Reviewerly/exHarmony huggingface dataset:
RESEARCH OF GENERATIVE ADVERSARIAL NETWORKS FOR THE SYNTHESIS OF NEW MEDICAL DATA

The paper considers the development of a Generative Adversarial Network (GAN) for the synthesis of new medical data. The developed GAN consists of two models trained simultaneously: a generative model (G - Generator), estimating the distribution of data, and a discriminating model (D - Discriminator), which estimates the probability that the sample is obtained from the training data, and not from generator G. To create G, we used own neural network architecture based on convolutional layers using experimental functions of Tensor Flow Addons. To create discriminator D, we used a Transfer Learning (TL) approach. The training procedure is to maximize the likelihood that discriminator D will make a mistake. Experiments show that the proposed GAN architecture completely copes with the task of synthesizing of new medical data.
Sentinel: Understanding Data Systems

The complexity of modern data systems and applications greatly increases the challenge in understanding system behaviour and diagnosing performance problems. When these problems arise, system administrators are left with the difficult task of remedying them by relying on large debug log files, vast numbers of metrics, and system-specific tooling. We demonstrate the Sentinel system, which enables administrators to analyze systems and applications by building models of system execution and comparing them to derive key differences in behaviour. The resulting analyses are then presented as system reports to administrators and developers in an intuitive fashion. Users of Sentinel can locate, identify and take steps to resolve the reported performance issues. As Sentinel's models are constructed online by intercepting debug logging library calls, Sentinel's functionality incurs little overhead and works with all systems that use standard debug logging libraries.
Predicting the Fix Time of a Reported Bug using Radoop: A Big Data Approach

The bug is reported by different users of the software and gets fixed after it is verified and diagnosed. Once a bug is reported, it is triaged. The fix time of a bug is the time difference between bug resolved date and open date of the bug. The prior prediction of bug fix time will assist in resource management and in predicting the quality of the software product. The size of software repositories which consist of bugs, code, and archive communication is also increasing with enormous rate in the open-source environment. In this scenario, it is difficult to handle the large amount of datasets with the traditional approach. In this paper, we have used an extension called Radoop to enable the integration of RapidMiner with Hadoop to handle the big data of bug reports. We have used classification techniques Naïve Bayes (NB) based on Hive and Decision Tree (DT) based on Spark to predict bug fix time of a reported bug. The experimental result is validated on 1,23,849 bug reports of Eclipse and 67,178 bug reports of Mozilla projects. The experimental evaluation has been validated on accuracy performance of bug fix time prediction. We observed that the proposed approach improves the accuracy as compared with work mentioned in Panjer (Proceedings of the fourth international workshop on mining software repositories, pp 1–4, 2007) [20].
Facial Recognition-Based Automatic Door Access System Using Extreme Learning Machine

Abstract Facial recognition is one of the best forms of security since it is biometric-based security that uses biological features of the face. One of the artificial neural networks that can be implemented in the face recognition case is Extreme Learning Machine with the assistance of Local Binary Pattern in obtaining the facial features. The testing process in this study used 150 face images for training data and 60 face images for test data. The test was carried out using several three parameters of the hidden neuron numbers, namely 10, 30, and 50, and also five parameters of the image condition, namely feature, expression, face direction, lighting, and webcam distance. The system achieved the highest accuracy of 90% using 50 hidden neurons.
End-to-End Multilingual Speech Recognition System with Language Supervision Training

End-to-end (E2E) multilingual automatic speech recognition (ASR) systems aim to recognize multilingual speeches in a unified framework. In the current E2E multilingual ASR framework, the output prediction for a specific language lacks constraints on the output scope of modeling units. In this paper, a language supervision training strategy is proposed with language masks to constrain the neural network output distribution. To simulate the multilingual ASR scenario with unknown language identity information, a language identification (LID) classifier is applied to estimate the language masks. On four Babel corpora, the proposed E2E multilingual ASR system achieved an average absolute word error rate (WER) reduction of 2.6% compared with the multilingual baseline system.
Generalized Points-to Graphs

Computing precise (fully flow- and context-sensitive) and exhaustive (as against demand-driven) points-to information is known to be expensive. Top-down approaches require repeated analysis of a procedure for separate contexts. Bottom-up approaches need to model unknown pointees accessed indirectly through pointers that may be defined in the callers and hence do not scale while preserving precision. Therefore, most approaches to precise points-to analysis begin with a scalable but imprecise method and then seek to increase its precision. We take the opposite approach in that we begin with a precise method and increase its scalability. In a nutshell, we create naive but possibly non-scalable procedure summaries and then use novel optimizations to compact them while retaining their soundness and precision. For this purpose, we propose a novel abstraction called the generalized points-to graph (GPG), which views points-to relations as memory updates and generalizes them using the counts of indirection levels leaving the unknown pointees implicit. This allows us to construct GPGs as compact representations of bottom-up procedure summaries in terms of memory updates and control flow between them. Their compactness is ensured by strength reduction (which reduces the indirection levels), control flow minimization (which removes control flow edges while preserving soundness and precision), and call inlining (which enhances the opportunities of these optimizations). The effectiveness of GPGs lies in the fact that they discard as much control flow as possible without losing precision. This is the reason GPGs are very small even for main procedures that contain the effect of the entire program. This allows our implementation to scale to 158 kLoC for C programs. At a more general level, GPGs provide a convenient abstraction to represent and transform memory in the presence of pointers. Future investigations can try to combine it with other abstractions for static analyses that can benefit from points-to information.
A trajectory planning method based on feedforward compensation and quintic polynomial interpolation

The live-working robot is placed on the aerial working platform to connect the drainage wire. Due to the sloshing of the manipulator base on the platform, the effective operation is seriously affected by the end position error of the manipulator. Aiming at this problem, a trajectory planning method based on feedforward compensation combined with quintic polynomial interpolation is proposed in this paper. The angle and angular velocity constraints of every joint of the manipulator are determined by establishing the kinematic model of the manipulator. Analyze the sloshing characteristics of the base of the manipulator, and combine the movement time of the manipulator from the initial point to the target point to predict the position error of the end of manipulator. The feedforward compensation is performed to correct the target point, and quintic polynomial interpolation is used to plan the compensation end trajectory. Based on the analysis of the sloshing law of the base of the manipulator, the effectiveness of the method is verified by simulation experiments, and the target point can be reached accurately.
FLY: A Domain-Specific Language for Scientific Computing on FaaS

Cloud Computing is widely recognized as distributed computing paradigm for the next generation of dynamically scalable applications. Recently a novel service model, called Function-as-a-Service (FaaS), has been proposed, that enables users to exploit the computational power of cloud infrastructures, without the need to configure and manage complex computations systems. FaaS paradigm represents an opportunity to easily develop and execute extreme-scale applications as it allows fine-grain decomposition of the application with a much more efficient scheduling on cloud provider infrastructure. We introduce fly, a domain-specific language for designing, deploying and executing scientific computing applications by exploiting the FaaS service model on different cloud infrastructures. In this paper, we present the design and the language definition of fly on several computing (local and FaaS) back-ends: Symmetric multiprocessing (SMP), Amazon AWS Lambda, Microsoft Azure Functions, Google Cloud Functions, and IBM Bluemix/Apache OpenWhisk. We also present the first fly source-to-source compiler, publicly available on GitHub, which supports SMP and AWS back-ends.
Prediction of Service-Level Agreement Violation in Cloud Computing Using Bayesian Regularisation

Service-level agreement (SLA) is a contract between the cloud service provider and consumer which includes terms and conditions of service parameters. The cloud service provider has to commit to service-level agreements, which ensures a specific quality of performance. A certain level of penalty is set if the provider performs SLA violations. Managing and applying penalties has become a critical issue for cloud computing. It is found to be of paramount importance that the violations are predicted well in advance so that the necessary measures can be taken. In this research work, various proactive SLA prediction models were designed, utilising the power of machine learning. We have used real-world data sets to highlight the accurate models for violation prediction in a cloud environment. Seeing violation prediction as a classification problem where incoming requests need to be predicted for the violation, we have used Bayesian regularised artificial neural network (BRANN) on different samples of real-world data set. Both the models show remarkable performance for predicting SLA violation. BRANN shows a significantly good average accuracy of 97.6%.
Exploiting the Most Similar Cases Using Decision Tree to Render Recommendation

The Internet connects globally available large information with growing numbers of users around the world. This information is increasing at a fast pace which leads to the difficulty in finding the right user, i.e. it becomes difficult to find what exactly the user wants. To meet the user requirements, the best option is a recommendation based on the user’s need. This paper provides a solution to the filtration of millions of cases stored in a prehistoric database, to generate the recommendation system for an earthquake. The algorithm presented in this paper worked on mechanism for matching similarity of input cases with stored cases. It generates a recommendation based on the similarity between the input case and the stored cases. This paper filters the cases of earthquake disaster domain to create a decision tree using the ID3 algorithm which in turn reduces the computation to match similar cases.
Towards evaluating complex ontology alignments

Abstract The development of semi-automated and automated ontology alignment techniques is an important part of realizing the potential of the Semantic Web. Until very recently, most existing work in this area was focused on finding simple (1:1) equivalence correspondences between two ontologies. However, many real-world ontology pairs involve correspondences that contain multiple entities from each ontology. These ‘complex’ alignments pose a challenge for existing evaluation approaches, which hinders the development of new systems capable of finding such correspondences. This position paper surveys and analyzes the requirements for effective evaluation of complex ontology alignments and assesses the degree to which these requirements are met by existing approaches. It also provides a roadmap for future work on this topic taking into consideration emerging community initiatives and major challenges that need to be addressed.
B2B and B2C E-commerce Platform for Mining Equipment Suppliers

E-commerce is widely used for buying and selling of products using the internet. It is the preferred way of shopping because of its convenience. This paper discusses the development of an ecommerce platform with inventory integration for a mining equipment supplier companies. Because of e-commerce's low barrier of entry, many vendors and distributors sell directly to customers. With this, there is an opportunity to automate the business processes of the mid-size mining equipment supplier company to improve the service and sales performance. In turn, it can ease the management profit of the distributor channel. The system produces an e-commerce web application that can be overall access by the administrator. Likewise, distributors and customers' accounts can be partly access through the web application. This research includes modules to improve the system such as admin module, vendor module, distributor module, customer module and IT management system.
Drown Prevention and Flood prediction using smart embedded devices

Drowning and Flood becomes major negative impact to the mankind and infrastructure. Drowning is caused by the when person go into deeper areas or else due to a person's health condition. Flood is a natural disaster commonly caused by the run of rivers due to excessively highly rainy season or due to environment effect or global warming effect. Hence IoT with sensor technology support us to efficiently cover up this impact for mankind. This research support for each mankind to survive from the drowning threat and this may help for people to survive from the natural disaster like flooding. This research presents two IoT Devices consisting with sensors and monitoring system to determine the flood level, the user condition and water level when user in the water. Then generating alert via the mobile application to notify the user. Machine learning algorithms were implemented to perform the level classification.
Comprehensive Forensic Data Extraction and Representation System for Windows Registry

Computer forensics is the process of methodically examining computer media (hard disks, diskettes, tapes, etc.) for evidence. When considering computer forensics, registry forensics plays a vital role because it helps identifying system configurations, application details, user configurations and helps in finding registry malware. Therefore, it is significant to extract this registry information to simplify the investigations for forensic professionals. At present, tools are limited to few commonly used registry information and there is a much border area to cover. Investigators have to manually search for the registries for required artifacts. But the nature and complexity of the registry file structure limits most of the investigators using these registries. Limiting this registry analysis only to the physical registry files and not considering the ability of extraction of registry information from Volatile Memory is another significant issue in registry forensics. Because these tools are only rely on the physical registry files and cannot extract registry artifacts from Volatile Memory. In order to cater to this problem, this research provide a comprehensive solution to registry analysis. This system is capable of extracting registry information from both physical registry files and Volatile Memory.
An Empirical Study on Joint Entities-relations Extraction of Chinese Text Based on BERT

Joint Entities-relations extraction (JERE), which can solve the error propagation problem in piped-line extraction, uses a single model to combine the two tasks by different strategies. The neural network-based methods for the joint extraction show better performances than the state-of-the-art results, but the constructed model are complicated. hyper-parameters selection and model training are also difficult. For simplifying the model construction and improving the accuracy of extraction, we transferred the joint extraction problem to a sequential label problem formally, combined different strategies in Encoding layer and Decoding Layer of end-to-end model, focused on the using of Bert on Encoding layer with different tagging scheme, then designed the comparative experiments on Chinese text dataset, the empirical results showed that 1)Using Bert as the embedding layer of the model is better than word2vec, but not so significant. 2)The final results of Bert followed by complex neural net-works such as BiLSTM and simple neural networks such as MLP on experiments are very close, which means Bert can simplify the model construction, it also means that we can get a good result with less training time by a simple neural networks. 3)The new tagging scheme is better than the BIO tagging scheme, and if it is combined with the pre-training language model, we can quickly build a simple and sufficiently accurate model on JERE tasks.
An evolutionary framework for automatic and guided discovery of algorithms

This paper presents Automatic Algorithm Discoverer (AAD), an evolutionary framework for synthesizing programs of high complexity. To guide evolution, prior evolutionary algorithms have depended on fitness (objective) functions that are often challenging to design. To make evolutionary progress, instead, AAD employs Problem Guided Evolution (PGE), which requires introduction of a group of problems. Solutions discovered for simpler problems are used to solve more complex problems in the group. PGE also enables new evolutionary strategies. The above enable AAD to discover algorithms of similar or higher complexity relative to the state-of-the-art. Specifically, AAD produces Python code for 29 array/vector problems ranging from min, max, reverse, to more challenging problems like sorting and matrix-vector multiplication.
DAG: A General Model for Privacy-Preserving Data Mining : (Extended Abstract)

Secure multi-party computation (SMC) allows parties to jointly compute a function over their inputs, while keeping every input confidential. SMC has been extensively applied in tasks with privacy requirements, such as privacy-preserving data mining (PPDM), to learn task output and at the same time protect input data privacy. However, existing SMC-based solutions are ad-hoc - they are proposed for specific applications, and thus cannot be applied to other applications directly. To address this issue, we propose a privacy model DAG (Directed Acyclic Graph) that consists of a set of fundamental secure operators (e.g., +, -, ×, /, and power). Our model is general - its operators, if pipelined together, can implement various functions, even complicated ones. The experimental results also show that our DAG model can run in acceptable time.
Scalable Hyper-Ellipsoidal Function With Projection Ratio for Local Distributed Streaming Data Classification

Learning streaming data with limited size of memory storage becomes an interesting problem. Although there have been several learning methods recently proposed, based on the interesting concept of discard-after-learn, the performance of these issues: the learning speed, number of redundant neurons, and classification accuracy of these methods can be further improved in terms of faster speed, less number of neurons, and higher accuracy. The following new concepts and approaches were proposed in this paper: (1) a more generic structure of hyper-ellipsoidal function called Scalable Hyper-Ellipsoidal Function (SHEF) capable of handling the problem of a curse of dimensionality by introducing a regularization parameter into the covariance matrix of SHEF; (2) a new recursive function to update the covariance matrix of SHEF based on only the incoming data chunk; (3) a fast and easy conditions to test the states of being overlapped, inside, and touching of two SHEFs; (4) a new distance measure for determining the class of a queried datum based on the projected distance on only one discriminant vector, namely the Projection Ratio. The experimental results show the significant improvement when compared with the results from VLLDA, ILDA, LOL, VEBF, and CIL in terms of classification accuracy, the number of generated neurons, and computational time.
Inbodied Interaction 102: Understanding the Selection and Application of Non-invasive Neuro-physio Measurements for Inbodied Interaction Design

As a means to validate the effects of interaction designs, particularly those involving physiological processes, like: breathing in mindfulness; heartrate in exertion games, and blood flow to the brain for cognitive load assessments, HCI researchers are increasingly turning to body-based signals as signals to quantify effects and guide design decisions. These design decisions can be informed by Inbodied Interaction principles of aligning knowledge of how the body performs optimally (physiologically, neurologically) with our designs. The purpose of this course is to present new-to-HCI neuro-physiological measures including peripheral awareness, deep HRV, and new pre-cortical assessments to open new design opportunities. Students will leave the course with this set of new assessments, as well as practical worked examples of how to choose and apply which measures as best suited for a particular design and evaluation context.
Method for developing virtual reality applications for cognitive intensive training tasks

Virtual Reality applications have become popular over the last decade, gaining significant interest in industry and research. While the technology has its roots in the entertainment industry, there is an increase of research experiments and pilot studies where the technology is applied in professional context for behaviour and skill training. However, the implementation of effective training systems is a complex task; the development process remains largely ad hoc because there are no sufficient tools that appropriately support the development process. This thesis proposes a method for developing Head Mounted Display based virtual reality training applications for cognitive intensive training tasks. The method extends the concept of developing a parametrized system with run-time modifiable attributes and provides the necessary toolkit for the application implementation. The primary target user group are the developers and by extension the designer and training administrator.