. Determining the number of classes constituting the profiles of a given population is often done by using the likelihood ratio test, however the use of such methodology is not correct theoretically. To overcome this problem, we propose an alternative for the classical latent class models selection methods based on the information criteria. This article aims to investigate the performance of information criteria for selecting the latent class analysis models. Nine information criteria are compared under various sample sizes and model dimensionality. We propose also an application of ICs to select the best model of breast cancer dataset.
DRAT and Propagation Redundancy Proofs Without New Variables.

We study the complexity of a range of propositional proof systems which allow inference rules of the form: from a set of clauses Gamma derive Gamma+C where, due to some syntactic condition, Gamma+C is satisfiable if Gamma is, but where Gamma does not necessarily imply C. In increasing order of strength the rules are BC, RAT, SPR and PR (respectively short for blocked clauses, resolution asymmetric tautologies, subset propagation redundancy and propagation redundancy). These arose from work in satisfiability (SAT) solving. We introduce a new, more general rule SR (substitution redundancy). If the new clause C is allowed to include new variables then the systems based on these rules are all equivalent to extended resolution. We focus on restricted systems that do not allow new variables. The systems with deletion, where we can delete a clause from our set at any time, are denoted DBC-, RAT-, DSPR-, DPR- and DSR-. The systems without deletion are BC-, RAT-, SPR-, PR- and SR-. With deletion, we show that DRAT-, DSPR- and DPR- are equivalent. By earlier work of Kiesl, Rebola-Pardo and Heule, they are also equivalent to DBC-. Without deletion, we show that SPR- can simulate PR- provided only short clauses are inferred by SPR inferences. We also show that many of the well-known hard principles have small SPR- refutations. These include the pigeonhole principle, bit pigeonhole principle, parity principle, Tseitin tautologies and clique-coloring tautologies. SPR- can also handle or-fication and xor-ification, and lifting with an index gadget. Our final result is an exponential size lower bound for RAT- refutations, giving exponential separations between RAT- and both DRAT- and SPR-.
Privately Answering Counting Queries with Generalized Gaussian Mechanisms.

We consider the problem of answering $k$ counting (i.e. sensitivity-1) queries about a database with $(\epsilon, \delta)$-differential privacy. We give a mechanism such that if the true answers to the queries are the vector $d$, the mechanism outputs answers $\tilde{d}$ with the $\ell_\infty$-error guarantee: $$\mathcal{E}\left[||\tilde{d} - d||_\infty\right] = O\left(\frac{\sqrt{k \log \log \log k \log(1/\delta)}}{\epsilon}\right).$$ This reduces the multiplicative gap between the best known upper and lower bounds on $\ell_\infty$-error from $O(\sqrt{\log \log k})$ to $O(\sqrt{\log \log \log k})$. Our main technical contribution is an analysis of the family of mechanisms of the following form for answering counting queries: Sample $x$ from a \textit{Generalized Gaussian}, i.e. with probability proportional to $\exp(-(||x||_p/\sigma)^p)$, and output $\tilde{d} = d + x$. This family of mechanisms offers a tradeoff between $\ell_1$ and $\ell_\infty$-error guarantees and may be of independent interest. For $p = O(\log \log k)$, this mechanism already matches the previous best known $\ell_\infty$-error bound. We arrive at our main result by composing this mechanism for $p = O(\log \log \log k)$ with the sparse vector mechanism, generalizing a technique of Steinke and Ullman.
On the adoption of scramble keypad for unlocking PIN-protected smartphones

Personal identification number (PIN) is a simple and effective mechanism for screen unlocking but is susceptible to a number of attacks. Scramble keypad is a method that can improve the security of PIN by changing the keypad layout in each PIN-entry process. However, scramble keypad has not been provided as a standard feature in Android and iOS. In this work, we conducted a security and usability analysis of scramble keypad through theoretical analysis and user studies. The security analysis shows that scramble keypad can perfectly defend smudge attacks and greatly reduce the threats of side-channel attacks. It also has a significantly better chance to defend shoulder surfing attacks than standard keypad. The compromising of usability of scramble keypad for the improved security was also investigated. The results suggest that it is worthy to provide scramble keypad as a standard option of mobile operating systems for unlocking PIN-protected smartphones.
Efficiently Reusing Natural Language Processing Models for Phenotype Identification in Free-text Electronic Medical Records: Methodological Study

Background:Many efforts have been put into the use of automated approaches, such as natural language processing (NLP), to mine or extract data from free-text medical records to construct comprehensive patient profiles for delivering better health-care. Reusing NLP models in new settings, however, remains cumbersome - requiring validation and/or retraining on new data iteratively to achieve convergent results.Objective:The aim of this work is to minimise the effort involved in reusing NLP models on free-text medical records.Methods:We formally define and analyse the model adaptation problem in phenotype identification tasks. We identify “duplicate waste” and “imbalance waste”, which collectively impede efficient model reuse. We propose a concept embedding based approach to minimise these sources of waste without the need for labelled data from new settings.Results:We conduct experiments on data from a large mental health registry to reuse NLP models in four phenotype identification tasks. The proposed approach can choose the best model for a new task, identifying up to 76% of phenotype mentions without the need for validation and model retraining, and with very good performance (93-97% accuracy). It can also provide guidance for validating and retraining the selected model for novel language patterns in new tasks, saving around 80% of the effort required in “blind” model-adaptation approaches.Conclusions:Adapting pre-trained NLP models for new tasks can be more efficient and effective if the language pattern landscapes of old settings and new settings can be made explicit and comparable. Our experiments show that the phenotype embedding approach is an effective way to model language patterns for phenotype identification tasks and that its use can guide efficient NLP model reuse.
An Extended Secondary Study to Characterize the Influence of Developers Sentiments on Practices and Artifacts in Open Source Software Projects

Context: Sentiment Analysis applies computational techniques for both automated and semi-automated identification of human behavior. There is a trend to use such techniques in Sentiment Analysis tasks in the Software Engineering context. Objective: Characterize the influence of developers sentiments on software practices and artifacts in open source software projects. Methods: We conducted a Systematic Literature Review (SLR) to identify references in the literature related to the influence of developers sentiments on software practices and artifacts. Results: Evidence showed an increasing number of studies in this theme shedding light on issues related to the influence of developers sentiments on software practices. Practices focusing on developers productivity and collaboration, as well as source code, are the most vulnerable to sentiments variation. Conclusions: Based on the results provided in this SLR, we intend to present an updated and comprehensive overview regarding how the sentiments of developers can positively or negatively impact software practices and artifacts.
Application of Constriction Coefficient Based Particle Swarm Optimization and Gravitational Search Algorithm for Solving Practical Engineering Design Problems

The constriction coefficient-based particle swarm optimisation and gravitational search algorithm (CPSOGSA) is a heuristic optimisation technique. It uses the diversification capability of the GSA and the convergence power of CPSO. In this paper, the hybrid CPSOGSA is applied to three constrained engineering design problems, namely welded beam design (WBD), compression spring design (CSD), and pressure vessel design (PVD), to find the optimal value of the engineering objective function and design variables. The investigation of experimental outcomes has been performed through various performance metrics like statistical measures, run time analysis, convergence rate, and box plots. Moreover, statistical verification of experimental results is carried out using a signed Wilcoxon rank-sum test. Furthermore, seven heuristic algorithms were employed for comparative analysis. The simulation results clearly indicate that CPSOGSA provides better outcomes for all the three engineering benchmarks than classical PSO, standard GSA, and most of the competing algorithms.
Toward a deep augmented reality medical images diagnosis generation

Augmented reality (AR) and deep learning (DL) are promising areas. In this paper, we present the impact of such assortment (AR/DL) on the enhancement of generating textual and oral descriptions from a target medical image. The main purpose is to assist medical practitioners to make an accurate decision about a generated diagnosis. Automatic medical image report generation (textual and vocal) is used up as a diagnostic aid system for disease diagnoses depend strongly on visual properties. In this paper, we will describe how we develop an augmented report for the X-ray image target. Primary results using prototypes are promising. Doctors, learner's task are more efficient and feedbacks are encouraging.
Research on fast de-duplication of text backup information in library database based on big data

In order to overcome the problems of poor effect and low efficiency of traditional information de-duplication methods, this paper proposes a fast de-duplication method of text backup information in library database based on big data. Firstly, this paper carries out parallel mining of text information features in library database, uses the features with strong classification ability to determine the parameter value of the repeated feature function, obtains the entries with the parameter value higher than the threshold value, determines the number of text repeated backup information and the group weight, sets the difference between the two as the remaining digits, and stops de-duplication when the remaining digits are lower than the threshold value. The experimental results show that the average accuracy of this method is 96.95%, the weight removal efficiency is always above 98%, and the weight removal effect is good.
Tree-Based Blockchain Architecture for Supply Chain

Since the development of blockchain technology, many industries are re-engineering business processes. Like all other industries, the supply chain is also trying to adopt blockchain as it reduces t...
Asymmetric Ciphers—RSA and Others

The notion of an asymmetric encryption system dates to the 1970s, with the first and still primary version of asymmetric encryption being the RSA algorithm of Rivest, Shamir, and Adleman. In asymmetric encryption, an encryption key that is made public is used to encrypt a message that is sent to the owner of the public key. That owner then uses a privately held key to decrypt. The RSA algorithm relies on a choice of two large primes p and q, multiplied together to produce a modulus $$N = pq$$ . The public encryption key e and private decryption key d are chosen so that $$e d \equiv 1 \pmod {\phi (N)}$$ . Current knowledge of the mathematics is that if N and e are public, but p, q, and d are kept private, then decrypting a message requires factoring N into p times q, and that is computationally hard. In this chapter we lay out the foundation of the RSA process, with an example, and we comment on the current records in factoring as a estimate of the security of RSA.
Improving performance of the symmetrical evolutionary ciphering system SEC

Nowadays computer security becomes, increasingly, an indispensable field. It represents a major challenge for all entities: economic, political, social... However, few are the cryptographic systems that ensure security and still resists the enormous growth in technology. Now, cryptanalysis tools are much more sophisticated and more powerful than before. Hence, the need to design new systems that can be competitive to the old ones. New cryptographic systems with new properties and the ability to support this technology watch. In this work we have focused on the SEC encryption system uses a new approach of ciphering based on evolutionary algorithms, hence its called: symmetrical evolutionary ciphering. Since the strength of this type of algorithm will be very beneficial in cryptography, we will present in this paper, a new evaluation function that we have developed to improve the performance of this system and improve its resistance against all possible types of attacks.
APPLICATIONS OF BLOCKCHAIN TECHNOLOGY IN THE BRAZILIAN GOVERNMENT

Blockchain technology has emerged as a trend that can influence business, government, and society. By validating the exact time an action is taken without the need for an intermediary, the technology has the potential to provide profound modifications in the public service. This paper proposes an exploratory study built from the perspective of institutional logics whose research question is: how are the institutional logics present in the Brazilian public sector responding to the incorporation of blockchain technology? A qualitative approach was used to understand initial perceptions and to learn about the motivations and mechanisms of government initiatives that employ blockchain technology. Our research has found that, despite its disruptive character, the blockchain has entered the Brazilian public service scenario through an already institutionalised structure consisting of a hybrid logic of different approaches to public administration. Changes in this logic will require greater popularisation and regulation of the technology.
A novel approach of image retrieval towards segmentation

At the present time, it becomes simple to stockpile vast quantity of images by means of image processing approach. The hasty right of entry to these ample groups of images and retrieve related images of a specified image from this massive assortment of images presents key challenges and requires competent algorithms. In this manuscript, the authors intend a structure which is proficient to select the most appropriate features to examine newly expected images in that way improving the retrieval accuracy and competence. An enhanced algorithm is projected now. The algorithm comprises of scheming feature vectors after segmentation which will be used in likeness contrast linking query image and database images. The structure is skilled for dissimilar images in the database. The projected method has been tested on a variety of real images and its concert is found to be reasonably acceptable when compared with the concert of conventional methods of content-based image retrieval. The major objective of the anticipated method is to endow with an exact outcome with lesser computational time.
STRENGHTENING STUDENTS’ READING COMPREHENSION THROUGH PLAN STRATEGY

Building a sense of connection between reader and text gives values of reading as a meaningful activity. Adequate instructions bridge struggle readers to read the texts strategically. The four main steps of PLAN strategy namely Predict, Locate, Add, and Note allow readers to have involvement in the text and recognize reading improvement and effort. This study aimed to enhance students’ reading skill using PLAN strategy by applying quasi experimental method.  Sixty students of the tenth grade were involved and divided into two groups, experimental and control group. To collect the data, reading comprehension test was distributed to both groups in the pre-test and post-test. The results of the study showed that experimental group made significant improvement on reading comprehension after being taught by using PLAN strategy. Furthermore, there was a significant difference on reading comprehension between the students who were taught by using PLAN strategy and those who were not. It indicates that a complete process of PLAN strategy could strengthen students’ engagement in reading and positively impact on their learning achievement and experiences.
A Comparative Study on Neural Architectures and Training Methods for Japanese Speech Recognition

End-to-end (E2E) modeling is advantageous for automatic speech recognition (ASR) especially for Japanese since word-based tokenization of Japanese is not trivial, and E2E modeling is able to model character sequences directly. This paper focuses on the latest E2E modeling techniques, and investigates their performances on character-based Japanese ASR by conducting comparative experiments. The results are analyzed and discussed in order to understand the relative advantages of long short-term memory (LSTM), and Conformer models in combination with connectionist temporal classification, transducer, and attention-based loss functions. Furthermore, the paper investigates on effectivity of the recent training techniques such as data augmentation (SpecAugment), variational noise injection, and exponential moving average. The best configuration found in the paper achieved the state-of-the-art character error rates of 4.1%, 3.2%, and 3.5% for Corpus of Spontaneous Japanese (CSJ) eval1, eval2, and eval3 tasks, respectively. The system is also shown to be computationally efficient thanks to the efficiency of Conformer transducers.
A model for strategic execution based on Grounded theory in Knowledge-based companies due to qualitative-quantitative combined method

Background: knowledge based economic can be known as an important factor in the progress of the country. This discovery has the potential for employment in addition with economic development, thus one of the main considerable issues is to conduct such a research in order to handle all the activities in Knowledge-based companies.Aim: the goal of this research is to present a model for strategies based on Grounded theory in Knowledge-based companiesMethodology: the method present research was done by Grounded theory and qualitative-quantitative combined. The tools of gathering data in the qualitative section is deep interviews and in the quantitative is questioner. Findings: the gathered data form about 15 deep interviews are evaluated due to presented method by Ashteras and Courbin (1998) and the qualitative model was extracted. In this research the effect of content based factors and the process of strategical execution was evaluated on the performance of Knowledge-based companies by the structural equations (by SPSS 18, SmartPLS). Conclusion: the “strategic execution of Knowledge-based companies” theory is the major issue due to the evaluation of data which is created from the minor issues such as “strategic fit”, “strategic translation” and “confidence of best and on time decisions”. The evaluation of different methods was represented in order to successful execution of strategy and also some methods were introduced in that way and Suggestions for future research are provided.