Reviewerly/exHarmony huggingface dataset:
QoE- and Energy-aware Content Consumption For HTTP Adaptive Streaming

Video streaming services account for the majority of today's traffic on the Internet, and according to recent studies, this share is expected to continue growing. Given this broad utilization, research in video streaming is recently moving towards energy-aware approaches, which aim at reducing the energy consumption of the devices involved in the streaming process. On the other side, the perception of quality delivered to the user plays an important role, and the advent of HTTP Adaptive Streaming (HAS) changed the way quality is perceived. The focus is not any more exclusively on the Quality of Service (QoS) but rather oriented towards the Quality of Experience (QoE) of the user taking part in the streaming session. Therefore video streaming services need to develop Adaptive BitRate (ABR) techniques to deal with different network conditions on the client side or appropriate end-to-end strategies to provide high QoE to the users. The scope of this doctoral study is within the end-to-end environment with a focus on the end-users domain, referred to as the player environment, including video content consumption and interactivity. This thesis aims to investigate and develop different techniques to increase the delivered QoE to the users and minimize the energy consumption of the end devices in HAS context. We present four main research questions to target the related challenges in the domain of content consumption for HAS systems.
Deep learning's fitness for purpose: A transformation problem frame's perspective

Abstract Combined sewer overflows represent significant risks to human health as untreated water is discharged to the environment. Municipalities, such as the Metropolitan Sewer District of Greater Cincinnati (MSDGC), recently began collecting large amounts of water‐related data and considering the adoption of deep learning (DL) solutions like recurrent neural network (RNN) for predicting overflow events. Clearly, assessing the DL's fitness for the purpose requires a systematic understanding of the problem context. In this study, we propose a requirements engineering framework that uses the problem frames to identify and structure the stakeholder concerns, analyses the physical situations in which the high‐quality data assumptions may not hold, and derives the software testing criteria in the form of metamorphic relations that incorporate both input transformations and output comparisons. Applying our framework to MSDGC's overflow prediction problem enables a principled way to evaluate different RNN solutions in meeting the requirements.
Seismic event classification based on a two-step convolutional neural network

The identification of unnatural earthquake events is one of the tasks of earthquake rapid report. The identification accuracy is of great significance for improving the quality of earthquake catalog and seismological research. In this study, a 7-layer convolutional neural network model was constructed to identify unnatural earthquakes. First, the three-component seismic waveform was input to obtain the waveform image classifier, and then, the time–frequency spectrum of blasting and collapse was input to obtain the time–frequency spectrum classifier. The two classifiers were used to identify natural earthquake, blasting, and collapse. The model was trained and tested using 3386 seismic events of Shandong seismic network from 2017 to 2022. The events identified as blasting by the waveform image classifier were reidentified by the time–frequency spectrum classifier. Finally, the identification accuracy of natural earthquake, blasting, and collapse is 97.50%, 95.87%, and 86.84%, respectively, with an average accuracy rate of 96.13%. The experimental results show that the two-step convolutional neural network can extract the characteristics of seismic signals from multiple angles, which get a good result in seismic event classification.
Personalized Explanations for Early Diagnosis of Alzheimer’s Disease Using Explainable Graph Neural Networks with Population Graphs

Leveraging recent advances in graph neural networks, our study introduces an application of graph convolutional networks (GCNs) within a correlation-based population graph, aiming to enhance Alzheimer's disease (AD) prognosis and illuminate the intricacies of AD progression. This methodological approach leverages the inherent structure and correlations in demographic and neuroimaging data to predict amyloid-beta (Aβ) positivity. To validate our approach, we conducted extensive performance comparisons with conventional machine learning models and a GCN model with randomly assigned edges. The results consistently highlighted the superior performance of the correlation-based GCN model across different sample groups in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, suggesting the importance of accurately reflecting the correlation structure in population graphs for effective pattern recognition and accurate prediction. Furthermore, our exploration of the model's decision-making process using GNNExplainer identified unique sets of biomarkers indicative of Aβ positivity in different groups, shedding light on the heterogeneity of AD progression. This study underscores the potential of our proposed approach for more nuanced AD prognoses, potentially informing more personalized and precise therapeutic strategies. Future research can extend these findings by integrating diverse data sources, employing longitudinal data, and refining the interpretability of the model, which potentially has broad applicability to other complex diseases.
Morphology &amp; word sense disambiguation embedded multimodal neural machine translation system between Sanskrit and Malayalam

Machine translation is the process of conversion from one language to another using computers. In spite of the existence of several traditional machine translation systems like rule-based, statistical etc., the development of neural machine translation has significantly improved translation quality. However, the morphological richness and agglutinative nature of Indian languages hampers the usage of neural machine translation for translation among Indian languages despite its potential to overcome the difficulties posed by conventional translation systems. Hence the combination of neural machine translation with morphology, part of speech tagger and word sense disambiguation is proposed to address these challenges and adapt it for bidirectional translation between Sanskrit and Malayalam. Conventional neural machine translations are unimodal systems that utilize textual data for translation. Translation quality of conventional neural machine translation is enhanced with a novel multimodal bidirectional neural machine translation system with text & speech as the two modalities based on parallel text corpus and speech database. The best performance score is obtained for two level fusion, the feature vectors extracted from speech signals using different transforms (Wavelet transform, 1-D sequency mapped real transform & 1-D GCD based mapped real transform) are fused and which is further fused with a context vector from the text modality. Automatic and manual review methods are employed to assess quality of translation. Findings from the experiments reveal that addition of speech modality improved the overall quality of translation. BLEU scores of 43.89 and 42.72 are obtained for Sanskrit to Malayalam and Malayalam to Sanskrit multimodal translation respectively.
A Dual-Decoding branch U-shaped semantic segmentation network combining Transformer attention with Decoder: DBUNet

Semantic Segmentation is an extremely important medical image auxiliary analysis method. However, existing networks have the following problems: 1) The amount of feature information of Encoder and Decoder is not equal under multi-branch architecture; 2) The direct processing of the original image by ViT Encoder is not sufficient; 3) Multi-channel features are too independent and lack of fusion. Combined with the ViT Encoder framework, this study proposes a 'Single Encoder – Double Decoder' structure: DBUNet. Firstly, ViT Encoder is employed as a part of the Decoder branches to enhance the shallow features. Then, a polarization amplification of channel weights is proposed and placed in front of the ViT Encoder module to achieve early image processing. Finally, a Bottleneck for feature fusion is proposed to solve the problem of channel independence. The comprehensive verification of 13 comparative networks in three aspects, combined with ablation experiments, jointly proves the superiority of DBUNet.
CFTracker: Multi-Object Tracking With Cross-Frame Connections in Satellite Videos

Multi-object tracking (MOT) in satellite videos is an essential topic with many applications, such as traffic monitoring and disaster response. However, many multi-object trackers that perform well in natural scenes show weak generalization in satellite videos due to low object discrimination caused by low spatial resolution and the widespread indistinguishable background, such as clouds and reflections. In this paper, we design a novel multi-object tracking framework called CFTracker (Cross-Frame Tracker) for satellite videos from the point of both network structure and training method. On the one hand, in network structure design, a cross-frame feature update module (CFU) is proposed to enhance object recognition and reduce the response to background noises by using rich temporal semantic information. On the other hand, we reveal that the picture-pair training approach used by the mainstream MOT network is not entirely conducive to the network learning temporal semantic information. To better grasp the cross-frame feature connections and output time-consistent motion predictions, we train CFTracker by a novel cross-frame training flow (CT). Experiments demonstrate the effectiveness of our CFTracker and obtain state-of-the-art tracking accuracy and precision of 72.9% score on the AIR-MOT dataset and 57.1% score on the VISO dataset. The code will be available online.
A taxonomy of short‐term solar power forecasting: Classifications focused on climatic conditions and input data

Abstract A review of the state‐of‐the‐art in short‐term Solar Power Forecasting (SPF) methodologies is presented in this paper. Over the last few years, developing and improving solar forecasting models has been the main focus of researchers, considering the need to efficiently increase their forecasting accuracy. Forecasting models aim to be used as an efficient tool to help with the stability and control of energy systems and electricity markets. Intending to further comprehend the factors affecting the quality of SPF models, this paper focuses on short‐term solar forecasting methodologies since they pose a crucial role in the daily operation and scheduling of power systems, since they focus on forecasting horizons typically ranging from 1 h to 1 day. The reviewed works are classified according to the climatic conditions, technical characteristics, and the forecasting errors of the different methodologies, providing readers with information over various different cases of SPF. Considering the need to improve the SPF efficiency, such classifications allow for important comparative conclusions to be drawn, depending on the location of each case and the meteorological data available. Future directions in the field of short‐term solar power forecasting are proposed considering the increasing development of SPF models’ architecture and their field of focus.
Cybersecurity threats and vulnerabilities experienced by small-scale African migrant traders in Southern Africa

Abstract Cybersecurity threats have increased as the world becomes increasingly interconnected. Whilst the use of technology to facilitate commercial activities is now common practice, there is a need to limit exposure to these threats so that traders can transact safely. This study aimed to identify and analyse common cybersecurity vulnerabilities and threats experienced by small-scale African migrant traders in Southern Africa. A qualitative approach was employed and semi-structured and key informant interviews were conducted to gather the primary data, with secondary data sourced from the relevant literature. The study found that the majority of the small-scale traders experienced hacking while using their mobile devices for transacting. Moreover, most reported a lack of knowledge of cybersecurity and were therefore vulnerable to further threats. It is recommended that small-scale traders be capacitated with relevant technical information to enhance their understanding of cybersecurity risks that can negatively affect their commercial activities. There is also a need for further research to identify mitigation techniques and infrastructure to protect small-scale traders.
A dictionary-guided attention network for biomedical named entity recognition in Chinese electronic medical records

Biomedical named entity recognition (BNER) is a critical task for biomedical information extraction. Most popular BNER approaches based on deep learning utilize words and characters as features to represent medical texts. However, many medical terminologies are composed of multiple words and characters, and splitting medical terminology into multiple words (or characters) and assigning weight values for each word (or character) by a standard attention mechanism may disperse the attention score and result in a lower weight value for the medical terminology. This paper proposes a Dictionary-guided Attention Network (DGAN) for BNER in Chinese electronic medical records (EMRs). First, the medical concepts are extracted as large-size words to supplement the comprehensive semantic information of the medical terminology by matching the EMR text to the biomedical dictionary. Then, based on the matched dictionary results, an optimized attention strategy is proposed to focus on the medical concept and adaptively assign higher weights to the characters contained in a concept. Furthermore, semisupervised learning is introduced to reduce the manual labeling of data and to handle the entities not defined in the medical dictionary. To validate our new model in recognizing biomedical named entities, we conduct comprehensive experiments on a real-world Chinese EMR dataset and the CCKS2017 dataset. Our promising results illustrate that our method not only achieves a state-of-the-art performance in BNER but also reduces manual data annotation.
Pupil Localization Algorithm Based on Improved U-Net Network

Accurately localizing the pupil is an essential requirement of some new human–computer interaction methods. In the past, a lot of work has been done to solve the pupil localization problem based on the appearance characteristics of the eye, but these methods are often specific to the scenario. In this paper, we propose an improved U-net network to solve the pupil location problem. This network uses the attention mechanism to automatically select the contribution of coded and uncoded features in the model during the skip connection stage of the U-net network in the channel and spatial axis. It can make full use of the two features of the model in the decoding stage, which is beneficial for improving the performance of the model. By comparing the sequential channel attention module and spatial attention module, average pooling and maximum pooling operations, and different attention mechanisms, the model was finally determined and validated on two public data sets, which proves the validity of the proposed model.
Game-theoretic APT defense: An experimental study on robotics

This paper proposes a novel game-theoretic framework for defending against Advanced Persistent Threats (APTs). It applies the original Cut-The-Rope model into an experimental study extending the previously studied attacker movements beyond the Poisson distribution to a realistic set of attack actions. More importantly, it demonstrates the value of this framework on an experimental study of an APT defense game on attack graphs, which lets a security officer establish an optimized defense policy against stealthy intrusions. The security model and algorithm under study is designed for practical use with attack graphs as threat models, possibly including vulnerability information if available. The game-theoretic optimization delivers a proactive defense policy under the following assumptions or requirements: first, we do not need to assume that the system is, or has been, clean from adversaries at any time. At the moment when the defender computes the defense policy, the attacker is assumed to already be in the system (also having penetrated it until an unknown depth). Second, the defender does not rely on any signaling or other indicators of adversarial activity, nor is there a reliable feedback mechanism to tell the defender if its actions were successful or not. Third, the model can use information on exploits, such as Common Vulnerabilities and Exposures (CVE) numbers, to refine the defense game, but can also operate without such information. We corroborate our findings on publicly documented attack graphs from the robotics domain; without and with CVE information. We run experiments against two different types of defense regimes, and compare the results against an intuitive baseline defense heuristic. The results show that the optimized defense strongly outperforms simple heuristics, like taking the shortest or easiest attack paths.
Histopathological Image Deep Feature Representation for CBIR in Smart PACS

Abstract Pathological Anatomy is moving toward computerizing processes mainly due to the extensive digitization of histology slides that resulted in the availability of many Whole Slide Images (WSIs). Their use is essential, especially in cancer diagnosis and research, and raises the pressing need for increasingly influential information archiving and retrieval systems. Picture Archiving and Communication Systems (PACSs) represent an actual possibility to archive and organize this growing amount of data. The design and implementation of a robust and accurate methodology for querying them in the pathology domain using a novel approach are mandatory. In particular, the Content-Based Image Retrieval (CBIR) methodology can be involved in the PACSs using a query-by-example task. In this context, one of many crucial points of CBIR concerns the representation of images as feature vectors, and the accuracy of retrieval mainly depends on feature extraction. Thus, our study explored different representations of WSI patches by features extracted from pre-trained Convolution Neural Networks (CNNs). In order to perform a helpful comparison, we evaluated features extracted from different layers of state-of-the-art CNNs using different dimensionality reduction techniques. Furthermore, we provided a qualitative analysis of obtained results. The evaluation showed encouraging results for our proposed framework. Graphical Abstract
Learning to Solve Optimization Problems With Hard Linear Constraints

Constrained optimization problems appear in a wide variety of challenging real-world problems, where constraints often capture the physics of the underlying system. Classic methods for solving these problems rely on iterative algorithms that explore the feasible domain in the search for the best solution. These iterative methods are often the computational bottleneck in decision-making and adversely impact time-sensitive applications. Recently, neural approximators have shown promise as a replacement for the iterative solvers that can output the optimal solution in a single feed-forward providing rapid solutions to optimization problems. However, enforcing constraints through neural networks remains an open challenge. This paper develops a neural approximator that maps the inputs to an optimization problem with hard linear constraints to a feasible solution that is nearly optimal. Our proposed approach consists of four main steps: 1) reducing the original problem to optimization on a set of independent variables, 2) finding a gauge function that maps the infty-norm unit ball to the feasible set of the reduced problem, 3)learning a neural approximator that maps the optimization's inputs to an optimal point in the infty-norm unit ball, and 4) find the values of the dependent variables from the independent variable and recover the solution to the original problem. We can guarantee hard feasibility through this sequence of steps. Unlike the current learning-assisted solutions, our method is free of parameter-tuning and removes iterations altogether. We demonstrate the performance of our proposed method in quadratic programming in the context of the optimal power dispatch (critical to the resiliency of our electric grid) and a constrained non-convex optimization in the context of image registration problems.
Self-Perception of Digital Competence in University Lecturers: A Comparative Study between Universities in Spain and Peru According to the DigCompEdu Model

Today’s technology presents a major challenge for the education system in terms of digital literacy and the quality of education. This study focused on analyzing the level of digital competence of university teachers at the University of Seville, Spain, and another university in Arequipa, Peru, using the DigCompEdu model (Digital Competence Framework for Educators). The aim was to obtain significant data that reflect the educational reality within the use of Information and Communication Technologies (ICT) and to highlight the importance of training actions that improve the digital competence of teachers. The study was carried out using a descriptive–inferential approach, which showed the reality of the groups and obtained general conclusions based on the collected data. The participating teachers answered a self-assessment questionnaire that was previously validated by experts. The analyses and the obtained results highlight the importance of offering personalized training adapted to the needs of each educational context. Furthermore, they raise the need to reflect on areas for improvement and how to approach similar work in the future. It is suggested that future studies include probability samples in each research area to obtain more representative and globally applicable data.
Photonic reservoir computing enabled by stimulated Brillouin scattering

Artificial intelligence (AI) drives the creation of future technologies that disrupt the way humans live and work, creating new solutions that change the way we approach tasks and activities, but it requires a lot of data processing, large amounts of data transfer, and computing speed. It has led to a growing interest of research in developing a new type of computing platform which is inspired by the architecture of the brain specifically those that exploit the benefits offered by photonic technologies, fast, low-power, and larger bandwidth. Here, a new computing platform based on the photonic reservoir computing architecture exploiting the non-linear wave-optical dynamics of the stimulated Brillouin scattering is reported. The kernel of the new photonic reservoir computing system is constructed of an entirely passive optical system. Moreover, it is readily suited for use in conjunction with high performance optical multiplexing techniques to enable real-time artificial intelligence. Here, a methodology to optimise the operational condition of the new photonic reservoir computing is described which is found to be strongly dependent on the dynamics of the stimulated Brillouin scattering system. The new architecture described here offers a new way of realising AI-hardware which highlight the application of photonics for AI.