Reviewerly/exHarmony huggingface dataset:
Multi-band sub-GHz technology recognition on NVIDIA’s Jetson Nano

Low power wide area networks support the success of long range Internet of things applications such as agriculture, security, smart cities and homes. This enormous popularity, however, breeds new challenging problems as the wireless spectrum gets saturated which increases the probability of collisions and performance degradation. To this end, smart spectrum decisions are needed and will be supported by wireless technology recognition to allow the networks to dynamically adapt to the ever changing environment where fair co-existence with other wireless technologies becomes essential. In contrast to existing research that assesses technology recognition using machine learning on powerful graphics processing units, this work aims to propose a deep learning solution using convolutional neural networks, cheap software defined radios and efficient embedded platforms such as NVIDIA's Jetson Nano. More specifically, this paper presents low complexity near-real time multi-band sub-GHz technology recognition and supports a wide variety of technologies using multiple settings. Results show accuracies around 99%, which are comparable with state of the art solutions, while the classification time on a NVIDIA Jetson Nano remains small and offers real-time execution. These results will enable smart spectrum management without the need of expensive and high power consuming hardware.
On Five-Diagonal Splitting for Cubic Spline Wavelets with Six Vanishing Moments on a Segment

In this study, we use the vanishing property of the first six moments for constructing a splitting algorithm for cubic spline wavelets. First, we construct the corresponding wavelet space that satisfies the orthogonality conditions for all fifth-degree polynomials. Then, using the homogeneous Dirichlet boundary conditions, we adapt spaces to the closed interval. The originality of the study consists in obtaining implicit relations connecting the coefficients of the spline decomposition at the initial scale with the spline coefficients and wavelet coefficients at the nested scale by a tape system of linear algebraic equations with a non-degenerate matrix. After excluding the even rows of the system, in contrast to the case with two zero moments, the resulting transformation matrix has five (instead of three) diagonals. The results of numerical experiments on calculating the derivatives of a discrete function are presented.
A Visibility-Based Approach to Computing Nondeterministic Bouncing Strategies

Inspired by motion patterns of some commercially available mobile robots, we investigate the power of robots that move forward in straight lines until colliding with an environment boundary, at which point they can rotate in place and move forward again; we visualize this as the robot “bouncing” off boundaries. Different boundary interaction rules can be defined for such robots, such as one that orients the robot relative to its heading prior to collision, or relative to the normal of the boundary. We introduce a new data structure, the bounce visibility graph, which is generated from a polygonal environment definition. The bounce visibility graph can be queried to determine the feasibility of path-based tasks such as navigation and patrolling, assuming we have unavoidable nondeterminism in our actuation. If the task is feasible, then this approach synthesizes a strategy (a sequence of nondeterministic rotations). We also show how to compute stable cyclic trajectories and use these to limit uncertainty in the robot’s position (Software implementation at https://github.com/alexandroid000/bounce_viz).
Protecting Real-time Video Chat against Fake Facial Videos Generated by Face Reenactment

With the rapid popularity of cameras on various devices, video chat has become one of the major ways for communication, such as online meetings. However, the recent progress of face reenactment techniques enables attackers to generate fake facial videos and use others' identities. To protect video chats against fake facial videos, we propose a new defense system to significantly raise the bar for face reenactment-assisted attacks. Compared with existing works, our system has three major strengths. First, our system does not require extra hardware or intense computational resources. Second, it follows the normal video chat process and does not significantly degrade the user experience. Third, our system does not need to collect training data from attackers and new users, which means it can be quickly launched on new devices. We developed a prototype and conducted comprehensive evaluations. Experimental results show that our system can provide an average true acceptance rate of at least 92.5% for legitimate users and reject the attacker with mean accuracy of at least 94.4% for a single detection.
A Novel Forgery Detection Algorithm Based on Mantissa Distribution in Digital Images

Nowadays, digital image forgery detection is one of important topics in research world. In this paper, we propose a novel forgery detection algorithm using the logarithmic basis of Benford's law which states the mantissa of the logarithm of all practical numbers should be uniformly distributed. Based on this fact, the proposed method uses extracted features from mantissa distribution of discrete cosine transform (DCT) coefficients in JPEG images. Support vector machine (SVM) is used for classification to detect authentic and forged images based on these features. Results show that our proposed algorithm has the highest mean accuracy (99.78%), sensitivity (99.77%) and specificity (99.79%) in comparison with previous works on CASIA V1.0 dataset.
Mobile based Augmented Reality Application Prototype for Remote Collaboration Scenario Using ARCore Cloud Anchor

This paper describes a development study for mobile based Augmented Reality (AR) application prototype for simple remote collaboration scenario. The AR technology is implemented by using ARCore with cloud anchor into two different mobile devices as the host and client. A host is a user device that able to create and share a session of a camera viewpoint that could be connected with another client device remotely through the internet. For testing purposes of remote collaboration scenario in this application prototype, both users are able to add and manipulate location of virtual objects that overlay on each screen from their own device. Based on the functionality test, before placing the virtual object on the screen, a calibration of cloud anchor is required with minimum success performance in 250 to 1200 lumens of lights. The result for cloud anchor is being hosted with average delay up to 700+ milliseconds. In this paper, the result of the experiment shows an early stage of the utilization of AR technology in simple remote collaboration scenario. However, it shows the potential of mobile based AR technology for future remote collaboration scenario development.
Analysis for the Knowledge Economy in GCC Countries

Gulf Cooperation Council (GCC) countries endowed by oil and gas. This towards their economies to be highly dependent on oil and gas extraction activities, which made its economy vulnerable to economic fluctuation as a result of uncertainty of oil and gas prices. This towards GCC governments to implement policies to support economic diversification to strengthen the business environment and increase the share of non-hydrocarbon sector. One of these sectors is the Knowledge economy which became one of the main engines of economic growth in recent decades. The purpose of this chapter is to analyse the knowledge economy in the GCC countries. The Knowledge Assessment Methodology (KAM) was developed to facilitate countries trying to make the transition to the knowledge economy. KAM consists of five main pillars: (i) Performance; (ii) Institutions; (iii) Education and HR index; (iv) Innovation system index and (v) Information Infrastructure index. The five pillars have been analysed for the GCC countries from 2010 to 2017. Although that KAM has been used widely, the last available data on the world bank databank was for 2012. This was a limitation of the study. To eliminate this limitation the researcher collected the data about the same five pillars from the global competitiveness report. The financings of this chapter that the macroeconomic environment and Institutions are the highest scores in the KEI for GCC, this reflects the stability of the macroeconomic environment and the being of strong the Institutional framework and facilitates the flow of new or existing knowledge. Innovation pillar is the latest contributor of KEI for GCC countries. There is the need for GCC countries to increase the capacity for innovation and the quality of scientific research institutions. Although the increase in the spending on R&D and attempts to increase the university-industry collaboration in R&D still more is required for these promising countries. Many GCC countries have taken speedy steps in this direction, however more are still required specially in the area of availability of scientists and engineers, application of patents and intellectual property protection.
Reliability Analysis of Distribution Transformer with Bayesian Mixture and Cox Regression Approach

The reliability of a transformer has become an important factor to measure considering that the damaged transformer may cause a local power outage that can lead to the profit loss for both the public and the PLN (State Electric Company) itself. The reliability data thought patterns observed during a certain period time might not be a singular distribution pattern, but a mixture distribution such as the Weibull mixture. Each period that generates different reliability patterns will be shown as a specific sub-population in the mixture. Therefore, if during the observation two reliability shifts occur, then the reliability would be a mixture with the sub-populations system. In this research, various sub-population possibilities will be used, starting from 3 sub-population up until 7 sub-population. The parameter estimation in the Weibull mixture is done by using Markov Chain Monte Carlo (MCMC). Then, the best mixture model is chosen by the Bayes Factor. Other than that, the Cox regression in this research is used to understand the factors that impact the transformer lifespan. The best model chosen by the Bayes Factor resulted that the mixture model with 3,4,5,6, and 7 sub-population is considered the same.
A Comparison of Self-Play Algorithms Under a Generalized Framework

Throughout scientific history, overarching theoretical frameworks have allowed researchers to grow beyond personal intuitions and culturally biased theories.They allow to verify and replicate existing findings, and to link disconnected results.The notion of self-play, albeit often cited in multiagent Reinforcement Learning, has never been grounded in a formal model.We present a formalized framework, with clearly defined assumptions, which encapsulates the meaning of self-play as abstracted from various existing self-play algorithms.This framework is framed as an approximation to a theoretical solution concept for multiagent training.On a simple environment, we qualitatively measure how well a subset of the captured self-play methods approximate this solution when paired with the famous PPO algorithm.We also provide insights on interpreting quantitative metrics of performance for self-play training.Our results indicate that, throughout training, various self-play definitions exhibit cyclic policy evolutions.
Augmented Reality Procedure Assistance System for Operator Training and Simulation

This study explores the design, implementation, and evaluation of an Augmented Reality (AR) prototype that assists novice operators in performing procedural tasks in simulator environments. The prototype uses an optical see-through head-mounted display (OST HMD) in conjunction with a simulator display to supplement sequences of interactive visual and attention-guiding cues to the operator’s field of view. We used a 2x2 within-subject design to test two conditions: with/without AR-cues, each condition had a voice assistant and two procedural tasks (preflight and landing). An experiment examined twenty-six novice operators. The results demonstrated that augmented reality had benefits in terms of improved situation awareness and accuracy, however, it yielded longer task completion time by creating a speed-accuracy trade-off effect in favour of accuracy. No significant effect on mental workload is found. The results suggest that augmented reality systems have the potential to be used by a wider audience of operators.
Estimation of Collision Priority on Traffic Videos using Deep Learning

With the advancement in technology, autonomous vehicles will have a great impact on the near future and share the road space with human beings. For ensuring safer navigation, a robust collision mitigation system becomes mandatory which in general is deployed with multi-modal approaches. In this paper, a novel heuristic unimodal approach based on the vision system is presented to estimate the collision priority of vehicles on road. The priorities are estimated from the perspective of an ego vehicle that may be equipped with a vision based driver-assist system or a fully autonomous vehicle. Crowd-sourced videos from YouTube involving vehicular collisions captured by the dashboard camera of vehicles are considered. To detect the moving vehicles in the video, a deep learning-based pre-trained object detection model and a tracking algorithm are used. From the bounding box output, an estimate of collision priority of detected vehicles concerning the ego vehicle is obtained using an empirical heuristic-based approach. A simpler collision warning and navigation suggestion is also incorporated as a credible advanced driver assistance system element. The proposed qualitative approach performs well for input videos and a more robust estimate can be achieved by combining with other quantitative semantics of traffic parameters.
Hotspot Prediction Using 1D Convolutional Neural Network

Every year, there are always forest and land fires in Indonesia. Riau Province is one area that often experiences forest and land fires. One of the factors causing forest fires is the emergence of hotspots. The hotspot is an indicator of forest fires that indicates a location has a relatively higher temperature than its surroundings is ≥42°. This research studies about hotspot prediction using a 1d convolutional neural network to give some information about the number of hotspots for early treatment of forest fires. Experiments carried out by changing the amount of output that is daily, monthly, and 12 months. It also experimented on the learning rate and number of nodes used on neural networks. The results of this research are using 20 nodes and a learning rate of 0.2 on daily predictions with a MAAPE of 0.9386, 40 nodes and a learning rate of 0.5 on a monthly prediction with a MAAPE of 0.4139, 40 nodes and a learning rate of 0.5 on a prediction of 12 months with a MAAPE of 0.4397.
Efficient Subset Predicate Encryption for Internet of Things

With the rapid development of Internet technologies, emerging network environments have been discussed, such as Internet of Things. In this manuscript, we proposed a novel subset predicate encryption for the access control in Internet of Things. Compared with the existing subset predicate encryption schemes, the proposed scheme enjoy the better efficiency due to the short private key and the efficient decryption procedure.
Kernel Entropy Based Fuzzy C-Means (KEFCM) for Acute Sinusitis

Abstract Sinusitis is a condition when sinuses membranes are plugged or inflamed or swollen due to infection. There are several types of sinusitis, one of them, which will be explained in this study, is acute and chronic sinusitis. There are many ways to diagnose sinusitis such as allergy tests, nasal endoscopy, CT Scans and MRI. In this study, a diagnosis will be made whether someone has acute sinusitis or chronic sinusitis by using clustering techniques with machine learning. In medical field machine learning can be used to help to analyse medical data more quickly and accurately therefore the patient can get the treatment sooner. in this study, the machine learning method used is kernel entropy fuzzy c-means (KEFCM). The kernel will be used in the Entropy Fuzzy C-means (EFCM) method which can represent multiplication in a high-dimensional space and the kernel that will be used is RBF and Polynomial. This sinusitis data used in this study were obtained from the Laboratory of Radiology at Cipto Mangunkusumo National General Hospital, Indonesia with this method it will get 97% Accuracy.
MEET: A Method for Embeddings Evaluation for Taxonomic Data

Taxonomies are the mainstay of a wide range of applications in the semantic web and provide formal support for syntactic and semantic exchanges. Hence, they are pivotal for machine understanding in natural language processing and business-oriented tasks. However, keeping such hierarchies updated and able to represent the domain from which they have been drawn, is still a time-consuming and error-prone activity. Here, word embeddings have proven to be effective in catching lexicon and semantic similarities to enrich taxonomies from text data. This, in turn, would require to evaluate the generated embeddings to estimate the extent to which they encode the semantic similarity derived from the hierarchy itself. In this paper, we propose and implement MEET a methodology that aims at generating and evaluating embeddings from a text corpus preserving the semantic similarity relations synthesised from a taxonomy. To this aim, we develop a new measure - namely Hierarchical Semantic Similarity (HSS) - to compute the semantic similarity between taxonomic elements. Experimental results show that (i) HSS outperforms state-of-the-art measures for measuring semantic similarity in taxonomy and (ii) the embedding selected through MEET obtains a clear victory against embeddings selected by the literature on benchmark tasks. We made available an open-source repository <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> gitlab anonymised project with all the material employed in this research, including the HSS for 35,000 word pairs.
Shortest reconfiguration of sliding tokens on subclasses of interval graphs

Suppose that two independent sets Ib and Ir of a graph such that |Ib|=|Ir| are given, and a token is placed on each vertex in Ib. The sliding token problem is to determine whether there exists a sequence of independent sets which transforms Ib into Ir so that each independent set in the sequence results from the previous one by sliding exactly one token along an edge in the graph. The sliding token problem is one of the reconfiguration problems that attract the attention from the viewpoint of theoretical computer science. Recently, the problems that aim at finding a shortest reconfiguration sequence are investigated. In general, even if it is polynomial time solvable to decide whether two instances are reconfigurable into each other, it can be NP-hard to find a shortest sequence between them. In this paper, we show that the problem for finding a shortest sequence between two independent sets is polynomial time solvable for some graph classes which are subclasses of the class of interval graphs. As far as the authors know, this is the first polynomial time algorithm for the shortest sliding token problem for a graph class that requires detours.
Art Font Image Generation with Conditional Generative Adversarial Networks

An art font is an artistic font that has an impressive and attractive shape with decoration and it is widely used in advertisements and posters, among others. The main contribution of this paper is to propose an art font generation method using machine learning approach using conditional generative adversarial networks. The first idea of the proposed method is using the separated two networks, the typeface network and the ornament network. The typeface network changes shape of an input font and the ornament network adds effects to the font. The second idea is providing the skeletons and edges of the character as auxiliary input to the typeface network to avoid disrupting the shape of the font. The experimental results show that our method is better than direct transfer of font and texture to generate better-looking art fonts, and it can generate a large number of art fonts from a very small number of observed characters of the same style at one time, which has higher practicability.
Cloud Displacement Vector Calculation in Satellite Images Based on Cloud Pixel Spatial Aggregation and Edge Matching for PV Power Forecasting

Cloud movement is the main factor affecting photovoltaic (PV) power fluctuations. This paper aims to calculate the displacement vector of clouds in satellite images. Due to the large amount of data and the deformation degree of clouds, the existing cloud displacement vector algorithms have poor speed and accuracy when processing satellite cloud images. Therefore, this paper proposes a cloud displacement vector calculation method in satellite images based on cloud pixel spatial aggregation and edge matching, which can improve the accuracy and speed. The algorithm also obtains the movement information of different positions of clouds and the trend of cloud extinction and deformation can be analyzed. The test was performed using data from FY-4 satellite images of China. The results show that the cloud displacement vector algorithm designed in this paper has been greatly improved in calculation speed and accuracy, and the calculation error is less than 10%.
A Cross-Camera Multi-Face Tracking System Based on Double Triplet Networks

The aim of this study is to track the faces of all pedestrians in the video surveillance. Face images from each camera can use Chinese Whisper face clustering algorithm to cluster the same person’s face together, and according to the results of face clustering to find out which people through the camera. Double Triplet Networks (DTN) designed in this study is used to learn the depth features of human face. DTN is trained on LFW data set, and the model trained can improve its recognition accuracy to 99.51% by Margin Sample Mining Loss (MSML) and Focal Loss hard sample equalization. Comparing the similarity of the facial features in same video surveillance areas can track the faces of pedestrians, and comparing the similarity of the facial features in different video surveillance areas can predict which camera area the face comes from and tracking the sequential paths of pedestrians through these areas. Cross-camera face tracking is possible by transmitting facial features between cameras in real-time.
A Structured Control Selection Methodology for Insider Threat Mitigation

An insider is a person or software that possesses positive authorization to access the asset(s) of an enterprise. In recent years, security incidents perpetrated by enterprise insiders have increased considerably. Enterprises attempt to mitigate such threats by implementing controls intuitively, on an ad-hoc basis. However, such intuitive control implementation is both time-consuming, as well as prone to errors, leading to insecure enterprise systems. The paper attempts to address this issue by proposing a structured methodology for the selection of relevant security controls. The technique is to model insider threats and security controls, and match their constituent components against each other. The proposed methodology has been illustrated with suitable examples.