Reviewerly/exHarmony huggingface dataset:
Human Activity Recognition Based on Multichannel Convolutional Neural Network With Data Augmentation

In view of the excellent portability and privacy protection of wearable sensor devices, human activity recognition (HAR) of wearable devices has increased applications in human-computer interaction, health care, etc.Therefore, it is necessary to recognize various human activities accurately and efficiently.In this paper, we propose a multi-channel convolutional neural network with data augmentation for HAR, denoted AMC-CNN.First, the sliding windows in time series are used to construct the feature window, and then the feature window is augmented by data transformation and data addition.Through the horizontal connection of the augmented feature windows, the reconstructed feature samples are obtained.Second, a relatively lightweight multi-channel convolutional neural network is designed.In order to mine deep correlations between sensor data more efficiently, we use reasonable convolution kernel settings to compute multi-channel convolutions and extract parallel multi-scale features, and then train the model to recognize human activities.Finally, experiments are carried out on WISDM and MHEALTH datasets to verify the effectiveness of the data augmentation.Moreover, the other three models are adopted for comparative experiments.The evaluation indexes show that the method proposed in this paper has a better recognition effect, which verifies that the AMC-CNN model is effective in both single-sensor and multi-sensor HAR.
Multimodal interaction: Input-output modality combinations for identification tasks in augmented reality

Multimodal interaction (MMI) is being widely implemented, especially in new technologies such as augmented reality (AR) systems since it is presumed to support a more natural, efficient, and flexible form of interaction. However, limited research has been done to investigate the proper application of MMI in AR. More specifically, the effects of combining different input and output modalities during MMI in AR are still not fully understood. Therefore, this study aims to examine the independent and combined effects of different input and output modalities during a typical AR task. 20 young adults participated in a controlled experiment in which they were asked to perform a simple identification task using an AR device in different input (speech, gesture, multimodal) and output (VV-VA, VV-NA, NV-VA, NV-NA) conditions. Results showed that there were differences in the influence of input and output modalities on task performance, workload, perceived appropriateness, and user preference. Interaction effects between the input and output conditions on the performance metrics were also evident in this study, suggesting that although multimodal input is generally preferred by the users, it should be implemented with caution since its effectiveness is highly influenced by the processing code of the system output. This study, which is the first of its kind, has revealed several new implications regarding the application of MMI in AR systems.
Fifteen years of quantum optics, quantum information, and nano-optics educational facility at the Institute of Optics, University of Rochester

The quantum optics/quantum information and nano-optics educational laboratory facility (QNOL) at the University of Rochester (UR) is located within three rooms of the Institute of Optics with a total area of 587 ft2. It has been used for teaching a 4-credit-hour QNOL class annually for 15 years. Four teaching labs were prepared on the generation and characterization of entangled and single (antibunched) photons demonstrating the laws of quantum mechanics: (1) entanglement and Bell’s inequalities, (2) single-photon interference (Young’s double slit experiment and Mach–Zehnder interferometer), (3) single-photon source I: confocal fluorescence microscopy of single nanoemitters, and (4) single-photon source II: a Hanbury Brown and Twiss setup, fluorescence antibunching. Further, based on QNOL, 1.5 to 3 h sturdy quantum “mini-labs” were developed and introduced into the required classes such that all optics students at the UR had experience with quantum labs. Monroe Community College (MCC) students participated in two mini-labs at the UR. Since 2006 to spring 2022, a total of ~850 students have utilized the labs for lab report submission (including 144 MCC students) and more than 250 students have used them for lab demonstrations. In addition, UR freshman research projects have become a very important educational activity in this facility. All developed materials and students’ reports are available at http://www.optics.rochester.edu/workgroups/lukishova/QuantumOpticsLab/. We present a description of sturdy, universally accessible experiments that can be introduced into either a separate advanced class or into classes with a large number of students. Assessment methods, evaluation of students’ knowledge, and their attitude toward their career in quantum information are discussed.
Data-Driven Predictive Modeling of Lithofacies and Fe In-Situ Grade in the Assen Fe Ore Deposit of the Transvaal Supergroup (South Africa) and Implications on the Genesis of Banded Iron Formations

Abstract The Assen Fe ore deposit is a banded iron formation (BIF)-hosted orebody, occurring in the Penge Formation of the Transvaal Supergroup, located 50 km northwest of Pretoria in South Africa. Most BIF-hosted Fe ore deposits have experienced post-depositional alteration including supergene enrichment of Fe and low-grade regional metamorphism. Unlike most of the known BIF-hosted Fe ore deposits, high-grade hematite (&gt; 60% Fe) in the Assen Fe ore deposit is located along the lithological contacts with dolerite intrusions. Due to the variability in alteration levels, identifying the lithologies present within the various parts of the Assen Fe ore deposit, specifically within the weathering zone, is often challenging. To address this challenge, machine learning was applied to enable the automatic classification of rock types identified within the Assen Fe ore mine and to predict the in-situ Fe grade. This classification is based on geochemical analyses, as well as petrography and geological mapping. A total of 21 diamond core drill cores were sampled at 1 m intervals, covering all the lithofacies present at Assen mine. These were analyzed for major elements and oxides by means of X-ray fluorescence spectrometry. Numerous machine learning algorithms were trained, tested and cross-validated for automated lithofacies classification and prediction of in-situ Fe grade, namely (a) k-nearest neighbors, (b) elastic-net, (c) support vector machines (SVMs), (d) adaptive boosting, (e) random forest, (f) logistic regression, (g) Naïve Bayes, (h) artificial neural network (ANN) and (i) Gaussian process algorithms. Random forest, SVM and ANN classifiers yield high classification accuracy scores during model training, testing and cross-validation. For in-situ Fe grade prediction, the same algorithms also consistently yielded the best results. The predictability of in-situ Fe grade on a per-lithology basis, combined with the fact that CaO and SiO 2 were the strongest predictors of Fe concentration, support the hypothesis that the process that led to Fe enrichment in the Assen Fe ore deposit is dominated by supergene processes. Moreover, we show that predictive modeling can be used to demonstrate that in this case, the main differentiator between the predictability of Fe concentration between different lithofacies lies in the strength of multivariate elemental associations between Fe and other oxides. Localized high-grade Fe ore along with lithological contacts with dolerite intrusion is indicative of intra-basinal fluid circulation from an already Fe-enriched hematite. These findings have a wider implication on lithofacies classification in weathered rocks and mobility of economic valuable elements such as Fe.
The effect of trade on the gender gap in labour markets: the moderating role of information and communication technologies

This study analyses the impact of international trade on the gender gap in labour force participation while considering the moderating role of information and communication technologies (ICT).Our analyses of panel data from 79 developing economies over 2006-2019 reveal that international trade narrows gender gap, and the interaction between trade and ICT is likely to widen it.Further, the impact of these two parameters on the gender gap varies across economies by development level and region.Developing economies should strategically focus on trade promotion and adopt gender-equitable ICT development policies to narrow gender gap.
Design and Acceleration of Field Programmable Gate Array-Based Deep Learning for Empty-Dish Recycling Robots

As the proportion of the working population decreases worldwide, robots with artificial intelligence have been a good choice to help humans. At the same time, field programmable gate array (FPGA) is generally used on edge devices including robots, and it greatly accelerates the inference process of deep learning tasks, including object detection tasks. In this paper, we build a unique object detection dataset of 16 common kinds of dishes and use this dataset for training a YOLOv3 object detection model. Then, we propose a formalized process of deploying a YOLOv3 model on the FPGA platform, which consists of training and pruning the model on a software platform, and deploying the pruned model on a hardware platform (such as FPGA) through Vitis AI. According to the experimental results, we successfully realize acceleration of the dish detection using a YOLOv3 model based on FPGA. By applying different sparse training and pruning methods, we test the pruned model in 18 different situations on the ZCU102 evaluation board. In order to improve detection speed as much as possible while ensuring detection accuracy, for the pruned model with the highest comprehensive performance, compared to the original model, the comparison results are as follows: the model size is reduced from 62 MB to 12 MB, which is only 19% of the origin; the number of parameters is reduced from 61,657,117 to 9,900,539, which is only 16% of the origin; the running time is reduced from 14.411 s to 6.828 s, which is only less than half of the origin, while the detection accuracy is decreased from 97% to 94.1%, which is only less than 3%.
Enhancing Context-Aware Recommendation Using Hesitant Fuzzy Item Clustering by Stacked Autoencoder Based Smoothing Technique

Context-aware recommender systems (CARS) are a key component in businesses, notably in the e-transactions domain, since they assume that reviews, ratings, demographics, and other factors may determine customer preferences. On contrary, while evaluating the sentiment underlying the reviews and the rating score, consumers’ opinion is typically conflicting. As a result, a framework that employs either a review or a rating for top-N recommendation directs to produce unsatisfied recommendations in addition to a meager rating problem and high computation time. To overcome all the problems, a novel sentiment enhanced stacked autoencoder (SSAE) with context-specific hesitant fuzzy item hierarchical clustering (CHFHC) approach is proposed which employs online and offline phases. In the offline-phase, the meager user-item rating matrix is smoothed by learning the users’ concrete preference to a complete matrix by the SSAE approach. They are clustered offline using the CHFHC approach into context-based similar item clusters. In the online-phase, the active user gets context-based recommendations from the most similar cluster that matches the active users’ current context situation. Hence the SSAE_CHFHC approach improves the quality of Top-N recommendation corresponding to the exact contextual situation of the active user with a minimal recommendation computation time. Experiments on the (5-core) Amazon and yelp datasets proved that the intended SSAE_CHFHC framework consistently outperforms state-of-the-art recommendation algorithms on a variety of evaluation measures.
Effect of vocal tract dynamics on neural network‐based speech recognition: A Bengali language‐based study

Abstract Although speech recognition has achieved significant success using integrated and efficient models, still some series of challenges remain as linguistic‐acoustic patterns are perturbed by speakers' individual articulation gestures and environmental noises. Due to dynamic changes in the vocal tract cavity, word utterances yield temporal and perturbed linguistic‐acoustic features, whereas vowel utterances yield less‐perturbed quasi‐stationary features. To recognize patterns as in vowels and words, the basic feedforward neural network (NN), among other methods, responds to these vocal tract‐induced variabilities and has shown promising results because of its simple yet effective modelling of nonlinear data. We, therefore, present a comprehensive study on how these variabilities of acoustical features affect the speech token classification performances using NNs. We chose vocal tract resonance (formant frequency) as linguistic‐acoustic feature. Our statistical evaluation of vocal tract‐induced variabilities in seven Bengali vowels and words revealed that words have more variations than vowels. We used four‐fold cross‐validation in an NN with Adam optimizer to compute classification performances using five different metrics. Our experiments found that formant transitions and dispersions do not contribute to classification, and five‐hidden‐layered NN is optimum. In all different test cases, we justified our hypothesis—word classification falls behind vowel classification due to the variability induced by vocal tract dynamics. The optimum NN with 28,263 trainable parameters achieved the highest accuracy and AUC scores: 0.89 and 0.99 in vowels, and 0.64 and 0.91 in words.
A Neuromorphic Electrothermal Processor for Near‐Sensor Computing

Abstract The statistical processing of sensor data using conventional digital computers is inefficient in terms of time, energy usage, and communication bandwidth, among others. Therefore, new approaches are sought to create context and make sense of the sensor data using special‐purpose computers that excel in specific computation tasks. Herein, the requirements for physical systems to perform sophisticated nonlinear computations needed for real‐time pattern recognition in data, specifically sensor data, are discussed. The focus is on physical reservoir computing as a neuromorphic computing approach. Considering energy flow as the coupling mechanism between nonlinear dynamic systems, it is demonstrated that many physical systems satisfy the basic requirements for building reservoir computers. Using physical reservoir computers brings up exciting opportunities for near‐ or in‐sensor computing as to how new data are collected and processed. The concepts are demonstrated through a novel physical computation platform, where off‐the‐shelf, temperature‐sensitive resistors are used to perform various standard and specific computational tasks. This platform is used as a near‐sensor processor to detect particular events. How a similar platform may be used for in‐sensor neuromorphic computations is further discussed.
Toward Higher Integration Density in Femtosecond-Laser-Written Programmable Photonic Circuits

Programmability in femtosecond-laser-written integrated circuits is commonly achieved with the implementation of thermal phase shifters. Recent work has shown how such phase shifters display significantly reduced power dissipation and thermal crosstalk with the implementation of thermal isolation structures. However, the aforementioned phase shifter technology is based on a single gold film, which poses severe limitations on integration density and circuit complexity due to intrinsic geometrical constraints. To increase the compactness, we propose two improvements to this technology. Firstly, we fabricated thermal phase shifters with a photolithography process based on two different metal films, namely (1) chromium for microheaters and (2) copper for contact pads and interconnections. Secondly, we developed a novel curved isolation trench design that, along with a state-of-the-art curvature radius, allows for a significant reduction in the optical length of integrated circuits. As a result, curved Cr-Cu phase shifters provide a compact footprint with low parasitic series resistance and no significant increase in power dissipation (∼38 mW) and thermal crosstalk (∼20%). These results pave the way toward the fabrication of femtosecond-laser-written photonic circuits with a steep increase in terms of layout complexity.
Is the Corpus Ready for Machine Translation? A Case Study with Python to Pseudo-Code Corpus

The availability of data is the driving force behind most of the state-of-the-art techniques for machine translation tasks. Understandably, this availability of data motivates researchers to propose new techniques and claim about the superiority of their techniques over the existing ones by using suitable evaluation measures. However, the performance of underlying learning algorithms can be greatly influenced by the correctness and the consistency of the corpus. We present our investigations for the relevance of a publicly available python to pseudo-code parallel corpus for automated documentation task, and the studies performed using this corpus. We found that the corpus had many visible issues like overlapping of instances, inconsistency in translation styles, incompleteness, and misspelled words. We show that these discrepancies can significantly influence the performance of the learning algorithms to the extent that they could have caused previous studies to draw incorrect conclusions. We performed our experimental study using statistical machine translation and neural machine translation models. We have recorded a significant difference ( ∼ 10% on BLEU score) in the models' performance after removing the issues from the corpus.
Entanglement and atomic inversion for two two-level atoms interacting with an intensity-dependent quantized field

Our aim is to present an analytical solution for the quantum optics model that has two two-level atoms interacting together and interacting with an intensity-dependent quantized field. The quantized field is initially in binomial state and based on the Lamb–Dicke parameter and associated Laguerre polynomials. While two atoms are symmetric and initially in superposition state. Besides, the quantum entanglement between the quantum system components under the effect of the parameters of detuning, coupling, binomial, Lamb–Dicke and the initial state of the model is discussed. Several phenomena have been clearly observed and analyzed, such as entanglement sudden death (ESD) followed by entanglement sudden birth (ESB) in the concurrence and the negativity curves, and long-lived quantum coherence in the von Neumann entropy curve. In addition, the presence of revivals and collapses in the atomic population inversion curves has been figured out. Our analysis aims to help the scientists to determine the critical values required of quantum system parameters to control entanglement dynamics.
A Survey of Autoscaling in Kubernetes

Autoscaling is the vital feature of cloud infrastructure to acquire or allocate computing resources ondemand, which allows users to automatically scale the resources provisioned to the applications without human action under a fluctuating workload to optimize the resource cost while satisfying the Quality of Service (QoS) requirements. Kubernetes (K8s), the most prevalent container orchestration, provides built-in autoscalers to deal with the scaling problem in terms of vertical and horizontal at container level but still has some limitations. In this paper, we survey the state of the art of existing approaches to solve the problem of container autoscaling in Kubernetes, their main characteristics as well as their current issues. Based on the analysis, new future directions that can be explored are proposed.
Privacy-Preserving Surveillance for Smart Cities

This work presents privacy-preserving surveillance system for smart cities based on perceptual encryption algorithm (PE). The encryption is block-based and provides necessary level of security while preserving intrinsic properties of an image necessary for compression. Unlike existing PE methods, the proposed method retains color information, thus can enable processing in encryption domain. The analysis shows that our method achieves same compression performance as existing methods while providing better security. In addition, we have performed face recognition on the encrypted images and demonstrated that the proposed method delivers the same recognition accuracy as that of the plain images.
MissVoxelNet: 3D Object Detection for Autonomous Vehicle in Snow Conditions

LiDAR (Light Detection and Ranging) sensors are widely used in self-driving cars with awareness of the surrounding environment. However, the LiDAR sensor is sensitive to harsh weather conditions that cause the collected data to be distorted. These types of weather reduce the safety of self-driving cars. The harsh weather conditions also cause missing points problems on the point clouds, and it causes the performance of 3D object detection to reduce. Therefore, we propose a new method using probability estimation, which includes a Deep Mixture of Factor Analyzers (DMFA) and a Miss-Convolution layer, to recover missing points caused by snow. The proposed work outperforms models which perform well in normal conditions. In summary, snow often causes detection errors for 3D modern detectors. By recovering missing points in the point cloud, we significantly make the performance of the 3D detector better in snowy weather conditions.
Accelerating deep neural network filter pruning with mask-aware convolutional computations on modern CPUs

Filter pruning, a representative model compression technique, has been widely used to compress and accelerate sophisticated deep neural networks on resource-constrained platforms. Nevertheless, most studies focus on reducing the cost of model inference, whereas the heavy burden of the pruning optimization process is neglected. In this paper, we propose MaskACC, a mask-aware convolutional computation method, which accelerates the prevailing mask-based filter pruning process on modern CPU platforms. MaskACC dynamically reorganizes the tensors used in convolutions with the mask information to avoid unnecessary computations, thereby improving the computational efficiency of the pruning process. Evaluation with state-of-the-art neural network models on CPU cloud platforms demonstrates the effectiveness of our method, which achieves up to 1.61× speedup under commonly-used pruning rates, compared to conventional computations.