Reviewerly/exHarmony huggingface dataset:
Combining Negative Selection Techniques for Triplet Mining in Deep Metric Learning

The selection of training samples for triplet network-based deep metric learning is referred to as triplet mining. It has been recently found, that the selection of these triplets has a crucial effect on the performance of the model. Based on the definition of triplet loss, different sampling techniques are known: random selection is referred to easy mining due to its low computational cost. The selecting of samples where the resulting loss is non-zero is referred to as hard mining. The maximal loss comes from selecting the hardest negative, which is also the computationally most expensive technique. In this paper a combined method for negative sampling is presented and evaluated. For each training step, an anchor is selected randomly, easy positive mining is applied, followed by choosing from the hardest, a hard or a semi-hard sampling policy. Results show that the method performs well when no initial pretraining of model parameters is done, and discriminative ability is similar when compared to models where multi-class pretraining was applied.
3D Face Reconstruction for Forensic Recognition - A Survey

3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make unclear its possible role in bringing evidence to a lawsuit. Shedding some light on this matter is the goal of the present survey, where we start by clarifying the relation between forensic applications and biometrics. To our knowledge, no previous work adopted this relation to make the point on the state of the art. Therefore, we analyzed the achievements of 3D face reconstruction algorithms from surveillance videos and mugshot images and discussed the current obstacles that separate 3D face reconstruction from an active role in forensic applications.
Variational Parameter Optimization of Quantum-classical Hybrid Heuristics on Near-term Quantum Computer

Currently available quantum processors are noisy intermediate-scale quantum (NISQ) devices. The variational quantum eigensolver (VQE) is an algorithm that is closer to near-term applicability due to lower quantum hardware requirements. In VQE, trial states with variational parameters are prepared by quantum computers, and the optimal parameters are determined by a classical optimizer. This optimization is known to be an NP-hard problem. In this work, we make a comparison between various gradient-free optimizers in terms of approximation ratio and function evaluations. As a result, we find that COBYLA method is the best to find the approximated solution with a lower number of executions of quantum computers.
Monitoring of children's independent work in the system of additional education using digital technologies

The article talks about organization of independent work of children in the modern system of additional education through digital technologies. The concept of "independent work" is disclosed. The role of a teacher in organizing independent work for children in additional education is analyzed. The article considers the introduction of digital technologies into the learning process, which activates motivational, cognitive, activity components of educational activity. Classes for children in an institution of additional education based on digital technologies make it possible to achieve a faster inclusion of a student in educational and cognitive activities. This is due to: focusing on children's interests; creation of motivational and problematic situations in practical implementation of the topic under study; preparation of didactic materials using digital technologies. In order to identify the level of formation of independent work skills through digital technologies, a study was carried out. The study showed that children's ability to work independently is mostly formed at a low or medium level. The introduction of multi-level tasks through digital technologies has increased the level of children's independence in additional education system.
The JPS Pathfinding System in 3D

The ability to quickly compute shortest paths in 3D grids is a technological enabler for several applications such as pipe routing and computer video games. The main challenge is how to deal with the many symmetric permutations of each shortest path. We tackle this problem by adapting Jump Point Search (JPS), a well-known symmetry breaking technique developed for fast pathfinding in 2D grids. We give a rigorous reformulation of the JPS pathfinding system into 3D and we prove that our new algorithm, JPS-3D, is optimality preserving. We also develop a novel method for limiting scan depth during jump operations, which can further reduce search time. Experimental results show significant improvements versus online A* search and previous attempts at generalising JPS. We demonstrate that searching with adaptive scan limits can yield additional speedups of over an order of magnitude.
Gesture Recognition Algorithm Introducing Ghost Feature Mapping and Channel Attention Mechanism

To solve the problem that the lightweight object detection network has insufficient ability to extract static gesture features, high false detection rate and missed detection rate, a lightweight gesture recognition algorithm is proposed based on YOLOv4-tiny network structure. First, a more powerful and low-cost ghost feature mapping is introduced to enhance the ability of network to obtain multi-scale gesture features. Then, the embedded channel attention mechanism realizes feature recalibration and achieves the purpose of reducing background interference. Finally, Swish is used as the main activation function to further improve the accuracy of gesture recognition. The experimental results on the gesture dataset show that the proposed algorithm has better recognition performance than YOLOv4-tiny. For multi-scale gestures under different environmental conditions, the algorithm achieves accurate classification as well as real-time detection, and has better recognition performance for small-scale gestures.
Real-time Human Tracking System using Histogram Intersection Distance in Firefly Optimization Based Particle Filter

Real-time human tracking in a video have numerous applications.For security and surveillance application, the tracking system with PTZ (Pan, Tilt, and Zoom) camera is expected to track an object correctly regardless of the object orientation.Numerous studies reported that Particle Filter (PF) is reliable for color object tracking.However, the PF algorithm still suffers from impoverishment and degeneration in the resampling process.These problems can be resolved by combining the PF with Firefly Optimization (FO) in the resampling process.This research proposes the use of Histogram Intersection distance to build a likelihood function in PF to achieve real-time implementation.The Firefly Optimization Algorithm-based Particle Filter (FOAPF) with Histogram Intersection distance was compared to FOAPF with Bhattacharyya distance, resulting in lower RMSE (Root Mean Square Error) in tracking TB datasets.The result shows that when the Histogram Intersection distance was implemented, a faster average time of 1.8 ms was achieved than 1.9 ms when using Bhattacharyya distance.It shows the time result slightly different.The FOAPF with Histogram Intersection distance results in the TB datasets perform a low RMSE of 4.96 and 12.07, and private datasets show a low RMSE of 16.92 and 8.80, with the real-time implementation of 30 FPS and 50 particles.The comparison presents the successful implementation of the proposed method as a tracker to enhance human movement tracking with real-time implementation.
Neuroimaging Harmonization Using cGANs: Image Similarity Metrics Poorly Predict Cross-Protocol Volumetric Consistency

Computer-aided clinical decision support tools for radiology often suffer from poor generalizability in multi-centric frameworks due to data heterogeneity. In particular, magnetic resonance images depend on a large number of acquisition protocol parameters as well as hardware and software characteristics that might differ between or even within institutions. In this work, we use a supervised image-to-image harmonization framework based on a conditional generative adversarial network to reduce inter-site differences in T1-weighted images using different dementia protocols. We investigate the use of different hybrid losses including standard voxel-wise distances and a more recent perceptual similarity metric, and how they relate to image similarity metrics and volumetric consistency in brain segmentation. In a test cohort of 30 multiprotocol patients affected by dementia, we show that despite improvements in terms of image similarity, the synthetic images generated do not necessarily result in reduced inter-site volumetric differences, therefore highlighting the mismatch between harmonization performance and the impact on the robustness of post-processing applications. Hence, our results suggest that traditional image similarity metrics such as PSNR or SSIM may poorly reflect the performance of different harmonization techniques in terms of improving cross-domain consistency.
Detecting and Locating Storage-Based Covert Channels in Internet Protocol Version 6

Increased usage of the Internet has risen the demand for more IP addresses across the globe resulting in replacement of IPv4 by IPv6 protocol. Hence, security of IPv6 has become a vital area of research. One of the serious threats to Internet security is the presence of Network Covert Channels (NCCs) that provide substantial aid for performing covered communications like exchanging secret data and/or exfiltrating secret information from the organizations. To detect such malicious activities, there is an urgent requirement to develop and deploy efficient detection mechanisms in real-time networks. Further, to decode the hidden communications, there is an additional need to identify the location of covert data. Thus, this paper proposes a system for detecting and locating storage-based NCC(s) in IPv6 using Deep Neural Network (DNN) and One-vs-Rest (OvR) technique with Support Vector Machine (SVM). The proposed system is a two-layered system. Layer 1 detects an IPv6 packet as a normal/covert packet. Layer 2 locates the storage area of secret data in the covert packets detected at Layer 1. For experimentation, a dataset of normal and covert IPv6 packets was created using CAIDA’s dataset and pcapStego tool. Experiments were conducted to select the appropriate classifiers at both layers of the proposed system. With DNN and OvR SVM selected as the classifiers at Layer 1 and Layer 2 respectively, the proposed system locates covert data in IPv6 packets with an Accuracy of 99.7% and an average prediction time of 0.0719 seconds per covert sample, making it suitable for real-time deployment.
Blocking Bugs Identification via Binary Relevance and Logistic Regression Analysis

Blocking bugs, a type of bugs that prevents other bugs from being fixed, significantly increase the fixed time of both themself and the blocked bugs. Thus, these blocking bugs bring a considerable negative impact on software evolution. Therefore, the timely identification of blocking bugs is essential for software maintenance. This paper proposes an approach based on Binary Relevance(BR) and Logistic Regression(LR) analysis, called BR-LR, to predict bugs' blocking and blocked labels. We first filter and build a dataset consisting of two sets with a specific type of blocking relationship based on the ideas of BR. Then, we extract several fields from the bug reports and train the model by applying the logistic regression analysis with the constructed dataset in the first step, resulting in two prediction models for bug blocked and blocking labels. Finally, our approach combines the two prediction results to identify whether the bug is blocking or blocked. We also conduct empirical studies on seven open-source projects to verify the effectiveness of our approach. The final experimental results show that our model performs better from a partially correct perspective and can accurately predict bug labels than benchmarks. Specifically, the average accuracy of our model is 54.86%, and the average F1-measure is 50.61 %.
Dynamic Long-Short Range Structure Learning for Low-Illumination Remote Sensing Imagery HDR Reconstruction

A promising way for low-illumination (LI) remote sensing images high-dynamic range (HDR) reconstruction is to model the mapping function from the input LI images to the corresponding high-quality counterpart using deep convolution neural networks. Due to various image contents, the key for achieving pleasing performance lies on comprehensively exploit the image-specific long-rang (e.g., non-local similarity, low-rank) and short-range (e.g., local similarity, texture etc.) structures in the LI images using appropriate network architecture. However, most existing methods can only exploit either short-range or long-range structures that are contentagnostic shared across all images, thus limiting their generalization capacity. To tackle this problem, we propose a dynamic long-short range structure learning framework for LR remote sensing images HDR reconstruction. In contrast to existing methods, we introduce a novel two-branch network architecture including a pixel-aware dynamic module that can adaptively exploit the pixel-aware short-range structure surrounding each pixel depending on its feature representation, and a long-range transformer module that dynamically exploit the long-range correlation between image patchesin the deep feature space. Then, the learned long-short range structures are integrated and cast into pixel-wise scaling factors of an illumination enhance module to restore the LI image. It empowers us to effectively exploit the image-specific long-short range structures of each input IL images for accurate HDR reconstruction. Experimental results on remote sensing images with different levels of IL demonstrate the effectiveness of the proposed method.
3D-PL: Domain Adaptive Depth Estimation with 3D-Aware Pseudo-Labeling

For monocular depth estimation, acquiring ground truths for real data is not easy, and thus domain adaptation methods are commonly adopted using the supervised synthetic data. However, this may still incur a large domain gap due to the lack of supervision from the real data. In this paper, we develop a domain adaptation framework via generating reliable pseudo ground truths of depth from real data to provide direct supervisions. Specifically, we propose two mechanisms for pseudo-labeling: 1) 2D-based pseudo-labels via measuring the consistency of depth predictions when images are with the same content but different styles; 2) 3D-aware pseudo-labels via a point cloud completion network that learns to complete the depth values in the 3D space, thus providing more structural information in a scene to refine and generate more reliable pseudo-labels. In experiments, we show that our pseudo-labeling methods improve depth estimation in various settings, including the usage of stereo pairs during training. Furthermore, the proposed method performs favorably against several state-of-the-art unsupervised domain adaptation approaches in real-world datasets.
Improvising Breast Cancer detection using CNN, VGG and SSD Algorithms

Healthcare is one area where artificial intelligence technology is having a rising impact. In the early stages of cancer, medical imaging is critical, to increase the effectiveness of cancer diagnosis. With an alarming rate of one Indian woman being diagnosed with breast cancer every four minutes, breast cancer is on the increase among women. Despite the fact that breast cancer is a curable disease, early identification is vital to a favourable prognosis. Until recently, mammograms were the only way to identify breast cancer. Mammography, however, fails to detect breast cancer in women of all ages, leading to a high rate of false positive and false negative instances. This has dire repercussions. This experimental study discusses about using several Deep Learning algorithms to identify the cancerous area in mammograms. Three deep learning algorithms—Convolutional Neural Network, Visual Geometry Group, and Single Shot Detector—were applied in this research. This experiment, which had already been approved by the scientific and ethical authorities, was run on a Real Time Data collection of mammograms acquired from a 1250 bed hospital. There are 90 patients, mammography images in all, which are classified as benign or malignant. The accuracy of the Single Shot Detector method was 74%, which was greater than that of the other deep learning algorithms CNN (64%) and VGG (60%). Findings suggest that these deep learning algorithms can tremendously help the radiologists to improve their findings with respect to accuracy and precision.
Teaching and learning Requirements Engineering concepts: Peer-review skills vs. problem solving skills

Teaching of Software Engineering courses is challenging from many perspectives, where one of the key aspects is authentic assessment of the student work. Assessment of students’ work becomes especially challenging when we are dealing with large cohorts of students. The assessment typically should be done in a very short time frame, either to provide an early feedback within the semester or to allow for finalisation of the students’ grades for the course. Providing timely and comprehensive feedback is especially important during the semester, when students are more keen to improve their skills on the course topics. In this work we (1) analyse the level of correlation between students’ peer-review skills and their problem solving skills within Requirements Engineering activities, and (2) to what extent the students’ peer-feedback can be used to provide an early feedback within the semester or to mark students’ assessments.
A Self-Supervised Solution for the Switch-Toggling Visual Task

How a robot explores and interacts with the real world by itself is a major research challenge. On the other hand, causal reasoning and combinatorial generalization are indispensable parts of human intelligence for exploration and survival. This paper presents SelfSVT, a self-supervised solution for the switch-toggling visual task, of which the goal is to infer causalities of visual combinatorial effects on the environment. Specifically, when a robot takes over a new place with a set of light switches and knows nothing about the switches’ functions, it has to figure out how to transfer the environment from the current visual state to a goal visual state by toggling these switches by itself. SelfSVT trains efficient learning models that are able to perform the goal-conditioned visual task by directly reasoning the causalities of different visual states or inferring the switch states from its observations. In particular, we use the switch state to directly represent the combinatorial effect to make self-supervised learning possible and our framework adopts a siamese network with a discrete contrastive loss. It can perform causal induction and combinatorial generalization in a new environment with a few interactions. Our solution outperforms previous methods in both simulated and real-world environments and both static and dynamic environments. SelfSVT could achieve 100% success reasoning rates in most cases when there are sufficient interactions with the environment.
Look for the Change: Learning Object States and State-Modifying Actions from Untrimmed Web Videos

Human actions often induce changes of object states such as “cutting an apple”, “cleaning shoes” or “pouring coffee”. In this paper, we seek to temporally localize object states (e.g. “empty” and “full” cup) together with the corresponding state-modifying actions (“pouring coffee”) in long uncurated videos with minimal supervision. The contributions of this work are threefold. First, we develop a self-supervised model for jointly learning state-modifying actions together with the corresponding object states from an uncurated set of videos from the Internet. The model is self-supervised by the causal ordering signal, i.e. initialobject state → manipulating action → end state. Second, to cope with noisy uncurated training data, our model incorporates a noise adaptive weighting module supervised by a small number of annotated still images, that allows to efficiently filter out irrelevant videos during training. Third, we collect a new dataset with more than 2600 hours of video and 34 thousand changes of object states, and manually annotate a part of this data to validate our approach. Our results demonstrate substantial improvements over prior work in both action and object state-recognition in video.
Overview of JPEG Snack: A Novel International Standard for the Snack Culture

Most people watch short-form content, especially when viewing it on their smartphones. While short-form content is widely used for entertainment, it is also for essential information sharing with broad audiences. The importance of short-form content is self-evident, as Facebook and YouTube have introduced Facebook Reels and YouTube Shorts, respectively. The rapid, widespread adoption of short-form content indicates the need for a new international standard to ensure the interoperability of broad content and content creation tools. This article overviews a new international standard development effort in the Joint Photographic Experts Group (JPEG) in ISO/IEC JTC1/WG1 to create JPEG Snack (ISO/IEC 19566-8) to enable image-rich content creation and sharing. JPEG Snack images, audio, videos, and captions are embedded in the standard JPEG-1 (ISO/IEC 10918-1) image file. The JPEG Snack file comprises two components: i) a default JPEG-1 image and ii) an ISO base media file format (ISOMBFF (ISO/IEC 14496-12)) box-based file extension. The JPEG-1 image portion makes the file compatible with existing JPEG viewers for preview and user navigation, while enhanced JPEG Snack viewers can display the rich image/audio/video/caption short-form content. The article presents essential features of the JPEG Snack and its comparison with JPEG-linked media format (JLINK (ISO/IEC 19566-7)) and JPEG 360 (ISO/IEC 19566-6). It also presents the pros and cons of JPEG Snack over high-efficiency image file format (HEIF (ISO/IEC 23008-12)).
Academy Phoenix: Will Universities Reborn in Industry 5.0 Era, or Will They Lie Down in Ashes?

Industry 5.0 had been envisioned as a new era of the Industry that goes beyond simple efficiency and productivity. Instead of trivial economic benefits, it puts human society on a pedestal. The human needs and society as a whole are prioritised against the rise of material wealth. Cyberspace and physical space are merged to find a solution to social problems. The life-quality improvement, ageing society, climate change and sustainable development are new challenges that require a novel approach. People must stop working for the industry, but on the contrary, the industry must start working for people. There was always an idea conflict between the universities and the industry. Even though the industry is asking to train people for present today, the universities are teaching for tomorrow to be ready for the future. This is not enough anymore. Fulfilling the Industry 5.0 vision requires Universities 5.0. Excellent science needs to meet the needs of society. University research should be the pathfinder, while the industry is the accelerator of change. University students need a limitless global education, merging nations on the way to new knowledge and breakthrough discoveries. The industry should not be the only recipient of university graduates but the direct participant of the education and supporter of the technology transfer. This paper discusses the challenges and solutions in cooperation between technical universities and employers in the context of the Industry 5.0 trend. It also tries to answer the question of what are the limits of academic freedom in university education when dealing with the demands of Industry 5.0.