Reviewerly/exHarmony huggingface dataset:
Deep Learning with Chaotic Encryption based Secured Ethnicity Recognition

Ethnicity recognition (ER) is an interesting research topic in diverse fields like surveillance system, image/video understanding, investigation and so on. Presently, deep learning models become more useful in those applications. In this paper, we present a new ER model based on convolution neural network (CNN). In addition, a chaotic encryption-based blind digital image watermarking method is applied to the recognized images for security images. A cover image is employed to conceal the recognized image to protect the images from attackers or third parties. For examining the results of the presented model, an experimental analysis is carried out using VNFaces dataset which contains a set of images gathered from Facebook pages of Vietnamese people. A comparison is made with the ER-VGG model interms of accuracy. The simulation outcome indicated that the presented EG-CNN model is superior to other model on the applied images.
Smart Contracts Search Engine in Blockchain

Recently blockchain technology has attracted increasing attention. It provides a distributed peer-to-peer network. It also allows to enlarge the contracting space using smart contracts. Smart contract is a numeric protocol which define a promises between parties. In a blockchain platform, we can find millions of smart contracts which are created by different users (developers). The smarts contracts created by users and saved in a blockchain can be similar in functionality, even if different users use different wording. This paper presents a new search engine in the blockchain. The novelty of our search engine is to help users to checks their smart contracts by referencing some similar existing smart contracts created and saved in a blockchain platform. Our search engine allows to give each user an adaptive set of similar smart-contracts based on the user's similarity needs. Our search engine is developed in the Ethereum blockchain platform. Experiments demonstrate the potential of our search engine system under different scenarios.
An investigation of numeral quantifiers in English

There exist a broad range of theories which model quantifier scope (Ruys &amp;amp; Winter 2011). The empirical coverage and predictions of three theories: Quantifier Raising (May 1985) with Scope Economy (Fox 2000), Feature Checking (Beghelli &amp;amp; Stowell 1997), and Choice Functions (Winter 1997) are tested using a sentence-picture verification task. Speakers rated the quality of sentences with bare and modified numeral quantifiers in surface and inverse scope conditions. In Experiment 1, it was discovered that bare numerals marginally resisted inverse scope, contra the predictions of (Beghelli &amp;amp; Stowell 1997). Experiment 2 revealed that the scope of numerals is clause-bound, in argument against the theory of Choice Functions which predict extra-clausal scope for existential quantifiers. The results suggest that a modified QR theory best accounts for the observed data.
Analyzing Topics of JUUL Discussions on Social Media Using a Semantics-assisted NMF model

JUUL has become a widely used brand of e-cigarettes which takes more than 70% of the market. Social media provides a popular platform for users to discuss the preference and perceptions of JUUL. The discussions are valuable for real-time monitoring of JUUL use. Current research on topic analysis of JUUL discussions mainly relies on human work, which takes much time and effort. This paper adopts a Semantics-assisted NMF topic analysis model to automatically discover topics from JUUL-related short posts on Reddit. By successfully merging the semantic relationships into traditional NMF, this model outperforms in discovering topics with keywords that are important but have a lower word frequency among the posts. Experimental results show the potential of this model in JUUL surveillance and control practice.
An Automatic and Intelligent Approach for Supporting Teaching and Learning of Software Engineering Considering Design Smells in Object-Oriented Programming

Design smells are software structures that may indicate a code or design problem that makes software difficult to evolve and maintain [3]. Know those errors is the first step to improve some development skills, such as refactoring. Current tools that detect software problems does not aim to help students to learn with their own errors neither help teachers to use it to improve the way they teach object oriented programming (OOP) and software engineering disciplines. This work aims to develop an expert system module to improve the existent tools and help students and teachers in their tasks.
Enhancement and Removal of Noise from Medical Images by Wavelet Transform Method

The medical images are often received in poor visibility qualities and conditions due to various factors such as poor scanning and transmission. As a result, it ends up with complexity in upcoming procedure to read and understand such images. This research paper gives a novel methodology based on wavelet transform to de-noise a given medical image even if a very high noise is present. The image noises are detected with information available in the surroundings and then removed accordingly. The proposed algorithm has been executed in MATLAB with original medical images that are contaminated by noise. The computer testing results on test medical images has given an improved image quality in terms of entropy and standard deviation. This de-noising of medical images yields in improved visibility and may help in accurate disease detection.
Optimistic scheduling with service guarantees

Data centers form the core of current information and commercial enterprise. At current scale, any improvement in data center resource utilization leads to substantial savings. We focus on the problem of scheduling jobs in distributed execution environments to improve resource utilization. Cluster schedulers like YARN and Mesos base their scheduling decisions on resource requirements provided by end users. It is hard for end-users to predict the exact amount of resources required for a task/job, especially since resource utilization can vary significantly over time and across tasks. In practice, users make highly conservative estimates of peak utilization across all tasks of a job to ensure job completion, leading to resource fragmentation and severe under utilization in production clusters. We present UBIS, a utilization-aware approach to cluster scheduling, to address resource fragmentation, and to improve cluster utilization and job throughput. UBIS considers actual usage of running tasks and schedules opportunistic work on under-utilized nodes. UBIS monitors resource usage on these nodes and preempts opportunistic containers in the event this over-subscription becomes untenable. In doing so, UBIS effectively utilizes wasted resources, while minimizing adverse effects on regularly scheduled tasks. Our implementation of UBIS on YARN demonstrates improvements of up to 30% in makespan for a representative workload and 25% in individual job durations.
Experimental Validation of Structured Receding Horizon Estimation and Control for Mobile Ground Robot Slip Compensation

To achieve high-accuracy tracking performance for wheeled mobile robots in spatially varying terrain conditions, it is necessary to estimate both the robot’s state and the slip conditions of the environment to a high degree of precision. The receding horizon estimation and control (RHEC) framework presents a systematic, adaptive optimisation approach to this problem, to which our prior work proposed a structured blocking (SB) extension to address performance limitations for motion both at high speeds and over varying terrain. In this work, we validate these results in a series of preliminary field experiments with the Swagbot platform, demonstrating performance improvements in position tracking of up to 7%, and up to 13% for speed tracking at speeds of 1.5 and 2.5 m/s.
GADA: Generative Adversarial Data Augmentation for Image Quality Assessment

We propose a No-reference Image Quality Assessment (NR-IQA) approach based on the use of generative adversarial networks. To address the problem of lack of adequate amounts of labeled training data for NR-IQA, we train an Auxiliary Classifier Generative Adversarial Network (AC-GAN) to generate distorted images with various distortion types and levels of image quality at training time. The trained generative model allow us to augment the size of the training dataset by introducing distorted images for which no ground truth is available. We call our approach Generative Adversarial Data Augmentation (GADA) and experimental results on the LIVE and TID2013 datasets show that our approach – using a modestly sized and very shallow network – performs comparably to state-of-the-art methods for NR-IQA which use significantly more complex models. Moreover, our network can process images in real time at 120 image per second unlike other state-of-the-art techniques.
Two-steps QoS-aware services composition algorithm for Internet of Things

The Internet of Things (IoT) is a global infrastructure that provides services having similar functionality but with different quality of service (QoS). The fast growing of these services leads to a difficulty of selecting the most appropriate services to fulfill a complex user's requirement. It is therefore necessary to automate the mechanism for selecting the appropriate services in order to satisfy both functional and non-functional user's requirements. In this paper, a two-steps QoS-aware services composition algorithm (TS-QCA) based on clustering and shuffled frog leaping algorithm (SFLA) is proposed in the context of large-scale IoT environments. This approach aims at minimizing the composition time through accelerating the algorithm convergence by using the clustering technique and the exploitation of the parallel aspect of the SFLA algorithm. The simulation results demonstrate that the proposed algorithm is scalable and achieves a near-to-optimal composition in a reduced amount of composition in comparison to other services composition approaches proposed in the literature.
Analysis of Accuracy K-Means and Apriori Algorithms for Patient Data Clusters

Abstract The stacking data is usefull to get a new information. Data mining is a methode to determine the important pattern in Frequent Itemset Mining (FIM). Apriori is part of association rule that is used to determine the assosiative relationship in items combination. But apriori has a high computational time weakness because frequent itemset process searching must scan the database repeatedly for each itemset combination. This study aims to see the effect of the k-means clustering algorithm on the apriori algorithm by combining these two algorithms. The test results show that the combination of k-means and apriori algorithms produces more information detaily and faster time computating than the apriori algorithm with a total computing time of 21.93 minutes and a combination of k-means and apriori algorithms 17.41 minutes.
Pre-trained Contextualized Representation for Chinese Conversation Topic Classification

Topic classification plays an important role in facilitating security-related applications, which can help people reduce data scope and acquire key information quickly. Conversation is one of the important ways of communication between people. The utterances in a conversation may contain vital clues, such as people's opinions, emotions and political slants. To explore more effective approaches for Chinese conversational topic classification, in this paper, we propose a neural network architecture with pre-trained contextualized representation. We firstly apply pretrained BERT model to fine-tune and generate the conversational embeddings, which are the inputs of our neural network models. Then we design several models based on neural networks to extract task-oriented advanced features for topic classification. Experimental results indicate that the models based on our neural network architecture all outperform the baseline only fine-tuned with the pre-trained BERT model. It demonstrates that the pretrained representations are effective to Chinese conversational topic classification, and the proposed architecture can further capture the salient features from the representations. And we release the code and dataset of this paper that can be obtained from https://github.com/njoe9/pretrained_representation.
Using Recurrent Neural Networks for Toponym Resolution in Text

Toponym resolution refers to the disambiguation of place names and other references to places present in textual documents, resolving them to unambiguous geographical identifiers (e.g., geographic coordinates of latitude and longitude). One of the major challenges in this task is that, usually, place names are highly ambiguous (e.g., there are several locations on the surface of the Earth that share the same name). In this paper, we propose to address the task through a recurrent neural network architecture with multiple inputs and outputs, specifically leveraging pre-trained contextual embeddings (ELMo) and bi-directional Long Short-Term Memory (LSTM) units, both commonly used for textual data modeling. The proposed model was tested on two datasets that were previously used to evaluate toponym resolution systems, namely the War of the Rebellion and the Local-Global Lexicon corpora. The obtained results outperform state-of-the-art results, confirming the superiority of the proposed method over other previous approaches.
An Image Inpainting Method for Interleaved 3D Stacked Image Sensor

This paper presents an image inpainting method for 3D stacked image sensor. A typical structure for 3D stacked image sensor includes a pixel array, an analog-to-digital converter (ADC) array, and an image signal processor (ISP), which are connected vertically by through silicon vias (TSVs) or microbumps (μbumps). To improve the quality of the output images when existing one or more μbump/TSV/ADC failures, it is proposed to interleave the connections from pixel array to ADC array and recover the inaccessible data in the ISP. The sub-array of pixels corresponding to the defective μbump/TSV/ADC would be convenient to be inpainted. An inpainting method used for the damaged block of pixels is proposed, the image is progressively reduced through a process of the discrete wavelet transform (DWT) and down-sampling until the damaged block converts to one pixel only. The reduced image is inpainted and optimized, and then the original image size would be restored by upsampling. Evaluations over a range of images demonstrate that the proposed method improves average peak-signal-to-noise-ratio (PSNR) by 1.93-3.25 dB compared with the exemplar-based method and the TV method in case single or multiple μbump/TSV/ADC defects.
Application of quantum secure communication technology in the power grid services

Abstract With the development of power communication networks, the demand of information security involved in power network services also increases. Traditional secure communication technology have not been able to guarantee the security of the information transmitted in absolute security. This paper focuses on the problem of secure transmission of power communication networks, and proposes a suitable quantum secure communication technology scheme to ensure the key rate of the long-distance quantum secure communication and the security of the power communication network.
Diffusion Approximation Models for Cloud Computations with Task Migrations

This paper proposes a novel analytical model for performance evaluation of cloud computations using G/G/m/m+K queue. The cloud computing infrastructure allows to execute the computations on virtual machines, which can be deployed, turned off or migrated between the physical servers. The performance evaluation of such systems is complicated, since the job arrival process as well as service time may have different distributions. The approximation approach allows us to make previously presented in literature models more general by assumption of general input flows and allowing analytical approach to transient analysis.
Quantum hypergraph states in continuous variables

The measurement-based, or one-way, model of quantum computation for continuous variables uses a highly entangled state called a cluster state to accomplish the task of computing. Cluster states that are universal for computation are a subset of a class of states called graph states. These states are Gaussian states and therefore require that the homodyne detection (Gaussian measurement) scheme is supplemented with a non-Gaussian measurement for universal computation, a significant experimental challenge. Here we define a non-Gaussian class of states based on hypergraphs which we demonstrate satisfy the requirements of the Lloyd-Braunstein criteria while restricted to a Gaussian measurement strategy. Our main result is to show that, taking advantage of the intrinsic multimode nonlinearity, a hypergraph consisting of 3-edges can be used to apply a three-mode operation to an input three-mode state. As a special case, this technique can be used to apply the cubic phase gate to a single mode.
Multi-modal foreground detection via inter- and intra-modality-consistent low-rank separation

Multi-modal foreground detection, which integrates multiple complementary data like visible and thermal infrared sources for moving object detection, has received more and more attention recently. In this paper, we propose a novel Multi-modal Foreground Detection approach that pursues the inter- and intra-modality consistencies in a unified Low-rank and Sparse separation model called MFDLS. In particular, we first introduce a soft cross-modal constraint to pursue the inter-modal consistency among different modalities, while allowing sparse inconsistency to model their heterogeneous properties. Second, in addition to the conventional local appearance consistency within each modality, we further propose to preserve the global appearance consistency via Gaussian Mixture Model as the intra-modality consistency. Extensive experimental results yield to a new state-of-the-art comparing to the prevalent multi-modal foreground detection methods.
Mimicking Human Movement with Robots: Control of an Anthropomorphic Robotic Arm using a Glove-Based System as an Educational Tool

The present paper proposes, as an educational activity, the design of a system that allows the control of a robotic manipulator by using a sensory glove and an arm sleeve. The robotic manipulator features a robotic arm and hand, with a total of 10 degrees of freedom, using ten servo motors as actuators. On the other hand, the controller component features six flex sensors and two Inertial Measurement Units (IMUs). Due to the low force exerted by the motors, along with an issue regarding the reference frame of one of the IMUs, the robotic hand and wrist were detached from the robotic arm and both were tested separately. The results from real-time testing showed that the system successfully replicated the movements performed by the user. However, the system requires further improvements, mainly due to its lack of precision and limited degrees of freedom, along with other hardware issues. Regardless, an optimized design of the proposed system can have various applications, such as the manipulation of objects in hazardous conditions.
Medium, calculation, play: On digital images in scientific practice

Latour’s notion of immutable mobiles relates the circulability of certain objects to, among other features, their immutability, readability and combinability. As such it does not distinguish between, say, hand-drawn maps and machine-generated graphs. How, though, does the medial ‘microstructure’ of immutable mobiles matter to socially shared uses? Would, for example, digital images as bounded grids of nonoverlapping square pixels, each representing a numerical value, shape how distributed scientific work unfolds? In this article, I begin with reviewing attempts to link the microstructure of media to their communicative uses, focusing on Luhmann’s relational account of media as loosely coupled substrates in which more rigid forms can become manifest. Drawing on an ethnography of astronomical research, I then inquire into how the scientists involved reasoned about their uses of media from within their practices. They used digital photographic exposures as ‘workable objects’ whose usefulness was not guaranteed initially. Local work was oriented to potential reuses of images (as processed exposures) by researchers elsewhere, as demonstrated by concerns over the integrity of images, the possibility of describing their work with reasonable efforts, as well as by negotiating acceptable elements in calculations so as to reveal stabilized phenomena. In doing so, scientists insisted on the need to play or experiment, suspending sequential work for explorations oriented toward deciding which action among alternatives to make consequential. This was part of a more extended ‘calculative game’ that points, through its orientation to reasonable agreement and delimitations of legitimate efforts, to the social order of this work. The medium, here conceived as images deemed accountable to reuses elsewhere, is a social achievement. Recognizing this may help to better understand the reusability of scientific data and its challenges.
Trakhtenbrot theorem for classical languages with three individual variables

We present a simple proof of Thrakhtenbrot's theorem for the classical predicate logic in the language with only three individual variables. Both forms of Thrakhtenbrot's theorem are established: we prove that the classical predicate logic QCL over finite domains is not recursively enumerable in the language with only three individual variables and that the set of theorems of QCL over arbitrary domains and the set of non-theorems of QCL over finite domains, in the language with only three individual variables, form a recursively inseparable pair of recursively enumerable sets. The techniques used here can be generalised to obtain similar results for non-classical predicate logics with further restrictions on their vocabularies.