Reviewerly/exHarmony huggingface dataset:
Let's Grab a Drink: Teacher-Student Learning for Fluid Intake Monitoring using Smart Earphones

This paper shows the feasibility of fluid intake estimation using earphone sensors, which are gaining in popularity. Fluid consumption estimation has a number of healthcare-related applications in tracking dehydration and overhydration which can be connected to issues in fatigue, irritability, high blood pressure, kidney stones, etc. Therefore, accurate tracking of hydration levels not only has direct benefits to users in preventing such disorders but also offers diagnostic information to healthcare providers. Towards this end, this paper employs a voice pickup microphone that captures body vibrations during fluid consumption directly from skin contact and body conduction. This results in the extraction of stronger signals while being immune to ambient environmental noise. However, the main challenge for accurate estimation is the lack of availability of large-scale training datasets to train machine learning models (ML). To address the challenge, this paper designs robust ML models based on techniques in data augmentation and semi-supervised learning. Extensive user study with 12 users shows a per-swallow volume estimation accuracy of 3.35 mL (≈ 19.17% error) and a cumulative error of 3.26% over an entire bottle, while being robust to body motion, container type, liquid temperature, sensor position, etc. The ML models are implemented on smartphones with low power consumption and latency.
Deep learning

This chapter starts by introducing machine learning tasks. We then dive deeper into the classification task and introduce the perceptrons, a type of linear classifier that is foundational for understanding modern convolutional neural networks (CNN). We discussed how the forward inference and backward propagation training paths are implemented for both single-layer and multilayer perceptrons. On the basis of the conceptual and mathematical understanding of perceptrons, we present a basic convolutional neural network and the implementation of its major types of layers. We then present a CUDA kernel implementation of the convolutional layer, the most computationally intensive layer of CNN. We then present techniques for reducing convolutional layers to matrix multiplications by unrolling the input feature maps into a matrix. The conversation allows the convolution layers to benefit from the highly optimized GEMM libraries for GPUs. We also present the C and CUDA implementations of the unrolling procedure for the input matrix and discuss the pros and cons of the unrolling approach. We end the chapter with an overview of the CUDNN library, which is used by most deep learning frameworks. Users of these frameworks can benefit from the highly optimized layer implementations without writing CUDA kernels themselves.
Multi-level Domain Adaptation for Lane Detection

We focus on bridging domain discrepancy in lane detection among different scenarios to greatly reduce extra annotation and re-training costs for autonomous driving. Critical factors hinder the performance improvement of cross-domain lane detection that conventional methods only focus on pixel-wise loss while ignoring shape and position priors of lanes. To address the issue, we propose the Multi-level Domain Adaptation (MLDA) framework, a new perspective to handle cross-domain lane detection at three complementary semantic levels of pixel, instance and category. Specifically, at pixel level, we propose to apply cross-class confidence constraints in self-training to tackle the imbalanced confidence distribution of lane and background. At instance level, we go beyond pixels to treat segmented lanes as instances and facilitate discriminative features in target domain with triplet learning, which effectively rebuilds the semantic context of lanes and contributes to alleviating the feature confusion. At category level, we propose an adaptive inter-domain embedding module to utilize the position prior of lanes during adaptation. In two challenging datasets, i.e. TuSimple and CULane, our approach improves lane detection performance by a large margin with gains of 8.8% on accuracy and 7.4% on F1-score respectively, compared with state-of-the-art domain adaptation algorithms.
Note: Towards Devising an Efficient VQA in the Bengali Language

Designing and implementing visual question answering tasks using Bengali datasets and native VQA based smart systems are important, as a huge number of people speak in Bengali who are relatively less advanced to technology adoption due to the language barrier. The important designing and implementing tasks are little explored in the literature. Therefore, we attempt to investigate the tasks in depth in this study. To do so, we follow a step-by-step procedure for overcoming different barriers encountered while adopting datasets as well as creating our own Bengali CLEVR and Bengali VQA. We perform different sets of experiments to demonstrate the efficacy of our proposed approach. Various VQA-based smart systems for Bengali speakers covering virtual doctors, navigation systems, smart glasses for the visually-impaired people, and so on can be benefited from this study through making the applications usable and understandable to those who are not fluent in a foreign language such as English.
On new Properties of the Maiorana-McFarland Ternary Bent Functions

Maiorana-McFarland ternary bent functions are analyzed with respect to their relationship with spectral invariant operations and with respect to self-duality. In analogy to the Maiorana-McFarland method to generate binary bent functions, an equation is introduced to generate ternary bent functions in an even number of variables. It is shown that Maiorana-McFarland ternary bent functions are strict bent and that their duals may not be Maiorana-McFarland. It is known that spectral invariant operations preserve the bentness of a function. We show that most spectral invariant operations preserve that Maiorana-McFarland bentness of ternary functions. The concept of generalized self-duality is introduced and it is shown how Maiorana-McFarland ternary bent functions exhibit this kind of self-duality. It is shown that Maiorana-McFarland ternary bent functions in two variables may be divided into 3 classes of 54 functions each. A list of these functions for a first class is given in an Appendix.
Computer Modeling, Simulation and Visualization in Teaching Probability to Mathematics Students

The purpose of the study is to identify and justify the main directions for improving the professional training of mathematics students in the context of informatization and digitalization. The article also considers examples of applying the obtained theoretical conclusions in the study of probability theory. The proposed lab provides an example of the application of information and communication technologies (ICT) for modeling and visualization to help students understand the basic concepts of probability theory. The authors conclude that there is a need for a consistent, expedient combination of traditional educational technologies and ICT in the educational process.
SBDF-Net: A versatile dual-branch fusion network for medical image segmentation

• SBDF-Net extract detail information and global context information. • The dual-branch encoder calibrates lesion area at a more fine-grained level. • SGFM obtain rich multi-scale features by integrating cross-scale information. • TDB and TUB modules reduce feature loss. • Extensive evaluation of medical image segmentation on four datasets. In the field of medical image analysis, image segmentation can help doctors diagnose diseases and plan treatments. U-net has become an important network in biomedical image segmentation. Inspired by U-Net, we propose a dual-branch encoder for aggregating multi-scale context information. A novelty Shuffle Grouped Fusion Module is used to fuse cross-scale information between dual branches. In addition, Skip Connection + calibrates the features extracted by encoder to optimize the feature mapping. Finally, Three-branch Down-sampling Block and Two-branch Up-sampling Block are designed to reduce the feature loss produced by sampling operations. We have evaluated the performance of our network on four datasets. The IoU, Dice and Sensitivity of the model reach 86.45%, 92.95% and 93.36% on the 2018 Data Science Bowl dataset, 81.85%, 89.35% and 88.90% on the GLAS dataset, 80.92%, 87.63% and 87.19% on the Kvasir-SEG dataset, 91.54%, 95.48% and 94.72% on the Aortic Dissection dataset. The experimental results show that our proposed model is superior to U-Net and other advanced segmentation networks in many metrics. The proposed model is available at https://github.com/1998supper/SBDF-Net .
Shift‐channel attention and weighted‐region loss function for liver and dense tumor segmentation

To assist physicians in the diagnosis and treatment planning of tumor, a robust and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Recently, numerous researchers have improved the segmentation accuracy of liver and tumor by introducing multiscale contextual information and attention mechanism. However, this tends to introduce more training parameters and suffer from a heavier computational burden. In addition, the tumor has various sizes, shapes, locations, and numbers, which is the main reason for the poor accuracy of automatic segmentation. Although current loss functions can improve the learning ability of the model for hard samples to a certain extent, these loss functions are difficult to optimize the segmentation effect of small tumor regions when the large tumor regions in the sample are in the majority.We propose a Liver and Tumor Segmentation Network (LiTS-Net) framework. First, the Shift-Channel Attention Module (S-CAM) is designed to model the feature interdependencies in adjacent channels and does not require additional training parameters. Second, the Weighted-Region (WR) loss function is proposed to emphasize the weight of small tumors in dense tumor regions and reduce the weight of easily segmented samples. Moreover, the Multiple 3D Inception Encoder Units (MEU) is adopted to capture the multiscale contextual information for better segmentation of liver and tumor.Efficacy of the LiTS-Net is demonstrated through the public dataset of MICCAI 2017 Liver Tumor Segmentation (LiTS) challenge, with Dice per case of 96.9 %${\bf \%}$ and 75.1 %${\bf \%}$ , respectively. For the 3D Image Reconstruction for Comparison of Algorithm and DataBase (3Dircadb), Dices are 96.47 %${\bf \%}$ for the liver and 74.54 %${\bf \%}$ for tumor segmentation. The proposed LiTS-Net outperforms existing state-of-the-art networks.We demonstrated the effectiveness of LiTS-Net and its core components for liver and tumor segmentation. The S-CAM is designed to model the feature interdependencies in the adjacent channels, which is characterized by no need to add additional training parameters. Meanwhile, we conduct an in-depth study of the feature shift proportion of adjacent channels to determine the optimal shift proportion. In addition, the WR loss function can implicitly learn the weights among regions without the need to manually specify the weights. In dense tumor segmentation tasks, WR aims to enhance the weights of small tumor regions and alleviate the problem that small tumor segmentation is difficult to optimize further when large tumor regions occupy the majority. Last but not least, our proposed method outperforms other state-of-the-art methods on both the LiTS dataset and the 3Dircadb dataset.
Use of Metric Learning for the Recognition of Handwritten Digits, and its Application to Increase the Outreach of Voice-based Communication Platforms

Initiation, monitoring, and evaluation of development programmes can involve field-based data collection about project activities. This data collection through digital devices may not always be feasible though, for reasons such as unaffordability of smartphones and tablets by field-based cadre, or shortfalls in their training and capacity building. Paper-based data collection has been argued to be more appropriate in several contexts, with automated digitization of the paper forms through OCR (Optical Character Recognition) and OMR (Optical Mark Recognition) techniques. We contribute with providing a large dataset of handwritten digits, and deep learning based models and methods built using this data, that are effective in real-world environments. We demonstrate the deployment of these tools in the context of a maternal and child health and nutrition awareness project, which uses IVR (Interactive Voice Response) systems to provide awareness information to rural women SHG (Self Help Group) members in north India. Paper forms were used to collect phone numbers of the SHG members at scale, which were digitized using the OCR tools developed by us, and used to push almost 4 million phone calls. The data, model, and code have been released in the open-source domain.
Topic-Aware Sentiment Analysis of News Articles

We consider the problem of sentiment analysis in news media articles cast as a three-way classification task: negative, positive, or neutral. We show that subdividing the training corpus by topic (local news, sports, hi-tech, and others) and training separate sentiment classifiers for each sub-corpus improves classification F1 scores. We use topics since some words carry different sentiments in different domains: e.g., the word “force” is typically positive in the sports domain but negative in the political domain. Our experiments on the Kaggle dataset with sentiment-labeled Kazakhstani news articles in Russian language using the Convolutional Neural Network (CNN) model partially proved our hypothesis, showing that for the most prominent “kz” (local news) topic, we achieve an F1 score of 0.70, which is greater than the baseline model trained without the topic-awareness showing just 0.67. Topic-aware improves F1 scores in some topics, but due to the topic/class imbalance further research is needed. However, the performance in terms of F1 over all the corpus does not improve or the improvements are very small. Moreover, our approach shows better results on topics with many text samples than those with relatively small amounts of articles.
Analysis of Agricultural Product Information Traceability and Customer Preference Based on Blockchain

For traditional agricultural product traceability system, there are some problems, such as information asymmetry and serious centralized storage. To solve the problems, a set of an agricultural product traceability information analysis system based on blockchain is designed. The overall framework of the agricultural product information traceability system is built, and the traceability model and process are designed in detail from the data layer, consensus layer, and contract layer. Collaborative verification function modules are added in the data layer to ensure the authenticity of data on the blockchain. The integral penalty mechanism is introduced to improve the practical Byzantine fault tolerance algorithm (PBFT), which ensures the security of the block network and the validity of data. Smart contracts are adopted to ensure that changes in agricultural information are actually recorded on blockchain. The results show that the designed blockchain-based agricultural product information traceability system solves the problem of difficult sharing and traceability of agricultural product information, which realizes the traceability of agricultural product production information, processing information, transportation information, and transaction query information, and has certain theoretical and practical value for the information traceability of agricultural products and other products.
Privacy-Preserving in-Bed Human Pose Estimation: Highlights from the IEEE Video and Image Processing Cup 2021 Student Competition [SP Competitions]

Every person spends around one third of his/her life in bed. For an infant or a young toddler, this percentage can be much higher, and for bed-bound patients it can go up to 100% of their time. In-bed pose estimation is a critical step in many human behavior monitoring systems that are focused on prevention, prediction, and management of at-rest or sleep-related conditions in both adults and children. The topic of automatic noncontact human pose estimation has received a lot of attention/success, especially in the last few years in the computer vision community, thanks to the introduction of deep learning and its power in artificial intelligence (AI) modeling. However, the state-of-the-art (SOTA) vision-based AI algorithms in this field can hardly work under the challenges associated with in-bed human behavior monitoring, such as significant illumination changes (e.g., full darkness at night), heavy occlusion (e.g., covering by a sheet or blanket), as well as the privacy concerns that mitigate large-scale data collection, necessary for any deep learning-based model training. The data quality challenges and privacy concerns have hindered the use of advanced vision-based in-bed behavior monitoring systems at home, which during the recent COVID-19 pandemic could have been an effective way to control the spread of the virus by avoiding in-person visits to clinics.
The Theoretical Topology and Implementation of Enterprise Social Security in the Digital Age Based on Big Data and Artificial Intelligence

At present, there are some problems in the social security of employees in enterprises, such as incomplete security and low reliability. According to this background, this paper studies a topological method for enterprise social security data analysis based on data adaptive analysis strategy and strong learning data stream coprocessing. According to this background, this paper studies a topology method of enterprise social security data analysis based on data adaptive analysis strategy and strong learning data flow and designed a convolutional neural network method based on mutual interference deepening strategy and advanced learning classification mode convolution neural network based on mutual interference deepening strategy and intensive learning classification pattern. According to the coverage error strategy of different types of data, the high-precision matching analysis of enterprise employee social security data is realized, and the Cartesian formula is used to correct the error and topology analysis of the analysis results. According to the experimental data and results, the topology method of enterprise social security data analysis is based on the experimental data and results. We can know that the topological method of enterprise social security data analysis is based on convolutional neural network. Enterprise social security theory can effectively improve the scope and speed of social security. This theory effectively completes the high-precision matching of different types of data and indirectly improves employees’ cognition of enterprise values.
Inharmonicity in plucked guitar strings

We have considered the vibration of various types of pinned guitar strings and have investigated the deviation of the partials from integer multiples of the string's fundamental vibration frequency. We measured the inharmonicity parameter B and compared it to a direct calculation based on a model equation. We generally found very good agreement between the two determinations of B for monofilament strings, but perhaps not surprisingly, we find rather poor agreement for wound strings. Furthermore, we show that the methodology used to carry out this experiment can easily serve as the basis for an upper division physics laboratory on physical acoustics including a more thorough investigation of the classical wave equation in a real-world application.
Quantum Multiple Valued Kernel Circuits

Quantum kernels map data to higher dimensions for classification and have been shown to have an advantage over classical methods. In our work, we generalize recent results in binary quantum kernels to multivalued logic by using higher dimensional entanglement to create a qudit memory and show that the use of qudits offers advantages in terms of quantum memory representation as well as enhanced resolution in the outcome of the kernel calculation. Our method is not only capable of finding the kernel inner product of higher dimensional data but can also efficiently and concurrently compute multiple instances of quantum kernel computations in linear time. We discuss how this method increases efficiency and resolution for various distance-based classifiers that require large datasets when accomplished with higher-dimensioned quantum data encodings. We provide experimental results of our qudit kernel calculations with different data encoding methods through the use of a higher-dimensioned quantum computation simulator.
Modeling Kansei Index for Images and Impression Estimation Using Fine Tuning

In this study, we propose an effective method for estimating image impressions that is based on a Kansei (affective) index and then show how a deep learning model can be used to evaluate the suitability of images based on that Kansei index. To accomplish this, we exhaustively collected words and phrases that express image impressions using the evaluation grid method and then conducted an evaluation experiment for those collected words using Yahoo! Cloud Sourcing. Next, factor analysis was performed on the collected evaluation data, and the obtained factor scores were used as the Kansei index. Finally, we used ResNet18 trained with ImageNet to fine-tune each image's factor scores for use as supervised data and confirmed that our deep learning model could infer an effective Kansei index that correlates strongly with the target images.
GRAPHITE: Generating Automatic Physical Examples for Machine-Learning Attacks on Computer Vision Systems

This paper investigates an adversary's ease of attack in generating adversarial examples for real-world scenarios. We address three key requirements for practical attacks for the real-world: 1) automatically constraining the size and shape of the attack so it can be applied with stickers, 2) transform-robustness, i.e., robustness of a attack to environmental physical variations such as viewpoint and lighting changes, and 3) supporting attacks in not only white-box, but also black-box hard-label scenarios, so that the adversary can attack proprietary models. In this work, we propose GRAPHITE, an efficient and general framework for generating attacks that satisfy the above three key requirements. GRAPHITE takes advantage of transform-robustness, a metric based on expectation over transforms (EoT), to automatically generate small masks and optimize with gradient-free optimization. GRAPHITE is also flexible as it can easily trade-off transform-robustness, perturbation size, and query count in black-box settings. On a GTSRB model in a hard-label black-box setting, we are able to find attacks on all possible 1,806 victim-target class pairs with averages of 77.8% transform-robustness, perturbation size of 16.63% of the victim images, and 126K queries per pair. For digital-only attacks where achieving transform-robustness is not a requirement, GRAPHITE is able to find successful small-patch attacks with an average of only 566 queries for 92.2% of victim-target pairs. GRAPHITE is also able to find successful attacks using perturbations that modify small areas of the input image against PatchGuard, a recently proposed defense against patch-based attacks.