Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital Art Therapy with Gen AI: Mind Palette

Proper and delightful intervention can reduce the progression of mental disorders, but numerous physical and psychological barriers continue to exist. In response, we developed a mobile application called “Mind Palette”, which incorporates art therapy methodologies and generative AI technology. This application aims to address the need for comprehensive interventions in the mental health crisis, particularly among younger age groups. With AI chatbot interactions and AI-generated artwork recommendations, the application may facilitate discussions about emotions, encourage self-expression through art creation, and provide cognitive-behavioral therapeutic advice in both verbal and visual ways. The project highlights the human-centered approach and investigates the potential of generative AI as an effective agent for conducting art therapy.
How Technology Solutions providers are Using Platform engineering to Improve Efficiency, Agility, Performance and Responsiveness

This paper explores the impact of platform engineering on technology-based solutions providers. It examines the benefits of platform engineering in terms of improving the efficiency and scalability of technology-based solutions. The study analyzes the role of platform engineering in enabling faster and more efficient development of customized solutions for businesses, and how this can be leveraged to improve the competitiveness of technology-based solutions providers. The paper provides an overview of platform engineering and its essential characteristics, as well as the potential opportunities and challenges that it presents for technology-based solutions providers. The report also includes case studies of businesses that have successfully implemented platform engineering to enhance their technology-based solutions and drive innovation. Finally, the paper provides recommendations for technology-based solutions providers looking to incorporate platform engineering into their operations. Overall, the study highlights the transformative potential of platform engineering for technology-based solutions providers, enabling them to deliver more innovative and competitive solutions to their clients.
Deep Learning Analysis of Electrophysiological Series for Continuous Emotional State Detection

The detection of emotions has a wide range of applications in psychology, marketing, and human-computer interaction. The detection of emotional states using biomedical signals is attractive because it is non-invasive and there is a large body of knowledge in digital signal processing that can be applied to the analysis of these signals. Additionally, artificial intelligence can be used to improve the accuracy of emotion detection by using machine learning algorithms to analyze large amounts of data. In this study, we explored the application of a parallel hybrid architecture called the parallel TCN-SBU-LSTM, which combines a temporal convolutional network and a stacked bi-and uni-directional LSTM. For the EPiC 2023 Challenge, we set out to estimate the arousal and valence states of different subjects in four different scenarios, using a continuous analysis of five physiological signals. To determine the best hyperparameters for each scenario and which signals to use for estimation, we used a methodology involving grid search and 5-fold cross validation. The models obtained an average root mean square error of 1.585 across scenarios, demonstrating the suitability of the parallel TCN-SBU-LSTM network to estimate the emotions of the subjects in different scenarios with consistent performance.
GRONA : A Framework for Gather-and-Reduce On Near-Memory Accelerators

Gather-and-reduce (GnR) is a collective operation widely used in Big-data analytic workloads. Memory-bound fine-grained data-accesses from massive lookup-tables during Gather, followed by compute-bound arithmetic and logical processing during Reduce, are suitable for low-latency computations on emerging Near-DRAM Processing (NDP) architectures. While NDP on general Dual Inline Memory Modules (DIMMs) offer simple integration and find commercial adoption, cross-level data-sharing over standard buses remains a bottleneck. Maintaining standard functionality of the DIMM require careful schemes for scheduling, coordination and memory management in order to achieve the potential throughput.We propose GRONA (Gather-and-Reduce On Near-Memory Accelerators), a framework for intra-DIMM throughput-oriented reprogrammable Gather-and-Reduce. GRONA adopts a multilevel reprogrammable PE platform equipped with small caches to support all GnR applications. GRONA framework achieves effective decoupling of bank-group and rank-level data accesses and GnR execution, and displayed up-to 2.9x performance gains for applications such as FM-Index query and SparseLengthSums compared to NDP state-of-the-arts.
How are suburbanization and peri-urbanization not the same? Key differences in urban regions in Central and Eastern European countries: the case of cities in southern Poland

The aim of the paper is to discuss differences between suburbanization and peri-urbanization using the Kraków, Tarnów, and Nowy Sącz urban regions in the Province of Małopolska as a case study. The paper highlights the lack of distinction in the use of these terms in Central and Eastern European countries and in the Polish research literature, where these two unique urban decentralization processes (suburbanization and peri-urbanization) are described using the same term: suburbanization. In spite of the terminological ambiguities discussed in the paper and lack of agreement on ways to distinguish the two concepts as well as significant difficulties in their operationalization the analysis presented in the current study demonstrates that there does exist a case for a distinction in the treatment of suburbanization and peri-urbanization in many post-socialist urban regions.
Integration of Monitoring and Actuation Electronics in a Smart Roof Waterproofing System

During the past years, roof structures are evolving to provide new waterproofing materials that increase the comfort of homes and roofs' lifespan. Some of the market roof waterproofing solutions present durability issues and may result in watertightness losses and consequently in water infiltration events causing a set of anomalies in the internal environment of buildings. Moreover, solutions need to be more sustainable and follow no environmental harm principles. Thereby, the main goal of Smart Roofs System project and Saint Gobain Weber is to develop a water-based polymeric membrane that presents equivalent technical performance to the best solutions of waterproofing liquid membrane solvent-based. Using an innovative reinforcing textile structure as a complement to the process of drying the aqueous membrane. This structure comprises the integration of an active heating system with temperature and moisture sensors working as indicators of the degree of drying/humidity and temperature in the system for the application of a new layer of the waterproofing membrane. Furthermore, the smart system comprises the development and integration of an electronic system capable of monitoring temperature and moisture inside the waterproofing membrane and activate the heating system when needed. The resultant product mitigates cracking and reduces durability problems of the water-based polymeric membrane. Also, the innovative monitorization system and textile sensors developed for this project were successfully embedded in the target textile substrate and could successfully monitor the moisture and temperature inside the structure. The heating wires showed to be capable of accelerating the membrane cure process.
Structural Analyses of Designed α-Helix and β-Sheet Peptide Nanofibers Using Solid-State Nuclear Magnetic Resonance and Cryo-Electron Microscopy and Introduction of Structure-Based Metal-Responsive Properties

The 21-residue peptide α3, which is artificially designed and consists of three repeats of 7 residues, is known to rapidly assemble into the α-helix nanofiber. However, its molecular structure within the fiber has not yet been fully elucidated. Thus, we conducted a thorough investigation of the fiber’s molecular structure using solid-state NMR and other techniques. The molecules were found to be primarily composed of the α-helix structure, with some regions near the C- and N-terminal adopting a 310-helix structure. Furthermore, it was discovered that β-sheet hydrogen bonds were formed between the molecules at both ends. These intermolecular interactions caused the molecules to assemble parallelly in the same direction, forming helical fibers. In contrast, we designed two molecules, CaRP2 and βKE, that can form β-sheet intermolecular hydrogen bonds using the entire molecule instead of just the ends. Cryo-EM and other measurements confirmed that the nanofibers formed in a cross β structure, albeit at a slow rate, with the formation times ranging from 1 to 42 days. To create peptide nanofibers that instantaneously respond to changes in the external environment, we designed several molecules (HDM1-3) based on α3 by introducing metal-binding sites. One of these molecules was found to be highly responsive to the addition of metal ions, inducing α-helix formation and simultaneously assembling into nanofibers. The nanofibers lost their structure upon removal of the metal ion. The change occurred promptly and was reversible, demonstrating that the intended level of responsiveness was attained.
Internet of Energy Applications for the Brazilian Energy System

The Brazilian energy system is facing challenges such as increasing energy demand, the need for a more sustainable energy matrix, and the integration of renewable energy sources. In this context, the Internet of Energy (loE) offers promising solutions to optimize the generation, transmission, and consumption of energy. This article explores the potential applications of the Internet of Energy in the Brazilian energy system, discussing its benefits, challenges, and future prospects.
TSiCV Based Silicon Carbide Interposer Technology

This paper describes the research on the development on SiC Interposer technology with Through SiC Vias (TSiCV). It demonstrates a SiC Interposer with the Flip-Chip assembled Chips with different thicknesses. It gives an overview over the different approaches of via formation and describes the fabrication of a SiC Interposer. It highlights the differences between the via formation between silicon and silicon carbide. The etching of SiC itself is described and two different isolation methods for the vias are discussed. Furthermore, two different methods to access the backside of the via are presented. The via diameters between 40 μm to 120 μm have been investigated and etch rates of up to 2 μm/min were achieved. A discussion about the possibilities and limitation of the different approaches are presented.
Innovation points mining from scientific papers with deep learning models

This paper proposes a framework for mining innovation points of scientific papers, which divides the mining of innovation points into two main sequential modules. The first module is innovation point identification, which identifies innovation points from the text of scientific papers. The second module is innovation point classification, which classifies the identified innovation points. Under this framework, this paper proposes an innovation point mining model that adopts deep learning models to guarantee the performance of innovation point identification and classification. For the innovation points identification module, this paper proposes a text convolutional neural network (Text-CNN) to identify the innovation points from the constructed statements of a given paper. For the innovation points classification module, this paper adopts a Sci-BERT to classify those identified innovation points and assign them a corresponding label. The experimental results demonstrate that the proposed innovation points mining model can effectively identify and classify innovations in scientific papers.
ICD-Face: Intra-class Compactness Distillation for Face Recognition

Knowledge distillation is an effective model compression method to improve the performance of a lightweight student model by transferring the knowledge of a well-performed teacher model, which has been widely adopted in many computer vision tasks, including face recognition (FR). The current FR distillation methods usually utilize the Feature Consistency Distillation (FCD) (e.g., L2 distance) on the learned embeddings extracted by the teacher and student models. However, after using FCD, we observe that the intra-class similarities of the student model are lower than the intra-class similarities of the teacher model a lot. Therefore, we propose an effective FR distillation method called ICD-Face by introducing intra-class compactness distillation into the existing distillation framework. Specifically, in ICD-Face, we first propose to calculate the similarity distributions of the teacher and student models, where the feature banks are introduced to construct sufficient and high-quality positive pairs. Then, we estimate the probability distributions of the teacher and student models and introduce the Similarity Distribution Consistency (SDC) loss to improve the intra-class compactness of the student model. Extensive experimental results on multiple benchmark datasets demonstrate the effectiveness of our proposed ICD-Face for face recognition.
Stream-Based Active Learning for Regression with Dynamic Feature Selection

In the era of big data, companies are increasingly driven to amass vast amounts of data, particularly in process industries where advanced sensor technologies are prevalent. However, obtaining accurate labels or product information through quality inspections can be prohibitively expensive. Active learning emerges as a promising approach to optimize data sampling by prioritizing the most informative data points. Nevertheless, active learning strategies heavily rely on predictive models that are iteratively updated. Aligning with the principles of data-centric AI, this study highlights the detrimental effects of passively incorporating all available process variables into a predictive model for guiding data collection. Specifically, in real-time sampling strategies based on online active learning, the inclusion of irrelevant features significantly hampers the efficiency of the learning process.
Image Degradation Characteristics and Optimization Method of Visual Surveillance System in Strong Radiation Environment

Radiation damage of visual surveillance system in strong radiation environment will induce degradation of image quality, which will lead to failure of observation results in severe cases. To meet the application requirements of high-definition imaging in strong radiation environment, we decompose the degradation characteristics of radiated images, then propose an adaptive filtering method based on multiscale morphological decomposition to optimize the mixed noise, finally use the visual adaptive model fusion method to enhance the detail information. The experimental results show that our method can effectively improve the image quality degradation induced by radiation damage and obtain better visual effect.
PimCity: A Compute in Memory Substrate featuring both Row and Column Parallel Computing

Processing-in-memory (PIM) substrates can perform parallel computation directly within the memory array. As a result, throughput performance and energy efficiency can reach unprecedented levels, however, there are limitations. Typical PIM architectures only support parallel computing in one dimension of the memory array: Computation is performed along either multiple rows or multiple columns (but not both). This restricts data layout and makes intra-array intermediate data transfers during computation inevitable - which require reads and writes, even for short-distance data movement. Inter-array data transfers, on the other hand, become a problem for larger scale algorithms which use more than one PIM array. Such data transfers incur large communication overheads and increase the complexity of the peripheral circuitry and interconnection network between arrays. Intermediate data transfers of any form limit scalability and efficiency. To overcome this limitation, we introduce PimCity, a new PIM substrate which can compute in both the rows and the columns of the memory array. PimCity replaces intraarray data transfer (memory) operations with lower overhead logic operations inside the memory array. Further, PimCity can perform logic operations directly across neighboring memory arrays, which in turn enables low-cost inter-array data transfers. PimCity hence represents a scalable PIM architecture suitable for both HPC and embedded IoT style applications.
Fostering Cybersecurity Awareness and Skills Development Through Digital Game-Based Learning

Technological innovations pose several security, safety, and privacy threats. An array of educational games has been developed to raise cybersecurity awareness and promote secure behaviours and knowledge pertinent to prevalent risks. This study explores both the benefits associated with digital game-based learning and the barriers constraining its exploitation. The exploration takes a holistic approach delving into underlying theories, pedagogical frameworks, current research and development efforts, existing cybersecurity games, and a pilot study to gain direct experience into educational cybersecurity game development. The findings highlight key game design features required to foster cybersecurity awareness and skills development across educational, professional, and social contexts.
Status Update on the IEEE Rebooting Computing Initiative

It's been 10 years since the IEEE launched the Rebooting Computing Initiative (RCI) with the intention to re-examine all levels of how we compute. Back then, the term “post-Moore” was just coming into vogue. The RCI held several invitation-only summits and then opened the doors to others by launching the International Symposium on Rebooting Computing. Through the intervening years, many new ideas in how to rethink our computing levels of abstraction have been proposed. This paper examines some of those proposals, discusses their current status, and reviews the road ahead
Pediatric Pulmonology

Correspondence Jianbo Shao, MD, Department of Imaging Center, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430015 Hubei, China. Email: xzhuge27@163.com Abstract Purpose: To discuss the different characteristics of clinical, laboratory, and chest computed tomography (CT) in pediatric patients from adults with 2019 novel coronavirus (COVID‐19) infection. Methods: The clinical, laboratory, and chest CT features of 20 pediatric inpatients with COVID‐19 infection confirmed by pharyngeal swab COVID‐19 nucleic acid test were retrospectively analyzed during 23 January and 8 February 2020. The clinical and laboratory information was obtained from inpatient records. All the patients were undergone chest CT in our hospital. Results: Thirteen pediatric patients (13/20, 65%) had an identified history of close contact with COVID‐19 diagnosed family members. Fever (12/20, 60%) and cough (13/20, 65%) were the most common symptoms. For laboratory findings, procalcitonin elevation (16/20, 80%) should be pay attention to, which is not common in adults. Coinfection (8/20, 40%) is common in pediatric patients. A total of 6 patients presented with unilateral pulmonary lesions (6/20, 30%), 10 with bilateral pulmonary lesions (10/20, 50%), and 4 cases showed no abnormality on chest CT (4/20, 20%). Consolidation with surrounding halo sign was observed in 10 patients (10/20, 50%), ground‐glass opacities were observed in 12 patients (12/20, 60%), fine mesh shadow was observed in 4 patients (4/20, 20%), and tiny nodules were observed in 3 patients (3/20, 15%). Conclusion: Procalcitonin elevation and consolidation with surrounding halo signs were common in pediatric patients which were different from adults. It is suggested that underlying coinfection may be more common in pediatrics, and the consolidation with surrounding halo sign which is considered as a typical sign in pediatric patients.
Yield response of soybean and cowpea to rock phosphate fertilizer blend and rhizobial inoculation on two benchmark soils of northern Ghana

A thesis presented to the Department of Crop and Soil Sciences, Faculty of Agriculture, College of Agriculture and Natural Resources, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana, in partial fulfilment of the requirements for the award of degree of Doctor of Philosophy in Soil Science, 2016
Reducing Child Marriage in India: A Model to Scale Up Results, New Delhi, 2016

This paper presents a model for contextual strategizing and scaling up of interventions to accelerate the pace of reduction of child marriage, with particular reference to India, and within India with greater attention to four states, namely, Andhra Pradesh, Gujarat, Rajasthan and West Bengal. The suggested model is based on a theory of change that was developed taking into consideration the existing evidence base for the known drivers of high prevalence as well as the drivers of change in different contexts. The development of the model has also taken the review of existing policies and interventions into consideration.
CLINICO-EPIDEMIOLOGICAL PROFILE AND HOSPITAL OUTCOMES OF COVID-19 PATIENTS DURING ALL THREE WAVES, AN EXPERIENCE FROM A RURAL TERTIARY CARE CENTER, EASTERN UTTAR PRADESH, INDIA

Objective: In this study, an attempt has been made to report the clinical profile, exposure characteristics and outcomes of the COVID-19 patients admitted to our rural tertiary care facility. Methods: It is a single-centric retrospective observational study and includes data from January 2020 to March 2022. The data were retrieved from patient files. We have recorded patient demography, clinical and laboratory parameters and outcome of the patients. Patients were categorized based on disease severity according to WHO guidelines. Appropriate statistical analysis was applied and p value<0.05 was considered as significant. Results: A total of 2339 patients were included in this study. Total males were 67%, mean age group was 45.43±18.48 y (0 to 101 y), with majority belonging to 46-60 y age group (27.2%). Total comorbid patients were 29.3% with 9.4% accounting for more than one comorbidities. Most common comorbidity noted was diabetes mellitus 15.8% followed by hypertension 13.4%. Total mortality was noted to be 16.2%. Fever was the most common symptom (92%) followed by cough (51%) and myalgia (30%). Disease severity and outcome was significantly correlated with advancing age and the presence of underlying comorbidities. Conclusion: Rural population had comparable proportion of comorbidities as well as mortality. Common risk factors for severe disease in rural setting were similar to urban setting and old age and more than one comorbidities. Country-wide rural data should be collected for a better understanding of COVID-19 disease in rural and remote population.