Reviewerly/exHarmony huggingface dataset:
Two New Mutation Techniques for Cartesian Genetic Programming

Cartesian Genetic Programming is often used with a point mutation as the sole genetic operator. In this paper, we propose two phenotypic mutation techniques and take a step towards advanced phenotypic mutations in Cartesian Genetic Programming. The functionality of the proposed mutations is inspired by biological evolution which mutates DNA sequences by inserting and deleting nucleotides. Experiments with boolean functions problem show a better search performance when the proposed mutations are used. The results of our experiments indicate that the proposed mutations are beneficial for the use of Cartesian Genetic Programming.
Geometrical Platform of Big Database Computing for Modeling of Complex Physical Phenomena in Electric Current Treatment of Liquid Metals

According to the principles of multiphysical, multiscale simulation of phenomena and processes which take place during the electric current treatment of liquid metals, the need to create an adjustable and concise geometrical platform for the big database computing of mathematical models and simulations is justified. In this article, a geometrical platform was developed based on approximation of boundary contours using arcs for application of the integral equations method and matrix transformations. This method achieves regular procedures using multidimensional scale matrices for big data transfer and computing. The efficiency of this method was verified by computer simulation and used for different model contours, which are parts of real contours. The obtained results showed that the numerical algorithm was highly accurate based on the presented geometrical platform of big database computing and that it possesses a potential ability for use in the organization of computational processes regarding the modeling and simulation of electromagnetic, thermal, hydrodynamic, wave, and mechanical fields (as a practical case in metal melts treated by electric current). The efficiency of this developed approach for big data matrices computing and equation system formation was displayed, as the number of numerical procedures, as well as the time taken to perform them, were much smaller when compared to the finite element method used for the same model contours.
A Framework for the Assessment and Training of Software Refactoring Competences

Long-living software systems are becoming increasingly complex and difficult to maintain. Software refactoring is considered important to achieve maintainability and extensibility of a software system over time. In practice, it is still often neglected, partly because of costs, the perceived risks of collateral damage and difficulties of individuals working on certain components of complex software. It is therefore important for software projects that software developers have the appropriate skills and competences to efficiently perform software refactoring. However, so far there is no systematization of competences in software refactoring to guide in the assessment or training of competences, e.g., for planning or evaluating training activities and paths. In this paper, we address this need by presenting a competence framework for software refactoring by applying Bloom’s revised taxonomy for educational objectives. In particular, we specify competence levels by combining knowledge and cognitive-process dimensions. Via a case study with two existing training environments (i.e. a tutoring system and a serious game), we demonstrate by example that the framework can support (1) in analyzing the competence levels addressed by the training environments and (2) in reflecting training paths for software refactoring. Finally, we discuss the limitations and the further potential of the framework.
Accelerated Approximate Nearest Neighbors Search Through Hierarchical Product Quantization

A fundamental recurring task in many machine learning applications is the search for the Nearest Neighbor in high dimensional metric spaces. Towards answering queries in large scale problems, state-of-the-art methods employ Approximate Nearest Neighbors (ANN) search, a search that returns the nearest neighbor with high probability, as well as techniques that compress the dataset. Product-Quantization (PQ) based ANN search methods have demonstrated state-of-the-art performance in several problems, including classification, regression and information retrieval. The dataset is encoded into a Cartesian product of multiple low-dimensional codebooks, enabling faster search and higher compression. Being intrinsically parallel, PQ-based ANN search approaches are amendable for hardware acceleration. This paper proposes a novel Hierarchical PQ (HPQ) based ANN search method as well as an FPGA-tailored architecture for its implementation that outperforms current state of the art systems. HPQ gradually refines the search space, reducing the number of data compares and enabling a pipelined search. The mapping of the architecture on a Stratix 10 FPGA device demonstrates over ×250 speedups over current state-of-the-art systems, opening the space for addressing larger datasets and/or improving the query times of current systems.
LiteMat, an Encoding Scheme with RDFS++ and Multiple Inheritance Support

In this paper, we extend LiteMat, an RDFS and owl:sameAs inference-enabled RDF encoding scheme, which is used in a distributed knowledge graph data management system. Our extensions enable to reach RDFS++ expressiveness by integrating owl:transitiveProperty and owl:inverseOf properties. Considering the latter, owl:inverseOf property, we propose a simple solution that involves a dictionary look-up at query run-time. For the former, we present an efficient approach to encode individuals involved in chain and tree structures of a transitive property. Moreover, our extension also provides an efficient solution to the multiple inheritance problem which sometimes encountered in the concept hierarchy of ontologies. We provide details of a distributed implementation and highlight the efficiency of our encoding and query processing approaches over large synthetic datasets.
Deep Learning Based Classification Using Academic Studies in Doc2Vec Model

The number of academic studies published on the internet is increasing day by day. Researchers spend a long part of their time studying academic studies. They examine the harmony of their fields by looking at the title and summary of the studies. In this study, academic studies are classified based on deep learning by using Doc2vec word embeddings method. During the classification process, the studies were repeated in 9 different categories using repeated neural networks (Rnn's) and LSTM architectures.
Blockchain Technology applied to Electronic Health Records

Blockchain technology is on the cusp of revolutionizing the way we handle healthcare data, in term of storage and utilization. The main goal is to empower patients to be the center of their own health record so that, the patient doesn't have to rely on different institutions or hospitals they might visit. Blockchain technology and smart contracts provide an interesting and innovative way to keep track of Electronic Health Records (EHRs). This technology could help the patients to have better control of their own data. Health professionals and institutions, such as hospitals, could have access to patient’s data owned by other institutions. In the present article, we discuss how blockchain technologies can be used to handle EHR while improving the efficiency of operations through streamlining processes and transparency. We propose an architecture to manage and share healthcare data among different organizations. The proposed work could significantly reduce the time needed to share patient data among different health organizations and reduce the overall cost.
Stationary measure for three-state quantum walk

We focus on the three-state quantum walk (QW) in one dimension. In this paper, we give the stationary measure in general condition, originated from the eigenvalue problem. Firstly, we get the transfer matrices by our new recipe, and solve the eigenvalue problem. Then we obtain the general form of the stationary measure for concrete initial state and eigenvalue. We also show some specific examples of the stationary measure for the three-state QW. One of the interesting and crucial future problems is to make clear the whole picture of the set of stationary measures.
Fine-Grained Gesture Recognition Based on High Resolution Range Profiles of Terahertz Radar

As one of the most commonly used natural languages, gesture has become an important approach for people to interact with machines. However, the flexibility of fingers has posed a challenge for detecting fine-grained gestures. In this paper, a novel method of high precision fine-grained gesture recognition is proposed based on a terahertz radar, which is able to sense any gesture movement when its range of motion is greater than 5mm. First, High Resolution Range Profile (HRRP) sequences are extracted from radar echoes. Then, the HRRP features are fed to Random Forest classifier after dimensionality reduction by PCA. In order to verify the proposed method is effective to detect fine-grained gestures, four kinds of similar gestures with multi-fingers are designed for experiments. The results indicate the recognition rate exceeds 99.7%, which demonstrates a great prospect in fine-grained gesture recognition using a terahertz radar.
Efficient identity based aggregate signcryption scheme using bilinear pairings over elliptic curves

Signcryption is a cryptographic primitive that performs signature and encryption simultaneously at a cost significantly lower than the traditional signature-then-encryption methodology. An aggregate signature scheme is a digital signature scheme that combines n signatures on n different messages from n different users to a single signature. In this paper, we propose an aggregate signcryption scheme in the identity based setting using bilinear pairings over elliptic curves by combining aggregate signatures with signcryption schemes. We prove that our scheme is secure and satisfies the unforgeability and confidentiality in the random oracle model with the assumptions that the CBDHP and BDHP are hard. Also our scheme is public verifiable and requires constant number of pairings for aggregate verification. Comparing with previous schemes of this kind, our scheme is more efficient in computation and communication.
Immersive Technologies in Marketing: State of the Art and a Software Architecture Proposal

After conducting the historical review of marketing and especially experiential marketing, which considers various types of experiences such as sensations, feelings, thoughts, actions and relationships, seeking in the consumer greater satisfaction and therefore greater effectiveness in the action of marketing, as well as establishing the state of the art of immersive technologies and their applications in marketing, the authors propose a software architecture model for hotel services, which includes the description of hardware and software elements for development and implementation.The model would make it possible to bring customers closer to experiences that are very close to reality, based on their profiles and characteristics, previously treated by a recommendation module included in the proposal, a fact that supports the decision of purchase, with a high degree of adaptation to their needs and requirements.The proposal and development of the model with attributes of originality, aims to contribute to the development and technological innovation of marketing in the hotel industry.Finally, conclusions and recommendations for future work are established.
Towards Somaesthetic Smarthome Designs

Have you ever experienced a mirror that not only "sees" how you see yourself, but tells you how others perceive you? May such a smartmirror help not only adapt/improve oneself externally (look) but also internally (mood, emotions)? What are the expectations, fears (privacy)? The acceptance of such a smart thing requires approaches that go beyond technology-driven designs. In this paper, we report on the construction of an affective emotion-sensitive smartmirror as a smart piece of furniture for smarthomes and explorative studies with that system which address somaesthetics and aims to help inhabitants in self-reflection. The paper presents and discusses the prototype implementation and the results of a study in which the ideas, concerns, and experiences for/with the self-reflection capabilities of the affective smartmirror were explored by participants using the prototype as a technology probe.
CUHK at MRP 2019: Transition-Based Parser with Cross-Framework Variable-Arity Resolve Action

This paper describes our system (RE-SOLVER) submitted to the CoNLL 2019 shared task on Cross-Framework Meaning Representation Parsing (MRP).Our system implements a transition-based parser with a directed acyclic graph (DAG) to tree preprocessor and a novel cross-framework variable-arity resolve action that generalizes over five different representations.Although we ranked low in the competition, we have shown the current limitations and potentials of including variable-arity action in MRP and concluded with directions for improvements in the future.
Improved shuffled frog Leaping Algorithm for Path Planning of multiple Mobile-Robot

Improved shuffled frog leaping algorithm (ISFLA) has been proposed in this paper for multi-robot path planning in a clutter environment. Basic SFLA algorithm has been improved in the local search process by adding the weight and acceleration factor in the original version of SFLA to avoid the local optima and premature convergence. ISFLA technique has been embedded into the multi-robot system in a dynamic framework for generating optimal trajectory path by determining an optimal subsequent position for each robot from their existing position. Fitness function have been formulated by considering the different constraints and then, it optimized through ISFLA for generating optimal trajectory smooth path by avoiding the obstacles present in the environment. Finally, the simulation and experiment have been carried out to validate the performance of the proposed algorithm. The results obtained through ISFLA, SFLA and IGSA in terms of runtime, path deviation, the path travelled and energy utilized during travel shows ISFLA has outperformed its competitors.
Say Anything: Automatic Semantic Infelicity Detection in L2 English Indefinite Pronouns

Computational research on error detection in second language speakers has mainly addressed clear grammatical anomalies typical to learners at the beginner-to-intermediate level.We focus instead on acquisition of subtle semantic nuances of English indefinite pronouns by non-native speakers at varying levels of proficiency.We first lay out theoretical, linguistically motivated hypotheses, and supporting empirical evidence on the nature of the challenges posed by indefinite pronouns to English learners.We then suggest and evaluate an automatic approach for detection of atypical usage patterns, demonstrating that deep learning architectures are promising for this task involving nuanced semantic anomalies.
A highly parameterizable framework for Conditional Restricted Boltzmann Machine based workloads accelerated with FPGAs and OpenCL

Conditional Restricted Boltzmann Machine (CRBM) is a promising candidate for a multidimensional system modeling that can learn a probability distribution over a set of data. It is a specific type of an artificial neural network with one input (visible) and one output (hidden) layer. Recently published works demonstrate that CRBM is a suitable mechanism for modeling multidimensional time series such as human motion, workload characterization, city traffic analysis. The process of learning and inference of these systems relies on linear algebra functions like matrix–matrix multiplication, and for higher data sets, they are very compute-intensive. In this paper, we present a configurable framework for CRBM based workloads for arbitrary large models. We show how to accelerate the learning process of CRBM with FPGAs and OpenCL, and we conduct an extensive scalability study for different model sizes and system configurations. We show significant improvement in performance/Watt for large models and batch sizes (from 1.51x up to 5.71x depending on the host configuration) when we use FPGA and OpenCL for the acceleration, and limited benefits for small models comparing to the state-of-the-art CPU solution.
Secure and efficient certificateless aggregate signature scheme from bilinear pairings

The most important contribution of modern cryptography is the invention of digital signatures. To deal with specific application scenarios, digital signature schemes have been evolved with different variants. One of such variants is the aggregate signature scheme, which allows aggregation of different signatures by different users on different messages, to achieve computational and communication efficiency. Such schemes are useful in the design of Wireless Sensor Networks, Mobile Ad-hoc Networks, and Vehicular Ad-hoc Networks, where storage, bandwidth, and computational complexity are major constraints. In order to improve the computational and communicational efficiency along with security, in this paper, we propose a novel Certificateless Aggregate Signature (CLAS) scheme and extended it to achieve full aggregation. The proposed CLAS scheme uses bilinear pairings over elliptic curves and is proven secure in the Random Oracle Model under the assumption that Computational Diffie–Hellman Problem is hard. The security of the proposed CLAS scheme is proven without using forking lemma to achieve tight security. We compared our scheme with well-known existing schemes. Efficiency analysis shows that our scheme is much efficient than existing schemes in terms of communication and computational costs.
RED-GENE: An Evolutionary Game Theoretic Approach to Adaptive Data Stream Classification

The extensive growth of digital technologies such as the Internet of Things (IoT), social media networks and forecasting systems has led to new challenges regarding computational complexity and big data mining. The classification task in such applications is not trivial due to the high volume of related data and limited time available for the task. It is particularly difficult when dealing with data streams, where each instance of data is typically processed once on its arrival (i.e. online) while the underlying data distribution often changes due to the changing environment. In this paper, we propose a novel ensemble-based framework called Replicator Dynamics & Genetic Algorithms Approach (RED-GENE) for effective data stream classification in the context of changing environment leading to concept drifts (i.e. evolution of data streams). RED-GENE employs three novel Replicator Dynamics (RD) strategies along with a Genetic Algorithm (GA) optimisation technique to flexibly adapt to different types of concept drifts when performing data stream classification tasks. The proposed framework works as follows. First, a set of random feature combinations is drawn from a given pool of features of the target data stream to create different classification types. Next, RD is used to allow the classification types achieving higher classification accuracy to grow and those with lower accuracy to shrink. A modified version of the classic GA is then employed to optimise the randomly drawn combinations of features in each classification type. The proposed framework was tested using nine data streams (including both real-world and synthetic datasets) to investigate different variations of the proposed framework and compare its performance to other state-of-the-art algorithms using immediate and delayed prequential evaluation methods. The results demonstrated that the proposed framework can provide the best accuracy on average when comparing to five other state-of-the-art algorithms.
Virtual Reality Training Improves Real-World Performance on a Speeded Task

We investigated whether virtual reality (VR) training transfers to real-world performance. Participants were trained to complete a real-world maze task, which involved quickly moving an object through a tabletop maze without hitting the maze walls. Participants either trained in VR, trained by physically simulating the task (PS), or received no training (NT). After five timed training trials (or no training in the control condition), participants completed five timed test trials using the real-world maze. The VR training condition completed the maze significantly faster than the other conditions across each test trial. Other than the initial test trial where the NT condition was the slowest, the PS and NT conditions were comparable on the remaining test trials. These results suggest that VR provided trainees with an opportunity for meaningful encoding of the task, beyond the act of physically simulating the motions of the task.
Using Deep Learning to Detect Polyethylene Terephthalate (PET) Bottle Status for Recycling

Following the recent ban of plastic waste import by China, many developed countries face challenges with their huge amount of plastic waste. Some of the countries have diverted their waste to other developing East-Asia countries like Philippines, Vietnam and Malaysia. However, Malaysian government has taken strict action to send back over 3000 tons of plastic waste to its origin citing contamination. The aim of this paper is to establish mechanisms to detect the status of post-consumer PET bottles for recycling. By encouraging recycling of clean pet bottles, we ensure high quality bottles for recycling. A research based as well as experimental design approach was adopted to develop mechanisms to detect PET bottle status. During the experiment, various PET bottles were collected and images captured. A total of 1749 images were captured using raspberry Pi camera.
An Open, Extendible, and Fast Turkish Morphological Analyzer

In this paper, we present a two-level morphological analyzer for Turkish which consists of five main components: finite state transducer, rule engine for suffixation, lexicon, trie data structure, and LRU cache.We use Java language to implement finite state machine logic and rule engine, Xml language to describe the finite state transducer rules of the Turkish language, which makes the morphological analyzer both easily extendible and easily applicable to other languages.Empowered with a comprehensive lexicon of 54,000 bare-forms including 19,000 proper nouns, our morphological analyzer is amongst the most reliable analyzers produced so far.The analyzer is compared with Turkish morphological analyzers in the literature.By using LRU cache and a trie data structure, the system can analyze 100,000 words per second, which enables users to analyze huge corpora in a few hours.
Almost Optimal Testers for Concise Representations.

We give improved and almost optimal testers for several classes of Boolean functions on n variables that have concise representation in the uniform and distribution-free model. Classes, such as k-Junta, k-Linear, s-Term DNF, s-Term Monotone DNF, r-DNF, Decision List, r-Decision List, size-s Decision Tree, size-s Boolean Formula, size-s Branching Program, s-Sparse Polynomial over the binary field and functions with Fourier Degree at most d.The approach is new and combines ideas from Diakonikolas et al. [Ilias Diakonikolas et al., 2007], Bshouty [Nader H. Bshouty, 2018], Goldreich et al. [Oded Goldreich et al., 1998], and learning theory. The method can be extended to several other classes of functions over any domain that can be approximated by functions with a small number of relevant variables.