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Topic Review
Machine Learning in T- and B-Cell Epitope Prediction
An antigenic determinant (AD) is a portion of an antigen molecule known as an epitope that is recognized by the human immune system, specifically by antibodies or T and B cells. Recognition of epitopes is considered important in EBPV design to contain pandemics, epidemics, and endemics due to the outbreak of infectious diseases. To design an effective and viable EBPV against different strains of a pathogen, it is important to identify the putative T- and B-cell epitopes. Using the wet-lab experimental approach to identify these epitopes is time-consuming and costly because the experimental screening of a vast number of potential epitope candidates is required. Fortunately, various available machine learning (ML)-based prediction methods have reduced the burden related to the epitope mapping process by decreasing the potential epitope candidate list for experimental trials. Moreover, these methods are also cost-effective, scalable, and fast. 
  • 900
  • 16 Feb 2022
Topic Review
Dell EMC ScaleIO
Dell EMC PowerFlex (previously known as ScaleIO and VxFlex OS), is a commercial software-defined storage product from Dell EMC that creates a server-based storage area network (SAN) from local server storage using x86 servers. It converts this direct-attached storage into shared block storage than runs over an IP-based network. PowerFlex can scale from three compute/storage nodes to over 1,000 nodes that can drive up to 240 million IOPS of performance. PowerFlex is bundled with Dell EMC commodity computing servers (officially called VxFlex Ready Nodes, PowerFlex appliance, and PowerFlex rack). PowerFlex can be deployed as storage only or as a converged infrastructure combining storage, computational and networking resources into a single block. Capacity and performance of all available resources are aggregated and made available to every participating PowerFlex server and application. Storage tiers can be created with media types and drive types that match the ideal performance or capacity characteristics to best suit the application needs.
  • 900
  • 01 Nov 2022
Topic Review
Overview of Deep Learning-Based Visual Multi-Object Tracking
Multi-target tracking is an advanced visual work in computer vision, which is essential for understanding the autonomous driving environment. Due to the excellent performance of deep learning in visual object tracking, many state-of-the-art multi-target tracking algorithms have been developed.
  • 899
  • 22 Nov 2022
Topic Review
Smart Cities and Financial Sustainability
Smart city initiatives have become recurrent strategies used by local governments to provide better services, improve their managerial effectiveness, and increase citizen participation in cities’ decision-making processes. Great potential exists to use data, information, and communication technologies (ICT) more extensively to improve city operations. However, depending on the size and financial situation of the cities, some smart city initiatives could be considered investments that are too expensive and not easy to maintain in the long term. If city governments want to achieve most of the benefits arising from the intense use of technology and data, building financially sustainable smart cities should be seen as a priority.
  • 898
  • 28 Jun 2022
Topic Review
Artificial Intelligence Techniques in Surveillance Video Anomaly Detection
The Surveillance Video Anomaly Detection (SVAD) system is a sophisticated technology designed to detect unusual or suspicious behavior in video surveillance footage without human intervention. The system operates by analyzing the video frames and identifying deviations from normal patterns of movement or activity. This is achieved through advanced algorithms and machine learning techniques that can detect and analyze the position of pixels in the video frame at the time of an event.
  • 900
  • 10 May 2023
Topic Review
Models for Evaluation Intrusion Detection Systems in IoT
Using the Internet of Things (IoT) for various applications, such as home and wearables devices, network applications, and even self-driven vehicles, detecting abnormal traffic is one of the problematic areas for researchers to protect network infrastructure from adversary activities. Several network systems suffer from drawbacks that allow intruders to use malicious traffic to obtain unauthorized access. Attacks such as Distributed Denial of Service attacks (DDoS), Denial of Service attacks (DoS), and Service Scans demand a unique automatic system capable of identifying traffic abnormality at the earliest stage to avoid system damage. Numerous automatic approaches can detect abnormal traffic. However, accuracy is not only the issue with current Intrusion Detection Systems (IDS), but the efficiency, flexibility, and scalability need to be enhanced to detect attack traffic from various IoT networks. 
  • 898
  • 27 May 2022
Topic Review
RT-11
RT-11 (Real-time 11) is a discontinued small, low-end, single-user real-time operating system for the full line of Digital Equipment Corporation PDP-11 16-bit computers. RT-11 was first implemented in 1970. It was widely used for real-time computing systems, process control, and data acquisition across all PDP-11s. It was also used for low-cost general-use computing.
  • 897
  • 22 Nov 2022
Topic Review
Microcontroller Unit-Based Wireless Sensor Network Nodes
Despite numerous research efforts in the fast-growing field of wireless sensor devices, energy consumption remains a challenge that limits the lifetime of wireless sensor networks (WSNs). The Internet-of-Things (IoT) technology utilizes WSNs for providing an efficient sensing and communication infrastructure. Thus, a comparison of the existing wireless sensor nodes is crucial. Of particular interest are the advances in the recent MCU-based wireless sensor node platforms, which have become diverse and fairly advanced in relation to the currently available commercial WSN platforms.
  • 897
  • 28 Nov 2022
Topic Review
Electrochemical Random-Access Memory
Electrochemical Random-Access Memory (ECRAM) is a type of non-volatile memory (NVM) with multiple levels per cell (MLC) designed for deep learning analog acceleration. An ECRAM cell is a three-terminal device composed of a conductive channel, an insulating electrolyte, an ionic reservoir, and metal contacts. The resistance of the channel is modulated by ionic exchange at the interface between the channel and the electrolyte upon application of an electric field. The charge-transfer process allows both for state retention in the absence of applied power, and for programming of multiple distinct levels, both differentiating ECRAM operation from the one of a field-effect transistor (FET). The write operation is deterministic and can result in symmetrical potentiation and depression, making ECRAM arrays attractive for acting as artificial synaptic weights in physical implementations of artificial neural networks (ANN). The technology challenges include open circuit potential (OCP) and semiconductor foundry compatibility associated with energy materials. Universities, government laboratories, and corporate research teams have contributed to the development of ECRAM for analog computing. Notably, Sandia National Laboratories designed a lithium-based cell inspired by solid-state battery materials, Stanford University built an organic proton-based cell, and International Business Machines (IBM) demonstrated in-memory selector-free parallel programming for a logistic regression task in an array of metal-oxide ECRAM designed for insertion in the back end of line (BEOL).
  • 897
  • 17 Nov 2022
Topic Review
Unfollow Everything
Facebook (and parent company Meta Platforms) has been the subject of criticism and legal action. Criticisms include the outsize influence Facebook has on the lives and health of its users and employees, as well as Facebook's influence on the way media, specifically news, is reported and distributed. Notable issues include Internet privacy, such as use of a widespread "like" button on third-party websites tracking users, possible indefinite records of user information, automatic facial recognition software, and its role in the workplace, including employer-employee account disclosure. The use of Facebook can have negative psychological effects that include feelings of sexual jealousy, stress, lack of attention, and social media addiction that in some cases is comparable to drug addiction. Facebook's operations have also received coverage. The company's electricity usage, tax avoidance, real-name user requirement policies, censorship policies, handling of user data, and its involvement in the United States PRISM surveillance program and Facebook–Cambridge Analytica data scandal have been highlighted by the media and by critics. Facebook has come under scrutiny for 'ignoring' or shirking its responsibility for the content posted on its platform, including copyright and intellectual property infringement, hate speech, incitement of rape, violence against minorities, terrorism, fake news, Facebook murder, crimes, and violent incidents live-streamed through its Facebook Live functionality. The company and its employees have also been subject to litigation cases over the years, with its most prominent case concerning allegations that CEO Mark Zuckerberg broke an oral contract with Cameron Winklevoss, Tyler Winklevoss, and Divya Narendra to build the then-named "HarvardConnection" social network in 2004, instead allegedly opting to steal the idea and code to launch Facebook months before HarvardConnection began. The original lawsuit was eventually settled in 2009, with Facebook paying approximately $20 million in cash and 1.25 million shares. A new lawsuit in 2011 was dismissed. Some critics point to problems which they say will result in the demise of Facebook. Facebook has been banned by several governments for various reasons, including Syria, China, Iran and Russia.
  • 895
  • 01 Dec 2022
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