Reviewerly/exHarmony huggingface dataset:
Spatio-Temporal Event Segmentation for Wildlife Extended Videos

Using offline training schemes, researchers have tackled the event segmentation problem by providing full or weak-supervision through manually annotated labels or self-supervised epoch-based training. Most works consider videos that are at most 10's of minutes long. We present a self-supervised perceptual prediction framework capable of temporal event segmentation by building stable representations of objects over time and demonstrate it on long videos, spanning several days at 25 FPS. The approach is deceptively simple but quite effective. We rely on predictions of high-level features computed by a standard deep learning backbone. For prediction, we use an LSTM, augmented with an attention mechanism, trained in a self-supervised manner using the prediction error. The self-learned attention maps effectively localize and track the event-related objects in each frame. The proposed approach does not require labels. It requires only a single pass through the video, with no separate training set. Given the lack of datasets of very long videos, we demonstrate our method on video from 10 d (254 h) of continuous wildlife monitoring data that we had collected with required permissions. We find that the approach is robust to various environmental conditions such as day/night conditions, rain, sharp shadows, and windy conditions. For the task of temporally locating events at the activity level, we had an 80% activity recall rate for one false activity detection every 50 min. We will make the dataset, which is the first of its kind, and the code available to the research community. Project page is available at https://ramymounir.com/publications/EventSegmentation/ .
Energy Tracing and Blockchain Technology: A Primary Review

Reducing greenhouse emission is a mission for many organizations. Since road transportation is a major contributor of CO $$_2$$ emission, there is a shift towards electric vehicles (EV) rather than fuel vehicles due to zero CO $$_2$$ emission during operation stage. With the gradual shift towards EV, the demand for electricity would also increase. Thus, it is important to move our attention on how electricity is generated because the provenance of electricity supply is closely linked to climate change. Energy system is a complex resulting in difficulty to truly verify the claims of only using green energy source to generate electricity. Blockchain technology caught the attention of researchers to adapt this technology to trace the end-to-end process of products. The purpose of this paper is to identify the current state-of-art focusing on energy tracing and blockchain in academic and commercial sector. From our search, we identified one literature and one commercial project that focus on energy tracing. Effort focusing on energy tracing remain small. One of the reasons is the electricity is a non-physical attribute matter which makes tracing of the source challenging. The volatility of renewable energy source (RES) such as wind and solar power farms, along with complex energy distribution system, makes tracing harder. Current work on energy tracing remain scarce and more work should focus on this section to prevent rebound effect of carbon emission due to the lack of a transparent carbon footprint.
On The Batch Outsourcing Of Pairing Computations

Abstract Pairing-based cryptography is utilized in a wide range of devices, such as servers, mobile devices, smart cards and sensors. Pairing computation would be a burden for power and/or computation-restricted devices. Protocols for outsourcing pairing computations from limited devices to more resourceful devices are already proposed. These protocols naturally require verification of the computation and secrecy of the inputs and/or outputs. Similarly, batch pairing outsourcing protocols aim to improve efficiency over multiple runs of the state-of-the-art single pairing delegation protocols. Here, we will cover efficient, privacy preserving, secure batch pairing outsource protocols for each type based on secrecy of inputs and outputs. We propose the first generic outsourcing protocol where inputs and outputs of the pairing function are secret. In addition to this, we give some methods to avoid certain type of attacks, increase efficiency and get rid of pairing arithmetic. The proposed protocols enable limited devices to outsource pairing computations with only elliptic curve arithmetic.
Total transmittance measurement using an integrating sphere calibrated by a screen image synthesis system

The measurement of bidirectional scattering distribution function (BSDF) and total transmittance (TT) is a complicated issue. We have proposed a screen image synthesis (SIS) BSDF meter that provides high-speed and precise measurement. However, the measurement of TT using integrating sphere cannot offer a precise measurement. Therefore, we propose a calibration method that uses SIS BSDF meter and responsivity of the integrating sphere. It brings us not only precise BSDF function but precise TT measurement.
Deep active learning for suggestive segmentation of biomedical image stacks via optimisation of Dice scores and traced boundary length

Manual segmentation of stacks of 2D biomedical images (e.g., histology) is a time-consuming task which can be sped up with semi-automated techniques. In this article, we present a suggestive deep active learning framework that seeks to minimise the annotation effort required to achieve a certain level of accuracy when labelling such a stack. The framework suggests, at every iteration, a specific region of interest (ROI) in one of the images for manual delineation. Using a deep segmentation neural network and a mixed cross-entropy loss function, we propose a principled strategy to estimate class probabilities for the whole stack, conditioned on heterogeneous partial segmentations of the 2D images, as well as on weak supervision in the form of image indices that bound each ROI. Using the estimated probabilities, we propose a novel active learning criterion based on predictions for the estimated segmentation performance and delineation effort, measured with average Dice scores and total delineated boundary length, respectively, rather than common surrogates such as entropy. The query strategy suggests the ROI that is expected to maximise the ratio between performance and effort, while considering the adjacency of structures that may have already been labelled – which decrease the length of the boundary to trace. We provide quantitative results on synthetically deformed MRI scans and real histological data, showing that our framework can reduce labelling effort by up to 60–70% without compromising accuracy.
Exploring Streaming Media and Course Reserves: A Case Study

This article will detail a case study of Concordia University’s path from operating separate services of physical course reserves and media collections to pivoting toward the pandemic-prompted implementation of a new, large-scale, integrated streaming media course reserve service. While many libraries have offered streaming media and course reserve services during and prior to the pandemic’s arrival, this article brings to light how institutions can quickly upscale and blend services when unexpected events – like pandemics – create needs for fast optimization.
Scene-Specialized Multitarget Detector with an SMC-PHD Filter and a YOLO Network

You only look once (YOLO) is one of the most efficient target detection networks. However, the performance of the YOLO network decreases significantly when the variation between the training data and the real data is large. To automatically customize the YOLO network, we suggest a novel transfer learning algorithm with the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter and Gaussian mixture probability hypothesis density (GM-PHD) filter. The proposed framework can automatically customize the YOLO framework with unlabelled target sequences. The frames of the unlabelled target sequences are automatically labelled. The detection probability and clutter density of the SMC-PHD filter and GM-PHD are applied to retrain the YOLO network for occluded targets and clutter. A novel likelihood density with the confidence probability of the YOLO detector and visual context indications is implemented to choose target samples. A simple resampling strategy is proposed for SMC-PHD YOLO to address the weight degeneracy problem. Experiments with different datasets indicate that the proposed framework achieves positive outcomes relative to state-of-the-art frameworks.
Federated Phish Bowl: LSTM-Based Decentralized Phishing Email Detection

With increasingly more sophisticated phishing campaigns in recent years, phishing emails lure people using more legitimate-looking personal contexts. To tackle this problem, instead of traditional heuristics-based algorithms, more adaptive detection systems such as natural language processing (NLP)powered approaches are essential to understanding phishing text representations. Nevertheless, concerns surrounding the collection of phishing data that might cover confidential information hinder the effectiveness of model learning. We propose a decentralized phishing email detection framework called Federated Phish Bowl (FedPB) which facilitates collaborative phishing detection with privacy. In particular, we devise a knowledge-sharing mechanism with federated learning (FL). Using long short-term memory (LSTM) for phishing detection, the framework adapts by sharing a global word embedding matrix across the clients, with each client running its local model with Non-IID data. We collected the most recent phishing samples to study the effectiveness of the proposed method using different client numbers and data distributions. The results show that FedPB can attain a competitive performance with a centralized phishing detector, with generality to various cases of FL retaining a prediction accuracy of 83%.
CCGG: A Deep Autoregressive Model for Class-Conditional Graph Generation

Graph data structures are fundamental for studying connected entities. With an increase in the number of applications where data is represented as graphs, the problem of graph generation has recently become a hot topic. However, despite its significance, conditional graph generation that creates graphs with desired features is relatively less explored in previous studies. This paper addresses the problem of class-conditional graph generation that uses class labels as generation constraints by introducing the Class Conditioned Graph Generator (CCGG). We built CCGG by injecting the class information as an additional input into a graph generator model and including a classification loss in its total loss along with a gradient passing trick. Our experiments show that CCGG outperforms existing conditional graph generation methods on various datasets. It also manages to maintain the quality of the generated graphs in terms of distribution-based evaluation metrics.
Unextendible product operator basis

Unextendible product basis (UPB), a widely used tool in quantum information, exhibits nonlocality which is the powerful resource for quantum information processing. In this work we extend the definitions of nonlocality and genuine nonlocality from states to operators. We also extend UPB to the notions of unextendible product operator basis, unextendible product unitary operator basis (UPUOB) and strongly UPUOB. We construct their examples, and show the nonlocality of some strongly UPUOBs under local operations and classical communications. As an application, we distinguish the two-dimensional strongly UPUOB which only consumes three ebits of entanglement.
Soft Actor-Critic With Integer Actions

Reinforcement learning is well-studied under discrete actions. Integer actions setting is popular in the industry yet still challenging due to its high dimensionality. To this end, we study reinforcement learning under integer actions by incorporating the Soft Actor-Critic (SAC) algorithm with an integer reparameterization. Our key observation for integer actions is that their discrete structure can be simplified using their comparability property. Hence, the proposed integer reparameterization does not need one-hot encoding and is of low dimensionality. Experiments show that the proposed SAC under integer actions is as good as the continuous action version on robot control tasks and outperforms Proximal Policy Optimization on power distribution systems control tasks.
Dynamic Fusion Network for RGBT Tracking

For both visible and infrared images have their own advantages and disadvantages, RGBT tracking has attracted more and more attention. The key points of RGBT tracking lie in feature extraction and feature fusion of visible and infrared images. Current RGBT tracking methods mostly pay attention to both individual features (features extracted from images of a single camera) and common features (features extracted and fused from an RGB camera and a thermal camera), while pay less attention to the different and dynamic contributions of individual features and common features for different sequences of registered image pairs. This paper proposes a novel RGBT tracking method, called Dynamic Fusion Network (DFNet), which adopts a two-stream structure, in which two non-shared convolution kernels are employed in each layer to extract individual features. Besides, DFNet has shared convolution kernels for each layer to extract common features. Non-shared convolution kernels and shared convolution kernels are adaptively weighted and summed according to different image pairs, so that DFNet can deal with different contributions for different sequences. DFNet has a fast speed, which is 28.658 FPS. The experimental results show that when DFNet only increases the Mult-Adds of 0.02% than the non-shared-convolution-kernel-based fusion method, Precision Rate (PR) and Success Rate (SR) reach 88.1% and 71.9% respectively.
A One-Point Calibration Design for Hybrid Eye Typing Interface

We present an eye typing interface with one-point calibration, which is a two-stage design. The characters are clustered in groups of four characters. Users select a cluster by gazing at it in the first stage and then select the desired character by following its movement in the second stage. A user study was conducted to explore the impact of auditory and visual feedback on typing performance and user experience of this novel interface. Results show that participants can quickly learn how to use the system, and an average typing speed of 4.7 WPM can be reached without lengthy training. The subjective data of participants revealed that users preferred visual feedback over auditory feedback while using the interface. The user study indicates that this eye typing interface can be used for walk-up-and-use interactions, as it is easily understood and robust to eye-tracking inaccuracies. Potential areas of application, as well as possibilities for further improvements, are discussed.
Smart auto mining (SAM) for industrial IoT blockchain network

This work proposes smart auto mining (SAM) for resource-efficient mining in a blockchain network. The SAM algorithm stops the miners when there is zero pending transaction and starts the miner when there is at least one transaction sent into the network. The miner listens to the network to identify when a transaction has been made by a node. The model does not need any instruction to start mining when there is a pending transaction. The results show that a private Ethereum network produced over 300% more blocks in a 12-h period with 599,950 transactions compared to when SAM is applied. The proposed algorithm is also able to reduce the storage used by the chaindata by 14%. The overhead of mining is decreased by reducing the production of empty blocks in the network which saves energy, storage space, network bandwidth and computational complexity.
Key challenges in security of <scp>IoT</scp> devices and securing them with the blockchain technology

Abstract Internet of Things (IoT) data processing enables rapid access to large volumes of data with the possibility of improving service‐oriented business models. As such, the rapid adoption of IoT devices has resulted in a large number of low‐powered devices integrating into traditional communication networks and influencing practically all assets of everyday life. However, the integration of IoT devices into traditional networks comes at a price, often in the form of added security challenges or administrative complexity. Some of these challenges may be addressed by leveraging technology developed from an unlikely source, crypto currencies. Crypto currencies introduced the notion of a blockchain, which is comprised of several underlying technologies that may be combined with IoT protocols to enable secure mechanisms for reaping the benefits of IoT devices. This paper introduces key challenges in securing IoT devices as well as important components of blockchain technology that may contain the keys to addressing these challenges. Emerging IoT security solutions are identified and evaluated based on their assessed effectiveness.
A Robust and Generalized Framework for Adversarial Graph Embedding

Graph embedding is essential for graph mining tasks. With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high-quality graph embedding for various types of graphs, among which the Generative Adversarial Networks (GAN) based methods attract increasing attention among researchers. However, most GAN-based generator-discriminator frameworks randomly generate the negative samples from the original graph distributions to enhance the training process of the discriminator without considering the noise. In addition, most of these methods only focus on the explicit graph structures and cannot fully capture complex semantics of edges such as various relationships or asymmetry. In order to address these issues, we propose a robust and generalized framework named AGE. It generates fake neighbors as the enhanced negative samples from the implicit distribution, and enables the discriminator and generator to jointly learn robust and generalized node representations. Based on this framework, we propose three models to handle three types of graph data and derive the corresponding optimization algorithms, namely the UG-AGE and DG-AGE for undirected and directed homogeneous graphs, respectively, and the HIN-AGE for heterogeneous information networks. Extensive experiments show that our methods consistently and significantly outperform existing state-of-the-art methods across multiple graph mining tasks.
Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics

A large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems.While most variation operators in existing metaheuristics are empirically designed, new operators are automatically designed in this work, which are expected to be search space independent and thus exhibit robust performance on different problems.This work first investigates the influence of translation invariance, scale invariance, and rotation invariance on the search behavior and performance of some representative operators.This work then deduces the generic form of translation, scale, and rotation invariant operators, and proposes a principled approach for the automated design of operators, which searches for high-performance operators based on the deduced generic form.The experimental results demonstrate that the operators generated by the proposed approach outperform state-of-the-art ones on a variety of problems with complex landscapes and up to 1000 decision variables.
Applications of Digital Mobile Technologies in Response to the COVID-19 Pandemic: Some Evidence from Frontline Healthcare Workers in Three Tertiary Hospitals in Ghana

The World Health Organization (WHO) declared the COVID-19 pandemic a public health disaster of global concern on the 30th January 2020. With the highly infectious COVID-19 virus inevitably spreading across nations and causing significant health and economic impacts, leveraging Digital Mobile Technologies (DMTs) by frontline healthcare practitioners has been touted as a lifeline. Since January 2020, various frontline health workers, health experts, and health institutions across the globe have progressively embraced the use of numerous DMTs as an essential means of restraining the highly infectious disease spread. DMTs such as smart mobile phones and other digital mobile technologies are the reliable means of sharing pertinent health information, awareness, and surveillance as well as facilitating access to crucial healthcare services, especially in designated COVID-19 health facilities. Therefore, this study employed, an online survey using an exploratory-based research design to investigate how the COVID-19 pandemic has revolutionized the usage of DMTs among (n=1,126) frontline healthcare practitioners in three leading tertiary hospitals in Ghana. We adopted technology acceptance model (TAM) to study the adoption and utilization of DMTs and their limitations by frontline healthcare workers in COVID-19 management. We found that 75.1% (n=846 out of 1,126) of frontline healthcare workers, comprising nurses, medical doctors, physician assistants, and COVID-19 contact tracers were aware of DMTs, adopted and used DMTs in response to the COVID-19 pandemic and its management. However, 93.0% (n=784 out of 846) of those who were aware of the DMTs employed them in their daily operations beyond the fight against the COVID-19 pandemic. Our analyses also indicate that increased usage of these DMTs has significantly enhanced public healthcare campaigns and education on the COVID-19 pandemic in Ghana. These mobile applications have also improved and boosted healthcare communications among practitioners on measures against the COVID-19 pandemic. In terms of originality, this study expands our understanding of digital technology in response to the COVID-19 Pandemic in less studied regions such as Africa, West Africa, and Ghana to be specific. Secondly, the study contributes to filling a gap in our understanding of how theories such as TAM function in crisis communication management. Finally, the study recommends health authorities and governments in developing countries incorporate DMTs in their current health care systems, especially in public health emergencies like COVID-19 preparedness.
Detection &amp; recognition of veiled and unveiled human face on the basis of eyes using transfer learning

Face detection and recognition are the most substantial research areas in computer vision and transfer learning due to the inspiring nature of faces as an object. In this paper, we show that we can obtain promising results on the standard face databanks when the features are extracted merely from the eye. The contributions of this work are divided into three parts, specifically face detection, eyes detection and recognition for individual identification. The key features for face recognition, used in this study are the eyes, nostrils, and mouth. The key features for eyes recognition are center of left eye, center of right eye, midpoint of eyes and extraction of eyebrows. Extracted Local Binary Pattern Histogram (LBPH) method is used to extract the facial features of face images whose computational complexity is very low and these features contain simple pixel values. Furthermore, neighborhood pixels are calculated to extract effective facial feature to realize eyes recognition and person verification. This study is able to identify an individual on the basis of even a single eye. The algorithm finds the brighter eye from the face and then, on the basis of that eye, the person is identified and the name of person is provided. The experimental results of this study show that faces are recognized accurately and LBPH method has achieved 98.2% accuracy.