Miracle-dz/UniFAR-FTUs huggingface dataset:
A Blockchain Enabled Rooms Implementation For Internet of Things

The number of IoT devices is growing at an exponential rate. It is expected that by 2020, there will be approximately 30 billion internet-connected devices and 500 billion by 2030. Not only, does it increase security concerns but will give rise to interoperability issues. In this paper, the recently introduced Ethereum network will be utilised with respect to Internet of Things to create an infrastructure compatible with IoT devices. With an addition of an automated, immutable smart contract that will aid the interoperability of various devices through the heterogenousfriendly Ethereum network. The true potential of Ethereum, when combined with IoT, will be explored and demonstrated. Demonstrated through an implementation in which power will be provided to a room embedded with various IoT devices upon payment (in the form of rent) into a landlord’s smart
Cross-border electricity trade for Nepal: a SWOT-AHP analysis of barriers and opportunities based on stakeholders’ perception

ABSTRACT Endowed with over 40,000 MW of economic hydropower potential relative to its nascent domestic demand, Nepal can export electricity to its power-deficit neighbours in the BBIN (Bhutan, Bangladesh, India and Nepal) region as a strategic economic opportunity. The untapped hydro potential in Nepal, coupled with BBIN’s complementary seasonal demand patterns, difference in peak load timing, and rapidly growing growth of electricity demand in Bangladesh and India, are favourable for regional electricity cooperation, especially for Nepal. This study uses the SWOT-AHP-TOWS approach to identify the key barriers and opportunities for cross-border electricity trade and to provide insights on possible strategies for Nepal.
Primary Care of the Functionally Disabled: Assessment and Management

Come with us to read a new book that is coming recently. Yeah, this is a new coming book that many people really want to read will you be one of them? Of course, you should be. It will not make you feel so hard to enjoy your life. Even some people think that reading is a hard to do, you must be sure that you can do it. Hard will be felt when you have no ideas about what kind of book to read. Or sometimes, your reading material is not interesting enough.
Is the CFA Franc Prone to Speculative Attacks or a Contagion Effect: A Stochastic-Markov Transition Analysis for Cameroon

The study employs the Markovian processs on annual nominal effective exchange rate of CFA Franc spanning 1975 to 2017 to examine whether the CFA franc is prone to speculative attacks or a contagion effect. The findings reveal that the expected duration for the CFA Franc to be undervalued is twice higher than for it to be overvalued. This validates the contagion effect of a Euro crisis on the CFA Franc. Though the level of growth increased significantly during the undervaluation era, the level of uncertainty remains equally high. The findings confirm that exchange rate devaluation influences the expectations of private agents, which in turn triggers an attack on the domestic currency.
Skeletal growth and development : clinical issues and basic science advances

Advances in the understanding of skeletal morphogenesis and growth have not been systematically applied to our understanding of clinical problems in orthopaedics. This new work should enable physicians to take full advantage of recent scientific progress to develop new, more effective treatment options. It seems clear that a fresh range of alternatives will develop from our understanding of pathogenesis and the response of cells and tissues to specific treatments. In this volume of the Symposium Series, more than 35 orthopaedic surgeons, cell and molecular biologists, and bioengineers, define current clinical issues and knowledge, identify potential applications for basic concepts to clinical problems, and list specific directions for future research. The work is based on a 1977 workshop sponsored by the Academy of Orthopaedic Surgeons, the Orthopaedic Research and Education Foundation, and the NIH.
Multi-objective distributed generation penetration planning with load model using particle SWARM optimization

The paper presents an approach for simultaneous optimization of Distributed Generation (DG) penetration level and network performance index to obtain the optimal numbers, sites, and sizes of DG units. Two objective functions are formulated. These are: (II) DG penetration level, (II) network performance index. The minimization of the first objective reduces the capital investment cost of a distribution network owner (DNO) to integrate DG. The minimization of the second objective helps in reduction of network losses and improvement in node voltage profile and line loading. The solution approach provides a set of non-dominated solutions with different values of DG penetration level and network performance index. Thus, it offers more flexibility to a DNO to choose a final solution from the set of solutions according to its strategic decisions, regulatory directives, and budget restrictions. The solution approach used is multi-objective particle swarm optimization. The approach is validated on a 38-node distribution system. The results are compared with some existing approaches
A study on classification techniques for network intrusion detection

Computer systems vulnerabilities such as software bugs are often exploited by malicious users to intrude into information systems. With the recent growth of the Internet such security limitations are becoming more and more pressing. One commonly used defense measure against such malicious attacks in the Internet are Intrusion Detection Systems (IDSs). In this paper, we compare the ability of three classification techniques (k-means classifiers, neural networks and support vector machines) to perform for network intrusion detection applications. The results indicate that Support Vector Machines train in the shortest amount of time with an acceptable accuracy whilst Neural Networks exhibit high accuracy at the cost of long training times.
Performance Metrics and Energy Evaluation of a Lightweight Block Cipher in Human Sensor Networks

The vast increase in the development of Human Sensor Networks (HSN) and nodes has led to the increase in the exchange of the data among the sensor devices. Usually, the sensor devices will be having limited resources such as low battery power, less memory, etc. and should be capable to handle sensitive and private data. Normal encryption methods will not be suited for these sensor devices as they require large resources. For this reason, lightweight block ciphers are used for encrypting data on sensor devices. These algorithms should balance the security requirements along with energy consumption. In this paper, different design parameters and performance metrics for computing the energy being consumed by an encryption algorithm have been discussed. Lightweight block ciphers may work on different block sizes. To have a fair assessment among the ciphers, energy cost has been considered in order to encode one bit of plaintext. Energy per bit is considered as an important performance metric in measuring the energy efficiency of a cipher algorithm in low resource constraint devices.
Automatic detection of microfabric defects with Gabor fractal network

Abstract. Automatic detection of fabric defects is an important process in the textile industry, which is required to locate and classify microdefects from a large fabric image. We propose a learning-based system for automatic detection of microfabric defects. A segmentation algorithm based on fractal and gray features is applied to extract microdefect regions. Gabor fractal network is designed to further improve identification ability of this approach. The proposed network achieves superior performance in terms of detection accuracy with a much smaller model size. The best testing accuracy rates on dark line, hole, broken yarn, and dirt are 96.9%, 98.0%, 92.9%, and 98.8%, respectively. Experimental results demonstrate the effectiveness of the proposed scheme in defect detection for microfabrics. The proposed system has great potential for automatic detection of microfabric defects.
Image stitching and partitioning algorithms for infrared thermal human-body images

The thermal infrared image of the human body directly reflects the temperature distribution of the human body surface. Based on in-depth analysis, the infrared image can provide intelligent diagnosis assistance for human diseases. This paper proposed two preprocessing algorithms, i.e., upper-lower body image-stitching and body image partitioning, for medical infrared image analysis. In the image stitching stage, the human body is first extracted from the background by local thresholding based on the characteristics of the actual imaging environment. Then the upper and lower body images are aligned and fused using binary and grayscale template matching. In the image partitioning stage, the key points of the part area are determined by the extremum-point analysis of the human contour. The human body is then partitioned into regions including head, trunk, limbs, etc. Experiments show that the proposed preprocessing algorithms produce satisfactory results in image-stitching and portioning, and can effectively support the quantitative and qualitative analysis of human body temperature distribution.
Determining the impact of window length on time series forecasting using deep learning

Time series forecasting is a method of predicting the future based on previous observations. It depends on the values of the same variable, but at different time periods. To date, various models have been used in stock market time series forecasting, in particular using deep learning models. However, existing implementations of the models did not determine the suitable number of previous observations, that is the window length. Hence, this study investigates the impact of window length of long short-term memory model in forecasting stock market price. The forecasting is performed on S&P500 daily closing price data set. A different window length of 25-day, 50-day, and 100-day were tested on the same model and data set. The result of the experiment shows that different window length produced different forecasting accuracy. In the employed dataset, it is best to utilize 100 as the window length in forecasting the stock market price. Such a finding indicates the importance of determining the suitable window length for the problem in-hand as there is no One-Size-Fits-All model in time series forecasting.
A study exploring the educational needs of African-American pastors' wives within Baptist congregations

For many years, the role of the pastor's wife has been an emerging role in American religious history (Sweet, 1983). While on their journey in building and nurturing their relationship with God, supporting their husbands, families, congregations, and communites, pastors' wives have experienced joys and blessings as well as challenges and issues. A tremendous joy for some pastors' wives has been the privilege, the opportunity, and the honor to serve, minister to, assist and care for others. However, one major challenge for many pastors' wives has been preparedness, or the lack of preparedness, knowledge, and instruction (Obleton, 1996). Wives of pastors are a large population of adult learners in need of educational programming opportunities. Providing adult and continuing education courses is one viable option and a resource that could assist with the needed knowledge, skills, and abilities for their role in ministry. The primary purpose of this qualitative study was to explore the educational needs of African-American pastors' wives from the perspective and voice of the participants. To explore these educational needs, Maslow's Hierarchy of Needs was used as a framework. The participants for thls study included three educational planning partner groups or a triangular component that encompassed adult learners, specialists, and educators. For the purposes of this study, the adult learners included a sample of five African-American pastors' wives. The specialists included persons who had been in a leadership role in a ministers' wives organization. The convenience sample for this group included four participants. Finally, the educators, were the administrators or the implementers from
Target State Estimation Based on Kalman Filter in Terminal Guidance

In order to settle the requirement of target location and line-of-sight (LOS) rate estimation with multi-angle measurement in terminal guidance process, the state estimation algorithm based on Kalman filter is proposed in this paper. The linearized system model is established, and the extended Kalman filter is used to estimate the position and LOS rate of the target. The simulation results show that this method exhibits high accuracy for position estimation, and it can precisely track the change of LOS angular rate in real time. The algorithm is simple in principle, easy to implement and of high value in practice.
Education, technological changes and economic development of Bosnia and Herzegovina

Accumulation of knowledge, the role of education and innovations in the stimulation of economic development are important for understanding the endogenous theory of growth. The aim of this paper is to determine the strength and direction of relations between independent variables of education, technological development, innovations, foreign investments and the economic growth of Bosnia and Herzegovina (B&H). The relations between variables are assessed by multiple regression analysis for the period 2005 - 2017. GDP per capita (pc), as a measure of general wellbeing and economic development is a dependent variable of the regression model. Education Years Average, Technological readiness and Innovations are explanatory variables, whereas Foreign investments and Macroeconomic environment are control variables. The results indicate statistical significance of variables Education Years Average, Technological readiness and Innovations. The first two variables indicate a positive effect on the economic development, whereas Innovations have a negative effect on the economic development. The control variables are statistically insignificant. Therefore, more investment is recommended in the field of education, science and research. Institutions should be more developed and scientific-research systems should be harmonized with the European Union. It is the only way for B&H to be able to respond to global challenges when it comes to competitiveness, export and the GDP growth. Examples and experiences of this country can be useful for research activities in similar post-transition countries.
An Investigation of Features for Fundamental Frequency Pattern Prediction in Electrolaryngeal Speech Enhancement

Despite abundance of research, natural voice restoration after total laryngectomy (i.e., removal of the vocal folds of the larynx), has remained a challenge. A typical way of producing a relatively intelligible speech for patients suffering from this inability is to use an electrolarynx. However, the outcome voice sounds artiﬁcial and has “robotic” quality owing to constant fundamental frequency ( F 0 ) patterns generated by the electro-larynx. In existing frameworks on natural F 0 patterns prediction, a model is trained on a massive amount of parallel training data to ﬁnd a mapping that maps spectral features of the source speech into F 0 contours of the target speech. However, creating big datasets for electrolaryngeal (EL) speech is considered as a cumbersome and expensive task. Moreover, EL speech spectral features are signiﬁcantly different from spectral features of the normal speech, and therefore, it is not straightforward to effectively use easily available normal speech datasets in training of the model for EL speech. Consequently, the quality of the models could be still low due to the lack of sufﬁcient training data. To address this problem, we investigate F 0 pattern prediction based on other features that could be shared between normal speech and EL speech. By using shared input features, we would be to train the prediction model using a large amount of training data. As such features, in this work, we examine F 0 prediction accuracy based on phoneme-related features. The ﬁndings show that by considering phoneme labels for both vowels and consonants and one-hot encoding of these labels, we are able to predict F 0 contours with high correlation coefﬁcients
An FPGA Based Practical Implementation of Stochastic Resonance For Image Enhancement

—In this paper, a non-linear non-dynamic stochastic resonance (SR) based technique is proposed for enhancement of dark and low contrast images. Noise-enhanced signal processing theory is applied to a low contrast image to improve the contrast. Insuf ﬁ cient illumination is the major cause of low contrast of the image, which can be stated as internal noise. This internal noise is neutralized by the addition of some pre-calculated external noise. The low contrast image is added to different frames of random noise and is thresholded repeatedly against a ﬁ xed parameter. After that, averaging produces a high contrast (enhanced) image. Noise-induced resonance is obtained at a particular optimum noise intensity. This optimum intensity is obtained by varying the noise intensities. Performance of the proposed technique is investigated for Gaussian noise. Quantitative evaluation of the performance is done in terms of color enhancement, contrast enhancement factor, and perceptual quality measure. Furthermore, a hardware implementation of the proposed SR algorithm using NEXYS 4 DDR Artix-7, which is a low power FPGA device is done to show and validate the performance of the proposed algorithm in a practical environment. Power consumption and resource utilization of the proposed algorithm is also addressed. Finally, three different quantitative parameters i.e., contrast enhancement factor (F), color enhancement factor (CEF) and perpetual quality metric (PQM) are calculated to compare the hardware and software result of the proposed method.
ESTIMATION OF FARM LEVEL TECHNICAL EFFICIENCY AND ITS SOCIO-ECONOMIC DETERMINANTS IN VANILLA PRODUCTION IN KANDY DISTRICT, SRI LANKA

Vanilla is one of the economically important crops in Sri Lanka although it is mainly confined as a home garden crop grown in mid and low country wet zone. Farmer productivity is one of the most important concerns in vanilla cultivation. Thus, the productivity of Vanilla farmers can be raised by improvement in efficiency in the short run. As a result of the near absence of empirical information on farm-level technical efficiency in small scale Vanilla Production in the country generally and Kandy District in particular, a Stochastic Frontier function which incorporated inefficiency factors was estimated using a Maximum Likelihood technique to provide estimates of technical efficiency and its determinants using data obtained from 80 Vanilla farmers in Ganga Ihala Korale Divisional Secretariat division since it has one of the largest numbers of small-holder vanilla producers in the country. The results reveal that Vanilla farmers are not fully technically efficient and the mean technical efficiency estimated is 37.32%. Estimated results of the inefficiency model show that experience and educational level of the vanilla farmers significantly influence the farmers ‘efficiency positively whereas age of the farmers contributes to increase the inefficiency. The findings imply that policies that would encourage youth to engage in vanilla farming and improvement in human capital should be made and implemented. Keywords: Kandy District, Maximum Like hood estimation, Stochastic frontier model, Technical efficiency, Vanilla farmers
Hybrid Pre-Coding Based on Minimum SMSE Considering Insertion Loss in mmWave Communications

Hybrid pre-coding design is a promising research direction in large antenna array millimeter wave (mmWave) systems. The insertion loss is an inherent and significant feature of hybrid pre-coding, resulting in lower energy efficiency and inferior bit error rate (BER) performance. This paper takes the minimum sum-mean-square-error (Min-SMSE) considering the insertion loss as the optimization objective function, which aims at increasing the sum-rate and improving the BER performance. Then a hybrid pre-coding is designed based on this criterion with an adaptive overlapped subarray (OSA) architecture. It is proved that the optimization problem is non-convex. Thus, we decompose the optimization problem into two sub-optimum ones. One is to design the digital pre-coding based on the Min-SMSE criterion under the equivalent channel condition. The other one is to design the analog pre-coding based on the simplified Min-SMSE with the insertion loss objective function. Theoretical analyses of the proposed scheme are conducted, including the upper bound of the average BER, the lower bound of the average sum-rate, and the computational complexity. Simulation results show that our proposed scheme outperforms three representative hybrid pre-coding schemes with different architectures in both BER and sum-rate, when the numbers of the phase shifters and combiners are relatively small.
The Breadth of Policy and Regulatory Issues Facing IoT

Policymakers face a conundrum — promoting the adoption of IoT services to reap its many benefits, while safeguarding societal concerns. This will be a balancing act of oversight and regulation from policymakers to drive investment and consumer adoption while ensuring that safety, security, and privacy frameworks are in place. This column will explore critical national and international IoT policy and regulatory efforts as well as take a deeper dive into specific topics of interest.
Liquidity and credit risk in sovereign yield spreads

This thesis studies the relative importance of liquidity risk compared to credit risk for ten euro area sovereign yield spreads over various periods of time. Using a unique decomposition technique based on R, the findings show that credit risk dominates liquidity risk especially in peripheral countries. However, time variation in the importance of liquidity risk relative to credit risk can be observed. As such, liquidity risk has a relatively big role in the boom period considered, while credit risk is more important in the bust period that followed. Yet, no relative change is visible between different stress levels.