Reviewerly/exHarmony huggingface dataset:
Leveraging Sensor Fusion and Sensor-Body Position for Activity Recognition for Wearable Mobile Technologies

&lt;p class="0abstract"&gt;Smart devices like smartphones and smartwatches have made this world smarter. These wearable devices are created through complex research methodologies to make them more usable and interactive with its user. Various interactive mobile applications such as augmented reality (AR), virtual reality (VR) or mixed reality (MR) applications solely depend on the in-built sensors of the smart devices. A lot of facilities can be taken from these devices with sensors such as accelerometer and gyroscope. Different physical activities such as walking, jogging, sitting, etc., can be important for analysis like health state prediction and duration of exercise by using those sensors based on artificial intelligence. In this paper, we have implemented machine learning and deep learning algorithms to detect and recognize eight activities namely, walking, jogging, standing, walking upstairs, walking downstairs, sitting, sitting-in-a-car and cycling; with a maximum of 99.3% accuracy. A few activities are almost similar in action, such as sitting and sitting-in-a-car, but difficult to distinguish; which makes it more challenging to predict tasks. In this paper, we have hypothesized that with more sensors (sensor fusion) and data collection points (sensor-body positions) a wide range of activities can be recognized and the recognition accuracies can be increased. Finally, we showed that the combination of all the sensors data of both pocket/waist and wrist can be used to recognize a wide range of activities accurately. The possibility of using the proposed methodologies for futuristic mobile technologies is quite significant. The adaptation of most recent deep learning algorithms such as convolutional neural network (CNN) and bi-directional Long Short Time Memory (Bi-LSTM) demonstrated high credibility of the methods presented as experimentation.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;
Development of Fall Detection Device Using Accelerometer Sensor

This paper presents the development of Fall Detection System in the form of a wearable device integrating an ADXL335 accelerometer as a fall detection sensor. The system means to detect fall occurrence of the wearer and able to distinguish the activity of daily livings and fall events. In this study, the fall detection system consists of two-part, the transmitter and receiver. Transmitter will be attached to the user’s garment, then the Arduino microcontroller will process the acceleration reading from the sensor and transmit the data via wireless Zigbee networks to the receiver. The receiver which is connected to a computer will display the acceleration graph reading through the monitor. The working hardware of wearable fall detection system in this work is validated through a comparison of data acquisition with previous research, SisFall. The results obtained in this work correspond well to SisFall thus indicate the sensor reliability satisfied the benchmark.
Sharing quantum steering among multiple Alices and Bobs via a two-qubit Werner state

Abstract Quantum steering, a type of quantum correlation with unique asymmetry, has important applications in asymmetric quantum information tasks. We consider a new quantum steering scenario in which one half of a two-qubit Werner state is sequentially measured by multiple Alices and the other half by multiple Bobs. We find that the maximum number of Alices who can share steering with a single Bob increases from 2 to 5 when the number of measurement settings N increases from 2 to 16. Furthermore, we find a counterintuitive phenomenon that for a fixed N , at most 2 Alices can share steering with 2 Bobs, while 4 or more Alices are allowed to share steering with a single Bob. We further analyze the robustness of the steering sharing by calculating the required purity of the initial Werner state, the lower bound of which varies from 0.503(1) to 0.979(5). Finally, we show that our both-sides sequential steering sharing scheme can be applied to control the steering ability, even the steering direction, if an initial asymmetric state or asymmetric measurement is adopted. Our work gives insights into the diversity of steering sharing and can be extended to study the problems such as genuine multipartite quantum steering when the sequential unsharp measurement is applied.
Leveraging Phone Mask Training for Phonetic-Reduction-Robust E2E Uyghur Speech Recognition

In Uyghur speech, consonant and vowel reduction are often encountered, especially in spontaneous speech with high speech rate, which will cause a degradation of speech recognition performance. To solve this problem, we propose an effective phone mask training method for Conformer-based Uyghur end-to-end (E2E) speech recognition. The idea is to randomly mask off a certain percentage features of phones during model training, which simulates the above verbal phenomena and facilitates E2E model to learn more contextual information. According to experiments, the above issues can be greatly alleviated. In addition, deep investigations are carried out into different units in masking, which shows the effectiveness of our proposed masking unit. We also further study the masking method and optimize filling strategy of phone mask. Finally, compared with Conformer-based E2E baseline without mask training, our model demonstrates about 5.51% relative Word Error Rate (WER) reduction on reading speech and 12.92% on spontaneous speech, respectively. The above approach has also been verified on test-set of open-source data THUYG-20, which shows 20% relative improvements.
Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes

While argument mining has achieved significant success in classifying argumentative relations between statements (support, attack, and neutral), we have a limited computational understanding of logical mechanisms that constitute those relations. Most recent studies rely on black-box models, which are not as linguistically insightful as desired. On the other hand, earlier studies use rather simple lexical features, missing logical relations between statements. To overcome these limitations, our work classifies argumentative relations based on four logical and theory-informed mechanisms between two statements, namely, (i) factual consistency, (ii) sentiment coherence, (iii) causal relation, and (iv) normative relation. We demonstrate that our operationalization of these logical mechanisms classifies argumentative relations without directly training on data labeled with the relations, significantly better than several unsupervised baselines. We further demonstrate that these mechanisms also improve supervised classifiers through representation learning.
Two-sample Test using Projected Wasserstein Distance

We develop a projected Wasserstein distance for the two-sample test, a fundamental problem in statistics and machine learning: given two sets of samples, to determine whether they are from the same distribution. In particular, we aim to circumvent the curse of dimensionality in Wasserstein distance: when the dimension is high, it has diminishing testing power, which is inherently due to the slow concentration property of Wasserstein metrics in the high dimension space. A key contribution is to couple optimal projection to find the low dimensional linear mapping to maximize the Wasserstein distance between projected probability distributions. We characterize theoretical properties of the two-sample convergence rate on IPMs and this new distance. Numerical examples validate our theoretical results.
Dual Branch Attention Network for Person Re-Identification

As a sub-direction of image retrieval, person re-identification (Re-ID) is usually used to solve the security problem of cross camera tracking and monitoring. A growing number of shopping centers have recently attempted to apply Re-ID technology. One of the development trends of related algorithms is using an attention mechanism to capture global and local features. We notice that these algorithms have apparent limitations. They only focus on the most salient features without considering certain detailed features. People’s clothes, bags and even shoes are of great help to distinguish pedestrians. We notice that global features usually cover these important local features. Therefore, we propose a dual branch network based on a multi-scale attention mechanism. This network can capture apparent global features and inconspicuous local features of pedestrian images. Specifically, we design a dual branch attention network (DBA-Net) for better performance. These two branches can optimize the extracted features of different depths at the same time. We also design an effective block (called channel, position and spatial-wise attention (CPSA)), which can capture key fine-grained information, such as bags and shoes. Furthermore, based on ID loss, we use complementary triplet loss and adaptive weighted rank list loss (WRLL) on each branch during the training process. DBA-Net can not only learn semantic context information of the channel, position, and spatial dimensions but can integrate detailed semantic information by learning the dependency relationships between features. Extensive experiments on three widely used open-source datasets proved that DBA-Net clearly yielded overall state-of-the-art performance. Particularly on the CUHK03 dataset, the mean average precision (mAP) of DBA-Net achieved 83.2%.
Experimental quantum learning of a spectral decomposition

Currently available quantum hardware allows for small-scale implementations of quantum machine learning algorithms. Such experiments aid the search for applications of quantum computers by benchmarking the near-term feasibility of candidate algorithms. Here we demonstrate the quantum learning of a two-qubit unitary by a sequence of three parameterized quantum circuits containing a total of 21 variational parameters. Moreover, we variationally diagonalize the unitary to learn its spectral decomposition, i.e., its eigenvalues and eigenvectors. We illustrate how this can be used as a subroutine to compress the depth of dynamical quantum simulations. One can view our implementation as a demonstration of entanglement-enhanced machine learning, as only a single (entangled) training data pair is required to learn a $4\ifmmode\times\else\texttimes\fi{}4$ unitary matrix.
CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization

Dialogue summarization has drawn much attention recently.Especially in the customer service domain, agents could use dialogue summaries to help boost their works by quickly knowing customer's issues and service progress.These applications require summaries to contain the perspective of a single speaker and have a clear topic flow structure, while neither are available in existing datasets.Therefore, in this paper, we introduce a novel Chinese dataset for Customer Service Dialogue Summarization (CSDS).CSDS improves the abstractive summaries in two aspects: (1) In addition to the overall summary for the whole dialogue, role-oriented summaries are also provided to acquire different speakers' viewpoints.(2) All the summaries sum up each topic separately, thus containing the topic-level structure of the dialogue.We define tasks in CSDS as generating the overall summary and different role-oriented summaries for a given dialogue.Next, we compare various summarization methods on CSDS, and experiment results show that existing methods are prone to generate redundant and incoherent summaries.Besides, the performance becomes much worse when analyzing the performance on role-oriented summaries and topic structures.We hope that this study could benchmark Chinese dialogue summarization and benefit further studies.
Feature Analysis

Many apps are designed to solve a problem or accomplish a task, such as managing a health condition, creating a to-do-list, or finding work. The solutions that app developers offer reflects how they believe that users and other stakeholders understand the problem. Each individual developer may have different ideas but analyzing many apps together can reveal the average or typical ways that developers in the set think about the problems that their apps are designed to solve. Building on content analysis, interface analysis, the concept of affordances, and speculative design, this article offers a new method that we call “feature analysis” to analyze what a set of apps designed to solve the same problem can tell us about the relationship between app design and ideology. By counting and classifying the features in a set of apps, feature analysis enables researchers to systematically answer questions about how app developers’ design choices reflect existing cultural norms, assumptions, and ideologies.
Temporal and non-temporal contextual saliency analysis for generalized wide-area search within unmanned aerial vehicle (UAV) video

Abstract Unmanned aerial vehicles (UAV) can be used to great effect for wide-area searches such as search and rescue operations. UAV enable search and rescue teams to cover large areas more efficiently and in less time. However, using UAV for this purpose involves the creation of large amounts of data, typically in video format, which must be analysed before any potential findings can be uncovered and actions taken. This is a slow and expensive process which can result in significant delays to the response time after a target is seen by the UAV. To solve this problem we propose a deep model architecture using a visual saliency approach to automatically analyse and detect anomalies in UAV video. Our Temporal Contextual Saliency (TeCS) approach is based on the state-of-the-art in visual saliency detection using deep Convolutional Neural Networks (CNN) and considers local and scene context, with novel additions in utilizing temporal information through a convolutional Long Short-Term Memory (LSTM) layer and modifications to the base model architecture. We additionally evaluate the impact of temporal vs non-temporal reasoning for this task. Our model achieves improved results on a benchmark dataset with the addition of temporal reasoning showing significantly improved results compared to the state-of-the-art in saliency detection.
Golos: Russian Dataset for Speech Research

This paper introduces a novel Russian speech dataset called Golos, a large corpus suitable for speech research. The dataset mainly consists of recorded audio files manually annotated on the crowd-sourcing platform. The total duration of the audio is about 1240 hours. We have made the corpus freely available to download, along with the acoustic model with CTC loss prepared on this corpus. Additionally, transfer learning was applied to improve the performance of the acoustic model. In order to evaluate the quality of the dataset with the beam-search algorithm, we have built a 3-gram language model on the open Common Crawl dataset. The total word error rate (WER) metrics turned out to be about 3.3% and 11.5%.
Three-Class Overlapped Speech Detection Using a Convolutional Recurrent Neural Network

In this work, we propose an overlapped speech detection system trained as a three-class classifier. Unlike conventional systems that perform binary classification as to whether or not a frame contains overlapped speech, the proposed approach classifies into three classes: non-speech, single speaker speech, and overlapped speech. By training a network with the more detailed label definition, the model can learn a better notion on deciding the number of speakers included in a given frame. A convolutional recurrent neural network architecture is explored to benefit from both convolutional layer's capability to model local patterns and recurrent layer's ability to model sequential information. The proposed overlapped speech detection model establishes a state-of-the-art performance with a precision of 0.6648 and a recall of 0.3222 on the DIHARD II evaluation set, showing a 20% increase in recall along with higher precision. In addition, we also introduce a simple approach to utilize the proposed overlapped speech detection model for speaker diarization which ranked third place in the Track 1 of the DIHARD III challenge.
Improved Variance Reduction Methods for Riemannian Non-Convex Optimization

Variance reduction is popular in accelerating gradient descent and stochastic gradient descent for optimization problems defined on both euclidean space and Riemannian manifold. This paper further improves on existing variance reduction methods for non-convex Riemannian optimization, including R-SVRG and R-SRG/R-SPIDER by providing a unified framework for batch size adaptation. Such framework is more general than the existing works by considering retraction and vector transport and mini-batch stochastic gradients. We show that the adaptive-batch variance reduction methods require lower gradient complexities for both general non-convex and gradient dominated functions, under both finite-sum and online optimization settings. Moreover, under the new framework, we complete the analysis of R-SVRG and R-SRG, which is currently missing in the literature. We prove convergence of R-SVRG with much simpler analysis, which leads to curvature-free complexity bounds. We also show improved results for R-SRG under double-loop convergence, which match the optimal complexities as the R-SPIDER. In addition, we prove the first online complexity results for R-SVRG and R-SRG. Lastly, we discuss the potential of adapting batch size for non-smooth, constrained and second-order Riemannian optimizers. Extensive experiments on a variety of applications support the analysis and claims in the paper.
A Three-Dimensional Infinite Collapse Map with Image Encryption

Chaos is considered as a natural candidate for encryption systems owing to its sensitivity to initial values and unpredictability of its orbit. However, some encryption schemes based on low-dimensional chaotic systems exhibit various security defects due to their relatively simple dynamic characteristics. In order to enhance the dynamic behaviors of chaotic maps, a novel 3D infinite collapse map (3D-ICM) is proposed, and the performance of the chaotic system is analyzed from three aspects: a phase diagram, the Lyapunov exponent, and Sample Entropy. The results show that the chaotic system has complex chaotic behavior and high complexity. Furthermore, an image encryption scheme based on 3D-ICM is presented, whose security analysis indicates that the proposed image encryption scheme can resist violent attacks, correlation analysis, and differential attacks, so it has a higher security level.
Assistive Device for the Translation from Mexican Sign Language to Verbal Language

In this work, we present the design and implementation of an assistive device for people with hearing disabilities, which allows words from Mexican Sign Language to be translated into verbal language. The device consists of a wearable embedded computer with a camera and a pair of gloves. The system captures the hand-gesture images, extracts features from the gloves, and runs an Artificial Neural Network as a classifier. Our system achieves an average precision of 88% and an average recall of 90% on 20 signals.
DMAPF: A Decentralized and Distributed Solver for Multi-Agent Path Finding Problem with Obstacles

Multi-Agent Path Finding (MAPF) is a problem of finding a sequence of movements for agents to reach their assigned location without collision. Centralized algorithms usually give optimal solutions, but have difficulties to scale without employing various techniques - usually with a sacrifice of optimality; but solving MAPF problems with the number of agents greater than a thousand remains a challenge nevertheless. To tackle the scalability issue, we present DMAPF - a decentralized and distributed MAPF solver, which is a continuation of our recently published work, ros-dmapf. We address the issues of ros-dmapf where it (i) only works in maps without obstacles; and (ii) has a low success rate with dense maps. Given a MAPF problem, both ros-dmapf and DMAPF divide the map spatially into subproblems, but the latter further divides each subproblem into disconnected regions called areas. Each subproblem is assigned to a distributed solver, which then individually creates an abstract plan - a sequence of areas that an agent needs to visit - for each agent in it, and interleaves agent migration with movement planning. Answer Set Programming, which is known for its performance in small but complex problems, is used in many parts including problem division, abstract planning, border assignment for the migration, and movement planning. Robot Operating System is used to facilitate communication between the solvers and to enable the opportunity to integrate with robotic systems. DMAPF introduces a new interaction protocol between the solvers, and mechanisms that together result in a higher success rate and better solution quality without sacrificing much of the performance. We implement and experimentally validate DMAPF by comparing it with other state-of-the-art MAPF solvers and the results show that our system achieves better scalability.
Typologies of anomalies in higher education

Under the transformation of the Russian higher education, its development is hindered by the wide spread of anomalies which carry serious risks for the universities, higher education and society. The article presents the authors interpretation of anomalies in higher education as violations of the substantial, structural and functional nature, determined by the mismatch of actions and interactions in universities with the fundamental norms of higher education. The research aims at providing typologies of anomalies in higher education. The practical meaning of these typologies is to use them as a basis for developing social technologies, mechanisms and forms of prevention and overcoming various types of violations in higher education. The authors use three methodological approaches - institutional, structural, and community-based - to introduce a theoretical framework for the study of anomalies in higher education. The typologies of anomalies were identified on the basis of the following criteria: 1) the nature of norms violated - anomalies of deviant (violation of moral norms) and delinquent (violation of legal norms) types; 2) the nature of anomalies manifestation - explicit and latent types; 3) the spheres of their manifestation - anomalies in the educational process, research, management, and social activity; 4) the levels of their manifestation - institutional, systemic and community-based anomalies; 5) subjects of abnormal behavior - anomalies in the communities of students, pedagogical, scientific stuff, and managers.
Generation of photonic tensor network states with circuit QED

We propose a circuit QED platform and protocol to generate microwave photonic tensor network states deterministically. We first show that using a microwave cavity as ancilla and a transmon qubit as emitter is a good platform to produce photonic matrix product states. The ancilla cavity combines a large controllable Hilbert space with a long coherence time, which we predict translates into a high number of entangled photons and states with a high bond dimension. Going beyond this paradigm, we then consider a natural generalization of this platform, in which several cavity-qubit pairs are coupled to form a chain. The photonic states thus produced feature a two-dimensional entanglement structure and can be interpreted as radial plaquette projected entangled pair states [Wei, Malz, and Cirac, Phys. Rev. Lett. 128, 010607 (2022)], which include many paradigmatic states, such as the broad class of isometric tensor network states, graph states, and string-net states.