Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge Management in SMEs Practical guidelines

A semiconductor memory device that operates as a normal memory device as long as existing memory is being addressed, but appears to external hardware and software to have a larger size (or standard size) memory array than is actually present in the semiconductor memory device is disclosed. To external hardware and software, the semiconductor memory device operates as if it has more addressable memory cells than in fact actually exist in the memory array. When addressing missing memory cells, the semiconductor memory device emulates or mimics their presence for the benefit of the external hardware or software. Preferably, the semiconductor memory device is a non-volatile electrically alterable semiconductor memory device. A method for emulating missing memory cells is also disclosed.
Efficient Power Saving Method for WiFi Direct Devices in IoT based on Hidden Markov Model

Internet of Things Technology demands interconnection of large number of devices and energy saving is the prime factor in this evolving field due to limited battery energy in the devices. Wi-Fi Direct seems to be an ideal connectivity mechanism for in-vehicular and smart home environment. Wi-Fi Direct has an opportunistic power saving mechanism where in interval between successive doze state is constant and doesn’t consider any external factors. Battery constraint and randomness in type of data received from clients demands statistical model to predict an appropriate interval. We have modelled Wi-Fi Direct group owner as a finite automaton which is irreducible, a priori and has well known emission states in the form of data size received and finite hidden state based on battery percentage. Hidden Markov Model (HMM) based approach for our model determines appropriate interval between successive doze state and thereby increasing battery longevity by close to 8%.
Alzheimer's disease and frontotemporal dementia are differentiated by discriminant analysis applied to 99mTc HmPAO SPECT data

OBJECTIVE Alzheimer's disease (AD) and frontotemporal dementia (FTD) are the most frequent neurodegenerative cognitive disorders. However, FTD remains poorly recognised clinically. The use of 99mHmPAO-single photon emission computed tomography (SPECT) has been demonstrated in the differentiation of AD and FTD. Nethertheless, there are very few comparative studies designed to assess its precise value in this differential diagnosis. The aim was to determine a simple decision rule, deduced from statistical analysis, which, if applied to regions of interest (ROIs) and mini mental state examination (MMSE), could improve the predictive value of SPECT in differential diagnosis between AD and FTD. METHODS Forty patients, 20 with probable AD and 20 with probable FTD were included. All patients underwent brain SPECT imaging, after an intravenous injection of 99mTc HmPAO-(555mBq). For each patient, 20 ROIs were determined on the Fleishig's slice and their activity was normalised to the mean cerebellar activity. Bivariate analysis (Wilcoxon rank tests) and multivariate analysis (stepwise discriminant analysis) were performed to determine the subgroup of variables able to give the highest predictive value for this differential diagnosis. A simple decision rule was built from a predictive score derived by factorial discriminant analysis. RESULTS As previously described, the fixation defect was found in frontal regions of interest (ROIs) in FTD and in the left temporoparietal-occipital ROIs in AD. Among the 21 variables, five were finally selected: right median frontal, left lateral frontal, left tempoparietal, left temporoparietal-occipital areas, and MMSE. One hundred per cent of patients with FTD were correctly classified by the decision rule (20/20 patients) and 90% of patients with AD (18/20). CONCLUSION AD and FTD are differentiated by SPECT. Automatic classification based on a decision rule deduced from factorial discriminant analysis could enhance its performance.
Finger Vein Recognition Using Modified Maximum Edge Position Octal Patterns

The vascular pattern-based recognition systems based on liveness of a person are more reliable and secure system as compare to conventional methods. This paper proposes a method for finger vein recognition using modified maximum edges Position to improve the performance in presence of uneven illumination. Unlike the available binary patterns, this feature collects the sign and magnitude of edge which capitalize the gradient difference in different directions. Highly gradient directions are identified by choosing maximum values of edges. The performances of the proposed show a significant improvement. From this Maximum Edge Position Octal Pattern (MEPOP), the classification accuracy of the system is of 89.4654% and Equal Error Rate to 12.4738%.
P(VDF‐TrFE)/BaTiO3 Nanoparticle Composite Films Mediate Piezoelectric Stimulation and Promote Differentiation of SH‐SY5Y Neuroblastoma Cells

Poly(vinylidene fluoride‐trifluoroethylene, P(VDF‐TrFE)) and P(VDF‐TrFE)/barium titanate nanoparticle (BTNP) films are prepared and tested as substrates for neuronal stimulation through direct piezoelectric effect. Films are characterized in terms of surface, mechanical, and piezoelectric features before in vitro testing on SH‐SY5Y cells. In particular, BTNPs significantly improve piezoelectric properties of the films (4.5‐fold increased d31). Both kinds of films support good SH‐SY5Y viability and differentiation. Ultrasound (US) stimulation is proven to elicit Ca2+ transients and to enhance differentiation in cells grown on the piezoelectric substrates. For the first time in the literature, this study demonstrates the suitability of polymer/ceramic composite films and US for neuronal stimulation through direct piezoelectric effect.
Automated Validation of Crowdsourced Data

Ensuring the accuracy of information or data contributed by the crowd is amongst the challenges in crowdsourcing initiatives. Data that do not meet certain criteria set by the crowdsourcer are also submitted by the crowd in a crowdsourcing initiative due to its openness. Thus, there is a need to ensure that only valid data are being captured before the data are being processed further. However, manually validating the data is not practical due to the high volume of data involved in crowdsourcing and their unpredictable nature. Therefore, in this research, an automated algorithm to validate crowdsourced data was developed. The objective was to identify the processes needed to enable the validation of crowdsourced data to be performed automatically. Two types of validation were included; task validation and worker validation. Kuder-Richardson Formula 20 was used to compute validity of task and mean formula converted to percentage was used in computing worker validity. The algorithm was implemented by embedding it in a prototype crowdsourcing application called AsnafCircle that crowdsourced information on eligible asnaf (alms recipient) from the public. Evaluation showed that the algorithm was able to automatically compute values that determine task and worker validity. Evaluation by experts also conformed the necessity of the processes that constitute the algorithm. The presence of this algorithm will help to ensure validity of contributed data in crowdsourcing initiatives, hence, improving their reliability.
Using Convolutional Neural Networks for Automated Fine Grained Image Classification of Acute Lymphoblastic Leukemia

Acute lymphoblastic leukemia can be diagnosed through a series of tests which include the minimally invasive microscopic examination of a stained peripheral blood smear. Manual microscopy is a slow process with variable accuracy depending on the laboratorian's skill level. Thus automating microscopy is a goal in cell biology. Current methods involve hand-selecting features from cell images as inputs to a variety of standard machine learning classifiers. Underrepresented in this filed, yet successful in practice, is the convolutional neural network that learns features from fine-grained images. This paper compares the performance of a convolutional neural network model with other models to determine the validity of using whole cell images rather than hand-selected features for acute lymphoblastic leukemia classification.
A Novel Compact and High-Speed CMOS Parity Generator/Checker

During the digital transmission of data, the designer is forced to use one of the various methods of data generation and checking, among them is the parity bit. However, CMOS circuits that generate the parity bit (parity generator) and perform the checking operation (parity checking) usually have wide fan-in and thus have a relatively low speed and high power consumption. In this paper, a novel CMOS scheme is presented for the realization of such functions. The proposed scheme is compared with the conventional static CMOS scheme and other schemes. The proposed scheme is verified by simulation using the 45 nm CMOS technology and shows 80.3% and 60% savings in the power-delay product and the area, respectively, assuming eight bits in a single group of data; however, at the expense of more power consumption and less immunity to process, voltage, and temperature (PVT) variations.
Predicting the Number of People for Road Traffic Accident on Highways by Hour of Day

In the present, the volume of vehicles has become crucial factor for the number of road accidents Then, this paper is predicting the number of people for traffic accident by hour of day regarding deaths and injuries on highways in Thailand. The predicting number of people are separated into two types: (i) predict the total number of people in both of deaths and injuries and (ii) predict the number of male and female in deaths and injuries separately. Time series predicting methods: Multiplicative Decomposition, Multiplicative Holt-Winters, and Seasonal Autoregressive Moving Integrated Moving Average (SARIMA) model are applied to predict the number of people deaths and injuries. The Predicting error is measured by using the mean absolute percentage error (MAPE). For this study, separately predicting number of male and female resulted in less predicting errors than predicting the total number of people in female type of deaths and injuries. However, the predicting total number of people method is appropriated to predicting the number of male for both deaths and injuries. With this predict, the transport and traffic policy department can determine the strategy for road traffic accidents for any period.
Subband Selection for Binaural Speech Source Localization

We consider the task of speech source localization using binaural cues, namely interaural time and level difference (ITD & ILD). A typical approach is to process binaural speech using gammatone filters and calculate frame-level ITD and ILD in each subband. The ITD, ILD and their combination (ITLD) in each subband are statistically modelled using Gaussian mixture models for every direction during training. Given a binaural test-speech, the source is localized using maximum likelihood criterion assuming that the binaural cues in each subband are independent. We, in this work, investigate the robustness of each subband for localization and compare their performance against the full-band scheme with 32 gammatone filters. We propose a subband selection procedure using the training data where subbands are rank ordered based on their localization performance. Experiments on Subject 003 from the CIPIC database reveal that, for high SNRs, the ITD and ITLD of just one subband centered at 296Hz is sufficient to yield localization accuracy identical to that of the full-band scheme with a test-speech of duration 1sec. At low SNRs, in case of ITD, the selected subbands are found to perform better than the full-band scheme.
Locating Heartbeats from Electrocardiograms and Other Correlated Signals

The electrocardiogram (ECG) is the main source of heartbeat analysis. However, analyzing the ECG alone can be problematic because ECG data can be noisy. This research analyzes the associations between ECG and a variety of biomedical signals, and uses these associations to detect heartbeat locations with a higher accuracy than just analyzing ECG alone.
Data-guided control: Clustering, graph products, and decentralized control

This paper presents a novel approach to form an interdependent network model from time-varying system data. The research incorporates system meta-data using k-means clustering to form a layered structure within the dynamics. To compactly encode the layering, a Cartesian product model is fit to time-varying data using convex optimization. We show that under special situations a closed form solution of this model can be acquired. The Cartesian form is particularly conducive to reasoning about the role of the interdependent network layers within the dynamics. This is illustrated through the derivation of a distributed LQR controller which requires only knowledge of local layers in the network to apply. To demonstrate the applicability of this work, the proposed methods and analysis is applied to time-series data from a high-fidelity interdependent infrastructure network simulation.
Impact of perceived HIV stigma within men who have sex with men community on mental health of seropositive MSM

ABSTRACT Emerging evidence has suggested that seropositive men who have sex with men (MSM) do not only face biased treatment from the general public but also from members of the MSM community. We conceptualized such biases perpetuated within the MSM community as human immunodeficiency virus (HIV) in-group stigma. This study examined the pathways by which perceived HIV in-group stigma impacted the mental health of seropositive MSM in Hong Kong. Internalized HIV stigma, serostatus disclosure concerns, and negative reactions towards HIV stigma were hypothesized as intermediate factors. Based on 100 Chinese seropositive MSM who were on antiretroviral therapy, results of a path analysis partially supported our hypotheses. Only negative reactions towards HIV stigma within the MSM community was a significant intermediate factor. The findings highlight the importance of understanding seropositive MSM’s different reactions to HIV stigma perpetuated within the MSM community. On top of stigma reduction research, further research may explore ways that help seropositive MSM cope with HIV in-group stigma and foster resilience.
Assessing Industry 4.0 Maturity: An Essential Scale for SMEs

In the last few years, the debate surrounding the Industry 4.0 phenomenon has experienced a constant rise. Publications on this topic are booming, and scholars are studying every related aspect. Previous research highlights the relevance of technologies in supporting the transformation to Industry 4.0. Moreover, many of the maturity scales designed to address this topic, neglected the area related to the personnel, focusing their attention mostly on the technological part. Furthermore, many SMEs encountered difficulties in using the tools designed for bigger corporations. This research paper aims to provide a new maturity scale for SMEs. In order to realise the new maturity scale, firstly this paper carries out a review of the main Industry 4.0 maturity models available in the literature. Then, moving from this analysis, a new essential maturity scale is proposed and discussed.
A Novel Method to Generate Frequent Itemsets in Distributed Environment

Frequent itemset mining (FIM) is an important topic in data mining, which extracts knowledge of the relationships among items in a transaction dataset. Apriori algorithm and its variants, apriori-like algorithms, are widely used FIM algorithms. However, in a big data environment, these algorithms are inefficient. Due to the iterative calculation and modification of intermediate results, if an apriori-like algorithm is applied on a high-dimension or large-scale dataset, the memory requirement is unacceptable for a single machine. Although parallel and distributed programming could be a solution to deal with big data problems, apriori-like algorithms are not quite suitable for parallel computing because they need extra time overhead of communication to update intermediate results iteratively in cluster memories. To solve this problem, we propose a novel FIM algorithm, Distributed Apriori Based on Itemset-Encoding (DABIE). Different from existing methods, DABIE has two main advantages. Firstly, it stores intermediate results encoded in the form of 0 and 1 to reduce memory usage. Secondly, generating frequent itemsets is based on logical operation of encoding to reduce modification of data in cluster memories. These two advantages make DABIE more friendly to cluster computing. We apply DABIE on datasets with different scales. Compared with other distributed apriori-like algorithms, the results of our experiments show that DABIE can efficiently improve the multi-iterative FIM in big data environment.
Investigation of a Modular Linear Doubly Salient Machine With Dual-PM in Primary Yoke and Slot Openings

A modular linear doubly salient machine with dual-PM in the primary yoke and slot openings is proposed in this paper. First, the machine topology is introduced, where the PMs are employed in both the primary yoke and slot openings. The magnetic fluxes excited by the PMs in the slot openings pass through the air gap and link the secondary, not short-circuited by the primary teeth. Hence, the magnetic fluxes excited by both PMs can be concentrated and enhanced in the air gap. Then, the operation principle is investigated based on the permeance model, which shows that it obeys the flux modulation principle. The electromagnetic performances are analyzed in detail using FEM, which shows that the force density and overload capability can be significantly improved with the slot PMs. Finally, the 2-D finite-element (FE) predicted results are validated by the 3-D FE results that considering the transverse end effect.
New Fibonacci Recurrent Systems Applied to Transmission Lines Input Impedance and Admittance

In this paper, new Fibonacci wave functions are introduced for the first time. These transfer functions are derived from a first order transfer function source. Their characteristics and behaviors are quiet impressive and their output signals present multiple intermediate steady-states. Their polynomial coefficients follow specific distribution with respect to the Golden ratio. The pole locations of these transfer functions follow the Fibonacci pattern and they can be created to an infinite degree following a simple recurrent process. They have multiple resonance and anti-resonance frequencies organized in a perfect way with respect to each other. Each Fibonacci system has two well defined Fibonacci boundary systems using Pascal’s triangle. These systems describe perfectly transmission lines LC ladder model input impedance and admittance.
ProphSoc: Probability-based Social-based routing Scheme in Mobile Social Network (MSN)

Opportunistic network (OppNet) inherently involves links disconnection and messages dropping due to mobility or power exhaustion in the nodes. Routing and disseminating data are key and challengeable issues in this environment. Several routing schemes such as flooding-based, prediction-based, and social based are proposed to address this important problem. However, combining the exploitation of social features and other routing techniques can optimize performance especially in term of overhead reduction. In this paper, we propose a social aware protocol, called ProphSoc, combines social features with prediction schemes. It utilizes social properties to predict the probability of a node’s data delivery. We evaluate our scheme against three benchmarks Epidemic, Prophet, and Bubble Rap. Imperial results using real dataset proves the superiority of ProphSoc over other approaches in term of overhead and average hop count reduction. In addition, In terms of delivery ratio and average latency, there is a fair tradeoff for comparison of ProphSoc with PRoPHET.
A Comprehensive Survey of Video Datasets for Background Subtraction

Background subtraction is an effective method of choice when it comes to detection of moving objects in videos and has been recognized as a breakthrough for the wide range of applications of intelligent video analytics (IVA). In recent years, a number of video datasets intended for background subtraction have been created to address the problem of large realistic datasets with accurate ground truth. The use of these datasets enables qualitative as well as quantitative comparisons and allows benchmarking of different algorithms. Finding the appropriate dataset is generally a cumbersome task for an exhaustive evaluation of algorithms. Therefore, we systematically survey standard video datasets and list their applicability for different applications. This paper presents a comprehensive account of public video datasets for background subtraction and attempts to cover the lack of a detailed description of each dataset. The video datasets are presented in chronological order of their appearance. Current trends of deep learning in background subtraction along with top-ranked background subtraction methods are also discussed in this paper. The survey introduced in this paper will assist researchers of the computer vision community in the selection of appropriate video dataset to evaluate their algorithms on the basis of challenging scenarios that exist in both indoor and outdoor environments.
Irradiance-Driven Partial Reconfiguration of PV Panels

Adaptive reconfiguration of a photo-voltaic (PV) panel by means of a switch network is a well-known approach to tackle shading issues dynamically and with a reasonable cost. Most of these approaches assume however that the entire panel is reconfigurable, resulting in high installation costs due to the large wiring overhead required by this solution.In this work we propose an architecture in which only a portion of the panel is made reconfigurable, while minimizing the loss in the extracted power with respect to a fully reconfigurable solution. The key feature of our approach is the use of environmental (irradiance and temperature) data to determine the reconfigurable subset at design time.Simulation results show that, by reconfiguring only about 50-70% of a panel, it is possible to achieve up to 45% power increase with respect to a static topology, while losing less than 5% power with respect to full reconfiguration.
NOx Prediction Method Based on Deep Extreme Learning Machine

Real-time prediction of NOx is important for the control of NOx emission from a coal-fired power plant. This paper presents a NOx prediction method based on deep extreme learning machine. First, an improved deep extreme learning machine is proposed. Then, a NOx prediction model is designed based on the proposed method. Finally, the model is evaluated by using the actual data. Simulations results show that the proposed method is effective.
Compliance with Open Data Principles: A Longitudinal Content Analysis of the Saudi’s National Open Data Platform in 2016 and 2018

This study attempts to analyze the content of the National Open Data Platform (NODP) of the Kingdom of Saudi Arabia (KSA) in two time intervals with the aim of identifying the main developments occurred with regard to participants, available datasets and the aspects they cover, as well as the volume, quantity and quality of available data. The researchers attempted, through the study, to evaluate the applicability of the original eight principles of open data (OD) regarding data available on the Platform. The study concluded that the data that are now available through the NODP are timely, accessible, machine processable, non-discriminatory, and license-free. It also concluded that there are three OD principles that are not applied. These three principles relate to the data being complete, primary, and available in open formats. The study concluded with a set of recommendations that should enhance tendency towards implementing OD in the KSA.