Reviewerly/exHarmony huggingface dataset:
Flexible Executor Allocation without Latency Increase for Stream Processing in Apache Spark

Widely used stream processing systems, e.g., Structured Streaming in Apache Spark, traditionally adopt static strategies for resource allocation. Despite the fact that streaming applications often involve variable loads and long run time, static strategies allocate a fixed amount of resources to meet the traffic peak, which leads to a severe inefficiency. Dynamic resource provisioning, which manipulates the number of Executors at run time, provides a great opportunity to improve resource utilization and to limit the platform cost. However, adapting the mechanism to latency-sensitive applications is difficult, since each newly added Executor incurs more initialization overhead. The overhead is mainly caused by class loading in JVM, and is incurred every time a new Executor comes in, which may result in service level objective (SLO) violations. In this paper, we propose a new warm-up mechanism that provides a semi-isolated environment inside a cluster to conduct the warm-up on newly added Executors. The proposed component, the named Warm-up Manager, automatically creates and manages warm-up jobs by modifying jobs in production to allow the new Executor to load the necessary classes. The mechanism aims to reduce the initialization overhead and to enable latency-sensitive streaming applications to apply dynamic strategies. We implemented a prototype on Structured Streaming, and the evaluation found that the warm-up overhead imposed on the task was reduced to 4% in the best case scenario.
Offline Signature Verification Using Feature Learning and One-Class Classification

Offline signature verification remains the most commonly employed authentication modality and enjoys global acceptance. From the view point of computerized verification, concluding the authenticity of a signature offers a challenging problem for the pattern classification community. A major proportion of computerized solutions treat signature verification as a two-class classification problem where both genuine and forged signatures are employed for training purposes. For most of the real world scenarios however, only genuine signatures of individuals are available. This paper presents a signature verification technique that relies only on genuine signature samples. More precisely, we employ convolutional neural networks for learning effective feature representations and a one-class support vector machine that learns the genuine signature class for each individual. Experiments are carried out in a writer-dependent as well as writer-independent mode and low error rates are reported by only employing genuine signatures in the training sets.
Technology-facilitated oral homework: leveraging technology to get students speaking outside the classroom

What is it? Technology-Facilitated Oral Homework (TFOH) is an umbrella term for the use of digital technologies that enable learners to record themselves speaking the target language and submit recordings to their teachers from outside the formal classroom environment.
RDFINT: A Benchmark for Comparing Data Warehouse with Virtual Integration Approaches for Integration of RDF Data

Users often need to integrate large amounts of RDF data from multiple sources. Although there has been a lot of work on data cleaning and integration for structured data, relatively little work has been done for RDF data. We consider two different approaches to data integration: 1) a traditional data warehouse approach where relevant RDF data is extracted from different sources and then integrated in a data warehouse; 2) a virtual integration approach where RDF data still resides at each source and data integration happens when the data is queried, through a mediator that coordinates with wrappers at each source. It is often unclear how to choose the appropriate approach given an application scenario. This paper proposes RDFINT, a benchmark to compare these two approaches for integrating RDF data. We describe typical data integration operations, metrics that can be used to compare these two approaches, and factors that affect these metrics. We also report preliminary results of an implementation of these two approaches using the Apache Jena Fuseki framework.
Application of Multi-agent Optimization Methods Based on the Use of Linear Regulators and Interpolation Search for a Single Class of Optimal Deterministic Control Systems

Two new multi-agent algorithms for controlling one class of deterministic systems are proposed: the hybrid multi-agent method of interpolation search and multi-agent method based on the use of linear regulators of agent movement control. Detailed descriptions of the strategies of these methods are given, and step-by-step algorithms for each multi-agent method are described. Since multi-agent algorithms are used to find optimal control of dynamic systems, step-by-step algorithms for finding optimal open-loop control using multi-agent methods are also given. Two approaches to the search for optimal open-loop control are considered: when control is sought in relay form with a certain number of switches and when control is sought in the form of an expansion in a system of basis functions. In this chapter, cosine curves were considered as basis functions. Based on the above algorithms, software has been formed that allows finding the optimal open-loop control. Recommended parameters are given for each multi-agent algorithm. To study the effectiveness of the above algorithms, a specially selected set of test problems for finding the optimal open-loop control is solved, where the model of the control object is described by an ordinary differential equation linear in bounded control. During the study, it was shown that the described algorithms successfully cope with the task and can find a solution close to the exact one.
Trading Bias for Expressivity in Artificial Learning

Bias, arising from inductive assumptions, is necessary for successful artificial learning, allowing algorithms to generalize beyond training data and outperform random guessing. We explore how bias relates to algorithm flexibility (expressivity). Expressive algorithms alter their outputs as training data changes, allowing them to adapt to changing situations. Using a measure of algorithm flexibility rooted in the information-theoretic concept of entropy, we examine the trade-off between bias and expressivity, showing that while highly biased algorithms may outperform uniform random sampling, they cannot also be highly expressive. Conversely, maximally expressive algorithms necessarily have performance no better than uniform random guessing. We establish that necessary trade-offs exist in trying to design flexible yet strongly performing learning systems.
Towards practical 3D ultrasound sensing on commercial-off-the-shelf mobile devices

Ultrasound based contactless sensing has the potential to extend the interactive range of mobile devices significantly. However, existing approaches either require multiple transceiver pairs that are not available on commercial mobile devices or can only recognize simple actions with one single stroke. These drawbacks significantly limit the practicability of prior work. This article presents UltraScr, an ultrasound sensing system that can recognize the sophisticated gestures with multiple strokes (e.g., writing a capital letter) using just a single sound transceiver pair. To do so, UltraScr first uses the frequency attenuation profile (FAP) to capture the subtle hand movements. It then employs a convolutional neural network (CNN) to extract the subtle hand movements’ discriminative features to build an accurate ultrasound sensing system. We go further by exploiting the rejection classification method (RCM) and incremental learning to improve the robustness of our sensing system in the end-user environment. We evaluate UltraScr by applying it to gesture recognition across different scenarios and users. Extensive experimental results show that while using only one transceiver pair, the performance of UltraScr is comparable to the multi-transceiver-paired implementations. We show that UltraScr is robust to the change of external conditions (i.e., different humidity and battery) and can work effectively on a wide range of locations, but requires 3× fewer training samples compared to the state-of-the-art.
Dark Web

The explosion of the internet has given rise to cybercrimes, online identity theft, and fraud. With the internet, these crimes are able to occur anywhere in the world and limitless to whatever selected target. The anonymity of the internet allows criminal activity to flourish, and the number of unsuspecting victims is growing. From script kiddies to nation-states, this new method of internet-enabled crimes has strained governments. This chapter provides insight into how crimes related to online identity theft and fraud are carried out. Examined within this chapter are the evolution of cybercrime, history of identity theft, applications for internet anonymity, and discussion on effects caused by romance scams and data breaches. Finally, recommendations are provided on what organizations and individuals can do to protect themselves against these vicious crimes.
Alice: A General-Purpose Virtual Assistant Framework

In this paper, a virtual assistant framework called Alice is presented. This virtual assistant is a combination of 3D avatar, face detection, face recognition and face expression recognition with a voice assistant that similar to Amazon’s Alexa. The 3D avatar (Alice) is a female character animated using Unity and the lip is animated to sync with the speech to make it looks like speaking. Besides that, the 3D avatar can display different facial expressions such as happy, sad and upset. Face detection and recognition makes the system aware of the human user’s identity. Whereas, face expression recognition enables the system to detect the facial expression of the human user. Whenever there is a question being asked, the system will use Speech-to-Text system to convert human speech to text and Natural Language Processing to interpret the intent behind the text. Based on the result of interpretation, the system decides which audio file to be used as response. Then, a realistic artificial voice is generated as response to the human user. The system can access database based on user’s identity to retrieve information about that user. This may create a personalized experience for the human user. This framework can be customized for other applications for different fields. For this Alice framework, two applications have been developed namely a question answering chatbot and a customer service agent.
3D deformable convolution for action classification in videos

Action recognition is one of the popular research areas in computer vision because it can be applied to solve many problems especially in security surveillance, behavior analysis, healthcare and so on. Some of the well-known Convolutional Neural Network (CNN) in action classification using 3D convolution are C3D, I3D and R(2 + 1)D. These 3D CNNs assume that the spatial and temporal dimensions of motion are uniform where the 3D filters are uniformly shaped. However, the path in motion can be in any directions and a uniform shape filter might not be able to capture nonuniform spatial motion and this limits the performance of the classification. To address the above problem, we incorporate a 3D deformable filter in a C3D network for action classification. The deformable convolution adds offsets to the regular grid sampling locations in the standard convolution resulting in non-uniform sampling location. We will also investigate the performance of the network when apply the 3D deformable convolution in different layers and the effect of different dilation size of the 3D deformable filter. UCF101 dataset is used in the experiments. From our experiments, it is found that applying the deformable convolution in lower layer yield better result compare to other layers. Our experiment shows that if we put the deformable convolution in Conv1a, the accuracy achieved is 48.50%.
Phenotypical ontology driven framework for multi-task learning

Despite the large number of patients in Electronic Health Records (EHRs), the subset of usable data for modeling outcomes of specific phenotypes are often imbalanced and of modest size. This can be attributed to the uneven coverage of medical concepts in EHRs. We propose OMTL, an Ontology-driven Multi-Task Learning framework, that is designed to overcome such data limitations.The key contribution of our work is the effective use of knowledge from a predefined well-established medical relationship graph (ontology) to construct a novel deep learning network architecture that mirrors this ontology. This enables common representations to be shared across related phenotypes, and was found to improve the learning performance. The proposed OMTL naturally allows for multi-task learning of different phenotypes on distinct predictive tasks. These phenotypes are tied together by their semantic relationship according to the external medical ontology. Using the publicly available MIMIC-III database, we evaluate OMTL and demonstrate its efficacy on several real patient outcome predictions over state-of-the-art multi-task learning schemes. The results of evaluating the proposed approach on six experiments show improvement in the area under ROC curve by 9% and by 8% in the area under precision-recall curve.
Classifying Medical Document in Bahasa Indonesia using Semi-Supervised Learning

Abstract The medical domain has always been an all-time important domain since healthiness is everyone’s purpose. People find medical document resources in the sea of data and information, such as the web. To support information retrieval and knowledge dissemination through the web, we analyze the use of semi-supervised learning to classify medical-related documents. The semi-supervised learning technique is chosen to show the possibilities of creating good classifiers with limited human supervision. In this research, we use the Naïve Bayes and Pseudo Labeling technique. We analyze different labeled:unlabeled data ratios of the training dataset in the experiment, starting from 4:3, 3:4, 2:5, and 1:6, to see the semi-supervised learning performance with different levels of human supervision. We get a relatively similar result in terms of classification average accuracy (81%-83%). Interestingly, in one experiment, the highest accuracy of the 1:6 ratio (85%) outperforms the 2:5 ratio (82%) and has the same accuracy as the 4:3 (85%). However, the standard deviation of the accuracy in the 1:6 ratio is the highest, amongst others (4.183). Finally, semi-supervised learning can be used to create a great classifier model of the medical domain in Bahasa Indonesia with less human supervision.
An Improved Collaborative Filtering Algorithm Based on Filling Missing Data

At present, most of the literature uses error metrics to evaluate the performance of recommendation algorithms. However, especially in Top-N recommendation tasks, the accuracy metrics can better show the pros and cons of the recommendation algorithm. Researching traditional recommendation algorithms have a big problem is that the data of recommendation system is always sparse. In order to solve the problem and improve the accuracy metrics, we add a filling matrix when predicting the rating. And then, we considering that different users have different scoring preferences, the user and item biases are added to the loss function. Finally, we use the improved alternating least square method as the optimization method to update the filling matrix in the iterative process. The experiment compared four recommendation algorithms and the results show that, in terms of accuracy metrics, the accuracy of the improved algorithm has been improved many times. In addition, since the filling of the rating matrix needs to consider both the item and the user, we compared the algorithm that considers both and only considers one. The improved algorithm that considered both has an improvement of about 1%.
The inverse problem of a dynamical system solved with genetic algorithms

Abstract In this work, we propose a complete methodology to identify the parameters of a dynamical system from a data set using genetic algorithms. Considering the search for the model parameters as an inverse problem, we numerically solve the differential equations of the dynamical system; each set of parameters is then considered as an individual of the population that evolves in the genetic algorithm. As a fitness function we use the distance L 1 between the numerical solution and the data.
Meta Preference Learning for Fast User Adaptation in Human-Supervisory Multi-Robot Deployments

As multi-robot systems (MRS) are widely used in various tasks such as natural disaster response and social security, people enthusiastically expect an MRS to be ubiquitous that a general user without heavy training can easily operate. However, humans have various preferences on balancing between task performance and safety, imposing different requirements onto MRS control. Failing to comply with preferences makes people feel difficult in operation and decreases human willingness of using an MRS. Therefore, to improve social acceptance as well as performance, there is an urgent need to adjust MRS behaviors according to human preferences before triggering human corrections, which increases cognitive load. In this paper, a novel Meta Preference Learning (MPL) method was developed to enable an MRS to fast adapt to user preferences. MPL based on meta learning mechanism can quickly assess human preferences from limited instructions; then, a neural network based preference model adjusts MRS behaviors for preference adaption. To validate method effectiveness, a task scenario "An MRS searches victims in an earthquake disaster site" was designed; 20 human users were involved to identify preferences as {"aggressive", "medium", "reserved"}; based on user guidance and domain knowledge, about 20,000 preferences were simulated to cover different operations related to {"task quality", "task progress", "robot safety"}. The effectiveness of MPL in preference adaption was validated by the reduced duration and frequency of human interventions.
STACK Assessment in Mathematics Classroom: Advantages and Disadvantages

E-Learning is considered a new philosophy of education that is involved in all existing forms of education including full-time education. Presently, there is a variety of software to develop interactive content. This study is aimed to examine the possibility of employing STACK (the System for Teaching and Assessment using a Computer algebra Kernel) assignments in the mathematics classroom at a technical university, to compare the use of the STACK questions activity with traditional training, and to evaluate the effectiveness of this method. The experience of using STACK questions has shown that their application helps students of all forms of education in learning mathematics, and considerably facilitates the learning process of teachers, allows you to implement a point-rating (cumulative) system of assessment of knowledge, makes the learning results more visible and convenient for analysis. In addition, we note such advantages of STACK as: the STACK assignments allows you to check analytical solutions to complex types of problems and organize hints that help solve tasks, the STACK assignments saves the teacher time to check solutions and gives students individual options for tasks, tasks prepared in STACK can be used in various modes, such as training mode and monitoring one. In the course of the study, the STACK assignments were analyzed that revealed a greater number of advantages of this assessment compared to its disadvantages and demonstrated the possibility of its application in the educational process.
A survey on Navigation Systems in Dynamic Environments

Mobile robot navigation is a method of guiding a robot to accomplish a mission through an environment with obstacles in a good and safe manner. The main challenge of current mobile robotics is to develop intelligent navigation systems, where autonomous navigation is a research focus that aims to give a machine the ability to move in an unassisted environment, without human intervention to accomplish the desired goal. The task of navigation is to give the robot the opportunity to obtain the information it needs to reason and equip it with a locomotion capacity adapted to its environment. However, it implies complex systems in the realization, where their control poses important problems not only technological but also scientific. An autonomous mobile robot is a mechanical system that must be able to make decisions to perform movements based on information about its position and the environment in which it operates.
Synthesising Reinforcement Learning Policies Through Set-Valued Inductive Rule Learning

Today's advanced Reinforcement Learning algorithms produce black-box policies, that are often difficult to interpret and trust for a person. We introduce a policy distilling algorithm, building on the CN2 rule mining algorithm, that distills the policy into a rule-based decision system. At the core of our approach is the fact that an RL process does not just learn a policy, a mapping from states to actions, but also produces extra meta-information, such as action values indicating the quality of alternative actions. This meta-information can indicate whether more than one action is near-optimal for a certain state. We extend CN2 to make it able to leverage knowledge about equally-good actions to distill the policy into fewer rules, increasing its interpretability by a person. Then, to ensure that the rules explain a valid, non-degenerate policy, we introduce a refinement algorithm that fine-tunes the rules to obtain good performance when executed in the environment. We demonstrate the applicability of our algorithm on the Mario AI benchmark, a complex task that requires modern reinforcement learning algorithms including neural networks. The explanations we produce capture the learned policy in only a few rules, that allow a person to understand what the black-box agent learned. Source code: https://gitlab.ai.vub.ac.be/yocoppen/svcn2.
The Efficiency of the FSV Model in an Intelligent Search Engine

It is enough to have 3 basic stages of the modules in the SPD of a diversified corporate network: (F) - the method of submitting the request, i.e. the method of forming the expression of the information needs of the system user (S) - the function of the correspondence of the electronic resource to the request the degree of compliance with the request and the found electronic resource; (V) - method of presenting electronic resources. Combining these three stages for models, methods and software modules of the AML, we call FSV technology (FSV platform, FSV Framework). FSV technology is an instrumental software platform based on a client-server architecture, integration and modification of models, methods and algorithms of AML in the information environment of corporate networks. The following architecture has been developed for the FSV technology proposed for the search index in data retrieval systems.