Reviewerly/exHarmony huggingface dataset:
MGRec: Multi-Graph Fusion for Recommendation

The rapid development of the Internet and social networks deepens the connection between people and bring more rich information. To solve the problems of information overload, many recommendation algorithms have flourished, such as collaborative filtering algorithms, social recommendation algorithms, and meta-path based algorithms. The common characteristic of these algorithms is that they introduce some additional information, e.g., user friendship or item category, to improve the accuracy of recommendations. However, how to fuse such different types of information effectively remains challenging: how to aggregate specific information within each type of relations and how to combine various information among different types of relations. To solve the aforementioned problems, we propose a graph-level information fusion recommendation algorithm, i.e., MGRec (Multi-Graph Recommendation). Firstly, it models three types of relations, that are, ratings between users and items, friendships between users, and categories between items, as sub-graphs. Then, it makes information aggregation within each sub-graph, using personalized feature extraction methods like GCN and attention mechanism. Finally, it makes information combination among sub-graphs, which is implemented by a gate mechanism. Extensive experiments are conducted on two real-world datasets and results demonstrate the irreplaceability of MGRec.
Efficient Multiparty Protocols Using Generalized Parseval's Identity and the Theta Algebra

We propose a protocol able to show publicly addition and multiplication on secretly shared values. To this aim, we developed a protocol based on the use of masks and FMPC (Fourier Multi-Party Computation). FMPC is a novel multiparty computation protocol of arithmetic circuits based on secret-sharing, capable to compute the addition and multiplication of secrets with no communication. We achieve this task by introducing the first generalization of Parseval's identity for Fourier series applicable to an arbitrary number of inputs and a new algebra referred to as the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Theta^{[n]}-algebra$</tex> . FMPC operates in a setting where users wish to compute a function over some secret inputs by submitting the computation to a set of nodes, without revealing those inputs. FMPC offloads most of the computational complexity to the end users and includes an online phase that mainly consists of each node locally evaluating specific functions. FMPC paves the way for a new kind of multiparty computation protocol; making it possible to compute the addition and multiplication of secrets stepping away from circuit garbling and the traditional algebra introduced by Donald Beaver in 1991. Our protocol is capable to compute addition and multiplication with no communication and its simplicity provides efficiency and ease of implementation.
Study and realization of a control system prototype for a two-axis LDR-based solar tracker*

This article examines solar tracker technology, focusing on how these components are controlled. It begins with a brief overview of photovoltaic solar panels before discussing the many current types and models of solar trackers. It focuses primarily on the design and modeling of the tracker control system, which includes two rotational axes and LDR sensors. In this work, the prototype was made to simplify the use of a tracker in all possible conditions, although the choice to use this technology depends mainly on the physical characteristics of the terrain. The simplification programs take into account the positions of the sun as well as the times of sunset and sunrise, the software constantly predicts this position after a predetermined period, and provides specific instructions to the engine to track the optimal position. This system does not stop at the control of the actuators, a sleep mode has been added for the idle time to optimize the consumption of the motors, also adding to this a reset of the system in the first position of the solar survey according to a single axis (east-west), in making easier the movement of the tracker to the initial position of the next day.
Floating Music Score: System Design

This paper aims to describe an approach to create a useful and effective solution to facilitate the reading of musical scores during the performance or study of pieces, using the aid of Mixed Reality (MR), mobile devices, digital scores, and Data Musical Instrument Digital Interface (MIDI). MR can be defined as a system that combines physical objects with virtual objects. MIDI is a digital file containing musical notation information that can be interpreted by electronic devices. In this project, we aim to use HoloLens glasses and mobile devices, such as tablets or smartphones. The HoloLens are devices that can be used to create realistic holograms to visually represent the scores. The use of the various devices is intended to make the performance easier. The proposed approach aims to interpret scores in MIDI format and present them in hologram format to the MR glasses and graphical format to the mobile devices. In parallel, the system should be able to synchronize the presentation between different devices.
Camera Authentication Using Physical Features based on Handle System ID

Surveillance cameras are now heavily employed in IoT systems, which is crucial for security and other smart services. However, its current authentication system is vulnerable to the impersonation attack from fake devices, whose MAC/IP address and product serial number are identical to legitimate devices. Thus, we present a novel authentication mechanism based on the fingerprints extracted from physical sensor features, which can distinguish the fake devices with exactly the same configurations. Specifically, we propose: 1) a mechanism to extract the sensor fingerprint of a camera applying Photo Response Non-Uniformity and using it in Handle system 2) a mechanism Handle System ID to identify the IoT device with physical features, device state information 3) a simple, yet practical and efficient technique to match a camera device with a given Handle ID. Experimental results illustrate that our mechanism can work more than 10 times faster than conventional methods. The accuracy of rogue connection detection is up to 95.6% and false rejection is below 2.8%.
Selective Memory-Augmented Document Translation with Diverse Global Context

Existing document-level neural machine translation (NMT) models have sufficiently explored different context settings to provide guidance for target generation. However, little attention is paid to inaugurate more diverse contexts for abundant context information. In this paper, we propose a Selective Memory-augmented Neural Document Translation model (SMDT) to expand the context and reduce the hypothesis space of attention. Specifically, we retrieve similar bilingual sentence pairs from the training corpus to augment global context and then extend the two-stream attention model with a selection mechanism to capture local context and diverse global contexts. This unified approach allows our model to be trained elegantly on three publicly document-level machine translation datasets and significantly outperforms previous document-level NMT models.
RtSNN: Building a Real-time Siamese Neural Networks Model for Few-shot Action Recognition

The technology of real-time action recognition based on wearable sensors has been widely used by organizations and individuals to provide high-quality healthcare services. For instance, real-time action recognition using sports sensors can be used to predict irregular actions of athletes to avoid serious sports injuries. However, the collection and annotation of action data are always expensive, and only few high-quality labeled data are available. Moreover, existing studies on few-shot action recognition mainly focus on videos analysis, rather than targeting real-time action recognition. Therefore, how to enable real-time action recognition with few-shot data is still challenging and remains to be fully explored. In this paper, we propose the first siamese neural networks for real-time few-shot action recognition. Our main idea is to use augmented data from the original minority labeled action data to recognize real-time actions. To address the challenge of overfitting in few-shot learning tasks, we devise a scheme to effectively augment the original data. The proposed model, named Real-time Siamese Neural Networks (RtSNN), can learn a feature space in which all real-time action data are transformed into feature vectors. By leveraging the design philosophy of metric learning, RtSNN can classify different actions by the distance learned in the feature space. We perform extensive experiments on real-world action datasets to evaluate the RtSNN model. The evaluation results confirm that RtSNN can effectively recognize real-time actions. It can maintain the highest classification accuracy compared with baseline models.
GroupDiff: Exploring A Unified Graph Structure and High-order Interactions for Group Recommendation

With the development of social networks, it is becoming popular and convenient for people to join groups and take activities together. How to recommend to a group of users is a challenging problem, since some groups might be ad-hoc with sparse historical interactions, which results in the cold-start problem. Besides, group members usually have different preferences, making it difficult to reach a consensus. Although prior works have proposed to address such issues, they only take the first-order interactions among groups, users and items into consideration, while the high-order interactions are neglected, which are actually very informative for representation learning of group preferences. Furthermore, the interactions among groups, users and items are complex, making it difficult to model and learn from their high-order relationships. To address these issues, in this paper, we design a unified graph structure with heterogeneous nodes. It integrates all three types of interactions, i.e. user-item, group-user and group-item interactions, in one framework, which can facilitate the information diffusion and utilization along with the high-order interactions, and thus be beneficial to group recommendation mechanism design. Besides, based on the unified graph, we also propose GroupDiff, which is a graph convolution network-based model and explores the high-order relationships for representation learning of groups, users and items. Experiment results show the effectiveness of our proposed model.
Personalized Differentially Private Online Minimum Bipartite Matching in Spatial Crowdsourcing

Mobile devices and mobile applications benefit people's mobility. In some applications such as Uber and Didi, drivers and passengers (i.e., users) submit locations to the server to match each other. However, the server may be untrusted and location privacy becomes a huge concern. Users expect to enjoy matching while protecting their locations and different users have different levels of location privacy. In this paper, we consider the personalized privacy-preserving online minimum bipartite matching problem. To solve this problem, we design a Personalized Quadtree-Based privacy-preserving matching Framework (PQBF). In this framework, we propose a privacy method using the truncated Geometric Mechanism on the quadTree (GMT). GMT allows users to intuitively select generalization regions with different sizes, which is user-friendly. We then formulate the distance on the quadtree using earth mover's distance (EMD) to select the closest worker-task pair. Considering the huge time overhead of distance calculation, we provide a way to accelerate. We show that our method satisfies <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\epsilon$</tex> - geo-indistinguishability on the quadtree and give the time complexity of PQBF. Experiments on synthetic data and real data show that our mechanism is more effective than previous mechanisms in terms of total distance. PQBF saves up to 35.9% total distance than a state-of-the-art algorithm.
Compass: A Penalty-based Routing with High Success Ratio in Payment Channel Networks

In the past decade, blockchain-based cryptocurrencies have shown great promise without trusted third parties. However, the lack of scalability prevents them from a wide range of use. Payment Channel Networks (PCNs) such as the Lightning Network are proposed as a promising solution. But unlike traditional networks, transactions on PCNs will fail under insufficient balance in any node of the transaction path. There are some existing multi-path routing schemes to improve the success ratio, while it still remains a challenge to discover the most appropriate paths for each transaction with minimal impact on its subsequent transactions. In this paper, we propose Compass, a novel multi-path routing scheme, to address this challenge. First, we introduce the concept of penalty points. The penalty points are increased for the channel that causes the transaction to fail, and decreased upon a successful transaction for each channel of the path. We use penalty points to choose more reliable paths. After the possible transmission paths are obtained, the transaction amount is split into several sub-transactions which are sent to different paths. The splitting process aims at minimizing the loss of success probability for all channels on the paths, thus making the channels more durable. Compared with state-of-the-art schemes, simulations show that Compass has a higher transaction success ratio, while maintaining a lower latency and transaction fees.
Impact of Inheritance and Reusability on Design Quality of Object Oriented System

Inheritance is the main property of object oriented paradigm. It mainly focuses on the attributes and methods shared among the classes which are based on the hierarchical structure. Reusability is a basic concept that is influenced by the inheritance hierarchical structure in determining the complexity of an object oriented software system. It has high impact on sharing of various attributes and methods between different modules of the system. In this work, a new metric set consisting of 5 indicators/metrics has been proposed. The metrics set has been analyzed on different levels of design of object oriented software and attempted to provide an alternative way to find the impact of inheritance and reusability on design quality of object oriented software systems.
Edge Pricing Mechanisms under Cloud Tiered Pricing

As a supplement to cloud computing, edge computing provides low-latency computing services by deploying edge servers on the user side. Therefore, users are more inclined to deploy tasks on edge platforms; however, there are few studies on the pricing mechanism of edge platforms. In this paper, we propose a model to solve the Edge Price under Cloud Tiered Pricing problem, which edge platforms can design ideal pricing strategy to maximize their own revenue under the tiered pricing strategy of cloud platforms. In order to design an ideal and truthful mechanism to solve the problem of the model, we successively propose three different truthful mechanism algorithms: greedy, dynamic programming and random sampling. Extensive simulations were subsequently performed on the Google Cluster dataset. From the experimental results, we find that the dynamic programming mechanism can produce results as similar as the optimal results in pseudo-polynomial time. With the increase of user requests, the greedy mechanism can obtain better results under the comprehensive consideration of running time and total results. When the total requests of users are less than the capacity of the edge platform, the random sampling mechanism is a truthful algorithm and the performance can also be guaranteed.
Crowdsourcing System for Multi-object Annotation in Surveillance Videos

The collection and labeling of data is a labor-intensive task and this has given rise to a large market for data crowdsourcing transactions. While there are many publicly available video datasets, task-specific data is still scarce and requires Customized annotation services are required. Even with many excellent auxiliary models and tools, video annotation is still a lengthy and time-consuming task. To address these challenges, this paper provides a new and effective annotation method in which the annotator no longer just provides annotations, but also plays the role of a reviewer to review the annotation results of other annotators. This method focuses on surveillance video data, in addition, it also supports adding additional custom tasks (e.g., action tagging, person relationship recognition, video summarization, etc.). And in this paper we mainly consider the additional custom temporal action annotation task. In this paper, we develop rules for filtering frames or segments that need to be re-labeled based on the temporal information of the model inference results and rely on the correlation between target and time to determine the task relevance, and asynchronously assign the task to different annotators for and dynamically portray the ability of the annotators while annotation is in progress, so as to allocate tasks to achieve annotation and mutual review of annotators. We have experimentally demonstrated that this method can reduce costs and improve labeling accuracy.
AFSN: Adaptive Frame Selection Network for Efficient Human Synthesis on Smartphones

Viewing real-time human synthesis results on smart-phones provides users a convenient interactive experience. How-ever, most human synthesis solutions relay on high-performance servers and lack interaction with users. In order to reduce traffic between smartphones and cloud, this paper introduces a novel adaptive frame selection network (AFSN) on mobile devices. Besides, we propose a reinforcement-learning based agent that selects the image frames according to the pose change, that reduces communications and the amount of calculations. Based upon AFSN, we build a multi-task human synthesis system, that can deal with background changing, pose transfer, virtual try-on, and other generate tasks in a unified framework. Experimental results on one real-world dataset demonstrate that our method can reduce the number of frames to be processed by more than 50%.
Prototype Model of Voice Activated Wheelchair

In this project, voice recognition controls the wheelchair. This invention helps disabled, ill, or elderly wheelchair users since the design allows sick or disabled people to navigate by voice. Disabled people need wheelchairs because of weak upper limbs. Since a 24-hour caretaker is unrealistic, a voice-controlled motorized wheelchair is beneficial. As many sick or disabled persons as possible must have inexpensive access. The main purpose is to design a fully voice-controlled wheelchair prototype that can assist disabled people. We used ADUINO to design the prototype and conducted numerous test-runs to measure the efficiency of the system. The built prototype was quick, effective, and highly specific. The use of ARDUINO makes this prototype one-of-a-kind.
Analyzing the Classification Performance of DenseNet121 on Pre-processsed MIAS Dataset

Breast cancer is the most diagnosed type of cancer as per the data that is collected by the World Health Organization (WHO) within the past few years. Over 600,000 deaths were recorded in 2021 due to breast cancer. Breast cancer screening is done using two-dimensional (2D) and three-dimensional (3D) mammography, but MRIs and Ultrasounds are also used in certain conditions. The diagnosis from the screenings is not always accurate as a practitioner must physically look at the digital images to find any signs of cancer. Approximately, each diagnosis has a variable chance of a false-positive or a false-negative. Many CAD (computer-aided detection) systems have been developed for the assistance of a practitioner with the diagnosis. However, in the past years, Deep Neural Networks (DNN) have seen a spike and the models are being used to aid breast cancer screening. Data shows a possibility of reaching Area under the curve (AUC) values as high as 0.99 under ideal conditions when the training dataset is cleaned of noise and properly pre-processed and in some studies, the accuracy and sensitivity are even compared to that of a practitioner’s, with the DNN model outperforming in numbers across the board. After performing a literature review on similar work, we have trained a model of our own on a publicly available dataset (MIAS) reaching promising results of an AUC of 0.87 and an Accuracy of 0.88 with the initial model built on a DenseNet121 architecture.
Implementation of Convolutional Neural Networks deep learning approach to Classify Melanoma Skin Cancer

A kind of deadly skin tumor called melanoma is arguably very lethal since it accounts for the majority of skin cancer fatalities. Melanomas are often brown or black in color because melanocyte cells, which produce melanin, are the source of the disease. The DNA of skin cells is damaged by UV light, which is the main cause of melanomas. The analysis of the dermoscopy examination’s findings and comparison with medical sciences are frequently used in the manual diagnosis of melanoma cancer. Human subjectivity has a strong impact on manual detection, which renders it unreliable in some circumstances. In order to classify the outcomes of the dermoscopy test and to determine the results more precisely with a comparatively shorter amount of time, computer-assisted technology is required. Problem statement, planning, execution, and testing are the first steps in the creation of this application. To identify picture data, the research study combines deep machine learning approach using Convolutional Neural Network technique along with LeNet-5 architectural model. With a variable number of training and testing epochs and 44 photos from the training results, the experiment with the best success rate (93% in training and 100% in testing) required 176 images and 100 epochs of training data. Programming language such as Python with utilization of Keras library, which serves as the Tensorflow back-end, were used to construct this application.
Low-Cost 3D Holographic Display With Gesture Control

A way behind the past where teaching and learning were only by video, images, and 2D animations. Now we have live sessions where a classroom is bounded on just a laptop screen. What happened if that live session and 3D imaging are presented in the form of real sculpture by using Augmented Reality? In this paper, we are introducing a low-cost 3D display that presents both recorded and live sessions. Also, this display will be controlled with our fingertips. This application covers sectors of education, medical imaging, and advertisements with good effects and cheap cost.
Regularized Estimation of Information via Canonical Correlation Analysis on a Finite-Dimensional Feature Space

This paper aims to estimate the information between two random phenomena by using consolidated second-order statistics tools. The squared-loss mutual information, a surrogate of the Shannon mutual information, is chosen due to its property of being expressed as a second-order moment. We first review the rationale for i.i.d. discrete sources, which involves mapping the data onto the simplex space, and we highlight the links with other well-known related concepts in the literature based on local approximations of information-theoretic measures. Then, the problem is translated to analog sources by mapping the data onto the characteristic space, focusing on the adaptability between the discrete and the analog case and its limitations. The proposed approach gains interpretability and scalability for its use on large data sets, providing a unified rationale for the free regularization parameters. Moreover, the structure of the proposed mapping allows resorting to Szegö’s theorem to reduce the complexity for high dimensional mappings, exhibiting a strong duality with spectral analysis. The performance of the developed estimators is analyzed using Gaussian mixtures.