Reviewerly/exHarmony huggingface dataset:
Efficient Private Set Intersection Based on Functional Encryption

Private Set Intersection (PSI) is a protocol that allows two parties (sender and receiver) to communicate and calculate the intersection of their sets without revealing any other information about their sets. In a typical case, PSI protocol runs on the unbalanced setting where the size of one party's set far exceeds that of the other. In this paper, we focus on the scenario of the receiver having a more extensive set with more substantial arithmetic power, which is essential in disease close contact tracing. In contrast, most of the current work focuses on the opposite scenario. We propose a PSI protocol that can compute efficiently in the unbalanced setting, and the communication only increases with the size of the smaller set owned by the sender. Specifically, to implement the PSI protocol, we construct a functional encryption scheme for the equivalence testing function. Then use cuckoo hash to optimize the comparison operation between elements. The benchmarks show that the PSI protocol takes 46 seconds and 20MB communication cost to compute the intersection with 16 million elements in the receiver's set and 5,000 elements in the sender's set. It achieves 1.93 × reduction in communication cost and 2.57 × reduction in computation cost compared to prior work.
Designing for Diverse Bodies: Toward a Constructionist Perspective

The concept of embodiment and the human body have been important subjects in various fields including human-computer interaction (HCI). The quality of embodied experiences shapes the ways how we live and act and is closely linked to well-being. While bodily experiences and interactions have been widely explored in HCI, the differences among bodies have not been a focus in the field. We argue that how we perceive and manage the differences of bodies affect the quality of embodied experiences of both ourselves and others. Therefore, we feel the urge to conduct a systematic study to establish a framework of designing for the diversity of bodies. We take the social constructionist perspective which asserts that bodies are shaped in the context of culture, society, and history. To understand the influences of the socio-cultural forces, an identity-based approach is adopted to analyse the differences among bodies. Subsequently, we explore how multimodal technologies could contribute to deconstructing and reconstructing bodies. In particular, we investigate the possibilities of three types of technological bodies which are classified based on the ways how the physical human bodies are involved and interacted with. These technological bodies (cyborg bodies, hybrid bodies, cyber bodies) are examined and illustrated with existing design cases.
A Prediction-based Online Cost Optimization Algorithm for 5G Vertical

End-to-end network slicing in 5G enables new business models and use cases across all industry verticals. Network slicing is an efficient solution that fulfills the diverse business requirements, characterized by a Service Level Agreement (SLA). 5G service providers offer the network slices through various plans differing in leasing period, allocated resources, and price. With uncertain future demand, it is challenging for businesses to select a cost-effective plan that supports the traffic. In this work, we propose a prediction-based online algorithm that minimizes the cost through plan selection for different applications in industry verticals. First, the future traffic demand is predicted using the Recurrent Neural Network (RNN)-based Long Short-Term Memory (LSTM) model, which continuously learns and adapts to the dynamic requirements with higher prediction accuracy. The traffic prediction model estimates the future demand, and the proposed algorithm leverages it for plan selection. The problem is formulated as an Integer Linear Program (ILP), which provides an offline optimal solution of the problem. Results from extensive simulations, with real-world datasets, illustrate that the proposed algorithm reduces the best worst-case expected Competitive Ratio (CR) by 20% over randomized ski-rental algorithm and 37% over deterministic algorithm.
HiFi-GAN based Text-to-Speech Synthesis in Serbian

In this paper we present a deep neural network based text-to-speech system in the Serbian language, which converts generated acoustic features into a speech signal using the HiFi-GAN vocoder. The HiFi-GAN model was fine-tuned using an existing multi-speaker model trained on an English speech corpus. To overcome the problem of inadequate training data, we introduce a data generation technique based on a guided acoustic neural network, which attempts to minimize the mismatch between data used in HiFi-GAN training and inference. The outputs of the acoustic network are intended to represent a trade-off between original feature trajectories and trajectories generated by the standard text-to-speech system. The results of subjective evaluation through listening tests show that the proposed system produces speech whose quality significantly surpasses the quality of speech generated by the best existing speech synthesis for Serbian, and that its MOS score is very close to the score given to natural speech.
Automatic Counting Passenger System Using Online Visual Appearance Multi-Object Tracking

In recent years, people-counting problems have increased in popularity, especially in crowded indoor spaces, e.g., public transport.In peak hours, trains move significant numbers of passengers, producing delays and inconveniences for their users.Therefore, analysing how people use public transport is essential to solving this problem.The current analysis estimates how many people are inside a train station by using the number of people entering and leaving the ticket gates or estimating the train occupancy based on conventional CCTV cameras.However, this information is insufficient for knowing the train occupancy.The required data includes vehicle usage: how many people enter or leave a vehicle or which door is the most used.This paper presents a solution to the stated problem based on a multi-object tracker with a sequential visual appearance predictor and a line-based counting strategy to analyse each passenger's trajectory using an overhead fisheye camera.The camera selection inside the train was made after profoundly studying the railway environment.The method proposes a module to compute the total train occupancy.The solution is robust against occlusions thanks to the selected tracker and the fisheye camera field of view.This work shows a proof of concept dataset containing pseudo-real case scenarios of people's affluence in train doors recorded by fisheye cameras.Its purpose is to prove the system's functionality in these scenarios.The proposed approach achieved an overall accuracy of counting people getting on and off of 90.78% in the pseudo-real dataset, proving that this approach is valid.
Research on Mask Wearing Detection Based on Faster RCNN

In the context of the global raging of the new coronavirus (COVID-19), to effectively prevent the spread of the new coronavirus in the crowd, many places require the wearing of masks in public places. In response to this problem, this paper proposes a mask wearing detection based on the FasterRCNN algorithm. The method uses ResNet-50 to extract convolution features and selects high-quality suggestion boxes through NMS (non-maximum suppression), which increases the detection of incorrectly wearing masks, which can play a reminder role in practical applications and further improve the prevention of epidemics, and the final experiments show that the wearing of masks can be accurately and efficiently detected through the steps of feature extraction and prediction frame generation.
A Multi-domain Sentiment Classification model based on Adversarial Multi-task learning and attention mechanisms

Introducing a multi-task learning framework into the model can improve the performance of emotion classification, but the existing multi-task learning model often ignores the potential correlation between multi-domain task sets, and has the disadvantage of mutual interference between shared feature space and private feature space. Therefore, we propose a multi-domain sentiment classification model based on adversarial multi-task learning framework and attention mechanism (MD-AMT-AT). Firstly, the attention mechanism is used for feature learning of multi-domain task sets, and the emotional information of important words is obtained by calculating the attention score between sentences, so as to obtain cross-domain feature labels, that is, the feature with the maximum weight of each sentence. Secondly, private encoders and shared encoders based on LSTM and one-dimensional convolutional neural networks are constructed. Antagonistic training is introduced in multi-task to enhance shared space representation and further improve classification performance. Finally, experiments are carried out on English data sets in six fields, and the results show that the model achieves better results than the single-task learning method and the traditional multitask learning method in terms of accuracy and F1 value evaluation index.
Multi-agent Path Finding and Acting with Small Reflex-Based Mobile Robots

Multi-agent pathfinding (MAPF) represents a core problem in robotics. In its abstract form, the task is to navigate agents in an undirected graph to individual goal vertices so that conflicts between agents do not occur. Many algorithms for finding feasible or optimal solutions have been devised. We focus on the execution of MAPF solutions with a swarm of simple physical robots. Such execution is important for understanding how abstract plans can be transferred into reality and vital for educational demonstrations. We show how to use a swarm of reflex-based Ozobot Evo robots for MAPF execution. We emulate centralized control of the robots using their reflex-based behavior by putting them on a screen’s surface, where control curves are drawn in real-time during the execution. In this way, we bridge the gap between simple discrete planning phase and continuous acting phase. We identify critical challenges and ways to address them to execute plans successfully with the swarm. The MAPF execution was evaluated experimentally on various benchmarks. We also compare our new reflex-based execution with compilation-based approach.
Intelligent Regulation Method of University Heating Water Flow Based on Adaptive Control Algorithm

With the increasing requirements of energy saving and indoor environment, variable flow technology is widely used in heating system, and the adjustment of resistance is the throttle adjustment of valve. However, the economic loss of this adjustment method is too great. Therefore, a new intelligent adjustment method of university heating water flow is designed based on adaptive control algorithm. First, a single loop network is connected. Secondly, an intelligent adjustment system of university heating water flow is designed based on adaptive control algorithm. Experiments show that the designed intelligent adjustment of heating water flow is achieved.
Switching Reference Image-Based Visual Servoing of Quadrotor for Homing and Docking on the Wall

Quadrotor operates on a limited amount of battery capacity. To overcome this limitation, docking is required so that the quadrotor is able to recharge the battery to full capacity. One of the ways to achieve docking is by means of visual servoing. Some of the commonly used visual servoing methods are position-based visual servoing (PBVS) and image-based visual servoing (IBVS). However, these methods stop working when there is no resemblance of target features between acquired image and desired image. Such problem arises when the target features from acquired image are out of image plane or disoriented due to quadrotor’s skewed position or appeared to be disfigured due to bad weather conditions. To solve this problem, a switching reference IBVS method is proposed in this study. The underlying concept of the proposed method is to switch the desired target features to match the currently acquired target features. During simulation, the proposed method is robust under the effect of missing target features.
Publicly Verifiable Conjunctive Keyword Search with Balanced Verification Overhead

Verifiable conjunctive search in Searchable Symmetric Encryption (VSSE) allows a client to search multiple keywords with sublinear computation overhead and ensures the integrity of search results. However, the state-of-the-art VSSE schemes are either probabilistically verifiable or have significant communication overhead. In this paper, we propose an efficient VSSE scheme that achieves a balance between communication and computation overheads. Our scheme supports public and private verification for different scenarios by utilizing RSA accumulator and has constant-size public parameters. Finally, we prove that our scheme is secure against malicious servers and conclude that our scheme is efficient by comparing and analyzing the performance.
NCRL: Neighborhood-Based Collaborative Residual Learning for Adaptive QoS Prediction

How to accurately predict vacant QoS has become a fundamental issue for service-oriented downstream tasks. However, most QoS prediction approaches based on model learning fail to discriminatively capture the latent feature representations of a user and a service, since they either leverage the shallow neural network such as MLP or take advantage of insufficient location information. Moreover, collaborative relationships of similar neighborhood have not been fully taken into account together with prediction model learning. To address these issues, we propose a novel framework for adaptive QoS prediction named Neighborhood-based Collaborative Residual Learning (NCRL). Location-aware two-tower deep residual network is designed to achieve neural QoS prediction by extracting latent features of users and services, which are fed to generate similar neighborhood for collaborative prediction based on historical QoS invocations. They are integrally combined to perform adaptive QoS prediction. Extensive experiments are conducted based on a large-scale real-world QoS dataset called WS-DREAM with almost 2,000,000 historical QoS invocations. The results indicate that NCRL can remarkably outperform state-of-the-art competing baselines.
Contrastive Monotonic Pixel-Level Modulation

Continuous one-to-many mapping is a less investigated yet important task in both low-level visions and neural image translation. In this paper, we present a new formulation called MonoPix, an unsupervised and contrastive continuous modulation model, and take a step further to enable a pixel-level spatial control which is critical but can not be properly handled previously. The key feature of this work is to model the monotonicity between controlling signals and the domain discriminator with a novel contrastive modulation framework and corresponding monotonicity constraints. We have also introduced a selective inference strategy with logarithmic approximation complexity and support fast domain adaptations. The state-of-the-art performance is validated on a variety of continuous mapping tasks, including AFHQ cat-dog and Yosemite summer-winter translation. The introduced approach also helps to provide a new solution for many low-level tasks like low-light enhancement and natural noise generation, which is beyond the long-established practice of one-to-one training and inference. Code is available at https://github.com/lukun199/MonoPix .
V-Net architecture based advanced visually meaningful image encryption technique

The visually meaningful image encryption (VMIE) technique is used to offer additional security to the noise-like encrypted image by implanting the secret image in the cover image. Generally, VMIE techniques follow two steps: pre-encryption and embedding. In this paper, an advanced V-Net architecture based VMIE technique is presented. V-Net architecture is based on a Convolution Neural Network (CNN) and offered medical image segmentation, that is utilized in the image into image steganography in the proposed work. In the proposed VMIE technique, the secret image is encrypted by utilizing the 5D chaotic map to provide high security, and the encrypted secret image is implanted into the cover image using a V-Net based embedding method. CNN-based image embedding methods offer high payload capacity and imperceptibility. In these methods, the encoder hides the secret image into the cover and the decoder reconstruct the secret image. Furthermore, a CNN-based efficient decoder is designed in the proposed method. Moreover, to validate the proposed method, standard evaluation parameters are analyzed.
COLLECTIVE GAME ACTIVITY - THE WAY OF ACTIVATING THE PROFESSIONAL ACTIVITY OF STUDENTS OF PROFESSIONAL HIGHER AND PREVIOUS EDUCATION

Individualization of learning is considered as a principle of individual approach and as an educational technology based on the principle of individualization. Individualized learning involves the realization of the positive natural endowments of primary
LS-YOLO: Lightweight SAR Ship Targets Detection Based on Improved YOLOv5

At present, two main problems, which are the multi-scale of ship targets and the lightweight of detection models, restrict the real-time and on-orbit detection of ship targets on SAR images. To solve two problems, we propose a lightweight ship detection network (LS-YOLO) based on YOLOv5 model for SAR images. In the proposed network, we propose two modules, namely, Feature Refinement Module (FRM) and DCSP. The FRM module is designed to solve the multi-scale problem of ship targets in SAR images. This structure can effectively expand the receptive field of the model and improve the detection ability of small target ships. DCSP is lightweight module based on YOLOv5 CSP. This module effectively reduces model parameters and computation while keeping feature extraction ability as much as possible. The LS-YOLO detection speed is up to 1.2 ms, the accuracy (AP) is 96.6%, and the model size is only 3.8 MB. It can balance detection accuracy and detection speed, and provide reference for the construction of real-time detection network.
Development of Android-Based Virtual Lab in Oxygen Gas Synthesis Experiment

The development of technology in education can be used inlearning media such as making virtual labs in elemental chemistry. Virtual lab as a technology-based learning media in chemistry practicum can be used as a solution for universities that cannot carry out practical work directly. The virtual lab visualizes the practical process and the laboratory environment realistically to provide students with a better understanding. One of the elemental chemistries that is difficult for students to understand is the element oxygen; doing an oxygen gas synthesis practicum to help students understand the element. This study aims to describe the appearance, validation test results, and the results of the feasibility test using virtual oxygen gas synthesis lab media. The research method used is Design-Based Research (DBR) with an Analyze, Design, Development, Implementation, and Evaluation (ADDIE) approach. The virtual display of the oxygen synthesis practicum lab includes the main page, main menu (Materials, procedures, MSDS, and material tools), profiles, instructions for using media, practicum processes, and observations. The results of the validation test from this virtual lab have an average r-value of 0.73, and the presentation of the feasibility test results is 98.87%. This shows that the virtual lab of oxygen synthesis practicum is valid and suitable to be used as a learning medium if it is hampered by the direct implementation of the practicum.
FusionSeg: Motion Segmentation by Jointly Exploiting Frames and Events

Segmentation of independently moving objects is an important stage in scene comprehension tasks like tracking and recognition. Frame-based cameras employed for dynamic scenes suffer from motion blur and exposure artifacts due to the sampling principle. In contrast, event-based cameras sample visual information based on scene dynamics and have the advantages of microsecond temporal resolution, high dynamic range, and more. Inspired by the complimentary of frame-based cameras and event-based cameras, we propose a cross-domain motion segmentation method, named FusionSeg, for fusing visual signals from frames and events to improve motion segmentation performance. To solve motion segmentation problem on the multi-objects scenario, we use the identification mechanism to embed multiple objects into the same feature space. In addition, to solve the feature matching and propagation problem, we design a long and short-term temporal-spatial attention. Our FusionSeg is evaluated on public datasets and outperforms the state-of-the-art by 4.7% in terms of detection rate. Experiments also demonstrate our method’s robustness in situations with varying motion patterns and numbers of moving objects.
An Efficient Method for Secured Data Communication using Chaos Encryption Algorithm

To prevent the information theft from attackers there is a need of mechanism to secure the data. In order to successfully deal with image information theft, it must look for a improved encryption algorithm. Past few decades several algorithms were developed, due to the preponderation of chaos technology, when the image is encrypted with chaos technology, the ciphertext presents an arbitrariness, which makes the possible action of deciphering greatly abbreviated. In this paper, another methodology of Chaos encryption has been proposed. This methodology shows irregular conduct that can be used for secure encryption and proficient information transmission. The unbending nature of this methodology can be investigated and observed by execution factors for example, testing towards numerous image processing attacks, measurement of Peak signal to noise ratio, Mean square error values. Image with various scaling factors provide low MSE rate and high PSNR by utilizing this chaotic strategy. This strategy is demonstrated to be extremely successful compared to the other existing systems. The proposed methodology is adaptable and highly effective because of its affectability to secret key and appropriate for different applications such as military, legal and medical systems, overall visual check by offering simultaneous security functions.
Simulation of Named Entity Label Prediction Based on Combined Neural Network

The current software named entity recognition method ignores the prediction of named entity labels, and there is a low F1 value, and KS value. A method of software named entity recognition based on the combined neural network is proposed. This paper presents a software named entity label prediction method based on combined neural network. Based on combined neural network model, character and word feature extraction, and the word vector features are combined to predict the optimal label sequence. The support vector machine classifier is constructed, and the label sequence is classified. According to the hyperplane, the classification results are divided. The optimal classification named entity is determined by decision function, and the software named entity recognition is realized. The experimental results show that the proposed method has a high F1 value, and KS value.