Reviewerly/exHarmony huggingface dataset:
Deblurring Using Analysis-Synthesis Networks Pair

Blind image deblurring remains a challenging problem for modern artificial neural networks. Unlike other image restoration problems, deblurring networks fail behind the performance of existing deblurring algorithms in case of uniform and 3D blur models. This follows from the diverse and profound effect that the unknown blur-kernel has on the deblurring operator. We propose a new architecture which breaks the deblurring network into an analysis network which estimates the blur, and a synthesis network that uses this kernel to deblur the image. Unlike existing deblurring networks, this design allows us to explicitly incorporate the blur-kernel in the network's training. In addition, we introduce new cross-correlation layers that allow better blur estimations, as well as unique components that allow the estimate blur to control the action of the synthesis deblurring action. Evaluating the new approach over established benchmark datasets shows its ability to achieve state-of-the-art deblurring accuracy on various tests, as well as offer a major speedup in runtime.
Look-Into-Object: Self-Supervised Structure Modeling for Object Recognition

Most object recognition approaches predominantly focus on learning discriminative visual patterns, while overlooking the holistic object structure. Though important, structure modeling usually requires significant manual annotations and therefore is labor-intensive. In this paper, we propose to ``look into object" (explicitly yet intrinsically model the object structure) through incorporating self-supervisions into the traditional framework. We show the recognition backbone can be substantially enhanced for more robust representation learning, without any cost of extra annotation and inference speed. Specifically, we first propose an object-extent learning module for localizing the object according to the visual patterns shared among the instances in the same category. We then design a spatial context learning module for modeling the internal structures of the object, through predicting the relative positions within the extent. These two modules can be easily plugged into any backbone networks during training and detached at inference time. Extensive experiments show that our look-into-object approach (LIO) achieves large performance gain on a number of benchmarks, including generic object recognition (ImageNet) and fine-grained object recognition tasks (CUB, Cars, Aircraft). We also show that this learning paradigm is highly generalizable to other tasks such as object detection and segmentation (MS COCO). Project page: https://github.com/JDAI-CV/LIO.
Symmetry and Group in Attribute-Object Compositions

Attributes and objects can compose diverse compositions. To model the compositional nature of these general concepts, it is a good choice to learn them through transformations, such as coupling and decoupling. However, complex transformations need to satisfy specific principles to guarantee the rationality. In this paper, we first propose a previously ignored principle of attribute-object transformation: Symmetry. For example, coupling peeled-apple with attribute peeled should result in peeled-apple, and decoupling peeled from apple should still output apple. Incorporating the symmetry principle, a transformation framework inspired by group theory is built, i.e. SymNet. SymNet consists of two modules, Coupling Network and Decoupling Network. With the group axioms and symmetry property as objectives, we adopt Deep Neural Networks to implement SymNet and train it in an end-to-end paradigm. Moreover, we propose a Relative Moving Distance (RMD) based recognition method to utilize the attribute change instead of the attribute pattern itself to classify attributes. Our symmetry learning can be utilized for the Compositional Zero-Shot Learning task and outperforms the state-of-the-art on widely-used benchmarks. Code is available at https://github.com/DirtyHarryLYL/SymNet.
GAN-Driven Personalized Spatial-Temporal Private Data Sharing in Cyber-Physical Social Systems

The cyber-physical social system (CPSS) enables human social interaction from cyberspace to the physical world by sharing an increasing volume of spatial-temporal data. The sensitive information in the shared data is appealing to the adversaries and various attacks. However, most existing research assumes that the privacy protection level is identical regardless of various requirements, which is not practical. Subsequently, this results in either over-protection or failing to resist the leading attacks like collusion attacks. Motivated by this, a personalized model is proposed by using generative adversarial nets (GAN) to achieve differential privacy and thereby enhancing spatial-temporal private data sharing. A Differential Privacy Identifier is added to the classic GAN with a Generator and a Discriminator. The deployment of the differentially Private GAN (P-GAN) enables the generation of the sanitized data that can perfectly approximate the spatial-temporal trajectory while providing high-level privacy protection. P-GAN optimizes the trade-off between personalized privacy protection and improved data utility while maintaining fast convergence. Simultaneously, the random noise generation guaranteed by GAN breaks the correlation of injected noises and make P-GAN attack-proof against the collusion attacks. Extensive evaluation results on real-world datasets show the superiority of P-GAN from the aspects of optimized trade-off and efficiency.
Mobile learning and pedagogical opportunities: A configurative systematic review of PreK-12 research using the SAMR framework

Scholars postulate that mobile devices can be used to transform learning. However, there is a paucity of evidence to determine if mobile learning is redefining learning or if these devices are being used to replicate past teaching practices. To fill this gap in scholarly understanding, this systematic review was conducted to examine studies from 2014 to 2019 involving mobile devices in PK-12 (2–18 years) learning. Technology use was coded using the Substitution, Augmentation, Modification, and Redefinition (SAMR) framework. This framework was empirically extended and presented in this study. The data revealed unique findings, including the concerning discovery that while mobile technologies can and were used to transform learning in 54% of the studies, 46% of the time devices were used to replicate activities that can be conducted without technology. This has larger implications on school integration of mobile devices and whether these devices are being used to redefine learning to provide the most benefit to PK-12 students.
Cross-View Tracking for Multi-Human 3D Pose Estimation at Over 100 FPS

Estimating 3D poses of multiple humans in real-time is a classic but still challenging task in computer vision. Its major difficulty lies in the ambiguity in cross-view association of 2D poses and the huge state space when there are multiple people in multiple views. In this paper, we present a novel solution for multi-human 3D pose estimation from multiple calibrated camera views. It takes 2D poses in different camera coordinates as inputs and aims for the accurate 3D poses in the global coordinate. Unlike previous methods that associate 2D poses among all pairs of views from scratch at every frame, we exploit the temporal consistency in videos to match the 2D inputs with 3D poses directly in 3-space. More specifically, we propose to retain the 3D pose for each person and update them iteratively via the cross-view multi-human tracking. This novel formulation improves both accuracy and efficiency, as we demonstrated on widely-used public datasets. To further verify the scalability of our method, we propose a new large-scale multi-human dataset with 12 to 28 camera views. Without bells and whistles, our solution achieves 154 FPS on 12 cameras and 34 FPS on 28 cameras, indicating its ability to handle large-scale real-world applications. The proposed dataset will be released at https://github.com/longcw/crossview_3d_pose_tracking.
Concept attribution: Explaining CNN decisions to physicians

Deep learning explainability is often reached by gradient-based approaches that attribute the network output to perturbations of the input pixels. However, the relevance of input pixels may be difficult to relate to relevant image features in some applications, e.g. diagnostic measures in medical imaging. The framework described in this paper shifts the attribution focus from pixel values to user-defined concepts. By checking if certain diagnostic measures are present in the learned representations, experts can explain and entrust the network output. Being post-hoc, our method does not alter the network training and can be easily plugged into the latest state-of-the-art convolutional networks. This paper presents the main components of the framework for attribution to concepts, in addition to the introduction of a spatial pooling operation on top of the feature maps to obtain a solid interpretability analysis. Furthermore, regularized regression is analyzed as a solution to the regression overfitting in high-dimensionality latent spaces. The versatility of the proposed approach is shown by experiments on two medical applications, namely histopathology and retinopathy, and on one non-medical task, the task of handwritten digit classification. The obtained explanations are in line with clinicians’ guidelines and complementary to widely used visualization tools such as saliency maps.
Using an autoencoder in the design of an anomaly detector for smart manufacturing

According to the smart manufacturing paradigm, the analysis of assets’ time series with a machine learning approach can effectively prevent unplanned production downtimes by detecting assets’ anomalous operational conditions. To support smart manufacturing operators with no data science background, we propose an anomaly detection approach based on deep learning and aimed at providing a manageable machine learning pipeline and easy to interpret outcome. To do so we combine (i) an autoencoder, a deep neural network able to produce an anomaly score for each provided time series, and (ii) a discriminator based on a general heuristics, to automatically discern anomalies from regular instances. We prove the convenience of the proposed approach by comparing its performances against isolation forest with different case studies addressing industrial laundry assets’ power consumption and bearing vibrations.
Differential Private Noise Adding Mechanism and Its Application on Consensus Algorithm

Differential privacy is a formal mathematical framework for quantifying the degree of individual privacy in a statistical database.To guarantee differential privacy, a typical method is to add random noise to the original data for data release. In this paper, we investigate the conditions of differential privacy (single-dimensional case) considering the general random noise adding mechanism, and then apply the obtained results for privacy analysis of the privacy-preserving consensus algorithm. Specifically, we obtain a necessary and sufficient condition of e-differential privacy, and the sufficient conditions of (e, δ)-differential privacy. We apply them to analyze various random noises. For the special cases with known results, our theory not only matches with the literature, but also provides an efficient approach to the privacy parameters' estimation; for other cases that are unknown, our approach provides a simple and effective tool for differential privacy analysis. Applying the obtained theory on privacy-preserving consensus algorithm, we obtain the necessary condition and the sufficient condition to ensure differential privacy.
Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses

Abstract Background The mitotic count in breast carcinoma is an important prognostic marker. Unfortunately substantial inter- and intra-laboratory variation exists when pathologists manually count mitotic figures. Artificial intelligence (AI) coupled with whole slide imaging offers a potential solution to this problem. The aim of this study was to accordingly critique an AI tool developed to quantify mitotic figures in whole slide images of invasive breast ductal carcinoma. Methods A representative H&amp;E slide from 320 breast invasive ductal carcinoma cases was scanned at 40x magnification. Ten expert pathologists from two academic medical centers labeled mitotic figures in whole slide images to train and validate an AI algorithm to detect and count mitoses. Thereafter, 24 readers of varying expertise were asked to count mitotic figures with and without AI support in 140 high-power fields derived from a separate dataset. Their accuracy and efficiency of performing these tasks were calculated and statistical comparisons performed. Results For each experience level the accuracy, precision and sensitivity of counting mitoses by users improved with AI support. There were 21 readers (87.5%) that identified more mitoses using AI support and 13 reviewers (54.2%) that decreased the quantity of falsely flagged mitoses with AI. More time was spent on this task for most participants when not provided with AI support. AI assistance resulted in an overall time savings of 27.8%. Conclusions This study demonstrates that pathology end-users were more accurate and efficient at quantifying mitotic figures in digital images of invasive breast carcinoma with the aid of AI. Higher inter-pathologist agreement with AI assistance suggests that such algorithms can also help standardize practice. Not surprisingly, there is much enthusiasm in pathology regarding the prospect of using AI in routine practice to perform mundane tasks such as counting mitoses.
Random forest explainability using counterfactual sets

Nowadays, Machine Learning (ML) models are becoming ubiquitous in today’s society, supporting people with their day-to-day decisions. In this context, Explainable ML is a field of Artificial Intelligence (AI) that focuses on making predictive models and their decisions interpretable by humans, enabling people to trust predictive models and to understand the underlying processes. A counterfactual is an effective type of Explainable ML technique that explains predictions by describing the changes needed in a sample to flip the outcome of the prediction. In this paper, we introduce counterfactual sets, an explanation approach that uses a set of counterfactuals to explain a prediction rather than a single counterfactual, by defining a sub-region of the feature space where the counterfactual holds. A method to extract counterfactual sets from a Random Forest (RF), the RandomForestOptimalCounterfactualSetExtractor(RF−OCSE), is presented. The method is based on a partial fusion of tree predictors from a RF into a single Decision Tree (DT) using a modification of the CART algorithm, and it obtains a counterfactual set that contains the optimal counterfactual. The proposal is validated through several experiments against existing alternatives on ten well-known datasets by comparing the percentage of valid counterfactuals, distance to the factual sample, and counterfactual sets quality.
Compensation for Power Loss by a Proof-of-Stake Consortium Blockchain Microgrid

Distributed generation in the microgrid becomes increasingly significant, as it eliminates the power losses from long-distance electricity transmission lines. Distributed energy resources owners who can both produce and consume energy are defined as prosumers. To encourage the peer-to-peer (P2P) energy trading between prosumers, blockchain as a thriving technology is utilized in the P2P network due to its transparency, security, and rapidity in executing transactions. Due to its decentralized quality, any intermediaries are eliminated so that transactions happen directly among traders. This article introduces a consortium blockchain trading model to support P2P energy trading, using a proof-of-stake protocol. The pre-selected miners are responsible for compensating the power losses in distribution lines by energy transactions. The specific process of the blockchain establishment, as well as the smart contract creation, are demonstrated. In addition, a type of crypto-currency named “elecoin” is created in the P2P market, which is published by the mining mechanism of the blockchain. Finally, a case study is introduced to realise the functions of the proposed blockchain model. Simulation results show the feasibility and effectiveness of the proposed approach.
Toward a catalog of software quality metrics for infrastructure code

Infrastructure-as-code (IaC) is a practice to implement continuous deployment by allowing management and provisioning of infrastructure through the definition of machine-readable files and automation around them, rather than physical hardware configuration or interactive configuration tools. On the one hand, although IaC represents an ever-increasing widely adopted practice nowadays, still little is known concerning how to best maintain, speedily evolve, and continuously improve the code behind the IaC practice in a measurable fashion. On the other hand, source code measurements are often computed and analyzed to evaluate the different quality aspects of the software developed. However, unlike general-purpose programming languages (GPLs), IaC scripts use domain-specific languages, and metrics used for GPLs may not be applicable for IaC scripts. This article proposes a catalog consisting of 46 metrics to identify IaC properties focusing on Ansible, one of the most popular IaC language to date, and shows how they can be used to analyze IaC scripts.
The Dynamics of Foreign Language Enjoyment: An Ecological Momentary Assessment

Following the recent shift from negative psychology to positive psychology, interest in foreign language enjoyment (FLE) has grown noticeably in second language acquisition. Given the fact that learners are "persons-in-context" and are not "ergodic ensembles," the particular learner-context ecosystem goes through ongoing momentary changes with respect to individual differences like FLE. Ecological momentary assessment (EMA) contributes to our understanding of the dynamics of this ecosystem in terms of the interaction between individual learners and their learning environments. In this study, using a time-based sampling scheme of EMA, we explored the dynamism of different facets of FLE across different timescales including seconds, minutes, weeks, and months in a course of intermediate English as a foreign language. To do this, we applied open-ended interviews with two intermediate English language learners in a private English language institute across months, journals across weeks, enjoymeters across minutes, and the idiodynamic approach across seconds. Findings indicated that enjoyment in foreign language fluctuates in terms of a hierarchy of temporal scales, from moment-to-moment changes to the ones over months. The emerging patterns of enjoyment across different timescales in terms of the tenets of complex dynamic systems theory are discussed.
Face Recognition based Attendance System using Haar Cascade and Local Binary Pattern Histogram Algorithm

The attendance system is used to track and monitor whether a student attends a class. There are different types of attendance systems like Biometric-based, Radiofrequency card-based, face recognition based and old paper-based attendance system. Out of them all, a Face recognition based attendance system is more secure and time-saving. There are several research papers focusing on only the recognition rate of students. This research focusing on a face recognition based attendance system with getting a less false-positive rate using a threshold to confidence i.e. euclidean distance value while detecting unknown persons and save their images. Compare to other euclidean distance-based algorithms like Eigenfaces and Fisherfaces, Local Binary Pattern Histogram (LBPH) algorithm is better [11]. We used Haar cascade for face detection because of their robustness and LBPH algorithm for face recognition. It is robust against monotonic grayscale transformations. Scenarios such as face recognition rate, false-positive rate for that and false-positive rate with and without using a threshold in detecting unknown persons are considered to evaluate our system. We got face recognition rate of students is 77% and its false-positive rate is 28%. This system is recognizing students even when students are wearing glasses or grown a beard. Face Recognition of unknown persons is nearly 60% for both with and without applying threshold value. Its false-positive rate is 14% and 30% with and without applying threshold respectively.
A Comprehensive Study of Load Balancing Approaches in the Cloud Computing Environment and a Novel Fault Tolerance Approach

The past few years have witnessed the emergence of a novel paradigm called cloud computing. CC aims to provide computation and resources over the internet via dynamic provisioning of services. There are several challenges and issues associated with implementation of CC. This research paper deliberates on one of CC main problems i.e. load balancing (LB). The goal of LB is equilibrating the computation on the cloud servers such that no host is under/ overloaded. Several LB algorithms have been implemented in literature to provide effective administration and satisfying customer requests for appropriate cloud nodes, to improve the overall efficiency of cloud services, and to provide the end user with more satisfaction. An efficient LB algorithm improves efficiency and asset's usage through effectively spreading the workload across the system's different nodes. This review research paper objective is to present critical study of existing techniques of LB, to discuss various LB parameters i.e. throughput, performance, migration time, response time, overhead, resource usage, scalability, fault tolerance, power savings, etc. The research paper also discusses the problems of LB in the CC environment and identifies the need for a novel LB algorithm that employs FT metrics. It has been found that traditional LB algorithms are not good enough and they do not consider FT efficiency metrics for their operation. Hence, the research paper identifies the need for FT efficiency metric in LB algorithms which is one of the main concerns in cloud environments. A novel algorithm that employs FT in LB is therefore proposed.
Bitcoin investment: a mixed methods study of investment motivations

Bitcoin is a well-established blockchain-based cryptocurrency that has attracted a great deal of attention from media and regulators alike. While millions of individuals invest in bitcoin, their motivations for doing so are less clear than with traditional investment decisions. We argue that the technical nature of bitcoin investments gives it unique characteristics and, consequently, that we lack a thorough understanding of how this affects the motivations behind bitcoin investment. We use a mixed method approach consisting of qualitative (n = 73) and quantitative (n = 150) studies and fuzzy-set qualitative comparative analysis (fsQCA) to identify seven bitcoin-specific motivations (profit expectancy, ease of bitcoin acquisition, support of bitcoin ideology, investment skills, risk affinity, anticipated and experienced inaction regret) and how configurations of them explain bitcoin investment. The findings reveal, among others, that some individuals invest in bitcoin because they support the bitcoin ideology. Contrary to the traditional investment literature, profit expectancy is not a necessary condition to the extent that there is one empirical configuration of motivations that explains that individuals also invest in bitcoin even if they do not expect profits. The results disclose non-trivial investment motivation configurations and lay the groundwork for future studies of the role of cryptocurrencies in society.