Miracle-dz/UniFAR-FTUs huggingface dataset:
Droid-IoT: Detect Android IoT Malicious Applications Using ML and Blockchain

: One of the most rapidly growing areas in the last few years is the Internet of Things (IoT), which has been used in widespread fields such as healthcare, smart homes, and industries. Android is one of the most popular operating systems (OS) used by IoT devices for communication and data exchange. Android OS captured more than 70 percent of the market share in 2021. Because of the popularity of the Android OS, it has been targeted by cybercriminals who have introduced a number of issues, such as stealing privateinformation.As reported by one of the recent studies Android malware are developed almost every 10 s. Therefore, due to this huge exploitation an accurate and secure detection system is needed to secure the communication and data exchange in Android IoT devices. This paper introduces Droid-IoT, a collaborative framework to detect Android IoT malicious applications by using the blockchain technology. Droid-IoT consists of four main engines: (i) collaborative reporting engine, (ii) static analysis engine, (iii) detection engine, and (iv) blockchain engine. Each engine contributes to the detection and minimization of the risk of malicious applications and the reporting of any malicious activities.All features are extracted automaticallyfrom the inspected applications to be classified by the machine learning model and store the results into the blockchain. The performance of Droid-IoT was evaluated by analyzing more than 6000 Android applications and comparing the detection rate of Droid-IoT with the state-of-the-art tools. Droid-IoT achieved a detection rate of 97.74% with a low false positive rate by using an extreme gradient boosting (XGBoost) classifier. This has led many researchers to investigate this area and to detect and miti-gate the existing threats. This paper presented Droid-IoT, a collaborative framework that detects Android malicious applications by using blockchain technology. The proposed system consists of four main engines: (i) a collaborative reporting engine, (ii) a static analysis engine, (iii) a detection engine, and (iv) a blockchain engine. Each engine contributes to the detection and minimization of the risk of malicious applications, reporting any malicious activities to the user. The results
Revealing deeper relationships between knowledge management leadership and organisational performance: a meta-analytic study

ABSTRACT Many empirical studies have tried to investigate the relationship between knowledge management (KM) leadership and organisational performance; however, the findings of such studies have been inconsistent. In response to this, we apply a meta-analysis technique to systematically assess the existing pool of empirical research results and derive conclusions. We also examine the impacts of contextual moderators, such as national culture, economy, and industry, on the KM leadership–organisational performance relationships. The findings, stemming from 6,272 research subjects making up 22 studies, demonstrate that KM leadership is positively associated with the overall (financial and non-financial) performance of firms. Specifically, it was found that the KM leadership–non-financial performance relationship is strengthened in collective cultures, developing economies, and the manufacturing industry. This paper is the first meta-analytic study concerning KM leadership and organisational performance, producing new understanding of contextual factors in the KM field.
Image multi-level-thresholding with Mayfly optimization

Image thresholding is a well approved pre-processing methodology and enhancing the image information based on a chosen threshold is always preferred. This research implements the mayfly optimization algorithm (MOA) based image multi-level-thresholding on a class of benchmark images of dimension 512x512x1. The MOA is a novel methodology with the algorithm phases, such as; i) Initialization, ii) Exploration with male-mayfly (MM), iii) Exploration with female-mayfly (FM), iv) Offspring generation and, v) Termination. This algorithm implements a strict two-step search procedure, in which every Mayfly is forced to attain the global best solution. The proposed research considers the threshold value from 2 to 5 and the superiority of the result is confirmed by computing the essential Image quality measures (IQM). The performance of MOA is also compared and validated against the other procedures, such as particle-swarm-optimization (PSO), bacterial foraging optimization (BFO), firefly-algorithm (FA), bat algorithm (BA), cuckoo search (CS) and moth-flame optimization (MFO) and the attained p-value of Wilcoxon rank test confirmed the superiority of the MOA compared with other algorithms considered in this work
Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems

: The extensive proliferation of modern information services and ubiquitous digitization of society have raised cybersecurity challenges to new levels. With the massive number of connected devices, opportunities for potential network attacks are nearly unlimited. An additional problem is that many low-cost devices are not equipped with effective security protection so that they are easily hacked and applied within a network of bots (botnet) to perform distributed denial of service (DDoS) attacks. In this paper, we propose a novel intrusion detection system (IDS) based on deep learning that aims to identify suspicious behavior in modern heterogeneous information systems. The proposed approach is based on a deep recurrent autoencoder that learns time series of normal network behavior and detects notable network anomalies. An additional feature of the proposed IDS is that it is trained with an optimized dataset, where the number of features is reduced by 94% without classification accuracy loss. Thus, the proposed IDS remains stable in response to slight system perturbations, which do not represent network anomalies. The proposed approach is evaluated under different simulation scenarios and provides a 99% detection accuracy over known datasets while reducing the training time by an order of magnitude.
Polarization dependent clamping intensity inside a femtosecond filament in air

Laser polarization and its intensity inside a filament core play an important role in filament-based applications. However, polarization dependent clamping intensity inside filaments has been overlooked to interpret the polarization-related filamentation phenomena. Here, we report on experimental and numerical investigations of polarization dependent clamping intensity inside a femtosecond filament in air. By adjusting the initial polarization from linear to circular, the clamping intensity is increased by 1.36 times when using a 30 cm focal length lens for filamentation. The results indicate that clamping intensity inside the filament is sensitive to laser polarization, which has to be considered to fully understand polarization-related phenomena.
Broadband instantaneous multi-frequency measurement based on chirped pulse compression

A broadband instantaneous multi-frequency measurement system based on chirped pulse compression, which potentially has a sub-megahertz (MHz) accuracy and a hundred-gigahertz (GHz) measurement range, is demonstrated. A signal-under-test (SUT) is converted into a carrier-suppressed double-sideband (CS-DSB) signal, which is then combined with an optical linearly frequency-modulated signal having the sweeping range covering the +1st-order sideband of the CS-DSB signal. With photodetection, low-pass filtering, and pulse compression, accurate frequencies of the SUT are obtained via locating the correlation peaks. In the experiment, single- and multi-frequency measurements with a measurement range from 3 to 18 GHz and a measurement accuracy of <±100 MHz are achieved.
Evaluation of Implant Stability Following Sinus Augmentation Utilizing Bovine Bone Mixed with Platelet-Rich Fibrin

Background: Lateral sinus augmentation and simultaneous insertion of dental implants is a highlypredictable procedure and associated with high rate of implants success.Aims: To evaluate implant stability changes following maxillary sinus augmentation utilizing deproteinizedbovine bone alone or mixed with platelet-rich fibrin.Materials and Methods: A total of 34 lateral sinus augmentation procedures were performed and 50 dentalimplants simultaneously installed. The lateral sinus augmentation cases were allocated randomly into 3groups: Group A comprised 13 procedures and 21 dental implants utilizing solely deproteinized bovine bone.Group B involved 10 cases and 16 dental implants using deproteinized bovine bone mixed with leukocyteand platelet-rich fibrin. Group C included 11 operations and 13 dental implants employing deproteinizedbovine bone mixed with advanced platelet-rich fibrin. Resonance frequency analysis test was performedimmediately after implant installation and 24 weeks postoperatively for the measurement of the implantstability.Results: Implant stability quotient values increased significantly for all study groups 24 weeks after dentalimplants installation (P= 0.001). The implant stability quotient at T1 (day of implant installation) was 56.93±12.01 for group A, 58.34 ±12.82 for group B, and 61.35 ±8.47 for group C. The implant stability quotientat T2 (24 weeks after implant insertion) was 69.17 ±5.10, 69.43 ±5.32, and 68.50 ±7.44, respectively.Conclusion: The addition of leukocyte and platelet-rich fibrin or advanced platelet-rich fibrin to the bovinebone for sinus floor augmentation did not increased the implant stability quotient value in comparison to thebovine bone alone.
Mental toughness of martial art athletes based on age and gender

Mental toughness is one of psyichological factors when athlete doing training or match. The function of mental toughness is to keep the atlethes fit when they have some difficulties in a match, feel the pressure during the match, and take behavior control so that they will not get stressed easily. This research aims to know mental toughness condition of the athletes through the age and gender. Data analysis techniques that we used for this research are descriptive statistic and cross tab. The sample was taken from 174 martial art atheletes (Age = 19.69 Â± 2.89 ). Instrument of mental toughness refers to mental toughness dimension made by Clogh et al. (2001) which is; control, commintment, challenge, and confidence. The result of this research shows that most of them are on the high level of mental thoughness, and the majority is the men at the age of 19 to 22. In addition, the relationship that we observed in this study emphasized the need of coping and optimism training in mental toughness intervension.
Optimized Convolutional Neural Network for Automatic Detection of COVID-19

The outbreak of COVID-19 affected global nations and is posing serious challenges to healthcare systems across the globe. Radiologists use X-Rays or Computed Tomography (CT) images to confirm the presence of COVID-19. So, image processing techniques play an important role in diagnostic procedures and it helps the healthcare professionals during critical times. The current research work introduces Multi-objective Black Widow Optimization (MBWO)-based Convolutional Neural Network i.e., MBWO-CNN technique for diagnosis and classification of COVID-19. MBWO-CNN model involves four steps such as preprocessing, feature extraction, parameter tuning, and classification. In the beginning, the input images undergo preprocessing followed by CNN-based feature extraction. Then, Multi-objective Black Widow Optimization (MBWO) technique is applied to fine tune the hyperparameters of CNN. Finally, Extreme Learning Machine with autoencoder (ELM-AE) is applied as a classifier to confirm the presence of COVID-19 and classify the disease under different class labels. The proposed MBWO-CNN model was validated experimentally and the results obtained were compared with the results achieved by existing techniques. The experimental results ensured the superior results of the ELM-AE model by attaining maximum classification performance with the accuracy of 96.43%. The effectiveness of the technique is proved through promising results and the model can be applied in diagnosis and classification of COVID-19. © 2021 Tech Science Press. All rights reserved.
Business capstone strategy simulations: Student perceptions of realism

Abstract Purpose Study fits JEBS category “innovative instructional classroom projects/best practices” by exploring questions in the literature about perceptions of Realism in strategy simulation. Findings First, student survey found that perceptions about Realism were lower than perceptions about Interface and Comprehension. Second, content analysis revealed teaching method to improve Realism reported lacking in the literature. Limitations Convenience sample of one university and one simulation, Glo-Bus© were used. Value For strategy simulations to have optimal impact, student perceptions of Realism are important; therefore, teaching methods must be informed by this type of analysis.
Mobility Load Management in Cellular Networks: A Deep Reinforcement Learning Approach

Balancing traffic among cellular networks is very challenging due to many factors. Nevertheless, the explosive growth of mobile data traffic necessitates addressing this problem. Due to the problem complexity, data-driven self-optimized load balancing techniques are leading contenders. In this work, we propose a comprehensive deep reinforcement learning (RL) framework for steering the cell individual offset (CIO) as a means for mobility load management. The state of the LTE network is represented via a subset of key performance indicators (KPIs), all of which are readily available to network operators. We provide a diverse set of reward functions to satisfy the operators’ needs. For a small number of cells, we propose using a deep Q-learning technique. We then introduce various enhancements to the vanilla deep Q-learning to reduce bias and generalization errors. Next, we propose the use of actor-critic RL methods, including Deep Deterministic Policy Gradient (DDPG) and twin delayed deep deterministic policy gradient (TD3) schemes, for optimizing CIOs for a large number of cells. We provide extensive simulation results to assess the efficacy of our methods. Our results show substantial improvements in terms of downlink throughput and non-blocked users at the expense of negligible channel quality degradation.
Pengaruh Social Media Marketing, Electronic Word Of Mouth (EWOM) Dan lifestyle Terhadap Keputusan Pembelian Online

Tujuan penelitian bertujuan untuk menganalisis beberapa hal yaitu: pengaruh Social Media Marketing terhadap keputusan pembelian, pengaruh electronic word of mouth terhadap keputusan pembelian, ,pengaruh Lifestyle terhadap keputusan pembelian, pengaruh electronic word of, Social Media Marketing dan Lifestyle terhadap keputusan pembelian. Dalam penelitian ini menggunakan jenis penelitian asosiatif dengan sampel sebanyak 96 responden yang merupakan Masyarakat Kota Medan. Teknik pengumpulan data menggunakan daftar pertanyaan seperti kuesioner dan teknik analisis data teknik analisis data menggunakan Statistical Package for the Social Sciences (SPSS) untuk menguji empat hipotesis yang diajukan dalam penelitian ini. Hasil penelitian menujunjukkan bahwa: (1) Electronic Word of Mouth secara persial berpengaruh signifikan terhadap Keputusan Pembelian; (2) Social Media Marketing secara persial berpengaruh signifikan terhadap Keputusan Pembelian; (3) Lifestyle secara parsial berpengaruh signifikan terhadap Keputusan Pembelian; (4) Electronic Word of Mouth , Social Media Marketing , dan Lifestyle berpengaruh terhadap Keputusan Pembelian.
Fofola e Fala ka e Talanoa e Kainga

There is limited knowledge of how Pacific-indigenous approaches can aid efforts to curtail violence within the kainga (families). This article documents aspects of the inaugural application of the Tongan conceptual framework of Fofola e fala ka e talanoa e kainga (laying out the mat so families can dialogue) as part of the faith-based Kainga Tu’umalie (prosperous families) family violence intervention and prevention program in Aotearoa New Zealand. Fofola e fala symbolizes a place of safety and refuge for every member of the kainga to freely express their feelings. The first two authors were involved in evaluating the program with the first author engaged in direct observations and being immersed in Kainga Tu’umalie retreats. Given their depth of cultural knowledge and involvement in the development of this program, seven faith-based community leaders were engaged in talanoa (Pacific-indigenous way of dialogue and discussion). Participant accounts form the core basis of our analysis, which highlights the significance of Kainga Tu’umalie as a violence prevention program for Tongan families. Of key consideration is the importance of Tongan-indigenous approaches to reducing family violence that draw from a combination of traditional cultural knowledge and Christian values that are central to the realities of being Tongan today.
Feeding milk supplemented with Ulva sp., Ascophyllum nodosum, or Saccharina latissima to preweaning dairy calves: Effects on growth, gut microbiota, gut histomorphology, and short-chain fatty acids in digesta.

Emerging knowledge shows the importance of preweaning nutrition on programming the gastrointestinal microbiome and development of the gut barrier function. The aim of this study was to assess the effects of supplementing cow milk with either intact dried Ulva sp. , Ascophyllum nodosum, or Saccharina latissima on growth performance and several gut health parameters of preweaning dairy calves. Forty male Holstein calves were selected based on birth weight (41 ± 4 kg) and plasma Brix percentage (≥8.7%) at d 2 of life. From d 2 to d 42 of life, the control calves (n = 10) were fed with cow milk (8 L/d) without seaweed supplementation, and the experimental calves were fed with cow milk (8 L/d) supplemented with either Ulva sp. (n = 10), A. nodosum (n = 10), or S. latissima (n = 10) at a concentration of 50 g/8 L of cow milk per day (i.e., 5% on a dry matter basis). Calves were weighed every week, and body weight gain and calf starter intake were monitored weekly. At d 42 ± 3 of life, calves were slaughtered. The organ weights and digesta pH from the reticulorumen, mid- and end small intestine, and mid-colon were recorded. A tissue sample (5 cm) collected from the mid-small intestine was analyzed for histomorphology. Digesta from the mid-small intestine and mid-colon were analyzed for lactobacilli, Escherichia coli, and Enterobacteriaceae, and short-chain fatty acid profile. Weight gain of the calves was not affected by seaweed supplementation. Proportional organ weights were not affected by seaweed supplementation except for reticulorumen weight, which was higher in calves fed Ulva sp. Both the mid-small intestinal and mid-colonic digesta populations of lactobacilli, Enterobacteriaceae, and E. coli, as well as the mid-small intestinal histomorphology in seaweed-supplemented calves were not different from control calves. However, acetic acid proportion in mid-colonic digesta was increased in calves fed Ulva sp. and A. nodosum, whereas butyric acid proportion was decreased compared with the control calves. Digesta pH in mid- and end small intestine and mid-colon were not affected, whereas ruminal pH was increased in calves fed Ulva sp. compared with the control calves. In conclusion, intact dried seaweed supplementation did not improve the growth or selected gut health parameters (i.e., histomorphology, digesta pH, bacteria, and short-chain fatty acids) in preweaning Holstein calves.
Maturity-associated variations in anthropometry, physical fitness, and sport-specific skills among young male and female futsal players

Purpose. The aim of this study was to determine the maturity-related variations in anthropometry, physical fitness, and sport-specific skills among young male and female futsal players. Methods. Overall, 77 athletes (44 boys and 33 girls) aged 10.5–13.7 (12.3 ± 0.9) years were measured for stature, body mass, 2 skinfolds, as well as 3 fitness and 2 sport-specific test items. Results. Compared with boys, girls were significantly more advanced in maturation and had greater body mass index and body fat percentage. Except for the countermovement jump result, boys outperformed girls in all fitness and sport-skill assessments. Regardless of gender, early maturing players were significantly taller and heavier and had higher body fat percentage and grip strength than their late maturing counterparts. In boys, none of the other physical fitness and sport-specific skill measures significantly differed between contrasting maturity groups. In turn, early maturing girls had significantly better scores in all physical fitness measurements than late maturing girls. Partial correlations, with chronological age controlled as a covariate, indicated moderate to high correlations between the maturation indicator and stature and body mass in both boys and girls. Except for grip strength and dribbling tests for boys and girls, respectively, other physical fitness and sport-specific skill measures did not show a significant correlation with maturity status. Conclusions. These results highlight that rather than with functional and sport-specific characteristics, physical maturity status may be associated with greater body size in young futsal players.
Improvement of One-Shot-Learning by Integrating a Convolutional Neural Network and an Image Descriptor into a Siamese Neural Network

Over the last few years, several techniques have been developed with the aim of implementing one-shot learning, a concept that allows classifying images with only a single image per training category. Conceptually, these methods seek to reproduce certain behavior that humans have. People are able to recognize a person they have only seen once, but they are probably not able to do the same with certain animals, such as a monkey. This is because our brains have been trained for years with images of people but not so much of animals. Among the one-shot learning techniques, some of them have used data generation, such as Generative Adversarial Networks (GAN). Other techniques have been based on the matching of descriptors traditionally used for object detection. Finally, one of the most prominent techniques involves using Siamese neural networks. Siamese networks are usually implemented with two convolutional nets that share their weights. They receive two images as input and can detect whether they belong to the same category or not. In the field of grocery products, there has been a lot of research on the one-shot learning problem but not so much on the use of Siamese networks. In this paper, several classifiers are firstly evaluated to decide on a convolutional model to be used with the Siamese and to improve the baseline results obtained in the dataset used. Then, two existing techniques are integrated within the Siamese model: a convolutional net and a Local Maximal Occurrence (LOMO) descriptor. The latter was initially used for the re-identification of people although it has shown its effectiveness to improve the values of a traditional Siamese with only convolutional sisters. The whole network is trained on categories and responds to different categories, showing its strong capacity to deal with the problem of having only one image per category.