Reviewerly/exHarmony huggingface dataset:
Using Open Data for Training Deep Learning Models: A Waste Identification Case Study

One of the main challenges of building commercial solutions with Supervised Deep Learning is the acquisition of large custom-labeled datasets. These large datasets usually fit neither commercial industries’ production times nor budgets. The case study presents how to use Open Data with different features, distributions, and incomplete labels for training a tailored Deep Learning multi-label model for identifying waste materials, type of packaging, and product brand. We propose an architecture with a CBAM attention module, and a focal loss, for integrating multiple labels with incomplete data and unknown labels, and a novel training pipeline for exploiting specific target-domain features that allows training with multiple source domains. As a result, the proposed approach reached an average F1-macro-score of 86% trained only with 13% tailored data, which is 15% higher than a traditional approach. In conclusion, using pre-trained models and highly available labeled datasets reduces model development costs. However, it is still required to have target data that allows the model to learn specific target domain features.
Evaluation of AI Techniques to Implement Proactive Container Auto-scaling Strategies

This paper evaluates techniques for improving the use of cloud computing resources through autoscaling. Autoscaling, also referred to as auto-scaling or automatic scaling, is a cloud computing technique for dynamically allocating computational resources. Autoscaling can be reactive (responding to resource needs as they arise) or proactive (anticipating future demands). Our study proposes the use of AI-based models to predict the creation of new computational entities under varying load conditions. The proposed methodology included data cleaning, correlation analysis to select relevant features, and the evaluation of several supervised and unsupervised machine learning models. The results shown that machine learning techniques can be used to anticipate and optimize the capacity of computing systems.
Planning Navigation Routes in Unknown Environments

Self-driving robots have to fulfill many different operations, as coordinating the motors’ traction, camera movement, or actuator arms mechanics, as well as more high-level operations like driving to different places. Autonomous navigation is of utmost importance for exploration robots, which must drive around exploring areas with unknown terrain conditions, as for example is the case of mars rovers and other space exploration vehicles. Given that the environment is unknown, planning a specific route and driving plan is challenging or even inappropriate due to blocking obstacles in the terrain. To overcome such problems we propose an adaptable plan for driving robots in different situations. Our solutions mixes both global and dynamic planning algorithms to take advantage of available information, if it exist beforehand, and to overcome unknown obstacles if they appear, while still moving towards the goal. In particular, we apply our algorithm to the movement of robots between posts in environments with partial information, as it is the case of space mission competitions. We evaluate our solution in a simulated environment taking into account the effectiveness in fulfilling a mission in the shortest time, using the shortest possible path. Our results show that of the A* algorithm with diagonals in combination with the ABEO algorithm offer the best combination reaching the goal in most cases, in optimal (planning + execution) time.
A task level fusion autonomous switching mechanism

Positioning technology is an important component of environmental perception. It is also the basis for autonomous decision-making and motion control of firefighting robots. However, some issues such as positioning in indoor scenarios still remain inherent challenges. The positioning accuracy of the fire emergency reaction dispatching (FERD) system is far from adequate to support some applications for firefighting and rescue in indoor scenarios with multiple obstacles. To solve this problem, this paper proposes a fusion module based on the Blackboard architecture. This module aims to improve the positioning accuracy of a single sensor of the unmanned vehicles within the FERD system. To reduce the risk of autonomous decision-making of the unmanned vehicles, this module uses a comprehensive manner of multiple channels to complement or correct the positioning of the firefighting robots. Specifically, this module has been developed to fusion a variety of relevant processes for precise positioning. This process mainly includes six strategies. These strategies are the denoising, spatial alignment, confidence degree update, observation filtering, data fusion, and fusion decision. These strategies merge with the current scenarios-related parameter data, empirical data on sensor errors, and information to form a series of norms. This paper then proceeds to gain experience data with the confidence degree, error of different sensors, and timeliness of this module by training in an indoor scenario with multiple obstacles. This process is from data of multiple sensors (bottom-level) to control decisions knowledge-based (up-level). This process can obtain globally optimal positioning results. Finally, this paper evaluates the performance of this fusion module for the FERD system. The experimental results show that this fusion module can effectively improve positioning accuracy in an indoor scenario with multiple obstacles. Code is available at https://github.com/lvbingyu-zeze/gopath/tree/master .
Fast estimation of the look-elsewhere effect using Gaussian random fields

Abstract We discuss the use of Gaussian random fields to estimate the look-elsewhere effect correction. We show that Gaussian random fields can be used to model the null-hypothesis significance maps from a large set of statistical problems commonly encountered in physics, such as template matching and likelihood ratio tests. Some specific examples are searches for dark matter using pixel arrays, searches for astronomical transients, and searches for fast-radio bursts. Gaussian random fields can be sampled efficiently in the frequency domain, and the excursion probability can be fitted with these samples to extend any estimation of the look-elsewhere effect to lower p values. In addition, in cases where the Gaussian random field is stationary and the parameter space is Euclidean, the look-elsewhere effect correction can be computed analytically. We demonstrate these methods using two example template matching problems. Finally, we apply these methods to estimate the trial factor of a $$4^3$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>4</mml:mn> <mml:mn>3</mml:mn> </mml:msup> </mml:math> accelerometer array for the detection of dark matter tracks in the Windchime project. When a global significance of $$3\sigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>3</mml:mn> <mml:mi>σ</mml:mi> </mml:mrow> </mml:math> is required, the estimated trial factor for such an accelerometer array is $$10^{14}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>14</mml:mn> </mml:msup> </mml:math> for a one-second search, and $$10^{22}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>22</mml:mn> </mml:msup> </mml:math> for a 1-year search.
Tracing the Visual Path: Gaze Direction in the 360 Video Experience

In traditional storytelling, a kind of predefined structure typically guides the narrative. However, when we move beyond the constraints of a rectangular screen, new possibilities emerge. 360-degree videos offer a unique opportunity to narrate with every element visible to the user in each frame. Nevertheless, some questions arise: What exactly are the end users seeing? Where is their attention directed? This paper deepens into an analysis of the data captured from the position and rotation of the headset worn by viewers while watching 360-degree videos. To accomplish this, two videos created by university students were examined, where gaze information was captured by testing it with a group of participants. Alongside watching the videos, participants provide their feedback on what elements they focused during the video. The findings revealed that participants tend to focus on elements intended to draw their attention. Furthermore, when the camera was stationary, participants found it easier to explore their surroundings, highlighting the value of utilizing the 360-degree format.
Towards efficient video-based action recognition: context-aware memory attention network

Abstract Given the prevalence of surveillance cameras in our daily lives, human action recognition from videos holds significant practical applications. A persistent challenge in this field is to develop more efficient models capable of real-time recognition with high accuracy for widespread implementation. In this research paper, we introduce a novel human action recognition model named Context-Aware Memory Attention Network (CAMA-Net), which eliminates the need for optical flow extraction and 3D convolution which are computationally intensive. By removing these components, CAMA-Net achieves superior efficiency compared to many existing approaches in terms of computation efficiency. A pivotal component of CAMA-Net is the Context-Aware Memory Attention Module, an attention module that computes the relevance score between key-value pairs obtained from the 2D ResNet backbone. This process establishes correspondences between video frames. To validate our method, we conduct experiments on four well-known action recognition datasets: ActivityNet, Diving48, HMDB51 and UCF101. The experimental results convincingly demonstrate the effectiveness of our proposed model, surpassing the performance of existing 2D-CNN based baseline models. Article Highlights Recent human action recognition models are not yet ready for practical applications due to high computation needs. We propose a 2D CNN-based human action recognition method to reduce the computation load. The proposed method achieves competitive performance compared to most SOTA 2D CNN-based methods on public datasets.
No Step-free Copyright Exceptions: The Role of the Three-step in Defining Permitted Uses of Protected Content (including TDM for AI-training purposes)

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Multistream BertGCN for Sentiment Classification Based on Cross-Document Learning

Very recently, the BERT graph convolutional network (BertGCN) model has attracted much attention from researchers due to its good text classification performance. However, just using original documents in the corpus to construct the topology of graphs for GCN-based models may lose some effective information. In this paper, we focus on sentiment classification, an important branch of text classification, and propose the multistream BERT graph convolutional network (MS-BertGCN) for sentiment classification based on cross-document learning. In the proposed method, we first combine the documents in the training set based on within-class similarity. Then, each heterogeneous graph is constructed using a group of combinations of documents for the single-stream BertGCN model. Finally, we construct multistream-BertGCN (MS-BertGCN) based on multiple heterogeneous graphs constructed from different groups of combined documents. The experimental results show that our MS-BertGCN model outperforms state-of-the-art methods on sentiment classification tasks.
Don't Forget the User

Network measurement has long focused on the bits and bytes --- low-level network metrics such as latency and throughput, which have the advantage of being objective and directly characterizing the performance of the network. We argue that users provide a rich and largely untapped source of implicit as well as explicit signals that could complement and expand the coverage of traditional methods. Implicit feedback leverages user actions to indirectly infer the network performance and the resulting quality of user experience. Explicit feedback leverages user input, typically provided offline, to expand the reach of network measurement, especially for newer ones.
A Case Against CXL Memory Pooling

Compute Express Link (CXL) is a replacement for PCIe. With much lower latency than PCIe and hardware support for cache coherence, programs can efficiently access remote memory over CXL. These capabilities have opened the possibility of CXL memory pools in datacenter and cloud networks, consisting of a large pool of memory that multiple machines share. Recent work argues memory pools could reduce memory needs and datacenter costs.
On-Fiber Photonic Computing

In the 1800s, Charles Babbage envisioned computers as analog devices. However, it was not until 150 years later that a Mechanical Analog Computer was constructed for the US Navy to solve differential equations. With the end of Moore's Law, photonic computing is revitalizing the promise of analog computing by leveraging photons' speed, bandwidth, and energy efficiency for faster, more efficient, and scalable analog computing systems. This paper argues that the networking community should augment pluggable transponders with photonic computing capabilities to enable a backward-compatible solution for in-network computing. We propose on-fiber photonic computing to perform computing operations inside network transponders while the data is in the optical domain. We discuss the components required to enable the seamless integration of computation into the very fabric of optical communication links. We then discuss several use cases of on-fiber photonic computing, including machine learning inference, video encoding, load balancing, and intrusion detection.
Harnessing ML For Network Protocol Assessment

In this paper, our primary objective is to showcase that the application of machine learning techniques extends beyond network protocol design. We aim to demonstrate that performance assessment of network protocols, a vital aspect of improving network infrastructures and developing better protocol designs, can be modernized through the utilization of machine learning. As a step towards this goal, we have designed and introduced Mahak, the first tool that harnesses active learning techniques to automate the performance assessment of congestion control schemes. Mahak actively learns to optimize the evaluation process of congestion control schemes so that they can generate their performance maps over a desired space without exhaustively testing them in every scenario. Mahak treats schemes under the test as black boxes. This protocol-agnostic aspect of Mahak enables users to directly assess the performance of the actual implementation of a protocol instead of their over-simplified mathematical models or simplified simulated versions.
An empirical study on user experience evaluation of VR interface in digital museums

Virtual reality (VR) has been increasingly introduced to digital museums to enable more immersive experiences. However, there is a lack of comprehensive evaluation for user experience in VR-based digital museums. A comprehensive framework for evaluating user experience of VR interfaces in digital museums was created. Based on this framework, the study conducted an experiment to investigate learning effectiveness in VR-based digital museums and compared user experience on desktop and mobile devices. A total of 45 participants were recruited. They were asked to visit a digital museum on either a desktop or a mobile device. After they completed their visit, they took a recognition test and a recall test to measure their effectiveness of learning. Their user experience was evaluated using a new scale, followed by an open-ended interview to collect their opinions and feedback. It was found that introducing VR to the digital museum helped increase users’ effectiveness of learning. Significant differences existed between the desktop and mobile VR interfaces in user experience. It means that previous research findings cannot be directly applied to the user experience of digital museums on mobile. These empirical evidences also suggest that the established framework is effective for evaluating the user experience in VR-based digital museums.
RTS with Certainty

Football promotes mental and physical benefits (cardiovascular, metabolic, musculoskeletal, psychological), and ensures social and emotional health. Purposeful heading is a common practice among football athletes, and rising injury rates have affected adult willingness to allow their children to participate in tackle football. Soccer has recorded a high number of injuries, more than basketball and volleyball, and carries a 1000 times higher risk compared to industrial occupations. Studies undertaken in 2012-2016 recorded that 22% soccer injuries are concussions, as a result of purposeful heading. There is an established link between purposeful heading, neurodegeneration, dementia, and motor impairment. Purposeful heading has a high incidence among youth players. Concussions may cause acute symptoms including blurred visions, loss of hunger, sleep, balance, concentration, and attention. Subconcussive impacts can cause functional and microstructural damage including ionic and metabolic shifts, microstructural damage, and impaired neurotransmission, often undiagnosable. Sometimes, loss of consciousness (LOC) may occur. Concussions can cause Traumatic Brain Injury (TBI), leading to disease, mortality, and morbidity worldwide. Concussion awareness has been rising between 2010-2019. A reduction in head impact exposure (HIE) has been addressed through the design of protective equipment, educational initiatives, and rule changes. Behavioral interventions, neck and trunk strength training, timely care, and the use of computational models are evidence-based interventions. A deeper understanding of the epidemiology of concussions is required to inform primary prevention in respective geographies and targeted age groups. Addressing safety concerns can improve participation. The text covers a Literature Review and Thematic Analysis of Influencing Factors, Consequences, Measurements, and Interventions Related to Heading in Soccer and Football
Postdigital Environmental Crises

Postdigital technologies can be a double edged sword for environmental crises. Various postdigital technologies are being used to good effect to address environmental crises including the climate crisis, biodiversity loss, and the destruction of rainforests. For example, there is potential for postdigital technologies to prevent over-fishing and to save the remaining rainforests. However, postdigital technologies can also add to environmental crises. Materials and energy are required for the production of postdigital technologies, and these technologies are not a silver bullet for addressing environmental problems. This entry considers the double edged sword in using postdigital technologies for addressing environmental crises.
Analysis of the Impact of the Pandemic on Social Inequalities in Enem 2019 and 2020 using Machine Learning

ENEM measures the ability and knowledge of students who are in high school or have already completed it. With the scores obtained in the exam, the student can enroll in SISU, which is one way to enter public universities. During pandemic, the planning of schools, mainly public, was affected so that many students gave up taking the ENEM in 2020. To identify the profile of those enrolled in ENEM and verify which portion was most affected, this research analyze their social inequalities using data from ENEM 2019 and 2020 and machine learning methods. The methodology is based on cluster analysis where K-Means was applied and on performance classification where Random Forest, K-Nearest Neighbors, and MultiLayer Perceptron were used, and Select K-Best was used to select features. The results of the grouping generated two groups, one composed of subscribers with lower financial conditions and another with greater ones. In the classification, the MultiLayer Perceptron obtained an accuracy of 85.18% for 2019 and 83.63% for 2020. The results showed that the proposed methodology was able to identify the differences between the subscribers and classify their performance.
Hybrid photonic integrated circuits for neuromorphic computing [Invited]

The burgeoning of artificial intelligence has brought great convenience to people’s lives as large-scale computational models have emerged. Artificial intelligence-related applications, such as autonomous driving, medical diagnosis, and speech recognition, have experienced remarkable progress in recent years; however, such systems require vast amounts of data for accurate inference and reliable performance, presenting challenges in both speed and power consumption. Neuromorphic computing based on photonic integrated circuits (PICs) is currently a subject of interest to achieve high-speed, energy-efficient, and low-latency data processing to alleviate some of these challenges. Herein, we present an overview of the current photonic platforms available, the materials which have the potential to be integrated with PICs to achieve further performance, and recent progress in hybrid devices for neuromorphic computing.
Evaluation of surface-based hippocampal registration using ground-truth subfield definitions

The hippocampus is an archicortical structure, consisting of subfields with unique circuits. Understanding its microstructure, as proxied by these subfields, can improve our mechanistic understanding of learning and memory and has clinical potential for several neurological disorders. One prominent issue is how to parcellate, register, or retrieve homologous points between two hippocampi with grossly different morphologies. Here, we present a surface-based registration method that solves this issue in a contrast-agnostic, topology-preserving manner. Specifically, the entire hippocampus is first analytically unfolded, and then samples are registered in 2D unfolded space based on thickness, curvature, and gyrification. We demonstrate this method in seven 3D histology samples and show superior alignment with respect to subfields using this method over more conventional registration approaches.