Reviewerly/exHarmony huggingface dataset:
A survey on rotation invariance of orthogonal moments and transforms

The theory of moments and transforms is well established and widely applied to a number of computer vision, pattern recognition and image processing applications. A sub-class of these moments and transforms are used as rotation invariant global descriptors. There exist a number of techniques in literature whose authors claim superiority of their approaches. Consequently, many researchers face difficulties in deciding about the best or the near-best rotation invariant technique for their use. This paper reviews numerous state-of-the-art rotation invariant techniques based on moments and transforms by conducting a comparative performance analysis of various prominent approaches available in literature. To support the analysis of the survey, detailed experiments are conducted to compare performance of the various invariants using Zernike moments (ZMs) as a representative of all moments and transforms. The ZMs are selected because they are the most commonly used rotation invariant moments. Experiments are conducted on three standard databases which represent a wide variety of objects in different proportions of the shape and color cues. It is shown that the magnitude of moments of the gray-scale and multi-channel representation of color objects provides the overall best recognition results across a variety of classifiers.
Personalizing 3D virtual fashion stores: Exploring modularity with a typology of atmospherics based on user input

This research develops a typology of atmospherics that contains user-identified modules and modular options for personalizing 3D virtual fashion stores. A content analysis of 46 focus group discussions (n = 170) was conducted to understand the user’s perspective for personalizing 3D virtual fashion store atmospherics. Based on three atmospheric categories (pathfinding assistant, environment, and the manner of product presentation), 17 modules and 207 modular options were identified for personalizing 3D virtual stores. This research pioneers the development of an atmospherics typology for personalizing 3D virtual shopping environments as a persuasive selling tool in the emerging field of 3D virtual reality (VR) retailing.
Using Trellis software to enhance high-quality large-scale network data collection in the field

Trellis is a mobile platform created by the Human Nature Lab at the Yale Institute for Network Science to collect high-quality, location-aware, off-line/online, multi-lingual, multi-relational social network and behavior data in hard-to-reach communities. Respondents use Trellis to identify their social contacts by name and photograph, a procedure especially useful in low-literacy populations or in contexts where names may be similar or confusing. We use social network data collected from 1,969 adult respondents in two villages in Kenya to demonstrate Trellis' ability to provide unprecedented metadata to monitor and report on the data collection process including artifactual variability based on surveyors, time of day, or location.
Influence of the Contact Center Systems Development on Key Performance Indicators

The role played by information and communication technologies in human life is growing by the year. During the current pandemic, communication systems have become an essential part of operations of many industries around the world. This has also brought a dynamic development of solutions implemented in Contact Center systems, which ensure quick contact with customers. This paper presents how the pace of the development of Contact Center systems affects optimization of selected Key Performance Indicators (KPIs). It contains a detailed analysis of how successive technologies deployed in the industry, including IVR, chat, WebRTC, Visual IVR, Social Media, MultiChannel, OmniChannel and bots, have affected KPIs. The authors selected essential indicators that are critical to customer service, namely Service Level, Cost per Contact, Customer Satisfaction, Average Handle Time, First Call Resolution, Abandon Rate, Average Waiting Time, and Occupancy Rate. The authors also presented possible trends in the development of the solutions discussed in this paper, including in particular broad possibilities of employing artificial intelligence. AI methods will soon enable equipping Contact Centers with mechanisms for multi-criteria content classification and smart emotion recognition and behavioral profiling methods, and will optimize the process of predicting both inbound and outbound traffic.
ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised Learning

We propose a new training algorithm, ScanMix, that explores semantic clustering and semi-supervised learning (SSL) to allow superior robustness to severe label noise and competitive robustness to non-severe label noise problems, in comparison to the state of the art (SOTA) methods. ScanMix is based on the expectation maximisation framework, where the E-step estimates the latent variable to cluster the training images based on their appearance and classification results, and the M-step optimises the SSL classification and learns effective feature representations via semantic clustering. We present a theoretical result that shows the correctness and convergence of ScanMix, and an empirical result that shows that ScanMix has SOTA results on CIFAR-10/-100 (with symmetric, asymmetric and semantic label noise), Red Mini-ImageNet (from the Controlled Noisy Web Labels), Clothing1M and WebVision. In all benchmarks with severe label noise, our results are competitive to the current SOTA.
Training Multi-Bit Quantized and Binarized Networks with a Learnable Symmetric Quantizer

Quantizing weights and activations of deep neural networks is essential for deploying them in resource-constrained devices, or cloud platforms for at-scale services. While binarization is a special case of quantization, this extreme case often leads to several training difficulties, and necessitates specialized models and training methods. As a result, recent quantization methods do not provide binarization, thus losing the most resource-efficient option, and quantized and binarized networks have been distinct research areas. We examine binarization difficulties in a quantization framework and find that all we need to enable the binary training are a symmetric quantizer, good initialization, and careful hyperparameter selection. These techniques also lead to substantial improvements in multi-bit quantization. We demonstrate our unified quantization framework, denoted as UniQ, on the ImageNet dataset with various architectures such as ResNet-18,-34 and MobileNetV2. For multi-bit quantization, UniQ outperforms existing methods to achieve the state-of-the-art accuracy. In binarization, the achieved accuracy is comparable to existing state-of-the-art methods even without modifying the original architectures.
Artificial intelligence for the real world of breast screening

Breast cancer screening with mammography reduces mortality in the women who attend by detecting high risk cancer early. It is far from perfect with variations in both sensitivity for the detection of cancer and very wide variations in specificity, leading to unnecessary recalls and biopsies. Over the last 12 months several papers have reported on AI algorithms that perform as well as human readers on large well curated population data sets. The nature of the test sets, the way the gold standard has been calculated, the definition of a positive call, and the statistics used all influence the results. Historically retrospective studies have not predicted the real-life performance of radiologist plus machine. So, it is important to perform prospective studies before introducing Artificial intelligence into real world breast screening.
Bézier Curves-Based Optimal Trajectory Design for Multirotor UAVs with Any-Angle Pathfinding Algorithms

Multirotor Unmanned Aerial Vehicles (UAVs) play an imperative role in many real-world applications in a variety of scenarios characterized by a high density of obstacles with different heights. Due to the complicated operation areas of UAVs and complex constraints associated with the assigned mission, there should be a suitable path to fly. Therefore, the most relevant challenge is how to plan a flyable path for a UAV without collisions with obstacles. This paper demonstrates how a flyable and continuous trajectory was constructed by using any-angle pathfinding algorithms, which are Basic Theta*, Lazy Theta*, and Phi* algorithms for a multirotor UAV in a cluttered environment. The three algorithms were modified by adopting a modified cost function during their implementation that considers the elevation of nodes. First, suitable paths are generated by using a modified version of the three algorithms. After that, four Bézier curves-based approaches are proposed to smooth the generated paths to be converted to flyable paths (trajectories). To determine the most suitable approach, particularly when searching for an optimal and collision-free trajectory design, an innovative evaluation process is proposed and applied in a variety of different size environments. The evaluation process results show high success rates of the four approaches; however, the approach with the highest success rate is adopted. Finally, based on the results of the evaluation process, a novel algorithm is proposed to increase the efficiency of the selected approach to the optimality in the construction process of the trajectory.
Quantitative Analysis and Evaluation of Coal Mine Geological Structures Based on Fractal Theory

With the increasing depth of coal mining, the quantitative evaluation of the degree of geological structure development is becoming increasingly important for the control of mine water hazards in coal mining areas. Understanding the complexity of geological structure development can improve the safety and efficiency of coal production. At present, various evaluation indicators of the geological structure development cannot fully reflect the complexity of faults and folds, and the evaluation process is usually affected by subjective human factors. In this paper, the fractal dimension from fractal theory is used as the evaluation indicator to quantitatively analyze and evaluate the complexity of fault and fold structure in the mining area. To verify the evaluation results, the mathematical geology method is applied in an analysis of the trend surface of fault and fold networks. The results indicate that the fractal dimension can be applied for the quantitative analysis and evaluation of the complexity of fault and fold networks. In addition, the outcome of this work provides new insights into how to characterize the fault and fold structures of coal mining areas in northern China, and has some important implications to ensure the coal production safety.
Long Short Term Memory Networks Based Anomaly Detection for KPIs

In real-world many internet-based service companies need to closely monitor large amounts of data in order to ensure stable operation of their business. However, anomaly detection for these data with various patterns and data quality has been a great challenge, especially without labels. In this paper, we adopt an anomaly detection algorithm based on Long Short-Term Memory (LSTM) Network in terms of reconstructing KPIs and predicting KPIs. They use the reconstruction error and prediction error respectively as the criteria for judging anomalies, and we test our method with real data from a company in the insurance industry and achieved good performance.
Key strategies for synthetic data generation for training intelligent systems based on people detection from omnidirectional cameras

To train Deep Neural Networks (DNNs)-based methods, suitable training data are key to help DNNs learn appropriate pattern recognition features. The use of synthetic data may help in generating sufficient and balanced data. However, models trained with such data often present a domain gap when applied to real-world scenarios. Many studies focus on techniques such as domain adaptation to minimize this gap, but little attention is paid to the data generation itself. Our work shows that this gap can be minimized by enhancing the generated data features. More specifically, we generate different synthetic training datasets with particular features and use them to train a DNN for people detection in large spaces using omnidirectional cameras. Experimental results with real-world data show that proper synthetic data minimize the domain gap. We also show that expanding a training dataset to include synthetic samples in addition to real samples, can improve the model's capabilities.
A certificateless keyword searchable encryption scheme in multi‐user setting for fog‐enhanced Industrial Internet of Things

Abstract Industrial Internet of Things (IIoT), an appendage of the Internet of Things (IoT), upgrades conventional industries to intelligent and autonomous industries. It integrates different intelligent terminals, sensors, actuators, mobile devices, cloud computing through wireless communication to enhance operational efficiency and safety. However, it relies on the powerful data processing and unlimited storage capabilities of cloud computing by outsourcing its data and on‐demand services to the cloud server. This introduces privacy and confidentiality issues, and a high cost of data access on IIoT, especially for location bound data retrieval. To enforce confidentiality in IIoT, encryption techniques are being adapted to secure the outsourced data, but not without impeding the data searching operation. Several searchable encryption schemes have been proposed for secure and privacy‐preserving data retrieval in IIoT. However, most of these schemes not only completely relied on the cloud server but also susceptible to inside keyword guessing attack (IKGA). Besides, some of them use high computation intensive cryptography operations that make them computationally intensive for resource‐constrained data users and owners. In this article, we develop a lightweight searchable public‐key encryption scheme. The scheme is provably secure against IKGA. It contains a secure key distribution approach for generating and distributing the symmetric encryption key and allows secure ciphertext data search and retrieval in a multi‐user setting without compromising privacy. Finally, we analyze and evaluate the performance of the proposed scheme by comparing the results with some recent state‐of‐the‐art schemes to quantitatively show the capabilities of the scheme.
Median Filter Aided CNN Based Image Denoising: An Ensemble Approach

Image denoising is a challenging research problem that aims to recover noise-free images from those that are contaminated with noise. In this paper, we focus on the denoising of images that are contaminated with additive white Gaussian noise. For this purpose, we propose an ensemble learning model that uses the output of three image denoising models, namely ADNet, IRCNN, and DnCNN, in the ratio of 2:3:6, respectively. The first model (ADNet) consists of Convolutional Neural Networks with attention along with median filter layers after every convolutional layer and a dilation rate of 8. In the case of the second model, it is a feed forward denoising CNN or DnCNN with median filter layers after half of the convolutional layers. For the third model, which is Deep CNN Denoiser Prior or IRCNN, the model contains dilated convolutional layers and median filter layers up to the dilated convolutional layers with a dilation rate of 6. By quantitative analysis, we note that our model performs significantly well when tested on the BSD500 and Set12 datasets.
Spectrum of Advancements and Developments in Multidisciplinary Domains for Generative Adversarial Networks (GANs)

The survey paper summarizes the recent applications and developments in the domain of Generative Adversarial Networks (GANs) i.e. a back propagation based neural network architecture for generative modeling. GANs is one of the most highlighted research avenue due to its synthetic data generation capabilities and benefits of representations comprehended irrespective of the application. While several reviews for GANs in the arena of image processing have been conducted by present but none have given attention on the review of GANs over multi-disciplinary domains. Therefore, in this survey, use of GAN in multidisciplinary applications areas and its implementation challenges have been done by conducting a rigorous search for journal/research article related to GAN and in this regard five renowned journal databases i.e. "ACM Digital Library"," Elsevier", "IEEE Explore", "Science Direct", "Springer" and proceedings of best domain specific conference are considered. By employing hybrid research methodology and article inclusion and exclusion criteria, 100 research articles are considered encompassing 23 application domains for the survey. In this paper applications of GAN in various practical domain and their implementation challenges its associated advantages and disadvantages have been discussed. For the first time a survey of this type have been done where GAN with wide range of application and its associated advantages and disadvantages issue have been reviewed. Finally, this article presents several diversified prominent developing trends in the respective research domain which will provide a visionary perspective regarding ongoing GANs related research and eventually help to develop an intuition for problem solving using GANs.
Improving Cold-Start Recommendation via Multi-prior Meta-learning

Optimization-based meta-learning has been applied in cold-start recommendations, where a good initialization of meta learner is obtained from past experiences and then reused for fast adaptation to new tasks. However, when dealing with various users with diverse preferences, meta-learning with a single prior might fail in cold-start recommendations due to its insufficient capability for adaptation. To address this problem, a multi-prior meta-learning (MPML) approach is proposed in this paper and applied in cold-start recommendations. More concretely, we integrate a novel accuracy-based task clustering scheme with double gradient to learn multiple priors. Experiments demonstrate the effectiveness of MPML.
Distributing Multipartite Entanglement over Noisy Quantum Networks

A quantum internet aims at harnessing networked quantum technologies, namely by distributing bipartite entanglement between distant nodes. However, multipartite entanglement between the nodes may empower the quantum internet for additional or better applications for communications, sensing, and computation. In this work, we present an algorithm for generating multipartite entanglement between different nodes of a quantum network with noisy quantum repeaters and imperfect quantum memories, where the links are entangled pairs. Our algorithm is optimal for GHZ states with 3 qubits, maximising simultaneously the final state fidelity and the rate of entanglement distribution. Furthermore, we determine the conditions yielding this simultaneous optimality for GHZ states with a higher number of qubits, and for other types of multipartite entanglement. Our algorithm is general also in the sense that it can optimise simultaneously arbitrary parameters. This work opens the way to optimally generate multipartite quantum correlations over noisy quantum networks, an important resource for distributed quantum technologies.
Comparing Score Aggregation Approaches for Document Retrieval with Pretrained Transformers

While BERT has been shown to be effective for passage retrieval, its maximum input length limitation poses a challenge when applying the model to document retrieval. In this work, we reproduce three passage score aggregation approaches proposed by Dai and Callan [5] for overcoming this limitation. After reproducing their results, we generalize their findings through experiments with a new dataset and experiment with other pretrained transformers that share similarities with BERT. We find that these BERT variants are not more effective for document retrieval in isolation, but can lead to increased effectiveness when combined with "pre–fine-tuning" on the MS MARCO passage dataset. Finally, we investigate whether there is a difference between fine-tuning models on "deep" judgments (i.e., fewer queries with many judgments each) vs. fine-tuning on "shallow" judgments (i.e., many queries with fewer judgments each). Based on available data from two different datasets, we find that the two approaches perform similarly.
A Decentralized and Encrypted National Gun Registry

Gun violence results in a significant number of deaths in the United States. Starting in the 1960’s, the US Congress passed a series of gun control laws to regulate the sale and use of firearms. One of the most important but politically fraught gun control measures is a national gun registry. A US Senate office is currently drafting legislation that proposes the creation of a voluntary national gun registration system. At a high level, the bill envisions a decentralized system where local county officials would control and manage the registration data of their constituents. These local databases could then be queried by other officials and law enforcement to trace guns. Due to the sensitive nature of this data, however, these databases should guarantee the confidentiality of the data.In this work, we translate the high-level vision of the proposed legislation into technical requirements and design a crypto- graphic protocol that meets them. Roughly speaking, the protocol can be viewed as a decentralized system of locally-managed end-to-end encrypted databases. Our design relies on various cryptographic building blocks including structured encryption, secure multi-party computation and secret sharing. We propose a formal security definition and prove that our design meets it. We implemented our protocol and evaluated its performance empirically at the scale it would have to run if it were deployed in the United States. Our results show that a decentralized and end-to-end encrypted national gun registry is not only possible in theory but feasible in practice.
Legal document recommendation system: A cluster based pairwise similarity computation

Legal practitioners analyze relevant previous judgments to prepare favorable and advantageous arguments for an ongoing case. In Legal domain, recommender systems (RS) effectively identify and recommend referentially and/or semantically relevant judgments. Due to the availability of enormous amounts of judgments, RS needs to compute pairwise similarity scores for all unique judgment pairs in advance, aiming to minimize the recommendation response time. This practice introduces the scalability issue as the number of pairs to be computed increases quadratically with the number of judgments i.e., O (n2). However, there is a limited number of pairs consisting of strong relevance among the judgments. Therefore, it is insignificant to compute similarities for pairs consisting of trivial relevance between judgments. To address the scalability issue, this research proposes a graph clustering based novel Legal Document Recommendation System (LDRS) that forms clusters of referentially similar judgments and within those clusters find semantically relevant judgments. Hence, pairwise similarity scores are computed for each cluster to restrict search space within-cluster only instead of the entire corpus. Thus, the proposed LDRS severely reduces the number of similarity computations that enable large numbers of judgments to be handled. It exploits a highly scalable Louvain approach to cluster judgment citation network, and Doc2Vec to capture the semantic relevance among judgments within a cluster. The efficacy and efficiency of the proposed LDRS are evaluated and analyzed using the large real-life judgments of the Supreme Court of India. The experimental results demonstrate the encouraging performance of proposed LDRS in terms of Accuracy, F1-Scores, MCC Scores, and computational complexity, which validates the applicability for scalable recommender systems.