Reviewerly/exHarmony huggingface dataset:
Graph-Grounded Goal Planning for Conversational Recommendation

Conversational recommendation casts the recommendation problem as a dialog-based interactive task, which could acquire user interest more efficiently and effectively by allowing users to express what they like. In this work, we move a step towards a new conversational recommendation task that is more suitable for real-world applications. In this task, the recommender proactively and naturally lead a dialog from non-recommendation content to approach an item being of interest to users, and allow users to ask questions for better support of user decisions. The challenge of this task lies in how to effectively control the dialog flow to complete the recommendation while appropriately responding to user utterances. To address this challenge, we first construct a Chinese recommendation dialog dataset DuRecDial. We then propose a two-stage Multi-Goal driven Conversation Generation framework, MGCG. In particular, the goal planning module leverages the global graph structure information and local goal-sequence information to effectively control the dialog flow step by step. The goal-guided responding module can produce an in-depth dialog about each goal by fully exploiting hierarchical goal information for response retrieval or generation. Results on DuRecDial demonstrate that MGCG can lead the dialog more proactively and naturally, and complete the recommendation task more effectively.
Dual-path image pair joint discrimination for visible–infrared person re-identification

Because the imaging spectra of infrared images and visible light images are different, there is a huge modal difference between visible light images and infrared ones. Existing methods use image conversion to solve the problem of modal difference between two images, but these methods usually fail to focus on the complete information of images, which lead to the results of cross modal person re-identification are unstable. To solve this problem, we propose a new visible–infrared person re-identification method, called dual-path image pair joint discriminant model (DPJD), which simultaneously optimizes the distance within and between classes, and supervises the network learning to identify feature representations. We generate images with different modalities for the samples, and separately compose the same modality image pair and different modality image pair so as to overcome the inconsistent alignment issues. In addition, we also propose a discriminant module based on dual-path (DMDP) to improve the generation quality and discrimination accuracy of image pairs. Experiments on two benchmark datasets SYSU-MM01 and RegDB demonstrate its effectiveness.
Chatbot Implementation in Customer Service Industry through Deep Neural Networks

It is the age of artificial intelligence (AI). As AI, machine learning, and Deep Learning (DL) have been progressing, machines have also advanced to emulate humans. A good example of such a machine is chatbots, which can talk like human, uses AI and NLP, can be a virtual assistant for customer experience. This paper provides a implementation of how chatbots work and the implementation of its techniques that can be used to create them. It compares and contrasts the current chatbots, as well as their strengths and weaknesses. Comparison is based on the functionalities and technical requirements of the most common chatbot application systems. According to research, nearly 75% of consumers have had bad customer service and coming up with substantive, lengthy, and insightful answers remains a difficult task. We used an encoder-decoder attention mechanism design to build the Seq2Seq AI Chatbot. This encoder-decoder makes use of LSTM cells in Deep RNNs. Instead of a specific chatbot that is used for a certain purpose, we wanted to design a general chatbot that can conduct general discussions with us like a friend. However, the model we'll employ can be trained on other datasets for different purposes. We have built a chatbot to talk about everyday conversations.
DPFDT: Decentralized Privacy-preserving Fair Data Trading System

In this paper, we introduce DPFDT - a decentralized privacy-preserving fair data trading system using smart contracts. In this system, data as a digital commodity can be sold by the seller to the buyer at a certain price. We first design a conditional anonymous scheme based on block-chain and a fair trading protocol. Then we integrate them to form DPFDT to provide fairness, conditional anonymity, privacy-preserving, and decentralization. A conditional anonymous scheme means that the user’s operations are carried out through anonymous accounts, but when an anonymous account does some malicious behaviors, its real account will be traced. A protocol is said to be fair if and only if the buyer pays, and he will be guaranteed to receive the correct data. While a few fair exchange protocols based on smart contracts have been proposed, the DPFDT has the following advantages: (1) Adding privacy-preserving into the system, including the privacy of both parties’ identities in trading and the privacy of the buyers’ needs; (2) Authorizing buyers the right to specify the order. That is, sellers will be asked in the order specified by the buyer; (3) Introducing a paid trial stage in the trading process, the loss of paying for unwanted goods will be minimized. Besides, security analysis shows that the proposed scheme has transaction security and trade security. Finally, experiments are done on the Ethernet platform. It shows that the DPFDT is efficient and practical in real-life applications.
LTU Attacker for Membership Inference

We address the problem of defending predictive models, such as machine learning classifiers (Defender models), against membership inference attacks, in both the black-box and white-box setting, when the trainer and the trained model are publicly released. The Defender aims at optimizing a dual objective: utility and privacy. Privacy is evaluated with the membership prediction error of a so-called “Leave-Two-Unlabeled” LTU Attacker, having access to all of the Defender and Reserved data, except for the membership label of one sample from each, giving the strongest possible attack scenario. We prove that, under certain conditions, even a “naïve” LTU Attacker can achieve lower bounds on privacy loss with simple attack strategies, leading to concrete necessary conditions to protect privacy, including: preventing over-fitting and adding some amount of randomness. This attack is straightforward to implement against any model trainer, and we demonstrate its performance against MemGaurd. However, we also show that such a naïve LTU Attacker can fail to attack the privacy of models known to be vulnerable in the literature, demonstrating that knowledge must be complemented with strong attack strategies to turn the LTU Attacker into a powerful means of evaluating privacy. The LTU Attacker can incorporate any existing attack strategy to compute individual privacy scores for each training sample. Our experiments on the QMNIST, CIFAR-10, and Location-30 datasets validate our theoretical results and confirm the roles of over-fitting prevention and randomness in the algorithms to protect against privacy attacks.
Performance Evaluation of Enhancement Algorithm for Contrast Distorted Images

The digital cameras have played the crucial role for the revolutionary growth of digital platform. The digital cameras capture the high-resolution images. But still, it is prone to the noise that might be due to improper acquisition settings, light conditions or any other noise artifacts. In this paper, our work was focused to improve the contrast and preserving the brightness in digital images. Firstly, the illumination was estimation through 2D-discrete wavelet transform. From the obtained value, logarithmic scale was calculated. The logarithmic scale is used to expand the dark scales in the image. With the obtained scale parameter, adaptive gamma correction was implemented. The nature of gamma was automated through logarithmic scale for each input image. The experiments were conducted on the TID2008 database. In comparison to the published algorithms, our proposed method had proved to be effective. The performance of the proposed method was measured from peak SNR, absolute mean brightness error, image quality index and entropy.
Application of Quantum Algorithms for Network Protocols

Cryptographic processes hold immense power in securing data transmissions across the Internet in the modern age. Preparing for future hacking possibilities is deemed essential and crucial to combat data breaches and leaks. With the advent of quantum computing, a field capable of performing complex operations within a short time, breaking into the cryptographic system keys with sheer brute force is visibly possible. This paper aims to create a framework that runs post-quantum cryptographic algorithms shortlisted by NIST for Round 3 on the TLS protocol. The framework is built using Python with the help of LibOQS packages, programmed to work as an API, and invoked with the help of a web application. It allows cross-platform execution as it is contained in a Docker container. The established framework utilizes digital signature cryptography to verify the authenticity of a signed message and key encapsulation mechanism (KEM) for secure communication between client and server. The algorithms can be integrated with various internet-based applications like IoT, blockchain, but the results are demonstrated using a web application for this research.
Implicit optimal variational collaborative filtering

Abstract Recently, variational autoencoder (VAE), a model that supports Bayesian inference and variational posterior distribution approximation, has been used to address the prevailing challenge of inefficient modelling of non-linear user-item interactions in collaborative filtering (CF). However, the choices of prior distributions in these VAE-based CF models are inadequate to determine the underlying distributions, leading to skewed maximum likelihood estimation of model parameters. In this paper, we present an Implicit Optimal Variational autoencoder model for collaborative filtering (IOVA-CF) to alleviate this challenge. Specifically, IOVA-CF utilizes a novel implicit optimal prior, which aids in generating excellent latent representations. This implicit optimal prior (IoP) is an aggregated posterior prior (AP), that is, the posterior’s expectation of the data distribution. However, unlike previous AP, IoP utilizes the density ratio technique to compute the Kullback-Leibler (KL) divergence without explicitly capturing the aggregated posterior. Besides, we can calculate the KL divergence between the aggregated posterior and the inference network in a closed-form using the density ratio technique, making IoP an optimal prior for maximizing the objective function. Additionally, unlike earlier VAE-based recommendation models, IOVA-CF significantly alleviates the over-regularization issue. Furthermore, IOVA-CF can adequately capture the latent space’s uncertainty. Finally, empirical evaluations with several competitive baseline models on four (4) real-world datasets reveal the superior performance of IOVA-CF.
A Self-Supervised Representation Learning of Sentence Structure for Authorship Attribution

The syntactic structure of sentences in a document substantially informs about its authorial writing style. Sentence representation learning has been widely explored in recent years and it has been shown that it improves the generalization of different downstream tasks across many domains. Even though utilizing probing methods in several studies suggests that these learned contextual representations implicitly encode some amount of syntax, explicit syntactic information further improves the performance of deep neural models in the domain of authorship attribution. These observations have motivated us to investigate the explicit representation learning of syntactic structure of sentences. In this article, we propose a self-supervised framework for learning structural representations of sentences. The self-supervised network contains two components; a lexical sub-network and a syntactic sub-network which take the sequence of words and their corresponding structural labels as the input, respectively. Due to the n -to-1 mapping of words to their structural labels, each word will be embedded into a vector representation which mainly carries structural information. We evaluate the learned structural representations of sentences using different probing tasks, and subsequently utilize them in the authorship attribution task. Our experimental results indicate that the structural embeddings significantly improve the classification tasks when concatenated with the existing pre-trained word embeddings.
Global vertical absolute measurement for a large-aperture interferometer

In this paper, we proposed a novel absolute measurement method for a 300-mm-aperture vertical interferometer, which contains gravity-induced deformation. To maintain the precision, the method does not require the operation of reference flat. It only needs one flip and one rotation of testing flat to get the global surface. The structure of the paper is as follows. First, the algorithm was developed with variety reduction and iteration. Then, the gravity-induced deformation of flat was analysed by Finite Element Method (FEM). The iteration factor and rotational angle were optimised. The accuracy of simulation reached 1.6 × 10−4 nm. Furthermore, according to a comparative experiment, the proposed method (4.0 nm, RMS) was consistent with liquid method (3.9 nm, RMS). Finally, misalignment and rotation error were analyzed, which can be controlled for absolute measurement.
Double Granularity Relation Network with Self-criticism for Occluded Person Re-identification

Occluded person Re-identification is still a challenge. Most existing methods capture visible human parts based on external cues, such as human pose and semantic mask. In this paper, we propose a double granularity relation network with self-criticism to locate visible human parts. We learn the region-wise relation between part and whole and pixel-wise relation between pixel and whole. The relations find non-occluded human body parts and exclude noisy information. To guide the relation learning, we introduce two relation critic losses, which score the parts and maximize the performance by imposing higher weights on large parts and lower ones on small parts. We design the double branch model based on the proposed critic loss and evaluate it on the popular benchmarks. The experimental results show the superiority of our method, which achieves mAP of 51.0% and 75.4% respectively on Occluded-DukeMTMC and P-DukeMTMC-reID. Our codes are available at DRNC .
A Survey of Big Data Analytics Using Machine Learning Algorithms

Big data is information management system through the integration of various traditional data techniques. Big data usually contains high volume of personal and authenticated information which makes privacy as a major concern. To provide security and effective processing of collected data various techniques are evolved. Machine Learning (ML) is considered as one of the data technology which handles one of the central and hidden parts of collected data. Same like ML algorithm Deep Learning (DL) algorithm learn program automatically from the data it is considered to enhance the performance and security of the collected massive data. This paper reviewed security issues in big data and evaluated the performance of ML and DL in a critical environment. At first, this paper reviewed about the ML and DL algorithm. Next, the study focuses towards issues and challenges of ML and their remedies. Following, the study continues to investigate DL concepts in big data. At last, the study figures out methods adopted in recent research trends and conclude with a future scope.
EPR: a Neural Network for Automatic Feature Learning from Code for Defect Prediction

Software defect prediction plays a significant role in the software development cycle but suffers from many difficulties. In this paper we propose a novel deep learning model (including algorithms) called Extractor, Parser, and Reviewer (EPR) for defect prediction in software. Two different networks, recurrent neural networks (RNNs) and one-dimensional convolutional networks(ODCNs), are employed by the EPR for different purposes. RNN is utilized to extract contextual features to represent semantic dependencies between code tokens and ODCN acts as a parser to establish dependencies between semantic features. Meanwhile, the attention mechanism of the two networks is used as a reviewer to assign different weights from location information to the importance of the features, respectively. Our proposed model is validated by the PROMISE repository, and the results show that the proposed model in this paper significantly outperforms several existing algorithms.
SETOKEN:A secure protection mechanism based on service API for 5G network access token

The introduction of new technologies, new mechanisms, and new architectures in 5G mobile communication system has made higher data rate, greater user capacity, and richer service provision possible. However, exploiting the reusability of tokens, malicious attacker can launch man-in-the-middle attack to obtain service illegally. In this paper, we propose SETOKEN, a 5G access token security enhancement mechanism. We grant the CA certificate to a trusted network function repository (NRF) and add digital signature, random factor and serial number in the token to defend against attacks. Moreover, we have optimized the usage of tokens in terms of efficiency. Evaluation results show the security and effectiveness of SETOKEN. Besides, the overheads introduced by SEAPP is acceptable.
Toward an Adaptive Skip-Gram Model for Network Representation Learning

The random walk process on network data is a widely-used approach for network representation learning.However, we argue that the sampling of node sequences and the subsampling for the Skip-gram's contexts have two drawbacks.One is less possible to precisely find the most correlated context nodes for every central node with only uniform graph search.The other is not easily controlled due to the expensive cost of hyperparameter tuning.Such two drawbacks lead to higher training cost and lower accuracy due to abundant and irrelevant samples.To solve these problems, we compute the adaptive probability of random walk based on Personalized PageRank (PPR), and propose an Adaptive SKip-gram (ASK) model without using complicated sampling process and negative sampling.We utilize k-most important neighbors for positive samples selection, and attach their corresponding PPR probability into the objective function.Based on benchmark datasets with three citation networks and three social networks, we demonstrate the improvement of our ASK model for network representation learning in tasks of link prediction, node classification, and embedding visualization.The results achieve more effective performance and efficient learning time.
Generative Adversarial Network-Based Visual-Aware Interactive Fashion Design Framework

Fashion image generation is the task of generating realistic fashion images from a real dataset distribution. Due to the subjectivity of design, fashion industries have always been striving to meet customers' needs. Although image generation techniques have become more advanced through time, the results are prone to visual inconsistencies, enormous artifacts, uncontrolled generation, and poor quality at large. This study aims to scale up the image generation process by integrating into the GAN for multiple fashion attributes such as color, shape, and texture to existing architectures. To accomplish this, 12,000 Ethiopian fashion images were collected from different sources. As deep learning is a data-intensive approach, image augmentation was used to enlarge the dataset to 90,000. Standard pre-processing was applied to normalize inputs to a common scale, compute average color, create a binary segmented mask, and label the dataset. The conditional inputs added in the proposed architecture are an average color, a segmented binary mask, and a 512-texture dimension organized as TensorFlow records fed to the existing progressive growing GAN generators. The discriminator was assigned to estimate the average color and classify the generated images. Besides, two experiments were done using the same dataset and training configuration, namely StyleGAN and its conditional version. Improved results were obtained from the three experiments with the evaluation metrics Fréchet inception distance, and perceptual path length 41 and 1500, respectively. Moreover, the human evaluation user study was performed to assess the user capability on identifying real and generated images and to examine the closeness of these images by using Google Forms in both paired and unpaired evaluation settings with the confusion rates of 46% and 47.6%, respectively. In general, the performance evaluation of the implemented conditional progressive growing generative adversarial network along with multiple conditional inputs shows an improved result was achieved. As a result, fashion design could be generated using such a GAN method.
Application of Particle Filter for Passive Underwater Bearing only Tracking

In underwater warfare and surveillance, tracking an object as accurately as possible is an important task. For this purpose, researchers are more inclined towards the passive mode of operation of Sonar. In this paper, the estimation of the object path with the help of Bearing-only tracking (BOT) is presented. The target parameters such as its course, speed and position are estimated with the help of BOT and this process is known as Target Motion Analysis (TMA). The Bearing angle is a non-linear measurement, and it requires an efficient algorithm to perform the estimation accurately. In this paper, the comparison of the Unscented Kalman filter (UKF) combined with Particle Filter (PF) and the UKF alone is performed. A simulator is generated to test these algorithms and to estimate the target path with the help of Matlab. The performance analysis of these two methods to estimate the target path is presented.
Electronic Payments based on Blockchain Technology. A Bibliometric Review

The conventional electronic payment systems are facing challenges on ensuring, safety, trust and accuracy. These issues can be addressed in blockchain technology. The bibliometric patterns of research on blockchain technology and electronic payments shows the domination of China, Chinese authors and China based Universities in the research. China was followed by United States of America. The leading journals were IEEE Access and the Lecture Notes in Computer Science. The future research can be on widening the application of blockchain technology in electronic payments and future researchers can concentrate the documents and resources from China and United States of America for updating knowledge and for research collaborations.
Dog Identification System Using Nose Print Biometrics

Advancement in technology has improved identification systems by integrating biometrics as a form of reliable authentication. Besides human biometrics, biomarkers are also present in nature and throughout various species of animals. Dogs are known to have unique patterns on their nose analogous to human fingerprints. This paper reports developing and implementing a system designed to recognize dogs with their nose prints as biometrics. The identification system employs various image processing techniques developed in past research papers. You Only Look Once (YOLO), and Scale-Invariant Feature Transform (SIFT) extract an image’s unique vital points and descriptors. These unique descriptors, along with the dog’s information, are saved for future matching. The matching is done with the Fast Library of Approximate Nearest Neighbor (FLANN). The system was able to identify 18 dogs out of 20 correctly. A total of 20 comparisons were made, making the overall accuracy of the system 90%.