Reviewerly/exHarmony huggingface dataset:
On-Device Training of Machine Learning Models on Microcontrollers with Federated Learning

Recent progress in machine learning frameworks has made it possible to now perform inference with models using cheap, tiny microcontrollers. Training of machine learning models for these tiny devices, however, is typically done separately on powerful computers. This way, the training process has abundant CPU and memory resources to process large stored datasets. In this work, we explore a different approach: training the machine learning model directly on the microcontroller and extending the training process with federated learning. We implement this approach for a keyword spotting task. We conduct experiments with real devices to characterize the learning behavior and resource consumption for different hyperparameters and federated learning configurations. We observed that in the case of training locally with fewer data, more frequent federated learning rounds more quickly reduced the training loss but involved a cost of higher bandwidth usage and longer training time. Our results indicate that, depending on the specific application, there is a need to determine the trade-off between the requirements and the resource usage of the system.
Boosting 3D Adversarial Attacks With Attacking on Frequency

Deep neural networks (DNNs) have been shown to be vulnerable to adversarial attacks in the image domain. Recently, 3D adversarial attacks, especially adversarial attacks on point clouds, have elicited mounting interest. However, adversarial point clouds obtained by previous methods show weak transferability and are easy to defend. To address these problems, in this paper we propose a strong point cloud attack method named AOF which pays more attention to the low-frequency component of point clouds. We combine the losses from point cloud and its low-frequency component to craft adversarial samples and focus on the low-frequency component of point cloud in the process of optimization. Extensive experiments validate that AOF can improve the transferability significantly compared to state-of-the-art (SOTA) attacks, and is more robust to state-of-the-art 3D defense methods. Otherwise, compared to adversarial point clouds generated by other adversarial attack methods, adversarial point clouds obtained by AOF contain more deformation than outlier.
GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design

Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art graph learning model. However, it can be notoriously challenging to inference GCNs over large graph datasets, limiting their application to large real-world graphs and hindering the exploration of deeper and more sophisticated GCN graphs. This is because real-world graphs can be extremely large and sparse. Furthermore, the node degree of GCNs tends to follow the power-law distribution and therefore have highly irregular adjacency matrices, resulting in prohibitive inefficiencies in both data processing and movement and thus substantially limiting the achievable GCN acceleration efficiency. To this end, this paper proposes a GCN algorithm and accelerator Co-Design framework dubbed GCoD which can largely alleviate the aforementioned GCN irregularity and boost GCNs&#x2019; inference efficiency. Specifically, on the algorithm level, GCoD integrates a split and conquer GCN training strategy that polarizes the graphs to be either denser or sparser in local neighborhoods without compromising the model accuracy, resulting in graph adjacency matrices that (mostly) have merely two levels of workload and enjoys largely enhanced regularity and thus ease of acceleration. On the hardware level, we further develop a dedicated two-pronged accelerator with a separated engine to process each of the aforementioned denser and sparser workloads, further boosting the overall utilization and acceleration efficiency. Extensive experiments and ablation studies validate that our GCoD consistently reduces the number of off-chip accesses, leading to speedups 15286&#x00D7;, 294&#x00D7;, 7.8&#x00D7;, and 2.5&#x00D7; as compared to CPUs, GPUs, and prior-art GCN accelerators including HyGCN and AWB-GCN, respectively, while maintaining or even improving the task accuracy. Additionally, we visualize GCoD trained graph adjacency matrices for a better understanding of its advantages.
Methodology of wavelet analysis in research of dynamics of phishing attacks

Safety of transfer and reception of various data over the internet can be accompanied by a presence of harmful components in a passed content. The phishing attack is one of versions of such harmful components. Thus it is important to know the relationship between the phishes verified as valid and suspected phishes Submitted. This is necessary for the forecast. To solve this problem, we use the wavelet analysis of time series which represent phishes verified as valid and suspected phishes submitted. We are considering the change of Hurst indicator; we analyse of a spectrum of wavelet energy. This allows you to identify the features of the main characteristics of time series which are considered. Conducted researches have shown the presence of essential duration of long-term dependence of investigated data. We also identified presence of trend component in structure of investigated series of data. It allows you to investigate recurrence of occurrence of phishing attacks that allows concentrating forces and means during the periods of activisation of such harmful influences. The analysis is spent on real data that reflects the importance of the conclusions obtained.
Social VR: A New Medium for Remote Communication and Collaboration

We are facing increasingly pressure on reducing travel and working remotely. Tools that support effective remote communication and collaboration are much needed. Social Virtual Reality (VR) is an emerging medium, which invites multiple users to join a collaborative virtual environment (VE) and has the potential to support remote communication in a natural and immersive way. We successfully organized a CHI 2020 Social VR workshop virtually on Mozilla Hubs, which invited researchers and practitioners to have a fruitful discussion over user representations and ethics, evaluation methods, and interaction techniques for social VR as an emerging immersive remote communication tool. In this CHI 2021 virtual workshop, we would like to organize it again on Mozilla Hubs, continuing the discussion about proxemics, social cues and VE designs, which were identified as important aspects for social VR communication in our CHI 2020 workshop.
Multi-swarm improved moth–flame optimization algorithm with chaotic grouping and Gaussian mutation for solving engineering optimization problems

Moth–Flame Optimization (MFO) is widely utilized to solve optimization problems in different fields since it has a simple structure and easy implementation. However, MFO cannot effectively balance exploration and exploitation and often suffers from the lack of population diversity in the search process, especially in solving some complex engineering optimization problems. To overcome the above problems, in this paper, a multi-swarm improved moth–flame algorithm (MIMFO) is proposed. In MIMFO, firstly, the population is grouped and dynamically reorganized through chaotic grouping mechanism and dynamic regrouping mechanism, which can improve the grouping quality and diversity of the population. Secondly, spiral search and linear search are carried out for the two sub-swarms to improve the search efficiency and balance exploration and exploitation. In addition, Gaussian mutation is used to generate flame, which can accelerate convergence and enhance the exploration. The MIMFO is verified on 13 benchmark problems with 30, 500, 1000, 2000 dimensions and CEC 2014 test functions. The results show that the MIMFO is superior to other swarm intelligence algorithms and MFO variants in finding the global optimum and convergence performance. Finally, MIMFO is used to solve 57 engineering constraint optimization problems, and the results show that MIMFO can solve real-world engineering problems.
Contrastive Self-Supervised Learning: A Survey on Different Architectures

Self-Supervised Learning (SSL) has enhanced the learning process of semantic representations from images. SSL has reduced the need for annotating or labelling the data by relying less on class labels during the training phase. SSL techniques dependent on Constrative Learning (CL) are acquiring prevalence because of their low dependency on training data labels. Different CL methods are producing state-of-the-art results on datasets which are used as the benchmarks for Supervised Learning. In this survey, we provide a review of CL-based methods including SimCLR, MoCo, BYOL, SwAV, SimTriplet and SimSiam. We compare these pipelines in terms of their accuracy on ImageNet and VOC07 benchmark. BYOL propose basic yet powerful architecture to accomplish 74.30 % accuracy score on image classification task. Using clustering approach SwAV outperforms other architectures by achieving 75.30 % top-1 ImageNet classification accuracy. In addition, we shed light on the importance of CL approaches which can maximise the use of huge amounts of data available today. At last, we report the impediments of current CL methodologies and emphasize the need of computationally efficient CL pipelines.
Consumer behavior in an augmented reality environment: Exploring the effects of flow via augmented realism and technology fluidity

Advances in information technologies today have created rich forms of reality to engage consumers. This study examines the effects of augmented realism and technology fluidity of augmented reality (AR) applications on consumer decision-making. A posttest-only between-group experiment was conducted in a laboratory setting to enable the completion of a simulated product-evaluation task via a web-based AR system, a mobile-app based AR system and a non-AR marketing device. Findings demonstrated that augmented realism and technology fluidity strongly influenced consumers’ flow experience, which led to cognitive and affective responses toward the brand and/or the AR-interface medium, in addition to increased purchase intention. The four sub-dimensions of flow also helped explicate the relationships between immersive shopping experience and cognitive, affective and behavioral outcomes. The study contributes to theory building in AR-based immersive and online marketing research.
Study on Virtual Experience Marketing Model Based on Augmented Reality: Museum Marketing (Example)

With the development of emerging digital technologies such as Augmented Reality and Artificial Intelligence, Augmented Reality (AR) technology-enabled experience marketing model can bring brand new virtual experience to the users, improve the brand attitudes of users, and increase the use and purchase intention of users. Based on the theoretical basis of experience marketing and AR, the AR application of Guilin Museum was designed and developed by using Unity as the software development tool and using AR Foundation as the AR development framework. The implementation of this application was mainly based on face detection and tracking, image detection, and tracking in the underlying API of AR Foundation. Subsequently, an AR virtual experience marketing model was constructed based on the Schmitt strategic experience module, and the usage data of AR applications were collected. Furthermore, the collected data were analyzed and evaluated using SPSS and AMOS software, and the relationships and influences of sensory experience, emotional experience, thinking experience, action experience, and association experience on the brand attitudes of users and use intention and purchase intention in AR application were tested.
UAV routing for reconnaissance mission: A multi-objective orienteering problem with time-dependent prizes and multiple connections

We address the route planning problem of an unmanned air vehicle (UAV) tasked with collecting information from a radar-monitored environment for a reconnaissance mission. The UAV takes off from a home base, visits a set of targets, and finishes its movement at a final base. Collectable information at a target depends on the time the target is visited by the UAV. There are multiple trajectory alternatives between the target pairs with different travel time and threat attributes. A route plan involves the selection of the targets to visit, the order of visit to the targets, and the trajectories to follow between the targets. Multiple routing objectives, information collection, mission duration and mission safety, are considered to present the trade-offs among the objectives to the route planner. The problem is classified as a multi-objective orienteering problem with time-dependent prizes and multiple connection options. A mixed integer programming model that can be used for small-sized problems is formulated. Larger problems are addressed with a hybrid algorithm involving heuristics and exact approaches. A case study based on a terrain in the State of Colorado is presented. Finally, some practical issues for the UAV route planning problem is discussed. • Routing a UAV for reconnaissance under radar surveillance. • Multiple objectives, time-dependent prizes, and multiple connections are considered. • A new type of orienteering problem is introduced. • An MIP model and a hybrid solution algorithm including a heuristic are developed. • A case study based on the terrain properties of the State of Colorado is presented.
Dataset on bitcoin carbon footprint and energy consumption

Due to data limitations on bitcoin-related emissions, assessing the environmental impacts of bitcoin appear difficult. This data in brief article presents constructed daily frequency dataset on bitcoin annualised carbon footprint spanning July 7, 2010 to December 4, 2021 with 4,158 observations. The 12 data variables capture floor, ceiling, and optimal annualised carbon footprint from coal, oil, gas, and the average from the 3 sources. The constructed bitcoin carbon footprint data are measured in kgCO2 using emission factors for electricity generation from IEA World Energy Outlook. The data will benefit multidisciplinary research on cryptocurrency from environmental, energy, and economics disciplines.
Linear quantum systems: A tutorial

The purpose of this tutorial is to give a brief introduction to linear quantum control systems. The mathematical model of linear quantum control systems is presented first, then some fundamental control-theoretic notions such as stability, controllability and observability are given, which are closely related to several important concepts in quantum information science such as decoherence-free subsystems, quantum non-demolition variables, and back-action evasion measurements. After that, quantum Gaussian states are introduced, in particular, an information-theoretic uncertainty relation is presented which often gives a better bound for mixed Gaussian states than the well-known Heisenberg uncertainty relation. The quantum Kalman filter is presented for quantum linear systems, which is the quantum analogy of the Kalman filter for classical (namely, non-quantum-mechanical) linear systems. The quantum Kalman canonical decomposition for quantum linear systems is recorded, and its application is illustrated by means of a recent experiment. As single- and multi-photon states are useful resources in quantum information technology, the response of quantum linear systems to these types of input is presented. Finally, coherent feedback control of quantum linear systems is briefly introduced, and a recent experiment is used to demonstrate the effectiveness of quantum linear systems and networks theory.
A study of transformer-based end-to-end speech recognition system for Kazakh language

Today, the Transformer model, which allows parallelization and also has its own internal attention, has been widely used in the field of speech recognition. The great advantage of this architecture is the fast learning speed, and the lack of sequential operation, as with recurrent neural networks. In this work, Transformer models and an end-to-end model based on connectionist temporal classification were considered to build a system for automatic recognition of Kazakh speech. It is known that Kazakh is part of a number of agglutinative languages and has limited data for implementing speech recognition systems. Some studies have shown that the Transformer model improves system performance for low-resource languages. Based on our experiments, it was revealed that the joint use of Transformer and connectionist temporal classification models contributed to improving the performance of the Kazakh speech recognition system and with an integrated language model it showed the best character error rate 3.7% on a clean dataset.
Full transformer network with masking future for word-level sign language recognition

Word-level sign language recognition (SLR) is a significant task which transcribes a sign language video into a word. Currently, deep-learning-based frameworks mostly combine spatial feature extractors based on convolution neural networks (CNNs) and sequence learners. These methods either lack the sufficient capacity to establish the high-level vision semantic knowledge and incorporate the details in images or perform weak intelligence on video frame sequence comprehension. Focusing on gestures and facial expressions is essential to interpreting sign language; however, it is challenging to crop these elements from pictures and distill them end-to-end. In this paper, a full self-attention framework for word-level SLR is proposed to tackle the above issue, which integrates a Vision Transformer as spatial encoder and an improved temporal Transformer. In addition, we introduce the masking future operation to improve the Transformer for the temporal module. The vision Transformer first refines the latent high-level semantic feature sequences from sign language videos and feeds them into the temporal module. Then the masking future Transformer enhances this sequence by making subsequent time invisible at each moment of frames and generates the final recognition. This approach integrates global and local spatial information; furthermore, it can also distinguish the latent semantic features contained in sign language action sequences. To validate the proposed approach, we perform extensive experiments on two datasets. The results and ablation studies demonstrate the effectiveness of this method, and it achieves new state-of-the-art performance on the WLASL dataset by using RGB images alone.
VAQEM: A Variational Approach to Quantum Error Mitigation

Variational Quantum Algorithms (VQA) are one of the most promising candidates for near-term quantum advantage. Traditionally, these algorithms are parameterized by rotational gate angles whose values are tuned over iterative execution on quantum machines. The iterative tuning of these gate angle parameters make VQAs more robust to a quantum machine&#x2019;s noise profile. However, the effect of noise is still a significant detriment to VQA&#x2019;s target estimations on real quantum machines &#x2014; they are far from ideal. Thus, it is imperative to employ effective error mitigation strategies to improve the fidelity of these quantum algorithms on near-term machines.While existing error mitigation techniques built from theory do provide substantial gains, the disconnect between theory and real machine execution characteristics limit the scope of these improvements. Thus, it is critical to optimize mitigation techniques to explicitly suit the target application as well as the noise characteristics of the target machine.We propose VAQEM, which dynamically tailors existing error mitigation techniques to the actual, dynamic noisy execution characteristics of VQAs on a target quantum machine. We do so by tuning specific features of these mitigation techniques similar to the traditional rotation angle parameters -by targeting improvements towards a specific objective function which represents the VQA problem at hand. In this paper, we target two types of error mitigation techniques which are suited to idle times in quantum circuits: single qubit gate scheduling and the insertion of dynamical decoupling sequences. We gain substantial improvements to VQA objective measurements &#x2014; a mean of over 3x across a variety of VQA applications, run on IBM Quantum machines.More importantly, while we study two specific error mitigation techniques, the proposed variational approach is general and can be extended to many other error mitigation techniques whose specific configurations are hard to select a priori. Integrating more mitigation techniques into the VAQEM framework in the future can lead to further formidable gains, potentially realizing practically useful VQA benefits on today&#x2019;s noisy quantum machines.
Revisiting the edge of chaos: Again?

Does biological computation happen at some sort of "edge of chaos", a dynamical regime somewhere between order and chaos? And if so, is this a fundamental principle that underlies self-organization, evolution, and complex natural and artificial systems that are subjected to adaptation? In this article, we will review the literature on the fundamental principles of computation in natural and artificial systems at the "edge of chaos". The term was coined by Norman Packard in the late 1980s. Since then, the concept of "adaptation to the edge of chaos" was demonstrated and investigated in many fields where both simple and complex systems receive some sort of feedback. Besides reviewing both historic and recent literature, we will also review critical voices of the concept.
An improved group teaching optimization algorithm based on local search and chaotic map for feature selection in high-dimensional data

The current study proposes a novel binary group teaching optimization algorithm with local search and chaos mapping (BGTOALC) as a wrapper-based feature selection method to solve high-dimensional feature selection problems. The local search and chaos mapping enhance the performance of the proposed algorithm. Also, two novel binary operators called Binary Teacher Phase Good Group (BTPGG) and Binary Teacher Phase Bad Group (BTPBG) are applied to the teacher’s phase for increasing the exploration and exploitation of the algorithm. Moreover, a new Binary Student Opposition-Based Learning (BSOBL) operator is introduced for the student phase, using an opposition-based strategy to achieve better exploitation. Finally, the teacher allocation phase is designed in a binary manner using the new Mean Binary Select (MBS) operator to increase the algorithm’s convergence rate. Subsequently, two other binary group teaching optimization algorithms, named BGTOAV and BGTOAS, are developed utilizing the S-shaped and V-shaped transfer functions to compare their performance with the BGTOALC algorithm. The proposed approaches are compared to other state-of-the-art binary algorithms on 30 datasets with different dimensions. Different experiments prove that the BGTOALC method outperforms the previous methods in terms of reducing the number of selected features and increasing the accuracy of the machine learning algorithm. Eventually, statistical analyses indicate the superiority of the BGTOALC method in terms of efficiency and convergence rate against other binary metaheuristic algorithms.