Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation Analysis on Society-Based Hospital Concept with Software-as-a-Service (SaaS) Technology

The high mortality rate of pregnant women in Indonesia due to the low monitoring of their health conditions needs to get special attention from all interested parties. Recording of the development of the health of pregnant women in the Cohort book at the Puskesmas or Clinic is currently only used as a routine when examining pregnant women and has not been used optimally. Data stored in the Cohort book can basically be used as an indicator of the health development of pregnant women, but technology is needed that can monitor and disseminate existing information so that relevant parties can use the information. The application of the concept of Society-Based Hospital, can be one solution for monitoring the condition of pregnant women so that action can be followed up. This concept involves hospitals in a particular area to reduce the limited resources of specialist doctors who are not owned by the Puskesmas or Clinic. The application of the RSBM concept and supported by existing technology, is expected to disseminate information to all relevant parties, so that it can provide rapid action, so as to reduce the high maternal and child mortality rates.
Insegt Fibre: A Powerful Segmentation Tool for Quantifying Fibre Architecture in Composites

Insegt Fibre is a tool for volumetric fibre segmentation, that is appropriate for densely packed fibres. The robustness to image quality of the underlying dictionary-based algorithm has opened up new opportunities for micro-structural characterisation of fibre-reinforced polymers. The recent user-friendly version of the robust fibre-detection algorithm makes image-based characterisation of fibrous materials simpler and more accessible.
Optimal Relay Deployment in Bidirectional AF Relaying Systems

Recently, the node deployment problem of a relay network has brought a hot discussion. This paper studies utility function minimum maximization problem of bidirectional amplify and forward relaying systems from the perspective of relay deployment. Since many optimization problems are considered separately, different algorithms are proposed for each optimization problem. So we consider a unified utility function instead of the separate optimization goals. Two different optimization problems are considered. One is to find the optimal relay deployment and optimize the utility function with fixed power allocation. The other is joint optimization of relay deployment and power allocation. Especially when power is fixed, the relay deployment is not a fixed location, but a piecewise function. In order to balance the two opposite transmission directions in bidirectional relaying networks, the relay deployment is related to channel gain and path loss. In addition, approximate and accurate expressions of average relay deployment are investigated when only statistical channel information is available. Simulation results validate that the reasonable relay deployment improves the system performance.
The Willingness to Adopt Fitness Wearables in Jamaica: A Study on Wearable Fitness Trackers in Kingston and St. Andrew

Research by the Jamaica Health and Lifestyle Survey Group has found that there is a high incidence of non-communicable disease in Jamaica (NCD) such as diabetes and hypertension, and these diseases have become one of the main causes of death and disability in Jamaica. Being physically active is one of the primary ways to reduce the chances of developing NCDs, however there is concern that persons are not engaging in sufficient levels of physical activity. Wearable fitness trackers are seen as a promising tool to facilitate health interventions for physical activity. However, little is known about Jamaicans’ behavioral intent towards using wearable fitness trackers. This research sought to investigate the adoption of wearable fitness trackers in Jamaica. This was achieved by examining the extent to which persons are willing to use wearable fitness trackers and the non-technical factors that affect this willingness. We used a quantitative cross-sectional survey approach, using quota sampling to select members of the sample population. The sample consisted of adult, 18 years and older, from Kingston and St. Andrew. We used ordinal regression and other statistical data association techniques for data analysis. A majority of respondents had a positive behavioral intent to user wearable fitness trackers. Facilitating conditions, performance expectancy, hedonic motivation, effort expectancy and privacy and security risks were found to be the significant factors that impacted behavioral intent. No associations were found between behavioural intent and age or sex.
Predicting the Standard and Deviant Patterns in EEG Signals Based on Deep Learning Model

In the recent years, there has been a significant growth in the area of brain computer interference. The main aim of such area is to read the brain activities, formulate a specific/desired output and power a specific approach using such output. Electroencephalography (EEG) may provide an insight into the analysis procedure of the human behavior and the level of the attention. Using the deep learning based neural network has a great success in different applications recently, such as making a decision, classifying a pattern and predicting an outcome by learning from a set of data and build the right weight matrices to represent the prediction outcome or the learning patterns. This research work proposes a novel model based on long short-term memory network to predict the standard and the deviant cases within EEG data sets. The EEG signals are acquired utilizing all the 128 electrodes that represent the 128 channels from infants aged between 5 and 7 months. Statistical approaches, principal component analysis (PCA) and autoregressive (AR) power spectral density estimate have been employed to extract the features from the EEG data sets. The proposed deep learning based model has shown great robustness dealing with different types of features extracted from the processed data sets. Very promising results have been achieved in predicting the standard and deviant cases. The standard case was presented with frequent, repetitive stimulus and the deviant case was presented with infrequent sounds.
Approaches to a social network groups clustering

— The research is devoted to the problem of the use of social networks as a tool of the illegal activity and as a source of information that could be dangerous to society. The article presents the structure of the multiagent system with which a social network groups could be clustered according to the criteria uniquely defines a group as a destructive. The agents’ of the system clustering algorithm is described.
Exploring Observability of Attractor Cycles in Boolean Networks for Biomarker Detection

Boolean Network (BN) is a simple and popular mathematical model that has attracted significant attention from systems biology due to its capacity to reveal genetic regulatory network behavior. In addition, observability, as an important network feature, plays a vital role in deciphering the underlying mechanisms driving a genetic regulatory network and has been widely investigated. Prior studies examined observability of BNs and other complex networks. That said, observability of attractor, which can serve as a biomarker for disease, has not been fully examined in the literature. In this study, we formulated a new definition for singleton or cyclic attractor observability in BNs and developed an effective methodology to resolve the captured problem. We also showed complexity is of <inline-formula> <tex-math notation="LaTeX">$O(P^{m}n)$ </tex-math></inline-formula>, when the maximal period of cyclic attractor is <inline-formula> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula>, the number of attractor is <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> and the number of genes is <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>. Importantly, we have confirmed our method can faithfully predict the expression pattern of segment polarity genes in Drosophila melanogaster and showed it can effectively and efficiently deal with the captured observability problem.
Passive Observations of a Large DNS Service: 2.5 Years in the Life of Google

In 2009 Google launched its Public DNS service, which has since become the largest DNS service in existence. A common problem with public resolvers is that Content Delivery Networks (CDNs) struggle to map end user origin. The EDNS Client Subnet (ECS) extension allows resolvers to reveal part of a client’s IP to authoritative name servers, helping CDNs pinpoint client origin. A side effect of ECS is that authoritative name server operators learn where in its network the public resolver handles queries. We leverage this side effect to study Google Public DNS (GPDNS). We perform a longitudinal analysis over data covering 2.5 years and 3.7 billion queries. Our study focuses on three aspects. First, we show that while GPDNS has PoPs in many countries, traffic is frequently routed out of country. This can reduce performance, and expose DNS requests to state level surveillance. We also show that end users are often served by a suboptimal PoP. Second, we show that end users switch to GPDNS en masse when their ISP resolver is unresponsive, and do not switch back. Finally, we also find that many e-mail providers configure GPDNS as resolver on their servers, causing serious privacy concerns due to information leakage.
Cognitive ergonomics for data analysis

Today's ever-increasing amount of data places new demands on cognitive ergonomics and requires new design ideas to ensure successful human–data interaction. Our aim is to identify the cognitive factors that require attention when designing systems to improve decision-making based on large amounts of data. We designed an experiment that simulates the typical cognitive demands people encounter in data analysis situations. We demonstrate some essential cognitive limitations using a behavioural experiment with 20 participants. The studied task presented the participants with critical and noncritical attributes that contained information on two groups of people. They had to select the response option (group) with a higher frequency of critical attributes. The results showed that accuracy of judgement decreased as the amount of information increased, and that judgement was affected by irrelevant information. Our results thus demonstrate critical cognitive limitations when people utilise data and suggest a cognitive bias in data-based decision-making. Therefore, when designing for cognition, we should consider the human cognitive limitations that are manifested in a data analysis context and develop general cognitive ergonomics guidelines for design to support the utilisation of data and improve data-based decision-making.
Reducing the Memory Cost of Training Convolutional Neural Networks by CPU Offloading

In recent years, Convolutional Neural Networks (CNNs) have enabled unprecedented progress on a wide range of computer vision tasks. However, training large CNNs is a resource-intensive task that requires specialized Graphical Processing Units (GPU) and highly optimized implementations to get optimal performance from the hardware. GPU memory is a major bottleneck of the CNN training procedure, limiting the size of both inputs and model architectures. In this paper, we propose to alleviate this memory bottleneck by leveraging an under-utilized resource of modern systems: the device to host bandwidth. Our method, termed CPU offloading, works by transferring hidden activations to the CPU upon computation, in order to free GPU memory for upstream layer computations during the forward pass. These activations are then transferred back to the GPU as needed by the gradient computations of the backward pass. The key challenge to our method is to efficiently overlap data transfers and computations in order to minimize wall time overheads induced by the additional data transfers. On a typical work station with a Nvidia Titan X GPU, we show that our method compares favorably to gradient checkpointing as we are able to reduce the memory consumption of training a VGG19 model by 35% with a minimal additional wall time overhead of 21%. Further experiments detail the impact of the different optimization tricks we propose. Our method is orthogonal to other techniques for memory reduction such as quantization and sparsification so that they can easily be combined for further optimizations.
BArt: Trading digital contents through digital assets

Since digital artworks are indeed digital content, they face the inherent problem digital content has: the link between content and its original author is very difficult to keep. Additionally, retaining control over digital copies of the content is also a challenging task. Digital coins have solved this very same problem through cryptocurrencies (for instance, Bitcoin) and have opened the door to apply the same techniques to other similar scenarios where ownership of digital assets needs to be preserved. In this paper, we propose BArt, a transparent and distributed mechanism for artists to commercialize their digital artwork, keeping control of the copies, monetizing its usage, and maintaining ownership. BArt allows artists to publicly register their work in the Bitcoin blockchain and sell usage rights in exchange for bitcoins. Buyers are allowed to exert the acquired rights. Proper behavior from all parties is enforced by the system with cryptography and economic incentives.
Monocular depth estimation using overlap information

Depth estimation is an important tool for machines to get spatial information. Human is realizing high-accuracy depth estimation by dividing problem area, but it is diﬃcult for machine to calculate depth from a single RGB image. Our goal is to improve the accuracy of monocular depth estimation. From the past research, it has been found that obtaining the information stepwise in the global and local areas is eﬀective for depth estimation. Therefore, In this research, we propose a method to utilize the anteroposterior relationship information of the object. Experimental results showed that the overlap information is useful for depth prediction.
Speech intelligibility enhancement and glimpse-based intelligibility models for known noise conditions

In this thesis, a series of context-sensitive speech modifications are proposed which aim at improving intelligibility in known noise conditions. In addition, attempts are made to improve the predictive power of objective intelligibility models for both modified and synthetic speech. The first study designed modifications which reallocate speech energy across time and frequency, with the aim of reducing masking under the constraints of constant input-output energy and preserved duration. Subjective evaluations demonstrated that reallocating energy to just-audible time-frequency regions at mid-high frequencies is the most beneficial strategy for listeners. A second study explored noise-dependent optimised spectral weightings derived using a genetic algorithm-based optimisation procedure, with an objective intelligibility measure (OIM) ? the glimpse proportion (GP) as the objective function. A clear dependence of noise type and global signal-to-noise ratio on energy reallocation was observed with the discovery of sparse, highly-selective spectral weightings, particularly in high noise conditions. In a subjective test using both stationary noise and competing speech maskers, spectral-weighted speech was more intelligible than unmodified speech in noise. In a third study, seven state-of-the-art OIMs were evaluated using three datasets from different subjective experiments, in which both synthetic speech and speech modified by algorithms designed to boost intelligibility were applied in a range of noise masking conditions. The GP metric is further extended. Four glimpse-based OIMs are also proposed to improve the predictive power of objective intelligibility models for both modified and synthetic speech. The proposed OIMs outperformed other evaluated metrics in various assessment conditions. In the final study, noise-dependent optimised spectral weightings are investigated further by maximising one of the proposed OIMs based on high-energy glimpses. In a subjective test, large intelligibility gains were seen for spectral-weighted speech in different noise conditions. Static noise-independent spectral weightings are also designed. A further listening test suggested that noise-independent spectral weightings can be as effective as the noise-dependent adaptive boosting suggested by the optimisation if a sufficient number of frequency channels are boosted. This modification has potential application to boosting speech intelligibility for public address systems and personal mobile devices.
Teaching Pepper Robot to Recognize Emotions of Traumatic Brain Injured Patients Using Deep Neural Networks

Social signal extraction from the facial analysis is a popular research area in human-robot interaction. However, recognition of emotional signals from Traumatic Brain Injured (TBI) patients with the help of robots and non-intrusive sensors is yet to be explored. Existing robots have limited abilities to automatically identify human emotions and respond accordingly. Their interaction with TBI patients could be even more challenging and complex due to unique, unusual and diverse ways of expressing their emotions. To tackle the disparity in a TBI patient’s Facial Expressions (FEs), a specialized deep-trained model for automatic detection of TBI patients’ emotions and FE (TBI-FER model) is designed, for robot-assisted rehabilitation activities. In addition, the Pepper robot’s built-in model for FE is investigated on TBI patients as well as on healthy people. Variance in their emotional expressions is determined by comparative studies. It is observed that the customized trained system is highly essential for the deployment of Pepper robot as a Socially Assistive Robot (SAR).
Logical Reasoning beyond Classical Logic: An Illustration with Pythagoras Theorem

We report on a study conceived with the idea that the use of logic in regard to mathematical reasoning as it actually occurs in practice is not limited to its prominent role in formal deductions and proofs. Interpretation of different mathematical situations elicits in fact the use of mostly unconscious forms of reasoning, close to those of narrative processing, which do not coincide with the expectations of traditional logic. This is pervasive, in particular, in educational situations at different levels, as we illustrate with interpretations which can emerge alongside an apparently obvious mathematical statement, namely, Pythagoras Theorem. We defend the position that analyses of “errors”, should start by understanding their prevalence and non arbitrariness. Accordingly, we use a nonclassical logics whose features may give new insights to the kind of learning obstacles often found in the literature, as well as in our results.
A Model for Stochastic Planning of Distribution Network and Autonomous DG Units

This article presents a mixed-integer linear stochastic model for the optimal expansion planning of electricity distribution networks and distributed generation (DG) units. In the proposed framework, autonomous DG units are aggregated and modeled using the well-known energy hub concept. In this model, the uncertainties of heat and electricity demand as well as renewable generation are represented using various scenarios. Although this is a standard technique to capture the uncertainties, it drastically increases the dimensions of this optimization problem and makes it practically intractable. In order to address this issue, a novel iterative method is developed in this article to enhance the efficiency of the optimization model. The proposed framework is further utilized to assess the benefits of the collaborative distribution network and autonomous distributed generation planning through various case studies performed on the 24-node distribution test grid. With 5.93% cost reduction, the obtained results indicate the importance of such collaborations in reaching an efficient network expansion solution. Moreover, the total planning cost for the stochastic model is 1.23% lower than the deterministic case. Various sensitivity analyses are also carried out to investigate the impacts of parameters of the proposed model on the optimal planning solution. The scalability of the model is also assessed by its implementation on the 54-node distribution test network.
Fast Convergence Time Synchronization in Wireless Sensor Networks Based on Average Consensus

Average consensus theory is intensely popular for building time synchronization in wireless sensor network (WSN). However, the average consensus-based time synchronization algorithm is based on the iteration that poses challenges for efficiency, as they entail high communication cost and long convergence time in large-scale WSN. Based on the suggestion that the greater the algebraic connectivity the faster the convergence, a novel multihop average consensus time synchronization (MACTS) is developed with innovative implementation in this article. By employing multihop communication model, it shows that virtual communication links among multihop nodes are generated and algebraic connectivity of the network increases. Meanwhile, a multihop controller is developed to balance the convergence time, accuracy, and communication complexity. Moreover, the accurate relative clock offset estimation is yielded by delay compensation. Implementing the MACTS based on the popular one-way broadcast model and taking multihop over short distances, we achieve hundreds of times the MACTS convergence rate compared to average TimeSync (ATS).
A new fast friendly window-based congestion control for real-time streaming media transmission

Real-time streaming media over the Internet is an important component of multimedia applications. For the sake of quality of service (QoS), they make rigid demands on bandwidth, delay and packet loss. However, the current Internet does not offer any QoS guarantees to real-time streaming media over it. How to maximize the transmission quality of real-time streaming applications in a best-effort network while friendly sharing bandwidth with non-real time applications like TCP has become an important issue. But now, many real-time streaming applications based on UDP rarely perform congestion control in a TCP-friendly manner, and they do not share the available bandwidth fairly with applications built on TCP. The Internet communication strongly fears that the current evolution could lead to congestion collapse and starvation of TCP traffic. For this reason, TCP-friendly protocols are being developed to behave fairly with respect to coexistent TCP flows. In this paper we present a new window-based congestion control method—fast fair binomial congestion control (FFBCC) for real-time applications. It provides a good performance of bandwidth distribution and TCP-friendliness for real-time streaming transmission while competing bandwidth with TCP flows.