Reviewerly/exHarmony huggingface dataset:
MUSENET: Multi-Scenario Learning for Repeat-Aware Personalized Recommendation

Personalized recommendation has been instrumental in many real applications. Despite the great progress, the underlying multi-scenario characteristics (e.g., users may behave differently under different scenarios) are largely ignored by existing recommender systems. Intuitively, modeling different scenarios properly could significantly improve the recommendation accuracy, and some existing work has explored this direction. However, these work assumes the scenarios are explicitly given, and thus becomes less effective when such information is unavailable. To complicate things further, proper scenario modeling from data is challenging and the recommendation models may easily overfit to some scenarios. In this paper, we propose a multi-scenario learning framework, MUSENET, for personalized recommendation. The key idea of MUSENET is to learn multiple implicit scenarios from the user behaviors, with a careful design inspired by the causal interpretation of recommender systems to avoid the overfitting issue. Additionally, since users' repeat consumptions account for a large part of the user behavior data on many e-commerce platforms, a repeat-aware mechanism is integrated to handle users' repurchase intentions within each scenario. Comprehensive experimental results on both industrial and public datasets demonstrate the effectiveness of the proposed approach compared with the state-of-the-art methods.
NGAME: Negative Mining-aware Mini-batching for Extreme Classification

Extreme Classification (XC) seeks to tag data points with the most relevant subset of labels from an extremely large label set. Performing deep XC with dense, learnt representations for data points and labels has attracted much attention due to its superiority over earlier XC methods that used sparse, hand-crafted features. Negative mining techniques have emerged as a critical component of all deep XC methods, allowing them to scale to millions of labels. However, despite recent advances, training deep XC models with large encoder architectures such as transformers remains challenging. This paper notices that memory overheads of popular negative mining techniques often force mini-batch sizes to remain small and slow training down. In response, this paper introduces NGAME, a light-weight mini-batch creation technique that offers provably accurate in-batch negative samples. This allows training with larger mini-batches offering significantly faster convergence and higher accuracies than existing negative sampling techniques. NGAME was found to be up to 16% more accurate than state-of-the-art methods on a wide array of benchmark datasets for extreme classification, as well as 3% more accurate at retrieving search engine queries in response to a user webpage visit to show personalized ads. In live A/B tests on a popular search engine, NGAME yielded up to 23% gains in click-through-rates. Code for NGAME is available at https://github.com/Extreme-classification/ngame
Disentangled Representation for Diversified Recommendations

Accuracy and diversity have long been considered to be two conflicting goals for recommendations. We point out, however, that as the diversity is typically measured by certain pre-selected item attributes, e.g., category as the most popularly employed one, improved diversity can be achieved without sacrificing recommendation accuracy, as long as the diversification respects the user's preference about the pre-selected attributes. This calls for a fine-grained understanding of a user's preferences over items, where one needs to recognize the user's choice is driven by the quality of the item itself, or the pre-selected attributes of the item.
Real-time detection of barely visible defects on composite structures based on surface reconstruction

Visual inspection is currently the most common way for surface defect detection in the damaged composite structure, which is considered unreliable for barely visible impact defects (BVIDs) of less than 0.3 mm in depth. This paper proposes a defect detection method using stereo vision-based surface reconstruction and curvature analysis, which can detect defects of less than 0.1 mm in depth. To meet the real-time requirement of on-site detection, a real-time image registration algorithm is proposed through deformation decomposition and deep learning-based image correlation, realizing 1 frame/s calculation speed for megapixel images. The detection experiments of barely visible defects on carbon fiber-reinforced plastic (CFRP) composite laminates are carried out to demonstrate the effectiveness and efficiency. The proposed method presents a low-cost and high-efficiency approach for non-destructive testing and health monitoring of composite structures.
NPDP benchmark suite for the evaluation of the effectiveness of automatic optimizing compilers

The paper presents a benchmark suite of ten non-serial polyadic dynamic programming (NPDP) kernels, which are designed to test the efficiency of tiled code generated by polyhedral optimization compilers. These kernels are mainly derived from bioinformatics algorithms, which pose a significant challenge for automatic loop nest tiling transformations. The paper describes algorithms implemented with examined kernels and unifies them in the form of loop nests presented in the C language. The purpose is to reconsider the execution and monitoring of codes, typically used in past and current publications. For carrying out experiments with introduced benchmarks, we applied the two source-to-source compilers, PLuTo and TRACO, to generate cache-efficient codes and analyzed their performance on four multi-core machines. We discuss the limitations of well-known tiling approaches and outline future tiling strategies to generate effective tiled code by means of optimizing compilers for introduced benchmarks.
An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends

Blockchain, which is the backbone of Bitcoin, has recently received a lot of attention. Blockchain functions as an immutable ledger that enables decentralized transactions. Numerous fields, such as the Internet of Things (IoT), reputation systems, and financial services, are being covered by blockchain-based applications. However, blockchain technology still faces numerous difficulties, such as scalability and security issues, that need to be resolved. A comprehensive overview of blockchain technology is provided in this paper. First, we compare some common consensus algorithms utilized by various blockchains and provide an overview of the architecture of blockchains. In addition, a brief list of recent advancements and technical difficulties is provided. In addition, we outline potential blockchain trends for the future.
Efficient Super-Resolution System With Block-Wise Hybridization and Quantized Winograd on FPGA

Super-resolution (SR) techniques aim to restore a high-resolution (HR) image from low-resolution (LR) images, which are often used to assist the enhancement of image/video quality under the rapid development of HR and high-frame-rate media. Recently, neural network (NN)-based methods perform much better image reconstruction quality than classical approaches. However, the unacceptable computation complexity as well as the huge memory footprints of NNs limit the throughputs and scalability of these SR systems. In this work, we analyze several key issues in the design of NN-based SR systems first. Then, we propose a three-level systematic optimization methodology for SR systems to reduce computation overhead and keep image quality. At the algorithm level, we introduce image blocking to SR tasks and develop a block-wise SR algorithm based on the hybrid of NN and interpolation with a consistent image block evaluation metric. The configurable hybrid parameters help the SR algorithm to achieve a flexible tradeoff between the computation overhead and image quality. At the operator level, we focus on the transpose convolution operators commonly used for upsampling in SR NNs. We propose an efficient Winograd-based transposed convolution acceleration method. Through the efficient subconvolutions conversion and the Winograd specialization, this methods enables unified Winograd transformations and simplified data access patterns. At the data level, we propose a novel quantization method for Winograd-aware SR NNs to get better-quantized accuracy. Comprehensive evaluations demonstrate the effectiveness of these optimizations. Our SR system reduces a large number of multiplications with great scalability and supports 4K@120 fps and 8K@30 fps outputs with acceptable image quality degradation.
Exploring Plausibility and Presence in Mixed Reality Experiences

Mixed Reality (MR) applications along Milgram's Reality-Virtuality (RV) continuum motivated a number of recent theories on potential constructs and factors describing MR experiences. This paper investigates the impact of incongruencies that are processed on different information processing layers (i.e., sensation/perception and cognition layer) to provoke breaks in plausibility. It examines the effects on spatial and overall presence as prominent constructs of Virtual Reality (VR). We developed a simulated maintenance application to test virtual electrical devices. Participants performed test operations on these devices in a counterbalanced, randomized 2x2 between-subject design in either VR as congruent or Augmented Reality (AR) as incongruent on the sensation/perception layer. Cognitive incongruence was induced by the absence of traceable power outages, decoupling perceived cause and effect after activating potentially defective devices. Our results indicate that the effects of the power outages differ significantly in the perceived plausibility and spatial presence ratings between VR and AR. Both ratings decreased for the AR condition (incongruent sensation/perception) compared to VR (congruent sensation/perception) for the congruent cognitive case but increased for the incongruent cognitive case. The results are discussed and put into perspective in the scope of recent theories of MR experiences.
GeoSynth: A Photorealistic Synthetic Indoor Dataset for Scene Understanding

Deep learning has revolutionized many scene perception tasks over the past decade. Some of these improvements can be attributed to the development of large labeled datasets. The creation of such datasets can be an expensive, time-consuming, and imperfect process. To address these issues, we introduce GeoSynth, a diverse photorealistic synthetic dataset for indoor scene understanding tasks. Each GeoSynth exemplar contains rich labels including segmentation, geometry, camera parameters, surface material, lighting, and more. We demonstrate that supplementing real training data with GeoSynth can significantly improve network performance on perception tasks, like semantic segmentation. A subset of our dataset will be made publicly available at https://github.com/geomagical/GeoSynth.
A Video-Based Augmented Reality System for Human-in-the-Loop Muscle Strength Assessment of Juvenile Dermatomyositis

As the most common idiopathic inflammatory myopathy in children, juvenile dermatomyositis (JDM) is characterized by skin rashes and muscle weakness. The childhood myositis assessment scale (CMAS) is commonly used to measure the degree of muscle involvement for diagnosis or rehabilitation monitoring. On the one hand, human diagnosis is not scalable and may be subject to personal bias. On the other hand, automatic action quality assessment (AQA) algorithms cannot guarantee 100% accuracy, making them not suitable for biomedical applications. As a solution, we propose a video-based augmented reality system for human-in-the-loop muscle strength assessment of children with JDM. We first propose an AQA algorithm for muscle strength assessment of JDM using contrastive regression trained by a JDM dataset. Our core insight is to visualize the AQA results as a virtual character facilitated by a 3D animation dataset, so that users can compare the real-world patient and the virtual character to understand and verify the AQA results. To allow effective comparisons, we propose a video-based augmented reality system. Given a feed, we adapt computer vision algorithms for scene understanding, evaluate the optimal way of augmenting the virtual character into the scene, and highlight important parts for effective human verification. The experimental results confirm the effectiveness of our AQA algorithm, and the results of the user study demonstrate that humans can more accurately and quickly assess the muscle strength of children using our system.
A Framework for Multi-Prototype Based Federated Learning: Towards the Edge Intelligence

Edge intelligence becomes the enabler to fulfill the privacy-preserving intelligent services and applications for next-generation networking. However, the heterogeneous data distribution of distributed edge clients often hinders the convergence rate and test accuracy. Federated Learning (FL), as a new paradigm for privacy-preserving distributed edge-artificial intelligence (edge-AI) that enables model training without the raw data of clients leaving their local sides. The differences in the data distribution of clients can easily lead to biased model inference results, especially when inferring through classifiers. In this paper, to enhance robustness against heterogeneity, a novel multiple-prototype based federated learning (MPFed) framework is proposed, in which clients communicate with server as typical federated training, but the model inference is performed by measuring the distance between the target prototype and multiple weighted prototypes. The weighted prototype of each class is calculated by executing the clustering algorithm (e.g., k-means) and weighted strategy at the client side before finishing the last federated iteration. The server aggregates these weighted prototypes collected from all clients, and then distributes to them for model inferences. Experimental analyses on multiple baseline datasets, such as MNIST, Fashion-MNIST, and CIFAR10 demonstrate our method has a higher test accuracy, at least 10%, and is relatively efficient in communication than baselines and state-of-the-art algorithms.
Perceptual Encryption-based Privacy-Preserving Deep Learning for Medical Image Analysis

The widespread adoption of deep learning (DL) solutions in the healthcare organizations is obstructed by their compute intensive nature and dependability on massive datasets. In this regard, cloud-services such as cloud storage and computational resources are emerging as an effective solution. However, when the image data are outsourced to avail such services, there is a privacy concern that the data should be kept protected not only during transmission but during computations as well. To meet these requirements, this study proposed a privacy-preserving DL (PPDL) scheme that enable computations without the need of decryption. The encryption is based on perceptual encryption (PE) that only hides the perceivable information in an image while preserves other characteristics that are necessary for DL computations. Precisely, we have implemented a binary classifier based on EfficientNetV2 for the COVID-19 screening in the chest X-ray (CXR) images. For the PE algorithm, the suitability of two pixel-based and two block-based PE methods was analyzed. The analysis have shown that when global contents are left unmodified (pixel-based PE), then the DL-based model achieved the classification accuracy same as that of the plain images. On the other hand, for block-based PE algorithms, there is up to 3% drop in the model's accuracy and sensitivity scores.
Gesture Controlled Mouse Navigation : Hand Landmark Approach

Euclidean Distance has been an important metric for the calculation of distances between points on a scaled space. Recent times have seen an increased use of this metric for the purposes of pattern recognition. Hand Landmarks are a group of twenty-one points which define the skeletal structure of a human’s hand. Calculation of distances between Hand Landmarks using Euclidean Distance can cater to the need of an efficient algorithm which could recognize patterns using mathematical calculations. The last few years have witnessed the introduction of technologies which promoted virtualization of important hardware thereby, increasing the portability and handling of the systems. The Mouse is an important aspect of the computer which helps to easily interfacing with the system. History has seen the use of trackballs, laser sensors and track pads as means of controlling the system however, each one of them consisted of costly hardware equipment be it the material or the sensors used. This paper proposes a virtual mouse system which can be controlled and navigated using gestures made using any one hand, whether left or right, without the involvement of any external hardware or sensor. All this is done by means of Euclidean distances and hand landmarks. It allows the user to control all the functioning of the mouse including its movement, mouse clicks and drag and drop without physically touching any hardware. It uses only the system’s inbuilt camera to carry-out the functioning of the mouse. The system is carried out using Python along with OpenCV, Mediapipe and the PyAutoGui libraries. All the gestures become visible on the camera screen with appropriate marking to facilitate representing the working of the system.
Performance Evaluation of Clustering Based Tone Mapping Operators with State-of-Art Methods

Tone mapping operators have a major role in visualizing the real time images. There exist various methods, the performance of the selected tone mapping operators is compared with Novel clustering-based Tone mapping Operators. Performance evaluation is done by calculating its objective metrics. The performance of existing TMOs which comes in the categories of global, local, and clustering based are evaluated and compared with Novel clustering-based techniques. We measured more than 100 high dynamic images of indoor and outdoor natural scenes. Real time images are taken at different places of Hyderabad, Bahrain and Las Vegas using Nikon D5100 camera. Few images are taken from open sources. The quality metrics such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Mean Absolute Error (MAE), modified Peak Signal to Noise Ratio (mPSNR) , Tone Mapped Quality Index (TMQI), Feature Similarity Index for Tone Mapped Image (FSITM) and execution time are calculated and compared with existing tone mapping operator (TMO).
Penetration Testing for IoT Security: The Case Study of a Wireless IP Security CAM

As the trend to use the Internet of Things (IoT) applications and devices increases, security and privacy have become key concerns. IoT application adoption has increased significantly over time, with sensitive data frequently gathered by IoT devices, accidentally or consciously. According to recent research, numerous types of IoT devices have substantial vulnerabilities, and in many cases, no security procedures are in place to secure them. The focus of this study was the security issues of Internet protocol (IP) cameras. The vulnerabilities of IP cameras were investigated in more depth, as well as their influence on security and privacy at the user level, in order to assist companies and security experts in predicting attacker behavior and securing the systems. The objective of this study was to research and uncover the security and privacy vulnerabilities associated with an IP camera. This was accomplished by performing a direct inspection of the camera. A real-world test was carried out with a VAVA Outdoor Wireless IP Security Cam, which was employed as a home security camera. Information was gathered from numerous sources on the Internet for this purpose, and then the software and hardware were used to examine the security features of this device. The findings of this study revealed that the IP camera contains security faults and vulnerabilities that put user safety at risk.
Design of QR Based Smart Student Attendance System

Student attendance system is used to measure student participation in a classroom. Before pandemic attendance was taken manually like in sheets or registers. But when the pandemic hit, everything was online, so even the classes. The attendance count is a very important problem that the administrator needs to be more careful about taking during the online classes as there are many chances of a proxy happening. So, we came up with this proposed system “Student attendance using QR code” This paper proposes an attendance system that is based on the QR code-based attendance system. The students need to scan the QR in the class according to the professor instruction. By implementing this proposed system, we can reduce proxy and time in taking attendance of students. In order to design this proposed system, we are using technologies like OpenCV through python, and some libraries like MYQR, PYbase64, Pyzbar.
Quantum Machine Learning for Network Intrusion Detection Systems, a Systematic Literature Review

Quantum computing presents potential advantages over classical computing in terms of computational complexity. Therefore, it is expected for quantum machine learning applications to have improvements in capacity and learning efficiency over classical machine learning methods. This paper aims to present a Systematic Literature Review of articles published between 2017 and 2022, identifying, analyzing, and comparing different proposals of quantum machine learning applications for network intrusion detection systems (IDS). This study focused on identifying papers that implemented quantum machine learning algorithms in the context of intrusion detection systems. The main algorithms found were variational hybrid quantum-classical, with models based on quantum support vector machines and quantum neural networks. Benefits compared to classical models were observed and described, such as reduced training time and improved classification accuracy for attacking traffic.