Reviewerly/exHarmony huggingface dataset:
Decentralized Multi-Bernoulli Multitarget Tracking Using Multistatic Doppler-Only Measurements

This paper proposes a decentralized multi-Bernoulli filter for multitarget tracking over a network of separately located Doppler sensors, in which each sensor exchanges its received measurements and posterior estimates with partially selected neighbors via one-iteration-only diffusion. The selection of an optimal neighbor subset for each sensor is formulated as a partially observable Markov decision process with the probability ratio of nonexistence to existence of targets being the cost function. In particular, the locally collected measurements and posteriors are handled by the particle-based iterated-corrector multi-Bernoulli filter and Gaussian-mixture-based arithmetic average fusion, respectively. The validity of the proposed filter is verified via computer simulations.
Supervisory Event Loop-based Autoscaling of Node.js Deployments

Autoscaling mechanisms are used widely to scale computing instances, under varying load conditions. Containerized cloud applications managed by orchestrators-such as Kubernetes with the Horizontal Pod Autoscaler (HPA)-can be scaled based on CPU utilization. However, the prevalent approach may not be ideal for every language runtime, such as the event-driven Node.js. Language runtime-specific metrics can be utilized to accurately describe the state of the application and use it to monitor and affect the application scalability in control loop-based systems at the desired conditions dynamically. Hence, we investigate event loop lag as an alternative and Node.js-specific metric to drive autoscaling with HPA controllers. Additionally, we synthesize a three-tier adaptive mechanism on top of the cluster autoscaler, which acts as a supervisory controller, to change the setpoint value dynamically based on the divergence from the service level objectives. We further extend the aforementioned architecture to re-evaluate the system model and the controller gain parameters at runtime. To assess the methodology, we evaluate and compare the performance of our adaptive autoscaling approach against the CPU-utilization-based autoscaling mechanism, under various load patterns and workloads.
Information Fusion Cascade Network for 3D Hand Pose Estimation Using Depth Data

With the development of metaverse, augmented reality and robot operation, hand pose estimation plays an increasingly important role. In this paper, an information fusion cascade network is proposed to improve the performance of hand pose estimation through a cascade network containing finger-palm information fusion and a biomechanical-constraint loss function. Extensive evaluations on two public datasets show that information fusion cascade network can generate accurate and fast 3D hand poses and achieve an excellent balance of 3D hand pose estimation accuracy and speed compared to state-of-the-art (SOAT) methods.
Enciphering Scheme using Planar Near-Rings and DNA Transform

Cryptography is a ubiquitous tool used to preserve the confidentiality and integrity of the information. For this purpose, various encryption algorithms have been designed by cryptographers. Customary, in algebraic cryptography, Galois fields are focused while constructing such ciphers because of the availability of the inverse elements. However, this article presents a novel secrecy-providing algorithm using the Planar Near-Rings. Since these rings do not bear distributive law, they are suitable for achieving confusion and diffusion in enciphered data. After applying the structure of Planar Near-Rings, we apply the DNA transform in parallel, enhancing diffusion in the adjacent data bytes. In the end, the enciphered data is examined through various tests and comes out with results that guarantee the strength of the proposed scheme against numerous attacks.
Marine Object Detection using Transformers

Underwater object detection is a challenging task due to low visibility and complex background scenes. Although different CNN architectures have been proposed in the literature for this task but they lack the ability to utilize global contextual information about the image. Visual transformer-based architectures have recently gained attention after the success of transformers in NLP. In this paper, DEtection TRansformer (DETR), a visual transformer-based object detection architecture, is utilized for the underwater object detection task. The fine-tuning of the model is carried out on the Brackish dataset with 6 different classes. Exploiting the global contextual information and relationship of objects in the scene reports promising performance on standard evaluation metrics.
An Effective Undersampling Approach to Deal with Class Imbalance Problem in Software Defect Prediction

Machine learning based software defect prediction (SDP) approaches assist in estimating where faults are likely to occur in source code. In real-life projects, fault-free modules are higher in number than faulty modules. This is referred to as class imbalance problem. Random over-sampling and random under-sampling (RUS) are the basic sampling strategies in data-level approaches for class imbalance problem. The RUS approach removes the instances randomly and due to randomness, loss of useful information may occur. On the other hand, over-sampling may lead to biased modeling. In this paper, we propose a new technique known as Structured Under-Sampling (SUS) to address both of these problems. The proposed approach systematically removes inconsistent and redundant instances. After that, most similar instances to the existing instances are removed to ensure minimum loss of information. For empirical evaluation, C4.5 classifier has been used to compare SUS with RUS and Tomek RUS while performance difference has been analyzed using F1-score and ROC. The results of our investigation show improved performance of SUS over other approaches. Moreover, it has been observed that the Imbalance ratio (IR) affects the performance of SUS. More specifically, when IR > 5 SUS outperformed in all results.
Sentiment Analysis using Ensemble Classifier for Entrepreneurs based on Twitter Analytics

When committing the time and resources to a startup idea, entrepreneurs must consider several aspects. Analyzing the thoughts of people about emergent businesses from popular social networking platforms can contribute positively in the decision making process of investors. Statistical methods are widely experienced in generation of business insights and estimation of future growth by determining a general trend in opinion of users. However, intelligent computing for efficient and affective sentiment analysis is still challenging and capture the interest of researchers. In this paper, we have aimed at using natural language processing to understand the attitude of twitter users about innovative business ideas and latest technologies in their surroundings. We have proposed an ensemble classifier to allow users to investigate the sentiment associated with a topic of interest in real time using data visualization. The objective of this research is to explore the abilities of machine learning algorithms in producing maximum accuracy on translating raw tweets into useful judgements through sentiment analysis. Our proposed application classifies positive and negative emotions taken from sentiment140 dataset. To assess the output of ensemble classifier, we have trained Support Vector Machine (SVM), Naive Bayes, and Long Short-Term Memory (LSTM) neural networks on a defined set of features. While, experimental results show that our methodology is more accurate than other known methods and may be used by entrepreneurs for improving business analytics. This paper also proposes a comparison among the current methodologies of sentiment analysis using artificial intelligence.
Forensic Data Analysis of Delivery and Transport Applications

The COVID-19 pandemic has changed many aspects of human life during last three years. One of these aspects is the adaption of new trends and technologies for everyday activities such as delivery and transportation. People now prefer to shop online and get their products delivered at home without wasting any time. Therefore, the security and importance of online and delivery applications is the main concern these days. The payment mode of these applications is online which involves personal data like bank information and user details. This problem led to the research contribution of our work. The main objective and implication of this study is to find personally identifiable information (PII) of users which uniquely identifies a person at personal and organizational scopes. In this paper, we present the forensics analysis of eight popular Android delivery and transport applications i.e. Daraz.pk, Foodpanda, Grocer app, Airlift express, Bykea, Indriver, Uber and Clicky shopping app. These applications have not been previously studied and investigated by other researchers. Furthermore, these applications are among the top android apps used by customers. It is expected that such an analysis can guide investigators towards obtaining useful information about a suspect who has used such an application on their device. The analysis process started with the installation of each application on a rooted Samsung S7 Edge smartphone. Then various activities were performed such as setting up an account, booking a ride, or ordering a delivery. After this, a physical image of the device was acquired. A detailed analysis of the image was carried out using Autopsy and all relevant artifacts were collected. A comparison of the results showed largest number of artifacts have been gathered from installation activity and the most number of unique artifacts have been collected from order and booking activity. A tabular form of analysis has also been shown with all of the source and path files from which the data has been gathered.
Pseudo Depth Maps for RGB-D SLAM

In visual SLAM, RGB-D cameras can actively obtain pixel depth information but are expensive and unsuitable for outdoor use. In contrast, monocular depth estimation is relatively inexpensive and more readily available. There have been some systems that fuse monocular depth estimation with SLAM algorithms, but no method that can directly generate depth maps has been tested with experiments. We propose that using monocular depth estimation results can generate 16-bit pseudo depth maps, which can be combined with monocular images as pseudo RGB-D. The relevant camera parameters can be configured according to the general RGB-D camera SLAM requirements. Experiments with Monodepth2 and ORB-SLAM3 on the KITTI dataset demonstrate that pseudo RGB-D can achieve as satisfactory results in SLAM as in stereo computation. The research paves the way for researchers can quickly examine monocular depth estimation results in more SLAM frameworks, reducing the cost of testing new depth monocular estimation frameworks.
Leveled Homomorphic Encryption Based on Finite Field Isomorphism Problem Over Matrix Algebra

Finite fields of order p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> are isomorphic. The elements of these fields can be denoted by a polynomial with coefficients modulo p or by matrices with entries chosen modulo p. The length of an element of field can be associated with the norm of that element. In general, a non-trivial isomorphic map between any two fields does not preserve the length of the elements. The entries of the images of short elements distribute randomly and uniformly over modulo p. This key feature yields new kind of problems, called finite field isomorphism problems. In this paper, we studied the finite field isomorphism problem and introduced a leveled homomorphic encryption scheme based on the finite field isomorphism problem over the matrix field.
Edge-AI based Face Recognition System: Benchmarks and Analysis

The performance of real-time video processing tasks such as facial recognition has improved by many folds with the emergence of Artificial Intelligence (AI) and Convolutional Neural Networks (CNNs). However, face recognition models are computationally intensive and lack flexibility in deployment. Edge computing devices like Jetson (Nano, TX2) have overcome this gap by bringing high-speed and high-throughput computing capabilities to the edge. In this paper, we propose reliable integration of multi-face recognition system into compact and low-power edge devices. The deployed inference system uses a novel and light-weight face detector ‘FaceBoxes’, which is used for multi-face detection and extraction. ‘FaceNet’ is used as a face recognizer which outputs 128-dimensional embedding as feature vectors which is fed to a ‘Multilayer Perceptron (MLP)’ for face classification. We analyze the performance of facial recognition inference system on the two NVIDIA Jetson Edge computing boards (Nano, TX2). We also compare the performance of a TensorRT-based deep learning model optimization against a typical Tensorflow model implementation. The paper provides a detailed benchmarking analysis in terms of accuracy, FPS, execution time, memory usage and energy consumption. Additionally, the performance of the proposed face recognition system is compared with the state-of-the-art Multi-task Cascaded Convolutional Network (MTCNN) based face recognition system. Our findings can aid researchers in determining how this face recognition framework, when deployed on a particular Edge-GPU platform, might suit their needs for a specific face recognition application.
Layered classification method for darknet traffic based on Weighted K-NN

In recent years, anonymous networks are used very frequently. Difficulties in tracking user's identities increase with the frequent usage of anonymity networks. This increases the difficulty of detecting cybercriminal activity. To prevent crime, darknet traffic needs to be monitored. Most of the existing dark-net researches focuses on the Tor without adequate consideration of other darknets. Moreover, most of the work content focuses on distinguishing normal traffic and darknet traffic, and lacks a fine-grained classification method for darknet traffic. This paper proposes a hierarchical classification method for the network traffic of FreeNet, one of the most frequently used darknets, which can distinguish between normal traffic and FreeNet traffic, as well as five FreeNet user behaviors. We train the classifier based on the weighted K-NN. The experimental results show that the proposed classifier distinguishes normal traffic from FreeNet traffic with an average accuracy of 99.6% and five user behaviors with an average accuracy of 95.8%. We compared our classifier with existing works such as decision tree (DT), Gaussian naive Bayes (Gaussian NB), and K-NN. The results show that the accuracy of the classifier is the highest when distinguishing user behavior. Compared with the above three models, the accuracy of the classifier is improved by 1.86%, 57.95%, and 3.10% respectively.
Harnessing the Power of Machine Learning for Mapping of Iron Oxide Mineralization using Sentinel Satellite Spectral Imagery

In recent decades, satellite-based mineral exploration has been recognized as one of the most cost-effective and time-efficient tools for regional exploration. In this study, three advanced digital image processing techniques based on machine learning were used on multispectral Sentinel satellite data for mapping of iron oxides: Spectral Band Combination (SBC), Spectral Band Ratio (SBR), and Spectral Angle Mapper (SAM). The satellite data was preprocessed in parallel using image enhancement algorithms on high computational machines. The results demonstrated that the SAM technique was more promising and efficient for accurate mapping of iron oxide mineralization in South Africa’s Northern Cape region, with an overall accuracy of 81.62 percent. The cost-effective, robust, and efficient methodology developed in this study can explore iron oxide mineralization resources in other regions with similar geographical and geological characteristics. The findings of this study can be a starting point for developing a mine plan, along with other ground data sets from the mining regions.
Node Embedding over Attributed Bipartite Networks in Dynamic Context

This work seeks an effective embedding representation by the exploitation of both the structure and attribute aspect of attributed bipartite networks in a dynamic context. Most of the plain or attributed network embedding works are limited to the static type homogeneous networks, but in reality, networks often evolve that certainly effect the node homophilic relations. Hence, we propose DABNE embedding framework that investigates the dynamics of evolved nodes/links over time through targeting both the explicit and implicit node interactions of different and same entity types of a bipartite network. Primarily, it targets the dynamics of the network (structural change) and dynamics on the network (explicit/implicit node interactions either through structure or attributes similarities) on the network. We adopt the KL divergence method to model the node’s explicit interactions, and for implicit node interactions, we employ the Skipgram model. Lastly, we concatenate these independently learned latent embeddings to perform the classification (multi-class and multi-label) and clustering task. Experiments are conducted on two real-world networks with both structure and attributed based embedding methods to corroborate the efficacy of our proposed framework.
Ciphertext storage scheme supporting data hierarchical management and control

As file management is widely used in e-government and enterprise office, the file exchange, as the main means of sharing and collaborating in office, has been far from able to meet the needs of data security. Encrypted Storage with its highly security, controllability, can be an effective solution to the limitations of existing authority control services. However, during the data sharing and exchange, the file is separated from the owner, which has the problem of insufficient or over -authorization, thus needs fine-grained hierarchical control and cooperation. In this article, we propose a hierarchical management and control scheme for encrypted storage combined with ciphertext retrieval. The proposed scheme supports top-down privilege division without increasing the number of managed keys, all the data are processed and authorized strictly, the high-level user can deduce the keys of lower authorized users, but not vice versa, thus solving data leakage caused by over-authorization. Through performance and security analysis, we demonstrate that the scheme can better meet the data security and precise authorization requirements.
Comparative Study of Bearing Only Tracking (BOT) System using Efficient Observer Trajectories

The main advantage of using passive target tracking is to identify the target state without exposing the presence of observing platform. The Bearing Only Tracking (BOT) is the field of underwater signal processing which is mainly focused on estimating the unknown position of target source by using the direction or angle only information. Therefore, observer can acquire the azimuth or angle information only; and cannot obtain the distance and velocity information about the target. For uniform motion targets, the target remains unobservable in the presence of stationary observing station and therefore the observing station must maneuver to track the unknown position and velocity of the target. The observability conditions for single moving observer are studied which is moving with constant velocity and acceleration using bearing only measurements. Keeping in view these observability conditions, the mathematical model for observer motion trajectory has been developed in this paper with finite number of constant velocity segments which are termed as Legs. These legs are straight line segments with observer course change in the beginning of each time interval. Secondly, the mathematical model for the sinusoidal or coordinated turn observer motion has been defined which changes the angular velocity to observe the unknown target. Thus, these two observer trajectories primarily fulfil the observability criteria in case of maneuvering observing station. The underwater BOT system has non-linear measurement model and therefore, we cannot use the standard estimation and filtration methods to estimate the target states. Thus, to resolve this non-linearity, the EKF algorithms are used throughout the paper. The simulation experiments are performed for above observer trajectories and it is analyzed that such observer trajectories have got higher observability in the form of increased bearing rate and ultimately in reducing estimation errors.
A Traceability Method for Bitcoin Transactions Based on Gateway Network Traffic Analysis

Cryptocurrencies like Bitcoin have become a popular weapon for illegal activities. They have the characteristics of decentralization and anonymity, which can effectively avoid the supervision of government departments. How to de-anonymize Bitcoin transactions is a crucial issue for regulatory and judicial investigation departments to supervise and combat crimes involving Bitcoin effectively. This paper aims to de-anonymize Bitcoin transactions and present a Bitcoin transaction traceability method based on Bitcoin network traffic analysis. According to the characteristics of the physical network that the Bitcoin network relies on, the Bitcoin network traffic is obtained at the physical convergence point of the local Bitcoin network. By analyzing the collected network traffic data, we realize the traceability of the input address of Bitcoin transactions and test the scheme in the distributed Bitcoin network environment. The experimental results show that this traceability mechanism is suitable for nodes connected to the Bitcoin network (except for VPN, Tor, etc.), and can obtain 47.5% recall rate and 70.4% precision rate, which are promising in practice.
Context-Based Narrative Generation Transformer (NGen-Transformer)

Text generation is an important domain of natural language processing where the plausibility of the generated text depends upon the context assimilation capabilities of the architecture being used. Recently the performance of automatic text generation task have greatly improved with the use of attention based language models. In this paper, we have explored the task of story generation based on some user defined context or prompt. We have proposed a GP2 based narrative generation architecture called NGen-Transformer. Our proposed architectures focuses specifically on the context provided by the user to produce meaningful stories. For the purpose of evaluation of our proposed model, we have used the WritingPrompts dataset which consists of a large number of human written sample stories based on corresponding titles or sentences (prompts). Experimental results show that our proposed NGen- Transformer model outperforms several sequence to sequence as well as attention based architectures on the task of story generation.
Lightweight Social Organization and Coordination System for Non-Profit Associations

Efficient use and management of resources and manpower is an important success factor in the organization of non-profit organizations as well as events. But the barriers to entry for available solutions are significant, especially for smaller associations. To overcame that shortcomings a device-independent lightweight mobile system for small non-profit organizations was created. It focuses on the ease of use and application by inexperienced users as well as the provision of key capabilities needed by them. They can improve their efficiency of use of resources and optimizing the coordination within the organization requiring a minimum of technical and financial resource.