Reviewerly/exHarmony huggingface dataset:
Online Knowledge Distillation via Multi-branch Diversity Enhancement

Knowledge distillation is an effective method to transfer the knowledge from the cumbersome teacher model to the lightweight student model. Online knowledge distillation uses the ensemble prediction results of multiple student models as soft targets to train each student model. However, the homogenization problem will lead to difficulty in further improving model performance. In this work, we propose a new distillation method to enhance the diversity among multiple student models. We introduce Feature Fusion Module (FFM), which improves the performance of the attention mechanism in the network by integrating rich semantic information contained in the last block of multiple student models. Furthermore, we use the Classifier Diversification (CD) loss function to strengthen the differences between the student models and deliver a better ensemble result. Extensive experiments proved that our method significantly enhances the diversity among student models and brings better distillation performance. We evaluate our method on three image classification datasets: CIFAR-10/100 and CINIC-10. The results show that our method achieves state-of-the-art performance on these datasets.
Unsupervised One-Shot Learning of Both Specific Instances and Generalised Classes with a Hippocampal Architecture

Established experimental procedures for one-shot machine learning do not test the ability to learn or remember specific instances of classes, a key feature of animal intelligence. Distinguishing specific instances is necessary for many real-world tasks, such as remembering which cup belongs to you. Generalisation within classes conflicts with the ability to separate instances of classes, making it difficult to achieve both capabilities within a single architecture. We propose an extension to the standard Omniglot classification-generalisation framework that additionally tests the ability to distinguish specific instances after one exposure and introduces noise and occlusion corruption. Learning is defined as an ability to classify as well as recall training samples. Complementary Learning Systems (CLS) is a popular model of mammalian brain regions believed to play a crucial role in learning from a single exposure to a stimulus. We created an artificial neural network implementation of CLS and applied it to the extended Omniglot benchmark. Our unsupervised model demonstrates comparable performance to existing supervised ANNs on the Omniglot classification task (requiring generalisation), without the need for domain-specific inductive biases. On the extended Omniglot instance-recognition task, the same model also demonstrates significantly better performance than a baseline nearest-neighbour approach, given partial occlusion and noise.
A Comparative Study of Deep Learning Architectures for Detection of Anomalous ADS-B Messages

Since the 1920's, air traffic is becoming more prevalent by the year which results in a steady increase of the number of aircrafts roaming the airspace. This requires the expansion of the air surveillance systems in order to be able to manage each one of these aircrafts. Such an accommodation is planned to be implemented using different technologies and notably the Automatic Dependent Surveillance Broadcast (ADS-B) system. The ADS-B protocol is based on the idea that aircrafts as well as air traffic controllers communicate with each other using messages. However, for practicality reasons, those messages are not encrypted thus malicious messages can be injected. Hence, these attacks need to be detected to ensure the safety of the protocol. In this paper, we evaluate deep learning architectures for the purpose of detecting anomalous/malicious ADS-B messages, especially LSTM architectures which appear to be the most promising ones.
Interaction and Behaviour Evaluation for Smart Homes

The smart home concept can significantly benefit from predictive models that take proactive management operations on home actuators, based on users' behavior evaluation. In this paper, we use a small-scale physical model, the ScaledHome-2 testbed, to experiment with the evolution of measurements in a suburban home under different environmental scenarios. We start from the observation that, for a home to become smart, in addition to IoT sensors and actuators, we also need a predictive model of how actions taken by inhabitants and home actuators affect the internal environment of the home, reflected in the sensor readings. In this paper, we propose a technique to create such a predictive model through machine learning in various simulated weather scenarios. This paper also contributes to the literature in the field by quantitatively comparing several machine learning algorithms (K-nearest neighbor, regression trees, Support Vector Machine regression, and Long Short Term Memory deep neural networks) in their ability to create accurate and generalizable predictive models for smart homes.
High-Throughput Optimizations of AES Algorithm for Satellites

The increase in the flow of information and high-speed communication has compelled researchers to use satellite networks for different applications. The satellite networks comprise different satellites in different orbits that require the data security algorithms for the providing security and privacy of consumers' information. The data security primarily consists of data confidentiality service, which is provided by data encryption algorithms. The data encryption algorithm for example AES algorithm is utilized in many applications, including the satellites. The challenges for AES algorithm utilization in satellite applications are high-speed computation, lightweight implementation, and catering the radiations in the space environment. In this work, optimizations are proposed for coping with the challenges in a data encryption algorithm for the space environment. Also, the high-throughput optimization for the AES algorithm is realized on FPGA, and it equated with previous work. Moreover, the recent security attacks, for instance the nonce misuse attack is challenging for the security of encryption algorithms. The high-throughput optimized encryption algorithm protects against the recent attacks by the modifications in the AES algorithm. Moreover, the security analysis is provided against the latest attacks for the validation of high-throughput optimizations.
Improved Asymmetric Time-varying Coefficients of Particle Swarm Optimization

In this work, a modified version of the newly-developed algorithm Improved Particle Swarm Optimization (PSOI) is proposed. PSOI is a type of a PSO algorithm that uses time-varying social coefficients to train the particles. It shows a better performance compared to the conventional PSO, and this inspires this work. The proposed method uses asymmetric polynomial curves, and hence, it is referred to as the Improved Asymmetric PSO (PSOAI). In this work, PSOAI is tested using several benchmarks and compared to several PSO versions including the PSOI. The results are promising compared to several versions of PSO.
Learning to Generate Explainable Plots for Neural Story Generation

Story generation is an important natural language processing task that aims to generate coherent stories automatically. While the use of neural networks has proven effective in improving story generation, how to learn to generate an explainable high-level plot still remains a major challenge. In this article, we propose a latent variable model for neural story generation. The model treats an outline, which is a natural language sentence explainable to humans, as a latent variable to represent a high-level plot that bridges the input and output. We adopt an external summarization model to guide the latent variable model to learn how to generate outlines from training data. Experiments show that our approach achieves significant improvements over state-of-the-art methods in both automatic and human evaluations.
Modeling and Abstraction of Network and Environment States Using Deep Learning

CANs promise to apply cognition to overcome shortcomings of self-organizing networks, such as limited flexibility and adaptability to changing environments. in CAN, machine-learning-based network automation functions, called CFs, learn context-specific policies for automating network operations. For this, CFs need a common abstract description of the network states to which they respond. This article presents a design and implementation of an EMA engine that could be tasked with learning the required abstract states in a consistent way across multiple CFs.
A Meta Learning Approach for Automating Model Selection in Big Data Environments using Microservice and Container Virtualization Technologies

For a given specific machine learning task, very often several machine learning algorithms and their right configurations are tested in a trial-and-error approach, until an adequate solution is found. This wastes human resources for constructing multiple models, requires a data analytics expert and is time-consuming, since a variety of learning algorithms are proposed in literature and the non-expert users do not know which one to use in order to obtain good performance results. Meta learning addresses these problems and supports non-expert users by recommending a promising learning algorithm based on meta features computed from a given dataset. In the present paper, a new generic microservice-based framework for realizing the concept of meta learning in Big Data environments is introduced. This framework makes use of a powerful Big Data software stack, container visualization, modern web technologies and a microservice architecture for a fully manageable and highly scalable solution. In this demonstration and for evaluation purpose, time series model selection is taken into account. The performance and usability of the new framework is evaluated on state-of-the-art machine learning algorithms for time series forecasting: it is shown that the proposed microservice-based meta learning framework introduces an excellent performance in assigning the adequate forecasting model for the chosen time series datasets. Moreover, the recommendation of the most appropriate forecasting model results in a well acceptable low overhead demonstrating that the framework can provide an efficient approach to solve the problem of model selection in context of Big Data.
Human presence detection by monitoring the indoor <i>CO</i> <sub>2</sub> concentration

Presence detection systems are becoming more and more important and are used in smart home environments, in the Ambient Assisted Living (AAL) domain or in surveillance technology. Common systems focus on using motion sensors or cameras, which have only a limited viewing angle and therefore monitoring gaps can easily occur within a room.
WITHDRAWN: Modern landscape design based on VR technology and wireless internet of things system

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
Cluster-clean-label

One of the major problems of applying supervised machine learning methods in real-world problems is the absence of labeled data. Labeling huge amounts of data is time consuming and cost intensive. Moreover, in many cases, labels can only be assigned by domain experts like medical doctors or engineers, who have little time and do not necessarily have profound machine learning knowledge. In this paper, we propose an efficient interactive cluster-clean-label approach. First, to visualize the potentially huge amount of data, principal component analysis followed by t-SNE projection is applied. On the 2-dimensional representation of the data, HDBSCAN clustering is utilized to identify groups of potentially similar class membership. Subsequently, anomaly detection in form of an autoencoder is applied on each cluster, and instances that are likely to belong to different classes are suggested to the user. The user decides which of these suggested instances to include and restarts the anomaly detection process with the remaining subset of instances. This iterative process is repeated until the user is satisfied with the clusters' purity. Eventually, labels are assigned to the clusters. The approach is evaluated by a user study with 25 participants using the initially unlabeled MNIST data set, where on average users were able to label 91.59% of the data set, with an accuracy of 98.99%. A video showing the approach is available: https://youtu.be/RsLI0dg90qE.
A Practical Calibration Method for Stripe Laser Imaging System

This article presents a new practical calibration method of a stripe laser imaging system for the 3-D shape measurement of an object. The proposed calibration method has an integrated mathematical approach for the camera imaging parameters and the stripe laser light plane. A simple calibration block with a checkerboard pattern attached is used that has different inclination angle at each face. Because the same scene with the laser light ON and OFF is used for the integrated calibration of the camera and the laser light plane, the calibration process is fast and efficient and the resultant calibration parameters are consistent. The experimental results demonstrated the performance of the proposed calibration method.
Traditional Features based Automated System for Human Activities Recognition

Human Activities Recognition (HAR) is an important research topic and its applications are spread in all the fields of computer vision and machine learning including video surveillance, robotics, and name a few more. In this paper, a new traditional feature fusion and selection-based method is proposed for automated HAR. The proposed methodology consists of three core steps- optical flow-based motion region extraction and later ROI detection, shape and gray level difference matrix (GLDM) features are combined in one matrix based on seniority value indexes, and finally, Reyni entropy-controlled Euclidean classifier based best features selection. The final selected features are put to Cubic SVM for final recognition. The validation of the proposed technique is conducted on three datasets- KTH, YouTube, and Weizmann and achieved an accuracy of 99.30%, 99.80%, and 99.60%, respectively. Overall, Cubic SVM outperforms among existing techniques.
An attention based method for offline handwritten Urdu text recognition

Compared to derivatives from Latin script, recognition of derivatives from Arabic handwritten script is a complex task due to the presence of two-dimensional structure, context-dependent shape of characters, high number of ligatures, overlap of characters, and placement of diacritics. While significant attempts exist for Latin and Arabic scripts, very few attempts have been made for offline, handwritten, Urdu script. In this paper, we introduce a large, annotated dataset of handwritten Urdu sentences. We also present a methodology for the recognition of offline handwritten Urdu text lines. A deep learning based encoder /decoder framework with attention mechanism is used to handle two-dimensional text structure. While existing approaches report only character level accuracy, the proposed model improves on BLSTM-based state-of-the-art by a factor of 2 in terms of character level accuracy and by a factor of 37 in terms of word level accuracy. Incorporation of attention before a recurrent decoding framework helps the model in looking at appropriate locations before classifying the next character and therefore results in a higher word level accuracy.
Stereo Matching Optimization with Multi-baseline Trinocular Camera Model

In this paper, we focused on accelerating the stereo matching by using multi-baseline trinocular camera model. To optimize matching cost calculation and cost aggregation steps, we designed a special scheme called the trinocular dynamic disparity range (T-DDR) by narrowing disparity searching range. Based on that, we proposed the T-DDR-SGM for trinocular stereo matching. Evaluation results showed that T-DDR-SGM could significantly reduce the computational complexity with slightly improving the accuracy. We proved that the proposed optimization methods for the trinocular stereo matching are effective and the trinocular stereo matching is useful for reducing computational complexity.
Creating IoT-ready XR-WebApps with Unity3D

The rise of IoT-ready devices is supported through well-established web concepts for communication and analytics, but interaction yet remains in the world of web browsers and screen-based 2D interaction during times of tablet and smartphone popularity. Transforming IoT interaction concepts into 3D for future exploitation with head-worn XR devices is a difficult task due to the lack of support and continued disengagement of game engines used in XR development. In this work, we present an approach to overcome this limitation, tightly including web technology into a 3D game engine. Our work leverages the versatility of web concepts to create immersive and scalable web applications in XR, without the need for deep-tech know-how about XR concepts or tiring customization work. We describe the methodology and tools in detail and provide some exemplary XR applications.
PEFS: A Validated Dataset for Perceptual Experiments on Face Swap Portrait Videos

Videos obtained by current face swapping techniques can contain artifacts potentially detectable, yet unobtrusive to human observers. However, the perceptual differences between real and altered videos, as well as properties leading humans to classify a video as manipulated, are still unclear. Thus, to support the research on perceived realism and conveyed emotions in face swap videos, this paper introduces a high-resolution dataset providing the community with the necessary sophisticated stimuli. Our recording process has been specifically designed to focus on human perception research and entails three scenarios (text-reading, emotion-triggering, and free-speech). We assess the perceived realness of our dataset through a series of experiments. The results indicate that our stimuli are overall convincing, even for long video sequences. Furthermore, we partially annotate the dataset with noticeable facial distortions and artifacts reported by participants.
Implementation of Two Factor Authentication (2FA) and Hybrid Encryption to Reduce the Impact of Account Theft on Android-Based Instant Messaging (IM) Applications

Instant messaging is an application that is widely used to communicate. Based on the wearesocial.com report, three of the five most used social media platforms are chat or instant messaging. Instant messaging was chosen for communication because it has security features in log in using a One Time Password (OTP) code, end-to-end encryption, and even two-factor authentication. However, instant messaging applications still have a vulnerability to account theft. This account theft occurs when the user loses his cellphone. Account theft can happen when a cellphone is locked or not. As a result of this account theft, thieves can read confidential messages and send fake news on behalf of the victim. In this research, instant messaging application security will be applied using hybrid encryption and two-factor authentication, which are made interrelated. Both methods will be implemented in 2 implementation designs. The implementation design is securing login and securing sending and receiving messages. For login security, QR Code implementation is sent via email. In sending and receiving messages, the message decryption process will be carried out when the user is authenticated using a fingerprint. Hybrid encryption as message security uses RSA 2048 and AES 128. Of the ten attempts to steal accounts that have been conducted, it is shown that the implementation design is proven to reduce the impact of account theft.
VUM: Understanding Requirements for a Virtual Ubiquitous Microscope

Augmented Reality (AR) and wearable sensors offer new possibilities to expand our senses and change how we interact with the world. Sensory augmentation can be integrated into everyday activities, but controls remain a challenge for user experience. In this paper, we investigate how users can control a futuristic interface that enables in-situ magnification. We designed an interactive system to enable users to zoom in on objects up to a microscopic level and implemented a prototype using the Microsoft Hololens. In a user-study, we compared full-screen to windowed visualizations and four interaction techniques for zooming: a clicker, two types of gestures, and voice. Our results indicate that the clicker enabled users to zoom at the fastest rate and lowered cognitive load. We also found a preference for windowed views. With our work, we provide insights for future augmented vision systems.
Multi-stage programming in the large with staged classes

Multi-stage programming (MSP) holds great promise, allowing the reliable generation of specialized, partially-evaluated code with static type- and scope-safety guarantees. Yet, we argue that MSP has not reached its full potential yet, as it has been traditionally limited to generating expressions, and has lacked principled facilities for generating modular programs and data structures. In that sense, we argue that MSP has been reserved for programming “in the small,” focused on generating efficient kernels of computation on the scale of single function bodies. We present a novel technique called staged classes, which extends MSP with the ability to manipulate class definitions as first-class constructs. This lets programmers use MSP “in the large,” on the level of applications, rather than mere functions. This way, applications can be designed in an abstract and modular way without runtime cost, as staged classes guarantee the removal of all staging-time abstractions, resulting in the generation of efficient specialized modules and data structures. We describe the design of a prototype relational database system in Scala, which uses several stages of runtime compilation to maximize the efficiency of query execution and data storage. We also show that staged classes can be used for defining type- and scope-safe implementations of type providers.