Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of gluteal subcutaneous and muscle thickness in infants and children for application to intramuscular injection, autologous fat grafting, and gluteal artery perforator flaps

Background To achieve improvements in intramuscular injection, autologous fat grafting, and gluteal artery perforator flaps in infants and children, the relationships of computed tomography (CT)-determined gluteal muscle thickness (MT) and subcutaneous tissue thickness (SCT) with age and body mass index (BMI) were analyzed. Methods Gluteal SCT and MT at ventrogluteal (VG) and dorsogluteal (DG) sites were measured in a standardized manner in 350 patients aged 0 to 6 years who had visited a tertiary hospital and had undergone abdominopelvic CT between January 2005 and December 2016. Recorded measurements were analyzed using one-way analysis of variance and stepwise multiple regression to identify the factors that were most closely related to MT and SCT. Results Subcutaneous tissue at VG sites was thinner than at DG sites, but not significantly so in any age group. Muscles tended to be thinner at VG sites in 4 to 7-year-old, but thicker at VG sites in 1 to 3-year-old, though the differences were not significant. MT in the VG and DG regions was found to be related to age, and SCT in these regions to be related to age and BMI. Conclusions The VG and DG sites should be considered as alternatives for intramuscular injection in infants and children when the anterolateral site is problematic. In addition, considering the gluteal MT and SCT of infants and children should help produce good results in autologous fat grafting and gluteal artery perforator flap harvesting.
Multi-stage image retrieval based on feature augmentation with truncated polynomial weight

In this paper, we propose an effective image retrieval method. Based on a conventional global image representation, multi-stage image retrieval pipeline with feature augmentation is constructed to improve retrieval accuracy. To suppress irrelevant images while boosting relevant images, a novel weighting scheme for feature augmentation is introduced. In addition, the relationship between database images is leveraged to update or re-rank shortlist of the retrieved images. The proposed method was evaluated on Google-landmarks dataset, and the experimental results validate the effectiveness of the proposed method.
SlimIoT: Scalable Lightweight Attestation Protocol for the Internet of Things

The Internet of Things (IoT) is increasingly intertwined with critical industrial processes, yet contemporary IoT devices offer limited security features, creating a large new attack surface. Remote attestation is a well-known technique to detect cyber threats by remotely verifying the internal state of a networked embedded device through a trusted entity. Multi-device attestation has received little attention although current single-device approaches show limited scalability in IoT applications. Though recent work has yielded some proposals for scalable attestation, several aspects remain unexplored, and thus more research is required. This paper presents slimIoT, a scalable lightweight attestation protocol that is suitable for all IoT devices. slimIoT depends on an efficient broadcast authentication scheme along with symmetric key cryptography. It is resilient against a strong adversary with physical access to the IoT device. Our protocol is informative in the sense that it identifies the precise status of every device in the network. We implement and evaluate slimIoT considering many factors. On the one hand, our evaluation results show a low overhead in terms of memory footprint and runtime. On the other hand, simulations demonstrate that slimIoT is scalable, robust and highly efficient to be used in static and dynamic networks consisting of thousands of heterogenous IoT devices.
PedNet: A Spatio-Temporal Deep Convolutional Neural Network for Pedestrian Segmentation

Articulation modeling, feature extraction, and classification are the important components of pedestrian segmentation. Usually, these components are modeled independently from each other and then combined in a sequential way. However, this approach is prone to poor segmentation if any individual component is weakly designed. To cope with this problem, we proposed a spatio-temporal convolutional neural network named PedNet which exploits temporal information for spatial segmentation. The backbone of the PedNet consists of an encoder–decoder network for downsampling and upsampling the feature maps, respectively. The input to the network is a set of three frames and the output is a binary mask of the segmented regions in the middle frame. Irrespective of classical deep models where the convolution layers are followed by a fully connected layer for classification, PedNet is a Fully Convolutional Network (FCN). It is trained end-to-end and the segmentation is achieved without the need of any pre- or post-processing. The main characteristic of PedNet is its unique design where it performs segmentation on a frame-by-frame basis but it uses the temporal information from the previous and the future frame for segmenting the pedestrian in the current frame. Moreover, to combine the low-level features with the high-level semantic information learned by the deeper layers, we used long-skip connections from the encoder to decoder network and concatenate the output of low-level layers with the higher level layers. This approach helps to get segmentation map with sharp boundaries. To show the potential benefits of temporal information, we also visualized different layers of the network. The visualization showed that the network learned different information from the consecutive frames and then combined the information optimally to segment the middle frame. We evaluated our approach on eight challenging datasets where humans are involved in different activities with severe articulation (football, road crossing, surveillance). The most common CamVid dataset which is used for calculating the performance of the segmentation algorithm is evaluated against seven state-of-the-art methods. The performance is shown on precision/recall, F 1 , F 2 , and mIoU. The qualitative and quantitative results show that PedNet achieves promising results against state-of-the-art methods with substantial improvement in terms of all the performance metrics.
Visual Information Pianists Use for Efficient Score Reading

When sight-reading music, pianists have to decode a large number of notes and immediately transform them into finger actions. How do they achieve such fast decoding? Pianists may use geometrical features contained in the musical score, such as the distance between notes, to improve their efficiency in reading them. The aim of this study is to investigate the visual information pianists rely on when reading music. We measured the accuracy of the musical score reading of 16 skilled pianists and investigated its relationship with the geometrical features. When a single note was presented, pianists easily read it when it was located within three ledger lines. When two notes with an octave interval were presented, interestingly, their readable range was extended compared to that of the single note. The pianists were also able to recognize the octave interval correctly even if they misread the height (or pitch) of the target notes. These results suggest that the pianists decoded two notes composing an octave interval as a single “two-tone geometric pattern. ” Analyzing the characteristics of incorrect responses, we also found that pianists used the geometrical features of the spatial relationship between the note head and the ledger line, and that the cause of the misreading could be categorized into four types: [Type I] Confusion to a neighboring note having the same ledger line configuration; [Type II] Interference from a commonly used height note having the same note name; [Type III] Misunderstanding based on the appearance probability; [Type IV] Combination of the above three. These results all indicate that the pianists' abilities in score reading rely greatly on the correlation between the geometric features and playing action, which the pianists acquired through long-time training.
Assessment of Expression of Ki-67 in Benign and Malignant Prostatic Lesions among Sudanese Patients

BACKGROUND: Prostatic cancer is one of the most common cancers of males in a Sudanese population. The early detection is very important, as it is only curable at an early stage. AIM: The objective of this study was to investigate the expression pattern of Ki-67 in benign and malignant prostatic lesions to improve the diagnosis that may help in better management and prevention of disease. MATERIAL AND METHODS: Fifty-eight formalin fixed paraffin blocks from diagnosed cases of prostatic tumours with different grade, and stages were included in this study. Ki-67 expression was examined immunohistochemically by using monoclonal mouse anti-human Ki67 IS626. The results were correlated with Gleason score and tumour differentiation and stage. RESULTS: The frequency of histological types was as follow: 11 cases of benign prostate, atic hyperplasia (19%) and 47 cases of prostatic cancer (81%). Our results stated that prostatic adenocarcinoma among Sudanese patients was of low grade which means tumours are less aggressive. Furthermore, the findings demonstrate that Ki-67 expression in prostatic carcinoma smears was correlated significantly with the degree of Gleason score (P < 0.05). CONCLUSIONS: We found that the prostatic adenocarcinoma among Sudanese patients was less aggressive. Furthermore, Ki-67 expression was proportional to the grade of a tumour and it was a useful prognostic and diagnostic biomarker.
Energy-efficient Offloading Policy for Resource Allocation in Distributed Mobile Edge Computing

Mobile edge computing (MEC) is a promising paradigm to integrate computing and communication resources in mobile networks. MEC can improve mobile service quality and enhance Quality of Experience (QoE) by offloading computation tasks to MEC servers. However, a MEC server only can provide limited computational resources for users. In this paper, we consider a mobile edge computing system that provides three offloading policies that are: (i) executing tasks in local device, (ii) offloading tasks to servers in a local region, (iii)offloading tasks to servers in a nearby region. In the policy (iii), mobile user equipment can utilize computational resources of MEC servers in nearby regions to solve the problem of insufficient computational resources in local region servers. We formulate the computation offloading problem as a potential game and propose a Distributed Offloading strategy based on Jacobi algorithm (DOJ) for solving the computation offloading problem in a short period. The simulation results show that our proposed algorithm can reduce overall system costs and guarantee the QoE of users.
Thalamocortical Communication in the Awake Mouse Visual System Involves Phase Synchronization and Rhythmic Spike Synchrony at High Gamma Frequencies

In the neocortex, communication between neurons is heavily influenced by the activity of the surrounding network, with communication efficacy increasing when population patterns are oscillatory and coherent. Less is known about whether coherent oscillations are essential for conveyance of thalamic input to the neocortex in awake animals. Here we investigated whether visual-evoked oscillations and spikes in the primary visual cortex (V1) were aligned with those in the visual thalamus (dLGN). Using simultaneous recordings of visual-evoked activity in V1 and dLGN we demonstrate that thalamocortical communication involves synchronized local field potential oscillations in the high gamma range (50–90 Hz) which correspond uniquely to precise dLGN-V1 spike synchrony. These results provide evidence of a role for high gamma oscillations in mediating thalamocortical communication in the visual pathway of mice, analogous to beta oscillations in primates.
The Effect of Using Attribute Information in Network Traffic Prediction with Deep Learning

It is crucial for network operators to predict network traffic in the future as accurate as possible for appropriate resource provisioning and traffic engineering. Recurrent neural network (RNN) methods are considered to be the most promising prediction methods because of their high prediction accuracy. In conventional studies, RNN methods use only time series of traffic volume as input, and do not use any attribute information (e.g., timestamp and day of the week) of the time series data. However, traffic volume changes depending on both time and day of the week. Therefore, it is possible that we can further improve the prediction accuracy of the RNN methods by using the attribute information as input, in addition to the time series of traffic volume. In this paper, we investigate the effect of using the attribute information of time series of traffic volume on prediction accuracy in network traffic prediction. We propose two RNN methods: RNN-VT method and RNN-VTD method. The RNN-VT method uses timestamp information and the RNN-VTD method uses both timestamp and day of the week information as input, in addition to the time series of traffic volume. Experimental results show that day of the week information is significantly effective for improving prediction accuracy of the RNN methods while timestamp information is not effective.
Recursive Sparse Pseudo-input Gaussian Process SARSA

The class of Gaussian Process (GP) methods for Temporal Difference learning has shown promise for data-efficient model-free Reinforcement Learning. In this paper, we consider a recent variant of the GP-SARSA algorithm, called Sparse Pseudo-input Gaussian Process SARSA (SPGP-SARSA), and derive recursive formulas for its predictive moments. This extension promotes greater memory efficiency, since previous computations can be reused and, interestingly, it provides a technique for updating value estimates on a multiple timescales
Cortical control of a tablet computer by people with paralysis

General-purpose computers have become ubiquitous and important for everyday life, but they are difficult for people with paralysis to use. Specialized software and personalized input devices can improve access, but often provide only limited functionality. In this study, three research participants with tetraplegia who had multielectrode arrays implanted in motor cortex as part of the BrainGate2 clinical trial used an intracortical brain-computer interface (iBCI) to control an unmodified commercial tablet computer. Neural activity was decoded in real time as a point-and-click wireless Bluetooth mouse, allowing participants to use common and recreational applications (web browsing, email, chatting, playing music on a piano application, sending text messages, etc.). Two of the participants also used the iBCI to “chat” with each other in real time. This study demonstrates, for the first time, high-performance iBCI control of an unmodified, commercially available, general-purpose mobile computing device by people with tetraplegia.
High Frequency Transformer Design and Optimization using Bio-inspired Algorithms

ABSTRACT High frequency (HF) transformer at the isolation stage of SST plays a vital role in deciding the power conversion efficiency of the SST system. This paper proposes a new design procedure for optimization of HF transformer used in SST applications such that it fulfils all the requirements pertaining to its operating conditions. The novel design procedure proposed is based on optimizing the core geometry (in 5th power of cm), which has direct influence on regulation and copper loss. An isolated half-bridge DC–DC converter topology is chosen for implementing the proposed design. Four bio-inspired algorithms, namely Particle swarm optimization (PSO), Whale Optimization algorithm (WOA), Dragonfly Algorithm (DA), and Ant Lion Optimizer (ALO) are used to solve the optimization problem and the results are compared with Genetic algorithm (GA). Finally, the optimization results are validated through PowerEsim, a web-based testing platform with huge database of real-time components from leading manufacturers across the globe.
Recognizing Gestures on Projected Button Widgets with an RGB-D Camera Using a CNN

Projector-camera systems can turn any surface such as tabletops and walls into an interactive display. A basic problem is to recognize the gesture actions on the projected UI widgets. Previous approaches using finger template matching or occlusion patterns have issues with environmental lighting conditions, artifacts and noise in the video images of a projection, and inaccuracies of depth cameras. In this work, we propose a new recognizer that employs a deep neural net with an RGB-D camera; specifically, we use a CNN (Convolutional Neural Network) with optical flow computed from the color and depth channels. We evaluated our method on a new dataset of RGB-D videos of 12 users interacting with buttons projected on a tabletop surface.
Non‐alcoholic fatty liver disease in pediatric type 2 diabetes: Metabolic and histologic characteristics in 38 subjects

Obesity and type 2 diabetes (T2D) is risk factors for non‐alcoholic fatty liver disease (NAFLD) and non‐alcoholic steatohepatitis (NASH). In children with T2D and liver biopsies, we investigated correlations between NAFLD/NASH and transaminase activity, A1c, lipids, and histologic changes in repeat biopsies.
Father’s roles and perspectives on healthcare seeking for children with pneumonia: findings of a qualitative study in a rural community of the Philippines

Objectives Pneumonia remains a primary cause of death for under-five children. It is possible to reduce the mortality impact from childhood pneumonia if caregivers recognise the danger signs of pneumonia and obtain appropriate healthcare. Among caregivers, research on fathers’ healthcare-seeking behaviours and perceptions are limited, whereas research on mothers is available. This study aims to reveal fathers’ roles and perspectives with respect to the selection of care and treatment for children with pneumonia in a remote island of the Philippines. Design A qualitative research was carried out using semistructured interviews. Setting and participants The interviews were conducted with 12 fathers whose children had pneumonia-like episodes in the 6 months prior to the interview. Data analysis was performed using the concept analysis method to identify codes which were merged into subcategories and categories. Finally, the themes were identified. Results Three themes were identified as part of fathers’ roles, and two were identified as fathers’ perspectives on various treatment options. Fathers took care of their sick children by not entrusting care only to mothers because they considered this as part of their role. Notably, fathers considered that arranging money for the child’s treatment was a matter of prime importance. They selected a particular treatment based on their experiences and beliefs, including herbal medicine, home treatment, and visiting traditional healers and health facilities. Their decision was influenced by not only their perception of the severity of illness but also cultural beliefs on the cause of illness. Visiting health facilities, particularly during hospital admissions, causes significant financial burden for the family which was the main concern of fathers. Conclusion It is crucial to consider the cultural background and also imperative to address issues related to medical cost and the credibility of health facilities to improve fathers’ healthcare-seeking behaviour.
Classical Algorithms from Quantum and Arthur-Merlin Communication Protocols

The polynomial method from circuit complexity has been applied to several fundamental problems and obtains the state-of-the-art running times. As observed in [Alman and Williams, STOC 2017], almost all applications of the polynomial method in algorithm design ultimately rely on certain low-rank decompositions of the computation matrices corresponding to key subroutines. They suggest that making use of low-rank decompositions directly could lead to more powerful algorithms, as the polynomial method is just one way to derive such a decomposition. Inspired by their observation, in this paper, we study another way of systematically constructing low-rank decompositions of matrices which could be used by algorithms. It is known that various types of communication protocols lead to certain low-rank decompositions (e.g., $\mathsf{P}$ protocols/rank, $\mathsf{BQP}$ protocols/approximate rank). These are usually interpreted as approaches for proving communication lower bounds, while in this work we explore the other direction. We have the two generic algorithmic applications of communication protocols. The first connection is that a fast $\mathsf{BQP}$ communication protocol for a function $f$ implies a fast deterministic additive approximate counting algorithm for a related pair counting problem. The second connection is that a fast $\mathsf{AM}^{\mathsf{cc}}$ protocol for a function $f$ implies a faster-than-bruteforce algorithm for $f\textsf{-Satisfying-Pair}$. We also apply our second connection to shed some light on long-standing open problems in communication complexity. We show that if the Longest Common Subsequence problem admits an efficient $\mathsf{AM}^{\mathsf{cc}}$ protocol, then polynomial-size Formula-$\textsf{SAT}$ admits a $2^{n - n^{1-\delta}}$ time algorithm for any constant $\delta > 0$.
Designing a Blended Course in Android App Development using 4C/ID

Developing an Android app is a complex skill that is difficult for students to master because many constituent skills have to be processed simultaneously, which might cause a cognitive overload. Cognitive Load Theory (CLT) offers a framework to analyze and reduce the cognitive load, but the question is how to apply CLT in a systematic way during course design. The Four-Component Instructional Design (4C/ID) model incorporates CLT and offers additional instructional methods that improve learning. This paper reports on a case study applying 4C/ID to develop a blended course in Android app development. The following 4C/ID components were designed using the Ten Steps to Complex Learning (Ten Steps) approach: learning tasks, supportive information and procedural information. Each designed component or part thereof was categorized as face-to-face or online, for both lecturer and student, resulting in a blended learning design. The Ten Steps approach proved to be valuable in designing a blended course in Android app development. Our case study will be the starting point of further research.
Societal determinants of HIV vulnerability among clients of female commercial sex workers in Indonesia

This study aimed to explore societal determinants of HIV vulnerability among the clients of female commercial sex workers (FCSWs) in Belu and Malaka districts, Indonesia. A qualitative inquiry using in-depth interviews was employed to collect data from participants (n = 42) recruited using a purposive and snowball sampling technique. Data analysis was guided by a qualitative data analysis framework. The study results revealed several societal determinants that supported vulnerability to HIV infection among the participants. They included low education level and sexual health literacy including the lack of knowledge and information about HIV transmission and prevention. Additional determinants identified were limited source of HIV/AIDS-related information, availability of and ease of accessibility of brothels and FCSWs, peer influence, and high mobility of the study participants. Findings of this study indicate the needs and call for interventions that aim to protect both FCSWs and their clients, through provision of HIV/AIDS and sexual health education and information and improvement in the availability and accessibility of condoms.