Reviewerly/exHarmony huggingface dataset:
Multilingual Inappropriate Text Content Detection System Based on Doc2vec

In this paper, an inappropriate multilingual text content detection method is proposed based on Neural Machine Translation - Doc2Vec (NMT-D2V) is proposed.. NMT-D2V has three features, i.e., it extends an existing detection system (e.g. based on English) to another language using a multilingual scheme, it improves detection system transparency based on similar text presentation, and it does not required text translation into the original language for each input. An experimental comparison using a Japanese dataset demonstrated that the evaluation index area under the curve of the Receiver Operating Characteristic of NMT-D2V (0.717) is better than that of an existing translation method(0.703).
A Non-template Based Automatic Landmarking on 3D Face Data

The standard initial stage for the extraction of information from human face image data is the detection of key anatomical landmarks, which is a vital stage for several face recognition, facial analysis and synthesis applications. Locating facial landmarks in images is an important task in image processing, and detecting these landmarks automatically still remains a challenge. The appearance of facial landmarks may vary tremendously due to facial expressions and pose variations. Detecting and localising landmarks from raw face data are often performed manually by trained and experienced scientists or clinicians, and this process is laborious and tedious. In order to overcome these challenges, a novel non-template based automatic landmarking method on 3D face data is presented. The geometric approach is employed through utilising the surface curvatures, primitive surfaces information to detect and extract potential features. Subsequently, K-means clustering is applied to categorise and obtain the centroid of extracted features, which is later use to estimate and localise facial landmarks. The overall performance and accuracy of the proposed approach demonstrates the effectiveness and robustness of its method. Results of 95.6% of accurately locating the feature proved the effectiveness of the algorithm.
Calculating transition amplitudes by variational quantum eigensolvers

Variational quantum eigensolver (VQE) is an appealing candidate for the application of near-term quantum computers. A technique introduced in [Higgot et al., Quantum 3, 156 (2019)], which is named variational quantum deflation (VQD), has extended the ability of the VQE framework for finding excited states of a Hamiltonian. However, no method to evaluate transition amplitudes between the eigenstates found by the VQD without using any costly Hadamard-test-like circuit has been proposed despite its importance for computing properties of the system such as oscillator strengths of molecules. Here we propose a method to evaluate transition amplitudes between the eigenstates obtained by the VQD avoiding any Hadamard-test-like circuit. Our method relies only on the ability to estimate overlap between two states, so it does not restrict to the VQD eigenstates and applies for general situations. To support the significance of our method, we provide a comprehensive comparison of three previously proposed methods to find excited states with numerical simulation of three molecules (lithium hydride, diazene, and azobenzene) in a noiseless situation and find that the VQD method exhibits the best performance among the three methods. Finally, we demonstrate the validity of our method by calculating the oscillator strength of lithium hydride in numerical simulations with shot noise. Our results illustrate the superiority of the VQD to find excited states and widen its applicability to various quantum systems.
Mix and Rank: A Framework for Benchmarking Recommender Systems

Recommender systems use big data methods, and are widely used in various social-network, e-commerce, and content platforms. With their increased relevance, online platforms and developers are in need of better ways to choose the systems that are most suitable for their use-cases. At the same time, the research literature on recommender systems describes a multitude of measures to evaluate the performance of different algorithms. For the end-user however, the large number of available measures do not provide much help in deciding which algorithm to deploy. Some of the measures are correlated, while others deal with different aspects of recommendation performance like accuracy and coverage. To address this problem, we propose a novel benchmarking framework that mixes different evaluation measures in order to rank the recommender systems on each benchmark dataset, separately. Additionally, our approach discovers sets of correlated measures as well as sets of evaluation measures that are least correlated. We investigate the robustness of the proposed methodology using published results from an experimental study involving multiple big datasets and evaluation measures. Our work provides a general framework that can handle an arbitrary number of evaluation measures and help end-users rank the systems available to them.
Deep Multimodal Image-Text Embeddings for Automatic Cross-Media Retrieval

This paper considers the task of matching images and sentences by learning a visual-textual embedding space for cross-modal retrieval. Finding such a space is a challenging task since the features and representations of text and image are not comparable. In this work, we introduce an end-to-end deep multimodal convolutional-recurrent network for learning both vision and language representations simultaneously to infer image-text similarity. The model learns which pairs are a match (positive) and which ones are a mismatch (negative) using a hinge-based triplet ranking. To learn about the joint representations, we leverage our newly extracted collection of tweets from Twitter. The main characteristic of our dataset is that the images and tweets are not standardized the same as the benchmarks. Furthermore, there can be a higher semantic correlation between the pictures and tweets contrary to benchmarks in which the descriptions are well-organized. Experimental results on MS-COCO benchmark dataset show that our model outperforms certain methods presented previously and has competitive performance compared to the state-of-the-art. The code and dataset have been made available publicly.
Analytical Approach to Cross Project Defect Prediction

Ensuring software quality has been an issue since the very beginning of software industry. Now with the advancement in technology, tools and development cycles, new paradigms have emerged to tackle the challenges of software quality management. Software defect prediction (SDP) is one of those to help recognize the code blocks, classes, methods or files which contain the most bugs. It helps the programmer to prioritize those classes or methods for testing and bug fixing. It gives maximum coverage and hence reduces the cost of testing, bug fixing and saves time. This paper provides a way to discover challenges encountered during the SDP. We provide a comprehensive study of different defect prediction techniques, tools, datasets, algorithms, modelling tools and application areas of cross-project defect prediction, granularity levels used and SDP challenges.
MLLR-PRSW for Kinematic-Independent Acoustic-to-Articulatory Inversion

This paper presents an improved method for kinematic-independent acoustic-to-articulatory inversion, using acoustic adaptation to estimate weights for articulatory model creation from reference speakers. Paired acoustic and articulatory data from the Marquette Electromagnetic Articulography corpus of Mandarin Accented English (EMAMAE) are used for experimental evaluation. The new method is a modification of the Parallel Reference Speaker Weighting (PRSW) inversion algorithm, in which two separate methods are used for acoustic and articulatory adaptation. A Maximum Likelihood Linear Regression (MLLR) approach is used for acoustic adaptation model and the PRSW weighted reference speaker approach is used for articulatory model adaptation. The new MLLR-PRSW adaptation method outperforms the baseline PRSW method on inversion of new test subjects where no kinematic data is used for training, providing estimated trajectories very close to the results from speaker dependent models that do use kinematic data.
Data-Centric Helicopter Failure Anticipation: The MGB Oil Pressure Virtual Sensor Case

This paper presents a virtual sensor for helicopter Main Gear Box (MGB) oil pressure. It is developed for anticipating failures of systems related to MGB lubrication. The virtual sensor is built using Machine Learning and massive in-service flight data collected from Airbus helicopters flying world-wide. The correlation between oil pressure values and other flight parameters is learnt during stable phases of flights in which the system is in its nominal state. At each flight, the values continuously estimated by the virtual sensor are compared to the measured ones, and an alert is raised when the difference becomes higher than a statistically predefined threshold. The virtual sensor was tested using normal and abnormal flights, and the results obtained so far in terms of anomaly detection performance are promising.
IMPLEMENTASI METODE SENTRA PERSIAPAN DALAM MENGEMBANGKAN KECERDASAN VERBAL LINGUISTIK ANAK USIA DINI

This study aims to describe: 1) the implementation of centers of preparation in developing verbal linguistic intelligence, 2) supporting factors and obstacles to the implementation of preparation centers in developing verbal linguistic intelligence. This type of research is a qualitative research method with the principal subject of research, teachers, students aged 5-6 years. Data collection is done using the method of observation, interviews and documentation. The results showed that: 1) the implementation of the method of preparation center in developing verbal linguistic intelligence was carried out with 3 stage, 2) supporting factors in the implementation of preparation centers, namely: a) the media provided varies or the availability of complete facilities and infrastructure, b) learning based on age classification c) programmed learning. while the inhibiting factors are: a) the condition of the guardian of the student who does not understand the method of preparation center, b) the mood of the changing child c) the media that do not support the difficult theme d) not all teachers graduate from PAUD.
Attention-Based Generative Graph Convolutional Network for Skeleton-Based Human Action Recognition

Skeleton-based action recognition is a challenging field in computer vision. Graph representations of skeleton are used to learn the connection patterns of human joints. However, the fixed handcraft graph of human skeleton topology can not well represent all the connection patterns. In this work, we propose an end-to-end generative graph convolution network to learn the joints graph connection patterns directly from data. We use self attention to construct the spatial adjacency matrix of each skeleton frame. Graph is generated progressively by learning the weighted adjacency matrix sequence from skeleton frames. In order to extract temporal dynamics effectively, velocity semantic information is also used to generate graphs. An online graph generative mechanism is proposed to enhance the model adaption. Extensive experiments on two large-scale action recognition datasets are preformed to verify the effectiveness of our approach. The comparison results show that our method achieves remarkable improvement compared with the state-of-the-art approaches.
Generation of Pedestrian Pose Structures using Generative Adversarial Networks

The safety of vulnerable road users is of paramount importance as transport moves towards fully automated driving. The richness of real-world data required for testing autonomous vehicles is limited, and furthermore, the available data does not have a fair representation of different scenarios and rare events. This work presents a novel approach for the generation of human pose structures, specifically the type of pose structures that would appear to be in pedestrian scenarios. The results show that the generated pedestrian structures are indistinguishable from the ground truth pose structures when classified using a suitably trained classifier. The paper demonstrates that the Generative Adversarial Network architecture can be used to create realistic new training samples, and, in future, new pedestrian events.
Towards the Development of Talend Open Studio Components for the Support of Semantic Sources

The Extract-Transform-Load (ETL) process is the most widely used mechanism to keep a Data Warehouse loading with data extracted from a variety of sources. Currently, tools offering graphical interfaces to facilitate the manipulation of ETL processes have become very popular and have reached a very advanced level of maturity. Talend Open Studio for Data Integration is one of the most popular and comprehensive tools in terms of functionality and performance. So far, this ETL tool provides a large number of components for different data sources. However, the advent of the Semantic Web brings the notion of ontology as a new source of data whose structure is characterized by its complex aspect related to the expressiveness of languages of the knowledge representation. The emergence of this notion is a new challenge. Indeed, to our knowledge, Talend Open Studio for Data Integration does not have any components intended to support ontological sources.In this contribution, we present our approach for the development of Talend Open Studio for Data Integration components in order to use Semantic Web data in ETL processes created with this tool. Using a strategy that promotes the abstraction of ontological sources, this approach can be adapted to different languages of representation of knowledge such as RDF and OWL.In order to assess the usefulness of our approach, we evaluated it as part of a hypothetical example set of a simplistic ontology.
ON SEMIGROUP ORBITS OF POLYNOMIALS AND MULTIPLICATIVE ORDERS

We show, under some natural restrictions, that some semigroup orbits of polynomials cannot contain too many elements of small multiplicative order modulo a large prime $p$ , extending previous work of Shparlinski [‘Multiplicative orders in orbits of polynomials over finite fields’, Glasg. Math. J. 60 (2) (2018), 487–493].
Data Driven Detection of Railway Point Machines Failures

In this paper, a novel approach to early detection of railway point machines failures is presented. Easily accessible data from Centralized Traffic Control (CTC) systems, along with meteorological data, are utilized to build a classification system recognizing risk factors for railway point machine failure. We present and discuss a framework that aims at extracting information from the raw railway logs, and discuss the issues that need to be solved to make the framework properly operational. We show that ensemble methods utilizing decision trees are able to provide meaningful classification accuracy for this problem.
Secure Location Verification: Why You Want Your Verifiers to Be Mobile

The integrity of location information is crucial in many applications such as access control or environmental sensing. Although there are several solutions to the problem of secure location verification, they all come with expensive requirements such as tight time synchronization, cooperative verification protocols, or dedicated hardware. Yet, meeting these requirements in practice is often not feasible which renders the existing solutions unusable in many scenarios. We therefore propose a new solution which exploits the mobility of verifiers to verify locations. We show that mobility can help minimize system requirements while at the same time achieves strong security. Specifically, we show that two moving verifiers are sufficient to securely verify location claims of a static prover without the need for time synchronization, active protocols, or otherwise specialized hardware. We provide formal proof that our method is secure with minimal effort if the verifiers are able to adjust their movement to the claimed location (“controlled mobility”). For scenarios in which controlled mobility is not feasible, we evaluate how more general claim-independent movement patterns of verifiers affect the security of our system. Based on extensive simulations, we propose simple movement strategies which improve the attack detection rate up to 290% with only little additional effort compared to random (uncontrolled) movements.
Overview Of Serverless Architecture Research

Abstract With the development of cloud computing, serverless computing, that is, function as a service (FaaS) is considered to be the next stage of cloud computing development. Serverless computing can be seen as a logical extension of cloud computing, a disruptive approach to application development. It is based on the code written by the developer for accurate resource allocation, and the resources on the platform are started when a predefined event is triggered. By comparing with the traditional architecture, the advantages of serverless architecture are highlighted. Although serverless is a relatively new concept in the field of software architecture, it is also a technological innovation with great influence.
Low-coherence phase-shifting shearing interferometer for measuring parallelism of parallel surfaces of transparent samples

A novel low-coherence phase-shifting shearing interferometer for measuring parallelism of parallel surfaces of transparent samples was proposed in this research; wherein the shear devices are two identical Savart prisms, the first one laterally separates the incident beam into two linearly polarized beams vibrating perpendicularly, and the second recombines the separated beams after they pass through the sample being examined; the phase-shifter is an assembly of the second Savart prism and a rotation stage; and the measurements are done by two operations: derivative determination and Frankot–Chellappa reconstruction. This paper sequentially introduces the configuration, measurement theory, and experimental setup of the interferometer; it then presents and discusses the results from the uses of the experiment setup. The results not only verify the validity and feasibility of the interferometer but also exhibit a maximum standard deviation of repeated measurements of 0.018 μm.
Rewarding Miners: Bankruptcy Situations and Pooling Strategies

In Proof-of-Work (PoW) based blockchains (e.g., Bitcoin), mining is the procedure through which miners can gain money on regular basis by finding solutions to mathematical crypto puzzles (i.e., full solutions) which validate blockchain transactions. In order to reduce the uncertainty of the remuneration over time, miners cooperate and form pools. Each pool receives rewards which have to be split among pool’s participants. The objective of this paper is to find an allocation method, for a mining pool, aimed at redistributing the rewards among cooperating miners and, at the same time, preventing some malicious behaviours of the miners. Recently, Schrijvers et al. (2017) have proposed a rewarding mechanism that is incentive compatible, ensuring that miners have an advantage to immediately report full solutions to the pool. However, such a mechanism encourages a harmful inter-pool behaviour (i.e., pool hopping) when the reward results insufficient to remunerate pool miners, determining a loss in terms of pool’s computational power. By reinterpreting the allocation rules as outcomes of bankruptcy situations, we define a new rewarding system based on the well-studied Constrained Equal Losses (CEL) rule that maintains the incentive compatible property while making pool hopping less advantageous.
UAV-based Person Re-Identification and Dynamic Image Routing using Wireless Mesh Networking

Person Re-Identification (PRID) has been one of the most challenging tasks in intelligent video surveillance. Most existing PRID based surveillance methods rely on a single camera mounted on emplacements. This approach assumes that any scrutinized person appears again in the field of view of that cam-era, which is unreliable in real-world settings. In this paper, we propose a system wherein we explore on-board re-identification of persons using cameras mounted on Unmanned Aerial Vehicles (UAVs). The captured images are dynamically shared between multiple UAVs by virtue of wireless mesh networking, also making use of the Robot Operating System (ROS) for on-ground control of the UAVs from a control station, facilitating the exchange of images between them. We demonstrate our approach on an institutional dataset we created, and our experimental results show that the system could be indispensable in aiding airborne surveillance operations.
VRT-Net: Real-Time Scene Parsing via Variable Resolution Transform

Urban scene parsing is a basic requirement for various autonomous navigation systems especially in self-driving. Most of the available approaches employ generic image parsing architectures designed for segmentation of object focused scene captured in indoor setups. However, images captured in car-mounted cameras exhibit an extreme effect of perspective geometry, causing a significant scale disparity between near and farther objects. Recognizing this, we formalize a unique Variable Resolution Transform (VRT) technique motivated from the foveal magnification in human eye. Following this, we design a Fovea Estimation Network (FEN) which is trained to estimate a single most convenient fixation location along with the associated magnification factor, best suited for a given input image. The proposed framework is designed to enable its usage as a wrapper over the available real-time scene parsing models, thereby demonstrating a superior trade-off between speed and quality as compared to the prior state-of-the-arts.
SketchTransfer: A New Dataset for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks

Deep networks have achieved excellent results in perceptual tasks, yet their ability to generalize to variations not seen during training has come under increasing scrutiny. In this work we focus on their ability to have invariance towards the presence or absence of details. For example, humans are able to watch cartoons, which are missing many visual details, without being explicitly trained to do so. As another example, 3D rendering software is a relatively recent development, yet people are able to understand such rendered scenes even though they are missing details (consider a film like Toy Story). This capability goes beyond visual data: humans are easily able to recognize isolated melodies from musical pieces when heard for the first time, even if the only piece they've listened to previously is from an orchestra. Thus the failure of machine learning algorithms to do this indicates a significant gap in generalization between human abilities and the abilities of deep networks. We propose a dataset that will make it easier to study the detail-invariance problem concretely. We produce a concrete task for this: SketchTransfer, and we show that state-of-the-art domain transfer algorithms still struggle with this task. The state-of-the-art technique which achieves over 95% on MNIST \xrightarrow SVHN transfer only achieves 59% accuracy on the SketchTransfer task, which is much better than random (11% accuracy) but falls short of the 87% accuracy of a classifier trained directly on labeled sketches. This indicates that this task is approachable with today's best methods but has substantial room for improvement.