Reviewerly/exHarmony huggingface dataset:
SmartTS: A Component-Based and Model-Driven Approach to Software Testing in Robotic Software Ecosystem

Validating the behaviour of commercial off-theshelf components and of interactions between them is a complex, and often a manual task.Treated like any other software product, a software component for a robot system is often tested only by the component developer.Test sets and results are often not available to the system builder, who may need to verify functional and non-functional claims made by the component.Availability of test records is key in establishing compliance and thus selection of the most suitable components for system composition.To provide empirically verifiable test records consistent with a component's claims would greatly improve the overall safety and dependability of robotic software systems in open-ended environments.Additionally, a test and validation suite for a system built from the model package of that system empirically codifies its behavioural claims.In this paper, we present the "SmartTS methodology": A component-based and model-driven approach to generate modelbound test-suites for software components and systems.SmartTS methodology and tooling are not restricted to the robotics domain.The core contribution of SmartTS is support for test and validation suites derived from the model packages of components and systems.The test-suites in SmartTS are tightly bound to an application domain's data and service models as defined in the RobMoSys (EU H2020 project) compliant SmartMDSD toolchain.SmartTS does not break component encapsulation for system builders while providing them complete access to the way that component is tested and simulated.
Comparative Analysis of Back-propagation Neural Network and K-Means Clustering Algorithm in Fraud Detection in Online Credit Card Transactions

The ubiquitous nature of the internet had been a major driving force of the digital transformation in our world today. It has necessarily become the main medium for conducting electronic commerce (e-commerce) and online transactions. With this development, various means of possible payment methods have also emerged, such as electronic cash/ cheques, debit/credit cards, and electronic wallets. However, debit/credit cards are by far the most common payment methods employed. As a result, different credit card fraud activities have rapidly increased all over the world and are still evolving. This menace has drawn a lot of research interest and a number of techniques, with special emphasis on Data Mining, Expert System and Machine Learning (ML), as a means of identifying fraudulent behaviors. This paper examines and investigates two ML algorithms trained on public online credit card datasets, to analyze and identify fraudulent transactions. The BPNN and the K-means clustering ML algorithms were designed and implemented using Python Programming Languages. It was determined that the BPNN has a much higher accuracy of 93.1% as compared to the K-means which has an accuracy of 79.9%. Other metrics used to evaluate their performance also shows that the BPNN algorithm outperformed K-means algorithm, while the low prediction time of K-means gave it an advantage over the BPNN.
Cross-Domain Slot Filling as Machine Reading Comprehension

With task-oriented dialogue systems being widely applied in everyday life, slot filling, the essential component of task-oriented dialogue systems, is required to be quickly adapted to new domains that contain domain-specific slots with few or no training data. Previous methods for slot filling usually adopt sequence labeling framework, which, however, often has limited ability when dealing with the domain-specific slots. In this paper, we take a new perspective on cross-domain slot filling by framing it as a machine reading comprehension (MRC) problem. Our approach firstly transforms slot names into well-designed queries, which contain rich informative prior knowledge and are very helpful for the detection of domain-specific slots. In addition, we utilize the large-scale MRC dataset for pre-training, which further alleviates the data scarcity problem. Experimental results on SNIPS and ATIS datasets show that our approach consistently outperforms the existing state-of-the-art methods by a large margin.
PARADISE: Exploiting Parallel Data for Multilingual Sequence-to-Sequence Pretraining

Despite the success of multilingual sequenceto-sequence pretraining, most existing approaches rely on monolingual corpora, and do not make use of the strong cross-lingual signal contained in parallel data.In this paper, we present PARADISE (PARAllel & Denoising Integration in SEquence-to-sequence models), which extends the conventional denoising objective used to train these models by (i) replacing words in the noised sequence according to a multilingual dictionary, and (ii) predicting the reference translation according to a parallel corpus instead of recovering the original sequence.Our experiments on machine translation and cross-lingual natural language inference show an average improvement of 2.0 BLEU points and 6.7 accuracy points from integrating parallel data into pretraining, respectively, obtaining results that are competitive with several popular models at a fraction of their computational cost. 1 mono (x) =log P x|g φ (x)(1)Common noising functions include sentencepermutation and span masking (Lewis et al., 2020;Liu et al., 2020).While these methods obtain strong cross-lingual performance without parallel data, they are usually
Improved Descriptional Complexity Results for Simple Semi-Conditional Grammars

A simple semi-conditional (SSC) grammar is a form of regulated rewriting system where the derivations are controlled either by a permitting string alone or by a forbidden string alone and this condition is specified in the rule. The maximum length i (j, resp.) of the permitting (forbidden, resp.) strings serves as a measure of descriptional complexity known as the degree of such grammars. In addition to the degree, the numbers of nonterminals and of conditional rules are also counted into the descriptional complexity measures of these grammars. We improve on some previously obtained results on the computational completeness of SSC grammars by minimizing the number of nonterminals and / or the number of conditional rules for a given degree (i, j). More specifically we prove, using a refined analysis of a normal form for type-0 grammars due to Geffert, that every recursively enumerable language is generated by an SSC grammar of (i) degree (2, 1) with eight conditional rules and nine nonterminals, (ii) degree (3, 1) with seven conditional rules and seven nonterminals (iii) degree (4, 1) with six conditional rules and seven nonterminals and (iv) degree (4, 1) with eight conditional rules and six nonterminals.
Visualizing Errors in Rendered High Dynamic Range Images

A new error metric targeting rendered high dynamic range images is presented. Our method computes a composite visualization over a number of low dynamic range error maps of exposure compensated and tone mapped image pairs with automatically computed, or manually provided, parameters. We argue that our new error maps predict errors substantially better than metrics previously used in rendering. Source code is released with the hope that our work can be a useful tool for future research.
Analisis Perbandingan Kinerja Algoritma Naïve Bayes, Decision Tree-J48 dan Lazy-IBK

This research is focused on knowing the performance of the classification algorithms, namely Naïve Bayes, Decision Tree-J48 and K-Nearest Neighbor. The speed and the percentage of accuracy in this study are the benchmarks for the performance of the algorithm. This study uses the Breast Cancer and Thoracic Surgery dataset, which is downloaded on the UCI Machine Learning Repository website. Using the help of Weka software Version 3.8.5 to find out the classification algorithm testing. The results show that the J-48 Decision Tree algorithm has the best accuracy, namely 75.6% in the cross-validation test mode for the Breast Cancer dataset and 84.5% for the Thoracic Surgery dataset.
Impact of Guidance and Counselling Services on Academic Performance of Students in Some Selected Secondary Schools in Sokoto Metropolis

The study aims to evaluating the impact of guidance and counseling services on the academic performance of students in some selected secondary schools in Sokoto metropolis. Causal-comparative (Ex-post facto) research design was adopted for this study. This study involves a total of 200 subjects comprising 5 principals, 25 class teachers, 10 teacher counselors, and 160 students from the five selected schools. Data were collected using the four sets of questionnaires. Data analysis was done using descriptive and inferential statistics. The finding revealed that eighty percent (80%) of the students responded that their academic performance was changed after receiving counseling services. Seventy-two percent (72%) of the class teachers perceived the level of students’ academic performance after counseling as good, while 20% perceived it very good and 8% rated it bad. Guidance and counseling services have positively influenced the academic performance of the students and should be strengthened to improve the students’ performance in national examinations.
Development of augmented reality application for introducing tangible cultural heritages at the lampung museum using the multimedia development life cycle

Museum Lampung is one of the largest museums in Lampung Province which has a collection of tangible cultural heritage. If the museum visitors are seeking information about the collections, they will be assisted by museum guides orally. However, the limited number of guides are not enough to serve visitors during the school holidays. Therefore, to help visitors to find information about its cultural heritage collection, Augmented Reality (AR) technology was developed. AR is a technology that can display 3D objects in a real environment. The AR application that will be built is developed with the MDLC approach, where this method is suitable for developing multimedia applications. This research produces an application that can display 3D objects when the user's camera is directed to the collections of Museum Lampung and provides information related to these objects. Based on the test results on aspects of perceived usefulness, convenience, intention, and user friendliness, generally respondents answered "Agree" with a percentage of 83%. This indicates that the application is acceptable to the user.
The Implication of NESREA Act and Financial Performance on Environmental Disclosure in Nigeria

The study assesses the implication of compliance and enforcement of the NESREA Act, profitability, and Growth on environmental disclosure of cement companies in Nigeria. Secondary data comprising financial and non-financial information were source from annual accounts and reports of the sample companies, spanning a period of five years (2015 – 2019). Panel regression models were considered in assessing the implication of the variables under study. The findings reveal that NDI has a significant P-value which signifies that compliance with NESREA Act increases environmental disclosure by 2.9%. ROA also exerts a significant impact on environmental disclosure. This implies that a 1% increase in the profitability of the sample companies will increase environmental disclosure by 1.4%. Firm Size is also positive and exerts significant impact, by implication, the result suggested that an increase in the total revenue will lead to about 9% increase in environmental disclosure. Hence the study recommends among others that measuring, treatment, disclosure, and reporting of environmental activities need to be standardized and mandated to give a true and fair view of environmental management practices. These will not only protect the environment but will also enhance the firm's competitiveness and subsequently lead to high corporate performance.
Meta-Reinforcement Learning by Tracking Task Non-stationarity

Many real-world domains are subject to a structured non-stationarity which affects the agent's goals and the environmental dynamics. Meta-reinforcement learning (RL) has been shown successful for training agents that quickly adapt to related tasks. However, most of the existing meta-RL algorithms for non-stationary domains either make strong assumptions on the task generation process or require sampling from it at training time. In this paper, we propose a novel algorithm (TRIO) that optimizes for the future by explicitly tracking the task evolution through time. At training time, TRIO learns a variational module to quickly identify latent parameters from experience samples. This module is learned jointly with an optimal exploration policy that takes task uncertainty into account. At test time, TRIO tracks the evolution of the latent parameters online, hence reducing the uncertainty over future tasks and obtaining fast adaptation through the meta-learned policy. Unlike most existing methods, TRIO does not assume Markovian task-evolution processes, it does not require information about the non-stationarity at training time, and it captures complex changes undergoing in the environment. We evaluate our algorithm on different simulated problems and show it outperforms competitive baselines.
Finite-time topological identification of complex network with time delay and stochastic disturbance

The topology identification issue of complex stochastic network with delay and stochastic disturbance is mainly introduced in this paper.It is known the time delay and stochastic disturbance are ubiquitous in real network, and they will impair the identification of network topology, and the topology capable of identifying the network within specific time is desired on the other hand.Based on these discussions, the finite-time identification method is proposed to solve similar issues problems.The validity of theoretical results is proved with the stochastic dynamical system stability theory and finite-time stability theory.Finally, a simple numerical simulation is proposed to verify the feasibility of the method.
Development of Intelligent Tools for Detecting Resource-intensive Database Queries

The detection of resource-intensive queries which consume an excessive amount of time, processor, disk, and memory resources is one of the most popular vulnerabilities of Database Management Systems (DBMS).The tools for monitoring and optimizing queries typically used in modern DBMS were analyzed, and their shortcomings were identified.Subsequently, the relevance of new intelligent tools' development for timely and reliable detection of resource-intensive queries to databases was distinctly justified.The study concluded a set of analysis of an extended statistical parameter which indicated to be of interest for identifying resource-intensive queries.The initial set of queries' parameters reduced by two consecutive methods.Firstly, normalizing the set of indicators using a sigmoid function.Secondly, selecting a finite number of principal components based on the Cattell test.Whereas the clustering of a set of queries performed using self-organizing Kohonen maps.Suggestions for further studies in the classification algorithm context were indicated in lights of the study's conclusions.
Optimization of the Alumni Site of SMK Muhammadiyah Sintang

&lt;p&gt;Alumni have an important role in the development of educational institutions in the public sphere, but regarding alumni, many schools have not used alumni in school contributions, one of which is SMK Muhammadiyah Sintang. Regarding information, SMK Muhammadiyah alumni have not implemented an online alumni data collection and management system. So far, data collection on alumni is still carried out by recording in written documents which results in data management that cannot be done quickly, and errors often occur, and it is difficult to update information about alumni. In conducting research, the authors use existing literature and literature study materials to build alumni websites using the WSDM (Website Development Design Method) research method, website development methods using Extreme Programming (XP), and the development of alumni information systems using a bootstrap framework, providing designs responsive web that gives the appearance of the website will automatically adjust the screen size of the browser used. The purpose of this research is to design or create a website that can help schools quickly and accurately in the process of data collection and data management of alumni in obtaining information about alumni.&lt;/p&gt;
Partition Function Estimation: A Quantitative Study

Probabilistic graphical models have emerged as a powerful modeling tool for several real-world scenarios where one needs to reason under uncertainty. A graphical model's partition function is a central quantity of interest, and its computation is key to several probabilistic reasoning tasks. Given the #P-hardness of computing the partition function, several techniques have been proposed over the years with varying guarantees on the quality of estimates and their runtime behavior. This paper seeks to present a survey of 18 techniques and a rigorous empirical study of their behavior across an extensive set of benchmarks. Our empirical study draws up a surprising observation: exact techniques are as efficient as the approximate ones, and therefore, we conclude with an optimistic view of opportunities for the design of approximate techniques with enhanced scalability. Motivated by the observation of an order of magnitude difference between the Virtual Best Solver and the best performing tool, we envision an exciting line of research focused on the development of portfolio solvers.
FPGA Realization of Logic Gates using Neural Networks

The real time systems are designed by using analog and digital sub systems with control logic.In the design of digital sub systems the logic gates are the major building blocks.The Neural Networks found many applications in the areas of pattern recognition, prediction, artificial intelligence and other applications.Most of the Neural Network designs are realized by using software domain and hardware domains.This paper presents the hardware realization of logic gates using Neural Network.The logic gates like NAND, AND, NOR, OR, NOT, BUFFER are realized by using single layer neural network.The XNOR and XOR logic gates are realized by using structural representation of neural network based universal NAND gates.All the logic gates are realized by using Verilog HDL with Xilinx Vivado Design suite targeting for Xilinx Zynq-7000 SoC Evaluation Board.The Neural Network based logic gates are simulated for different test cases.
Multi-Target Invisibly Trojaned Networks for Visual Recognition and Detection

Visual backdoor attack is a recently-emerging task which aims to implant trojans in a deep neural model. A trojaned model responds to a trojan-invoking trigger in a fully predictable manner while functioning normally otherwise. As a key motivating fact to this work, most triggers adopted in existing methods, such as a learned patterned block that overlays a benigh image, can be easily noticed by human. In this work, we take image recognition and detection as the demonstration tasks, building trojaned networks that are significantly less human-perceptible and can simultaneously attack multiple targets in an image. The main technical contributions are two-folds: first, under a relaxed attack mode, we formulate trigger embedding as an image steganography-and-steganalysis problem that conceals a secret image in another image in a decipherable and almost invisible way. In specific, a variable number of different triggers can be encoded into a same secret image and fed to an encoder module that does steganography. Secondly, we propose a generic split-and-merge scheme for training a trojaned model. Neurons are split into two sets, trained either for normal image recognition / detection or trojaning the model. To merge them, we novelly propose to hide trojan neurons within the nullspace of the normal ones, such that the two sets do not interfere with each other and the resultant model exhibits similar parameter statistics to a clean model. Comprehensive experiments are conducted on the datasets PASCAL VOC and Microsoft COCO (for detection) and a subset of ImageNet (for recognition). All results clearly demonstrate the effectiveness of our proposed visual trojan method.
Code Switching In Instruction: Pre-Service Teachers’ Lived Experiences

Code switching is really needed in instruction and should be implemented to ensure students’ understanding towards various things (e.g. translating of difficult vocabulary, lessons to be acquired and even in giving instructions), especially for teaching students in lower levels. It can also help in teaching students with multiple intelligences, and in general, makes the pre-service teachers’ life easy inside the room in terms of clarifying things due to any confusion that may arise in the duration of the discussion. This survey interview research explored the Pre-Service Teachers of Ramon Magsaysay Technological University lived experiences in using code switching in instruction. The study found out that the Pre-Service teachers prefer to use code switching in instruction. The Pre-Service teachers believe that using code-switching in class can help them gain students’ interest, manage the class, and make their students understand a particular lesson. Aside from Mattson and Burenholt’s three functions of code-switching, pre-service teachers use code-switching in class so that they can express themselves more during class discussions. The Pre-Service Teachers believe that although code-switching might be useful in class, too much use of it can lead to students being used in speaking their native tongue, thus, weakening the chances to improve their skills in using the English language. The Pre-Service Teachers agreed that there is a higher success in students’ academic achievement when code switching is being applied in instruction. The study recommends that the Department of Education must set clear guidelines regarding the use of code-switching inside an English class. Schools, in all levels, must strengthen their curriculum so that students’ communicative competence will further be developed.
Relational Gating for ''What If'' Reasoning

This paper addresses the challenge of learning to do procedural reasoning over text to answer "What if..." questions. We propose a novel relational gating network that learns to filter the key entities and relationships and learns contextual and cross representations of both procedure and question for finding the answer. Our relational gating network contains an entity gating module, relation gating module, and contextual interaction module. These modules help in solving the "What if..." reasoning problem. We show that modeling pairwise relationships helps to capture higher-order relations and find the line of reasoning for causes and effects in the procedural descriptions. Our proposed approach achieves the state-of-the-art results on the WIQA dataset.