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Topic Review
Developments in Algorithms for Sequence Alignment
Pairwise sequence alignment is the basis of multiple sequence alignment and mainly divided into local alignment and global alignment. The former is to find and align the similar local region, and the latter is end-to-end alignment. A commonly used global alignment algorithm is the Needleman–Wunsch algorithm, which has become the basic algorithm that is used in many types of multiple sequence alignment software. The algorithm usually consists of two steps: one is calculating the states of the dynamic programming matrix; and the other is tracking back from the final state to the initial state of the dynamic programming matrix to obtain the solution of alignment. Time and space complexity of pairwise sequence alignment algorithms based on dynamic programming is O(l1l2), where l1 and l2 are the lengths of the two sequences to be aligned. Such overheads are acceptable for short sequences but not for sequences with more than several thousand sites. As a space-saving strategy of the dynamic programming algorithm, the Hirschberg algorithm is able to complete alignment by the space complexity of O(l) without any sacrifice of quality.
  • 792
  • 22 Apr 2022
Topic Review
SeAttE—Embedding Model Based on Knowledge Graph Completion
SeAttE is a novel tensor ecomposition model based on Separating Attribute space for knowledge graph completion. SeAttE is the first model among the tensor decomposition family to consider the attribute space separation task. Furthermore, SeAttE transforms the learning of too many parameters for the attribute space separation task into the structure’s design. This operation allows the model to focus on learning the semantic equivalence between relations, causing the performance to approach the theoretical limit. 
  • 566
  • 21 Apr 2022
Topic Review
Parallel K-Means Algorithms in Java
K-means is a well-known clustering algorithm often used for its simplicity and potential efficiency. K-means, though, suffers from computational problems when dealing with large datasets with many dimensions and great number of clusters. 
  • 1.1K
  • 21 Apr 2022
Topic Review
Modelling Energy Transition in Germany
The expression energy transition indicates a long-term structural change in energy systems. It is not a new phenomenon: man has made several energy transitions in his short history on Earth. One of the most significant was shifting to an energy system based on fossil fuels (coal, oil, and natural gas) from one essentially based on wood; however, since the 1980s, it has been realized that fossil fuels are at the root of climate change due to carbon emissions into the atmosphere.
  • 607
  • 20 Apr 2022
Topic Review
Distributed Machine Learning in Edge Computing
Distributed edge intelligence is a disruptive research area that enables the execution of machine learning and deep learning (ML/DL) algorithms close to where data are generated. Since edge devices are more limited and heterogeneous than typical cloud devices, many hindrances have to be overcome to fully extract the potential benefits of such an approach (such as data-in-motion analytics).
  • 3.1K
  • 20 Apr 2022
Topic Review
KinectGaitNet
Gait recognition had gained a lot of attention in various research and industrial domains. These include remote surveillance, border control, medical rehabilitation, emotion detection from posture, fall detection, and sports training. The main advantages of identifying a person by their gait include unobtrusiveness, acceptance, and low costs. Researchers proposes a convolutional neural network KinectGaitNet for Kinect-based gait recognition. The 3D coordinates of each of the body joints over the gait cycle are transformed to create a unique input representation. The proposed KinectGaitNet is trained directly using the 3D input representation without the necessity of the handcrafted features. The KinectGaitNet design allows avoiding gait cycle resampling, and the residual learning method ensures high accuracy without the degradation problem.
  • 524
  • 20 Apr 2022
Topic Review
Academic and Administrative Role of AI in Education
Using AI in education can have a dramatic impact on the way academic and administrative staff use their time and the manner in which students are served individually. Artificial Intelligence Applications are assisting the education sector organizations at two main levels.1. Administrative level (admission, counseling, library services, etc.)2. Academic Level (assessment, feedback, tutoring, etc.)
  • 2.2K
  • 20 Apr 2022
Topic Review
Smart Libraries
With the rapid development of artificial intelligence (AI) and Internet-of-Things (IoT), thousands of smart devices can be interconnected with each other. A series of innovative concepts have emerged and penetrated into all aspects of human life, e.g., “Smarter Planet”, “Smart City”, “Smart Community”, and “Smart Campus”. As a key and indispensable field, librarianship has become a convenient scenario aided by AI and IoT. Distinctive advanced AI-based approaches applied in libraries include, but are not limited to, natural language processing (NLP), deep learning (DL), recommender systems, machine vision, and smart acquisition.
  • 2.4K
  • 19 Apr 2022
Topic Review
Applications in Computational Pathology
Deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and graph neural networks (GNNs) have, over the past decade, changed the accuracy of prediction in many diverse fields. In recent years, the application of deep learning techniques in computer vision tasks in pathology has demonstrated extraordinary potential in assisting clinicians, automating diagnoses, and reducing costs for patients.
  • 513
  • 19 Apr 2022
Topic Review
Decentralized Multi-Robot Collision Avoidance
When deploying a multi-robot system, it is ensured that the hardware parts do not collide with each other or the surroundings, especially in symmetric environments. Two types of methods are used for collision avoidance: centralized and decentralized. The decentralized approach has mainly been used in recent times, as it is computationally less expensive.
  • 595
  • 19 Apr 2022
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