Reviewerly/exHarmony huggingface dataset:
AI BOT: An Intelligent Personal Assistant

Intelligent Personal Assistants (IPA) such as Google's Assistant, Apple's Siri and Microsoft's Cortana excels in their natural language user interface. Still there is a need for single bundled IPA with multi-functionalities is increasing day by day. This paper presents the design and development of an artificial Intelligent personal assistant (AIPA), an open end-to-end web service application which receives queries in the form of voice and text and responds with natural language. The paper emphasizes on the implementation of different ways of interacting with a personal assistant and incorporating various functionalities which aren't found in any of the existing virtual assistants with the help of several AI algorithms and open-source tools. Understanding context is important for any AI and increase in the context leads to the effective handling of open-ended requests. Unlike commercial products, this AI bot uses open ended requests than specific tasks to provide multifunctional to artificial brain.
A method to assist in the proper management of rule bases in Web information systems

Purpose One key issue of maintaining Web information systems is to guarantee the consistency of their knowledge base, in particular, the rules governing them. There are currently few methods that can ensure that rule bases management can scale to the amount of knowledge in these systems environment. Design/methodology/approach In this paper, the authors propose a method to detect correct dependencies between rules. This work represents a preliminary step for a proposal to eliminate rule base anomalies. The authors previously developed a method that aimed to ameliorate the extraction of rules dependency relationships using a new technique. In this paper, they extend the proposal with other techniques to increase the number of extracted rules dependency relationships. The authors also add some modules to filter and represent them. Findings The authors evaluated their own method against other semantic methods. The results show that this work succeeded in extracting better numbers of correct rules dependency relationships. They also noticed that the rule groups deduced from this method’s results are very close to those provided by the rule bases developers. Originality/value This work can be applied to knowledge bases that include a fact base and a rule base. In addition, it is independent of the field of application.
Bilateral Grid Statistics Combined with BRISK for Robust Matching

Feature-based matching in image registration methods has always been a research hotspot. Matching using traditional robust features, while accurate, is often time consuming. How to improve the precision by simple descriptors for robust matching has become a new direction in feature matching research. Grid-based Motion Statistic is a fast and effective algorithm, but it lacks matching accuracy and does not perform well in large deformations. In this paper, we present a method combined bilateral grid statistics and BRISK algorithm on the basis of GMS. The original large number of messy matches are scored by the bilateral grid statistics, the correct and false matches are distinguished, and more accurate results are got. Compared with GMS, our method has higher correct rate, wider range of matching, better robustness than SIFT and SURF, and greatly reduces computation and time-consuming operation.
FP-AK-QIEA-ℝ on the Knapsack problem

FP-AK-QIEA-ℝ implements and evaluates a new quantum-inspired evolutionary algorithm based on the concept of quantum superposition for initialization. This model is based on QIEA-ℝ for initialization, and for next iterations, it uses probability density functions where the best individuals are rewarded; then, the sampling process gets closer to the best region. FP-AK-QIEA-ℝ was evaluated in real domain problems such as benchmark functions, protein folding, and it obtained excellent results. This work evaluates the algorithm in a binary domain, and several experiments were performed on the knapsack problem, showing good results for the experiments.
Towards a more reliable evaluation of the Morocco’s solar resource

The current national energy policy aims to develop and promote renewable and clean energy. To this end, one of the Morocco’s most available energies that could contribute appreciably to improving national energy mix is solar energy. Thus; any solar project must rely (at least in large part) on modeled (satellite-based) irradiance data. However, a key question remains: the reliability of these data sets and generated maps. It is in this outlook that our study comes to highlight the problems with the assessment of the solar resource and to stress the concept of the calibration of satellite data to ground measurements (site-specific adaptation). In order to achieve this desired goal, we have introduced the different procedures of local calibration used in the solar energy industry and operated the calibration of the Moroccan Solar Atlas (MSA). In this context, an innovative protocol of generalization of the calibration to cover the entire territory of Morocco has been implemented and has demonstrated the quality of the proposed method and its contribution compared to conventional methods.
Deep Incremental Learning for Big Data Stream Analytics

Internet of Things (IoT) consists of many physical devices that generate data continuously and communicate these data among them. This data is continuously evolving large in volume with time varying nature. Processing these data is critical in all applications including monitoring and control applications. The continuous flow of data and time critical decisions make it infeasible to process the data after storage. Hence, the data must be processed on the fly or online. Online learning or incremental learning algorithms are suitable for processing stream data. Traditional machine learning algorithms are not scalable and so, Deep learning would be of great benefit to analyse steam data. This paper provides a survey on the different approaches used for processing data streams. Concept drift is a significant challenge identified and the paper compares the different works to handle concept drift.
Facial expression recognition based on conjugate gradient extreme learning machine

This paper proposed a face recognition algorithm based on conjugate gradient extreme learning machine. General extreme learning machine algorithm, which is gained by using method of calculating generalized inverse, the process is a large amount of computation and memory consumption. For this problem, this paper proves the positive definiteness of the calculated matrix, and based on this, an extreme learning machine solution algorithm based on conjugate gradient algorithm was proposed and kernel function is introduced to improve its nonlinear classification performance. At the same time, DAG method is used to extend the binary classification conjugate gradient extreme learning machine to multi-classification problems. Experimental results show that the computational speed of the algorithm in this paper is faster than that of the general extreme learning machine algorithm, and the classification accuracy is higher than that of the general extreme learning machine algorithm.
Incorporating Gradient Direction for Assessing Multiple Distortions

Quantifying the perceived quality of digital images matters when various artifacts cause unpleasant experiences for users of multimedia applications and TV viewers. With an automatic metric for image quality assessment, content providers can monitor what they share and network resources can be assigned in an optimized way. However, it is difficult to model the way humans perceive the quality of an image. Existence of multiple distortions in an image is another challenge, since it makes it more difficult to evaluate the severity of the artifacts in comparison to a situation where there is only one distortion in an image. In this paper, we have adopted the information provided by the direction of gradient vectors, in addition to the gradient magnitudes, so that we can have better performance in dealing with multiple distortions. Doing so, we were able to deliver a full-reference method with an acceptable performance and time complexity for multiply distorted images.
Multi-Branch Context-Aware Network for Person Re-Identification

Most existing methods on person re-identification pay redundant attention to global features or local features which ignore contextual dependencies which are equally important in representing pedestrian images. In this paper, we propose a Multi-Branch Context-Aware Network (MBCAN) for person re-identification to exploit rich context information. MBCAN learns global features and local-part features in two separate branches to take full advantages of both coarse-grained and fine-grained features. Additionally, two types of attention modules are introduced to capture contextual dependencies in spatial dimension and channel dimension, respectively. A module called feature vector extraction block is designed to find an efficient way to integrate features from coarse to fine. Extensive experiments with ablation analysis show the effectiveness of our method, and state-of-the-art results are achieved on Market-1501, DukeMTMC-reID and CUHK03 datasets.
Semantic GAN: Application for Cross-Domain Image Style Transfer

Image style transfer has attracted much attention from many fields and received promising performance. However, style transfer in the cross-domain field, e.g., the transfer between near-infrared and visible light images, is rarely studied. In the cross-domain image style transfer, one key issue is mismatching problem existing in the generated semantic regions. In this paper, we propose a novel model of Semantic GAN, which integrates the semantic guidance and the recent CycleGAN. In particular, we present a semantic style loss with Gram matrix to well preserve the semantic information in the generated images. The proposed Semantic GAN can control the transfer in the right way with semantic masks and solve the mismatching problem. We apply our approach to two outdoor scene datasets to evaluate the performance of all competing methods. The experimental results show that our approach outperforms previous methods in addressing the mismatching problem and providing a good quality result.
An Efficient Nearest Neighbor Design for 2D Quantum Circuits

In the last couple of years, synthesis of quantum circuits has received huge impetus among the research communities after the evolution of an efficient and powerful computational technology called “quantum computing”. But physical implementation of these circuits considers the nearest neighbor qubit interaction as the desirable one otherwise a computational error can result. Realization of such an architecture in which qubit interacts only with its adjacent neighbors is termed as the Nearest Neighbor (NN) property. To attain such design architecture, SWAP gates plays a significant role of bringing the qubits to adjacent locations. But this in turn introduces design overhead so NN-based realization using limited number of SWAP gates has become significant. In order to explore this area, in this article, we introduced an efficient design technique for NN realization of quantum circuits in 2D architecture. The design algorithm has been partitioned into three phases of qubit selection, qubit placement and SWAP gate implementation. To verify the exactness of the stated design approach, its functionality has been evaluated over a wide set of benchmark function and subsequently witnessed an improvement on its cost metrics. By running our algorithm an overall improvement of about 17%, 3% against existing 2D works and 35%, 22% against 1D works over SWAP gate count and quantum cost metrics have been recorded, respectively.
Performance Analysis and Optimization of Alluxio with OLAP Workloads over Virtual Infrastructure

With the popularity of cloud computing, decoupled compute-storage architecture has become a trend. While being able to independently scale compute and storage results in large cost savings and more flexibility, this architecture also increases the latency of data access, reducing the performance. To solve this problem, Alluxio was proposed. Alluxio achieves the goal of reducing data access latency by providing a near-compute cache when Alluxio is deployed with compute nodes. Applications and compute frameworks send requests through Alluxio and are automatically served through the cached copy. Alluxio is used in many production environments and research work, but there is no comprehensive analysis of Alluxio’s acceleration effects. In this paper, we evaluate and analysis the performance of Alluxio with OLAP workloads in different application scenarios. We also summarize the shortcomings of Alluxio and optimize it. Finally, the improved performance results and conclusion are given.
On the Impact of Generative Policies on Security Metrics

Policy based Security Management in an accepted practice in the industry, and required to simplify the administrative overhead associated with security management in complex systems. However, the growing dynamicity, complexity and scale of modern systems makes it difficult to write the security policies manually. Using AI, we can generate policies automatically. Security policies generated automatically can reduce the manual burden introduced in defining policies, but their impact on the overall security of a system is unclear. In this paper, we discuss the security metrics that can be associated with a system using generative policies, and provide a simple model to determine the conditions under which generating security policies will be beneficial to improve the security of the system. We also show that for some types of security metrics, a system using generative policies can be considered as equivalent to a system using manually defined policies, and the security metrics of the generative policy based system can be mapped to the security metrics of the manual system and vice-versa.
A Comprehensive Collaborative Filtering Approach using Autoencoder in Recommender System

Recommender System is such kind of method where a user can get a recommendation by analyzing the user's previous preferences or behavior. It is an approach which helps a user to find items and contents by predicting their rating and showing them the recommended products. Items in the user based model are recommended to a user based on his/her similar user's preferences. In this paper, a strategy has been proposed in which calculation of the similarity between users have been done by using Autoencoder (AE) feature on the movielens data set. By using the autoencoder user-features have been calculated. Two users might like the same type of products and might give the same ratings to common products, so their features should be identical. By taking this relevant information into account, the similarity between users has been estimated and a collaborative filtering system based on user has been proposed. A visualization of the results of the recommendation system after using the evaluation methods have also been provided.
Data Analytics for the Cryptocurrencies Behavior

The cryptocurrencies are a new paradigm of transferring money between users. Their anonymous and non-centralized is a subject of debate around the globe that paired with the massive spikes and declines in value that are inherit to an unregistered asset. These facts make difficult for the common daily use of the cryptocurrencies as an exchange currency as instead they are being used as a new way to invest. What we propose in this article is a system for the better understanding of the cryptocurrencies economical behavior against the global market. For that we are using Data Analytics techniques to build a predictor that uses as inputs said external financial variable. These forecasts would help determine if a coin is safe to trade with, if those forecasts can be precise by only using this external data. The results obtained indicates us that there is a certain degree of influence of the global market to the cryptocurrencies, but that is it not enough to correctly predict the fluctuations in price of the coins and that they care more about others factors and that they have their own bubbles, like the crypto collapse in late 2017.
Online Panorama Image Generation for a Disaster Rescue Vehicle

We introduce our online panorama generation system for armored rescue vehicles in an extreme disaster. To make the whole system fireproof and heatproof, all the cameras are embedded around the vehicle body, and this makes the panorama stitching suffer from binocular parallax. Because vehicle and remote operators should observe its current surroundings, the panorama image should be generated online in an acceptable time.We explain how to calculate the initial alignment of input camera image on the cylindrical canvas in advance, and how to merge the overlapped image by removing the binocular parallax. Structures for the data transfer is also discussed. Experiments show that the current implementation can generate and share the panoramic image with remote operators successfully.
An integrated deep-learning and geometric approach to 1D barcode

Vision-based 1D barcode reading gains increasing research due to great demand of high degree of automation. Aiming at detecting image region of 1D barcodes, existing geometric approaches barely balance speed and precision. Deeplearning- based methods can locate 1D barcode fast but lack effective and accurate segmentation process, while pure geometric-based methods take unnecessary computational cost when processing high resolution image. We propose to integrate the deep-learning and geometric approaches, to tackle robust barcode localization in the presence of complicated background and accurate barcode detection within the localized region, respectively. Our integrated solution benefits the complementary advantages of the two methods. Through extensive experiments on standard benchmarks, we show our integrated approach outperforms the state-of-the-arts by at least 5 percentages.
Self-Adaption Multi-classifier Fusion Networks for Image Recognition

Recently, many visual recognition related studies have proved that making full use of different levels of features can effectively enhance the representational ability of convolutional neural networks (CNNs). Different from other CNN architecture which are devoted to aggregate features of different scales, we proposed a multi-classifier network (MCN) to make more effective use of these feature maps. Specifically, MCN can directly make full use of features of different levels and fuse intermediate results in a self-adaption way. Note that the auxiliary classifiers not only can optimize the internal features of CNNs directly, but also bring additional gradient which further solves the problem of vanishing-gradient. In addition, MCN is a very flexible architecture and can be combined with existing state-of-the-art networks (ResNet, DenseNet, ResNeXt, etc.) easily. Extensive experiments on three highly competitive benchmark datasets, CIFAR-10, CIFAR-100 and ImageNet, clearly demonstrate superior performance of the proposed MCN over state-of-the-arts.
Uncovering Hidden Links Between Images Through Their Textual Context

Using hyperlinks to enhance page ranking has been widely studied in the literature. The main motivation is that an hyperlink underlines a page relevance. However, several hyperlinks in the web are used for navigation or marketing purposes. In addition, hyperlinks are created manually, so it is impossible to semantically link all similar pages. In our work, we propose to uncover hidden semantic links and create them automatically between all the collection’s images. For this aim, we propose first to format textual context of images into topic distributions via LDA technique, and then compute semantic similarities to create links. Experiments carried out in the Wikipedia Retrieval Task of ImageClef 2011 showed that the whole textual context of images is useful for uncovering hidden links and consequently enhancing the retrieval accuracy.
Predicting the difficulty of automatic item generators on exams from their difficulty on homeworks

To design good assessments, it is useful to have an estimate of the difficulty of a novel exam question before running an exam. In this paper, we study a collection of a few hundred automatic item generators (short computer programs that generate a variety of unique item instances) and show that their exam difficulty can be roughly predicted from student performance on the same generator during pre-exam practice. Specifically, we show that the rate that students correctly respond to a generator on an exam is on average within 5% of the correct rate for those students on their last practice attempt. This study is conducted with data from introductory undergraduate Computer Science and Mechanical Engineering courses.
Control Method for Rollover Recovery of Rescue Robot Considering Normalized Energy Stability Margin and Manipulating Force

Various robots that operate in dangerous environments have been developed. A robot falling down is a critical incident, and many studies have been conducted to avoid the rollover situation. However, in a disaster site, the robot may rollover even if previous control methods are implemented. Therefore, it is necessary to develop a control method to recover from the rollover situation. The objective of this study is to realize autonomous control for the rollover recovery of a rescue robot. Initially, subcrawlers are controlled to reduce the normalized energy stability margin. Subsequently, if the robot cannot recover by using the subcrawlers, a manipulator is used to push the ground, rotate the robot, and then recover from the rollover situation. The experiment was performed to verify the effectiveness of the proposed control method using the Gazebo simulator where a dynamics engine, named the Open Dynamics Engine, was installed.
Training quantized one-stage object detection neural networks via selective feature imitation

The quantization of neural networks using low-precision numeric is an efficient approach to reduce the requirements for storage and computation resources, which makes it a reality to run the neural network on the resource-constrained devices. However, the accuracy of the neural network deteriorates after the normal quantization, especially for the object detection task. To overcome this dilemma, we propose a teacher–student based quantization training scheme for the one-stage object detection neural network. First, the quantization configurations of weights and activations are determined according to their statistical probability densities of each layer, thus ensuring a lower representation error of the fixed-point numeric. Second, the supplementary supervision information from a high-performance floating-point teacher detection neural network is used to assist the training of the quantized student detection neural network. Therefore, the expression and fitting ability of the quantized student can be significantly improved by selectively imitating the teacher’s key features that are indicated in the feature selection matrix. The superiority of our method has been verified on several benchmark datasets in comparison with other related methods. On the PASCAL VOC2007 test set, the mean average precision (mAP) of the 4-bit quantized tiny-YOLOv2 trained with our method can reach 61.1%, compared with an mAP of 57.4% of its 32-bit floating-point counterpart trained with the plain method. Further, the 3-bit quantized detection neural networks can still achieve satisfactory mAP even without the pretrained weights, demonstrating the robustness of the proposed method.