Reviewerly/exHarmony huggingface dataset:
Exploration of memory hybridization for RDD caching in Spark

Apache Spark is a popular cluster computing framework for iterative analytics workloads due to its use of Resilient Distributed Datasets (RDDs) to cache data for in-memory processing. We have revealed that the performance of Spark RDD cache can be severely limited if its capacity falls short to the needs of the workloads. In this paper, we have explored different memory hybridization strategies to leverage emergent Non-Volatile Memory (NVM) devices for Spark's RDD cache. We have found that a simple layered hybridization approach does not offer an effective solution. Therefore, we have designed a flat hybridization scheme to leverage NVM for caching RDD blocks, along with several architectural optimizations such as dynamic memory allocation for block unrolling, asynchronous migration with preemption, and opportunistic eviction to disk. We have performed an extensive set of experiments to evaluate the performance of our proposed flat hybridization strategy and found it to be robust in handling different system and NVM characteristics. Our proposed approach uses DRAM for a fraction of the hybrid memory system and yet manages to keep the increase in execution time to be within 10% on average. Moreover, our opportunistic eviction of blocks to disk improves performance by up to 7.5% when utilized alongside the current mechanism.
Problems and Decision-Making Models in the First Cycle of Education

There are two main issues addressed in this chapter. The first one concerns the management of interpersonal relations in the school, in particular the relationship between teachers and students, between teachers and families of the students, and between students and students. The other is the realization of an interdisciplinary teaching that exceeds the fragmentation of knowledge, and unifies the logical structures of the various disciplines. We show that the formal modeling of problems—represented as decisional problems, eventually under uncertainty, with many objectives and decision makers—offers a substantial aid to the solution, or at least to clarify the problems, and to reach a consensus. Moreover, fuzzy reasoning, more flexible and adaptable of the classic reasoning, helps in the analysis of relations and in the evaluation of alternatives.
Side Channel Analysis of SPARX-64/128: Cryptanalysis and Countermeasures

SPARX family of lightweight block cipher was introduced in Asiacrypt 2016. The family consists of three variants (a) SPARX-64/128, (b) SPARX-128/128 and (c) SPARX-128/256. In this work, first, we propose a technique to perform Correlation Power Analysis (CPA) on the SPARX-64/128 cipher. Our technique uses a combination of first-order, second-order and modulo addition CPA methods. Using our proposed technique we extract 128 key bits of SPARX-64/128 cipher with low complexities in general; key guess complexity of $$2^{12}$$ and $$65000\approx 2^{16}$$ power traces. We initially propose a countermeasure of SPARX-64/128 block cipher against side-channel attacks in terms of power analysis, a threshold implementation based on a serialized design of SPARX-64/128 core. The serialized design of SPARX-64/128 core is implemented in hardware and occupies 60 slices in FPGA. As a countermeasure, this serialized implementation is extended to propose a provably secure threshold implementation of SPARX-64/128 core (TI-SPARX). The TI-SPARX core occupies 131 slices in FPGA and runs at 144 MHz thus, giving a throughput of 9 Mbps. To the best of our knowledge, this is the first side channel attack and countermeasure result on SPARX-64/128 cipher.
An Instrumented Software Framework for the Rapid Development of Experimental User Interfaces

One of the more demanding aspects of formal evaluations in user-centered system design is the underlying requirement for an experimental platform that accurately reflects the functionality being tested and captures relevant user performance data. While we are unaware of any truly turn-key solutions for avoiding many of the inherent costs associated with the iterative cycle of user-centered system development and formal testing, in our mediated multitasking research, we have found that a multi-application host environment, instrumented for user tracking and a playback function for reviewing what individuals attended to, experienced, and did during the task-performance segments of interaction studies, could be implemented as a reusable infrastructure of services and control functions. In this short paper, we introduce the Testbed Framework System, a flexible and extensible software platform for use in the development and conduct of formal usability testing, and outline a number of its core functions and capabilities.
UAV Path Planning for Local Defense Systems

This work presents an unmanned aerial vehicle (UAV) planning algorithm for local defense of a system from enemy UAVs that come to attack or reconnoiter the system. Planning with non-cooperative moving targets often leads to difficulties in utilizing the widely-used path planning algorithms, since their intention and path plans are not known. Furthermore, because a destination of our UAVs path plan can be changed over time, a fast path planning algorithm which can deal with various obstacles is needed. To handle these problems, two key methods are adopted in this work: First, an informative planning is used for predicting each path of enemy UAVs. Second, the iterative linear quadratic regulator algorithm (iLQR) is utilized to derive feasible paths in a mission environment. Utilizing the two methods, the system predicts paths of invading UAVs and allocates friend UAVs to dominate enemies. Finally, each path for an allocated task is computed via iLQR.
On the Simulation (and Energy Costs) of Human Intelligence, the Singularity and Simulationism

For many the Holy Grail of robotics and AI is the creation of artificial persons: artefacts with equivalent general competencies as humans. Such artefacts would literally be simulations of humans. With the theme of simulation this essay reflects on both the simulation of intelligence and the associated energy costs across three broad and controversial topics: first, how to design (or evolve) human-equivalent AI, second, the prospects of an intelligence explosion once the first has been achieved, and third, simulationism—the idea that we are ourselves simulations in a simulated universe.
Presburger Concept Cardinality Constraints in Very Expressive Description Logics

Quantitative information plays an increasingly important role in knowledge representation. To this end, many formalisms have been proposed that enrich traditional KR formalisms by counting and some sort of arithmetics. Baader and Ecke (2017) propose an extension of the description logic $$\mathcal {ALC}$$ by axioms which express correspondences between the cardinalities of concepts by means of Presburger arithmetics. This paper extends their results, enhancing the expressivity of the underlying logic as well as the constraints while preserving complexities. It also widens the scope of investigation from finite models to the classical semantics where infinite models are allowed. We also provide first results on query entailment in such logics. As opposed to prior work, our results are established by polynomially encoding the cardinality constraints in the underlying logic.
Reactivity and Dynamic Obstacle Avoidance

This chapter presents methods for aerial manipulators that allow to react against unexpected obstacles during navigation. The local replanning method presented in more detail considers jointly the state of the aerial platform and the manipulator configuration at joint level. Moreover, the proposed planner generates trajectories that account for the dynamics, which is a crucial issue due to the strong dynamical coupling between the aerial platform and the arms. Finally, simulation and experimental results have been included for validation.
Extracting Keywords from Short Government Documents Using Reinforcement Learning

In this paper, we proposed a novel approach to extract keywords from massive amount of unlabelled short government documents using reinforcement learning. To guide policy network to keep important words, we introduced the average rate regularization, as the sparsity constraints of the model's loss function. Analysis on the results shows that the proposed model outperforms the traditional unsupervised keyword extraction approaches on massive amount of unlabelled government document headlines.
Visualizing Sequences of Debugging Sessions using Swarm Debugging

In Software Engineering, one of the most important activities is debugging. Debugging is a set of techniques to detect, locate, and correct faults in a computer program. Modern Integrated Development Environments (IDEs), such as Eclipse or Visual Studio, provide infrastructure to support interactive debugging, during which a developer explores the source code of the system under development or maintenance. Although IDEs encourage developers to work collaboratively, debugging is still an individual activity. Furthermore, interactive debugging activity is limited by IDE debugging features that do not store previous debugging sessions. This condition forces developers to repeat debugging execution sessions to review the debugging information. In this paper, using the concept of Swarm Debugging, we present the Sequence Debugging Session View (SDV) tool. The primary goal is to capture the debugging information from a developer IDE (as Visual Studio) and store it. Then, the tool enables developers to retrieve the data in 3D interactive visualization and understand software behavior through the analysis and sharing of debugging session data. The main contribution of the tool is to assist on program comprehension and to reduce effort during software maintenance. To validate the solution, we performed two usage studies in real situations at a software house. The feedback from the evaluation of the tool suggests that the team could be helped on the software arrangement.
Organization of the activities of the student scientific society: best practice and prospects for further development

The article reveals the experience and effective pedagogical conditions of the organization of research activities of students at the faculty of pedagogical and art education of the Modrovian state pedagogical Institute. M. E. Evsev'eva". The prospects of further development of research work with students at the faculty.
Bidirectional Gated Recurrent Unit Networks for Relation Classification with Multiple Attentions and Semantic Information

Relation classification is an important part in natural language processing (NLP) field. The main task of relation classification is extracting the relations between target entities. In recent years, there are many methods for relation classification and some of them have achieved quite good results, but these methods have not given enough attention to the target words, and the semantic information of words is also lack of utilization. In order to make good use of the contextual information in the sentences as much as possible, we adopt the bidirectional gated recurrent unit networks (BGRU). On this basis, in order to focus on the computing process of target entities and target sentences, we add the multiple attention mechanism. Meanwhile, other semantic information such as the named entity and part of speech information of the word are also added as input data so as to make full use of the words’ information in the corpus. We have conducted some experiments on the widely used datasets, and we got up to 3% improvement in the F1 value compared to previous optimal method.
Generating Real Context Data to Test User Dependent Systems - Application to Multi-agent Systems

This paper, deals with the usually need of data to simulate behavior and efficiency of proposed solutions in several fields, and also knowing that personal data always bring privacy and security issues. This work wants to promote a balanced solution between the need of personal information and the user’s privacy expectations. We propose a solution to overcome these issues, and don’t compromise the balance between security and personal comfort based on generating real context data of users, that allow to test user dependent systems.
Integration data models based on ontology

Evaluation of the performance of university staff can be carried out with different goals, consistent with the strategic goals of the institution. To provide an appropriate data set, an appropriate structure must be provided that ensures that neither incomplete nor reliable data will be used. To provide such a structure with the best possible features data from the various available sources should be integrated. The article provides an overview of the existing integration problems and approaches to solving this problem.
Structure Learning of Bayesian Network Based on Adaptive Thresholding

Direct dependencies and conditional dependencies in restricted Bayesian network classifiers (BNCs) are two basic kinds of dependencies. Traditional approaches, such as filter and wrapper, have proved to be beneficial to identify non-significant dependencies one by one, whereas the high computational overheads make them inefficient especially for those BNCs with high structural complexity. Study of the distributions of information-theoretic measures provides a feasible approach to identifying non-significant dependencies in batch that may help increase the structure reliability and avoid overfitting. In this paper, we investigate two extensions to the k-dependence Bayesian classifier, MI-based feature selection, and CMI-based dependence selection. These two techniques apply a novel adaptive thresholding method to filter out redundancy and can work jointly. Experimental results on 30 datasets from the UCI machine learning repository demonstrate that adaptive thresholds can help distinguish between dependencies and independencies and the proposed algorithm achieves competitive classification performance compared to several state-of-the-art BNCs in terms of 0-1 loss, root mean squared error, bias, and variance.
Method of Safety of Informational Resources Utilizing the Indirect Steganography

The direction for support of information security by means of the use of indirect steganography method is explained. The steganography method on the basis of the use of the adaptive positional coding is developed. On the basis of the developed method, the indirect steganography embedding system which allows to build in information by modification of the bases of image container elements is designed
Research on Gesture Recognition Based on Improved GBMR Segmentation and Multiple Feature Fusion

Aiming at addressing the problem of interactive gesture recognition between lunar robot and astronaut, a novel gesture detection and recognition algorithm is proposed.In gesture detection stage, based on saliency detection via Graph-Based Manifold Ranking (GBMR) algorithm, the depth information of foreground is added to the calculation of superpixel.By increasing the weight of connectivity domains in graph theory model, the foreground boundary is highlighted and the impact of background is weakened.In gesture recognition stage, Pyramid Histogram of Oriented Gradient (PHOG) feature and Gabor amplitude also phase feature of image samples are extracted.To highlight the Gabor amplitude feature, we propose a novel feature calculation by fusing feature in different directions at the same scale.Because of the strong classification capability and not-easy-to-fit advantage of Adaboosting, this paper applies it as the classifier to realize gesture recognition.Experimental results show that the improved gesture detection algorithm can maintain the robustness to influences of complex environment.Based on multi-feature fusion, the error rate of gesture recognition remains at about 4.2%, and the recognition rate is around 95.8%.
Chinese Address Similarity Calculation Based on Auto Geological Level Tagging

How to quickly measure the similarity of addresses has become an urgent need in various fields including financial anti-fraud. Traditional string-based similarity calculation methods have not completed this task perfectly. Taking into account the hierarchical nature of addresses, we constructed a framework for calculating the similarity of Chinese addresses. First, the whole address strings are split and annotated with proper level by a LM-LSTM-CRF model, and then sub-string level similarities are calculated. Last, similarity scores are combining by BP neural networks. This framework has achieved good results in practice for financial anti-fraud tasks.
Knowledge-Based Multi-agent System for Smart Factory of Small-Sized Manufacturing Enterprises in Korea

This paper focuses on the development of practical application prototype of information and communication model that can be applied in the field in order to solve the problems of SME manufacturing and manufacturing companies in each manufacturing industry. We are trying to establish a process to implement a smart factory by adding a specialist network through problem-solving data management based on the cases of bad people in the manufacturing process including field workers. It also has the implication of supporting field work by providing Manufacturing Problem Solving (MPS) processes to shop floor workers.
A GENERALIZED MODEL OF FREDHOLM'S CRYPTOSYSTEM

The problem of cyber security in the era of the creation of quantum computers is of particular relevance. Specifically, the data are at risk which are confidential or whose value depends on their integrity. In order to find a way out of the situation which happened in the article, a thorough comprehensive analysis of the current state of the known cryptosystems was carried out, based on a systematic approach. In particular, the advantages and disadvantages of models of cryptosystems which were created on the basis of cognitive cryptography are stated, the theory of dynamic chaos, constructive, quantum and post-quantum cryptography. The issue of cryptosystem models based on DNA algorithms is also raised, proxy cryptosystem models, attribute cryptosystems, packet and non-commutative cryptography. As a result of the research, it was found out that the greatest interest in terms of security today is integral cryptography. The lack of scientifically justified models of cryptosystems based on integrated cryptography has led to the development of one of these models. The model is developed on the basis of the proposed concept, which is based on the main principals of integral cryptography. As a result of the research, a generalized model of the cryptosystem was developed, which in the future is proposed to be called the cryptosystem of Fredholm. It is shown that the essence of the encryption and decryption procedures is reduced to solving the direct and inverse problem, which is described by the integral equation of Fredholm of the first-order. The article emphasizes in particular that compared to the known models of cryptosystems, the proposed model has a number of significant advantages. The first advantage is the guaranteed theoretical and practical cryptostability, which is due to the incorrectness of the inverse decryption problem. The second advantage of the proposed model is the absence of effective algorithms for cryptanalysis due to the lack of prevalence of integral cryptography in modern cyber security systems. The developed model serves the theoretical basis for the further development of appropriate cryptographic algorithms and research of their security parameters.
Design and Implementation of Sensory Data Collection and Storage Based on Hadoop Platform

At present, modern manufacturing and management concepts such as digitization, networking, and intelligence are widely used in the industry. Industry automation and information have been unprecedentedly improved, and therefore the entire life of industrial production link involves massive amounts of data, and the status monitoring data of industrial machine have large, multiple source, heterogeneous, and complex data characteristics. What is more, the traditional processing methods and tools could not meet the requirements for massive data, and may miss the best time to repair machine. So, to resolve the challenges that the industrial sensory big data faces, this paper proposes the sensory data collection and storage based on Hadoop platform.
Online selective training for faster neural network learning

Training neural networks is a computationally challenging problem that requires significant time efforts. In this paper, we propose two approaches that improve efficiency of this task by actively selecting most relevant points from a training data set. The first approach forms a batch that maximizes the reduction of the estimator's entropy, while the second approach only trains on datapoints whose predicted probability is below a predetermined threshold. Both techniques rely on data metrics to speed up training while retaining the epoch-based neural network training framework. The results demonstrate that the proposed methods enable significant reduction of training time in experiments on the CIFAR10 dataset without compromising the accuracy.
Non-static Vision Guided Pneumatic Pick and Place Robotic System with Shared Supervisory Control

The purpose of this research is to develop a vision guided pneumatic pick and place robotic system with shared supervisory control where it is highly desirable in the current industry and is in line with the industrial revolution 4.0 that is currently being developed around the globe. In this research, a non-static Pixy camera CMUcam5 is being used as the vision sensor that provides cues to the movement of the pick and place system in an automatic mode. The sensor will receive the data from the image captured and process it to do the desired task. The system uses Arduino microcontroller and the PS2 controller starter kit wireless transmitter or SKPSW, which is tested in the manual and automatic mode. The supervisory control project is developed by using the PS2 controller as a base system that will send the data from the remote control and send output data to the system. Operational test results show that the shared supervisory control is well executed in both manual and automatic mode. The vision sensor also guides the pick and place robot to handle two objects in the automatic modes with successful attempts.
Analysis of the Construction of Similarity Matrices on Multi-core and Many-Core Platforms Using Different Similarity Metrics

Similarity matrices are 2D representations of the degree of similarity between points of a given dataset which are employed in different fields such as data mining, genetics or machine learning. However, their calculation presents quadratic complexity and, thus, it is specially expensive for large datasets. MPICorMat is able to accelerate the construction of these matrices through the use of a hybrid parallelization strategy based on MPI and OpenMP. The previous version of this tool achieved high performance and scalability, but it only implemented one single similarity metric, the Pearson’s correlation. Therefore, it was suitable only for those problems where data are normally distributed and there is a linear relationship between variables. In this work, we present an extension to MPICorMat that incorporates eight additional metrics for similarity so that the users can choose the one that best adapts to their problem. The performance and energy consumption of each metric is measured in two platforms: a multi-core platform with two Intel Xeon Sandy-Bridge processors and a many-core Intel Xeon Phi KNL. Results show that MPICorMat executes faster and consumes less energy on the many-core architecture. The new version of MPICorMat is publicly available to download from its website: https://sourceforge.net/projects/mpicormat/