Miracle-dz/UniFAR-FTUs huggingface dataset:
Object classification with deep convolutional neural network using spatial information

This paper proposes a prosthetic control method which incorporates a novel object classifier with a conventional EMG-based motion classifier. The proposed method uses not only color information but spatial information to reduce the misclassification in previous research. The depth images are created based on spatial information which is acquired by Kinect. The deep convolutional neural network is adopted for the object classification, and the posture of the prosthetic hand is controlled based on the classification result of the object. To verify the validity of the proposed control method, the experiments have been carried out with 6 target objects. The 300 images for each target object were acquired in various directions. Their shapes resemble each other in particular perspective. We trained the deep convolutional neural network using the hybrid images which involve gray scale and depth information. In the experiments, the depth information improved the learning performance with high classification accuracy. These results revealed that the proposed method has high potential to improve object classification ability.
Understanding dynamic capabilities through knowledge management

Purpose – This paper sets out to integrate research on knowledge management with the dynamic capabilities approach. This paper will add to the understanding of dynamic capabilities by demonstrating that dynamic capabilities can be seen as composed of concrete and well‐known knowledge management activities.Design/methodology/approach – This paper is based on a literature review focusing on key knowledge management processes and activities as well as the concept of dynamic capabilities, the paper connects these two approaches. The analysis is centered on knowledge management activities which then are compiled into dynamic capabilities.Findings – In the paper eight knowledge management activities are identified; knowledge creation, acquisition, capture, assembly, sharing, integration, leverage, and exploitation. These activities are assembled into the three dynamic capabilities of knowledge development, knowledge (re)combination, and knowledge use. The dynamic capabilities and the associated knowledge manage.
Auto-identification of background traffic based on autonomous periodic interaction

Background traffic of web applications refers to the traffic not generated directly due to user activities (e.g. user behavior profiling) that is usually useful to the application providers, but not the users. A recent study indicated that background traffic, contributing 51.8% bandwidth, has exceeded user-generated traffic. Accurate identification of background traffic can help network managers to optimize network resource allocation and avoid network congestion. However, identification of background traffic is not easy and the solution must be robust enough for all applications. In this paper, we propose the first method that can self-learn background traffic rules from unlabeled data and automatically identify online background traffic. The accuracy of the extracted rules is 90.51%. When applying our method in a real enterprise network, the false positive rate (FPR) is only 3% showing that our method is accurate and effective. Our method is derived from a critical observation that the background traffic exhibits a periodic behavior (referred as autonomous periodic interaction (AuPI)). Technically, we propose two indexes, Time Regularity Factor (TRF) and Time Interval Factor (TIF), to capture this AuPI pattern from unlabeled communication traffic. As a side contribution, we created a public benchmark dataset of 45 hot applications with 97,000+ background traffic flows that can be used by researchers to further investigate background traffic.
Investigation of power flow control device in grid

This article is devoted to our scientific and technical solutions on developing a semiconductor device that integrates self-generated power supply and can be used as distribution power controller in 5 kV lines. Power Flow Control Device block diagram is given, and analyze overviewed. The system simulation model is established by MTALAB/SIMULINK software. The simulation results show that: The random SVPWM which adds the Markov chain can reduce the harmonic amplitude. The harmonic wave becomes smooth at the switching frequency and the frequency of integer multiple times. It can get the continuous output voltage frequency spectrum. Finally achieve the purpose of suppressing electromagnetic noise. At the same time, the modulation frequency is more uniform distribution around the desired frequency. Therefore, it can improve the electromagnetic compatibility. The simulation results show the effectiveness and superiority of the proposed method.
Radar Cross Section reduction using plasma blobs: 3-D Finite Difference Time Domain simulations

Bistatic scattering cross-section or radar cross section (RCS) of a flat plate covered with cold, collisional, steady-state, inhomogeneous plasma blobs have been studied in this paper. We have performed 3-D finite difference time domain (FDTD) simulations for this study which provides a detailed spatio-temporal evolution of electromagnetic fields. The simulation has been performed using a 10 GHz sinusoidal wave for the case of vertical polarization. Two configurations have been studied, firstly a plate covered with a single blob and secondly the same plate covered with several blobs of smaller dimension. The simulation shows several interesting results and confirms that plasma absorber can be successfully used for reducing the scattering cross section of a plate for almost all azimuthal angles. To our knowledge, this is the first detailed three-dimensional computation of bistatic RCS of plasma shielded objects.
Estimating Gradual-Emotional Behavior in One-Minute Videos with ESNs

In this paper, we describe our approach for the OMG- Emotion Challenge 2018. The goal is to produce utterance-level valence and arousal estimations for videos of approximately 1 minute length. We tackle this problem by first extracting facial expressions features of videos as time series data, and then using Recurrent Neural Networks of the Echo State Network type to model the correspondence between the time series data and valence-arousal values. Experimentally we show that the proposed approach surpasses the baseline methods provided by the organizers.
A Distributed Trust Based Secure Communication Framework for Wireless Sensor Network

The wireless sensor network is an emerging technology, which is used to sense and monitor the environment. As the nodes are deployed in an open environment, the security is one of the essential factors. The cryptography techniques can ensure confidentiality, integrity and authentication. However, wireless sensor network also needs to deal with inside and outside attackers. To deal with outside attackers, attacks by compromised or malicious nodes, trust management system is suggested by many researchers in the area of wireless sensor network. Trust management system can be implemented in various applications for security management such as secure data aggregation, secure cluster head selection, trusted routing, access control, etc. Many researchers provide different kind of solutions for these secure applications based on trust management. However, to incorporate, all such applications on a single sensor node in the network, it is essential to design and develop a trust management system, which considers various aspects and applications of wireless sensor network. As a result, in this paper, we would like to propose a parameter and trust factor based secure communication framework and design a trust management system for wireless sensor networks. Our main contribution is to identify various parameters and trust factors which influences on trust in wireless sensor network and developing a framework for a trust management system based on various parameters and trust factors. The working of the proposed model is shown by simulation experiments conducted in MATLAB for the application of secure communication, data aggregation and intrusion detection in wireless sensor networks.
Estimating the Rating of Reviewers Based on the Text

User-generated texts such as reviews and social media are valuable sources of information. Online reviews are important assets for users to buy a product, see a movie, or make a decision. Therefore, rating of a review is one of the reliable factors for all users to read and trust the reviews. This paper analyzes the texts of the reviews to evaluate and predict the ratings. Moreover, we study the effect of lexical features generated from text as well as sentimental words on the accuracy of rating prediction. Our analysis show that words with high information gain score are more efficient compared to words with high TF-IDF value. In addition, we explore the best number of features for predicting the ratings of the reviews.
Adapting HOPNET algorithm for Wireless Sensor Networks

HOPNET is a novel hybrid routing protocol based on Ant Colony Optimization (ACO) and Zone Routing Protocol (ZRP) for Mobile Ad Hoc Networks (MANETs). In this paper, we present an adaptation of the HOPNET algorithm in a real ad hoc Wireless Sensor Network (WSN), which tends to be more critical than MANETs in terms of energy amount, processing power, memory and communication. Our proposed algorithm has been validated through a prototype implementation for ZigBit, an IEEE 802.15. 4/ZigBee(TM) OEM module. We are collecting measures about overhead, data delivery ratio, latency, and evaluating the routing algorithm in a sensor network environment.
GLOBAL UNIFORM RISK BOUNDS FOR WAVELET DECONVOLUTION ESTIMATORS

We consider the statistical deconvolution problem where one observes n replications from the model Y = X + ∈, where X is the unobserved random signal of interest and ∈ is an independent random error with distribution ϕ. Under weak assumptions on the decay of the Fourier transform of ϕ, we derive upper bounds for the finite-sample sup-norm risk of wavelet deconvolution density estimators f n for the density f of X, where f :R → R is assumed to be bounded. We then derive lower bounds for the minimax sup-norm risk over Besov balls in this estimation problem and show that wavelet deconvolution density estimators attain these bounds. We further show that linear estimators adapt to the unknown smoothness of f if the Fourier transform of ϕ decays exponentially and that a corresponding result holds true for the hard thresholding wavelet estimator if ϕ decays polynomially. We also analyze the case where f is a "supersmooth"/analytic density. We finally show how our results and recent techniques from Rademacher processes can be applied to construct global confidence bands for the density f.
Effect of fortifying Aflatoxin-contaminated feeds with Ugandan bentonite on performance of broiler birds

Newcastle One hundred fifty day-old commercial broiler chicks were reared in six groups with each group containing 25 chicks. The chicks were maintained on two diets namely BSA: broiler starter feed containing 25-35 ppb of aflatoxin and BSB: broiler starter feed containing 25-35 ppb of aflatoxin+0.5% Ugandan bentonite (UB). After three weeks, broiler starter diets were replaced with two diets of broiler finisher mash namely BFA (broiler finisher feed containing 25-35 ppb of aflatoxin) and BFB (broiler finisher feed containing 25-35 ppb of aflatoxin+0.5% UB). During starter and finisher growth phases, highest weight gains (t-test; P<0.0001, df=2) were observed in groups fed of diets containing UB. During the starter phase, the mean weight gained by chicks fed on BSB was 29% higher than the mean weight gained by chicks fed on BSA. Also, feed intake for chicks fed BSB was 15% higher than the mean feed intake for chicks fed on BSA. A similar trend was observed during the finisher phase with the mean weight gain and feed intake for birds fed BFB being 22 and 14% respectively higher than values for birds fed BFA. The mortality in groups fed the diet containing UB was 141% lower than the percentage mortality for groups maintained on diets without UB. These findings are suggestive that UB can be incorporated in animal feeds to detoxify the aflatoxins, reduce mortality, improve dressing percentage of broiler chickens and eventually boost profitability of broiler production systems. © 2013 PSCI Publisher All rights reserved.
Establishing the Resilience of a Coastal-marine Social-ecological System to the Installation of Offshore Wind Farms

Offshore wind farming is a contentious new form of sea use and a prominent driver of change across Europe. Drawing on the results of the research program Zukunft Kuste Coastal Futures, this contribution considers the resilience of the social-ecological system to the introduction of offshore wind farming in a northern German case study region. We do so by focusing on regime shifts and cross-scale effects, described through the concepts of adaptive cycles and ecosystem services. Offshore wind farming is shown to lead to a potential slow regime shift in the marine ecosystem, as well as a more rapid regime shift in the seascape. These shifts lead to changes in the available ecosystem services and conflicts between new and traditional sea and seascape values. We then explore the impact of these changes on the socioeconomic system on the coast. Against the background of the system’s current state and constraints, we argue this impact could be creative and innovative, but this trajectory depends on an internal socio-political shift and willingness to change.
Effect of dietary starch source on milk production and composition of lactating Holstein cows

The objective of this study was to evaluate the effects of four sources of starch on milk production and composition, nutrient digestion and blood metabolites of lactating Holstein cows. Four multiparous Holstein cows (708 ± 70 kg of body weight; 83 ± 9 days in milk) were used in a 4 × 4 Latin square design with 21-d periods. The concentrate portion of the diet contained wheat, barley, maize or potato as the primary source of starch. Intake of dry matter (DM) ranged from 18.7 kg/d to 19 kg/d, and was similar among treatments. Milk production was higher in cows fed the wheat-based diet compared with other diets while the milk fat concentration of the cows fed the maize-based diet was the highest. Milk protein concentration was unaffected by the source of dietary starch. Cows fed the potato-based diet had a lower milk protein, lactose and solid-non-fat yield. Milk yield/kg of DM intake and net energy for lactation (NE L )/NE L intake were higher in cows fed wheat-, barley- or maize-based diets compared with those fed the potato-based diet. Feed nitrogen efficiency was higher in cows fed the maize-based diet compared with the other experimental diets. Total tract apparent digestibility of organic matter, crude protein and ether extract were higher in cows fed the wheat- or maize-based diets compared with those fed barley- or potato-based diets; however, total tract apparent digestibility of neutral detergent fibre and acid detergent fibre were higher in cows fed the wheatbased diet compared with those fed the potato-based diet. These results showed that improved production performance in cows fed the wheat-based diet appeared to be because of greater nutrient digestibility and greater nutrient utilization efficiency. Furthermore, potato starch is not superior to grain starch as a readily available energy source for lactating dairy cows. Keywords: Nutrient digestibility, nutrient efficiency, plasma metabolites, wheat, barley, potato, maize
A 12.5 Gsample/s optical digital-to-analog converter with 3.8 effective bits

Optical techniques have the potential to overcome speed limitations: for digital-to-analog conversion. In this paper, we present a novel scheme for an optical digital-to-analog converter based on the coherent summation of optical phase-modulated signals. A first demonstration using commercial discrete components yielded in a 12.5 Gsample digital-to-analog converter with a nominal resolution of 6 bits and an effective number of bits (ENOB) of 3.8.
REMOVAL OF HEXAVALENT CHROMIUM USING ACTIVATED CARBONS DERIVED FROM MARINE ALGAE GRACILARIA AND SARGASSUM SP.

Industrial wastewater contain higher quantities of salts besides chromium (VI) ions, therefore, the effect of these salts on the adsorption of chromium (VI) should be studied. In this research, the batch removal of toxic hexavalent chromium ions from wastewater and aqueous solution using two different activated carbon marine algae Gracilaria (red algae) and Sargassum sp. (brown algae) were examined. Activated carbon prepared from Gracilaria and Sargassum sp. The chromium uptake was dependent on the equilibrium pH and chromium concentration, with pH=2.0, being the optimum pH value. The experimental adsorption data were fitted to the Langmuir and Freundlich adsorption model. The maximum efficiencies of chromium removal were 91.53% and 91.98% for activated carbon prepared of Gracilaria and Sargassum sp. , respectively. A significant fraction of the total chromium (VI) uptake was achieved within 60 min. The chromium (VI) uptake by the biosorbents was best described by pseudo-second order rate model. This investigation verifies the possibility of using activated carbon prepared from algae as valuable material for the removal of chromium from aqueous solution and wastewater.
Direct Position‐Based Solver for Stiff Rods

In this paper, we present a novel direct solver for the efficient simulation of stiff, inextensible elastic rods within the position‐based dynamics (PBD) framework. It is based on the XPBD algorithm, which extends PBD to simulate elastic objects with physically meaningful material parameters. XPBD approximates an implicit Euler integration and solves the system of non‐linear equations using a non‐linear Gauss–Seidel solver. However, this solver requires many iterations to converge for complex models and if convergence is not reached, the material becomes too soft. In contrast, we use Newton iterations in combination with our direct solver to solve the non‐linear equations which significantly improves convergence by solving all constraints of an acyclic structure (tree), simultaneously. Our solver only requires a few Newton iterations to achieve high stiffness and inextensibility. We model inextensible rods and trees using rigid segments connected by constraints. Bending and twisting constraints are derived from the well‐established Cosserat model. The high performance of our solver is demonstrated in highly realistic simulations of rods consisting of multiple 10 000 segments. In summary, our method allows the efficient simulation of stiff rods in the PBD framework with a speedup of two orders of magnitude compared to the original XPBD approach.
Identification of maintainers and restorers for WA and Kalinga sources of CMS lines in rice (Oryza sativa L.).

The availability of stable cytoplasmic male sterility and fertility restoring system is vital for commercial exploitation of heterosis in rice. This study using three CMS lines viz., APMS 6A, CRMS 32A and PUSA 6A and six testers viz., SR-6- SW-8-1, R 1099-2569-1-1, R-1557-1306-1-568-1, Super rice-7, Super rice-8 and Jitpiti. Based on the pollen fertility (%) and spikelet fertility (%) two genotypes i.e. Super rice-8 for APMS 6A and R 1099-2569-1-1 for CRMS 32A were identified as potential restorers and two genotypes i.e. SR-6-SW-8-1 for APMS 6A and R-1557-1306-1-568-1 for CRMS 32A were identified as maintainers.
Bangla grapheme to phoneme conversion using conditional random fields

Integrated with handheld devices, toys, KIOSKs, and call centers, Text to Speech (TTS) and Speech Recognition (SR) have become widely used applications in everyday life. One of the core components of said applications is Grapheme to Phoneme (G2P) conversion. The task at hand is the mapping of the written form to the spoken form, i.e. mapping one sequence to another. In Natural Language Processing (NLP), it is typically referred to as a sequence to sequence labeling task. The task however, is a language dependent one and has primarily been implemented for English and similar resource-rich languages. In comparison, very little has been done for digitally under-resourced languages such as Bangla (ethnonym: Bangla; exonym: Bengali). The current state-of-the-art Bangla Grapheme to Phoneme conversion is limited to rule-based and lexicon based approaches, the development of which requires a significant contribution of linguistic experts. In this paper, we propose a data-driven machine learning approach for Bangla G2P conversion. We evaluate the existing rule based approaches and design a machine learning model using Conditional Ran-dom Fields (CRFs). To train the machine learning models we have only used character level contextual features due to the fact that extracting hand crafted features requires specialized knowledge. We have evaluated the systems using two publicly available datasets. We have obtained promising results with a phoneme error rate of 1.51% and 14.88% for CRBLP and Google pronunciation lexicons, respectively.
An application of fractional factorial method to obtain robust solutions at a glove manufacturing environment in sri lanka

Variation is inevitable in any phenomenon. It is a crucial requirement to make a continuous effort to improve the performance and quality of products or processes to clutch the competitive advantage of the industry. Hence, the concept of ‘robustness’ was developed as a major characteristic of design engineering and product development in modern manufacturing to make the process and/or the product insensitive to variations transmitted from noise factors. Thus, the intent of this paper is to provide an application of fractional factorial design techniques to improve the level of abrasion resistance of industrial gloves by making a robust process in a Sri Lankan manufacturing context. The results show that one main factors (vulcanizing agent), and 04 two way interactions—32 experimental runs were conducted by manipulating 11 factors-have a significant impact (p<0.10) on abrasion level of the industrial gloves. The confirmation run results also verified the robust solution obtained from fractional factorial experimental design.
Misery Digraphs: Delaying Intrusion Attacks in Obscure Clouds

When remote command injection attacks succeed at the entry points of a cloud (servers exposed to the outside Internet), attackers targeting a specific asset in the cloud will pursue further exploration to find their targets. Attack targets, such as database servers, are often running on separate machines, forcing an extra step for a successful attack. However, compromising two or three machines is all an attacker needs to reach an isolated database through a simple attack path. The goal of this paper is to investigate the possibility of frustrating attackers by constructing a cloud network architecture that hides the path to a target asset in the network, utilizing multiple moving decoy virtual machines and confusing firewall configurations. A deceiving cloud network architecture can significantly delay attacks (by stretching the attack path from a handful of steps to thousands), providing time for system administrators to intervene and resolve the intrusion. This paper introduces the concept of misery digraphs, which provide a theoretical foundation for creating intrusion deception in clouds. This paper describes the necessary steps to convert a cloud to one that includes a misery digraph, and evaluates the feasibility and effectiveness of using the approach with Amazon Web Services. Our simulation results demonstrate that for a cloud implementing misery digraphs with a simple attack path of length five, there is a 91% probability that an attack requires at least 1000 steps to reach the target.