Reviewerly/exHarmony huggingface dataset:
A Simple But Effective Bert Model for Dialog State Tracking on Resource-Limited Systems

In a task-oriented dialog system, the goal of dialog state tracking (DST) is to monitor the state of the conversation from the dialog history. Recently, many deep learning based methods have been proposed for the task. Despite their impressive performance, current neural architectures for DST are typically heavily-engineered and conceptually complex, making it difficult to implement, debug, and maintain them in a production setting. In this work, we propose a simple but effective DST model based on BERT. In addition to its simplicity, our approach also has a number of other advantages: (a) the number of parameters does not grow with the ontology size (b) the model can operate in situations where the domain ontology may change dynamically. Experimental results demonstrate that our BERT-based model outperforms previous methods by a large margin, achieving new state-of-the-art results on the standard WoZ 2.0 dataset 1. Finally, to make the model small and fast enough for resource-restricted systems, we apply the knowledge distillation method to compress our model. The final compressed model achieves comparable results with the original model while being 8x smaller and 7x faster.
Microservice-Based Architecture for an Energy Management System

This article proposes a microservice-based architecture for an energy management system (MS-EMS) to address the fragility, poor flexibility, and hardware dependence of EMSs. Compared with the service-oriented architecture (SOA), the proposed architecture can significantly improve the load performance and scalability of an EMS through fine-grained decomposition of the system and decentralized data management. Container and cluster technologies are used to manage the microservices. A k-fault (k ≥ 2)-tolerant model is proposed to improve the reliability of the MS-EMS. The model employs containerized microservices as essential components to achieve a parallel connection of the essential components using the horizontal-scale technology of the containers. On the other hand, a MILP model-based algorithm for managing computing resources is also suggested. By minimizing the number of worker nodes of the cluster, where the MS-EMS is deployed, we can improve the utilization of the computing resources and avoid unnecessary costs. The result of the performance analysis showed that the reliability of the MS-EMS is 99.99965625%, which is two orders and one order of magnitude higher than those of the existing EMSs and the SOA-based EMS (S-EMS), respectively. Moreover, the cost of the MS-EMS is also lower than those of the existing EMSs and S-EMSs. The proposed architecture is implemented in a realpower system and has shown favorable operation performances, indicating a promising prospect for future applications.
EvoCluster: An Open-Source Nature-Inspired Optimization Clustering Framework in Python

EvoCluster is an open source and cross-platform framework implemented in Python which includes the most well-known and recent nature-inspired metaheuristic optimizers that are customized to perform partitional clustering tasks. The goal of this framework is to provide a user-friendly and customizable implementation of the metaheuristic based clustering algorithms which can be utilized by experienced and non-experienced users for different applications. The framework can also be used by researchers who can benefit from the implementation of the metaheuristic optimizers for their research studies. EvoCluster can be extended by designing other optimizers, including more objective functions, adding other evaluation measures, and using more data sets. The current implementation of the framework includes ten metaheristic optimizers, thirty datasets, five objective functions, and twelve evaluation measures. The source code of EvoCluster is publicly available at ( http://evo-ml.com/2019/10/25/evocluster/ ).
Balanced Binary Neural Networks with Gated Residual

Binary neural networks have attracted numerous attention in recent years. However, mainly due to the information loss stemming from the biased binarization, how to preserve the accuracy of networks still remains a critical issue. In this paper, we attempt to maintain the information propagated in the forward process and propose a Balanced Binary Neural Networks with Gated Residual (BBG for short). First, a weight balanced binarization is introduced and thus the informative binary weights can capture more information contained in the activations. Second, for binary activations, a gated residual is further appended to compensate their information loss during the forward process, with a slight overhead. Both techniques can be wrapped as a generic network module that supports various network architectures for different tasks including classification and detection. The experimental results show that BBG-Net performs remarkably well across various network architectures such as VGG, ResNet and SSD with the superior performance over state-of-the-art methods.
ADI17: A Fine-Grained Arabic Dialect Identification Dataset

In this paper, we describe a method to collect dialectal speech from YouTube videos to create a large-scale Dialect Identification (DID) dataset. Using this method, we collected dialectal Arabic from known YouTube channels from 17 Arabic speaking countries in the Middle East and Northern Africa. After a refinement process, a total of 3,000 hours of speech was available for training DID systems, with an additional 57 hours of speech for development and testing. For detailed evaluations, the DID data was divided into three sub-categories based on the segment duration: short (less than 5s), medium (5-20s), and long (over 20s). We compare state-of-the-art DID techniques on these data, and also analyze a DID system trained on these data. Since the training and test data share the same channel domain, we also used the Multi-Genre Broadcast 3 (MGB-3) test set to evaluate on domain mismatched condition.
Augmented Reality as a Didactic Resource for Teaching Mathematics

This paper is an example of how to use new technologies to produce didactic innovative resources that ease the teaching and learning process of mathematics. This paper is focused on augmented reality technology with the aim of achieving the creation of didactic resources related to the polyhedra taught in a course of compulsory secondary education in Spain. First, we introduce the basis of this technology and present the theoretical framework in which we make an exhaustive analysis that justifies its usage with educative purposes. Secondly, we explain how to build the polyhedra in augmented reality using the Unity game engine and the Vuforia software development kit (SDK), which enables the use of augmented reality. Using both tools, we create an augmented reality application and some augmented reality notes with the purpose of helping in the process of visualization and comprehension of the three-dimensional geometry related to polyhedra. Finally, we design an innovative, didactic proposal for teaching the polyhedra in the third course of compulsory Secondary Education in Spain, using the resources created with the augmented reality technology.
SPIDER: Enabling Fast Patch Propagation In Related Software Repositories

Despite the effort of software maintainers, patches to open-source repositories are propagated from the main codebase to all the related projects (e.g., forks) with a significant delay. Previous work shows that this is true also for security patches, which represents a critical problem. Vulnerability databases, such as the CVE database, were born to speed-up the application of critical patches; however, patches associated with CVE entries (i.e., CVE patches) are still applied with a delay, and some security fixes lack the corresponding CVE entries. Because of this, project maintainers could miss security patches when upgrading software.In this paper, we are the first to define safe patches (sps). An sp is a patch that does not disrupt the intended functionality of the program (on valid inputs), meaning that it can be applied with no testing; we argue that most security fixes fall into this category. Furthermore, we show a technique to identify sps, and implement SPIDER <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , a tool based on such a technique that works by analyzing the source code of the original and patched versions of a file. We performed a large-scale evaluation on 341,767 patches from 32 large and popular source code repositories as well as on 809 CVE patches. Results show that SPIDER was able to identify 67,408 sps and that most of the CVE patches are sps. In addition, SPIDER identified 2,278 patches that fix vulnerabilities lacking a CVE; 229 of these are still unpatched in different vendor kernels, which can be considered as potential unfixed vulnerabilities.
Multi-objective prediction of coal-fired boiler with a deep hybrid neural networks

Our works frequently examine the emission of pollutants and the prediction of the thermal efficiency of boilers from power plants. Power plant systems are strongly coupled. Thus, multi-objective modelling and prediction is always a difficult problem. Artificial neural network (ANN) modelling is one of the methods used to meet this challenge. With the increasing requirements of environmental protection, the classical shallow neural network can no longer meet the needs of high precision. In recent years, deep neural networks have gradually demonstrated their powerful capabilities. However, can deep neural networks be used to improve model prediction performance? After many experiments, we successfully construct a sophisticated and stable deep hybrid neural network model to achieve this requirement. The experimental results show that the performance of the hybrid model is superior to that of the classical model; we diagram the detailed structure of the model and provide the corresponding parameter settings in this paper.
Sentiment Cognition From Words Shortlisted by Fuzzy Entropy

Sentiment analysis (SA) is the process of evaluating human emotions, opinions, and reviews expressed in text to detect the writer's mental outlook toward a particular event, topic, product, service, etc., and assign a relevant sentiment. In SA, to highlight the correct words which contribute toward sentiment cognition is very difficult. Simulating this task of shortlisting of words by human observers is challenging due to complexity of human mind's processing. The use of fuzzy entropy is proposed in this article as an innovative step to tap sentiment quotients of online movie reviews. We have proposed a novel approach of shortlisting of words that help in sentiment cognition using a combination of fuzzy entropy, k -means clustering, and sentiment lexicon SentiWordNet. We have addressed this challenging task of simulating the human cognition of words by developing a model that recognizes sentiment based on fuzzy scores derived from SentiWordNet in an automatic manner. Experiments on two benchmark movie review data sets-IMDB and polarity data sets by Pang and Lee-with training by long short-term memory neural networks, yields high accuracy for our approach as compared to other state-of-the-art-methods of SA.
Short text similarity measurement using context‐aware weighted biterms

Summary With the development of internet technologies, social media and mobile devices, short texts have become an increasingly popular medium among users to communicate with friends, search information and review products. Measuring the similarity between short texts is a fundamental task due to its importance in many applications, such as text retrieval, topic discovery, and event detection. However, short texts generally comprise sparse, noisy, and ambiguous information. Hence, effectively measuring the distance between short texts is a challenging task. In this paper, we exploit the advantageous corpus‐wide word co‐occurrence information into document‐level feature enrichment to mitigate the challenges caused by the sparseness of short texts for distance measurement. We propose a novel context‐aware weighted Biterm method for short text Distance Measurement (BDM). In BDM, we extract biterms (ie, word pairs) from a short text corpus and exploit a biterm topic model to determine the global weights of biterms in the corpus. We then determine the local importance of a biterm in different contexts (ie, short texts) based on the corpus‐level biterm weight. The distance between two short texts is computed using the context‐aware weighted biterms. Experimental results on three real‐world datasets demonstrate better accuracy and effectiveness of the proposed BDM.
A New Side-Channel Vulnerability on Modern Computers by Exploiting Electromagnetic Emanations from the Power Management Unit

This paper presents a new micro-architectural vulnerability on the power management units of modern computers which creates an electromagnetic-based side-channel. The key observations that enable us to discover this sidechannel are: 1) in an effort to manage and minimize power consumption, modern microprocessors have a number of possible operating modes (power states) in which various sub-systems of the processor are powered down, 2) for some of the transitions between power states, the processor also changes the operating mode of the voltage regulator module (VRM) that supplies power to the affected sub-system, and 3) the electromagnetic (EM) emanations from the VRM are heavily dependent on its operating mode. As a result, these state-dependent EM emanations create a side-channel which can potentially reveal sensitive information about the current state of the processor and, more importantly, the programs currently being executed. To demonstrate the feasibility of exploiting this vulnerability, we create a covert channel by utilizing the changes in the processor's power states. We show how such a covert channel can be leveraged to exfiltrate sensitive information from a secured and completely isolated (air-gapped) laptop system by placing a compact, inexpensive receiver in proximity to that system. To further show the severity of this attack, we also demonstrate how such a covert channel can be established when the target and the receiver are several meters away from each other, including scenarios where the receiver and the target are separated by a wall. Compared to the state-of-the-art, the proposed covert channel has >3x higher bit-rate. Finally, to demonstrate that this new vulnerability is not limited to being used as a covert channel, we demonstrate how it can be used for attacks such as keystroke logging.
Estimation of the parameters of a weighted nuclear norm model and its application in image denoising

In this study, a method for optimizing a weighted nuclear norm model is proposed. This model optimization solution employs a soft-thresholding operation on the singular values of an observation matrix. To determine the soft-thresholding, we focused on the unique properties of a class of special matrices, termed rank-order similar matrices (ROSMs). The threshold determination problem was solved during training by exploiting these properties. In addition, we applied this optimization method to image denoising. In this application, the denoising does not work on the whole image at once, but rather relies on a set of ROSMs. Each matrix in the set is an ROSM, built by stacking non-local similar patch vectors. This optimization method is applied to every ROSM in the set to obtain estimates of the underlying patches, which are aggregated to reconstruct a restored image. Simulation results of 154 noisy images indicate that the proposed optimization method achieves the same peak signal to noise ratio/mean structural similarity index measure results as those achieved by several other state-of-the-art methods such as block-matching and 3D filtering and weighted nuclear norm minimization. It also outperforms many state-of-the-art methods such as nuclear norm minimization, in terms of the visual quality of images.
Implementation and evaluation of smart contracts using a hybrid on‐ and off‐blockchain architecture

Summary Smart contracts can be implemented using either centralized or decentralized (blockchain) platforms. However, for a large class of applications, neither of these two alternatives on its own can provide adequate scalability, performance, quality of service, security, and trust requirements. We argue that for many applications, hybrid solutions that combine both on and off‐blockchain components are more adequate. In this article, we introduce the design and implementation of a novel hybrid smart contract architecture built using the Ethereum blockchain connected to a centralized smart contract management system developed by us. We then compare and evaluate the implementation of an asset tracking service using three different architectures: on‐chain, off‐chain, and hybrid. We demonstrate that using a hybrid architecture, we can substantially improve performance of applications while retaining security and trust for critical tasks.
Efficient Multi-authority Access Control using Attribute-based Encryption in Cloud Storage

Cloud users are outsourcing their data day-by-day resulting in huge amount of data storage in Cloud server. Cloud service provider (CSP) themselves may go for modifying the data that leads to data integrity problem. Unauthorized users illegally gain access to the data stored in cloud server. Therefore, providing protection, achieving privacy and access control for user’s data are big challenges in the Cloud. Ciphertext-policy attribute-based encryption (CP-ABE) is prominent encryption technique to solve the exigent dilemma of secure data sharing in cloud computing. The shared data files generally have the characteristic of multilevel hierarchy, predominantly in the areas of IT companies, healthcare and military; these areas require a multi-authority access control. However, the hierarchy construction of shared files and multi-authority access control approach are not been investigated in CP-ABE. This paper proposes an efficient multi-authority access control using attribute-based encryption scheme that achieves efficient, fine grained access control. The proposed scheme consists of one algorithm – (Role Hierarchy Algorithm (RHA) and Hierarchy Access Structure (HAS) to protect user’s data, achieve privacy, multi-authority access control and fine grained access to a stored data. The RHA algorithm classifies Cloud users into groups based on their assigned attributes. The HAS helps to define an access structure for fine grained and multi-authority access control of Cloud resources. Simulation results prove that the proposed RHA, HAS are efficient (in terms of time consumption and storage consumption for encryption, decryption) compared to existing works. The advantages of proposed scheme become more and more conspicuous when there is more number of files at Cloud storage server.
Product image recognition with guidance learning and noisy supervision

This paper considers to recognize products from daily photos, which is an important problem in real-world applications but also challenging due to background clutters, category diversities, noisy labels, etc. We address this problem by two contributions. First, we introduce a novel large-scale product image dataset, termed as Product-90. Instead of collecting product images by laborious and time-intensive image capturing, we take advantage of the web and download images from the reviews of several e-commerce websites where the images are casually captured by consumers. Labels are assigned automatically by the categories of e-commerce websites. Totally the Product-90 consists of more than 140K images with 90 categories. Due to the fact that consumers may upload unrelated images, it is inevitable that our Product-90 introduces noisy labels. As the second contribution, we develop a simple yet efficient guidance learning (GL) method for training convolutional neural networks (CNNs) with noisy supervision. The GL method first trains an initial teacher network with the full noisy dataset, and then trains a target/student network with both large-scale noisy set and small manually-verified clean set in a multi-task manner. Specifically, in the stage of student network training, the large-scale noisy data is supervised by its guidance knowledge which is the combination of its given noisy label and the soften label from the teacher network. We conduct extensive experiments on our Products-90 and four public datasets, namely Food101, Food-101N, Clothing1M and synthetic noisy CIFAR-10. Our guidance learning method achieves performance superior to state-of-the-art methods on these datasets.
Mental Retrieval of Remote Sensing Images via Adversarial Sketch-Image Feature Learning

Searching the targets of interest in large-scale remote sensing images is a fundamental problem, which becomes a very challenging issue when there is no relevant example at hand but a mental picture in mind. Hand-drawn sketch as a precise and convenient expression of the mental picture makes sketch-based remote sensing image retrieval (SBRSIR) an ideal choice to cope with this issue. However, the accuracy of SBRSIR algorithm, which is critical to effective retrieval, is still far behind classic query-by-example image retrieval. Two central limiting factors for this performance gap are: 1) the lack of effective cross-domain representations for bridging the domain gap between sketches and remote sensing images and 2) the absence of large sketch/remote sensing image data sets for developing, evaluating, and comparing the SBRSIR approaches. In this article, we first develop a novel SBRSIR model to learn a deep joint embedding space with discriminative losses, where adversarial training is used for the embedding space to learn domain-invariant representations. Then, we contribute a sketch/remote sensing image data set specifically for SBRSIR and provide a benchmark for subsequent researchers. Extensive experiments on the data set and large scene images demonstrate the effectiveness and superiority of the method for both the seen and unseen categories.
Gender Classification using Facial Embeddings: A Novel Approach

Image Processing for Human recognition involves using bio-metric traits such as Face, Iris, Voice and other physical traits to uniquely identify human faces. With the increase in Image Data on the Internet, there is a huge demand for Artificial Intelli-gence(AI) algorithms that can perform classification tasks like Race and Gender Classification. The advent of Deep Learning Techniques like Convolutional Networks has led to a rapid ascent in accuracy in various image classification tasks. Through this paper, a novel method to predict Gender of a person by applying various Machine Learning Classification Techniques on Facial Em-beddings has been proposed. The facial embeddings are found by passing through a Pretrained Inception Network. The maximum accuracy obtained by the proposed work to classify gender is 97%.