Reviewerly/exHarmony huggingface dataset:
Automated Neural and On-Device Learning for Micro Controllers

mW power consumption envelope, and below, is a pillar of the TinyML community, which aggregates contributions on algorithms, tools, software, and hardware for machine learning at the edge. In the tinyML context, this paper presents the state-of-the-art review of different approaches for Neural Architecture Search targeting resource constrained devices such as microcontrollers (MCU). As well as the implementations of On-Device Learning techniques for them. On the former, part of the paper reviews different approaches of Neural Architecture Search which consider the hardware constraints into the search mechanism and how they have been able to design a tiny neural network with low memory and computational requirements which can be deployed on cheap and off-the-shelf MCU. On the latter, the paper reviews the approaches and advanced solutions for On-Device Learning with MCU to address the concept drift and cope with the accuracy drop on real time data. Moreover, this paper also introduces Extreme Learning Machines (ELM) for feature extraction and some preliminary results using Restricted Coulomb Energy neural network for On-Device Classification Learning which is highly storage conservative and more suitable for tiny, embedded devices.
The relationship between simulator sickness and driving performance in a high-fidelity simulator

• Twenty-one participants completed a drive in a high-fidelity driving simulator. • Simulator sickness was measured before, during, and after a 25 km driving task. • Weak-moderate correlations between simulator sickness and driving performance were found. • Our findings suggest that simulator sickness is not strongly related to driving performance. Driving simulators are highly valuable tools for various applications such as research, training, and rehabilitation. However, they are also known to cause simulator sickness, a special form of traditional motion sickness. Common side effects of simulator sickness include nausea, headache, dizziness, eye-strain, and/or disorientation, all symptoms which may negatively impact driving performance. The goal of the present study was to investigate the relationship between simulator sickness and driving performance obtained in a high-fidelity driving simulator. Twenty-one healthy participants were engaged in a simulated driving task containing rural, city, and highway sections for approx. 25 min. Participants were asked to drive naturally while obeying traffic rules and completing common driving maneuvers (including reactions to sudden events). Driving performance was evaluated based on various driving measures, such as lane positioning, speed measures, following distance, or the number of steering reversals. Simulator sickness was measured before, during, and after the simulated drive using a combination of the Simulator Sickness Questionnaire and the Fast Motion Sickness scale. Overall, correlations between the level of simulator sickness and driving performance measures were low to moderate ( r ’s from -0.37 to 0.40) and were not significant. Additionally, participants who reported higher levels of simulator sickness did not differ with regards to their driving performance from those who reported lower simulator sickness scores. Our results suggest that the presence of simulator sickness is not strongly related to performance in a driving simulator.
Accurate segmentation of breast tumor in ultrasound images through joint training and refined segmentation

Objective.This paper proposes an automatic breast tumor segmentation method for two-dimensional (2D) ultrasound images, which is significantly more accurate, robust, and adaptable than common deep learning models on small datasets.Approach.A generalized joint training and refined segmentation framework (JR) was established, involving a joint training module (Jmodule) and a refined segmentation module (Rmodule). InJmodule, two segmentation networks are trained simultaneously, under the guidance of the proposed Jocor for Segmentation (JFS) algorithm. InRmodule, the output ofJmoduleis refined by the proposed area first (AF) algorithm, and marked watershed (MW) algorithm. The AF mainly reduces false positives, which arise easily from the inherent features of breast ultrasound images, in the light of the area, distance, average radical derivative (ARD) and radical gradient index (RGI) of candidate contours. Meanwhile, the MW avoids over-segmentation, and refines segmentation results. To verify its performance, the JR framework was evaluated on three breast ultrasound image datasets. Image dataset A contains 1036 images from local hospitals. Image datasets B and C are two public datasets, containing 562 images and 163 images, respectively. The evaluation was followed by related ablation experiments.Main results.The JR outperformed the other state-of-the-art (SOTA) methods on the three image datasets, especially on image dataset B. Compared with the SOTA methods, the JR improved true positive ratio (TPR) and Jaccard index (JI) by 1.5% and 3.2%, respectively, and reduces (false positive ratio) FPR by 3.7% on image dataset B. The results of the ablation experiments show that each component of the JR matters, and contributes to the segmentation accuracy, particularly in the reduction of false positives.Significance.This study successfully combines traditional segmentation methods with deep learning models. The proposed method can segment small-scale breast ultrasound image datasets efficiently and effectively, with excellent generalization performance.
Technical reporting in digital forensics

Abstract One of the primary roles of a practitioner in the field of digital forensics (DF) is to conduct the examination of any lawfully seized digital device content and report upon any findings that may support an inquiry being conducted. While there are many intricacies to this task, in some cases, an inquiry will commence with a practitioner carrying out the necessary examination work required to report any findings at a “technical level.” Such technical reports are often used for intelligence gathering purposes in an attempt to establish the potential evidential value of a device or data set and are often a precursor to, and catalyst for, further and often more extensive forensic work being commissioned. Therefore, the ability to report at a technical level should be considered a fundamental skill required of all practitioners in this discipline and any attempts to provide guidance and support for conducting this task effectively should be encouraged. This work explores the role of technical reporting, where a series of reporting examples are presented that explore the intricacies involved with conveying digital forensic findings at a technical level. Procedural and linguistic challenges are investigated and evaluated in order to acknowledge the pitfalls that practitioners may encounter and to identify potential technical reporting best practices.
A coverage path planning approach for autonomous radiation mapping with a mobile robot

In nuclear and radiation-related industries, it is crucial to ensure that the radiation dose exposure to the radiation worker is maintained below the permissible dose limit. A radiation map is a useful tool for visualizing the radiation distribution across the work area and for coordinating activities involving the hotspots (high radiation areas). The goal of this work was to design and implement a coverage path planning approach for autonomous radiation mapping carried out by a mobile robot. Given a 2D occupancy map, a method to generate uniformly distributed sampling points was proposed. The geometry of the region of interest, the radiation detector module, and the radiation measurement parameters were considered in formulating the sampling positions. Next, the coverage path planning planner integrates the nearest neighbor and depth-first search algorithms to create a continuous path that enables the robot to visit all the sampling points. The K-means clustering algorithm is added for systematic coverage of a large number of sampling points. The clustering provides options to partition the region of interest into smaller spaces, where the robot would perform the mapping cluster by cluster. Finally, the method of building the radiation map from the acquired data was also presented. The approach was implemented in ROS using a commercial mobile robot equipped with a Geiger–Muller detector. The performance and reliability of the proposed approach were evaluated with a series of simulations and real-world experiments. The results showed that the robot is able to perform autonomous radiation mapping at various target areas. The accuracy of the generated radiation map and the hotspots classifications were also compared and evaluated with conventional manual measurements. Overall, the theoretical frameworks and experiments have provided convincing results in the automation of hazardous work and subsequently toward improving the occupational safety of radiation workers.
Training a Neural Network with Exciton-Polariton Optical Nonlinearity

In contrast to software simulations of neural networks, hardware implementations have limited or no tunability. While such networks promise great improvements in terms of speed and energy efficiency, their performance is limited by the difficulty of applying efficient training. We propose and realize experimentally an optical system where highly efficient backpropagation training can be applied through an array of highly nonlinear, nontunable nodes. The system includes exciton-polariton nodes realizing nonlinear activation functions. We demonstrate high classification accuracy in the MNIST handwritten digit benchmark in a single-hidden-layer system.
PersonGONE: Image Inpainting for Automated Checkout Solution

In this paper, we present a solution for automatic checkout in a retail store as a part of AI City Challenge 2022. We propose a novel approach that uses the "removal" of unwanted objects — in this case, body parts of operating staff, which are localized and further removed from video by an image inpainting method. Afterwards, a neural network detector can detect products with a decreased detection false positive rate. A part of our solution is also automatic detection of ROI (the place where products are shown to the system). We reached 0.4167 F1-Score with 0.3704 precision and 0.4762 recall which placed us at the 7th place of AI City Challenge 2022 in corresponding Track 4. The code is made public and available on GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
A Visually-Aware Food Analysis System for Diet Management

This demo illustrates a visually-aware food analysis (VAFA) system for socially-engaged diet management. VAFA is able to receive multimedia inputs, such as the images of food with/without a description to record a user’s daily diet. Such information will be passed to AI algorithms for food classification, ingredient recognition, and nutrition analysis, to produce a nutrition report for the user. Moreover, VAFA profiles the users’ eating habits to make personalized recipe recommendation and identify the social communities with similar eating preferences. VAFA is empowered by state-of-the-art AI algorithms and a large-scale dataset with 300K users, 400K recipes, and over 10M user-recipe interactions.
An Efficient Proxy Signature–Based Authority Delegation Scheme for Medical Cyber Physical Systems

The use of technology in the field of critical illness treatment has been one of the most powerful gifts to mankind. Medical cyber physical systems (MCPSs) are joining the virtual world with the real one. However, along with the facilities that MCPS brings is concern about the compromise of health records of patients. The safety and security of these records are a new challenge and a mission-critical task. The cyber security attacks on healthcare facilities are having a direct impact on human lives. The privacy and authenticity of the documents are a major security concern. In this chapter, we propose the use of a proxy signature-based authority delegation scheme for securely delegating the patients’ electronic health records (EHRs). In the case of a critical illness, the patient has to undergo treatment by various specialists whose treatments are in line with one other. In such a situation, delegation-based access to the her could be provided securely without compromising confidentiality. Proxy signature schemes provide delegation-based authentication and authorization. We provide an end-to-end secure solution for the implementation of a proxy signature-based delegation scheme. The proposed scheme is verified through mathematical modeling, formal verification, simulation, and security analysis.
Toward an integrated framework for misinformation and correction sharing: A systematic review across domains

Although misinformation and correction sharing is a topic that spans various domains and disciplines, the ultimate aim of such research is to better understand how to reduce misinformation sharing while motivating correction sharing in an increasingly decentralized and dispersed informational landscape. This review aims to (a) provide a systematic and structured overview of empirical studies on both misinformation sharing and correction sharing, as differentiated phenomenon, by examining article elements such as theoretical lenses, methodologies, topics of research, and (b) collect and organize factors predicting both misinformation sharing and correction sharing into an integrated model, which provides the foundation for an interdisciplinary framework of misinformation sharing and correction sharing. A total of 64 relevant empirical articles published before October 2021 were identified for analysis. Finally, a discussion regarding the academic and practical implications of this study, and gaps in the literature aim to provide direction for future research.
BERT-Based Mixed Question Answering Matching Model

Compared with the current question answering system, similarity matching methods are largely separated into two categories: deep learning methods and conventional ways. Conventional ways rely heavily on artificial features, have weak generalization ability, and insufficient accuracy. RNN and CNN also have text global feature extraction. Limitations. This paper proposes a BERT-based hybrid question answering matching model, which uses the BERT -base pre-training model to capture and represent the semantic information of the QAS sentence and the semantic relevance between the two. The feature vector generated by the BERT model is used as Bi-LSTM &#x005F; GCN for the input of the model, feature extraction is performed to further obtain the syntactic features of the sentence, and finally the attention mechanism is added to find the target answer, and the effectiveness of the proposed algorithm is verified on the two types of data sets.
Machine Intelligence for Language Translation from Kashmiri to English

Machine translation (MT) is an emerging research as well as application area in the contemporary world. It is receiving significant attention from academia, industry and corporate houses. A wide range of translation techniques are being applied either individually or in combination for machine translation of different languages across the globe. However, there are still many languages that are either completely missing or poorly visible on the machine translation map. The Kashmiri language is one such language where very little or negligible work has been done related to its machine translation. This paper aims to present a Kashmiri-to-English Machine Translation System and highlight various features of the Kashmiri language. The system is based on machine intelligence having the ability to learn various translation rules from the translated set of input sentences, using Long Short-term Memory (LSTM) architecture for deep sequence learning. The paper also discusses various challenges related to machine translation from Kashmiri to English or other languages. The work presented in this paper is the first of its nature and can serve as a bedrock for research community interested to work on machine translation of Kashmiri language.
Influence of lip position, lip force and blowing pressure on the tuning and playability of an alto saxophone mouthpiece

The embouchure of a musician plays a crucial role in the correct tuning of an instrument. This work examines the influence of lip position, lip force and blowing pressure on the tuning of an alto saxophone mouthpiece. The measurements were collected by means of an artificial mouth setup which mimics the realistic playing condition. The range of lip forces and blowing pressures in which the mouthpiece generates a chosen note, is defined as the total playability area (TPA) for that note. Sound pressure level within the TPA and correct tuning playability area (CTPA) are measured for four different lip positions. Sound pressure is nearly independent of blowing pressure but increases strongly with decreasing lip force. The slope of this decrease is a characterization of the mouthpiece. In the airflow/pressure zone where sound is being produced, airflow increases as blowing pressure decreases, so an extrapolated airflow at zero blowing pressure can be calculated. The hydrodynamic resistance and extrapolated airflow are calculated for all notes and averages and variabilities are determined as parameters which characterize the mouthpiece. Results confirm that the lowest notes on a saxophone are difficult to play in an acceptable tuning. Adjusting lip position extends the total dynamic range of the sound pressure. The hydrodynamic resistance is similar across notes. The extrapolated airflow strongly depends on note. The study provides foundation for objective characterization of the playability of different mouthpiece types.
DSSDPP: Data Selection and Sampling Based Domain Programming Predictor for Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) refers to recognizing defective software modules in one project (i.e., target) using historical data collected from other projects (i.e., source), which can help developers find defects and prioritize their testing efforts. Unfortunately, there often exists large distribution difference between the source and target data. Most CPDP methods neglect to select the appropriate source data for a given target at the project level. More importantly, existing CPDP models are parametric methods, which usually require intensive parameter selection and tuning to achieve better prediction performance. This would hinder wide applicability of CPDP in practice. Moreover, most CPDP methods do not address the cross-project class imbalance problem. These limitations lead to suboptimal CPDP results. In this paper, we propose a novel data selection and sampling based domain programming predictor (DSSDPP) for CPDP, which addresses the above limitations. DSSDPP is a non-parametric CPDP method, which can perform knowledge transfer across projects without the need for parameter selection and tuning. By exploiting the structures of source and target data, DSSDPP can learn a discriminative transfer classifier for identifying defects of the target project. Extensive experiments on 22 projects from four datasets indicate that DSSDPP achieves better <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MCC</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AUC</i> results against a range of competing methods both in the single-source and multi-source scenarios. Since DSSDPP is easy, effective, extensible, and efficient, we suggest that future work can use it with the well-chosen source data to conduct CPDP especially for the projects with limited computational budget.
MSGAN: Multi-Stage Generative Adversarial Networks for Cross-Modality Domain Adaptation

Domain adaptation has become an important topic because the trained neural networks from the source domain generally perform poorly in the target domain due to domain shifts, especially for cross-modality medical images. In this work, we present a new unsupervised domain adaptation approach called Multi-Stage GAN (MSGAN) to tackle the problem of domain shift for CT and MRI segmentation tasks. We adopt the multi-stage strategy in parallel to avoid information loss and transfer rough styles on low-resolution feature maps to the detailed textures on high-resolution feature maps. In detail, the style layers map the learnt style codes from the Gaussian noise to the input features in order to synthesize images with different styles. We validate the proposed method for cross-modality medical image segmentation tasks on two public datasets, and the results demonstrate the effectiveness of our method. Clinical relevance— This technique paves the way to translate cross-modality images (MRI and CT) and it can also mitigate the performance degradation when applying deep neural networks in a cross-domain scenario.
Point-Supervised Video Temporal Grounding

Given an untrimmed video and a language query, Video Temporal Grounding (VTG) aims to locate the time interval in the video semantically relevant to the query. Existing fully-supervised VTG methods require accurate annotations of temporal boundary, which is time-consuming and expensive to obtain. On the other hand, weakly-supervised VTG methods where only paired videos and queries are available during training lag far behind the fully-supervised ones. In this paper, we introduce point supervision to narrow the performance gap with affordable annotating cost and propose a novel method dubbed Point-Supervised Video Temporal Grounding (PS-VTG). Specifically, an attention-based grounding network is first employed to obtain a language activation sequence (LAS). Then pseudo segment-level label is generated based on the LAS and the given point supervision to assist the training process. In addition, multi-level distribution calibration and cross-modal contrast are framed to obtain discriminative feature representations and precisely highlight the language-relevant video segments. Experiments on three benchmarks demonstrate that our method trained with point supervision can significantly outperform weakly-supervised approaches and achieve comparable performance with fully-supervised ones.