Reviewerly/exHarmony huggingface dataset:
Automated Evaluation of the Virtual Assistant in Bleu and Rouge Scores

The advanced field of natural language processing refers to create an intelligent conversational agent. The main goal is to develop bot are capable of generating responses over the last measurements collected over the IoT network. The virtual assistant classifies the user utterance and extract the valuable information to predict the optimal response. The contribution of this paper was performed by the automatics metrics BLEU and ROUGE used in machine translation. However, these metrics were implemented in NLG system to perform the effectiveness of the system. In this work, we evaluated our model over others containing a large set of data in several languages along with the Paradise survey prompts the user satisfaction the results provide F1-score is 74%, the Precision is 85% and Accuracy 75%. Although, the BLEU 0.8 and ROUGE 0.7. The bot has a strong capability to understand dialog human judgments over various features structured based on qualitative and quantitative experiments.
Real-Time Authentication with A.I

Wrongdoing conception in surveillance videos is more challenging than detection. This work introduces the design of a accurate, lightweight and fast variance understanding neural network. AI capabilities can improve the way current security solutions are working today. It is projected that there will be need for security in area of AI to overcome issues like securing AI training data, training pipelines and ML models, also how AI modelled applications can be operated through negotiating with them. How can AI be leveraged to enhance security of critical infrastructure in the landscape of critical information which may contain large amount of critical business data. Now a day’s state sponsored attacks are growing on an alarming rate, due to which government and private organization are more vigilant in protecting critical infrastructure. This can be achieved by creating business application which will solve the headache of securing critical information for NCIIPC. (National Critical Information Infrastructure protection centre) This can be simulating in high end laptop with Intel Core i5 – 9th generation processor, 6 GB NVIDIA GPU GTX 1660 Ti, which has high FPS which we think will work more smoothly with MTCNN model to detect and recognize the face. Also, at various stages authentication function and alert function will work on top of recognition function. This application will prevent the user to enter into critical infrastructure and prevent wrong doing post validating itself through fake ID Card.
Needs Analysis on ESP Course for Business and Engineering Students in a Chinese Local University

The current study tends to discuss the issue of concocting a needs analysis for ESP course which may provide useful information for the curriculum design of ESP course for business and engineering students in a Chinese applied university.The author used close-ended questionnaires to investigate the special needs of ESP course for students major in business and engineering and tried to indicate whether there is a correlation between students major and their specific needs in ESP course as well as whether the proficiency of English correlates with their needs in ESP course.Questionnaires were also used to collect data from different perspectives like instructors, employees, and employers in relevant industries, trying to explore whether there are different beliefs about ESP needs among different participants.
DPGraph: A Benchmark Platform for Differentially Private Graph Analysis

Differential privacy has become an appealing choice for analyzing sensitive data while offering strong privacy protection, even for complex data types like graphs. Despite a decade of academic efforts in designing differentially private algorithms for graph analysis, few works have been used in practice. This is due to their complexity in the choice of privacy guarantees and parameter/environmental configurations, or due to their scalability issues for large datasets.
Distributional reinforcement learning with unconstrained monotonic neural networks

The distributional reinforcement learning (RL) approach advocates for representing the complete probability distribution of the random return instead of only modelling its expectation. A distributional RL algorithm may be characterised by two main components, namely the representation of the distribution together with its parameterisation and the probability metric defining the loss. The present research work considers the unconstrained monotonic neural network (UMNN) architecture, a universal approximator of continuous monotonic functions which is particularly well suited for modelling different representations of a distribution. This property enables the efficient decoupling of the effect of the function approximator class from that of the probability metric. The research paper firstly introduces a methodology for learning different representations of the random return distribution (PDF, CDF and QF). Secondly, a novel distributional RL algorithm named unconstrained monotonic deep Q-network (UMDQN) is presented. To the authors’ knowledge, it is the first distributional RL method supporting the learning of three, valid and continuous representations of the random return distribution. Lastly, in light of this new algorithm, an empirical comparison is performed between three probability quasi-metrics, namely the Kullback–Leibler divergence, Cramer distance, and Wasserstein distance. The results highlight the main strengths and weaknesses associated with each probability metric together with an important limitation of the Wasserstein distance.
Distribution Disagreement via Lorentzian Focal Representation

Error disagreement-based active learning (AL) selects the data that maximally update the error of a classification hypothesis. However, poor human supervision (e.g., few labels, improper classifier parameters) may weaken or clutter this update; moreover, the computational cost of performing a greedy search to estimate the errors using a deep neural network is intolerable. In this paper, a novel disagreement coefficient based on distribution, not error, provides a tighter bound on label complexity, which further guarantees its generalization in hyperbolic space. The focal points derived from the squared Lorentzian distance, present more effective hyperbolic representations on aspherical distribution from geometry, replacing the typical euclidean, kernelized, and Poincaré centroids. Experiments on different deep AL tasks show that, the focal representation adopted in a tree-likeliness splitting, significantly performs better than typical baselines of centroid representations and error disagreement, and state-of-the-art neural network architectures-based AL, dramatically accelerating the learning process.
A priority-driven optimal guidance approach to moving target tracking for safe navigation in a narrow space

This paper presents an ‘optimally designed’ anti-collision trajectory plan suitable for moving target tracking in narrow spaces like corridors, aisles, etc. Narrow spaces constitute of highly constrained reachable states in the context of dynamic motion. Tracking is the default mode, while the safe trajectory is triggered on a priority-basis. The optimal switching point plays a key role in preventing a possible collision due to a ‘mis-timed’ change of manoeuvre, in case the obstacle shows unanticipated motion. Moreover, it also ensures that tracking is resumed at a point which minimises tracking error under the said deviation from the desired path. A smoothening filter has been proposed to make seamless transitions at the switching points.
Chatbots in the tourism industry: the effects of communication style and brand familiarity on social presence and brand attitude

Text-based chatbots are increasingly being implemented in the tourism sector to supplement online customer service encounters. However, customers often perceive conversations with chatbots as unnatural and impersonal. Therefore, we investigated whether a humanlike communication style enhances users’ chatbot and brand perceptions. Two experiments were conducted in which the effects of informal language (vs. formal language) and invitational rhetoric (present vs. absent) were examined separately. In both experiments, participants engaged in conversations with a customer service chatbot in the tourism sector after which they evaluated social presence and attitude towards the brand. Also, brand familiarity was included as a factor in both experiments as users’ brand familiarity affects their perceptions of the communication style in human-to-human interaction. The results showed chatbots using informal language or invitational rhetoric increase one's brand attitude via social presence. Moreover, brand familiarity only moderated the findings when the chatbot used invitational rhetoric: participants who were familiar with the brand experienced more social presence when the chatbot messages contained invitational rhetoric. We conclude that the perceived humanness of chatbots can be increased by adopting a communication style consisting of informal language and invitational rhetoric. Implications for the design and evaluation of chatbot messages are discussed.
DiffNet: Discriminative Feature Fusion Network of Multisurface Skeleton Project Images for Action Recognition

In this work, we discuss the feature fusion approach of multisurface skeleton projection images for action recognition. Multisurface skeleton projection images are generated from human skeleton joint motion trajectories on three surfaces: horizontal-vertical, horizontal- and vertical-depth surfaces. The vision features of these skeleton projection images contain complementary action information on different surfaces and are generally combined by early fusion or late fusion. To learn and fuse the discriminative features of each surface effectively, this paper proposes a new feature fusion method called the discriminative feature fusion network, which uses a two-task framework to recognize action and surface categories simultaneously. In the proposed network, the features of three skeleton projection images are computed by the same convolutional network. To retain the complementary action feature of these skeleton projection images, the surface classification loss is defined and added into the action classification loss to train the feature learning network. The experimental results show that the performance of the proposed feature fusion method is better than traditional early and late fusion. Compared with skeleton visualization image-based action recognition methods, the proposed method achieves state-of-art performance on the NTU RGB+D action dataset.
Which, Who and How: Detecting fraudulent sale accumulation behavior from multi-dimensional sparse data

In a distribution channel, product manufacturers will reward retail traders who achieve a high value of sales. In order to obtain reward from the manufacturer, distributors might form an alliance where a cheating retail trader could accumulate the amount of sales via collaborative fraud with other distributors. In order to detect such fraud behavior, we mainly focus on answering three questions: WHO conduct the sale accumulation? WHICH sale records are modified? HOW they conduct the sale accumulation? However, if any one of the three questions cannot be answered, the other two may not be handled either. To cope with this difficulty, we propose the new concepts of data chunk and partially ordered lattice of the data warehouse schema. Based on these concepts, we propose the concepts of sale accumulation point(SAP) and sale accumulation pattern(SAPAT). SAP can answer WHICH sale record is accumulated by WHO. SAPAT can answer HOW the distributors conduct the sale accumulation. By designing algorithm to detect SAP and SAPAT all together, the three questions can be answered at the same time. The experimental results demonstrate that the proposed algorithm based on the SAP to solve the WHICH and WHO problems can achieve an average AUC value of 0.65. Traditional feature extraction methods can only achieve an average AUC of 0.25. The proposed algorithm based on the SAPAT to handle the HOW problem can achieve an average accuracy of 75.2%. The baseline algorithm can only achieve average accuracy of 27.1%. Furthermore, the experiments demonstrate that the proposed method can well handle the WHICH and HOW problems on both synthetic and real data.
LTNet: Light Transfer Network for Depth Guided Image Relighting

Relighting is an interesting yet challenging low-level vision problem, which aims to re-render the scene with new light sources. In this paper, we introduce LTNet, a novel framework for image relighting. Unlike previous methods, we propose to solve this challenging problem by decoupling the enhancement process. Specifically, we propose to train a network that focuses on learning light variations. Our key insight is that light variations are the critical information to be learned because the scene stays unchanged during the light transfer process. To this end, we employ a global residual connection and corresponding residual loss for capturing light variations. Experimental results show that the proposed method achieves better visual quality on the VIDIT dataset in the NTIRE2021 relighting challenge.
Natural Language Processing with “More Than Words – BERT”

Question-Answering (QA) has become one of the most popular natural language processing (NLP) and information retrieval applications. To be applied in QA systems, this paper presents a question classification technique based on NLP and Bidirectional Encoder Representation from Transformers (BERT). We performed experimental investigation on BERT for question classification with TREC-6 dataset and a Thai sentence dataset. We propose an improved processing technique called “More Than Words – BERT” (MTW – BERT) that is a special NLP Annotation tags for combining Part-Of-Speech tagging and Named Entities Recognition to be able for learning both pattern of grammatical tag sequence and recognized entities together as input before classifying text on BERT model. Experimental results showed that MTW – BERT outperformed existing classification methods and achieved new state-of-the-art performance on question classification for TREC-6 dataset with 99.20%. In addition, MTW-BERT also applied for question classification for Thai sentences in wh-question category. The proposed technique remarkably achieved Thai wh-classification with accuracy rate of 87.50%.
Multi-View Texture Learning for Face Super-Resolution

In recent years, single face image super-resolution (SR) using deep neural networks have been well developed. However, most of the face images captured by the camera in a real scene are from different views of the same person, and the existing traditional multi-frame image SR requires alignment between images. Due to multi-view face images contain texture information from different views, which can be used as effective prior information, how to use this prior information from multi-views to reconstruct frontal face images is challenging. In order to effectively solve the above problems, we propose a novel face SR network based on multi-view face images, which focus on obtaining more texture information from multi-view face images to help the reconstruction of frontal face images. And in this network, we also propose a texture attention mechanism to transfer high-precision texture compensation information to the frontal face image to obtain better visual effects. We conduct subjective and objective evaluations, and the experimental results show the great potential of using multi-view face images SR. The comparison with other state-of-the-art deep learning SR methods proves that the proposed method has excellent performance.
A method to acquire cross‐domain requirements based on Syntax Direct Technique

Abstract With the rapid increase in the number of Apps, the requirement of users has also become extremely complex. Developers have to continuously acquire innovative requirements that provide the guideline for developing more competitive products. However, traditional methods to acquire requirements are not suitable for the App development due to the disadvantage that it cannot interact with users directly. Besides, some methods that use text and data analysis to acquire requirements automatically are hard to expand innovative products because they are often confined to the specific App or the same domain. Therefore, to attract more new users, developers try to find new portable inspiration from other domains for enriching the functions of the App. In this article, we propose a feature extraction method from the descriptions of Apps and use similarity matching to acquire cross‐domain requirements. Our experiments have verified that the Precision, the Recall, and the F‐measure are all as high as 80% of our feature extraction method. Besides, the requirements list we recommend also makes a good performance in terms of reusability with the average Reuse Rank of 59.33% and average Adjusted Functional Points of 7.49, the adaptability gets an average score of 3.3, and the average score of operability is 3.
Scene Text Recognition With Linear Constrained Rectification

Scene Text Recognition remains a challenging problem because of various text styles and image distortions. This paper proposed an end-to-end trainable model with a rectification module network.The rectification module adopts a polynomial based spatial transform network to rectify the distorted input image, the feature representation between the rectification and encoding step is shared. The model can be trained with the scene text images and the corresponding word labels. With the flexible rectifying and feature sharing, this model outperforms previous works through the extensive evaluation results on the standard benchmarks, especially on irregular datasets, 80.2% on IC15 and 85.4% on CUTE, more specifically.
CBDC and Monetary Policy

Improving the conduct of monetary policy is unlikely to be the main motivation for central banks to issue a central bank digital currency (CBDC). While some argue that a CBDC could allow more complex transfer schemes or the ability to break below the zero lower bound, we find these benefits might be small or difficult to realize in practice.
A Novel Multi-Task Self-Supervised Representation Learning Paradigm

Self-supervised learning can be adopted to mine deep semantic information of visual data without a large number of human-annotated supervision by using a pretext task to pretrain a model. In this study, we proposed a novel self-supervised learning paradigm, namely multi-task self-supervised (MTSS) representation learning. Unlike existing self-supervised learning methods, which pretrain neural networks on the pretext task and then fine-tune the parameters of neural networks on the downstream task, in our scheme, downstream and pretext tasks are considered primary and auxiliary tasks, respectively, and are trained simultaneously. Our method involves maximizing the similarity of two augmented views of an image as an auxiliary task and using a multi-task network to train the primary task alongside the auxiliary task. We evaluated the proposed method on standard datasets and backbones through a rigorous experimental procedure. Experimental results revealed that proposed MTSS can achieve better performance and robustness than other self-supervised learning methods on multiple image classification data sets without using negative sample pairs and large batches. This simple yet effective method can inspire people to rethink self-supervised learning.
Launching the Digital Learning Portal for Primary School Teacher Training

The paper investigates the experience of launching a digital learning portal for primary school teacher training at SHEI "Donbas State Pedagogical University". It presents the theoretical substantiation of the relevance of developing a new type of digital learning resources that correspond to the concepts of the Next Generation of Digital Learning Environment (NGDLE). Some digital resources are developed and involved in the digital learning portal. The effectiveness of its launching is studied at the initial stage and it is proved by the surveys, conducted among university lecturers, schoolteachers and students.
Utilizing UAVs Technology on Microscopic Traffic Naturalistic Data Acquirement

Research into collecting and measuring reliable, accurate, and naturalistic microscopic traffic data is a fundamental aspect in road network planning scientific literature. The vehicle trajectory is one of the main variables in traffic flow theory that allows to extract information regarding microscopic traffic flow characteristics. Several methods and techniques have been applied regarding the acquisition of vehicle trajectory. The forthcoming applications of intelligent transport systems on vehicles and infrastructure require sufficient and innovative tools to calibrate existing models on more complex situations. Unmanned aerial vehicles (UAVs) are one of the most emerging technologies being used recently in the transportation field to monitor and analyze the traffic flow. The aim of this paper is to examine the use of UAVs as a tool for microscopic traffic data collection and analysis. A comprehensive guiding framework for accurate and cost-effective naturalistic traffic surveys and analysis using UAVs is proposed and presented in detail. Field experiments of acquiring vehicle trajectories on two multilane roundabouts were carried out following the proposed framework. Results of the experiment indicate the usefulness of the UAVs technology on various traffic analysis studies. The results of this study provide a practical guide regarding vehicle trajectory acquirement using UAVs.
Interfacing &amp; Embodiment: ”Baby Tango” Dancing Robot Attempts to Communicate

For decades, robotic arts and dancing robots have been the focus of many research studies and artistic experiments. They started in the early 1920s and began realizing some of their potential as technology advanced in the last decade. This paper presents observations on the current state of the art in dancing robots, wearable haptics, and other theoretical frameworks; then proposes a prototype "Baby Tango" as an artistic experience, based on these observations. This experience reflects on the idea of interfacing and embodiment and their potential effects on the future of human-robot interactions by exploring new means of verbal and non-verbal communication with robots.
Prewitt and Canny Methods on Inversion Image Edge Detection: An Evaluation

Abstract The use of the edge detection method in an image will produce the edges of the image object. The goal is to mark the part that becomes the image’s detail and fix the point of the blurred vision, which occurs because of an error or the effect of the image acquisition process. This study aims to see the combination of the Prewitt and Canny methods in detecting the edges of the inverted image. The image dataset used is a bonsai image consisting of 10 typical images, and ten bonsai images reversed based on the standard image dataset. The research dataset was obtained from the Caltech 101 website http://www.vision.caltech.edu/Image_Datasets/Caltech101/ with an image size of approximately 200×300 pixels. Based on the analysis of 10 experiments that have been carried out, the combination of the Prewitt and Canny methods can perform edge detection quite well with an average accuracy of 78.90% and an error rate of 21.10%. Thus it can be concluded that these methods combine to yield a reasonable level of precision, though the extent is very limited.