Reviewerly/exHarmony huggingface dataset:
Object Detection and Classification from a Real-Time Video Using SSD and YOLO Models

In computer vision, real-time object detection and recognition is considered as a challenging task in uncontrolled environments. In this research work, an improved real-time object detection and recognition technique from web camera video is introduced. Objects such as people, vehicles, animals, etc. are detected and recognized by this technique. Single Shot Detector (SSD) and You Only Look Once (YOLO) models are used in our paper shown promising results in the task of object detection and recognition for getting better performance. Our system can detect objects even in adverse as well as uncontrolled environments like excess or lack of light, rotation, mirroring and a variety of backgrounds, etc. Here, the convolutional neural network (CNN) has been used for the purpose of classifying the object. Our investigated technique is able to gain real-time performance with satisfactory detection as well as classification results and also provides better accuracy. The percentage of accuracy in the detection and classification of an object through our investigated model is about 63–90%.
Intelligent Visual-IoT-Enabled Real-Time 3D Visualization for Autonomous Crowd Management

Real-time crowd management systems play an important role in urban planning, disaster warning, and emergency evacuation. For example, through closed-circuit television (CCTV) cameras and artificial intelligence (AI) algorithms, crowd counting and abnormal event recognition can be realized. However, most of these applications are limited within the 2D coordinate system of each single camera, lacking a unified understanding of the 3D world as a whole. In addition, overly simple functionalities, such as framing certain areas or displaying text, limit the system intelligence and user experience. This article proposes a solution of realtime 3D visualization of outdoor scenes enabled by AI and the Visual Internet of Things (V-IoT). First, fixed and airborne cameras, infrared cameras, LiDAR, gas sensors, and so on are deployed to collect multi-modal data and send them to the clouds. Then AI algorithms are executed in the clouds to calculate the location of people, vehicles, and other moving objects, and to detect fights, stampedes, fire, or other abnormal events. Finally, the clouds send the AI algorithm results to the visualization system in terminal devices. The system displays the scene in real time through 3D animation and charts, and provides user interaction interfaces and simulation functionalities in a gamification environment. Experiments show that the proposed solution realistically visualizes the real world to sup-nort the user's decision.
Bengali Abstractive News Summarization Using Seq2Seq Learning with Attention

Text summarization is the technique for generating short and succinct summaries from long texts that focuses on the most important information but keeps the overall exhaustive signification of the whole text. This paper presents a method of generating short abstractive summaries from long Bengali news articles using some basic NLP approaches with various Recurrent Neural Network (RNN) architectures, such as Bidirectional RNN, Encoder-Decoder RNN, Sequence to Sequence (Seq2Seq) Learning with Attention mechanism, Longest Short-term Memory (LSTM), etc. Dataset collected by the authors from different Bengali online newspapers is used here. Then, the dataset is preprocessed. After that, word embedding and vocabulary counts are done. Finally, the deep learning approaches are applied to generate an abstractive summary for each news article. The model used the Seq2Seq algorithm with an attention mechanism which reduced the training loss up to 0.001 successfully. Another existing dataset is used to evaluate the results and found satisfactory results for own dataset than the other.
IoTs, Machine Learning (ML), AI and Digital Transformation Affects Various Industries - Principles and Cybersecurity Risks Solutions

Our purpose to conduct this research is that we would like to present advantages and applications of internet of things (IoTs), Machine learning (ML), AI - Artificial intelligence and digital transformation in Education, Medicine-hospitals, Tourism and Manufacturing Sectors. In this paper authors will use methods such as empirical research and practices and experiences in infrared rays system applications in emerging markets such as Vietnam. Research Results find out that in education sector, ML and IoTs and AI has affected methods of teaching and methods of evaluating students in classroom and from then, teachers or instructors can decide suitable career development path for learners. Last but not least, ML and IoTs and AI together also has certain impacts in hospitals and medicine sector where public health data and patients information and diseases information are recorded and processed faster with Big Data. Till the end, we have enough information to propose implications for future researches on applications of machine learning in each specific sector and also, cybersecurity Risk management also need for implementing and applying ML and IoTs and AI.
The impact of human resource management practices on employee performance and the mediating role of employee commitment

Employees represent the essential assets of any organization. The best organizations oversee human capital in the most efficient and effective way (Nethmini &amp; Ismail, 2019). Blau (2017) highlighted the relevance between human resources management (HRM) practices and the employees’ success at work through enhanced inspiration and commitment. Hence, employee commitment cannot be overlooked as the degree of employee commitment can decide employee performance (Ahmad, 2014). This paper aims to explore the significance of employee commitment as a mediator in the relationship between HRM practices and employee performance. The target population is employees in the construction industry in Jordan. The respondents were selected utilizing a simple random sampling method. The data was gathered through a self-administered questionnaire and analyzed utilizing structural equation modeling (SEM) in IBM SPSS AMOS 24.0. The researcher obtained an aggregate of 297 usable and completed questionnaires. The study found that: 1) HRM practices have significant effects on employee performance, 2) employee commitment has significant effects on employee performance, 3) HRM practices have significant effects on employee commitment, and more importantly, and 4) employee commitment partially mediates the relationship between HRM practices and employee performance. The implications of the study are also examined
Quadrotor path planning using A* search algorithm and minimum snap trajectory generation

In this study, we propose a practical path planning method that combines the A* search algorithm and minimum snap trajectory generation. The A* search algorithm determines a set of waypoints to avoid collisions with surrounding obstacles from a starting to a destination point. Only essential waypoints (waypoints necessary to generate smooth trajectories) are extracted from the waypoints determined by the A* search algorithm, and an appropriate time between two adjacent waypoints is allocated. The waypoints so determined are connected by a smooth minimum snap trajectory, a dynamically executable trajectory for the quadrotor. If the generated trajectory is invalid, we methodically determine when intermediate waypoints are needed and how to insert the points to modify the trajectory. We verified the performance of the proposed method by various simulation experiments and a real-world experiment in a forested outdoor environment.
Sentiment Analysis of Bengali Facebook Data Using Classical and Deep Learning Approaches

Chakraborty, Partha Nawar, Farah Chowdhury, Humayra AfrinThe process of assessing the emotional tone behind a document in order to comprehend the expressed opinions, views and emotions is known as sentiment analysis. A sentiment detecting approach is presented in this paper that uses seven machine learning algorithms to detect the polarity of textual Facebook posts and comments in Bengali, including five classical approaches and two deep learning approaches. We preprocessed our initial raw data through several steps and applied the TF-IDF technique for feature extraction. In classical approaches, we have used Naive Bayes (NB), Support vector machine (SVM), Decision tree (DT), AdaBoost, Random forest classifier (RF) and in deep learning approaches we have used Long Short Term Memory Network (LSTM), and Convolutional Neural Network. We have shown a comparative analysis of the classifiers used in sentiment detection. In our study, deep learning approaches have shown better performance than classical approaches with an accuracy of 96.95% by LSTM. Among the classical approaches, Support vector machine and Random forest classifier have achieved maximum accuracy of 78.23% and 78.37%, respectively.
Cryptanalysis of ForkAES

Forkciphers are a new kind of primitive proposed recently by Andreeva et al. for efficient encryption and authentication of small messages. They fork the middle state of a cipher and encrypt it twice under two smaller independent permutations. Thus, forkciphers produce two output blocks in one primitive call. Andreeva et al. proposed ForkAES, a tweakable AES-based forkcipher that splits the state after five out of ten rounds. While their authenticated encrypted schemes were accompanied by proofs, the security discussion for ForkAES was not provided, and founded on existing results on the AES and KIASU-BC. Forkciphers provide a unique interface called reconstruction queries that use one ciphertext block as input and compute the respective other ciphertext block. Thus, they deserve a careful security analysis. This work fosters the understanding of the security of ForkAES with three contributions: (1) We observe that security in reconstruction queries differs strongly from the existing results on the AES. This allows to attack nine out of ten rounds with differential, impossible-differential and yoyo attacks. (2) We observe that some forkcipher modes may lack the interface of reconstruction queries, so that attackers must use encryption queries. We show that nine rounds can still be attacked with rectangle and impossible-differential attacks. (3) We present forgery attacks on the AE modes proposed by Andreeva et al. with nine-round ForkAES.
Weighted superpixel segmentation

Image boundaries and regularity are two important factors in superpixel segmentation. Balancing the influence of image boundaries and regularity is key to producing superpixels. In this paper, we present a novel superpixel segmentation algorithm, called weighted superpixel segmentation (WSS), which is capable of generating superpixels with high boundary adherence and regular shape in a linear time. In WSS, superpixels are generated according to a distance metric defined by the combination of a weight function term, color distance term and plane distance term. Unlike other superpixel algorithms, the weight function is calculated for each pixel to determine the weight of the color distance term and plane distance term in the distance metric. To increase superpixel regularity, superpixel seeds are initialized in a hexagonal manner. Then, we use the distance metric to obtain the initial superpixels. Determining the seed search range is an essential factor to improve algorithm accuracy; thus, a dynamic circle search range is designed in our algorithm that can provide better superpixel results. Finally, a merging strategy is applied to obtain the final superpixels and ensure that the number of superpixels agrees with expectations. Experimental results demonstrate that WSS performs as well as or even better than the existing methods in terms of several commonly used evaluation metrics in superpixel segmentation.
Cross-Spectral Image Patch Matching by Learning Features of the Spatially Connected Patches in a Shared Space

Cross-spectral image patch matching is a challenging problem due to the significant difference between two images caused by different imaging mechanisms. We consider cross-spectral image patches can be matched because there exists a shared semantic feature space among them, in which the semantic features from different spectral images will be more independent of the spectral domains. To learn this shared feature space, we propose a progressive comparison of spatially connected feature metric learning with a feature discrimination constrain (SCFDM). The progressive comparison of spatially connected feature network keeps the property of each spectral domain in its corresponding low-level feature space, and interacts the cross-spectral features in a high-level feature space. The feature discrimination constrain enforces the framework to refine the shared semantic space for feature extraction. Extensive experiments shows that SCFDM outperforms the state-of-the-art methods on the cross-spectral dataset in terms of FPR95 and the training convergence. Meanwhile, it also demonstrates a better generalizability on a single spectral dataset.
Conditional nonclassical field generation in cavity QED

We introduce a method for the conditional generation of nonclassical states of light in a cavity. We consider two-level atoms traveling along the transverse direction to the cavity axis and show that by conditioning on one of the output measurements nonclassical field states are generated. The two-level atoms are prepared in the ground state and we conditioned on the events in which they are also detected in the ground state. Nonclassical properties of the cavity mode are identified and characterized. This includes: quadrature squeezing, sub-Poissonian photon-number distributions, and negative Wigner functions. We determine the optimal parameter regions where the corresponding nonclassical features are most distinct.
Cross-Domain Recommendation via Coupled Factorization Machines

Data across many business domains can be represented by two or more coupled data sets. Correlations among these coupled datasets have been studied in the literature for making more accurate cross-domain recommender systems. However, in existing methods, cross-domain recommendations mostly assume the coupled mode of data sets share identical latent factors, which limits the discovery of potentially useful domain-specific properties of the original data. In this paper, we proposed a novel cross-domain recommendation method called Coupled Factorization Machine (CoFM) that addresses this limitation. Compared to existing models, our research is the first model that uses factorization machines to capture both common characteristics of coupled domains while simultaneously preserving the differences among them. Our experiments with real-world datasets confirm the advantages of our method in making across-domain recommendations.
DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters

We propose a novel spectral convolutional neural network (CNN) model on graph structured data, namely Distributed Feedback-Looped Networks (DFNets). This model is incorporated with a robust class of spectral graph filters, called feedback-looped filters, to provide better localization on vertices, while still attaining fast convergence and linear memory requirements. Theoretically, feedback-looped filters can guarantee convergence w.r.t. a specified error bound, and be applied universally to any graph without knowing its structure. Furthermore, the propagation rule of this model can diversify features from the preceding layers to produce strong gradient flows. We have evaluated our model using two benchmark tasks: semi-supervised document classification on citation networks and semi-supervised entity classification on a knowledge graph. The experimental results show that our model considerably outperforms the state-of-the-art methods in both benchmark tasks over all datasets.
Guiding creative design in online advertising

Ad creatives (text and images) for a brand play an influential role in online advertising. To design impactful ads, creative strategists employed by the brands (advertisers) typically go through a time consuming process of market research and ideation. Such a process may involve knowing more about the brand, and drawing inspiration from prior successful creatives for the brand, and its competitors in the same product category. To assist strategists towards faster creative development, we introduce a recommender system which provides a list of desirable keywords for a given brand. Such keywords can serve as underlying themes, and guide the strategist in finalizing the image and text for the brand's ad creative. We explore the potential of distributed representations of Wikipedia pages along with a labeled dataset of keywords for 900 brands by using deep relevance matching for recommending a list of keywords for a given brand. Our experiments demonstrate the efficacy of the proposed recommender system over several baselines for relevance matching; although end-to-end automation of ad creative development still remains an open problem in the advertising industry, the proposed recommender system is a stepping stone by providing valuable insights to creative strategists and advertisers.
Nb-based Josephson parametric amplifier for superconducting qubit measurement*

We report a fabrication process and characterization of the Josephson parametric amplifier (JPA) for the single-shot quantum state measurement of superconducting multiqubit system. The device is prepared using Nb film as its base layer, which is convenient in the sample patterning process like e-beam lithography and film etching. Our results show that the JPA has a bandwidth up to 600 MHz with gain above 15 dB and noise temperature approaching the quantum limit. The qubit state differentiation measurements demonstrate the signal-to-noise ratio around 3 and the readout fidelity above 97% and 91% for the ground and first-excited states, respectively.
Constructing Chinese Macro Discourse Tree via Multiple Views and Word Pair Similarity

Macro-discourse structure recognition is an important task in macro-discourse analysis. At present, the research on macro-discourse analysis mostly uses the manual features (e.g., the position features), and ignores the semantic information in topic level. In this paper, we first propose a multi-view neural network to construct Chinese macro discourse trees from three views, i.e., the word view, the context view and the topic view. Besides, we propose a novel word-pair similarity mechanism to capture the interaction among the discourse units and the topic. The experimental results on MCDTB, a Chinese discourse corpus, show that our model outperforms the baseline significantly.
The freezing Rènyi quantum discord

Abstract As a universal quantum character of quantum correlation, the freezing phenomenon is researched by geometry and quantum discord methods, respectively. In this paper, the properties of Rènyi discord is studied for two independent Dimer System coupled to two correlated Fermi-spin environments under the non-Markovian condition. We further demonstrate that the freezing behaviors still exist for Rènyi discord and study the effects of different parameters on this behaviors.
Feature Encoder Guided Generative Adversarial Network for Face Photo-Sketch Synthesis

Face photo-sketch synthesis often suffers from many problems, such as low clarity, facial distortion, contents loss, texture missing and color inconsistency in the synthesized images. To alleviate these problems, we propose a feature Encoder Guided Generative Adversarial Network (EGGAN) for face photo-sketch synthesis. We adopt the cycle-consistent generative adversarial network with skipped connections as the general framework, which can train the models for both sketch synthesis and photo synthesis simultaneously. The two generators can constrain each other. In addition, a feature auto-encoder is introduced to refine the synthetic results. The feature encoder is trained to explore a latent space between the photo domain and sketch domains, assuming that there exists a uniform feature representation for a photo-sketch pair. Instead of participating in the generation process, the feature encoder is only utilized to guide the training process. Meanwhile, the feature loss and the feature consistency loss between the fake images and real images from the latent space are calculated to prevent the important identity-specific information from missing and reduce the artifacts in the synthesized images. Extensive experiments demonstrate that our method can achieve state-of-the-art performance on public databases both in terms of perceptual quality and quantitative assessments.
Reliable Access Control for Mobile Cloud Computing (MCC) With Cache-Aware Scheduling

In mobile cloud computing (MCC), data are stored and processed by more powerful computing platforms located in clouds to enhance the capability of mobile devices. Although MCC brings many benefits to users and organizations, security has become the main concern for MCC because it involves third party environment where data confidentiality is not a guarantee. To overcome this challenge, attribute-based encryption (ABE) technique, a promising cryptography method was introduced to support both data confidentiality and access control simultaneously. However, most of the ABE used single certification authority (CA) and disjoint attribute authority (AA) to verify user identity which can be a single point of failure. Therefore, a new approach was proposed in this research to deal with the single point of failure in the authority. Furthermore, the cache-based scheduling algorithm was upgraded to improve the response time of user jobs. The experimental results show that the approach performed better compared to its competitor in terms of reliability. Additionally, it significantly reduced the number of read operations compared to its counterpart.
Capturing Sentence Relations for Answer Sentence Selection with Multi-Perspective Graph Encoding

This paper focuses on the answer sentence selection task. Unlike previous work, which only models the relation between the question and each candidate sentence, we propose Multi-Perspective Graph Encoder (MPGE) to take the relations among the candidate sentences into account and capture the relations from multiple perspectives. By utilizing MPGE as a module, we construct two answer sentence selection models which are based on traditional representation and pre-trained representation, respectively. We conduct extensive experiments on two datasets, WikiQA and SQuAD. The results show that the proposed MPGE is effective for both types of representation. Moreover, the overall performance of our proposed model surpasses the state-of-the-art on both datasets. Additionally, we further validate the robustness of our method by the adversarial examples of AddSent and AddOneSent.
AutoRemover: Automatic Object Removal for Autonomous Driving Videos

Motivated by the need for photo-realistic simulation in autonomous driving, in this paper we present a video inpainting algorithm AutoRemover, designed specifically for generating street-view videos without any moving objects. In our setup we have two challenges: the first is the shadow, shadows are usually unlabeled but tightly coupled with the moving objects. The second is the large ego-motion in the videos. To deal with shadows, we build up an autonomous driving shadow dataset and design a deep neural network to detect shadows automatically. To deal with large ego-motion, we take advantage of the multi-source data, in particular the 3D data, in autonomous driving. More specifically, the geometric relationship between frames is incorporated into an inpainting deep neural network to produce high-quality structurally consistent video output. Experiments show that our method outperforms other state-of-the-art (SOTA) object removal algorithms, reducing the RMSE by over 19%.