Reviewerly/exHarmony huggingface dataset:
CompassbAR: A Technique for Visualizing Out-of-View Objects in a Mixed Reality Environment

The introduction of the new generation of Head Mounted Displays (HMD) makes users’ experiences in Mixed Reality (MR) environments more engaging. However, these devices still have a limited field of view, which negatively affects the spatial localization process of virtual objects in the 3D environment. The literature presents several visualization techniques to address this issue, but they currently have several drawbacks, such as visual clutter, occlusion of the real scene, high user workload, and there is still no visualization technique that solves such issues definitively. Therefore, inspired by the gaming industry, we present CompassbAR a visualization technique for out-of-view objects. CompassbAR encodes the position of all out-of-view objects surrounding the user, in a 2D bar positioned at the top of the field of view. In addition, we propose a validation procedure and metrics that aim to evaluate the ability of the CompassbAR visualization technique to guide users towards the out-of-view objects.
Iterative Algorithm for Parameterization of Two-Region Piecewise Uniform Quantizer for the Laplacian Source

Motivated by the fact that uniform quantization is not suitable for signals having non-uniform probability density functions (pdfs), as the Laplacian pdf is, in this paper we have divided the support region of the quantizer into two disjunctive regions and utilized the simplest uniform quantization with equal bit-rates within both regions. In particular, we assumed a narrow central granular region (CGR) covering the peak of the Laplacian pdf and a wider peripheral granular region (PGR) where the pdf is predominantly tailed. We performed optimization of the widths of CGR and PGR via distortion optimization per border–clipping threshold scaling ratio which resulted in an iterative formula enabling the parametrization of our piecewise uniform quantizer (PWUQ). For medium and high bit-rates, we demonstrated the convenience of our PWUQ over the uniform quantizer, paying special attention to the case where 99.99% of the signal amplitudes belong to the support region or clipping region. We believe that the resulting formulas for PWUQ design and performance assessment are greatly beneficial in neural networks where weights and activations are typically modelled by the Laplacian distribution, and where uniform quantization is commonly used to decrease memory footprint.
The serverless shell

Serverless computing is a recent paradigm to program the cloud. It allows to execute user-defined functions at scale, on demand, and in a pay-per-use manner. This paper reports on adapting the Unix shell for serverless. Our software, called the serverless shell (sshell), runs shell scripts on a serverless platform much like with a regular computer. It permits to reuse an existing code base while benefiting from the massive power of serverless and paying only for the resources used. sshell is built around a small set of components that includes a new inter-process communication layer for serverless. We evaluate it in AWS Lambda using several microbenchmarks and a large-scale application. Our results show that sshell achieves comparable or better performance than a high-end server. Moreover, it can be faster and more cost-efficient than a cluster-based solution to mine large datasets.
Software Cost Estimation for Mobile Application Development - A Comparative Study of COCOMO Models

Software effort estimation is a crucial stage in the process of software project development including mobile application development. It is required to assist the project manager on deciding the project outlining, staff resources evaluation, cost estimation, quality control, and benchmarking. The methods of estimation can be categorized into parametric and non-parametric methods. This paper analyses and compares two algorithmic methods, namely Constructive Cost Model II (COCOMO II) and Constructive System Engineering Cost Model (COSYSMO) in calculating the effort for mobile application development. The experiment is conducted based on COCOMO datasets, and the experiment result is evaluated using the Mean Magnitude of Relative Error (MMRE). We found that the COCOMO II is a more suitable parametric effort estimation for mobile applications with the MMRE value of 0.4805. For future research, the cost drivers which are related to the mobile software aspects should be added.
Mitigating Coherent Noise by Balancing Weight-2 <i>Z</i>-Stabilizers

Physical platforms such as trapped ions suffer from coherent noise where errors manifest as rotations about a particular axis and can accumulate over time. We investigate passive mitigation through decoherence free subspaces, requiring the noise to preserve the code space of a stabilizer code, and to act as the logical identity operator on the protected information. Thus, we develop necessary and sufficient conditions for all transversal $Z$-rotations to preserve the code space of a stabilizer code, which require the weight-$2$ $Z$-stabilizers to cover all the qubits that are in the support of some $X$-component. Further, the weight-$2$ $Z$-stabilizers generate a direct product of single-parity-check codes with even block length. By adjusting the size of these components, we are able to construct a large family of QECC codes, oblivious to coherent noise, that includes the $[[4L^2, 1, 2L]]$ Shor codes. Moreover, given $M$ even and any $[[n,k,d]]$ stabilizer code, we can construct an $[[Mn, k, \ge d]]$ stabilizer code that is oblivious to coherent noise. If we require that transversal $Z$-rotations preserve the code space only up to some finite level $l$ in the Clifford hierarchy, then we can construct higher level gates necessary for universal quantum computation. The $Z$-stabilizers supported on each non-zero $X$-component form a classical binary code C, which is required to contain a self-dual code, and the classical Gleason's theorem constrains its weight enumerator. The conditions for a stabilizer code being preserved by transversal $2\pi/2^l$ $Z$-rotations at $4 \le l \le l_{\max} <\infty$ level in the Clifford hierarchy lead to generalizations of Gleason's theorem that may be of independent interest to classical coding theorists.
Accurate estimation of camera response function for high dynamic range measurement

A high dynamic range surface has large reflectivity variations and measuring it with a structured light technique could cause pixel saturation that will seriously degrade the measurement accuracy. Accurate identification of saturated pixels will significantly improve the measurement accuracy. This paper introduces an accurate estimation approach to the camera response function (CRF) for high dynamic range measurement and proposes a method that uses the estimated CRF to identify the saturated pixels in captured images. We perform experiments using the proposed saturation identification method and the other existing methods to compare both the saturation identification accuracy and measurement accuracy. The experiment results verify both the accuracy and suitability of the proposed method.
Research on recommendation algorithm of Graph attention Network based on Knowledge graph

Abstract The introduction of knowledge graph as the auxiliary information of recommendation system provides a new research idea for personalized intelligent recommendation. However, most of the existing knowledge graph recommendation algorithms fail to effectively solve the problem of unrelated entities, leading to inaccurate prediction of potential preferences of users. To solve this problem, this paper proposes a KG-IGAT model combining knowledge graph and graph attention network, and adds an interest evolution module to graph attention network to capture user interest changes and generate top-N recommendations. Finally, experimental comparison between the proposed model and other algorithms using public data sets shows that KG-IGAT has better recommendation performance.
Face recognition algorithm based on open CV

With the rapid development of information technology and the continuous improvement of people's security awareness, people pay more and more attention to the research of face recognition. Open CV is an open source cross platform computer vision library, which realizes many common algorithms in image processing and computer vision. QT is a cross platform C++ GUI application development framework, which can be used to develop both GUI and non GUI programs. This paper first introduces the face recognition method. The process of face recognition includes three parts: face detection, face representation and face recognition. Secondly, the face recognition algorithm is studied, and PCA algorithm is used for face recognition. Then we use Visual Studio 2013 to build a face recognition system based on open CV, design the user interface with QT, and realize the face model detection and recognition training: open the image, carry out face detection and location, face feature annotation, and then open the second photo for recognition. At the same time, each module and the whole process of the system are tested, the test results are good, and a complete face detection and recognition system based on open CV is realized.
Model Adaptation through Hypothesis Transfer with Gradual Knowledge Distillation

The ability to adapt their perception to changing environments is a core characterization of intelligent robots. At present, Unsupervised Domain Adaptation (UDA) methods are used to address this problem where the adaptation task is formulated as a transfer problem from a well-described scenario (source domain) to a new scenario (target domain). In order to implement the domain adaptation, these methods require access to the source data for achieving the distribution matching between both domains. However, in many real-world applications, the source data is inaccessible and only a source model pre-trained on the source domain is available during the transfer process. Therefore, the traditional UDA methods cannot support the challenging setting. This paper developed a new hypothesis transfer method to achieve model adaptation with gradual knowledge distillation. Specifically, we first prepare a source model through training a deep network on the labeled source domain by supervised learning. Then, we transfer the source model to the unlabeled target domain by self-training. To implement gradual knowledge distillation, we sliced the self-training into several epochs and then used the soft pseudo-labels from the latest epoch to guide the current epoch. In this process, the soft labels were generated by a semantic fusion on a proposed geometry of the neighborhood. To regulate the self-training, we developed a new objective constructed on the neighborhood. Experiments on three benchmarks have confirmed the state-of-the-art results of our method.
<scp>R‐ICTS</scp>: Recognize the Indian cautionary traffic signs in <scp>real‐time</scp> using an optimized adaptive boosting cascade classifier and a convolutional neural network

Abstract Cautionary traffic signs are of immense significance to traffic safety. In this study, a robust and optimal real‐time approach to recognize the Indian cautionary traffic signs (ICTS) is proposed. ICTS are all triangles with a white backdrop, a red border, and a black pattern. A dataset of 34,000 real‐time images has been acquired under various environmental conditions and categorized into 40 distinct classes. Pre‐processing techniques are used to transform RGB images to gray‐scale images and enhance contrast in images for superior performance. To find the ICTS, an optimized adaptive boosting cascade classifier is used. To classify the specific category of signs found by the optimized adaptive boosting cascade classifier, an 11‐layer CNN model is built. Finally, using computer vision methods, this model is tested in real‐time. Evaluation metrics such as precision, recall, F1 measure, error rate, and mAP are expressed on the ICTS, GTSDB, LISA, STSD, and DITS‐based datasets to evaluate the proposed method and compare the results of predictions with other investigations. When compared to other state‐of‐the‐art objects detection models such as SSD, YOLOv3, and Faster RCNN, the proposed model outperformed them all, with a precision of 97.15%, recall rate of 96.74%, an error rate of 3.26%, f 1 ‐score of 96.94%, and mAP@0.5IoU of 95.6%. (Dataset available: 10.21227/yy4h‐rc98 )
Geological Feature Modeling and Reserve Estimation of Uranium Deposits Based on Multiple Interpolation Methods

Uranium resource distribution and accurate reserve evaluation are important references for mineral investment and production. Eight kinds of interpolation methods in the Groundwater Modeling System (GMS), including ordinary kriging (OK), are used in this study to predict the spatial distribution of reserve-related parameters, such as uranium grade, ore thickness and uranium content per square meter. The present study draws the following conclusions: (1) Cross-validation found that the uranium grade value using the spherical method is the closest to the actual value. The spherical method has the best interpolation effect. (2) The relative error, which is +3.62%, between the uranium reserves that is calculated by the spherical interpolation method and that by the traditional calculation value is the smallest. (3) The setting of the number of interpolation grids is related to the actual number of boreholes. The ratio between the two will affect the accuracy of reserve estimation, and different interpolation methods have different degrees of influence on reserve estimation. This method is applicable to all in-situ leaching sandstone uranium mines. Further study needs to be carried out toward heterogeneity of three-dimensional space, which will make the estimation more accurate.
Network Architecture of Telemedicine System for Monitoring the Person’s Condition

Problem of creating telemedicine system architecture for monitoring the human state is discussed. As an example, a monitoring system for patients with dementia is used. The assessment of the health status of a person with dementia is performed on the basis of medical data that is transmitted to a cloud-based medical information system. Based on the data obtained, the attending physician forms recommendations. The network architecture must meet the security requirements for providing medical services remotely. At the same time, the telemedicine system being developed should be reliable under high loads and also be able to scale if necessary.
Deep image compression with lifting scheme: Wavelet transform domain based on high‐frequency subband prediction

Image compression is the most important image processing method extensively deployed in different appliances. “Discrete wavelet transform (DWT)” is one of the well-adopted transforming methods exploited for compressing images. The extremely deployed version of DWT is convolution-oriented. Nevertheless, the lifting-oriented DWT scheme requires more contemplation on more proficient performance and lesser computation cost. This paper intends to propose a deep learning-based image compression model with a lifting scheme for predicting high-frequency subbands. Moreover, the fine-tuning in lifting factorization is done by a new Sea Lion with Averaged Update Evaluation that includes new cosine estimation under the COordinate Rotation DIgital Computer algorithm. Similarly, this study defines a new single objective function that merges the multiconstraints, like, “Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR)”. At last, the supremacy of the presented approach is proved with respect to varied measures, like, CR, PSNR and so on.
Dynamic sparsity and model feature learning enhanced training for convolutional neural network-pruning

为了减轻深度学习算法在实际应用中庞大的参数量和繁重的计算量, 剪枝已经被广泛地应用在模型压缩任务中. 然而，大多数剪枝方法仅仅利用已经训练好的模型参数作为训练的初始参数, 而模型本身的特征信息没有被利用. 为此, 本文提出了一种基于模型特征学习增强的动态剪枝方法, 在训练过程中, 提出的方法不需要数据集类别标签. 一方面, 本文利用基准模型(训练好的模型)输出的预测类别信息和中间层特征作为监督信息指导压缩子模型的任务学习, 增强了压缩模型学习基准模型特征的能力；另一方面, 本文利用不同压缩子模型的输出信息互相学习, 增强了压缩子模型之间特征学习的能力. 此外, 本文使用一种动态的结构化稀疏正则方式, 仅仅在预期稀疏的参数上施加正则, 同时使用基于泰勒级数的通道敏感性准则确定预期稀疏参数. 在优化过程中, 本文使用动态稀疏的迭代阈值收缩算法(iterative shrinkage-thresholding algorithm, ISTA)求解器解决了约束剪枝率的优化问题. 模型训练结束后, 提出的方法直接移除冗余的参数, 剪枝后的模型不需要微调. 在多个网络结构和数据集下, 本文提出的方法均获得了很好的压缩性能.
Leveraging Unsupervised Machine Learning to Discover Patterns in Linguistic Health Summaries for Eldercare

The Center for Eldercare and Rehabilitation Technology, at University of Missouri, has researched the use of smart, unobtrusive sensors for older adult residents' health monitoring and alerting in aging-in-place communities for many years. Sensors placed in the apartments of older adult residents generate a deluge of daily data that is automatically aggregated, analyzed, and summarized to aid in health awareness, clinical care, and research for healthy aging. When anomalies or concerning trends are detected within the data, the sensor information is converted into linguistic health messages using fuzzy computational techniques, so as to make it understandable to the clinicians. Sensor data are analyzed at the individual level, therefore, through this study we aim to discover various combinations of patterns of anomalies happening together and recurrently in the older adult's population using these text summaries. Leveraging various computational text data processing techniques, we are able to extract relevant analytical features from the health messages. These features are transformed into a transactional encoding, then processed with frequent pattern mining techniques for association rule discovery. At individual level analysis, resident ID 3027 was considered as an exemplar to describe the analysis. Seven combinations of anomalies/rules/associations were discovered in this resident, out of which rule group three showed an increased recurrence during the COVID lockdown of facility. At the population level, a total of 38 associations were discovered that highlight the health patterns, and we continue to explore the health conditions associated with them. Ultimately, our goal is to correlate the combinations of anomalies with certain health conditions, which can then be leveraged for predictive analytics and preventative care. This will improve the current clinical care systems for older adult residents in smart sensor, aging-in-place communities.
Exploring accidental triggers of smart speakers

Voice assistants like Amazon’s Alexa, Google’s Assistant, Tencent’s Xiaowei, or Apple’s Siri, have become the primary (voice) interface in smart speakers that can be found in millions of households. For privacy reasons, these speakers analyze every sound in their environment for their respective wake word like “Alexa,” “Jiǔsì’èr líng,” or “Hey Siri,” before uploading the audio stream to the cloud for further processing. Previous work reported on examples of an inaccurate wake word detection, which can be tricked using similar words or sounds like “cocaine noodles” instead of “OK Google.” In this paper, we perform a comprehensive analysis of such accidental triggers, i. e., sounds that should not have triggered the voice assistant, but did. More specifically, we automate the process of finding accidental triggers and measure their prevalence across 11 smart speakers from 8 different manufacturers using everyday media such as TV shows, news, and other kinds of audio datasets. To systematically detect accidental triggers, we describe a method to artificially craft such triggers using a pronouncing dictionary and a weighted, phone-based Levenshtein distance. In total, we have found hundreds of accidental triggers. Moreover, we explore potential gender and language biases and analyze the reproducibility. Finally, we discuss the resulting privacy implications of accidental triggers and explore countermeasures to reduce and limit their impact on users’ privacy. To foster additional research on these sounds that mislead machine learning models, we publish a dataset of more than 350 verified triggers as a research artifact.
Object Detection in Self Driving Cars Using Deep Learning

In the Computer Vision domain, there has been continuous growth and development with main focus so as to facilitate a smooth interaction between Machines and human. Perception, planning and control are the main aspects that make up the Self-driving system. Perception subsystem converts the raw data collected by sensors or other information capturing devices into a model of the environment surrounding us. Planning subsystem analyses this model of the surrounding environment and makes certain purposeful decisions based on the inferences obtained from the analysis. Finally, the Control Subsystem is responsible for execution of the actions or the decisions planned previously. The scope of this project is to study and analyze the problems faced in the Perception subsystem in the domain of detecting objects for autonomous cars. Previously, technologies like Radar, LiDAR, GPS and various other sensors had been employed for Driverless cars for mapping the surroundings of the car. However, in the recent past, some deep neural network (DNN) architectures like YOLO (You Only Look Once), and SSD (Single Shot MultiBox Detector) have been developed which are capable of detecting objects even when live video is considered as the input, thus having potential to be included as a part of the Driverless car systems. Selection of a model having considerable accuracy and producing results at a faster rate is very much essential so as to meet the requirements of object detection in driverless cars. In this project, we have used Caffe, which is developed by Berkeley AI Research and Community contributors as the deep learning framework. Keeping in mind the factors that contribute to the selection of a good model, we have chosen SSD model along-side MobileNet Neural network as the base architecture as it results in both faster rate of result production and has a moderate accuracy.
Short-term Photovoltaic Power Prediction of Bi-LSTM Based on MIC

Photovoltaic power generation has certain volatility and randomness. In order to reduce the impact of photovoltaic power on the stable operation of power grid, this paper proposes a short-term Bi-LSTM photovoltaic power method based on MIC. Firstly, the correlation degree between the predicted PV power and meteorological factors was analyzed based on the maximum information coefficient (MIC), so as to screen the characteristics of high-dimensional meteorological data in the power grid. Then, the historical similar days were selected by the weighted sum of the maximum information coefficient. Finally, a bi-directional long neural network was built short-term memory (Bi-LSTM) was used to predict the short-term photovoltaic power. Case simulation shows that the proposed MIC-Bi- LSTM power prediction model has lower prediction bias and higher prediction accuracy.