Anomaly detection aims to identify the true anomalies from a given set of data instances. Unsupervised anomaly detection algorithms are applied to an unlabeled dataset by producing a ranked list based on anomaly scores. Unfortunately, due to the inherent limitations, many of the top-ranked instances by unsupervised algorithms are not anomalies or not interesting from an application perspective, which leads to high false-positive rates. Active anomaly discovery (AAD) is proposed to overcome this deficiency, which sequentially selects instances to get the labeling information and incorporate it into the anomaly detection algorithm to improve the detection accuracy iteratively. However, labeling is often costly. Therefore, the way to balance detection accuracy and labeling cost is essential. Along this line, this paper proposes a novel AAD method to achieve the goal. Our approach is based on the state-of-the-art unsupervised anomaly detection algorithm, namely, Isolation Forest, to extract features. Thereafter, the sparsity of the extracted features is utilized to improve its anomaly detection performance. To enforce the sparsity of the features and subsequent improvement of the detection analysis, a new algorithm based on online gradient descent, namely, Sparse Approximated Linear Anomaly Discovery (SALAD), is proposed with its theoretical Regret analysis. Extensive experiments on both open-source and additive manufacturing datasets demonstrate that the proposed algorithm significantly outperforms the state-of-the-art algorithms for anomaly detection.

According to Hebbian theory, synaptic plasticity is the ability of neurons to strengthen or weaken the synapses among them in response to stimuli. It plays a fundamental role in the processes of learning and memory of biological neural networks. With plasticity, biological agents can adapt on multiple timescales and outclass artificial agents, the majority of which still rely on static Artificial Neural Network (ANN) controllers. In this work, we focus on Voxel-based Soft Robots (VSRs), a class of simulated artificial agents, composed as aggregations of elastic cubic blocks. We propose a Hebbian ANN controller where every synapse is associated with a Hebbian rule that controls the way the weight is adapted during the VSR lifetime. For a given task and morphology, we optimize the controller for the task of locomotion by evolving, rather than the weights, the parameters of the Hebbian rules. Our results show that the Hebbian controller is comparable, often better than a non-Hebbian baseline and that it is more adaptable to unforeseen damages. We also provide novel insights into the inner workings of plasticity and demonstrate that “true” learning does take place, as the evolved controllers improve over the lifetime and generalize well.

Vector Quantized Variational Autoencoder (VQ-VAE) has shown promise in representing diverse and complex data distributions in deep learning, making it a potential solution for various applications including wireless communications. In this paper, we propose a joint source-channel coding scheme based on VQ-VAE for point-to-point wireless communication. Our approach leverages the dependence of the encoder and decoder on a given dataset and channel conditions to develop efficient encoding and decoding schemes, leading to improved reliability and efficiency even in the presence of noisy wireless channels. We demonstrate the effectiveness of our proposed approach through extensive simulations in handling realistic wireless communication scenarios. In addition, we discuss potential connections to semantic communication and highlight the secure and energy-efficient nature of our approach. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Upon acceptance in IEEE: Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to [email protected]. (Copyright (c) 2015 IEEE.)

Testing software before deploying is a mandatory task in SDLC. Various types of testing tools are used to test the software. This research focuses on JUnit framework to perform unit testing for Java applications. We have developed a Banking Inventory application using spring framework by connecting the application to the MongoDB. The application contains operations such as Create, Update, Delete and find for the customers and Unit test cases has been developed for all the modules using JUnit framework and the test cases are discussed and validated.

The growing population and energy demand, coupled with the depleting fresh water resources has resulted in great progress in sea water desalination (SWD) technologies. Nanopores of 2D materials like graphene and its structural analogs are the latest innovations in membrane technology for SWD. The performance of these novel atomically thin nanopores as seen from various experimental and theoretical studies is highly encouraging with reports of water permeability 2-3 orders of magnitude greater than the conventional reverse osmosis (RO). The potential for high efficiency and the low energy requirements of these nanopores for desalination has led to tremendous efforts in fabrication and commercialization. We present here a review of the very recent patents associated with the preparation of these nanopores, the process and the efficiency of SWD.

Autonomous driving requires accurate, robust, and fast decision-making perception systems to understand the driving environment. Object detection is critical in allowing the perception system to understand the environment. The perception systems, especially 2D object detection and classification, have succeeded because of the emergence of deep learning (DL) in computer vision (CV) applications. However, 2D object detection lacks depth information, which is crucial to understanding the driving environment. Therefore, 3D object detection is fundamental for the perception system of autonomous driving and robotics applications to estimate the objects’ location and understand the driving environment. The CV community has been giving much attention recently to 3D object detection because of the growth of DL models and the need to know accurate locations of objects. However, 3D object detection is still challenging because of scale changes, the lack of 3D sensor information, and occlusions. Researchers have been using multiple sensors to solve these problems and further enhance the performance of the perception system. This survey presents the multisensor (camera, radar, and LiDAR) fusion-based 3D object detection methods. The fully autonomous vehicles need to be equipped with multiple sensors for robust and reliable driving. Camera, LiDAR, and radar sensors and their corresponding advantages and disadvantages are also presented. Then, relevant datasets are summarized, and state-of-the-art multisensor fusion-based methods are reviewed. Finally, challenges, open issues, and possible research directions are presented.

Autonomous driving is a anticipated technology in both research groups and industry. Naturalistic human driving behavior offers guidance for autonomous driving in terms of human driver reaction under certain scenario, ground truth of detection and path planning, statistics for functional safety and SOTIF, etc. How to efficiently obtain the target market naturalistic driving scenario for SAE L3 and above highly automated driving (HAD) systems is a major challenge on the road of their series production. Based on above demand, this paper uses Unmanned Aerial Vehicle (UAV) to collect traffic data from congested highways and expressways in China, conducts data preprocessing and quality evaluation, target vehicle recognition and tracking, and structured driving scenarios mining. It is expected to provide data support for HAD development in the Chinese market, including human driver behavior analysis, quantitative metric extraction, real driving test scenario generation, etc. We call this large-scale naturalistic traffic flow dataset as Aerial Dataset for China Congested Highway & Expressway (AD4CHE). It contains 5.12 hours aerial survey data of 53761 vehicles in 4 different cities in China, with a total driving mileage of 6540.7 km, 16099 lane changes and 3331 cut-ins. Some initial findings from AD4CHE are introduced. The dataset will be open sourced at https://auto.dji.com/cn.

For several years people have realized the importance of archiving and finding information. With the need of computers, finding useful information from such collections has become a necessity. Information retrieval has become an important research area in the field of computer science and gained importance in several fields like business, healthcare, agriculture, medicine, law and many other fields. Information retrieval is finding material that could be in the form of a document consisting of unstructured nature that provides the required information. This research paper focuses on the need, models and the processes involved in information retrieval. A case study on INSYDER system has been proposed to gain holistic knowledge of information retrieval in the field of business.

Today, mobile devices like smartphones are supported with various wireless radio interfaces including cellular (3G/4G/LTE) and Wi-Fi (IEEE 802.11) [42]. The legacy devices can only communicate with only one interface. The Transmission Control Protocol, or TCP, has a limitation inability to change connection settings without breaking the connection. In this paper, we explain how multi-path TCP (MPTCP) protocol has been proposed to solve TCP single-interface limitation and provides a huge improvement on application performance by using multiple paths transparently (auto path changing). We discuss the last mile, which is the final networking segment that carried all network traffic. Indeed, the available bandwidth in last-mile link can effectively harm the network throughput as it limits the amount of transmitted data. We found that the quality of the last mile networks significantly determines the reliability and quality of the carrying network. We believe MPTCP can provide a convenient solution for the last mile problem. We provide a holistic view of the challenges and potential enablers in details.

Gate driver circuits to ensure proper turn-on and turn-off for power switches are essential parts of a power converter design. They become even more important for multilevel converters where multiple switches are operated at active voltage domains. Recent favorable use of Gallium-Nitride (GaN) devices for power switches makes gate driving even more challenging as the switch performance and reliability are more sensitive to variations of the gate driving signals and power compared with traditional power MOSFETs. This paper discusses gate driving methods using a multi-level multi-inductor hybrid (MIH) converter as the demonstration prototype to address two key challenges in designing gate drivers: 1) providing level-shifted PWM signals to active voltage domains and 2) powering schemes for gate driver circuits. To solve the first challenge, an optimal use of available half-bridge drivers is devised to eliminate the need for separate signal isolator chips. This method was implemented and verified in a MIH converter prototype for 48-V Point-of-Load (PoL) applications using three different powering schemes for gate drivers, including isolated power modules, regulated supplies from switch blocking voltages, and cascaded bootstrap power rails with regulations. The gate driver techniques and powering schemes are compared experimentally in terms of performance to illustrate their benefits and trade-offs.

Searching digitized detector signals for piled-up delayed components with distinct energy and delay time signatures is a smart method to provide common NaI(Tl) detectors with additional neutron detection capabilities at no extra cost. This technique nicely complements the idea of neutron detection by analyzing events with high energy depositions above the range of common gamma-ray energies. In combination, both approaches can provide half of the neutron sensitivity offered by a commercial 6Li co-doped NaI(Tl) (NaIL™) scintillator of the same size, at the price of higher and load-dependent background contributions. Delayed-coincidence techniques are most suitable for neutron monitoring or long-term measurements, where the statistics of the acquired delay-time distributions allows separate fitting of the effect and background contributions. In this case, the thermal neutron flux can be quantified in parallel to gamma-ray spectroscopy at overall detector loads exceeding 10 kcps.

Due to the recent popularity and growth of social media platforms such as Facebook and Twitter, cyberbullying is becoming more and more prevalent. The current research on cyberbullying and the NLP techniques being used to classify this kind of online behaviour was initially studied. This paper discusses the experimentation with combined Twitter datasets by Maryland and Cornell universities using different classification approaches like classical machine learning, RNN, CNN, and pretrained transformer-based classifiers. A state of the art (SOTA) solution was achieved by optimising BERTweet on a Onecycle policy with a Decoupled weight decay optimiser (AdamW), improving the previous F1-score by up to 8.4%, resulting in 64.8% macro F1. Particle Swarm Optimisation was later used to optimise the ensemble model. The ensemble developed from the optimised BERTweet model and a collection of models with varying data representations, outperformed the standalone BERTweet model by 0.53% resulting in 65.33% macro F1 for TweetEval dataset and by 0.55% for combined datasets, resulting in 68.1% macro F1.

Focusing on GaN-based light-emitting diode (LED) design optimization, this paper evaluates simulation-based machine learning approaches from a device physics point of view. Strategies are suggested for achieving more realistic results.

The main idea of this research originates from the patients such that each patient with neural disorder should refer to a medical center to check his brain's health condition to sample his EEG signals and present the results to a specialist for further investigation. If this process can be done remotely by tele-medicine techniques, it will save cost and time. In tele-medicine method, the patient can record the EEG signal alone at home and send the results to his physician. To this end, this research employs Bluetooth to connect the interface system to the computer, and the patient can send the results to his physician after saving the data. Thus, the main purpose of designing this BCI system is to record EEG signals using a microcontroller and transmit them via Bluetooth to a computer and mobile phone such that the signal can be represented instantaneously in a GUI.

Differences in contact impedance of the ECG measurement electrodes lead to asymmetries of the signal paths and thus result in reduced common-mode rejection and artifacts. Here, the imbalance of contact impedance is investigated for different types of electrodes with capacitive coupling in terms of static imbalance as well as dynamic variation during body movement. Flexible and incompressible materials like conductive foam and fabric showed the best overall performance. The negative effect of rigidity can partly be compensated by adding conducting foam, while soft materials can profit from an increase of electrode area.

This is a pre-print for the submission to IEEE LISS 2020 Conference, Budapest, Hungary.

Intelligent omni-surfaces (IOSs) have been considered for assisting outdoor-to-indoor millimeter-wave (mmWave) communications. Nevertheless, the existing works have not adequately investigated how the number or the deployment locations of IOSs should be optimized for serving multiple indoor users. In this paper, we study IOS-assisted outdoor-to-indoor mmWave communications where IOSs are installed in an exterior wall of a building to refract mmWave signals from an outdoor base station (BS) to indoor users that locate among indoor blockages. Given a fixed total number of refracting elements, we formulate an optimization problem to maximize the downlink energy efficiency of the outdoor BS while satisfying the dowlink data rate requirements of the indoor users by jointly optimizing the number, locations and phase shifts of IOSs and the beamforming vectors of the BS. To address the varying dimensionality and the non-convexity of the optimization problem, we decompose it into two subproblems that optimize the IOSs' phase shifts together with the BS beamforming vectors and the number and locations of IOSs, respectively, and devise successive convex approximation and Continuous Population-Based Incremental Learning-based algorithms to solve them alternately. Simulation results demonstrate that the proposed algorithms can obtain the optimal number and locations of IOSs, resulting in significantly enhanced energy efficiency of the outdoor BS compared to benchmark schemes.

Human activities are progressively defined by interactions with products that embed increasingly higher levels of immaterial component (software, patents, design principles and patterns). After presenting the continuity between seminal concepts in Cybernetics, Information Theory, Network Analysis, AI practices on one side and cognitive behaviour by leveraging a minimalist approach to cybernetic systems based on the concept of capacity of network; the text tries to establish the concept of interface to define logical rule-based automata that can be considered shareable instances of a self-model approach to consciousness: in first instance, a rule-based descriptive construct for an ‘economy of information exchange’ between systems (or an ‘economy of time’), and in second instance a rule-based framework for approaching the hard problem of consciousness. These are critical waypoints in navigating fast-growing knowledge-intensive landscapes. This review into patterns of growing complexity is concluded with an hypothesis that aims to extend Ashby’s definition of machine to include interfaces to enable self-modeling for possibly intelligent digital machines. Starting from this background, conclusion tries to define a clearer pathway to tackle the difficult problems about the positioning of biological and digital machines in what the paper points out to be the sentience-intelligence-consciousness spectrum.