Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence Control in 4D Cylindrical Space for Industrial Robotic Applications

This article argues that an efficient artificial intelligence control algorithm needs the built-in symmetries of an industrial robot manipulator to be further characterized and exploited. The product of this enhancement is a four-dimensional (4D) discrete cylindrical grid space that can directly replace complex robot models. A* is chosen for its wide use among such algorithms to study the advantages and disadvantages of steering the robot manipulator within the 4D cylindrical discrete grid. The study shows that this approach makes it possible to control a robot without any specific knowledge of the robot kinematic and dynamic models at planning and execution time. In fact, the robot joint positions for each grid cell are pre-calculated and stored as knowledge, then quickly retrieved by the pathfinding algorithm when needed. The 4D cylindrical discrete space has both the advantages of the configuration space and the three-dimensional Cartesian workspace of the robot. Since path optimization is the core of any search algorithms, including A*, the 4D cylindrical grid provides for a search space that can embed further knowledge in form of cell properties, including the presence of obstacles and volumetric occupancy of the entire industrial robot body for obstacle avoidance applications. The main trade-off is between a limited capacity for pre-computed grid knowledge and the path search speed. This innovative approach encourages the use of search algorithms for industrial robotic applications, opens up to the study of other robot symmetries present in different robot models and lays a foundation for the application of dynamic obstacle avoidance algorithms.
Automated Recommendation of Templates for Legal Requirements

Context: In legal requirements elicitation, requirements analysts need to extract obligations from legal texts. However, legal texts often express obligations only indirectly, for example, by attributing a right to the counterpart. This phenomenon has already been described in the Requirements Engineering (RE) literature [1]. Objectives: We investigate the use of requirements templates for the systematic elicitation of legal requirements. Our work is motivated by two observations: (1) The existing literature does not provide a harmonized view on the requirements templates that are useful for legal RE; (2) Despite the promising recent advancements in natural language processing (NLP), automated support for legal RE through the suggestion of requirements templates has not been achieved yet. Our objective is to take steps toward addressing these limitations. Methods: We review and reconcile the legal requirement templates proposed in RE. Subsequently, we conduct a qualitative study to define NLP rules for template recommendation. Results and Conclusions: Our contributions consist of (a) a harmonized list of requirements templates pertinent to legal RE, and (b) rules for the automatic recommendation of such templates. We evaluate our rules through a case study on 400 statements from two legal domains. The results indicate a recall and precision of 82,3% and 79,8%, respectively. We show that introducing some limited interaction with the analyst considerably improves accuracy. Specifically, our human-feedback strategy increases recall by 12% and precision by 10,8%, thus yielding an overall recall of 94,3% and overall precision of 90,6%.
Potential Bottleneck and Measuring Performance of Serverless Computing: A Literature Study

Trending form of cloud computing is Serverless computing, where developer just needs to focus on his code rather than worrying about server management. In serverless computing, application is nothing but collection of one or more functions, written for specific business functionality, which triggers on an event. There are various cloud service providers, i.e. Amazon, Microsoft, Google, IBM, etc. who provide serverless services, on pay as you use and auto scalable solution to execute the application code as a function. The developer just needs to upload the code for execution. The performance of the serverless computing may vary due to dynamic configuration of the solution, technologies and different technology used by the service provider.This paper reviews various past and recent work in the serverless computing to identify possible bottlenecks and the scope of measuring performance of serverless computing. It will also put some light to leverage machine learning in various possible ways to do performance engineering for future research.
Ranking Secure Coding Guidelines for Software Developer Awareness Training in the Industry

Secure coding guidelines are essential material used to train and raise awareness of software developers on the topic of secure software development. In industrial environments, since developer time is costly, and training and education is part of non-productive hours, it is important to address and stress the most important topics first. In this work, we devise a method, based on publicly available real-world vulnerability databases and secure coding guideline databases, to rank important secure coding guidelines based on defined industry-relevant metrics. The goal is to define priorities for a teaching curriculum on raising cybersecurity awareness of software developers on secure coding guidelines. Furthermore, we do a small comparison study by asking computer science students from university on how they rank the importance of secure coding guidelines and compare the outcome to our results.
Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm

Personalized tag recommender systems recommend a set of tags for items based on users' historical behaviors, and play an important role in the collaborative tagging systems. However, traditional personalized tag recommendation methods cannot guarantee that the collaborative signal hidden in the interactions among entities is effectively encoded in the process of learning the representations of entities, resulting in insufficient expressive capacity for characterizing the preferences or attributes of entities. In this paper, we proposed a graph neural networks boosted personalized tag recommendation model, which integrates the graph neural networks into the pairwise interaction tensor factorization model. Specifically, we consider two types of interaction graph (i.e. the user-tag interaction graph and the item-tag interaction graph) that is derived from the tag assignments. For each interaction graph, we exploit the graph neural networks to capture the collaborative signal that is encoded in the interaction graph and integrate the collaborative signal into the learning of representations of entities by transmitting and assembling the representations of entity neighbors along the interaction graphs. In this way, we explicitly capture the collaborative signal, resulting in rich and meaningful representations of entities. Experimental results on real world datasets show that our proposed graph neural networks boosted personalized tag recommendation model outperforms the traditional tag recommendation models.
Variance Reduction for Better Sampling in Continuous Domains

Design of experiments, random search, initialization of population-based methods, or sampling inside an epoch of an evolutionary algorithm uses a sample drawn according to some probability distribution for approximating the location of an optimum. Recent papers have shown that the optimal search distribution, used for the sampling, might be more peaked around the center of the distribution than the prior distribution modelling our uncertainty about the location of the optimum.We confirm this statement, provide explicit values for this reshaping of the search distribution depending on the population size $$\lambda $$ and the dimension d, and validate our results experimentally.
Genetic Algorithm Assisted Support Vector Machine for M-QAM Classification

Automatic modulation classification (AMC) has wide spread applications in today's communication system.AMC has vast applications both in military as well as civilian.In intelligent communication systems such as software defined radios networks and cognitive radio networks, AMC is the most important issue, when there is no prior information about the signal.In this research article, pattern recognition approach has been utilized for classification of M-ARY quadrature amplitude modulated (M-QAM) signals.Higher order cumulants are selected as feature set and Genetic Algorithm assisted Support Vector Machine (SVM) classifier is used for classification of M-QAM signals.The performance of classifier is evaluated on fading channels in the presence of additive white Guassain noise.The classification accuracy is also compared with and without optimized classifier.
Scaling agile on large enterprise level with self-service kits to support autonomous teams

Organizations are looking for ways of establishing agile and lean delivery processes.In this paper, we propose a particular way which based on self-service kits (SSK's).The SSK approach can be used to share expert knowledge in an agile and scalable way to the teams by offering them approaches, methods and tools with background information about the addressed topic.An SSK is provided as a digital bundle of artifacts that help solving an issue related to agile teams.Built upon the pullprinciple, it supports team autonomy during teams' delivery procedures.An SSK addresses generic as well as domain specific topics.As all SSK's share a common structured approach to supporting an agile organization, they help systematically scaling expert knowledge.This leverages establishing best practices elaborated by experts in a large scale organization in a native agile manner.As an SSK is structured as a "how-to" guide including templates for learning by doing, it helps emphasizing quality aspects too.We demonstrate an example of the systematic application of the SSK approach as well as its scaling in the Volkswagen Group IT.
AMP Chain Graphs: Minimal Separators and Structure Learning Algorithms

This paper deals with chain graphs (CGs) under the Andersson–Madigan–Perlman (AMP) interpretation. We address the problem of finding a minimal separator in an AMP CG, namely, finding a set Z of nodes that separates a given non-adjacent pair of nodes such that no proper subset of Z separates that pair. We analyze several versions of this problem and offer polynomial time algorithms for each. These include finding a minimal separator from a restricted set of nodes, finding a minimal separator for two given disjoint sets, and testing whether a given separator is minimal. To address the problem of learning the structure of AMP CGs from data, we show that the PC-like algorithm is order dependent, in the sense that the output can depend on the order in which the variables are given. We propose several modifications of the PC-like algorithm that remove part or all of this order-dependence. We also extend the decomposition-based approach for learning Bayesian networks (BNs) to learn AMP CGs, which include BNs as a special case, under the faithfulness assumption. We prove the correctness of our extension using the minimal separator results. Using standard benchmarks and synthetically generated models and data in our experiments demonstrate the competitive performance of our decomposition-based method, called LCD-AMP, in comparison with the (modified versions of) PC-like algorithm. The LCD-AMP algorithm usually outperforms the PC-like algorithm, and our modifications of the PC-like algorithm learn structures that are more similar to the underlying ground truth graphs than the original PC-like algorithm, especially in high-dimensional settings. In particular, we empirically show that the results of both algorithms are more accurate and stabler when the sample size is reasonably large and the underlying graph is sparse
An approach for non-singleton generalized Type-2 fuzzy classifiers

In recent years, successful applications of singleton fuzzy inference systems have been made in a plethora of different kinds of problems, for example in the areas of control, digital image processing, time series prediction, fault detection and classification. However, there exists another relatively less explored approach, which is the use of non-singleton fuzzy inference systems. This approach offers an interesting way for handling uncertainty in complex problems by considering inputs with uncertainty, while the conventional Fuzzy Systems have their inputs with crisp values (singleton systems). Non-singleton systems have as inputs Type-1 membership functions, and this difference increases the complexity of the fuzzification, but provides the systems with additional non-linearities and robustness. The main limitations of using a non-singleton fuzzy inference system is that it requires an additional computational overhead and are usually more difficult to apply in some problems. Based on these limitations, we propose in this work an approach for efficiently processing non-singleton fuzzy systems. To verify the advantages of the proposed approach we consider the case of general type-2 fuzzy systems with non-singleton inputs and their application in the classification area. The main contribution of the paper is the implementation of non-singleton General Type-2 Fuzzy Inference Systems for the classification task, aiming at analyzing its potential advantage in classification problems. In the present paper we propose that the use of non-singleton inputs in Type-2 Fuzzy Classifiers can improve the classification rate and based on the realized experiments we can observe that General Type-2 Fuzzy Classifiers, but with non-singleton fuzzification, obtain better results in comparison with respect to their singleton counterparts.
The Effects of Facial Expressions on Face Biometric System’s Reliability

The human mood has a temporary effect on the face shape due to the movement of its muscles. Happiness, sadness, fear, anger, and other emotional conditions may affect the face biometric system’s reliability. Most of the current studies on facial expressions are concerned about the accuracy of classifying the subjects based on their expressions. This study investigated the effect of facial expressions on the reliability of a face biometric system to find out which facial expression puts the biometric system at greater risk. Moreover, it identified a set of facial features that have the lowest facial deformation caused by facial expressions to be generalized during the recognition process, regardless of which facial expression is presented. In order to achieve the goal of this study, an analysis of 22 facial features between the normal face and six universal facial expressions is obtained. The results show that the face biometric systems are affected by facial expressions where the disgust expression achieved the most dissimilar score, while the sad expression achieved the lowest dissimilar score. Additionally, the study identified the five and top ten facial features that have the lowest facial deformations on the face shape in all facial expressions. Besides that, the relativity score showed less variances between the sample using the top facial features. The obtained results of this study minimized the false rejection rate in the face biometric system and subsequently the ability to raise the system’s acceptance threshold to maximize the intrusion detection rate without affecting the user convenience.
Emotion Detection of Twitter Post using Multinomial Naive Bayes

In research fields, emotion analysis and opinion mining using data from different platforms are up burning field. In this paper, we tried to represent sentiment of twitter data on core text. But tweets can only be in 140 characters, with lots of noise. Tweets contain few words which is in short forms, ambiguous and noisy, so it is hard to figure out the user's sentiments. So, it becomes very difficult to have the right opinion with these noise and short forms of words. The main job is to preprocess the data and then extract the features from there. But preprocessing demands, different theories, methods, steps which always varies. Our goal is to improve the outcomes using Naive Bayes classifier and an almost a good trained data set. Finally, we have our average accuracy for happy class 60%, surprise class 61%, relief class it is 71% and worry class has the highest 81%, by using unigram model for preprocessing. On the other hand, using unigram with POS tag model we have average accuracy of 63% same for happy and surprise class, 72% for relief and 83% for worry class.
Promoting quantum correlations in deterministic quantum computation with a one-qubit model via postselection

The deterministic quantum computation with one qubit (DQC1) model is a restricted model of quantum computing able to calculate efficiently the normalized trace of a unitary matrix. In this work, we analyze the quantum correlations called entanglement, Bell's nonlocality, quantum discord, and coherence generated by the DQC1 circuit considering only two qubits (auxiliary and control). For the standard DQC1 model, only quantum discord and coherence appear. By introducing a filter in the circuit we purify the auxiliary qubit, taking it out from the totally mixed state and consequently promoting other quantum correlations between the qubits, such as entanglement and Bell's nonlocality. Through the optimization of the purification process, we conclude that even a small purification is enough to generate entanglement and Bell's nonlocality. We obtain, that by applying the purification process repeatedly an average of 12 times, the auxiliary qubit becomes $99%$ pure. In this situation, almost maximally entangled states are achieved, which almost maximally violate Bell's inequality. This result suggests that with a simple modification, the DQC1 model can be promoted to a universal model of quantum computing.
A Sentiment Analysis System for the Hindi Language by Integrating Gated Recurrent Unit with Genetic Algorithm

The growing availability and popularity of opinion rich resources such as blogs, shopping websites, review portals, and social media platforms have attracted several researchers to perform the sentiment analysis task. Unlike English, Chinese, Spanish, etc. the availability of Indian languages such as Hindi, Telugu, Tamil, etc., over the web have also been increased at a rapid rate. This research work understands the growing popularity of Hindi language in the web domain and considered it for the task of sentiment analysis. The research work analyses the hidden sentiments from the movie reviews collected from the review section of Hindi language e-newspapers. The reviews are multilingual, which makes sentiment analysis a challenging task. To overcome the challenges, this research work proposes a deep learning based approach where a Gated Recurrent Unit network is combined with the Hindi word embedding model. The strategy enables the network to efficiently capture the semantic and syntactic relation between Hindi words and accurately classify them into the sentiment classes. Gated Recurrent Unit network's performance is profoundly dependent upon the selection of its hyper-parameters; therefore, this research work also utilizes a Genetic Algorithm to automatically build a gated recurrent network architecture enabling it to select the best optimal hyper-parameters. It has been observed that the proposed Genetic Algorithm-Gated Recurrent Unit (GA-GRU) model is effective and achieves breakthrough performance results on the Hindi movie review dataset as compared to other traditional resource-based and machine learning approaches
Low Cost and Low Power Stacked Sparse Autoencoder Hardware Acceleration for Deep Learning Edge Computing Applications

Nowadays, Deep Learning DL becoming more and more interesting in many areas, such as genomics, security, data analysis, image, and video processing. However, DL requires more and more powerful and parallel computing. The calculation performed by super-machines equipped with powerful processors, such as the latest GPUs. Despite their power, these computing units consume a lot of energy, which makes their use very difficult in small embedded systems and edge computing. To overcome the problem for which we must keep the maximum performance and satisfy the power constraint, it is necessary to use a heterogeneous strategy. Some solutions are promising when using less energy consuming electronic circuits, such as FPGAs associated with less expensive topologies such as Stacked Sparse Autoencoders. Our target architecture is the Xilinx ZYNQ 7020 SoC, which combines a dual-core ARM processor and an FPGA in the same chip. In the interest of flexibility, we decided to leverage the performance of Xilinx's high-level synthesis tools, evaluate and choose the best solution in terms of size and performance of the data exchange, synchronization and pipeline processing. The results show that our implementation gives high performance at very low energy consumption. Indeed, the evaluation of our accelerator shows that it can classify 1160 MNIST images per second, consuming only 0.443 W; 2.4 W for the entire system. More than the low energy consumption and the high performance, the platform used only costs $ 125.
Automatic Evaluation vs. User Preference in Neural Textual QuestionAnswering over COVID-19 Scientific Literature

We present a Question Answering (QA) system that won one of the tasks of the Kaggle CORD-19 Challenge, according to the qualitative evaluation of experts.The system is a combination of an Information Retrieval module and a reading comprehension module that finds the answers in the retrieved passages.In this paper we present a quantitative and qualitative analysis of the system.The quantitative evaluation using manually annotated datasets contradicted some of our design choices, e.g. the fact that using QuAC for fine-tuning provided better answers over just using SQuAD.We analyzed this mismatch with an additional A/B test which showed that the system using QuAC was indeed preferred by users, confirming our intuition.Our analysis puts in question the suitability of automatic metrics and its correlation to user preferences.We also show that automatic metrics are highly dependent on the characteristics of the gold standard, such as the average length of the answers.
EFFECTIVE CLASSROOM MANAGEMENT TECHNIQUES FOR CURRICULUM OF 21ST CENTURY

Schooling aims to grow multilateral perfect generation for the future life. However, teaching children no matter what kinds of subject should be taught, is really complicated and challenging. As well as teachers are facing different issues in teaching process and communicating with children. In that time, they are required to use some useful strategies to handle learners easily. For this reason, this paper purposes to demonstrate the importance and effective strategies of classroom management and focuses on some principles of this term in the modern education.