Reviewerly/exHarmony huggingface dataset:
Recommendation of Compatible Outfits Conditioned on Style

Recommendation in the fashion domain has seen a recent surge in research in various areas, for example, shop-the-look, context-aware outfit creation, personalizing outfit creation, etc. The majority of state of the art approaches in the domain of outfit recommendation pursue to improve compatibility among items so as to produce high quality outfits. Some recent works have realized that style is an important factor in fashion and have incorporated it in compatibility learning and outfit generation. These methods often depend on the availability of fine-grained product categories or the presence of rich item attributes (e.g., long-skirt, mini-skirt, etc.). In this work, we aim to generate outfits conditional on styles or themes as one would dress in real life, operating under the practical assumption that each item is mapped to a high level category as driven by the taxonomy of an online portal, like outdoor, formal etc. and an image. We use a novel style encoder network that renders outfit styles in a smooth latent space. We present an extensive analysis of different aspects of our method and demonstrate its superiority over existing state of the art baselines through rigorous experiments.
Aligning Internal Regularity and External Influence of Multi-granularity for Temporal Knowledge Graph Embedding

Representation learning for the Temporal Knowledge Graphs (TKGs) is an emerging topic in the knowledge reasoning community. Existing methods consider the internal and external influence at either element level or fact level. However, the multi-granularity information is essential for TKG modeling and the connection in between is also under-explored. In this paper, we propose the method that Aligning-internal Regularity and external Influence of Multi-granularity for Temporal knowledge graph Embedding (ARIM-TE). In particular, to prepare considerate source information for alignment, ARIM-TE first models element-level information via the addition between internal regularity and the external influence. Based on the element-level information, the merge gate is introduced to model the fact-level information by combining their internal regularity including the local and global influence with external random perturbation. Finally, according to the above obtained multi-granular information of rich features, ARIM-TE conducts alignment for them in both structure and semantics. Experimental results show that ARIM-TE outperforms current state-of-the-art KGE models on several TKG link prediction benchmarks.
Intelligent Deep Transfer Learning Based Malaria Parasite Detection and Classification Model Using Biomedical Image

Malaria is a severe disease caused by Plasmodium parasites, which can be detected through blood smear images.The early identification of the disease can effectively reduce the severity rate.Deep learning (DL) models can be widely employed to analyze biomedical images, thereby minimizing the misclassification rate.With this objective, this study developed an intelligent deep-transfer-learning-based malaria parasite detection and classification (IDTL-MPDC) model on blood smear images.The proposed IDTL-MPDC technique aims to effectively determine the presence of malarial parasites in blood smear images.In addition, the IDTL-MPDC technique derives median filtering (MF) as a pre-processing step.In addition, a residual neural network (Res2Net) model was employed for the extraction of feature vectors, and its hyperparameters were optimally adjusted using the differential evolution (DE) algorithm.The k-nearest neighbor (KNN) classifier was used to assign appropriate classes to the blood smear images.The optimal selection of Res2Net hyperparameters by the DE model helps achieve enhanced classification outcomes.A wide range of simulation analyses of the IDTL-MPDC technique are carried out using a benchmark dataset, and its performance seems to be highly accurate (95.86%), highly sensitive (95.82%), highly specific (95.98%), with a high F1 score (95.69%), and high precision (95.86%), and it has been proven to be better than the other existing methods.
Recent Advancement in Fine-Grained Access Control and Secure Data Sharing Scheme for Distributed Environment

The demand for Big Data also increases with the increasing rate of usage of Internet. The storage, usage, and management of Big Data are the biggest challenge in today’s world. Cloud computing and fog computing are new eras of IT industries which provide various computing services, distributed storage and resources over the internet which is available any-time-anywhere in the world using Internet but still, the security and privacy of these data is the major concern for business enterprises. The data shared among different users over distributed storage must be configured with fine-grained access control mechanisms. The distributed platform can use various encryption standards like DES, AES, Blowfish, Elliptic curve, RSA, etc. but they only offer confidentiality and integrity but for the fine-grained access control policies the mechanisms like identity-based encryption and attribute-based encryption algorithm are the best possible solution for authentication and confidentiality. Another option is homomorphic encryption which allows the processing of encrypted data. In this paper, the identity-based encryption, attribute-based encryption, and homomorphic encryption techniques are analyzed with their use cases and limitations which can be useful in the field of Industrial IoT, Industrial cloud, Big Data generated manufacturing industries, etc.
Non-Autoregressive Fully Parallel Deep Convolutional Neural Speech Synthesis

Deep learning-based speech synthesis evolves by employing a sequence-to-sequence (seq2seq) structure with an attention mechanism. The seq2seq speech synthesis model consists of a pair of the encoder for delivering the linguistic features and the decoder for predicting the mel-spectrogram, and learns the alignment between text and speech through the attention mechanism. The decoder predicts the mel-spectrogram by an autoregressive flow that considers the current input and what they have learned from previous inputs. This is beneficial when processing the sequential data, as in speech synthesis. However, the recursive generation of speech typically requires extensive training time, which slows the speed of synthesis. To overcome these obstacles, we propose a non-autoregressive framework for fully parallel deep convolutional neural speech synthesis. Firstly, we design a new synthesis paradigm that integrates a time-varying metatemplate (TVMT), whose length is modeled with a separate conditional distribution, to prepare the decoder input. The decoding step converts the TVMT into spectral features, which eliminates the autoregressive flow. Secondly, we propose a structure that uses multiple decoders interconnected by up-down chains with an iterative attention mechanism. The decoder chains distribute the burden of decoding, progressively infusing the information obtained from the training target example into the chains to refine the predicted spectral features at each decoding step. For each decoder, the attention mechanism is repeatedly applied to produce the elaborated alignment between the linguistic features and the TVMT, which is gradually transformed into the spectral features. The proposed architecture substantially improves the synthesis speed, and the resulting speech quality is superior to that of a conventional autoregressive model.
Enhancing the web image search results through direct and indirect relevance model

Due to the Internet revolution, search engines have become an inevitable part of our everyday life. However, retrieving required information in the form of text and images is still a challenging task. Unlike extracting text content from the web, extracting the images and ranking them based on the query is even more difficult as the number of images on the web is increasing day by day due to social media sites. Several methods exist in fetching the relevant images from the web. However, most of the methods are ineffective in retrieving relevant images as it involves outliers due to polysemy problem. This paper presents the framework for improving the performance of the search engine concerning the web image search results. The method utilizes direct as well as indirect relevance score computation for the images using text based and context based information. The direct relevance score computation is carried out based on the given query and the text representation of the images whereas, indirect relevance score computation uses the topmost relevant topics exist in the text documents and the relevant topics exist in the contextual content of an image thereby eliminating the duplicate images. Based on the experiments performed, the proposed method improves the results of the image search significantly with proper re-ranking of images. The overall increase in the precision for the proposed model than other models under comparison is around 5% to 10%.
Power- and QoS-Aware Job Assignment With Dynamic Speed Scaling for Cloud Data Center Computing

As the usage of mission-critical mobile applications increases in Industry 4.0, such as smart manufacturing and self-driving cars, the cloud computing paradigm and its supporting data centers have become more crucial.However, a common practice in the cloud data center computing industry tends to supply a surfeit of computing resources mainly for a robust quality-of-service (QoS).In this paper, we propose a simple real-time algorithm which combines a power-aware job assignment policy for a centralized job dispatcher and a power-and QoS-aware dynamic speed scaling policy for each physical machine (PM).The job assignment policy is called ''Join the Least Power Consuming (LPC) Server'' that routes an incoming cloud job to a server spending minimum power upon request.The server-side adaptive speed scaling policy expedites energy efficiency and satisfies response time-associated QoS condition.We call this policy ''Minimizing Earliness (ME)'' since it manages the server speed towards finishing jobs at their deadlines as precisely as possible, reducing the earliness of job completion.The design principle of LPC-ME combination supports both energy efficiency and service quality required in cloud data centers.Numerical experiments compare the proposed algorithm's power consumption and response time with those of existing popular policies and demonstrate better energy efficiency with negligible degradation of service quality.
Hazard Integrated: Understanding Security Risks in App Extensions to Team Chat Systems

Team Chat (TACT) systems are now widely used for online collaborations and project management.A unique feature of these systems is their integration of third-party apps, which extends their capabilities but also brings in the complexity that could potentially put the TACT system and its end-users at risk.In this paper, for the first time, we demonstrate that thirdparty apps in TACT systems indeed open the door to new security risks, such as privilege escalation, deception, and privacy leakage.We studied 12 popular TACT systems, following the key steps of a third-party app's life cycle (its installation, update, configuration, and runtime operations).Notably, we designed and implemented a pipeline for efficiently identifying the security risks of TA APIs, a core feature provided for system-app communication.Our study leads to the discovery of 55 security issues across the 12 platforms, with 25 in the install and configuration stages and 30 vulnerable (or risky) APIs.These security weaknesses are mostly introduced by improper design, lack of fine-grained access control, and ambiguous data-access policies.We reported our findings to all related parties, and 8 have been acknowledged.Although we are still working with the TACT vendors to determine the security impacts of the remaining flaws, their significance has already been confirmed by our user study, which further reveals users' concerns about some security policies implemented on mainstream TACT platforms and their misconceptions about the protection in place.Also, our communication with the vendors indicates that their threat models have not been well-thought-out, with some assumptions conflicting with each other.We further provide suggestions to enhance the security quality of today's TACT systems.
Spam Reviews Detection in the Time of COVID-19 Pandemic: Background, Definitions, Methods and Literature Analysis

During the recent COVID-19 pandemic, people were forced to stay at home to protect their own and others’ lives. As a result, remote technology is being considered more in all aspects of life. One important example of this is online reviews, where the number of reviews increased promptly in the last two years according to Statista and Rize reports. People started to depend more on these reviews as a result of the mandatory physical distance employed in all countries. With no one speaking to about products and services feedback. Reading and posting online reviews becomes an important part of discussion and decision-making, especially for individuals and organizations. However, the growth of online reviews usage also provoked an increase in spam reviews. Spam reviews can be identified as fraud, malicious and fake reviews written for the purpose of profit or publicity. A number of spam detection methods have been proposed to solve this problem. As part of this study, we outline the concepts and detection methods of spam reviews, along with their implications in the environment of online reviews. The study addresses all the spam reviews detection studies for the years 2020 and 2021. In other words, we analyze and examine all works presented during the COVID-19 situation. Then, highlight the differences between the works before and after the pandemic in terms of reviews behavior and research findings. Furthermore, nine different detection approaches have been classified in order to investigate their specific advantages, limitations, and ways to improve their performance. Additionally, a literature analysis, discussion, and future directions were also presented.
Accelerating Federated Edge Learning via Topology Optimization

Federated edge learning (FEEL) is envisioned as a promising paradigm to achieve privacy-preserving distributed learning. However, it consumes excessive learning time due to the existence of straggler devices. In this article, a novel topology-optimized FEEL (TOFEL) scheme is proposed to tackle the heterogeneity issue in federated learning and to improve the communication-and-computation efficiency. Specifically, a problem of jointly optimizing the aggregation topology and computing speed is formulated to minimize the weighted summation of energy consumption and latency. To solve the mixed-integer nonlinear problem, we propose a novel solution method of penalty-based successive convex approximation (SCA), which converges to a stationary point of the primal problem under mild conditions. To facilitate real-time decision making, an imitation-learning-based method is developed, where deep neural networks (DNNs) are trained offline to mimic the penalty-based method, and the trained imitation DNNs are deployed at the edge devices for online inference. Thereby, an efficient imitation-learning-based approach is seamlessly integrated into the TOFEL framework. Simulation results demonstrate that the proposed TOFEL scheme accelerates the federated learning process and achieves a higher energy efficiency. Moreover, we apply the scheme to 3-D object detection with multivehicle point cloud data sets in the CARLA simulator. The results confirm the superior learning performance of the TOFEL scheme over conventional designs with the same resource and deadline constraints.
A recommendation prediction method based on the estimation of PSD of sampled signals on graph

In recommendation system, the rating prediction is a very challenging problem. In this paper, we model users (items) and their relationships as a weighted undirected graph, and propose a rating prediction model based on graph signal reconstruction. Different from the existing reconstruction model, the proposed model takes into account the frequency structure of the signal itself, and tends to protect the frequency contents with higher power spectral density (PSD) and impose large penalties on those with lower PSD when reconstructing a signal. Furthermore, based on the assumption of the stationarity of the graph signal, we propose a method to estimate the PSD of a stationarity stochastic graph signal directly from some sampled signals without its any other complete implementation. Related theoretical analysis is also given. Finally, a rating prediction algorithm based on the graph signal reconstruction is derived. Experimental results show that the proposed model leads a significant improvement of the predictive accuracy.
Self-supervised stereo depth estimation based on bi-directional pixel-movement learning

Stereo depth estimation is an efficient method to perceive three-dimensional structures in real scenes. In this paper, we propose a novel self-supervised method, to the best of our knowledge, to extract depth information by learning bi-directional pixel movement with convolutional neural networks (CNNs). Given left and right views, we use CNNs to learn the task of middle-view synthesis for perceiving bi-directional pixel movement from left-right views to the middle view. The information of pixel movement will be stored in the features after CNNs are trained. Then we use several convolutional layers to extract the information of pixel movement for estimating a depth map of the given scene. Experiments show that our proposed method can significantly provide a high-quality depth map using only a color image as a supervisory signal.
Threat model and security analysis of video conferencing systems as a communication paradigm during the COVID-19 pandemic

During the age of the COVID-19 pandemic, video conferencing and collaborative tools have seen an enormous demand. This technology allows us to have real-time interaction during this social distancing era, primarily to facilitate remote working, file sharing, and social interaction. While video conferencing technology is not new, it has emerged as a new paradigm of communication. As video conferencing becomes commonplace and people increasingly rely on it, meetings are becoming a target for cyberattackers. Enforcing appropriate security and privacy settings prevents attackers from exploiting the system. Threat modeling optimizes security by identifying objectives, vulnerabilities and defining the plan to mitigate or prevent potential threats to the system. In this paper, we use the widely accepted STRIDE threat modeling technique to identify all possible risks to video conferencing tools and suggest mitigation strategies for creating a safe and secure system.
Assessing the Share of the Artificial Ad-Related Traffic: Some General Observations

Online advertising campaigns are adversely affected by bot traffic. In this paper, we develop and test a method for the estimation of its share, which is necessary for the evaluation of campaign efficiency. First, we present the nature of the problem as well as the underlying business rationale. Next, we describe the essential features of Internet traffic, which ought to be accounted for, and the potential methodologies, which can be used to reach the objective of the project. Finally, some of the results are provided, along with the respective discussion, followed by both technical and also more general conclusions.
Context Availability and Sentence Availability Ratings for 3,000 English Words and their Association with Lexical Processing

Words that can be easily placed in contexts are more easily processed, yet norms for context availability are limited. Here, participants rated 3,000 words for context availability and sentence availability, a new metric predicted to capture information relating to textual variation. Both variables were investigated alongside other word-level characteristics to explore lexical-semantic space. Analyses demonstrated that context availability and sentence availability are distinct. Context availability covaries with concreteness and imageability, while sentence availability captures information relating to contextual variation, frequency and ambiguity. Analyses of megastudy data showed that both context availability and sentence availability uniquely facilitated lexical decision performance.
An Empirical Investigation of the Underlying Cognitive Process in Complex Problem Solving

Meetings are one of the most common collaboration formats for complex problem-solving (CPS). This research aims to formulate cognitive-oriented guidelines for productive synchronous CPS discussions. The study proposes a method to analyze the cognitive process and identifies the cognitive process associated with better CPS discussions. A conversation-analysis method was developed. Two indicators—source–outcome retrieval ratio and count of overlapped solution utterances—were proposed to evaluate the CPS discussion’s efficiency and effectiveness. Sixteen experimental CPS discussions were analyzed using this method. Correlation coefficients were applied to ascertain the cognitive features in CPS discussions with different levels of effectiveness and confirmed the applicability and reliability of the proposed methods. The results revealed that a good CPS discussion includes a regular progress summary, discussion conclusion, and high utilization of cognitive sources.
Human Fall Detection Model with Lightweight Network and Tracking in Video

In order to real time and accurately detect the action of human falling, combined with lightweight detection network, Kalman filter tracking, posture estimation network and spatiotemporal graph convolutional network, a joint algorithm for human fall detection in video is proposed. Firstly, the lightweight YOLOv3-Tiny algorithm is used to locate the frame of human in video, which can quickly detect the human-frame; among them, for the situation that the human body is likely to be missed in video, the Kalman filter tracking algorithm is integrated into the stage of target-detection and the accuracy of detecting is improved. Secondly, the human-frame detected or tracked in video is sent to the AlphaPose network to estimate the posture graph about human body. Finally, the spatiotemporal graph convolutional network is exploited to extract the spatiotemporal features of the human body, and eventually the result for classification is output. Experimental results show that the algorithm proposed in this paper, which is more appealing and successful than the other algorithm.
Visual Reasoning for Uncertainty in Spatio-Temporal Events of Historical Figures

The development of digitized humanity information provides a new perspective on data-oriented studies of history. Many previous studies have ignored uncertainty in the exploration of historical figures and events, which has limited the capability of researchers to capture complex processes associated with historical phenomena. We propose a visual reasoning system to support visual reasoning of uncertainty associated with spatio-temporal events of historical figures based on data from the China Biographical Database Project. We build a knowledge graph of entities extracted from a historical database to capture uncertainty generated by missing data and error. The proposed system uses an overview of chronology, a map view, and an interpersonal relation matrix to describe and analyse heterogeneous information of events. The system also includes uncertainty visualization to identify uncertain events with missing or imprecise spatio-temporal information. Results from case studies and expert evaluations suggest that the visual reasoning system is able to quantify and reduce uncertainty generated by the data.