Reviewerly/exHarmony huggingface dataset:
SecureRx: A blockchain-based framework for an electronic prescription system with opioids tracking

The proposed research aims at attaining several objectives. To start with, it assesses the limitations associated with the current electronic prescription systems. Accordingly, it proposes a proactive method for providing real-time monitoring to prevent overdose prescriptions. The proposed approach should ensure that health providers have access to a comprehensive dataset without state or geographical limitations. More importantly, the research aims to improve drug monitoring to enhance accountability and trust, which are central to promoting drug legitimacy. A blockchain-based framework for secure, interoperable, and efficient access to prescription records. The proposed solution, SecureRx, is a web-based application developed on the Ethereum blockchain and RxCheck hub. Based on the evaluation results, SecureRx is functional as it allows health providers to verify patient history and make informed decisions regarding whether to prescribe opioids. The proposed solution exhibits adequate scalability and handles transactions quickly when the number of nodes is reduced. It is also robust and secure, supports HIPAA compliance, and requires minimal operational expenses. The safe utilization of medications, especially opioids, continues to be a major problem in the United States and around the world. Part of the reason for this is the inability of health providers to access health databases for patients. Current drug prescription systems are limited in terms of tracking the usage of prescription drugs. The SecureRx framework has highlighted the potential of the record-keeping technology to nurture effective and efficient sharing of information about prescriptions while ensuring the safety and privacy of the original sources of the data.
Stochastic attentions and context learning for person re-identification

The discriminative parts of people’s appearance play a significant role in their re-identification across non overlapping camera views. However, just focusing on the discriminative or attention regions without catering the contextual information does not always help. It is more important to learn the attention with reference to their spatial locations in context of the whole image. Current person re-identification (re-id) approaches either use separate modules or classifiers to learn both of these; the attention and its context, resulting in highly expensive person re-id solutions. In this work, instead of handling attentions and the context separately, we employ a unified attention and context mapping (ACM) block within the convolutional layers of network, without any additional computational resources overhead. The ACM block captures the attention regions as well as the relevant contextual information in a stochastic manner and enriches the final person representations for robust person re-identification. We evaluate the proposed method on 04 public benchmarks of person re-identification i.e., Market1501, DukeMTMC-Reid, CUHK03 and MSMT17 and find that the ACM block consistently improves the performance of person re-identification over the baseline networks.
Accelerate Neural Image Compression with Channel-Adaptive Arithmetic Coding

We have witnessed the revolutionary progress of learned image compression despite a short history of this field. Some challenges still remain such as computational complexity that prevent the practical application of learning-based codecs. In this paper, we address the issue of heavy time complexity from the view of arithmetic coding. Prevalent learning-based image compression scheme first maps the natural image into latent representations and then conduct arithmetic coding on quantized latent maps. Previous arithmetic coding schemes define the start and end value of the arithmetic codebook as the minimum and maximum of the whole latent maps, ignoring the fact that the value ranges in most channels are shorter. Hence, we propose to use a channel-adaptive codebook to accelerate arithmetic coding. We find that the latent channels have different frequency-related characteristics, which are verified by experiments of neural frequency filtering. Further, the value ranges of latent maps are different across channels which are relatively image-independent. The channel-adaptive characteristics allow us to establish efficient prior codebooks that cover more appropriate ranges to reduce the runtime. Experimental results demonstrate that both the arithmetic encoding and decoding can be accelerated while preserving the rate-distortion performance of compression model.
Online Voting System Based on IoT and Ethereum Blockchain

Voting process is a democratic practice that has been used over the years as a primary method used by people in democratic countries to express their opinions on issues and discussions that concern them. This paper seeks to facilitate and protect the voting process by making an online voting system for elections and referendums connected with voting devices. The IoT and Blockchain have been used with this system to ensure that users' data are protected from theft and prevent eavesdropping or vote tampering to guarantee the integrity of the voting. The blockchain encrypts votes in order to protect every vote from forgery. This system is not directed to governments only, but to all governmental and private agencies. For example, governments can establish referendums or elections, and anyone who has reached the legal age and has a voting card issued by the government will be able to vote, thus we get rid of the traditional methods and dispense with ballot boxes, standing in long queues and delay counting the votes that cost governments a lot of time, effort and money. Also, any institution or private corporation that wishes to conduct polls and questionnaires or to conduct a specific study in order to collect opinions from people of society can simply use this system to reach them. The system assists the concerned authorities in obtaining results quickly without delay, taking into account the differences in voting process between government and private organizations.
A blockchain-based quantum-secure reporting protocol

The reporting systems are needed to design so that the whistleblower's privacy, report confidentiality, and report integrity should be under-consideration. Additionally, it is expected that the approved report will be accessible publicly and not changed. We believe that blockchain technology is the best choice for reporting systems' infrastructure since it provides a transparent and immutable database. This paper presents the first blockchain-based quantum-secure reporting protocol (QS-RP) using multivariate public key cryptography (MPKC). In the QS-RP, a fast verification mechanism is applied, which makes use of the Merkle technique. The QS-RP provides confidentiality to the selectively secure multi-key (C-SSMK) and unforgeability to selectively secure multi-key (UF-SSMK). Additionally, the QS-RP provides several new features such as report confidentiality before report generation, user/whistleblower privacy, and report integrity. The most important feature of the QS-RP is providing the whistleblower's privacy and report confidentiality against quantum computers. Analysis of the security of the QS-RP indicated the mentioned claims in the random oracle model (ROM). Finally, the QS-RP is compared with other blockchain-based reporting protocols. The comparison shows the QS-RP provides more security features than other reporting protocols, and the performance analysis's results show that it is $$90 \%$$ faster in the execution time on the user side, and it is $$66 \%$$ efficient in the communication overhead in compared to other blockchain-based reporting protocols. Moreover, the QS-RP has no on-chain overhead for whistleblowers.
A Tucker Decomposition Based on Adaptive Genetic Algorithm for Efficient Deep Model Compression

Model compression is a prevalent technique to efficiently deploy CNNs on resource-constrained devices, which show outstanding performance in computer vision. Recent efforts devote to approximately representing the higher-order tensors in CNNs without hurting original accuracy. However, the selection of ranks for various decomposition algorithms is still an open problem. We propose an efficient model compression method which leverage Tucker decomposition for global compression. An improved genetic algorithm is applied to selecting and optimizing the ranks for Tucker decomposition. The improved genetic algorithm can also actively detect and get rid of local optimal, which is main drawback of search algorithms. Different decomposition strategies for convolutional layer and fully connected layer are designed based on their dimensional difference. Extensive experiments show that with negligible loss of accuracy, proposed method gives a compression ratio of 7.57$\times$ in number of parameters and 2.69$\times$ in FLOPs to VGG-19 on CIFAR-100, which has advanced the state-of-the-art methods.
The Making of Women

This paper investigates how making activities and participation in makerspaces supports the wellbeing and empowerment of women, particularly in making domains that are typically male-dominated. We spent six months undertaking participant observations in a women-only makerspace that runs workshops aimed at teaching women skills in using power tools and woodwork. We conducted contextual interviews with 12 workshop attendees as well as with the makerspace founder and lead instructor. Through the lens of feminist HCI and legitimate peripheral participation, we present trajectories of participation within a women-only makerspace - from beginning as a peripheral participant to becoming a competent and confident maker. We found that through structured workshops in a women-only space that actively teach making skills, the women-only makerspace works to transform the current makerspace landscape so more women can engage with these spaces and participate within them. We contribute three core qualities to foster participation: women-only but without a 'feminist' label, configuring a formal and collaborative learning environment, and reification through artefacts. Collectively these work towards new configurations of makerspaces for women that enable their participation within them, and we detail how such configurations work to create trajectories for women's participation.
Auxiliary task guided mean and covariance alignment network for adversarial domain adaptation

Adversarial domain adaptation (ADA) learns representations with strong transferability by eliminating the discrepancy between the probability distributions of the source and the target domains. Conventional ADA methods usually employ the Wasserstein distance as a discrepancy measure and train the classifier only from the source domain data. We show that such methods actually only consider first-order statistics in the latent feature space and the discriminability of the learned representations is not fully explored. In this paper, we propose a novel method called auxiliary task guided mean and covariance alignment network (AT-MCAN) for unsupervised domain adaptation. To take the second-order statistics differences into consideration, AT-MCAN introduces a covariance-aware divergence metric to align the distributions of two domains. To enhance the discriminability of the features, AT-MCAN introduces an auxiliary clustering task to the target domain so that the classifier can employ the data from both domains. We provide both theoretical analysis on the generalization bound and empirical evaluations on standard benchmarks to show the effectiveness of our proposed AT-MCAN.
Biomarker CA125 Feature Engineering and Class Imbalance Learning Improves Ovarian Cancer Prediction

Ovarian cancer is a fatal female reproductive cancer because it has no specific clinical manifestations and effective screening methods in the early stage.When it is discovered, it is already an advanced stage with a low cure rate.Therefore, it is of great significance to improve the diagnostic ability of early screening for ovarian cancer.In this study, we feature engineered CA125 by calculating the rate of change of CA125 in addition to selecting a few top-ranked important features from PLCO ovarian cancer dataset.The dataset was extremely imbalanced; the imbalance ratio (ratio of negative samples in the majority class to positive examples in the minority class) was 143.7.Twenty-three types of class-imbalanced learning methods were used in this study to improve the predictive ability of the model.We identified the decision tree method with the highest AUC value among nine classic classifiers (decision tree, AdaBoost, etc.) to build a model with the class imbalance method, and showed their comparison.We identified the decision tree using SVMSMOTE has the most robust predictive ability for ovarian cancer, with a PPV of 0.9041, AUC of 0.9532, the sensitivity of 0.7792, and specificity of 0.9982.The high PPV shown by the model selected in this study indicates that the true positive samples predicted by the model account for 90.41%.Compared with other studies, the PPV of this study increased by 81.3%.This study helps to improve the accuracy of early screening for ovarian cancer and makes the diagnosis of ovarian cancer more reliable.
Exploiting limited players’ behavioral data to predict churn in gamification

The number of users attracted and engaged in a system dictates the value of the system itself. In gamification, timely detection of churners can produce more successful applications by informing both designers and algorithms. While churn prediction has been extensively studied in entertainment games, gamified systems often implement simpler mechanics, leading to a limited set of features compared to full-featured games. In this work, we studied whether limited players’ telemetry data describing in-game activity can be used to train a Random Forest model for churn prediction in a gamified application. Specifically, we analyzed different approaches for data preprocessing and sampling. Then, data from an online free-to-play (F2P) game was used as a validation set. Results show how in-game activity can be successfully used to predict churn. Moreover, from the tree’s visualization and interpretation, we found how players’ likelihood of abandoning the game is proportional to their time investment, both in the game and gamified system.
Siamese network for object tracking with multi-granularity appearance representations

A reliable tracker has the ability to adapt to change of objects over time, and is robust and accurate. We build such a tracker by extracting semantic features using robust Siamese networks and multi-granularity color features. It incorporates a semantic model that can capture high quality semantic features and an appearance model that can describe object at pixel, local and global levels effectively. Furthermore, we propose a novel selective traverse algorithm to allocate weights to semantic models and appearance models dynamically for better tracking performance. During tracking, our tracker updates appearance representations for objects based on the recent tracking results. The proposed tracker operates at speeds that exceed the real-time requirement, and outperforms nearly all other state-of-the-art trackers on OTB-2013/2015 and VOT-2016/2017 benchmarks.
An Implementation of Blockchain Technology in Forensic Evidence Management

Evidence management is crucial in the field of forensic science. Evidences obtained from a crime scene are important in solving the case and delivering justice to the parties involved. Hence, protecting these evidences from any form of alteration is of utmost important. Chain of Custody is the process which maintains the integrity of evidence. Inability to maintain the chain of custody will make the evidence inadmissible in court, eventually leading to the case dismissal. Digitalization of forensic evidence management system is a need of time as it is an environment friendly model. Blockchains are digitally distributed ledgers of transactions signed cryptographically in chronological order that are sorted into blocks and is completely open to anyone in the blockchain network. Hyperledger Fabric is a consortium blockchain framework created by the Linux foundation and is mainly used for enterprise use. Based on the concept of Hyperledger Fabric, present study aimed to create a framework and further propose an algorithm to implement Blockchain Technology to digitalize forensic evidence management system and maintain Chain of Custody.
Extrinsic Calibration of Radar Mount Position and Orientation With Multiple Target Configurations

Radars are part of the sensor suite installed on modern vehicles for environmental perception. The position and orientation of the radar must be known in order to transform the detections from the radar coordinate system to a vehicle coordinate system (VCS), which is a common requirement for multisensor fusion. In this work, 77-GHz automotive radar sensors are extrinsically calibrated by registering the radar detections of corner reflector targets with known locations of the targets in the VCS; the procedure estimates the position and orientation parameters needed to transform radar detections onto the VCS. Radar detections are noisy and very sparse; hence, an effort is put into achieving good calibration accuracy by taking advantage of multiple target configurations. Two multitarget methods are discussed; one models estimation error as white noise and averages multiple estimates, and another combines all observations to make the data points denser for a one-time global estimation. The methods are tested with both synthetic and experimental data. The synthetic data result shows that, with sparse data points per target configuration, the estimation errors obtained due to the global method tend to decay faster than those obtained due to the averaging method as the number of configurations increases. The experimental data obtained from just ten target configurations result in estimation errors of about 0.35° and 1 cm for the orientation and position parameters, respectively.
Software Requirements Selection with Incomplete Linguistic Preference Relations

Software requirements (SRs) selection is a multicriteria group decision making (MCGDM) problem whose objective is to select the SRs from the pool of the requirements on the basis of different criteria. In MCGDM, different decision makers have different opinions of the same requirement so it is difficult to decide which set of SRs to implement during the different releases of the software. During the MCGDM process, decision makers may use linguistic variables to specify preferences of requirements over other requirements. In real life applications, it has been observed that sometimes decision makers cannot evaluate the SRs due to their lack of knowledge and limited expertise related to the problem domain. In this situation, incomplete linguistic preference relations (LPRs) are constructed. In literature, SRs selection with incomplete LPRs is still an unresearched problem. Therefore, to address this issue, a method is presented for the selection of SRs with incomplete LPRs. Finally, the applicability of the proposed method is explained with the help of an example.
Dialect Identification using Chroma-Spectral Shape Features with Ensemble Technique

The present work proposes a text-independent dialect identification system. Generally, dialects of a language exhibit varying pronunciation styles followed in a specific geographical region. In this paper, chroma features familiar with music-related systems are employed for identification of dialects. In addition, eight significant spectral shape related features from short term spectra are computed and combined along with chroma features and named as chroma-spectral shape features. Chroma features try to aggregate spectral information and attempt to encapsulate the evidential variations, concerning timbre, correlated melody, rhythmic, and intonation patterns found prominently among dialects of few languages. The effectiveness of the proposed features and approach is evaluated on five prominent Kannada dialects spoken in Karnataka, India and internationally known standard Intonation Variation in English (IViE) dataset with nine British English dialects. Discriminative models such as, single classifier based Support Vector Machine (SVM) and ensemble based support vector machines (ESVM) are employed for classification. The proposed features have shown better performance over state-of-the-art i-vector features on both datasets. The highest recognition performance of 95.6% and 97.52% are achieved in the cases of Kannada and IViE dialect datasets respectively using ESVM. Proposed features have also demonstrated robust performance with small sized (limited data) audio clips even in noisy conditions.
DAEANet: Dual auto-encoder attention network for depth map super-resolution

Recently, depth map super-resolution (DSR) has obtained remarkable performance with the development of convolutional neural networks (CNNs). High-resolution (HR) depth map can be inferred from a low-resolution (LR) one with the guidance of its corresponding HR intensity image. However, most of the existing CNNs-based methods unilaterally transfer structures information of guidance image to the input depth map, which ignores the corresponding relations between the depth map and the intensity map. In this paper, we propose a novel dual auto-encoder attention network (DAEANet) for DSR. The proposed DAEANet includes two auto-encoder networks, where guidance auto-encoder network (GAENet) and target auto-encoder network (TAENet) aim to extract feature information from intensity image and depth map. Specifically, all auto-encoder networks are similar and trained simultaneously to ensure structural consistency. Furthermore, to preserve the structure information in the process of training, the attention mechanism is employed to our DAEANet. Extensive experiments on several popular benchmarks show that the proposed DAEANet outperforms existing state-of-the-art algorithms.
Anshimi

In this paper we examine Anshimi, a mobile safety application that hosts women's safety services. We present a case study based on qualitative data gathered from semi-structured interviews and a participatory design workshop with women in Seoul, South Korea. We examine women's perceptions of various kinds of safety data that Anshimi provides and collects from its users, such as GPS data, security camera footage, messages, personal information, etc. Exploring how women negotiate the tension between feeling protected and surveilled, we account for the nuanced ways in which women understand, feel, and use safety data in their daily lives. We also present scenarios for engaging with women's safety data, in the hopes of developing a guiding framework for designing women's safety applications and safety data practices.
Pedestrian Detection System with Edge Computing Integration on Embedded Vehicle

The article proposes pedestrian detection system with edge computing with multi-network integration on embedded vehicle. When camera of lens design in machine learning, the proposal design uses AdaBoost, support vector machine (SVM) and convolutional neural network (CNN). The disadvantage is that a large number of samples are needed for training, and the amount of operation and the large number of parameters cannot be used in the embedded system for vehicles. This article proposes to reduce the amount of computation and the number of parameters required by the network by integrating different optimization operations between networks of different architectures, so as to achieve prediction by using the Renesas R-car H3 of the embedded system on vehicle. The proposed design can maintain above a certain accuracy and cost lower than camera of lens with sensors of radar and lidar.