Reviewerly/exHarmony huggingface dataset:
Development and Validation of Manually Modified and Supervised Machine Learning Clinical Assessment Algorithms for Malaria in Nigerian Children

It is currently estimated that 67% of malaria deaths occur in children under-five years (WHO, 2020). To improve the identification of children at clinical risk for malaria, the WHO developed community (iCCM) and clinic-based (IMCI) protocols for frontline health workers using paper-based forms or digital mobile health (mHealth) platforms. To investigate improving the accuracy of these point-of-care clinical risk assessment protocols for malaria in febrile children, we embedded a malaria rapid diagnostic test (mRDT) workflow into THINKMD's (IMCI) mHealth clinical risk assessment platform. This allowed us to perform a comparative analysis of THINKMD-generated malaria risk assessments with mRDT truth data to guide modification of THINKMD algorithms, as well as develop new supervised machine learning (ML) malaria risk algorithms. We utilized paired clinical data and malaria risk assessments acquired from over 555 children presenting to five health clinics in Kano, Nigeria to train ML algorithms to identify malaria cases using symptom and location data, as well as confirmatory mRDT results. Supervised ML random forest algorithms were generated using 80% of our field-based data as the ML training set and 20% to test our new ML logic. New ML-based malaria algorithms showed an increased sensitivity and specificity of 60 and 79%, and PPV and NPV of 76 and 65%, respectively over THINKD initial IMCI-based algorithms. These results demonstrate that combining mRDT "truth" data with digital mHealth platform clinical assessments and clinical data can improve identification of children with malaria/non-malaria attributable febrile illnesses.
Improving Evaluation of Facial Attribute Prediction Models

CelebA is the most common and largest scale dataset used to evaluate methods for facial attribute prediction, an important benchmark in imbalanced classification and face analysis. However, we argue that the evaluation metrics and baseline models currently used to compare the performance of different methods are insufficient for determining which approaches are best at classifying highly imbalanced attributes. We are able to obtain results comparable to current state-of-the-art using a ResNet-18 model trained with binary cross-entropy, a substantially less sophisticated approach than related work. We also show that we can obtain near-state-of-the-art results on accuracy using a model trained with just 10% of CelebA, and on balanced accuracy simply by maximizing recall for imbalanced attributes at the expense of all other metrics. To deal with these issues, we suggest several improvements to model evaluation including better metrics, stronger baselines, and increased awareness of the limitations of the dataset.
On the feasibility of adversarial machine learning in malware and network intrusion detection

Nowadays, Machine Learning (ML) solutions are widely adopted in modern malware and network intrusion detection systems. While these algorithms offer great performance, several researches demonstrate their vulnerability to adversarial attacks, which slightly modifies the input samples to compromise the correct behavior of the detector. Although this issue acquires extreme relevance in security-related contexts, the defenses are still immature. On the positive hand, cybersecurity poses additional challenges to the practicability of these attacks with respect to other domains. Previous studies focus exclusively on the degree of effectiveness of the proposals, but they do not discuss their actual feasibility. Based on this insight, in this paper we provide an overview of adversarial attacks and countermeasures for ML-based malware and network intrusion detection systems to assess their applicability in real world scenarios. In particular, we identify the constraints that need to be considered in the cybersecurity domain and discuss limitations of meaningful examples of previous proposals. Our work can guide practitioners to devise novel hardening solutions against more realistic threat models.
Detection of Locomotive Signal Lights and Pedestrians on Railway Tracks Using Improved YOLOv4

Intelligent detection of locomotive signal lights and pedestrians on railway tracks is of great significance to the safe operation of locomotives, especially under bad illumination conditions. Due to the highly complicated operation environment of railway locomotives, it is relatively difficult to apply deep neural network-based object detection methods in the recognition of locomotive signal lights and railway pedestrians. This work, for the first time, proposes a real-time detection method based on improved YOLOv4 to recognize locomotive signal lights and railway pedestrians. In our method, the Region-of-Interest is introduced into the original network of YOLOv4 to improve the detection precision of pedestrians on railway tracks. Importantly, we establish a dataset called detection of locomotive signal lights and railway pedestrians (DLSLRP), which is dedicated to the training, testing, and validation of related convolutional neural networks. We evaluated the proposed detector on DLSLRP dataset, the experimental results suggest that our method can detect locomotive signal lights and railway pedestrians with high speed and accuracy under different illumination conditions. The mAP reaches 93.52%, and the detection speed achieves 25 FPS.
Privacy Preservation in Multi-Cloud Secure Data Fusion for Infectious-Disease Analysis

It is often observed that people's data are scattered across various organizations and these data can be used to generate usable insights when integrated. However, data fusion from multiple data hosting sites could put user privacy at risk albeit with some security mechanisms. This paper studies a data-analytic platform that adopts the Kulldorff scan statistic to determine infectious-disease spatial hotspots by integrating and analyzing users’ health and location data that are respectively stored in two clouds. We examine the privacy threats to this platform which has a key-oblivious inner product encryption (KOIPE) mechanism in place to ensure that only coarse-grained statistical data is revealed to the honest-but-curious (HbC) entity. To protect user privacy from the designed inference attack, we exploit a game-theoretic approach to incentivize users to form anonymous clusters with a quantitative privacy guarantee. We conduct extensive simulations based on real-life datasets to demonstrate the performance of our scheme in terms of design overhead and privacy level.
Variation of quantum speed limit under Markovian and non-Markovian noisy environment

Abstract The time required for a given quantum system to evolve is known as the quantum speed limit (QSL), which can be used to characterize the speed of evolution of a quantum system. We study QSL time for Markovian and non-Markovian dynamics. Investigating the effect of QSL under non-unitary evolution is of fundamental interest. In this work, we derive QSL time for maximally entangled state and apply its results under various noise channels. We discuss measure of QSL, how correlations affect the rate of QSL. Further, we carried an investigation on connection between non-Markovianity and QSL. We show that the non Markovian evolution can speed up quantum evolution, therefore lead to smaller QSL time. We have established the connections between correlated channels, information loss and non-Markovianity. This theory may suggest some further connections to retrieve back information from surrounding and preserves decoherence.
Fine-Tuning BERT Models for Intent Recognition Using a Frequency Cut-Off Strategy for Domain-Specific Vocabulary Extension

Intent recognition is a key component of any task-oriented conversational system. The intent recognizer can be used first to classify the user’s utterance into one of several predefined classes (intents) that help to understand the user’s current goal. Then, the most adequate response can be provided accordingly. Intent recognizers also often appear as a form of joint models for performing the natural language understanding and dialog management tasks together as a single process, thus simplifying the set of problems that a conversational system must solve. This happens to be especially true for frequently asked question (FAQ) conversational systems. In this work, we first present an exploratory analysis in which different deep learning (DL) models for intent detection and classification were evaluated. In particular, we experimentally compare and analyze conventional recurrent neural networks (RNN) and state-of-the-art transformer models. Our experiments confirmed that best performance is achieved by using transformers. Specifically, best performance was achieved by fine-tuning the so-called BETO model (a Spanish pretrained bidirectional encoder representations from transformers (BERT) model from the Universidad de Chile) in our intent detection task. Then, as the main contribution of the paper, we analyze the effect of inserting unseen domain words to extend the vocabulary of the model as part of the fine-tuning or domain-adaptation process. Particularly, a very simple word frequency cut-off strategy is experimentally shown to be a suitable method for driving the vocabulary learning decisions over unseen words. The results of our analysis show that the proposed method helps to effectively extend the original vocabulary of the pretrained models. We validated our approach with a selection of the corpus acquired with the Hispabot-Covid19 system obtaining satisfactory results.
Multi-Object Recognition Based on Improved YOLOv4

When traditional deep learning model is used for multiple object recognition on the road, it has some problems such as large amount of calculation and poor timeliness. In order to solve these problems, this paper proposes a lightweight improvement strategy based on the YOLOv4 algorithm. First, in basic network architecture, the lightweight network MobileNetv2 is used to replace CSPDarknet53, which effectively reduces the amount of parameter extraction; then, in the deep network structure, the ordinary convolution layer is replaced with the deepthwise separable convolution, which further reduces the network parameters. Finally, we realized the lightweight YOLOv4 network. The multi-object recognition data set is used to train and test YOLOv4 and lightweight YOLOv4 respectively. The experimental results show that the mAP of the improved YOLOv4 network is 85.12%, which can effectively achieve the multi-object recognition task on the road. At the same time, compared with the YOLOv4, the detection speed of the improved network is increased by 75%, and the parameters are significantly reduced, thus saving about 83% of the storage space. The improved YOLOv4 has better application prospects in multi-object recognition task on the road.
STAM: A SpatioTemporal Attention Based Memory for Video Prediction

Video prediction has always been a very challenging problem in video representation learning due to the complexity in spatial structure and temporal variation. However, existing methods mainly predict videos by employing language-based memory structures from the traditional Long Short-Term Memories (LSTMs) or Gated Recurrent Units (GRUs), which may not be powerful enough to model the long-term dependencies in videos, consisting of much more complex spatiotemporal dynamics than sentences. In this paper, we propose a SpatioTemporal Attention based Memory (STAM), which can efficiently improve the long-term spatiotemporal memorizing capacity by incorporating the global spatiotemporal information in videos. In the temporal domain, the proposed STAM aims to observe temporal states from a wider temporal receptive field to capture accurate global motion information. In the spatial domain, the proposed STAM aims to jointly utilize both the high-level semantic spatial state and the low-level texture spatial states to model a more reliable global spatial representation for videos. In particular, the global spatiotemporal information is extracted with the help of an Efficient SpatioTemporal Attention Gate (ESTAG), which can adaptively apply different levels of attention scores to different spatiotemporal states according to their importance. Moreover, the proposed STAM are built with 3D convolutional layers due to their advantages in modeling spatiotemporal dynamics for videos. Experimental results show that the proposed STAM can achieve state-of-the-art performance on widely used datasets by leveraging the proposed spatiotemporal representations for videos.
Constrained Deep Reinforcement Learning for Low-Latency Wireless VR Video Streaming

Wireless virtual reality (VR) systems are able to provide users with immersive experiences, and require low latency and high data rate. To meet these conflicting requirements with limited radio resources, edge intelligence is a promising architecture. It exploits the edge server co-located at the base station to predict the field of view (FoV) of the next VR video segment, pre-render the three-dimensional video within the predicted FoV, and transmit it to the user in advance. Since the prediction is not error-free, the predicted FoV may not cover the actual FoV requested by the user, and hence may result in video quality loss. To address this issue, we first formulate a constrained partially observable Markov decision process problem to optimize the redundant range of the FoV according to the head motion prediction and the redundant range for the previous video segment. Then, we develop a constrained deep reinforcement learning algorithm to minimize the video quality loss ratio subject to the latency constraint. Simulation results show that the proposed algorithm outperforms the existing methods in terms of video quality loss ratio (from 6.9% to 4.9%) and latency (from 0.72 s to 0.63 s).
Framing ethical issues associated with the UK COVID-19 contact tracing app: exceptionalising and narrowing the public ethics debate

This paper explores ethical debates associated with the UK COVID-19 contact tracing app that occurred in the public news media and broader public policy, and in doing so, takes ethics debate as an object for sociological study. The research question was: how did UK national newspaper news articles and grey literature frame the ethical issues about the app, and how did stakeholders associated with the development and/or governance of the app reflect on this? We examined the predominance of different ethical issues in news articles and grey literature, and triangulated this using stakeholder interview data. Findings illustrate how news articles exceptionalised ethical debate around the app compared to the way they portrayed ethical issues relating to 'manual' contact tracing. They also narrowed the debate around specific privacy concerns. This was reflected in the grey literature, and interviewees perceived this to have emerged from a 'privacy lobby'. We discuss the findings, and argue that this limited public ethics narrative masked broader ethical issues.
Emerging Privacy and Trust Issues for Autonomous Vehicle Systems

In the awakening of cutting-edge technology, companies such as Apple, Waymo, and Tesla are racing to launch the industry&#x2019;s first fully autonomous car. Besides the technical challenges such as safety and infrastructure, privacy and data protection have attracted the autonomous vehicle industry and researchers&#x2019; attention. In particular, it is hard for autonomous vehicle manufacturers to impose substantive privacy and security protections when different vendors and suppliers are involved in vehicle production. Although we know how much data autonomous vehicles will generate per day, there is a lack of knowledge of how the collected data will be used (e.g., real-time broadcasting and offline analytic). The privacy risks associated with data collection raise individual concerns in autonomous vehicle systems. For instance, when location information is combined with personal information, a person&#x2019;s details such as wealth status, profession, sexual association, and religion can be deduced. The misuse of present and historical travel patterns also puts someone susceptible to physical harm or stalking. Driven by mutual benefits or regulations, specific data must be shared in real-time or published for analysis or research purposes. This paper discusses the emerging privacy and trust issues that are essential to motivate the acceptance of autonomous vehicles operating on public roads.
Real Time Monitoring and Tracking Objects for Pandemic

COVID-19 infections have been quickly growing over the world since its outbreak in December 2019. There were more than 247 million confirmed cases worldwide as of November 1, 2021, with more than 5 million deaths. The COVID-19 outbreak has had a significant impact on a variety of industries, with the healthcare industry being one of the hardest hit. It confronts significant challenges in allocating resources, providing services, and controlling the spread of the COVID-19 virus. Adopting health interventions utilising contemporary technology such as mobile health applications using Android coupled with the global positioning system (GPS) can considerably improve remote healthcare delivery while adhering to preventive measures such as social distancing. The primary goal of this app is to ensure that users who use it can receive meaningful and relevant information from it. This enables users to use GPS Tracking and Self-Assessment Test if any symptoms arise.
Social Research with Gender-Neutral Voices in Chatbots – The Generation and Evaluation of Artificial Gender-Neutral Voices with Praat and Google WaveNet

Artificial voice generation enables the creation of gender-neutral voice-based chatbots, such as "Q", and with this, new possibilities for social scientists to investigate effects of gender in chatbots on users. However, for this research, gender-neutral and matching male and female voices are necessary. This paper aims to match this need by providing a method for the creation of acoustically gender-neutral voices which can serve as a base for further investigation. The method requires less technical knowledge than most other approaches while, at the same time, theoretical assumptions about gender perception and neutrality can be modeled into the voices. Specifically, fundamental frequency and formant frequencies are outlined as major influences and subsequently included in voice production. In addition, an efficient two-step test for assessing gender perception is demonstrated. Applied to the produced voices, the test proved to be successful in determining gender-neutral variants. In sum, the paper shows that the production method and underlying theory are suited to create gender-neutral voices as well as matching male and female variants.
Face Trees for Expression Recognition

We propose an end-to-end architecture for facial expression recognition. Our model learns an optimal tree topology for facial landmarks, whose traversal generates a sequence from which we obtain an embedding to feed a sequential learner. The proposed architecture incorporates two main streams, one focusing on landmark positions to learn the structure of the face, while the other focuses on patches around the landmarks to learn texture information. Each stream is followed by an attention mechanism and the outputs are fed to a two-stream fusion component to perform the final classification. We conduct extensive experiments on two large-scale publicly available facial expression datasets, AffectNet and FER2013, to evaluate the efficacy of our approach. Our method outperforms other solutions in the area and sets new state-of-the-art expression recognition rates on these datasets.
Architecture and Building the Medical Image Anonymization Service: Cloud, Big Data and Automation

Medical images provide significant information to assist patients to obtain correct treatments. In order to create more innovative medical applications, NCHC with the three major medical institutions builds Taiwan's first medical image annotation database. However, under the restriction of personal information law in Taiwan, data privacy is a critical issue. Besides, the data from institutions are a huge amount to process. Thus, we propose “Anonymization Cloud Service” (ACS), based on cloud computing and big data architecture to serve privacy data to be anonymized with DICOM or CSV format. The research has three main components: (1) data conversion between DICOM and CSV, and transmission between transform node and big data cluster, (2) parallelized anonymous process with automation API, and (3) privacy control mechanism for risk evaluation. The results are implemented in more than tens of thousands of cases, over 10 TB of data, and 10 million DICOM files. Those actual cases show the proposed method effectively and efficiently achieves the goals of high performance and low risk.
A Conceptual Framework to Improve Cyber Forensic Administration in Industry 5.0: Qualitative Study Approach

As organizations strive to be compliant in a digitally evolving world, they need to ensure that they are forensically ready. Digital forensic readiness ensures compliance in legal, regulatory, functional, and operational structures. A literature review revealed a gap in detailed and comprehensive guidance on how such readiness ought to be accomplished. This is as a result of unfamiliar concepts and terms that revolve around digital forensic readiness. This research paper highlights and elaborates on a framework that can be achieved from research within focus groups. The insights drawn from the focus groups are used to critically assess the issues affecting practitioners in achieving complete digital forensic readiness.
On-The-Fly Data Augmentation for Text-to-Speech Style Transfer

Recent advanced text-to-speech (TTS) systems synthesize natural speeches. However, in many applications, it is desirable to synthesize utterances in a specific style. In this paper, we investigate synthesizing audios with three styles — news-casting, public speaking and storytelling — for a speaker who provides only neutral speech data. Firstly, considerable speech data were collected from the neutral speaker, and small amounts of speech from the wanted styles were collected from other speakers such that no speakers uttered in more than one style. All the data were used to train a basic multi-style multi-speaker TTS model. Secondly, augmented audios were created on-the-fly with the latest TTS model during its training and were used to further train the TTS model. Specifically, augmented data were created by ‘forcing’ a speaker to imitate stylish speeches of other three speakers by requiring their attention alignment matrices as similar as possible. Objective evaluation on the rhythm and pitch profile of the synthesized speech shows that the TTS model trained with our proposed data augmentation successfully transfers speech styles in these aspects. Subjective ABX evaluation also shows that stylish speeches synthesized by our proposed method are overwhelmingly preferred than those from a baseline TTS model by 40-60%.