Reviewerly/exHarmony huggingface dataset:
An Improved Genetic Algorithm-based Routing Protocol for Multimedia Data Transmission

The purpose of this study is to obtain an improved and optimized network with minimum end-to-end delay and improved throughput by using the Genetic Algorithm (GA) for multimedia communication. The proposed study also increases the Quality of Service (QoS) with the consideration of the optimal path. Nowadays, the transformation of multimedia information is essential for the users however, many factors affect the transmission of multimedia communication, such as quality of service, end-to-end delay, energy efficiency, and many more. This paper is intended to provide an efficient algorithm for multimedia communication that overcomes the factors mentioned above. For validating our proposed methodology simulation will be carried out for multimedia traffic and video streamed network traffic using OPNET Simulator. Comparison with existing algorithms will prove that the suggested algorithm outperforms them and provides better efficiency.
Low Fidelity Prototype Design to Facilitate BEELIC Website Navigation Process for Information Center

The impact of this pandemic situation is a significant crisis for SMEs (Small and Medium Enterprises), especially for those who rely on foot traffic. A strategic step and breakthrough are needed so that SMEs can also take advantage of the market potential in the industrial era 4.0. The website's communication becomes an important medium to achieve the message and its objectives. This study intends to design a low-fidelity prototype on the BEELIC website so that users can easily find information and determine which type of BEELIC design suits their needs. The design direction of the low-fidelity prototype design for this research was obtained from the research team's analysis of previous studies. The method used is the branching method by John Bower to design prototypes that are easy to understand and understand. To organize the layout and information, the research team uses design principles and elements to show the website's hierarchy so that users can see the order and priority of the information needed. That way, when Beelic starts up, many creative products from SMEs will be showcased on the website digitally. In this regard, the research team will see how aesthetic aspects in visual communication design can contribute to how the user experiences a user interface design.
Teman Berlindung: Mobile-Based Student Counseling Service Application

The existence of Covid-19, which is sweeping the whole world, has caused Indonesia to make a policy to change the learning system that was previously face-to-face to online. As a result, students are prevented from interacting directly with their friends, making them bored and stressed. Increased stress levels can also be influenced by other factors, such as an unsupportive home environment and the number of tasks assigned with relatively few deadlines. In addition, other problems, from friends and family, are often a burden for students, significantly affecting stress levels and mental health. The data collection method used in this study uses observation and literature studies on object-oriented applications. This study uses the Waterfall method to make information systems in the designed application and Unified Modeling Language as the language used in analyzing needs. The application's design can explain in detail how this application will run, and the appearance of this application will be seen. So, the purpose of designing this application is expected to reduce stress levels by accommodating students who need counselling services. Based on the design of the Teman Berlindung that the author carried out, the author stated that there were many cases where college students had no space to tell stories or just a place to complain. Regarding mental health, Teman Berlindung is expected to be useful not only during the pandemic but can also be used in the long term and help students channel complaints to related parties.
Clustering of Tidal Swamp Soil Based on NPK Macronutrients using the FCM Method

Tidal swamp resistance is divided into several types, namely type A, B, C and type D. This is divided based on the type of seawater overflow, with the presence of several types, each type has different characteristics, this study aims to enrich the soil, by clustering tidal swamp land using the variables sodium, phosphorus and Potassium (NPK). NPK is a macro nutrient that is needed by plants to grow and develop plants planted on the soil, considering that tidal swamp land is not fertile soil. This research was conducted in the village of Mulia Sari, Tanjung Lago District, Banyuasin Regency with type A. In solving this soil clustering problem, the researcher chose the FCM (Fuzzy C-Miens Clustering) method, because this method is included in the unsupervised learning technique. This method is used to find patterns in a tidal soil dataset. FCM is a popular fuzzy algorithm and can achieve a convergent cluster center. The initial step taken in this study was to measure the NPK macro elements contained in the soil dataset, by taking soil samples to be examined in the soil laboratory, determining the number of clusters, the study determined 3 (three) clusters based on the NPK variable. Then carry out the data processing using the FCM method. From the data processing, the sodium cluster was found in the 15th iteration, while the phosphorus cluster was found in the 17th iteration and Potassium was found in the 16th iteration.
Improving Healthcare Outcomes with Learning Models for Machine Commonsense Reasoning Systems

Machine commonsense reasoning (MCR) systems can significantly improve the way we interact with machines. MCR systems are therefore an important element in any human-centric applications. Recent advances in machine learning (ML) have enabled breakthroughs in MCR technologies. This paper aims to improve healthcare outcomes by making human-machine interactions more intuitive than before. It presents learning models developed for MCR. Specifically, it presents a critical analysis of state-of-the-art deep learning (DL) models for MCR. These include recurrent neural network (RNN), transfer learning (TL), and transformers. Transformers, in particular, have been found to be effective for a range of natural language processing (NLP) applications, including MCR. Based on the analysis, another contribution of this paper is to assemble useful MCR tools into an adaptable MCR toolbox. To ensure broad applicability, the toolbox can be customizable for different MCR applications. Our research focuses on two specific MCR applications: commonsense validation and commonsense explanation. The former concerns identifying statements that do not make commonsense. The latter aims at explaining the reason why a given statement does not make commonsense. The paper presents some preliminary results of applying elements of the assembled toolbox to the two MCR applications. These results indicate that it is possible to achieve near human performances using finely-tuned state-of-the-art DL methods for the two MCR applications.
A Novel Chaotic Image Encryption Algorithm Based on Pixel and Bit Level Scrambling-diffusion

A color chaotic image cryptosystem algorithm based on pixel level and bit level scrambling-diffusion is proposed. The cryptosystem includes key stream generation, scrambling process and diffusion process. In scrambling process, the proposed algorithm uses row-column exchange to get the scrambled pixel matrix at pixel level; it also manipulates the random exchange of higher bits and lower bits of the scrambled pixels based on sorting the key streams. The scrambling process effectively weakens the correlation between adjacent pixels as well as higher bit-planes. In diffusion process, the algorithm establishes a two-way diffusion mechanism using bitwise XOR and additive modular operations to improve the security and performance of the proposed image cryptosystem. The key streams applied to control the encryption process are produced by 3D skew tent map, which has large range of cipher key and excellent chaotic performance. Experimental results demonstrate that the proposed cryptosystem has high security and good performance.
Delivering 360-degree Video With Viewport-Adaptive Truncation

Delivering Virtual Reality (VR) content wirelessly involves projecting a 360-video into a 2D format and then encoding it to satisfy the wireless bitrate requirements. However, the popular equirectangular and cubemap projections offer little flexibility to adapt to changing bitrates and headset motion. In this work, we show that the truncated square pyramid projection offers high flexibility for network and headset motion adaptation. We adapt by tuning a truncation parameter that controls the video quality for different spatial regions in the 360-video. Depending on the video, our scheme improves average video quality by up to 1.1dB in PSNR and up to 4.6 in VMAF score compared to a non-adaptive baseline.
Research on Smart Site of Power Grid Based on Edge Computing Framework

With the rapid development of the new generation of information technology, the communication and interaction between man and machine are becoming more and more frequent. Supported by massive data, the continuous optimization of software and algorithm model promotes the continuous innovation of power grid infrastructure mode and improves the “intelligent” level of construction site management. Wisdom around grid project site overall construction goal, project wisdom site construction overall architecture of the power grid is proposed, according to the power of wisdom calculation of edge of couplet system framework, with its wisdom in grid project application practice on site as the breakthrough point, the wisdom of the current power grid project site for the construction of the bottleneck analysis and summary, and put forward the solution.
Design &amp; Development of Deep Learning Algorithm with Convolutional Neural Networks for Breast Cancer Classification

This paper presents a method for classification of normal and abnormal tissues in mammograms using a deep learning approach. Breast biopsies based on the results of mammnography and ultrasound have been diagnosed as benign at a rate of approximately 40 to 60 percent. Breast cancer is one of the primary causes of cancer death among the women world population. For early detection of breast cancer in women Mammography and ultrasound methods are used. This is a holistic methodology which can help classify a whole mammography exam, containing the MLO views along with CC and the segmentation maps, as compared to the classification of individual lesions, which is the dominant approach in the field. We are also demonstrating that the system discussed in this paper is capable of using the segmentation maps that are generated by micro-calcification detection systems and automated mass detection systems, and still produces highly accurate results.
Information Encryption Security System Based on Chaos Algorithm

Chaotic cryptography is structurally related to the concepts of confusion and diffusion in traditional cryptography theory. Chaotic cryptography is formed by the inevitable connection between chaos theory and pure cryptography. In order to solve the shortcomings of the existing research on information encryption security system, this paper discusses the realization technology of information security, the design principles of encryption system and three kinds of chaotic mapping systems, and discusses the selection of development tools and programmable devices. And the information encryption security system based on chaos algorithm is designed and discussed, and the randomness test of three groups of encrypted files is carried out by the proposed algorithm and the AES (Advanced Encryption Standard) algorithm. Experimental data show that the uniformity of P-value value of chaos algorithm is 0.714 on average. Therefore, it is verified that the information encryption security system using chaos algorithm has high security.
Computing and Explaining Knowledge Relevance in Exploratory Search Environment

The main goal of the exploratory search is to find the knowledge that can meet user's information needs and solve a problem. The searching is also a knowledge acquiring and learning process and user's requirements often evolve with the process. It will be helpful to explain why the information found is relevant to users' requirements during searching. The relevance between a query and a resource not only depend on representation of resource but also is affected by user's cognitive state, base on this assumption, a knowledge relevance model and knowledge space are built to represent resources semantics and user's cognitive state by combining description logics and conceptual graph in this work. By analyzing the structure of knowledge space, this work defines the sequential relation on the concepts space that generated by refinement operator and concepts neighbors according to topology structure of conceptual graph, which provide key information for data relevance computing. Probability based on model theoretic semantics of description logics is adopted to show the quantity relationships between concepts. The knowledge relevance is determined from both structure perspective and statistical perspective of knowledge space. Based on the knowledge space structure. Interactive knowledge visualization techniques are adopted to explain the relevance between key knowledge concepts. A dataset collected from WebMD is collected and a knowledge visualization prototype is developed, which prove the rationality and interpretability of the knowledge relevance computing method proposed.
Application of Knowledge Graph Technology in the Field of Power Grid Infrastructure

Starting from the definition and construction method of knowledge graph construction, this paper selects the knowledge graph construction method suitable for the field of power grid infrastructure, builds a power grid infrastructure knowledge graph platform with platform layer, service layer and application layer, and discusses in detail the construction process and application scenarios of power grid infrastructure knowledge graph. Construct a power grid infrastructure corpus and dictionary based on power grid infrastructure management specifications, guide the construction of power grid infrastructure knowledge graphs, and obtain power grid infrastructure knowledge graphs that have important use value in intelligent assisted decision-making and intelligent search and in-depth Q&A scenarios.
Application and Research of Knowledge Graph in the Management of Teachers' Profession Archives in Military Academies

New archival data service mode with artificial intelligence as the core and integrating knowledge graph, data mining and intelligent service is emerging. In view of the problems of the current teachers' profession archives management process, prefer to collect rather than use, the lack of in-depth processing of archives resources and organic integration with the training of teaching staff, this research adopts the theory and method of knowledge graph construction, starting from the aspects of teachers' profession archives information extraction, teachers' profession archives knowledge fusion and reasoning, constructed teachers' profession archives management model based on knowledge graph, applying this model to the teachers' profession archives management system can realize the intelligent collection of archives and the semantic organization of archives, intelligently extract valuable knowledge of teachers' profession archives, draw archives knowledge graph, and realize the refined management of archives resources to provide data support for the construction of the teaching staff.
Data-Driven Target Tracking Methods of UAS/UAM in Dynamic Environment

The unmanned aerial system (UAS)/urban air mobility (UAM) aircraft market is expected to expand rapidly in the coming years. As this happens, these UASs will be expected to operate in increasingly dense and dynamic environments. It would be increasingly important to be able to know the current state of the environment, predict the future state and take decisions accordingly. This included knowing the current position and future trajectory of other UASs in nearby vicinity. This is done conventionally using Kalmon filter-based target tracking approaches, such as extended Kalman Filter (EKF) and Interacting Multiple Model (IMM) based models. While these prediction models work reasonably, they have inherent shortcomings such as being constraint to a discrete number of motion models and inability to consider environment related information such as nearby obstacles. In this study, multiple data-driven target tracking models have been proposed and their performance has been verified by comparing it with EKF and IMM based models. Based on requirements, (1) Gaussian Process Regression (GPR), (2) Long Short-Term Memory (LSTM) encoder decoder neural network, (3) Occupancy map-based LSTM-CNN neural network, and (4) conditional generative adversarial network (CGAN) based models have been proposed and developed. For the training of the data-driven models, trajectory datasets were prepared from realistic UAV scenarios with ~60000 datapoints. A similar validation dataset was prepared, and the data-driven and traditional models were validated on it. The results showed that data-driven methods outperformed the traditional target tracking methods under multiple scenarios.
A New Parallel Resampling Algorithm for GPU-Accelerated Particle Filter

Particle filtering is a sequential Monte Carlo method widely used to solve non-linear and non-Gaussian filtering problems. Particle filters represent the posterior distributions as a set of particles, so they demand a sizeable computational load for satisfactory accuracy. In order to accelerate it, there have been many attempts to apply to graphics processing units (GPUs) through parallelization. However, parallelization of the resampling process has several challenges due to its sequential nature and, worse, can cause a bottleneck. Therefore, this paper addresses a new resampling method suitable for parallel computing. We focus on maximizing the number of processes to run independently on each particle and demonstrate the performance in terms of root mean square error (RMSE), the computation times, and Jensen-Shannon divergence (JSD) in an application of terrain referenced navigation (TRN). The results were compared to rejection, Metropolis, and systematic resampling methods.
Energy-Aware Motion Planning using Experimental Flight Data from a Tailsitter UAV

Hybrid vehicles with hover and wing-borne forward flight capabilities offer the performance advantages of fixedwing counterparts while retaining the diverse mission capabilities of traditional rotorcraft. The ability to operate in different flight modes also presents opportunities for trajectory planning strategies that best leverage the capabilities of multi-mode aircraft. In this work, experimental flight data collected from a small tailsitter UAV operating in fixed-wing mode is used to generate energy cost heuristics for the Rapidly-exploring Random Tree (RRT*) algorithm with motion primitives based on Dubins path. From this, mission-level energy and distance statistics are generated providing insight into the vehicle-level design implications of employing a wing with variable angle of incidence. Planning parameters that were tested include the maximum number of iterations (i.e. number of nodes added to the graph), the maximum step size between nodes (i.e. graph resolution), and the energy gain for the energy cost heuristics. Results showed that for the same number of iterations, optimizing for both energy and Dubins path reduced energy consumption by 16.2% and the resultant path length by 40 meters at the higher step size. There was an average of 9.8% reduction in energy and 9.4% reduction in total path length when minimizing for both Dubins path and energy cost across all test cases. As expected, these trajectories also minimized heading changes such that new randomly sampled nodes were more likely to be added along a straight path to the goal.
Safe Path Planning of UAV Based on Reinforcement Learning in Probabilistic Environments

Safety is a critical concern for flights in probabilistic environments of autonomous Unmanned Aerial Vehicles (UAVs). The traditional model-based path planning methods for probabilistic environments are often computationally demanding. When using model-free Reinforcement Learning (RL), an action with a higher risk could be chosen if the performance to the long-term goal is high enough. This paper will incorporate a probabilistic safety constraint to the model with Q-Learning that will dynamically adjust the collision penalty to achieve the required collision probability threshold. To train and test the proposed safe-RL model in an uncertain environment, a probabilistic collision space map is introduced where the collision areas are determined from a data-driven distribution. With the probabilistic environments, the goal of RL is to maximize the expected reward while maintaining an expected low collision chance. This model is then further tested on a realistic city map with a flow simulation around a group of urban buildings, where areas of high velocity are considered as probabilistic collision spaces and will be safety-critical for urban flights.
Survey of Fake Image Synthesis and its Detection

In recent times, image synthesis has attracted significant attention of people for both positive and negative reasons. Images can be easily synthesized using various techniques. This paper surveys various techniques for image synthesis as well as its detection in a unique structured manner, to enable a perspective on this iterative phenomenon. The paper describes both advantages and limitations starting from simple fake image detection to AI synthesized image detection approaches that are available in the literature. Generative Adversarial Network (GAN) is the trending algorithm for artificial image synthesis, because the faces generated by GAN are highly realistic. As discriminators are already present in the GAN’s structure, any attempt to create a distinguisher that detects fake images synthesized by GAN, needs to structure itself to detect all existing patterns of fake image synthesis including that of GAN.
Explanation models as a component of the intelligent computer-aided diagnosis systems in medicine: a brief review

The paper considers the most important and effective approaches and models for explaining and interpreting diagnostic results obtained using intelligent computer-aided diagnosis systems. The need to use them is due to the fact that the intelligent computer-aided diagnosis system itself is a “black box” and it is important for the doctor not only to get the patient’s diagnosis, but also to understand why such a diagnosis is stated, what elements of the patient information are the most significant from the point of view of the diagnosis. Reviews of the main approaches to explain predictions of machine learning models applied to general areas as well as to medicine are presented. It is shown how different types of the initial patient information impact on the choice of explanation models. Models are considered when visual or tabular information is available. Example-based explanation models are also studied. The purpose of the work is to review the main explanation models and their dependence on types of information about the patient.