Reviewerly/exHarmony huggingface dataset:
Analysis of Cytology Pap Smear Images Based on Ensemble Deep Learning Approach

The fourth most prevalent cancer in women is cervical cancer, and early detection is crucial for effective treatment and prognostic prediction. Conventional cervical cancer screening and classifying methods are less reliable and accurate as they heavily rely on the expertise of a pathologist. As such, colposcopy is an essential part of preventing cervical cancer. Computer-assisted diagnosis is essential for expanding cervical cancer screening because visual screening results in misdiagnosis and low diagnostic effectiveness due to doctors' increased workloads. Classifying a single cervical cell will overwhelm the physicians, in addition to the existence of overlap between cervical cells, which needs efficient algorithms to separate each cell individually. Focusing on the whole image is the best way and an easy task for the diagnosis. Therefore, looking for new methods to diagnose the whole image is necessary and more accurate. However, existing recognition algorithms do not work well for whole-slide image (WSI) analysis, failing to generalize for different stains and imaging, and displaying subpar clinical-level verification. This paper describes the design of a full ensemble deep learning model for the automatic diagnosis of the WSI. The proposed network discriminates between four classes with high accuracy, reaching up to 99.6%. This work is distinct from existing research in terms of simplicity, accuracy, and speed. It focuses on the whole staining slice image, not on a single cell. The designed deep learning structure considers the slice image with overlapping and non-overlapping cervical cells.
Effects of Display Layout on Spatial Memory for Immersive Environments

In immersive environments, positioning data visualisations around the user in a wraparound layout has been advocated as advantageous over flat arrangements more typical of traditional screens. However, other than limiting the distance users must walk, there is no clear design rationale behind this common practice, and little research on the impact of wraparound layouts on visualisation tasks. The ability to remember the spatial location of elements of visualisations within the display space is crucial to support visual analytical tasks, especially those that require users to shift their focus or perform comparisons. This ability is influenced by the user's spatial memory but how spatial memory is affected by different display layouts remains unclear. In this paper, we perform two user studies to evaluate the effects of three layouts with varying degrees of curvature around the user (flat-wall, semicircular-wraparound, and circular-wraparound) on a visuo-spatial memory task in a virtual environment. The results show that participants are able to recall spatial patterns with greater accuracy and report more positive subjective ratings using flat than circular-wraparound layouts. While we didn't find any significant performance differences between the flat and semicircular-wraparound layouts, participants overwhelmingly preferred the semicircular-wraparound layout suggesting it is a good compromise between the two extremes of display curvature.
Efficient Unitary Designs with a System-Size Independent Number of Non-Clifford Gates

Many quantum information protocols require the implementation of random unitaries. Because it takes exponential resources to produce Haar-random unitaries drawn from the full $n$-qubit group, one often resorts to $t$-designs. Unitary $t$-designs mimic the Haar-measure up to $t$-th moments. It is known that Clifford operations can implement at most $3$-designs. In this work, we quantify the non-Clifford resources required to break this barrier. We find that it suffices to inject $O(t^{4}\log^{2}(t)\log(1/\varepsilon))$ many non-Clifford gates into a polynomial-depth random Clifford circuit to obtain an $\varepsilon$-approximate $t$-design. Strikingly, the number of non-Clifford gates required is independent of the system size -- asymptotically, the density of non-Clifford gates is allowed to tend to zero. We also derive novel bounds on the convergence time of random Clifford circuits to the $t$-th moment of the uniform distribution on the Clifford group. Our proofs exploit a recently developed variant of Schur-Weyl duality for the Clifford group, as well as bounds on restricted spectral gaps of averaging operators.
A peer-to-peer file storage and sharing system based on consortium blockchain

In the era of big data, data is playing an increasingly important role in scientific study, and reliable storage and secure sharing of data have become a research hotspot. At present, centralized solutions based on data centers and cloud storage have problems with data-right confirmation and center trust. A large number of decentralized storage solutions are public systems, in which blockchain technology, as a tool for value exchange, does not solve the problems of data verification and system supervision. We propose a peer-to-peer storage system with identity access, which achieves data validation, cross-organizational data retrieval, trusted authorization, and sharing based on the consortium blockchain. Our solution proposes a peer-to-peer data storage scheme based on the consortium blockchain and a set of identity authentication mechanisms compatible with the consortium blockchain. Based on this, we propose a blockchain-based permission control scheme and a set of retrieval, authorization, and sharing processes. Finally, we implemented and tested the system to prove the feasibility of the scheme.
Review and Evaluation of Eye Movement Event Detection Algorithms

Eye tracking is a technology aimed at understanding the direction of the human gaze. Event detection is a process of detecting and classifying eye movements that are divided into several types. Nowadays, event detection is almost exclusively done by applying a detection algorithm to the raw recorded eye-tracking data. However, due to the lack of a standard procedure for how to perform evaluations, evaluating and comparing various detection algorithms in eye-tracking signals is very challenging. In this paper, we used data from a high-speed eye-tracker SMI HiSpeed 1250 system and compared event detection performance. The evaluation focused on fixations, saccades and post-saccadic oscillation classification. It used sample-by-sample comparisons to compare the algorithms and inter-agreement between algorithms and human coders. The impact of varying threshold values on threshold-based algorithms was examined and the optimum threshold values were determined. This evaluation differed from previous evaluations by using the same dataset to evaluate the event detection algorithms and human coders. We evaluated and compared the different algorithms from threshold-based, machine learning-based and deep learning event detection algorithms. The evaluation results show that all methods perform well for fixation and saccade detection; however, there are substantial differences in classification results. Generally, CNN (Convolutional Neural Network) and RF (Random Forest) algorithms outperform threshold-based methods.
The Future of Cybersecurity in the Age of Quantum Computers

The first week of August 2022 saw the world’s cryptographers grapple with the second shocker of the year. Another one of the four post-quantum cryptography (PQC) algorithms selected by the NIST (National Institute of Standards and Technology) in a rigorous 5-year process was cracked by a team from Belgium. They took just 62 min and a standard laptop to break the PQC algorithm to win a USD 50,000 bounty from Microsoft. The first shocker came 6 months earlier, when another of the NIST finalists (Rainbow) was taken down. Unfortunately, both failed PQC algorithms are commercially available to consumers. With 80 of the 82 PQC candidates failing the NIST standardization process, the future of the remaining two PQC algorithms is, at best, questionable, placing the rigorous 5-year NIST exercise to build a quantum-safe encryption standard in jeopardy. Meanwhile, there is no respite from the quantum threat that looms large. It is time we take a step back and review the etiology of the problem de novo. Although state-of-the-art computer security heavily relies on cryptography, it can indeed transcend beyond encryption. This paper analyzes an encryption-agnostic approach that can potentially render computers quantum-resistant. Zero-vulnerability computing (ZVC) secures computers by banning all third-party permissions, a root cause of most vulnerabilities. ZVC eliminates the complexities of the multi-layered architecture of legacy computers and builds a minimalist, compact solid-state software on a chip (3SoC) that is robust, energy-efficient, and potentially resistant to malware as well as quantum threats.
Phase retrieval of programmable photonic integrated circuits based on an on-chip fractional-delay reference path

Programmable photonic integrated circuits (PICs), offering diverse signal processing functions within a single chip, are promising solutions for applications ranging from optical communications to artificial intelligence. While the scale and complexity of programmable PICs are increasing, their characterization, and thus calibration, becomes increasingly challenging. Here we demonstrate a phase retrieval method for programmable PICs using an on-chip fractional-delay reference path. The impulse response of the chip can be uniquely and precisely identified from only the insertion loss using a standard complex Fourier transform. We demonstrate our approach experimentally with a four-tap finite-impulse-response chip. The results match well with expectations and verify our approach as effective for individually determining the taps’ weights without the need for additional ports or photodiodes.
Designing the Future? The Metaverse, NFTs, &amp; the Future as Defined by Unity Users

The “metaverse” and non-fungible tokens (NFTs), though not necessarily “new” terms or technologies, have risen to mainstream prominence post-2020. This paper, based on survey data obtained from Unity Technologies, examines the metaverse, NFTs, and the future of development within the Unity engine from the perspective of current Unity users. Specifically, the paper examines how users define the metaverse, their goals in metaverse and NFT development, and their future questions and concerns concerning these concepts. This data is then used to place the metaverse and NFTs into broader historical, present, and future contexts. The paper ultimately argues: (1) the metaverse and NFTs follow previous historical trends in communication technology development, (2) development within Unity will continue to be split between game development and non-game development, and (3) arguments of the “newness,” “uniqueness,” or “future-facing” of the metaverse and NFTs help to obfuscate legitimate concerns about these technologies.
Patch-based deep multi-modal learning framework for Alzheimer’s disease diagnosis using multi-view neuroimaging

The computer-aided diagnosis contributes to the early detection of mild cognitive impairment (MCI). Though many deep learning methods have achieved favorable performance on Alzheimer’s disease (AD) diagnosis tasks, the single-modality method will lead to lopsidedness in modeling disease features. Secondly, existing patch-based methods usually ignore the spatial information between local image patches when modeling the global feature representation of the brain. In addition, existing patch-based methods rely on anatomical landmark detection algorithms to pre-determine informative locations in the brain, which would result in the localization of brain atrophy locations that not only require extensive expert experience but also misses some potential lesion areas. In this paper, we propose a patch-based deep multi-modal learning (PDMML) framework for brain disease diagnosis. Specifically, we design a discriminative location discovery strategy to filter normal regions without prior knowledge. Multimodal imaging features were integrated at the patch level to capture multi-view brain disease representations. The local patches are further jointly learned to prevent the loss of spatial information caused by the direct flattening of the patches. Experimental results on 842 subjects from the ADNI dataset demonstrate that our proposed method excels in discriminative location discovery and brain disease diagnosis.
A systematic review of the purposes of Blockchain and fog computing integration: classification and open issues

The fog computing concept was proposed to help cloud computing for the data processing of Internet of Things (IoT) applications. However, fog computing faces several challenges such as security, privacy, and storage. One way to address these challenges is to integrate blockchain with fog computing. There are several applications of blockchain-fog computing integration that have been proposed, recently, due to their lucrative benefits such as enhancing security and privacy. There is a need to systematically review and synthesize the literature on this topic of blockchain-fog computing integration. The purposes of integrating blockchain and fog computing were determined using a systematic literature review approach and tailored search criteria established from the research questions. In this research, 181 relevant papers were found and reviewed. The results showed that the authors proposed the combination of blockchain and fog computing for several purposes such as security, privacy, access control, and trust management. A lack of standards and laws may make it difficult for blockchain and fog computing to be integrated in the future, particularly in light of newly developed technologies like quantum computing and artificial intelligence. The findings of this paper serve as a resource for researchers and practitioners of blockchain-fog computing integration for future research and designs.
Deep Learning Approach For Objects Detection in Underwater Pipeline Images

In this paper, we present automatic, deep-learning methods for pipeline detection in underwater environments. Seafloor pipelines are critical infrastructure for oil and gas transport. The inspection of those pipelines is required to verify their integrity and determine the need for maintenance. Underwater conditions present a harsh environment that is challenging for image recognition due to light refraction and absorption, poor visibility, scattering, and attenuation, often causing poor image quality. Modern machine-learning object detectors utilize Convolutional Neural Network (CNN), requiring a training dataset of sufficient quality. In the paper, six different deep-learning CNN detectors for underwater object detection were trained and tested: five are based on the You Only Look Once (YOLO) architectures (YOLOv4, YOLOv4-Tiny, CSP-YOLOv4, YOLOv4@Resnet, YOLOv4@DenseNet), and one on the Faster Region-based CNN (RCNN) architecture. The models’ performances were evaluated in terms of detection accuracy, mean average precision (mAP), and processing speed measured with the Frames Per Second (FPS) on a custom dataset containing underwater pipeline images. In the study, the YOLOv4 outperformed other models for underwater pipeline object detection resulting in an mAP of 94.21% with the ability to detect objects in real-time. Based on the literature review, this is one of the pioneering works in this field.
"I'm not sure what difference is between their content and mine, other than the person itself"

How social media platforms could fairly conduct content moderation is gaining attention from society at large. Researchers from HCI and CSCW have investigated whether certain factors could affect how users perceive moderation decisions as fair or unfair. However, little attention has been paid to unpacking or elaborating on the formation processes of users' perceived (un)fairness from their moderation experiences, especially users who monetize their content. By interviewing 21 for-profit YouTubers (i.e., video content creators), we found three primary ways through which participants assess moderation fairness, including equality across their peers, consistency across moderation decisions and policies, and their voice in algorithmic visibility decision-making processes. Building upon the findings, we discuss how our participants' fairness perceptions demonstrate a multi-dimensional notion of moderation fairness and how YouTube implements an algorithmic assemblage to moderate YouTubers. We derive translatable design considerations for a fairer moderation system on platforms affording creator monetization.
Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotation

Machine learning models are commonly used to detect toxicity in online conversations. These models are trained on datasets annotated by human raters. We explore how raters' self-described identities impact how they annotate toxicity in online comments. We first define the concept of Specialized Rater Pools: rater pools formed based on raters' self-described identities, rather than at random. We formed three such rater pools for this study - specialized rater pools of raters from the U.S. who identify as African American, LGBTQ, and those who identify as neither. Each of these rater pools annotated the same set of comments, which contains many references to these identity groups. We found that rater identity is a statistically significant factor in how raters will annotate toxicity for identity-related annotations. Using preliminary content analysis, we examined the comments with the most disagreement between rater pools and found nuanced differences in the toxicity annotations. Next, we trained models on the annotations from each of the different rater pools, and compared the scores of these models on comments from several test sets. Finally, we discuss how using raters that self-identify with the subjects of comments can create more inclusive machine learning models, and provide more nuanced ratings than those by random raters.
Destructive Criticism in Software Code Review Impacts Inclusion

The software industry lacks gender diversity. Recent research has suggested that a toxic working culture is to blame. Studies have found that communications in software repositories directed towards women are more negative in general. In this study, we use a destructive criticism lens to examine gender differences in software code review feedback. Software code review is a practice where code is peer reviewed and negative feedback is often delivered. We explore differences in perceptions, frequency, and impact of destructive criticism across genders. We surveyed 93 software practitioners eliciting perceived reactions to hypothetical scenarios (or vignettes) where participants are asked to imagine receiving either constructive or destructive criticism. In addition, the survey collected general opinions on feedback obtained during software code review as well as the frequency that participants give and receive destructive criticism. We found that opinions on destructive criticism vary. Women perceive destructive criticism as less appropriate and are less motivated to continue working with the developer after receiving destructive criticism. Destructive criticism is fairly common with more than half of respondents having received nonspecific negative feedback and nearly a quarter having received inconsiderate negative feedback in the past year. Our results suggest that destructive criticism in code review could be a contributing factor to the lack of gender diversity observed in the software industry.
Using reclaimed water to cope with water scarcity: an alternative for agricultural irrigation in Spain

Abstract In water-stressed agricultural regions, reuse of reclaimed water has emerged as a promising alternative that improves supply reliability, alleviates water scarcity and contributes to circular economy. The European Union has recently launched several initiatives to facilitate the adoption of water reuse for irrigation. However, its adoption is still far below its potential in most areas. This is the case of the Western La Mancha aquifer, in central Spain, where reclaimed water reuse is considered an alternative source to groundwater that may contribute to reduce overexploitation. A stakeholder-based fuzzy cognitive map (FCM) was developed to provide insights into the current situation of reclaimed water reuse in this area, as well as to explore the outcomes of different simulated scenarios ( cost recovery, agricultural transformation, social awareness and political will increase ). The FCM-based dynamic simulations showed that political will increase would generate the highest increase of reclaimed water reuse in agriculture in the study area, providing the highest increase of water reuse in agriculture. Agricultural extensification and increased social awareness delivered similar positive outcomes, however, only public awareness campaigns would increase water reuse, with agricultural extensification outcomes being more oriented towards the reduction of water abstractions and pollution. The cost recovery scenario was the only one that resulted in non-desired changes, mainly caused by reduced farmers’ income due to higher costs of reclaimed water. Finally, the analysis also evidenced the key role that the newly enacted EU Water Reuse Regulation may play in promoting reclaimed water use, even reversing the negative outcomes of the cost recovery scenario.
A genetic algorithm for multivariate missing data imputation

Some data mining, AI and data processing tasks might have data loss whose estimation/imputation is an important problem to be solved. Genetic algorithms are efficient and flexible global optimization methods able to deal with both multiple missing observations and multiple features such as continuous/discrete/binary data which are often found in multivariate databases unlike classical missing data estimation methods which only deal with univariate–continuous data. This paper presents a genetic algorithm to impute multiple missing observations in multivariate data which minimizes a new multi–objective (fitness) function based on the Minkowski distance of the means, variances, covariances and skewness between available/completed data. To do so, two sets of examples were tested: a continuous/discrete dataset which is compared to both the EM algorithm and auxiliary regressions, and a comparison over seven benchmark datasets.