Reviewerly/exHarmony huggingface dataset:
Semantic transparency effects in German compounds: A large dataset and multiple-task investigation

In the present study, we provide a comprehensive analysis and a multi-dimensional dataset of semantic transparency measures for 1810 German compound words. Compound words are considered semantically transparent when the contribution of the constituents’ meaning to the compound meaning is clear (as in airport), but the degree of semantic transparency varies between compounds (compare strawberry or sandman). Our dataset includes both compositional and relatedness-based semantic transparency measures, also differentiated by constituents. The measures are obtained from a computational and fully implemented semantic model based on distributional semantics. We validate the measures using data from four behavioral experiments: Explicit transparency ratings, two different lexical decision tasks using different nonwords, and an eye-tracking study. We demonstrate that different semantic effects emerge in different behavioral tasks, which can only be captured using a multi-dimensional approach to semantic transparency. We further provide the semantic transparency measures derived from the model for a dataset of 40,475 additional German compounds, as well as for 2061 novel German compounds.
Design of Accelerator for MobileNet Convolutional Neural Network Based on FPGA

MobileNet is a convolutional neural network with high recognition rate, less calculation and parameters, which is very suitable for embedded devices. This work designs a special parallelization acceleration unit for MobileNet architecture. On this basis, the whole operation of the architecture is realized in the form of reuse. Secondly, through configurable design, a depthwise separable convolution layer can support multilayer operations with different configurations. Finally, this work optimizes the timing of the architecture and implements pipeline operations between convolutional layers. In this way, the real-time performance of the system and the resource utilization of hardware acceleration unit can be improved. The maximum frame rate is 5.52fps and the power consumption of the system is 2.149W, while the design is run on the ZynqXC7z045 platform with a 100MHz work clock frequency.
Enhancing Fall Detection for Elderly with Smart Helmet in a Cloud-Network-Edge Architecture

Falls commonly occur for elder citizens which may lead to severe injuries. In this paper, we propose a cloud-network-edge architecture to enhance fall detection, prevention and protection which consists of the medical cloud, edge networks and end-devices, such smart helmet. The smart helmet is integrated with wearable cameras, accelerometers, gyroscope sensors; hence, it is able to sense daily activities of the elderly based on data fusion from multi sensors collaboratively. The processing of the sensor data can be offloaded at the edge for reducing delay and preserving privacy. The medical data can be delivered over a secure medical network to the medical cloud for services, such as fall alarm. In particular, we evaluate the performance of multimode processing algorithms for video and accelerometer data to enhance fall detection. Our experimental results show that the proposed algorithms are able to increase the accuracy and reduce the false alarm effectively.
Mesothermal gold prospectivity mapping of the southern Kibi-Winneba belt of Ghana based on Fuzzy analytical hierarchy process, concentration-area (C-A) fractal model and prediction-area (P-A) plot

In this paper, the southern Kibi-Winneba belt, which shares geological resemblance with the renowned Ashanti belt in Ghana has been investigated to delineate prospective zones of gold mineralization occurrences. The generation of a mineral prospectivity map (MPM) for the study area comprised the integration of nine (9) exploration layers obtained from geological, remote sensing and geophysical datasets based on the Fuzzy analytical hierarchy process (FAHP) approach and geographical information systems (GIS). The employability of the FAHP in the assignment of weight to the nine (9) evidential layers based on their comparative importance with respect to the mineral being sought (which is gold in this study) was carried out by experts in remote sensing and geophysical techniques for mineral exploration. The weights of the evidential layers were subsequently normalized and integrated to produce an MPM of gold deposits within the study area based on a fuzzy operator of γ = 0.9 in GIS. For an elaborate evaluation of the MPM, the concentration-area (C-A) fractal model was employed to classify the generated MPM to obtain 6 thresholds for the generation of a final MPM for the study area. Further evaluation of the MPM was carried out by the prediction-area (P-A) plot based on 15 locations of known gold occurrences, with results obtained predicting that 25% of the study area as prospective regions of gold deposits, within which 75% of the known gold occurrences have been delineated.
A novel fuzzy clustering algorithm by minimizing global and spatially constrained likelihood-based local entropies for noisy 3D brain MR image segmentation

In this paper, we propose a novel fuzzy clustering algorithm by minimizing global and spatially constrained likelihood-based local entropies (FCMGsLE) for segmenting noisy 3D brain magnetic resonance (MR) image volumes. For each voxel, in order to measure uncertainties that arise while identifying its class, two different entropies are defined. In particular, they measure the amount of uncertainties in terms of global entropy using fuzzifier weighted global membership function and spatially constrained likelihood-based local entropy using fuzzifier weighted local membership function. To mitigate the effect of noise and intensity inhomogeneity (IIH) or radio frequency (RF) inhomogeneity, the local membership function is induced by spatially constrained likelihood measure. These entropies are minimized through a fuzzy objective function to obtain the cluster prototypes and membership functions. The final membership function is obtained by integrating these global and local membership functions using weighted parameters. The algorithm is assessed both qualitatively and quantitatively on ten 3D volumes of simulated and clinical brain MR image data having high levels of noise and intensity inhomogeneity and a synthetic 3D image volume with Rician noise. The simulation results reveal that the proposed algorithm outperforms several state-of-the-art algorithms devised in recent past when evaluated in terms of segmentation accuracy, Dice similarity coefficient, partition coefficient, and partition entropy
An Efficient Method for Mining Closed Potential High-Utility Itemsets

High-utility itemset mining (HUIM) has become a key phase of the pattern mining process, which has wide applications, related to both quantities and profits of items. Many algorithms have been proposed to mine high-utility itemsets (HUIs). Since these algorithms often return a large number of discovered patterns, a more compact and lossless representation has been proposed. The recently proposed closed high utility itemset mining (CHUIM) algorithms were designed to work with certain types of databases (e.g., those without probabilities). In fact, real-world databases might contain items or itemsets associated with probability values. To effectively mine frequent patterns from uncertain databases, several techniques have been developed, but there does not exist any method for mining CHUIs from this type of databases. This work presents a novel and efficient method without generating candidates, named CPHUI-List, to mine closed potential high-utility itemsets (CPHUIs) from uncertain databases. The proposed algorithm is DFS-based and utilizes the downward closure property of high transaction-weighted probabilistic mining to prune non-CPHUIs. It can be seen from the experiment evaluations that the proposed algorithm has better execution time and memory usage than the CHUI-Miner.
Bootstrapping Non-Parallel Voice Conversion from Speaker-Adaptive Text-to-Speech

Voice conversion (VC) and text-to-speech (TTS) are two tasks that share a similar objective, generating speech with a target voice.However, they are usually developed independently under vastly different frameworks.In this paper, we propose a methodology to bootstrap a VC system from a pretrained speaker-adaptive TTS model and unify the techniques as well as the interpretations of these two tasks.Moreover by offloading the heavy data demand to the training stage of the TTS model, our VC system can be built using a small amount of target speaker speech data.It also opens up the possibility of using speech in a foreign unseen language to build the system.Our subjective evaluations show that the proposed framework is able to not only achieve competitive performance in the standard intra-language scenario but also adapt and convert using speech utterances in an unseen language.
Robustness to adversarial examples can be improved with overfitting

Abstract Deep learning (henceforth DL) has become most powerful machine learning methodology. Under specific circumstances recognition rates even surpass those obtained by humans. Despite this, several works have shown that deep learning produces outputs that are very far from human responses when confronted with the same task. This the case of the so-called “adversarial examples” (henceforth AE). The fact that such implausible misclassifications exist points to a fundamental difference between machine and human learning. This paper focuses on the possible causes of this intriguing phenomenon. We first argue that the error in adversarial examples is caused by high bias, i.e. by regularization that has local negative effects. This idea is supported by our experiments in which the robustness to adversarial examples is measured with respect to the level of fitting to training samples. Higher fitting was associated to higher robustness to adversarial examples. This ties the phenomenon to the trade-off that exists in machine learning between fitting and generalization.
Tethered Tool Manipulation Planning With Cable Maneuvering

In this letter, we present a planner for manipulating tethered tools using dual-armed robots. The planner generates robot motion sequences to maneuver a tool and its cable while avoiding robot-cable entanglements. Firstly, the planner generates an object manipulation motion sequence (OMMS) to handle the tool and place it in desired poses. Secondly, the planner examines the tool movement associated with the OMMS and computes candidate positions for a cable slider, to maneuver the tool cable and avoid collisions. Finally, the planner determines the optimal slider positions to avoid entanglements and generates a cable manipulation motion sequence (CMMS) to place the slider in these positions. The robot executes both the OMMS and CMMS to handle the tool and its cable to avoid entanglements and excess cable bending. Simulations and real-world experiments help validate the proposed method.
Oblivious Transfer for Privacy-Preserving in VANET’s Feature Matching

In the feature matching of some Vehicular Ad Hoc Network (VANET) technologies, users' privacy disclosure issue has seriously threatened personal safety and caused considerable economic loss. In this paper, we proposed Oblivious Transfer (OT) protocol and Private Set Intersection (PSI) protocol to protect the users' privacy in the situation of VANET's feature matching. In our schema, an efficient k-out-of- n OT protocol is constructed. Then, this OT protocol is adopted to give a PSI protocol with equality test. Based on the PSI protocol, the two parties of VANET can obtain the intersection of their characteristic sets and any information outside such intersection is unavailable. Accordingly, internal attacker fails to get any useful information from the two parties in the feature matching, and the two parties cannot obtain extra data of each other. Moreover, to reduce the computational cost of the OT protocol, the non-communicative algebraic structure-group ring Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> [S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> ] is employed. In addition, we analyzed that the proposed scheme does not use complex calculations and can resist against the current quantum attacks.
Fraud detection in credit card transaction using machine learning techniques

Credit card transactions are nowadays more and more frequent. Using your credit card to buy online, as a mobile wallet or for a simple payment to a merchant has become a daily action for most cardholders. The virtual world and technological development that we know, makes banking transactions become digitized. As a result, a flow of millions of online transactions is subject to various types of fraud. Traditional techniques for detecting fraud cannot detect sophisticated fraudulent techniques. To be limited to an analysis of the cardholder behavior's, or to static rules of risk management of the frauds, had never stopped the fraudulent to commit their crimes. However, machine-learning techniques have been able to meet this need, as we found in literature [1]. In this paper, we will present a comparative study of some machine learning techniques, which gave the best results, according to our state of art [1] but applied to the same set of data. The objective of this study is to choose the best credit card fraud detection techniques to implement in our future work.
Active Learning for Feasible Region Discovery

Often in the design process of an engineer, the design specifications of the system are not completely known initially. However, usually there are some physical constraints which are already known, corresponding to a region of interest in the design space that is called feasible. These constraints often have no analytical form but need to be characterised based on expensive simulations or measurements. Therefore, it is important that the feasible region can be modeled sufficiently accurate using only a limited amount of samples. This can be solved by using active learning techniques that minimize the amount of samples w.r.t. what we try to model. Most active learning strategies focus on classification models or regression models with classification accuracy and regression accuracy in mind respectively. In this work, regression models of the constraints are used, but only the (in) feasibility is of interest. To tackle this problem, an information-theoretic sampling strategy is constructed to discover these regions. The proposed method is then tested on two synthetic examples and one engineering example and proves to outperform the current state-of-the-art.
Paper Recommendation Based on Citation Relation

Searching for relevant literature is a fundamental part of academic research. The search for relevant literature is becoming a more difficult and time-consuming task as millions of articles are published each year. As a solution, recommendation systems for academic papers attempt to help researchers find relevant papers quickly. This paper focuses on graph-based recommendation systems for academic papers using citation networks. This type of paper recommendation system leverages a graph of papers linked by citations to create a list of relevant papers. In this study, we explore recommendation systems for academic papers using citation networks incorporating citation relations. We define citation relation based on the number of times the origin paper cites the reference paper, and use this citation relation to measure the strength of the relation between the papers. We created a weighted network using citation relation as citation weight on edges. We evaluate our proposed method on a real-world publication data set, and conduct an extensive comparison with three state-of-the-art baseline methods. Our results show that citation network-based recommendation systems using citation weights perform better than the current methods.
Class expression induction as concept space exploration: From DL-Foil to DL-Focl

The Web of Data is one of the perspectives of the Semantic Web. In this context, concept learning services, supported by multirelational machine learning, have been integrated in various tools for knowledge engineers to carry out several tasks related to the construction, completion and maintenance of the knowledge bases: essentially they are used to elicit new candidate concept definitions (i.e. axioms regarding classes) to be incorporated in the knowledge bases possibly also as replacements for previous ones. Sundry reference approaches rely on a covering strategy to generalize input examples that can be regarded as a form of hill-climbing search that explores a huge discrete conceptual space. Methods adopting this strategy are known to be affected by an inherent problem of myopia. In particular, our DL-Foil has been shown to suffer from this problem as its algorithm is based on a stochastic yet informed exploration of the concept space, by means of a refinement operator, to generate partial descriptions iteratively. To tackle this problem and enhance the performance of our system we have introduced a series of extensions of the original DL-Foil algorithm, that have led to various releases of its spin-off DL-Focl. Essentially they aim at reducing the aforementioned problem through specific strategies grounded on either the integration of meta-heuristics, such as repeated hill-climbing and tabu search, or the employment of some form of lookahead. In this work, we present consolidated and extended releases of both DL-Foil and DL-Focl along various dimensions: better heuristics and stop conditions, more complex refinement operators with the possibility to perform the specialization adopting iterative deepening or lookahead strategies, improved versions of the algorithm based on the repeated hill-climbing strategy with new quality criteria and of the tabu search with a different policy for managing the local memory. All the implementations of these approaches have been extensively evaluated in three experimental sessions, involving various publicly available knowledge bases and fragments extracted from the Linked Data cloud, showing interesting results and indicating some lessons to be learned: our approaches outperformed a popular reference system from the DL-Learner framework on learning problems when the open-world semantics is explicitly considered. They also exhibited an analogous performance on a benchmark of datasets from contexts with an intended underlying closed-world semantics.
Deep Learning Cyberbullying Detection Using Stacked Embbedings Approach

The Cyberspace is one of the humanity's great inventions that bring great benefits but also exposes us to cyber threats. Cyberbullying commonly happened to each and every person on social platforms. In this paper we propose a framework to detect cyberbullying messages in the form of text data using deep neural networks and word embeddings. We stack together the state-of-the-art Bert and Glove embeddings to improve the performance of the classifier. As a result, the model outperforms the majority of the traditional machine learning methods such as SVM and Logistic Regression.
Fast and scalable algorithms for mining subgraphs in a single large graph

Mining frequent subgraphs is an important issue in graph mining. It is defined as finding all subgraphs whose occurrences in the dataset are greater than or equal to a given frequency threshold. In recent applications, such as social networks, the underlying graphs are very large. Algorithms for mining frequent subgraphs from a single large graph have been developing rapidly lately. Among all such algorithms, GraMi is considered the state-of-the-art. However, GraMi still consumes a lot of time and memory in the mining of a large graph. In this paper, we propose two effective strategies to optimize the GraMi algorithm, which help to increase performance as well as reduce memory consumption during execution. Firstly, GraMi only lists all frequent subgraphs, without computing the support of each mined subgraph. This is disadvantageous in decision support systems, which require information about the support of all subgraphs. Therefore, we optimize GraMi to compute the support values during the mining process. Secondly, we apply the strategy of sorting all edges in graphs by their frequencies, which means that edges with low frequencies will be mined first, and vice versa. This sorting strategy can reduce the number of possibly infrequent subgraph candidates, especially on large subgraphs that are usually derived from those edges with high frequency. Thirdly, we apply a parallel processing technique, in which each frequent edge is executed simultaneously in a separate thread, and improve our parallel strategy by combination with the sorting strategy. Our experiments were performed on three real datasets and the results showed that the performance, as well as memory requirements, are better than those of the original GraMi algorithm
Does design matter when visualizing Big Data? An empirical study to investigate the effect of visualization type and interaction use

Abstract The need for good visualization is increasing, as data volume and complexity expand. In order to work with high volumes of structured and unstructured data, visualizations, supporting the ability of humans to make perceptual inferences, are of the utmost importance. In this regard, a lot of interactive visualization techniques have been developed in recent years. However, little emphasis has been placed on the evaluation of their usability and, in particular, on design characteristics. This paper contributes to closing this research gap by measuring the effects of appropriate visualization use based on data and task characteristics. Further, we specifically test the feature of interaction as it has been said to be an essential component of Big Data visualizations but scarcely isolated as an independent variable in experimental research. Data collection for the large-scale quantitative experiment was done using crowdsourcing (Amazon Mechanical Turk). The results indicate that both, choosing an appropriate visualization based on task characteristics and using the feature of interaction, increase usability considerably.
Bat Algorithm Based Non-linear Contrast Stretching for Satellite Image Enhancement

The remote sensing images acquired from the satellites are low contrast images. The availability of low contrast images and failure of the traditional methods such as Histogram Equalization and Gamma correction in preserving the brightness levels in the image are the main issues in satellite image processing. This paper proposes an optimized contrast stretching using non-linear transformation for image enhancement. The non-linear transformation is influenced by the appropriate choice of control parameters for the sample images since manual tuning for individual images is tedious. A Bat algorithm based tuning is employed for the automated selection of control parameters in the transformation. The performance of the optimization algorithm is compared against other metaheuristic algorithms such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). It is noted that the bat algorithm based contrast enhancement outperforms the other optimization techniques in terms of metrics such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Absolute Mean Brightness Error (AMBE), Entropy and CPU time (Central Processing Unit).