Miracle-dz/UniFAR-FTUs huggingface dataset:
Hand gesture recognition using an adapted convolutional neural network with data augmentation

Hand gestures provide a natural way for humans to interact with computers to perform a variety of different applications. However, factors such as the complexity of hand gesture structures, differences in hand size, hand posture, and environmental illumination can influence the performance of hand gesture recognition algorithms. Recent advances in Deep Learning have significantly advanced the performance of image recognition systems. In particular, the Deep Convolutional Neural Network has demonstrated superior performance in image representation and classification, compared to conventional machine learning approaches. This paper proposes an Adapted Deep Convolutional Neural Network (ADCNN) suitable for hand gesture recognition tasks. Data augmentation is initially applied which shifts images both horizontally and vertically to an extent of 20% of the original dimensions randomly, in order to numerically increase the size of the dataset and to add the robustness needed for a deep learning approach. These images are input into the proposed ADCNN model which is empowered by the presence of network initialization (ReLU and Softmax) and L2 Regularization to eliminate the problem of data overfitting. With these modifications, the experimental results using the ADCNN model demonstrate that it is an effective method of increasing the performance of CNN for hand gesture recognition. The model was trained and tested using 3750 static hand gesture images, which incorporate variations in features such as scale, rotation, translation, illumination and noise. The proposed ADCNN was compared to a baseline Convolutional Neural Network and the results show that the proposed ADCNN achieved a classification recognition accuracy of 99.73%, and a 4% improvement over the baseline Convolutional Neural Network model (95.73%).
Predictive Control for a Marine Hybrid Diesel-Electric Plant During Transient Operation

We investigate the implementation of model predictive control to the power split problem of a hybrid diesel-electric marine power plant during transient loading. The modeling procedure, the controller design and the experimental validation of the proposed control scheme are covered. System behavior is described by models based on system identification from experimentally obtained data. These models were used to design predictive controllers that cope with physical and operating constraints of the hybrid power train. Experiments were conducted at HIPPO-1 testbed, at the Laboratory of Marine Engineering. Based on the experimental results it can be concluded that predictive control can be utilized as an efficient energy management strategy in marine power plants for disturbance rejection, and operation of the plant within operator's desirable limits.
Vehicle classification method using compound kernel functions

The focus of this paper is to explore the use of the Support Vector Machine (SVM) classifier. Though several literatures have already discussed the idea of using this method in vehicle classification, however, SVM accuracy is limited on the type of Kernel function used. Each Kernel functions has their own characteristics and limitations that is highly dependent on its parameter. Thus, in order to overcome these limitations, a method of compounding Kernel function for vehicle classification is hereby implemented.
Szervezeti kultúra és tanulmányi teljesítmény a műszaki menedzser hallgatók körében

Our study analyses the connection between perceived and ideal organizational cultures and academic results. A modified, self-managed OCAI questionnaire was used to measure perceived and ideal organizational cultures among engineering management students of the University of Debrecen Faculty of Engineering (N = 114). Our results reveal weak positive linear correlations between academic performance and perceived or ideal level of clan culture. In case of other culture types we did not find any consistent relationship. Based on our results students who consider clan culture ideal can expect better results than those who prefer other types. Among students who visit at least 61% of the classes those who reported fitting (or better fitting) perceived and ideal cultures tend to perform better in their studies.
Textural features selection for image classification by Bayesian method

This article proposes an algorithm to optimize the performance in texture classification by Bayesian method. Specifically, we extract several features from the Grey level co-foccurrence matrices (GLCMs) with different distances d and directions θ. We then apply Genetic algorithm to select the suitable features that can minimize the error rate of using the cross validation set. This choice of features continues to be used for classifying test data. Three numerical examples performed with synthetic and real images show the superiority of proposed algorithm over some existing ones. They also present the feasibility and applicability of the proposed method for texture recognition, especially for some practical problems such as material and handwritten digit recognition.
A Morphological Approach to Counting BPH in Light-Trap Images with Overlapping Individuals

Brown planthoppers (BPH) damage rice directly through feeding and transmitting two the most dangerous kinds of virus on rice, i.e. rice ragged stunt and rice grassy stunt virus. This is one of the most dangerous species of insect for rice plant. Therefore, detecting and estimating BPH population in rice fields are concerned recently to support the BPH monitoring. In this research, an approach to detecting and counting BPH in images taken by light traps, the most used device to catch BPH in rice fields, is proposed. This approach uses morphological operations to analyse BPH structure and the sequential region labeling algorithm to count the separate BPH. In addition, an approach to dealing with overlapped BPH is also introduced. The experimental results show that our proposed approach is suited to identify and count the BPH in images. Particularly, our approach can deal with images that contain overlapping individuals.
TKSE: Trustworthy Keyword Search Over Encrypted Data With Two-Side Verifiability via Blockchain

As a very attractive computing paradigm, cloud computing makes it possible for resource-constrained users to enjoy cost-effective and flexible resources of diversity. Considering the untrustworthiness of cloud servers and the data privacy of users, it is necessary to encrypt the data before outsourcing it to the cloud. However, the form of encrypted storage also poses a series of problems, such as: How can users search over the outsourced data? How to realize user-side verifiability of search results to resist malicious cloud servers? How to enable server-side verifiability of outsourced data to check malicious data owners? How to achieve payment fairness between the user and the cloud without introducing any third party? Towards addressing these challenging issues, in this paper, we introduce TKSE, a trustworthy keyword search scheme over encrypted data without any third party, trusted or not. In TKSE, the encrypted data index based on digital signature allows a user to search over the outsourced encrypted data and check whether the search result returned by the cloud fulfills the pre-specified search requirements. In particular, for the first time, TKSE realizes server-side verifiability which protects honest cloud servers from being framed by malicious data owners in the data storage phase. Furthermore, blockchain technologies and hash functions are used to enable payment fairness of search fees without introducing any third party even if the user or the cloud is malicious. Our security analysis and performance evaluation indicate that TKSE is secure and efficient and it is suitable for cloud computing.
DALEX: explainers for complex predictive models

Predictive modeling is invaded by elastic, yet complex methods such as neural networks or ensembles (model stacking, boosting or bagging). Such methods are usually described by a large number of parameters or hyper parameters - a price that one needs to pay for elasticity. The very number of parameters makes models hard to understand. This paper describes a consistent collection of explainers for predictive models, a.k. a. black boxes. Each explainer is a technique for exploration of a black box model. Presented approaches are model-agnostic, what means that they extract useful information from any predictive method despite its internal structure. Each explainer is linked with a specific aspect of a model. Some are useful in decomposing predictions, some serve better in understanding performance, while others are useful in understanding importance and conditional responses of a particular variable. Every explainer presented in this paper works for a single model or for a collection of models. In the latter case, models can be compared against each other. Such comparison helps to find strengths and weaknesses of different approaches and gives additional possibilities for model validation. Presented explainers are implemented in the DALEX package for R. They are based on a uniform standardized grammar of model exploration which may be easily extended. The current implementation supports the most popular frameworks for classification and regression.
The establishment of an integrated automation production line with multiple monitoring and control loops

The paper is mainly to establish an integrated automation production with multiple monitoring and control loops. Multiple transmission loops is applied to perform the functions of IoT and signals communication for systems, based on the concept of signals flow, so as to avoid the hazard of action missing or working schedule interrupt for incomplete or abnormal signals through the way of variously independent transmission loops. The multiple monitoring and control loops are the combination of the loops of Wi-Fi, Bluetooth and Wireless modules. While one loop has trouble happens, others will inform the condition immediately and take the responsibility of signals transmission and the works of the monitoring and control temporarily. The experiments for validation are performed by the responses of some auto-mobiles, assumed to apply the carrying mission under the condition of lost schedule or into a dead zone of signals, to verify the functions of status information immediately and continuous working schedule without interrupts under the condition of signals missing for one loop.
High avalanche capability specific diode part structure of RC-IGBT based upon CSTBT™

A thin RC-IGBT is facing to a difficulty of certifying a stable BV characteristic among the three portions, i.e. IGBT (pnp), Diode (pin) and termination (pnp too). We propose a high avalanche capability specific Diode part structure of the RC-IGBT based upon CSTBT™. This stabilization of the BV characteristic was realized by adjusting the BV balance of three portions described above. One of the successful approach was lowering the Diode's BV by widening Diode's trench pitch (WDTP).
Virtual Transcendence Experiences: Exploring Technical and Design Challenges in Multi-Sensory Environments

In this paper1, we introduce the concept of Virtual Transcendence Experience (VTE) as a response to the interactions of several users sharing several immersive experiences through different media channels. For that, we review the current body of knowledge that has led to the development of a VTE system. This is followed by a discussion of current technical and design challenges that could support the implementation of this concept. This discussion has informed the VTE framework (VTEf), which integrates different layers of experiences, including the role of each user and the technical challenges involved. We conclude this paper with suggestions for two scenarios and recommendations for the implementation of a system that could support VTEs.
Socio-demographic determinants of malnutrition among primary school aged children in Enugu, Nigeria

Introduction Several factors including the parental literacy, illness, socioeconomic status, poor sanitation and hygienic practices affect the physical growth of children. The aim of this study was to determine the socio-demographic determinants of malnutrition among primary school aged children in Enugu, Nigeria. Methods A cross-sectional descriptive study involving primary school children in Enugu was carried out over a 3 month period. Subjects were selected using multistage sampling technique. Weight and height were measured using a digital scale and a wooden stadiometer, respectively. Body Mass Index (BMI), weight-for-age (WAZ), Height-for-age (HAZ) and BMI-for-age z scores were then derived using the new WHO reference standards. Results 348 children (40.4%) were recruited from 5 public schools while 512 (59.6%) were recruited from 9 private schools. The mean age of the study participants was 9.2 ± 1.8 years. 7 (0.8%) children were stunted, 26 (3.3%) wasted and 28 (3.3%) underweight. Of all the study participants, overweight and obesity were observed in 73 (8.5%) and 35 (4.1%) children, respectively. Children of lower socioeconomic class were more stunted, underweight and wasted, while overweight and obesity were more prevalent among children from the upper socioeconomic class. Conclusion Factors such as age and sex, parental education and socioeconomic class had a significant impact on nutritional status. Overweight and obesity were more prevalent among the children from the upper socioeconomic class, attending private schools, while stunting and wasting were more in children of the lower class attending public schools.
How to identify and prioritize procedures suitable for simulation-based training: Experiences from general needs assessments using a modified Delphi method and a needs assessment formula

Abstract Simulation-based training (SBT) is a revolutionary addition to the education of residents in different specialties. However, the selection of training programs is often reliant on commercially available equipment or on experiential notions, local interests or coincidence. The first step to developing training programs should be problem identification and general needs assessment to ensure that these are aligned with current trainee needs. We describe a stepwise approach to performing a needs assessment following the Delphi process and using a needs assessment formula (NAF) to prioritize technical procedures for simulation training. This technique relies on a panel of experts to gather information and achieve a convergence of opinion. We used three rounds of survey iterations. Round 1: A brainstorming phase to gather all procedures that a newly qualified resident should be able to perform. Round 2: Rating and preliminary prioritization with a NAF including frequency of the procedure, number of doctors, impact, and feasibility. Round 3: Elimination and final prioritization of procedures. The needs assessment identifies and prioritizes procedures for simulation training. It can guide medical educators in the planning of SBT programs to address current trainee needs with the aim of improving the education and training of future doctors.
MeLight: Embodied Appreciation of "Inouzu"

The purpose of this study was to make the appreciation in the museum that visitors understood precious cultural artifacts more deeply. We designed an embodied appreciation system called MeLight that allows various visitors including children to actively appreciate exhibits they are interested in. MeLight was installed in the Tokyo National Museum. We observed users during use of MeLight, and it seemed that they often traced the contour of coastlines with the flashlight device. As a result, it was effective to design, implement and introduce a system in which visitors used physical interaction to trigger active appreciation of and interest in the museum's cultural assets.
Two-Oracle Optimal Path Planning on Grid Maps

Path planning on grid maps has progressed significantly in recent years, partly due to the Grid-based Path Planning Competition GPPC. In this work we present an optimal approach which combines features from two modern path planning systems, SRC and JPS+, both of which were among the strongest entrants at the 2014 edition of the competition. Given a current state s and a target state t, SRC is used as an oracle to provide an optimal move from s towards t. Once a direction is available we invoke a second JPS-based oracle to tell us for how many steps that move can be repeated, with no need to query the oracles between these steps. Experiments on a range of grid maps demonstrate a strong improvement from our combined approach. Against SRC, which remains an optimal solver with state-of-the-art speed, the performance improvement of our new system ranges from comparable to more than one order of magnitude faster.
Staphylococcus aureus biofilms release leukocidins to elicit extracellular trap formation and evade neutrophil-mediated killing

Significance Methicillin-resistant Staphylococcus aureus (MRSA) infections are responsible for approximately 11,285 deaths per year in the United States. With the ability to acquire antibiotic resistance and counteract host immunity, there is an emergent need for novel therapeutic agents for these infections. Biofilms persist in chronic infections and can tolerate significantly higher antimicrobial concentrations than single cells, greatly complicating the prognosis of MRSA infections. Our knowledge of the persistence mechanisms associated with these infections is limited. This report describes some major strategies used by MRSA biofilms to evade the host innate immune response and shows that S. aureus utilizes the combined activity of leukocidins to persist during chronic infections. Host cells efficiently cleared biofilms that were unable to express leukocidins, presenting an exciting avenue for antiinfection therapeutic agents. Bacterial biofilms efficiently evade immune defenses, greatly complicating the prognosis of chronic infections. How methicillin-resistant Staphylococcus aureus (MRSA) biofilms evade host immune defenses is largely unknown. This study describes some of the major mechanisms required for S. aureus biofilms to evade the innate immune response and provides evidence of key virulence factors required for survival and persistence of bacteria during chronic infections. Neutrophils are the most abundant white blood cells in circulation, playing crucial roles in the control and elimination of bacterial pathogens. Specifically, here we show that, unlike single-celled populations, S. aureus biofilms rapidly skew neutrophils toward neutrophil extracellular trap (NET) formation through the combined activity of leukocidins Panton–Valentine leukocidin and γ-hemolysin AB. By eliciting this response, S. aureus was able to persist, as the antimicrobial activity of released NETs was ineffective at clearing biofilm bacteria. Indeed, these studies suggest that NETs could inadvertently potentiate biofilm infections. Last, chronic infection in a porcine burn wound model clearly demonstrated that leukocidins are required for “NETosis” and facilitate bacterial survival in vivo.
Exploring Auditory-Inspired Acoustic Features for Room Acoustic Parameter Estimation From Monaural Speech

Room acoustic parameters that characterize acoustic environments can help to improve signal enhancement algorithms such as for dereverberation, or automatic speech recognition by adapting models to the current parameter set. The reverberation time (RT) and the early-to-late reverberation ratio (ELR) are two key parameters. In this paper, we propose a blind ROom Parameter Estimator (ROPE) based on an artificial neural network that learns the mapping to discrete ranges of the RT and the ELR from single-microphone speech signals. Auditory-inspired acoustic features are used as neural network input, which are generated by a temporal modulation filter bank applied to the speech time-frequency representation. ROPE performance is analyzed in various reverberant environments in both clean and noisy conditions for both fullband and subband RT and ELR estimations. The importance of specific temporal modulation frequencies is analyzed by evaluating the contribution of individual filters to the ROPE performance. Experimental results show that ROPE is robust against different variations caused by room impulse responses (measured versus simulated), mismatched noise levels, and speech variability reflected through different corpora. Compared to state-of-the-art algorithms that were tested in the acoustic characterisation of environments (ACE) challenge, the ROPE model is the only one that is among the best for all individual tasks (RT and ELR estimation from fullband and subband signals). Improved fullband estimations are even obtained by ROPE when integrating speech-related frequency subbands. Furthermore, the model requires the least computational resources with a real time factor that is at least two times faster than competing algorithms. Results are achieved with an average observation window of 3 s, which is important for real-time applications.
Two Deletion Correcting Codes from Indicator Vectors

Construction of capacity achieving deletion correcting codes has been a baffling challenge for decades. A recent breakthrough by Brakensiek et al., alongside novel applications in DNA storage, have reignited the interest in this longstanding open problem. In spite of recent advances, the amount of redundancy in existing codes is still orders of magnitude away from being optimal. In this paper, a novel approach for constructing binary two-deletion correcting codes is proposed. By this approach, parity symbols are computed from indicator vectors (i.e., vectors that indicate the positions of certain patterns) of the encoded message, rather than from the message itself. Most interestingly, the parity symbols and the proof of correctness are a direct generalization of their counterparts in the Varshamov- Tenengolts construction. Our techniques require $7\log(n)+o(\log(n)$ redundant bits to encode an n-bit message, which is near-optimal.
Robust Entity Resolution Using a CrowdOracle

Entity resolution (ER) seeks to identify which records in a data set refer to the same real-world entity. Given the diversity of ways in which entities can be represented, ER is a challenging task for automated strategies, but relatively easier for expert humans. We abstract the knowledge of experts with the notion of a boolean oracle, that can answer questions of the form “do records u and v refer to the same entity? ”, and formally address the problem of maximizing progressive recall and F-measure in an online setting