Reviewerly/exHarmony huggingface dataset:
A novel dense descriptor based on structure tensor voting for multi-modal image matching

Automatic and robust matching of multi-modal images can be challenging owing to the nonlinear intensity differences caused by radiometric variations among these images. To address this problem, a novel dense feature descriptor and improved similarity measure are proposed for enhancing the matching performance. The proposed descriptor is built on a voting scheme of structure tensor that can effectively capture the geometric structural properties of images. It is not only illumination and contrast invariant but also robust against the degradation caused by significant noise. Further, the similarity measure is improved to adapt to the reversal of orientation caused by the intensity inversion between multi-modal images. The proposed dense feature descriptor and improved similarity measure enable the development of a robust and practical template-matching algorithm for multi-modal images. We verify the proposed algorithm with a broad range of multi-modal images including optical, infrared, Synthetic Aperture Radar (SAR), digital surface model, and map data. The experimental results confirm its superiority to the state-of-the-art methods.
Balance Adjustment of Power-Line Inspection Robot Using General Type-2 Fractional Order Fuzzy PID Controller

In this study, a general type-2 fractional order fuzzy PID (GT2FO-FPID) controller is proposed to fulfil the balance adjustment of the Power-line Inspection (PLI) robot system. It is a combination of Mamdani general type-2 fuzzy logic controller (GT2-FLC) and fractional PID controller. Since the PLI robot system is an under-actuated system, it’s necessary to get complete information of the system. However, when all state variables are treated as input to the controller, there is a problem with the rule explosion. Because of this, the information fusion method is adopt to solve the problem and simplify the controller design. At the same time, fractional-order integral-differential operators and input-output scaling factors, which are taken as design variables and optimized by genetic algorithm (GA). To assess the performance of proposed controller based on symmetry criterion, we compared it against existing controllers, i.e., interval type-2 fractional order fuzzy PID (IT2FO-FPID), type-1 fractional order fuzzy PID (T1FO-FPID), and conventional fractional order (FOPID) controllers. Furthermore, to show the anti-inference ability of the proposed controller, three common perturbed process are tested. Finally, simulation results show that the GT2FO-FPID controller outperforms other controllers in the presence of external perturbations on the PLI robot system.
Real-Time Hand Gesture Recognition Using the Leap Motion Controller and Machine Learning

Currently, gesture recognition is presented as a problem of feature extraction and pattern recognition, in which we label a movement as belonging to a given class. The response of a gesture recognition system can be applied for different problems in different fields, such as medicine, teaching, robotics among others. There are different models proposed in the scientific literature that try to solve the problem of hand gesture recognition. These works do not meet the demand for real-time processing and high recognition accuracy, simultaneously. In this context, the present work describes a project to develop a new model for the recognition of hand gestures using infrared information, acquired with the Leap Motion Controller, and also using machine learning techniques. The proposed model is intended to recognize 5 static and 4 dynamic gestures of the hand in real-time and with high accuracy, simultaneously. The methodology that will be used for developing this work is composed of two phases, the training and testing: In the first phase, we will design, classify, preliminary validation and tune the proposed model. In the second phase, we will test the proposed model by estimating its accuracy of recognition and time of processing.
Autonomous Navigation for Exploration of Unknown Environments and Collision Avoidance in Mobile Robots Using Reinforcement Learning

This paper proposes the use of reinforcement learning to provide navigation and adaptation skills to a mobile robot in unknown environments. The use of sensor information is developed instead of the spatial location of the robot with the aim to perform the navigation and exploration process. The use of the Q-learning algorithm is proposed, however, the states are established based on the information coming from the sensors. A reward policy is applied to focus on guide the robot away from obstacles and allowing the exploration of unknown environments. Additionally, an exploration policy is generated which collaborates in the adaptation process of the robot. The complete system is evaluated using a robot simulation environment known as V-Rep. The results show that the robot learns to navigate avoiding to collide with obstacles, besides presenting skills to explore unknown environments.
Employee Attrition Prediction Using Classification Models

The term Attrition refers to the voluntary or involuntary discontinuation of employees in an organization. This paper focuses on discussing a systematic flow for predicting Attrition using Data Analysis and Machine Learning techniques. The steps include Data Acquisition, Data Conditioning, Visualization, and Classification by applying the following Classification Models: Support Vector Machine, Decision Tree, K-Nearest Neighbor, Random Forest, and Naive Bayes algorithms in the Python environment. The resulting predictions of classification were evaluated using three performance metrics: Accuracy Score, Confusion Matrix and the ROC Curve. Based on the obtained results, we inferred that Random Forest classifier delivered the highest accuracy being 83.3% whereas Naive Bayes and Support Vector Machine was better in terms of classifying True Positives and indicated greater Area Under Curve values. This paper intends to be of great use to the Organizations aiming at detecting the key causes of Attrition and minimizing them using the power of data.
Optimising Resource Management for Embedded Machine Learning

Machine learning inference is increasingly being executed locally on mobile and embedded platforms, due to the clear advantages in latency, privacy and connectivity.In this paper, we present approaches for online resource management in heterogeneous multi-core systems and show how they can be applied to optimise the performance of machine learning workloads.Performance can be defined using platform-dependent (e.g.speed, energy) and platform-independent (accuracy, confidence) metrics.In particular, we show how a Deep Neural Network (DNN) can be dynamically scalable to trade-off these various performance metrics.Achieving consistent performance when executing on different platforms is necessary yet challenging, due to the different resources provided and their capability, and their time-varying availability when executing alongside other workloads.Managing the interface between available hardware resources (often numerous and heterogeneous in nature), software requirements, and user experience is increasingly complex.
JPAS: Job-progress-aware flow scheduling for deep learning clusters

Deep learning (DL) is an increasingly important tool for large-scale data analytics and DL workloads are also common in today's production clusters due to the increasing number of deep-learning-driven services (e.g., online search and speech recognition). To handle ever-growing training datasets, it is common to conduct distributed DL (DDL) training to leverage multiple machines in parallel. Training DL models in parallel can incur significant bandwidth contention on shared clusters. As a result, the network is a well-known bottleneck for distributed training. Efficient network scheduling is essential for maximizing the performance of DL training. DL training is feedback-driven exploration (e.g., hyper-parameter tuning, model structure optimization), which requires multiple retrainings of deep learning models that differ in terms of their configuration. The information at the early stage of each retraining can facilitate the direct search for high-quality models. Thus, reducing the early-stage time can accelerate the exploration of DL training. In this paper, we propose JPAS, which is a flow scheduling system for DDL training jobs that aims at reducing the early-stage time. JPAS uses a simple greedy mechanism to periodically order all DDL jobs. Each host machine sets priorities for its flows using the corresponding job order and offloads the flow scheduling and rate allocation to the underlying priority-enabled network. We evaluate JPAS over a real testbed that is composed of 13 servers and a commodity switch. The evaluation results demonstrate that JPAS can reduce the time to reach 90% or 95% of the converged accuracy by up to 38%. Hence, JPAS can remarkably reduce the early-stage time and thus accelerate the search for high-quality models.
Efficacy of the Metropolis Algorithm for the Minimum-Weight Codeword Problem Using Codeword and Generator Search Spaces

This article studies the efficacy of the Metropolis algorithm for the minimum-weight codeword problem. The input is a linear code C given by its generator matrix and our task is to compute a nonzero codeword in the code C of least weight. In particular, we study the Metropolis algorithm on two possible search spaces for the problem: 1) the codeword space and 2) the generator space. The former is the space of all codewords of the input code and is the most natural one to use and hence has been used in previous work on this problem. The latter is the space of all generator matrices of the input code and is studied for the first time in this article. In this article, we show that for an appropriately chosen temperature parameter the Metropolis algorithm mixes rapidly when either of the search spaces mentioned above are used. Experimentally, we demonstrate that the Metropolis algorithm performs favorably when compared to previous attempts. When using the generator space, the Metropolis algorithm is able to outperform the previous algorithms in most of the cases. We have also provided both theoretical and experimental justification to show why the generator space is a worthwhile search space to use for this problem.
Studying English On-Line as a Part of a Course “English for Special Purpose” in Technological University

Popularity of information technologies in modern society leads to implementation of on-line accompaniment of full-time education. The purpose of the study is to analyse the on-line elements of the course “English for Special Purpose”, developed for Masters of Kazan National Research Technological University. Since this discipline is aimed at the development and improvement of language competence of students, the study is primarily focused on training vocabulary for special purpose (engineering). The objectives of the on-line training are the following: development of speech, language and socio-cultural competence as well as motivation and self-discipline. The on-line part of the course “English for Special Purposes” consists of 4 modules, the choice of which varies depending on the learning objectives and the major of the group. The structure of each module is based on guidelines for the creation of electronic educational resources: guidelines for students, introduction of new vocabulary, training, video resources, assessment, self-testing and additional tasks for training communication skills. The course is flexible as certain modules can be chosen depending on the number of academic hours and the major. Assessment procedure is carried out in the form of intermediate and final testing.
Natural Key Discovery in Wikipedia Tables

Wikipedia is the largest encyclopedia to date. Scattered among its articles, there is an enormous number of tables that contain structured, relational information. In contrast to database tables, these webtables lack metadata, making it difficult to automatically interpret the knowledge they harbor. The natural key is a particularly important piece of metadata, which acts as a primary key and consists of attributes inherent to an entity. Determining natural keys is crucial for many tasks, such as information integration, table augmentation, or tracking changes to entities over time.
Methods and visualization tools for the analysis of medical, political and scientific concepts in Genealogies of Knowledge

Abstract An approach to establishing requirements and developing visualization tools for scholarly work is presented which involves, iteratively: reviewing published methodology, in situ observation of scholars at work, software prototyping, analysis of scholarly output produced with the support of text visualization software, and interviews with users. This approach is embodied by the software co-designed by researchers working on the Genealogies of Knowledge project. This paper describes our co-design methodology and the resulting software, presenting case studies demonstrating its use in test analyses, and discussing methodological implications in the context of the Genealogies of Knowledge corpus-based approach to the study of medical, scientific, and political concepts.
Leveraging Context for Neural Question Generation in Open-domain Dialogue Systems

Question generation in open-domain dialogue systems is a challenging but less-explored task. It aims to enhance the interactivity and persistence of human-machine interactions. Previous work mainly focuses on question generation in the setting of single-turn dialogues, or investigates it as a data augmentation method for machine comprehension. We propose a Context-augmented Neural Question Generation (CNQG) model that leverages the conversational context to generate questions for promoting interactivity and persistence of multi-turn dialogues. More specifically, we formulate the task of question generation as a two-stage process. First, we employ an encoder-decoder framework to predict a question pattern, which denotes a set of representative interrogatives, and identify the potential topics from the conversational context by employing point-wise mutual information. Then, we generate the question by decoding the concatenation of the current dialogue utterance, the pattern, and the topics with an attention mechanism. To the best of our knowledge, ours is the first work on question generation in multi-turn open-domain dialogue systems. Our experimental results on two publicly available multi-turn conversation datasets show that CNQG outperforms the state-of-the-art baselines in terms of BLEU-1, BLEU-2, Distinct-1 and Distinct-2. In addition, we find that CNQG allows one to efficiently distill useful features from long contexts, and maintain robust effectiveness even for short contexts.
Concurrency in DD-based Quantum Circuit Simulation

Despite recent progress in physical implementations of quantum computers, a significant amount of research still depends on simulating quantum computations on classical computers. Here, most state-of-the-art simulators rely on array-based approaches which are perfectly suited for acceleration through concurrency using multi- or many-core processors. However, those methods have exponential memory complexities and, hence, become infeasible if the considered quantum circuits are too large. To address this drawback, complementary approaches based on decision diagrams (called DD-based simulation) have been proposed which provide more compact representations in many cases. While this allows to simulate quantum circuits that could not be simulated before, it is unclear whether DD-based simulation also allows for similar acceleration through concurrency as array-based approaches. In this work, we investigate this issue. The resulting findings provide a better understanding about when DD-based simulation can be accelerated through concurrent executions of sub-tasks and when not.
Assessment, Distribution and Regional Geochemical Baseline of Heavy Metals in Soils of Densely Populated Area: A Case Study

To understand the content, pollution, distribution and source and to establish a geochemical baseline of heavy metal elements in soil under the influence of high-density population, the concentrations of heavy metal elements Cr, Mn, Co, Ni, Cu, Zn, As, Cd, Hg, Pb and Fe were determined in 23 soil samples in Suzhou University, and geo-accumulation index, enrichment factor, principal component analysis, spatial analysis and regression analysis were completed. The results showed the following: The elements Cu and As were slightly polluted, while the other heavy metal elements were not. The elements Cd, Cu, Ni and As in soils were mainly caused by agricultural activities of chemical fertilizer, whereas the elements Zn and Hg were impacted by the chemicals and batteries. The heavy metal elements in the north were lower than in the south of the campus, as a whole. The enrichment of elements Cu, As and Cd was caused by the east-west river on the campus, and the enrichment of the elements Mn, Ni and Zn was induced by the reservoir. Biochemical experiments and vehicle parking influenced the spatial enrichment of Cr, Co and Pb, while domestic waste led to the spatial differentiation of Hg concentrations. The regression curve between heavy metal elements and Fe was established, and the background values of the heavy metals Cr, Mn, Co, Ni, Cu, Zn, As, Cd, Hg and Pb are 50.90, 489.37, 11.76, 37.74, 55.70, 58.22, 20.07, 0.09, 0.08 and 24.13 mg/kg, respectively.
An Unsupervised Approach for Knowledge Construction Applied to Personal Robots

The employment of personal robots or service robots has aroused much interest in recent years with an amazing growth of robotics in different domains. Although sophisticated humanoid robots have been developed, much more effort is needed for improving their cognitive capabilities. Interactions with humans and/or with other agents are still limited and not considered satisfactory. So, the way we store and represent knowledge in a cognitive architecture is fundamental in order to overcome these limitations and improve the human-machine and machine-machine interactions. In this article, we propose an unsupervised approach for knowledge construction based on the robot's perception. Our approach makes use of Kohonen maps as an unsupervised machine learning technique and allows the definition of semantic clusters from visual features perceived by the robot. Besides, a multimedia graph knowledge base using a pure formalism is presented, which can be actively used by personal robots in their classic activities, such as environment exploration or information gathering, to represent and share the acquired knowledge, linking it to abstract concepts gifted with semantic relations.
A Content-Based Approach for Detecting Smishing in Mobile Environment

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Maintaining Voting Integrity using Blockchain

The potentials of using blockchains and distributed ledgers to support voting processes have attracted significant attention in the electronic voting community. Most of these recent ideas are centered on blockchain-based e-voting protocols. Others focus on how blockchain can be exploited to simultaneously deliver auditability and anonymity of voters in the voting process. A common feature of these research efforts is the use of blockchain within e-voting contexts. We elaborate in this work the integrity requirements that must be supported by blockchain in online voting as well as offline voting prevalent in developing countries. The framework conditions for blockchain-based voting are also discussed.
Pooling in Graph Convolutional Neural Networks

Graph convolutional neural networks (GCNNs) are a powerful extension of deep learning techniques to graph-structured data problems. We empirically evaluate several pooling methods for GCNNs, and combinations of those graph pooling methods with three different architectures: GCN, TAGCN, and GraphSAGE. We confirm that graph pooling, especially DiffPool, improves classification accuracy on popular graph classification datasets and find that, on average, TAGCN achieves comparable or better accuracy than GCN and GraphSAGE, particularly for datasets with larger and sparser graph structures.
On Instantiating the Algebraic Group Model from Falsifiable Assumptions

We provide a standard-model implementation (of a relaxation) of the algebraic group model (AGM, [Fuchsbauer, Kiltz, Loss, CRYPTO 2018]). Specifically, we show that every algorithm that uses our group is algebraic, and hence "must know" a representation of its output group elements in terms of its input group elements. Here, "must know" means that a suitable extractor can extract such a representation efficiently. We stress that our implementation relies only on falsifiable assumptions in the standard model, and in particular does not use any knowledge assumptions. As a consequence, our group allows to transport a number of results obtained in the AGM into the standard model, under falsifiable assumptions. For instance, we show that in our group, several Diffie-Hellman-like assumptions (including computational Diffie-Hellman) are equivalent to the discrete logarithm assumption. Furthermore, we show that our group allows to prove the Schnorr signature scheme tightly secure in the random oracle model. Our construction relies on indistinguishability obfuscation, and hence should not be considered as a practical group itself. However, our results show that the AGM is a realistic computational model (since it can be instantiated in the standard model), and that results obtained in the AGM are also possible with standard-model groups.
SemanticCloneBench: A Semantic Code Clone Benchmark using Crowd-Source Knowledge

Not only do newly proposed code clone detection techniques, but existing techniques and tools also need to be evaluated and compared. This evaluation process could be done by assessing the reported clones manually or by using benchmarks. The main limitations of available benchmarks include: they are restricted to one programming language; they have a limited number of clone pairs that are confined within the selected system(s); they require manual validation; they do not support all types of code clones. To overcome these limitations, we proposed a methodology to generate a wide range of semantic clone benchmark(s) for different programming languages with minimal human validation. Our technique is based on the knowledge provided by developers who participate in the crowd-sourced information website, Stack Overflow. We applied automatic filtering, selection and validation to the source code in Stack Overflow answers. Finally, we build a semantic code clone benchmark of 4000 clones pairs for the languages Java, C, C# and Python.