Reviewerly/exHarmony huggingface dataset:
Cross-Layer Joint Optimization Algorithm for Adaptive Video Streaming in MEC- Enabled Wireless Networks

HTTP Adaptive Streaming (HAS) is one of the most promising solutions for delivering streaming video services to users. However, due to its client-driven nature, it is challenging for telecom operators and mobile service providers to guarantee a certain level of quality of experience (QoE) for their mobile users when using HAS. Researches have shown that when various HAS clients compete simultaneously to obtain bandwidth resources from a shared network, this leads to unfairness and under-utilization of the system and degrades customers' QoE. In order to address these issues and provide some coordination between HAS clients, this paper introduces an innovative solution which employs a recently introduced MPEG solution, called Server and Network Assisted Dynamic Adaptive Streaming over HTTP (SAND-DASH) and Multi-Access Edge Computing (MEC) support in the existing wireless network environment. The solution is based on an optimisation algorithm which maximizes QoE, fairness, and system utilization while considering the radio resource constraints. The paper formulates a novel cross-layer joint optimization model which considers QoE, fairness, system utilization, and radio resource limitation. Then it describes the MEC-OP-SA, an efficient online greedy-based algorithm for solving the modeled optimization problem. The results indicate that the proposed algorithm, MEC-OP-SA, improves the overall system utilization and fairness while maintaining a high QoE level for HAS clients compared to existing solutions.
SPEEDY on Cortex–M3: Efficient Software Implementation of SPEEDY on ARM Cortex–M3

The SPEEDY block cipher suite announced at CHES 2021 shows excellent hardware performance. However, SPEEDY was not designed to be efficient in software implementations. SPEEDY’s 6-bit S-box and bit permutation operations generally do not work efficiently in software. We implemented SPEEDY block cipher by applying the implementation technique of bit-slicing. As an implementation technique of bit-slicing, SPEEDY can be operated in software very efficiently and can be applied in microcontroller. By calculating the round key in advance, the performance on ARM Cortex-M3 for SPEEDY-5-192, SPEEDY-6-192, and SPEEDY-7-192 are 65.7, 75.25, and 85.16 clock cycles per byte (i.e. cpb), respectively. It showed better performance than AES-128 constant-time implementation and GIFT constant-time implementation in the same platform. Through this, we conclude that SPEEDY can show good performance on embedded environments.
Detection of wearing safety helmet for workers based on YOLOv4

As a necessary protective equipment for workers to enter and exit the construction environment, safety helmets are of great significance to ensure the safe operation of workers. However, there are still some workers who lack safety awareness and do not wear safety helmets from time to time, and there are great safety hazards. This paper is based on the target detection algorithm of YOLOv4, focusing on the real construction site, and real-time detection of workers' helmet wearing in complex scenes. In order to solve the common phenomenon that only one type of helmet is detected, the helmet that is standing on the table or held in the hand is also recognized as a worker wearing a helmet. This article adds a human body model based on the helmet training. Training makes the detected helmet and the human body have a one-to-one correspondence. Experimental results show that the model achieves 93% accuracy on 9986 hard hat data sets. At the same time, the model has been deployed to the actual construction site to meet the daily detection of workers' hard hats, which verifies the effectiveness of the algorithm.
Photovoltaic Potential Spatial Estimation Considering Shading Effects

Several works estimate the photovoltaic potential using light detection and ranging data. However, such data may not be available in some countries. This paper presents a novel methodology to estimate the potential photovoltaic spatial distribution considering shading effects caused by tall buildings. The proposal uses reduction factors and buildings'3D models. Shading effects are analyzed in representative blocks for the study area. The proposed method is applied in an urban zone in Sao Paulo in Brazil, where tall buildings and residential houses are built. The estimate indicates a reduction of 3.2%in the PV potential due to shading effects. Furthermore, the proposed method allows the calculation of the daily average PV potential. The potential photovoltaic estimate is helpful for distribution utilities and governmental authorities to perform hosting capacities and sustainable planning studies.
Superpixel Clustering

This paper offers the simplest understanding of superpixels as maximal pixel clusters providing error-free obtaining of a limited sequence of accessible optimal piecewise constant image approximations with the minimum achievable approximation error (total squared error) E for each number of pixel clusters. The superpixel hierarchy is generated by the intersection of optimal image partitions into an increasing number of clusters g. It is irregular and differs markedly from the non-hierarchical sequence of optimal approximations, since described by a non-convex dependence of the approximation error E on the cluster number g. Therefore, the task of hierarchical superpixel clustering is posed and solved, which is briefly described in the paper.
Image Quality Assessment on Identity Documents

This paper developed a method for performing Image Quality Assessment (IQA) on ID-card images. First, we built the dataset, consisting of 204 images from Chilean ID cards, containing real and tampered images with varying quality levels. Then, we evaluated different features, obtaining the best results using the BRISQUE features and a newly trained SVR, with an R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score of 0.5868. This proposed method is called BRISQUE-ID. The IQA on ID cards can be used as a pre-processing stage for discarding lousy quality images and helping the subsequent steps in the processing pipeline.
Multi WGAN-GP loss for pathological stain transformation using GAN

In this paper, we proposed a new loss function to train the conditional generative adversarial network (CGAN). CGANs use a condition to generate images. Adding a class condition to the discriminator helps improve the training process of GANs and has been widely used for CGAN. Therefore, many loss functions have been proposed for the discriminator to add class conditions to it. Most of them have the problem of adjusting weights. This paper presents a simple yet new loss function that uses class labels, but no adjusting is required. This loss function is based on WGAN-GP loss, and the discriminator has outputs of the same order (the reason for no adjusting). More specifically, the discriminator has K (the number of classes) outputs, and each of them is used to compute the distance between fake and real samples of one class. Another loss to enable the discriminator to classify is also proposed by applying SoftMax to the outputs and adding cross-entropy to our first loss. The proposed loss functions are applied to a CGAN for image-to-image translation (here stain transformation for pathological images). The performances of proposed losses with some state-of-the-art losses are compared using Histogram Intersection Score between generated images and target images. The accuracy of a classifier is also computed to measure the effect of stain transformation. Our first loss performs almost similar to the loss that achieved the best results. (Abstract)
Two case studies of very long-term retention

Here, we present two case studies of extremely long-term retention. In the first, Richard C. Atkinson (RCA) had learned word sequences during experiments for his dissertation. Sixty-seven years later, RCA relearned the same words either in the original order or in a scrambled order. RCA reported no conscious awareness that the words were those used in the dissertation, but his relearning was considerably better for the words in the original order. In the second case study, Denis Cousineau had searched displays of objects for the presence of a target. The targets and foils had been novel at the beginning of training, and his search rate improved markedly over about 70 sessions. After 22 years, retraining showed retention of much of this gain in rate of search, and the rate was markedly faster than search for new objects with the same structure as the trained set. We consider interpretations of these case studies for our understanding of long-term retention.
Non-exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks

Supervised learning, while deployed in real-life scenarios, often encounters instances of unknown classes. Conventional algorithms for training a supervised learning model do not provide an option to detect such instances, so they miss-classify such instances with 100% probability. Open Set Recognition (OSR) and Non-Exhaustive Learning (NEL) are potential solutions to overcome this problem. Most existing methods of OSR first classify members of existing classes and then identify instances of new classes. However, many of the existing methods of OSR only makes a binary decision, i.e., they only identify the existence of the unknown class. Hence, such methods cannot distinguish test instances belonging to incremental unseen classes. On the other hand, the majority of NEL methods often make a parametric assumption over the data distribution, which either fail to return good results, due to the reason that real-life complex datasets may not follow a well-known data distribution. In this paper, we propose a new online non-exhaustive learning model, namely, Non-Exhaustive Gaussian Mixture Generative Adversarial Networks (NE-GM-GAN) to address these issues. Our proposed model synthesizes Gaussian mixture based latent representation over a deep generative model, such as GAN, for incremental detection of instances of emerging classes in the test data. Extensive experimental results on several benchmark datasets show that NE-GM-GAN significantly outperforms the state-of-the-art methods in detecting instances of novel classes in streaming data.
BoundaryNet: An Attentive Deep Network with Fast Marching Distance Maps for Semi-automatic Layout Annotation

Precise boundary annotations of image regions can be crucial for downstream applications which rely on region-class semantics. Some document collections contain densely laid out, highly irregular and overlapping multi-class region instances with large range in aspect ratio. Fully automatic boundary estimation approaches tend to be data intensive, cannot handle variable-sized images and produce sub-optimal results for aforementioned images. To address these issues, we propose BoundaryNet, a novel resizing-free approach for high-precision semi-automatic layout annotation. The variable-sized user selected region of interest is first processed by an attention-guided skip network. The network optimization is guided via Fast Marching distance maps to obtain a good quality initial boundary estimate and an associated feature representation. These outputs are processed by a Residual Graph Convolution Network optimized using Hausdorff loss to obtain the final region boundary. Results on a challenging image manuscript dataset demonstrate that BoundaryNet outperforms strong baselines and produces high-quality semantic region boundaries. Qualitatively, our approach generalizes across multiple document image datasets containing different script systems and layouts, all without additional fine-tuning. We integrate BoundaryNet into a document annotation system and show that it provides high annotation throughput compared to manual and fully automatic alternatives.
Explainable AI for Healthcare: A Study for Interpreting Diabetes Prediction

With extensive use of machine learning in field of healthcare, explainable AI becomes a vital part of machine learning process by helping healthcare practitioners for understanding critical decision-making process. Machine learning has greatly impacted medical field by identification of factors responsible for vulnerable health risks and factors affecting them. Hence, interpretation of machine learning model is extremely critical in the healthcare sector. Healthcare practitioners must understand factors that affect decision-making process for diseases like diabetes made by machine learning model. Explainable artificial intelligence helps clinical practitioners in interpretation of black-box models and their decision-making process to verify why a particular decision was taken by machine learning model that is extremely important in medical field. Interpretability in machine learning methods can help healthcare practitioners to understand the features that affect the decision-making process for detection of diseases like diabetes. In the past few years, researchers have been successful in detecting diabetes using machine learning models. Explainable artificial intelligence is exceptionally useful in turning black-box into glass box machine learning model and explains their respective decision-making process in healthcare. The paper presents various interpretable machine learning methods for understanding factors affecting decision-making in case of diabetes prediction that could be explained using model agnostic methods.
Generative synthesis of logos across DCT domain

Generative learning in pixel domain has achieved great success in exploiting their correlations in processing images towards desired objectives, yet learning in frequency domain could provide added benefits in exploiting pixel correlations without worrying about their spatial locations and increasing their modeling costs. In this paper, we analyze the spectral bias from a frequency perspective to overcome such limitations and hence propose a dynamic self-adaptive optimization on GAN-based generative learning, leading to a dynamic and generative logo synthesis in DCT domain. To achieve exploitation of all the pixel correlations inside the whole image regardless of their spatial locations, we introduce an approximated DCT transformation and decompose both the input images and the generated images into relatively independent DCT frequency bands. As a result, a new channel of DCT domain generative learning can be established to support the existing pixel domain learning towards improved logo synthesis. Since learning across different frequency band constantly varies, we further propose a dynamic optimization scheme to maximize the effectiveness of contributions from each individual DCT frequency band. Extensive experiments are carried out and the results in comparison with the existing state of the arts illustrate that our proposed achieves significant superiority in terms of both synthesized logo quality, integrity and variety.
Giving an Adversary Guarantees (Or: How to Model Designated Verifier Signatures in a Composable Framework)

When defining a security notion, one typically specifies what dishonest parties cannot achieve. For example, communication is confidential if a third party cannot learn anything about the messages being transmitted, and it is authentic if a third party cannot impersonate the real (honest) sender. For certain applications, however, security crucially relies on giving dishonest parties certain capabilities. As an example, in Designated Verifier Signature (DVS) schemes, one captures that only the designated verifier can be convinced of the authenticity of a message by guaranteeing that any dishonest party can forge signatures which look indistinguishable (to a third party) from original ones created by the sender.
Mathematical Model of Identification of Radar Targets for Security of Objects of Critical Infrastructure

The research is devoted to the development of a mathematical model for identifying radar targets while protecting critical infrastructure facilities. The features of identification of dangerous targets during radar observation are determined. The solution to the problem of identification of radar targets is disclosed. It is shown that the prototype for creating Fourier images of radar targets is a function that approximates the function for single, point targets, and four variations of the comb function for distributed, extended targets. The proposed mathematical model for identifying radar targets is a system of five analytical dependencies that make it possible to create typical and specific images for various classes of radar targets observed during the protection of critical infrastructure objects against the background of masking stationary interference.
Collision Avoidance for Mobile Robots Using Proximity Sensors

Due to the rise of e-commerce material handling industry has been experiencing significant changes, especially in the COVID-19 pandemic. Notwithstanding the broad utilization of Automated Guided Vehicles (AGVs) for many years, the demand for Autonomous Mobile Robot (AMR) is rapidly increasing. One of the main challenges in autonomous operation in an unstructured environment is gapless perception. In this paper, we present a concept for reactive collision avoidance using Capacitive Proximity Sensor (CPS), with the goal to augment robot perception in close proximity situations. We propose a proximity-based potential field method using capacitive measurement for collision avoidance. A local minima problem is solved by applying tangential forces around the virtual obstacle points. We evaluate the proof-of-concept both in simulation and on a real mobile robot equipped with CPS. The results have shown that capacitive sensing technology can compensate localization tolerance and odometry drift closing the perception gap in close proximity scenarios.
DAE-GCN: Identifying Disease-Related Features for Disease Prediction

Learning disease-related representations plays a critical role in image-based cancer diagnosis, due to its trustworthy, interpretable and good generalization power. A good representation should not only be disentangled from the disease-irrelevant features, but also incorporate the information of lesion’s attributes (e.g., shape, margin) that are often identified first during cancer diagnosis clinically. To learn such a representation, we propose a Disentangle Auto-Encoder with Graph Convolutional Network (DAE-GCN), which adopts a disentangling mechanism with the guidance of a GCN model in the AE-based framework. Specifically, we explicitly separate the encoded features into disease-related features and others. Among such features that all participate in image reconstruction, we only employ the disease-related features for disease prediction. Besides, to account for lesions’ attributes, we propose to leverage the attributes and adopt the GCN to learn them during training. Take mammogram mass benign/malignant classification as an example, our DAE-GCN helps improve the performance and the interpretability of cancer prediction, which can be verified by state-of-the-art performance on one public dataset DDSM and three in-house datasets.
Transferability of neural network clinical deidentification systems

Neural network deidentification studies have focused on individual datasets. These studies assume the availability of a sufficient amount of human-annotated data to train models that can generalize to corresponding test data. In real-world situations, however, researchers often have limited or no in-house training data. Existing systems and external data can help jump-start deidentification on in-house data; however, the most efficient way of utilizing existing systems and external data is unclear. This article investigates the transferability of a state-of-the-art neural clinical deidentification system, NeuroNER, across a variety of datasets, when it is modified architecturally for domain generalization and when it is trained strategically for domain transfer.We conducted a comparative study of the transferability of NeuroNER using 4 clinical note corpora with multiple note types from 2 institutions. We modified NeuroNER architecturally to integrate 2 types of domain generalization approaches. We evaluated each architecture using 3 training strategies. We measured transferability from external sources; transferability across note types; the contribution of external source data when in-domain training data are available; and transferability across institutions.Transferability from a single external source gave inconsistent results. Using additional external sources consistently yielded an F1-score of approximately 80%. Fine-tuning emerged as a dominant transfer strategy, with or without domain generalization. We also found that external sources were useful even in cases where in-domain training data were available. Transferability across institutions differed by note type and annotation label but resulted in improved performance.
Visualization on charts – manipulations and distortions

The article tackles the issues of manipulations and distortions in the visualization of information on charts. These are serious problems as nowadays more and more often decisions are made and actions are taken on the basis of information provided in a graphic form. The article shows both unconscious and deliberate distortions in the construction of charts. The article is also a review. Various forms of information distortion have been analysed and presented, the awareness of which will increase concentration when reading the visualization and may allow to avoid misinterpreting facts and making wrong decisions, which is significant, especially in the field of business.
Experience Sampling Tool for Repetitive Skills Training in Sports using Voice User Interface

Repetitive skills training (RST) is a commonly used method for improving skills. Although wearable devices and existing context-aware technologies allow us to easily detect objective data during RST, subjective data have not been collected effectively, even though both objective and subjective data are important for RST. In this paper, we propose and implement a prototype system, called MiQ, to collect subjective data with minimum workload during RST for sports. MiQ allows us to record subjective data hands-free via a voice user interface (VUI). We also discuss the future scope of the proposed prototype system.