Reviewerly/exHarmony huggingface dataset:
Metadata-Driven Semantic Coordination

Reuse and combination of disparate datasets on the Semantic Web require semantic coordination, i.e. the ability to match heterogeneous semantic models. Systematic evaluations raised the performance of matching systems in terms of compliance and resource consumption. However, it is equally important to be able to identify diverse matching scenarios, covering a range of variations in the datasets such as different modeling languages, heterogeneous lexicalizations, structural differences and to be able to properly handle these scenarios through dedicated techniques and the exploitation of external resources. Furthermore, this should be achieved without requiring manual tinkering of low-level configuration knobs. As of the Semantic Web vision, machines should be able to coordinate and talk to each other to solve problems. To that end, we propose a system that automates most decisions by leveraging explicit metadata regarding the datasets to be matched and potentially useful support datasets. This system uses established metadata vocabularies such as VoID, Dublin Core and the LIME module of OntoLex-Lemon. Consequently, the system can work on real-world cases, leveraging metadata already published alongside self-describing datasets.
Synthetic Data Augmentation for Improving Low-Resource ASR

Although the application of deep learning to automatic speech recognition (ASR) has resulted in dramatic reductions in word error rate for languages with abundant training data, ASR for languages with few resources has yet to benefit from deep learning to the same extent. In this paper, we investigate various methods of acoustic modeling and data augmentation with the goal of improving the accuracy of a deep learning ASR framework for a low-resource language with a high baseline word error rate. We compare several methods of generating synthetic acoustic training data via voice transformation and signal distortion, and we explore several strategies for integrating this data into the acoustic training pipeline. We evaluate our methods on an indigenous language of North America with minimal training resources. We show that training initially via transfer learning from an existing high-resource language acoustic model, refining weights using a heavily concentrated synthetic dataset, and finally fine-tuning to the target language using limited synthetic data reduces WER by 15% over just transfer learning using deep recurrent methods. Further, we show improvements over traditional frameworks by 19% using a similar multistage training with deep convolutional approaches.
A Speaker-Adaptive HMM-based Vietnamese Text-to-Speech System

This paper describes the first attempt in developing a Vietnamese HMM-based Text-to-Speech system using the speaker-adaptive approach. Although speaker-dependent systems have been built widely, no speaker-adaptive system has been developed for Vietnamese so far. We collected speech data from several Vietnamese native speakers and employed state-of-the-art speech analysis, model training and speaker adaptation techniques to develop the system. Besides, we performed perceptual experiments to compare the quality of speaker-adapted (SA) voices built on the average voice model and speaker-dependent (SD) voices built on SD models, and to confirm the effects of contextual features including word boundary (WB) and part-of-speech (POS) on the quality of synthetic speech. Evaluation results show that SA voices have significantly higher naturalness than SD voices when the same limited contextual feature set excluding WB and POS was used. In addition, SA voices trained with limited contextual features excluding WB and POS still have better quality than SD voices trained with full contextual features including WB and POS. These results show the robustness of the speaker-adaptive over the speaker-dependent approach for Vietnamese statistical parametric speech synthesis.
Perceived formal and informal sanctions on the willingness to commit cyber attacks against domestic and foreign targets

Scholars have expressed concerns over the effectiveness of formal sanctions in deterring cybercrime, particularly regarding computer intrusions and cyber attacks. Little empirical research, however, examines deterrence in the cyber world. This study examines the effects of perceived formal and informal sanctions, computer skill, online behaviors, cyber deviance, and beliefs on college students’ willingness to commit cyber attacks against domestic and foreign targets, specifically defacing websites, compromising bank servers, and searching government servers. The study found that anticipated formal sanctions did not deter students from their willingness to commit caused-based cyber attacks, but that perceived informal sanctions did. The implications for future studies examining intentions to commit cause-based computer intrusions are explored.
Facefetch: An Efficient and Scalable Face Retrieval System That uses Your Visual Memory

Often in many situations in our life, we wish to envision the person we met but we could not recall what the person exactly look like except for a slight impression of the face. Yugo Sato et.al. introduced a face retrieval system for this problem, which utilises visual inputs from the users and attempts to retrieve the target face. The major drawback of their approach was that their system was slow and only applicable for small databases like Chicago Face Database. In this paper, we introduce a robust and scalable face retrieval system that is capable of retrieving the envisioned face from a large-scale database. Furthermore, instead of information specific to the target, our face retrieval system asks the user to select common face attributes that they remember their target face had, using which the system filters out the irrelevant faces thus speeding up the search process. Then our system asks the user to select several images that resemble with the envisioned face. On the basis of this selection, our system automatically reduces the "semantic gap" between human description and the computer based description of the target image. In order to evaluate our system, We conducted user studies on a large-scale database and established that our framework succeeded in beating the state of the art results in this particular task and thus proved itself to be very effective for retrieving the envisioned face image in approximately half the total number of search iterations and taking one-third of the overall search time thereby putting much less burden on the user.
Similarity Analysis of Action Trajectories Based on Kick Distributions

This paper discusses the validity of similarity measures for action trajectories based on kick distributions. We focus on action trajectories for analyzing team strategies. Kick distribution is then obtained from the action trajectories, which allows us to quantitatively calculate the dissimilarity (or distance) between two team strategies. In this paper, three distance metrics are investigated as the similarity measure: Earth mover's distance, $$L^2$$ distance, and Jensen-Shannon divergence. A series of numerical experiments are conducted to compare the evaluation of the similarity obtained by the distances with human subjective evaluations. The effectiveness of the distance metrics is also discussed in terms of the computational cost for calculating the distance.
Parallelizing Machine Learning as a service for the end-user

As ML applications are becoming ever more pervasive, fully-trained systems are made increasingly available to a wide public, allowing end-users to submit queries with their own data, and to efficiently retrieve results. With increasingly sophisticated such services, a new challenge is how to scale up to evergrowing user bases. In this paper, we present a distributed architecture that could be exploited to parallelize a typical ML system pipeline. We propose a case study consisting of a text mining service and discuss how the method can be generalized to many similar applications. We demonstrate the significance of the computational gain boosted by the distributed architecture by way of an extensive experimental evaluation.
Concept Drift Detection in Data Stream Clustering and its Application on Weather Data

This article presents a stream mining framework to cluster the data stream and monitor its evolution. Even though concept drift is expected to be present in data streams, explicit drift detection is rarely done in stream clustering algorithms. The proposed framework is capable of explicit concept drift detection and cluster evolution analysis. Concept drift is caused by the changes in data distribution over time. Relationship between concept drift and the occurrence of physical events has been studied by applying the framework on the weather data stream. Experiments led to the conclusion that the concept drift accompanied by a change in the number of clusters indicates a significant weather event. This kind of online monitoring and its results can be utilized in weather forecasting systems in various ways. Weather data streams produced by automatic weather stations (AWS) are used to conduct this study.
Video Summarization using Global Attention with Memory Network and LSTM

Videos are one of the most engaging and interesting mediums of effective information delivery and constitute the majority of the content generated online today. As human attention span shrinks, it is imperative to shorten videos while maintaining most of its information. The premier challenge is that summaries more intuitive to a human are difficult for machines to generalize. We present a simple approach to video summarization using Kernel Temporal Segmentation (KTS) for shot segmentation and a global attention based modified memory network module with LSTM for shot score learning. The modified memory network termed as Global Attention Memory Module (GAMM) increases the learning capability of the model and with the addition of LSTM, it is further able to learn better contextual features. Experiments on the benchmark datasets TVSum and SumMe show that our method outperforms the current state of the art by about 15%.
Sequential Model-Free Anomaly Detection for Big Data Streams

We study sequential anomaly detection for big data streams where the nominal and anomalous high-dimensional probabilistic data models are unknown. We propose a model-free solution approach in that we firstly compute a set of univariate summary statistics from a nominal dataset in an offline phase where the summary statistics are useful to distinguish anomalous data from nominal data. We then evaluate whether the online summary statistics deviate from the nominal case via a cumulative sum-like detector. Our experiments with real-world data illustrate the advantages of the proposed detector in early and reliable anomaly detection in big data settings compared to the existing alternatives.
Preserving Spatial Information to Enhance Performance of Image Forgery Classification

As there are a huge range of powerful tools to edit images now, the need for verifying the authentication of images is more urgent than ever. While forgery methods are increasingly more subtle that even human vision seems hard to recognize these manipulations, conventional algorithms, which try to detect tampering traces, often pre-define assumptions that limit the scope of problem. Therefore, such methods are unable to encounter forgery methods in general applications. In this paper, we propose a framework that utilizes Deep Learning techniques to detect tampered images. Concretely, the MobileNetV2 network in [21] is modified so that it can be consistent to the task of image forgery detection. We argue that by remaining spatial dimension of early layers, the model is likely to learn rich features in these layers, and then following layers are to abstract these rich features for making a decision whether an image is forged. Besides, we also conduct a comprehensive experiment to prove those arguments. Experimental results show that the architecture-modified network achieves a remarkable accuracy of 95.15%, which surpasses others relying on the original architecture by a large margin up to 12.09%.
Linear Two-Time-Scale Stochastic Approximation A Finite-Time Analysis

We consider two-time-scale stochastic approximation for finding the solution of a linear system of two equations. Such methods have found broad applications in many areas, especially in machine learning and reinforcement learning. A critical question in this area is to analyze the convergence rates (or sample complexity) of this method, which has not been fully addressed in the existing literature. Our contribution in this paper is, therefore, to provide a new analysis for the finite-time performance of the two-time-scale stochastic approximation. Our key idea is to leverage the common techniques from optimization, in particular, we utilize a residual function to capture the coupling between the two iterates. This will allow us to explicit design the two step sizes used by the two iterations as well as to provide a finite-time error bound on the convergence of the two iterates. Our analysis in this paper provides another aspect to the existing techniques in the literature of two-time-scale stochastic approximation, which we believe is more elegant and can be more applicable to many scenarios.
Stability analysis of brain emotional intelligent controller with application to electrically driven robot manipulators

This study discusses stability analysis of the brain emotional learning-based intelligent controller (BELBIC) for model-free tracking control of a class of uncertain non-linear systems. Due to the unique adaptation laws for the controller gains, stability analysis of these controllers is a challenging task. This issue constructs the motivation of this study to undertake this task. Similar to direct adaptive fuzzy control, the BELBIC plays the role of a direct intelligent controller. Thus, the stability of the BELBIC can be guaranteed based on the stability analysis of direct adaptive fuzzy control. The main objective of this study is that the stability of the BELBIC in tracking control of a class of uncertain non-linear systems is guaranteed. Based on the proposed stability analysis and Barbalat's lemma, BELBIC can yield in the asymptotic convergence of the tracking error to zero. In comparison with the existing works in the literature, the proposed approach has fewer tuning parameters. Therefore, the developed approach has a simple structure. The simulation results on an articulated manipulator actuated by permanent magnet DC motors and comparison with other model-free approaches are presented to show the superiority of the proposed approach.
Accuracy and Long-Term Tracking via Overlap Maximization Integrated with Motion Continuity

The baseline is ATOM which aims at solving the problem of accurate target state estimation by proposing a novel tracking architecture. The architecture consists of dedicated target estimation and classification components. Classification component is trained online to guarantee high discriminative power in the presence of distractors. Target estimation is performed by the IoU-predictor network inspired by the IoU-Net which was recently proposed for object detection as an alternative to typical anchor-based bounding box regression techniques. In this work, we further enhance the performance of ATOM by embedding Squeeze-and-Excitation (SE) blocks into IoU-Net in ATOM to recalibrate useful features and suppress useless features and obtain ATOMFR. To solve the abnormal changes in the target box in ATOMFR, we add the Relocation Module on ATOMFR and get ATOMFR (RL). To solve the occlusion problem, we introduce the Inference Module into ATOMFR (RL) and obtain ATOMFR (RL + InF). Experimental results on VisDrone2019-SOT test set demonstrate the state-of-the-art performance of ATOMFR (RL + InF) compared with several existed trackers and it ranks the second place among all competitors.
Arabic Text Keywords Extraction using Word2vec

Automatic keywords extraction is very useful for text summarization, information retrieval and other natural language applications. This paper proposes a keyword extraction method that extracts keywords from Arabic documents based on the semantic similarity of words. The proposed method groups words into classes and put the words that are similar in the same class. Word2vec word embedding model is used to represent words using vectors. Accordingly, the semantic similarity between words can easily be computed using cosine similarity. Therefore, the words that have high context semantic similarity will be grouped in the same class. This method can also generate abstractive keywords that do not exist in original text using Word2vec. The experiments are conducted using three documents. Results showed that, the proposed model enhances the performance of keyword extraction with comparison to the previous models in terms of precision, recall and F-measure.
Skeleton-Based View Invariant Deep Features for Human Activity Recognition

Recently skeleton-based human activity recognition is receiving significant attention due to its efficient localization of human pose in 3D space. However, the state-of-the-arts are utilizing majorly the temporal information of the skeleton joint coordinate values and few transformed the skeleton coordinate values into images to process spatial patterns. These images needs to be up-sampled to the size of the CNN models that introduces noise. This paper introduces a novel view-invariant human action recognition framework by using skeleton joints coordinates which exploit spatial shape structures of the skeletons and RGB Dynamic Images (DIs). Skeleton coordinates based features help to localize human pose and its variations in orientation during the action performed and reduces the noise element when fed to CNN models. Whereas, DIs [1] encrypt the temporal dynamics of action by using the concept of Average Rank pooling. This hybrid representation of human features is projected into higher dimensional space by applying the concept of transfer learning on InceptionV3 [1] architecture, which is fine-tuned for two multi-view human action datasets. In addition, the final prediction of the action class is made by applying late fusion on skeleton-based features and DIs features. The performance of the proposed scheme is tested for two multi-view NUCLA and NTU RGB+D Dataset.
How Effective Is Super-Resolution to Improve Dense Labelling of Coarse Resolution Imagery?

Coarse resolution remote sensing images, such as LANDSAT and MODIS are easily found in public open repositories and, therefore, are widely used in many studies. But their use for automatic creation of thematic maps is very restrict since most of the deep-based semantic segmentation (a.k.a dense labelling) approaches are only suitable for subdecimeter data. In this paper, we design a straightforward framework in order to evaluate the effectiveness of deep-based super-resolution in the semantic segmentation of low-resolution remote sensing images. We carried out an extensive set of experiments on three remote sensing datasets with distinct nature/properties. The results show that super-resolution is effective to improve semantic segmentation performance on low-resolution aerial imagery. It not only outperforms unsupervised interpolation but also achieves semantic segmentation results comparable to high-resolution data.
Practical Security Analysis of Reference Pulses for Continuous-Variable Quantum Key Distribution

Abstract By manipulating the reference pulses amplitude, a security vulnerability is caused by self-reference continuous-variable quantum key distribution. In this paper, we formalize an attack strategy for reference pulses, showing that the proposed attack can compromise the practical security of CVQKD protocol. In this scheme, before the beam splitter attack, Eve intercepts the reference pulses emitted by Alice, using Bayesian algorithm to estimate phase shifts. Subsequently, other reference pulses are re-prepared and resubmitted to Bob. In simulations, Bayesian algorithm effectively estimates the phase drifts and has the high robustness to noise. Therefore, the eavesdropper can bias the excess noise due to the intercept-resend attack and the beam splitter attack. And Alice and Bob believe that their excess noise is below the null key threshold and can still share a secret key. Consequently, the proposed attack shows that its practical security can be compromised by transmitting the reference pulses in the continuous-variable quantum key distribution protocol.
Chess Position Identification using Pieces Classification Based on Synthetic Images Generation and Deep Neural Network Fine-Tuning

Chess pieces recognition using computer vision is a problem generally approached in various ways, with different kinds of results and complexity. Deep learning is a state of the art approach to solve problems on image recognition although facing necessity of huge data sets. This paper discusses a method to identify synthetically generated chess images on Blender using its Python API via fine-tuning of VGG16 convolutional network obtaining close to 97% accuracy on piece classification. Possible applications include automated record of real chess games and real-time play between online players using real boards.
Utilization of PTE and LDPE Plastic Waste and Building Material Waste as Bricks

Plastic waste is becoming a problem in various countries because of the difficulty of natural decomposition. One type is PET plastic(Polyethylene Terephthalate), which is often used as a bottle for soft drink packaging, and LDPE(Low Density Polyethylene), which is also widely used as a food or beverage packaging material. The use of these two types of plastic continuously, without good recycling, will have a negative impact on the environment. Building material waste is also becoming a serious environmental problem. This study aims to provide a solution to the problem of the above plastic waste and building material waste by making them into a mixture to be used as bricks. Research is carried out by mixing both materials, namely plastic heated at a temperature of 180-220°C and building material waste that had been crushed and sized to 30-40 mesh with homogeneous stirring. The ratios of PET and LDPE plastic to building material waste are 9 : 1, 8 : 2, 7 : 3, 6 : 4 and 5 : 5. After heating and printing, density, water absorption and compressive strength tests are carried out. Addition of PET and LDPE plastic can increase compressive strength, and reduce water absorption, porosity and density. A maximum compressive strength of 10.5 MPa is obtained at the ratio of 6 : 4.
What Did You Mean? An Evaluation of Online Character Recognition Approaches

This paper presents a project that deals with online character recognition based on various time series such as acceleration, gyro and force. One important aspect of this work is the comparison and evaluation between a complex neural network - more precisely a Long-Short-Term-Memory network (LSTM) - and more comprehensible models such as K-Nearest Neighbours (KNN), Linear Discriminant Analysis (LDA) and Naive Bayes (NB). LSTMs have shown to not only be suitable for speech recognition but also for the recognition of handwriting based on time series due to its ability to learn long-time dependencies of the provided data. Online handwriting describes the usage of a digitizer in order to convert analogue sensor data to a digital representation of the handwriting. The digitizer used in our approach refers to a sensor enhanced ball pen developed by STA-BILO. The data set used for training consists of approximately 20,000 lowercase English alphabet characters in total contributed by 15 different people. The discussed approaches are evaluated and compared against each other using the recorded dataset, while taking recognition rates (i.e., accuracy) into account.