Reviewerly/exHarmony huggingface dataset:
“I Have to Ask”: A Mixed-Methods Study on the Collection of Sexual Orientation and Gender Identity Data Within the San Francisco County COVID-19 Case Investigation and Contact Tracing Program

Objective: To understand how the San Francisco (SF) COVID-19 case investigation and contact tracing (CICT) workforce documented sexual orientation and gender identity (SOGI) data, as well as a qualitative assessment of the workforce's capacity to successfully collect that data. Methods: This mixed-methods project analyzed data from 2 sources: SOGI item completeness among adult completed/partially completed interviews in the SF digital CICT COVID-19 database, and a secondary data analysis of qualitative data from 16 semistructured 90-minute virtual interviews with the SF CICT workforce, between November 14, 2020, and April 14, 2021. Results: Among 15 416 COVID-19 cases and 7836 close contacts, sexual orientation data are missing from 20% of cases and 17% of contacts. The proportion of transgender/nonbinary individuals was 0.32% and 0.5%, respectively. The SF CICTs participants discussed challenges in collecting SOGI data, not understanding SOGI measure rationale, and feeling uncomfortable asking the questions. Conclusion: Qualitative interviews with the COVID-19 CICT workforce and quantitative data on SOGI parameters in COVID-19 surveillance suggest that these data may have been underreported. Our results strongly suggest that comprehensive training is crucial in the collection of SOGI data among COVID-19 cases and their close contacts. If SOGI data are not collected accurately, the true impact of COVID-19 among lesbian, gay, bisexual, transgender, and queer populations remains unknown, preventing data-driven allocation of COVID-19 funds to lesbian, gay, bisexual, transgender, and queer communities.
Quantum Blockchain

Quantum blockchain is a distributed database that is decentralized, encrypted, and based on quantum information theory and computation. This comes as a result of the recent progress made in quantum computing and the need for quantum equivalents of classical blockchains. Algorithms, frameworks, models, tools, architectures, and databases, of which quantum blockchain is a part, are still being standardized. Recently, the growth of quantum information theory and computation has resulted in a rise in the number of research ongoing in this domain. This chapter presents an insight into quantum blockchain using the PRISMA technique with results registered and analyzed. The literature is analyzed based on some parameters or categorizations accompanied by graphical and tabular representations.
MotherScholar and MotherLeader

Rarely explored in the literature, the professional lives of MotherLeaders in mid-career remains virtually unaddressed. The relative silence around both the challenges and career successes faced by academic mothers in leadership roles in the middle of their professional lives is distinct and needs to be given voice. The current chapter will depict some reflections on the highlights and frustrations of one MotherLeader currently navigating through the middle of her career path in academia. Lessons learned and suggestions for maintaining career vibrancy will also be offered.
Privacy-Preserving Convolution Neural Network Inference with Edge-assistance

Nowadays the wide application of Convolution Neural Network(CNN) has promoted the rapid development of new intelligence fields. However, the inference of complex CNN usually includes a large number of computational operations. It is hard for the resource-limited user to complete the complicated CNN inference by his own. Privacy-preserving convolution neural network inference schemes allow the user to leverage the edge/cloud server to complete CNN inference under the condition that the privacy is protected. Nevertheless, existing schemes either require time-consuming cryptographic techniques, such as secure multi-party computing and homomorphic encryption, or fail to protect the privacy of the trained CNN model. To address this problem, we propose a novel privacy-preserving CNN inference scheme with edge-assistance. In this scheme, the privacy of the input data and the trained model is well protected and no complex cryptographic technique is involved. We blind the data to be predicted and the trained model to the edge servers, and then the edge servers calculate the most time-consuming layers. The user only deals with computationally efficient layers, fast encryption and recovery. Analysis and experimental results demonstrate that the proposed scheme is secure and efficient, which obviously saves the computational overhead of the user.
AN EMPIRICAL SURVEY TO SUBSTANTIATE THE NEED FOR IMPROVEMENT IN USER SECURITY AWARENESS IN MOBILE BANKING IN NIGERIA

Mobile banking application solutions are now the most preferable means of performing financial transactions and almost all banks in Nigeria have developed and deployed at least one mobile application to service their Client (Customers).However, the security of users of these solutions is a critical issue that cannot be overlooked and understand the concerns of users (Customers) when it comes to their privacy, data, and their perceptions on the issues of access delegation, an empirical survey was conducted to identify what user opinion and concerns are.This study seeks to investigate if users of mobile banking applications have sufficient security awareness and if it has translated into better security protection measures.Also, the knowledge level on the security features available on mobile banking apps and the best way users can be educated to understand their role in securing their privacy.The research work investigates the security risk in access delegation by the account holders to third parties.The findings from the survey identified a low level of security awareness among users in terms of security safety measures as only 14.4% of respondents admitted that they will consider security as a factor which further that there is a lack of security awareness among users and this has heightened the concerns of users with 58.2% expressing serious concern over their privacy and the problem of Access delegation as a risky behavior most users indulge in.The study also reveals that most users believe that the current security measures in mobile banking are at a low level.An Empirical Survey using quantitative descriptive analysis was used for analyzing the data collected and the tool used for data collection was a structured questionnaire and Statistical Package for Social Science (SPSS) to code the data for analysis.
Generalized mixed prediction chain model and its application in forecasting chronic complications

The risk of severe complications poses a major threat to patients suffering from chronic diseases, and risk prediction models can assist with identifying patients’ risks of developing complications. Different from those static prediction models (logistic regression, decision trees, etc.) which use the patients’ cross-section data to predict whether they will suffer a complication, this research aims to provide a dynamic prediction method that comprehensively utilizes the patients’ longitudinal Electronic Health Records (EHR) to predict their complication progressions. The proposed generalized mixed prediction chain model (GMPC) takes the patient’s complication status as the response variable and takes the patient’s EHR data as the covariate. A mixed effect model is then employed to demonstrate the relationship between the response variable and the covariate. Additionally, to reduce the time delay between the historical EHR and the future complication status, GMPC constructs a prediction chain that uses the predicted value of the response variable at the previous time to support the prediction of the response variable at the next time. Internal cross-validation and external test verify the effectiveness of GMPC, and the results show that GMPC outperforms existing static prediction models and dynamic prediction models.
Inversion method of particle size distribution of milk fat based on improved MPGA

Milk fat's particle size and distribution not only affect product quality, but also have great impacts on food safety in the economy and society. Based on total light scattering method, this paper has studied the inversion method of particle size distribution under dependent mode condition by combining multi-population genetic algorithm (MPGA) with Tikhonov smooth function. It has minimized the influence from light-absorb medium to improve the inversion accuracy. The approach introduces Tikhonov smooth function and apparent optical parameters to build an objective fitness function and weaken the ill condition of the particle size inversion equation. It also introduces multi-population genetic algorithm to solve the premature convergence of genetic algorithms. The results show that the relative error of the milk fat simulation solution with a nominal diameter is -3.52%, which meets the national standard of ±8% and better than the relative error of -5.01% of the standard genetic algorithm. Thus, the improved MPGA can reconstruct particle size distribution, with a good reliability and stability.
A novel integrated framework based on multi-view features for multidimensional social bot detection

The ravage of malicious bots in online social networks has profoundly affected normal users. In this article, we propose a novel integrated framework to detect social bots. Specifically, social bot detection is performed from two dimensions: binary-class and fine-grained detection. Moreover, 35 features from three views are extracted to detect social bots, including eight newly defined features. Then, a category balancing based on resampling technology is designed to balance the training data. Finally, a divide-and-conquer strategy is integrated into Random Forest, and the interference of noise in the training process is reduced. Feature effectiveness evaluation found that extracting features from multi-views can describe bots more comprehensively. It is also noted from the category imbalance test that the balanced data set can prevent the detection result from tilting. Comparative experiments show that the integrated framework is more effective than the baseline both in social bot detection and the type detection of bots.
Investigation of Blockchain Technology Integration within Food Supply Chain Management

Abstract Blockchain technologies (BT) began to be used in many areas because of the many advantages they offer. In this respect, BT applications take place in different types of supply chains. One of the areas where blockchain is used is the food supply chain (FSC). FSC is a general concept that also covers different types such as agri-FSC and cold chain. These supply chains need the advantages of modern BT for crucial reasons such as the products’ time-dependent nature and direct impact on human health. We evaluate the use of blockchain applications in the field of FSC in order to provide a deep understanding of problems inherent to the FSC and present insights to companies into developing and implementing their own blockchain-driven solutions to address the FSC performance challenges. As the FSC is a complex and dynamic system, where multi-stakeholders are involved, a systematic approach should be established to reveal the BT integration behavior of the FSC. An extensive literature review was conducted within the scope of the study. A critical review of the studies shows which BT applications are used in the FSC chain to drive supply chain performance, their application areas, and main problems in various aspects in the applications. Using the causal loop diagram (CLD) as a system dynamics approach, the interrelationship of causes and effects is visualized and prescriptive information is generated to guide researchers and practitioners in the field of new technology adoption in FSCs in general and BT integration in particular. This study is intended to encourage the use of BT in FSC applications.
Walking on paintings: Assessment of passive haptic feedback to enhance the immersive experience

Virtual reality has been used in recent years for artistic expression and as a tool to engage visitors by creating immersive experiences. Most of these immersive installations incorporate visuals and sounds to enhance the user’s interaction with the artistic pieces. Very few, however, involve physical or haptic interaction. This paper investigates virtual walking on paintings using passive haptics. More specifically we combined vibrations and ultrasound technology on the feet using four different configurations to evaluate users’ immersion while they are virtually walking on paintings that transform into 3D landscapes. Results show that participants with higher immersive tendencies experienced the virtual walking by reporting illusory movement of their body regardless the haptic configuration used.
VFLF: A verifiable federated learning framework against malicious aggregators in Industrial Internet of Things

Abstract As an important approach to overcome data silos and privacy concerns in deep learning, federated learning, which can jointly train the global model and keep data local, has shown remarkable performance in a range of industrial applications. However, federated learning still suffers from the problem that shared gradients may be subject to tampering, inference functions, and falsification. To address this issue, we propose a verifiable federated learning framework to deal with malicious aggregators. Initially, we propose a reputation calculation mechanism to solve the problem of selecting a reliable aggregator based on a multiweight subjective logic model. Furthermore, we design a verifiable federated learning scheme to ensure data confidentiality, integrity, and verifiability, as well as support the client's dynamic withdrawal. Security analyses indicate that our framework is secure against malicious adversaries. Furthermore, experimental results on real datasets show that our verifiable federated learning has high accuracy and feasible efficiency.
To Deprecate or to Simply Drop Operations? An Empirical Study on the Evolution of a Large OpenAPI Collection

OpenAPI is a language-agnostic standard used to describe Web APIs which supports the explicit deprecation of interface features. To assess how APIs evolve over time and observe how their developers handle the introduction of breaking changes, we performed an empirical study on a dataset composed of more than one million API operations described using OpenAPI and Swagger format. Our results focus on detecting breaking changes engendered by operations removal and whether and to which extent deprecation is used to warn clients and developers about dependencies they should no longer rely on. Out of the 41,627 APIs considered, we found only 263 (0.6%) in which some operations are deprecated before being removed, while the developers of 10,242 (24.6%) of them directly remove operations without first informing their clients about the potentially breaking change. Furthermore, we found that only 5.2% of the explicit-deprecated operations and 8.0% of the deprecated-in-description operations end with a removal, suggesting a tendency to deprecate operations without removing them. Overall, we observed a low negative correlation between the relative amount of deprecated operations and the age of the corresponding APIs.
A Systematic Survey of Architectural Approaches and Trade-Offs in Data De-identification

Data de-identification refers to the process of removing or hiding the direct and indirect (quasi-)identifiers from data elements through transformation, generalization, or removal. Through active research over twenty years, diverse de-identification approaches, algorithms and tools have emerged. Existing survey studies focus predominantly on the application of de-identification within specific domains and to different data types, with attention to intrinsic characteristics of the algorithms and methods, and the impact on data utility –the extent to which the data can still serve its functional purpose after de-identification. However, from a software architecture design perspective, de-identification affects a number of additional non-trivial and impactful non-functional attributes, and a comprehensive overview of the involved architectural concerns and trade-offs is currently lacking. To address this gap, we present the outcome of a systematic literature review (SLR) study that: (1) outlines the current state of the art in an architecture tactic tree which classifies different architectural approaches to de-identification, (2) provides a further exploration of the relevant architectural trade-offs discussed in literature.
Hiding Image within Image Based on Deep Learning

Abstract Information hiding technology is a technology that transmits secret information through a carrier, and is a research hotspot in the field of information security. The traditional information hiding algorithm relies on the meticulous design of human beings, and obtains the dense image through modification. With the development of deep learning, the integration of information hiding technology and deep learning has resulted in many information hiding technologies based on deep learning. Among them, image hiding has become a research hotspot due to its large steganographic capacity. Therefore, this paper reviews the information hiding technology based on deep learning. According to the difference of hidden models, it is analysed from four aspects: (1) information hiding model based on encoder-decoder; (2) information hiding model based on generative adversarial network; (3) information hiding model based on invertible network; (4) information hiding model based on neural network information hiding models for style transfer. Finally, these models are analysed and compared, and the future development direction is discussed and prospected.
Improved Secure Deep Neural Network Inference Offloading with Privacy-Preserving Scalar Product Evaluation for Edge Computing

Enabling deep learning inferences on resource-constrained devices is important for intelligent Internet of Things. Edge computing makes this feasible by outsourcing resource-consuming operations from IoT devices to edge devices. In such scenarios, sensitive data shall be protected while transmitted to the edge. To address this issue, one major challenge is to efficiently execute inference tasks without hampering the real-time operation of IoT applications. Existing techniques based on complex cryptographic primitives or differential privacy are limited to either efficiency or model accuracy. This paper addresses this challenge with a lightweight interactive protocol by utilizing low-latency IoT-to-edge communication links for computational efficiency. We achieve this with a new privacy-preserving scalar product evaluation technique that caters to the unique requirements of deep learning inference. As compared to the state-of-the-art, our solution offers improved trade-offs among privacy, efficiency, and utility. Experimental results on a Raspberry Pi 4 (Model B) show that our construction can achieve over 14× acceleration versus local execution for AlexNet inference over ImageNet. The proposed privacy-preserving scalar-product-evaluation technique can also be used as a general primitive in other applications.
Understanding Politeness in an Online Community of Practice for Chinese ESL Teachers: Implications for Sustainable Professional Development in the Digital Era

Although politeness contributes to the effectiveness and sustainable development of online learning communities, it remains unclear how Chinese teachers of English as a second language (ESL), with differing social statuses, use varying levels of politeness when engaging in such communities. Accordingly, this paper analyses how Hong Kong ESL teachers, who tend to observe hierarchical relationships and the idiosyncrasies of “face”, conveyed politeness in an online professional community when making positive and negative evaluations and suggestions. A total of 174 interactive comments offered by 24 student teachers and 8 mentors were coded under three discourse functions and 15 types of politeness markers. Interviews with six participants indicated that, through their role in the community, they adopted ways of thinking and linguistic knowledge that influenced their choice and frequency of the politeness markers. Despite having different roles in the hierarchy, the participants exhibited similar patterns of politeness marker choice, implying that their language and cultural background influenced how they chose to convey politeness in the absence of face-to-face cues; however, their role in the community seemed to have affected the frequency of using the politeness markers. These results shed light on politeness from the perspective of the culture, language, and online community membership.
Content-aware dynamic filter salient object detection network in multispectral polarimetric imagery

Salient object detection (SOD) is widely applied in image segmentation, image fusion, and adaptive compression. However, the SOD of visible images in complex scenes remains a prominent problem due to the lack of low-level features. To solve this problem, a Content-aware Dynamic Filter salient object detection Network using visible and polarized mask images is proposed. It can use the prior information on polarization dimension to guide the SOD of visual features. First, to extract information from the MSPI, a salient polarization mask M composed of three channels is generated. Secondly, deep fused features of the M and RGB images are generated by Encoder and DenseNet fusion structures with receptive fields. Finally, the fused features guide the decoder to generate saliency maps through the content-aware dynamic filtering model. The indexes of the salient object detection results given in this paper are superior to the state-of-the-art algorithms, especially for objects in dim, low contrast, or high transparency and other complex scenarios.
Encountering Cover Versions of Songs Derived from Personal Music-Listening History Data: a Design and Field Trial of Musée in Homes

Abstract We designed and implemented Musée to capture the novel experience of interpreting cover versions of music, which contain both familiar and unfamiliar musical components and are curated based on the user’s music-streaming history data. Musée is a tangible music player that enables users to explore and listen to professional or amateur covers of songs (via YouTube) in two categories: covers of songs from users’ most-liked artists and covers of users’ most-played songs. To investigate its potential value in situ, we conducted field trials of Musée in four households for 1 month. Findings showed that unfamiliar musical elements in cover music provided a sense of ‘freshness’ to past songs and helped the listener appreciate over-consumed music in new ways. In addition, restricting detailed information about cover songs that were playing helped users focus on the sound, thus priming them to infer and reflect on the original song and their memories associated with it. Our findings point to new insights for the design of interfaces that use historical personal data to expand users’ experience beyond solely revisiting prior tastes.