Reviewerly/exHarmony huggingface dataset:
Deep Vision for Breast Cancer Classification and Segmentation

(1) Background: Female breast cancer diagnoses odds have increased from 11:1 in 1975 to 8:1 today. Mammography false positive rates (FPR) are associated with overdiagnoses and overtreatment, while false negative rates (FNR) increase morbidity and mortality. (2) Methods: Deep vision supervised learning classifies 299 × 299 pixel de-noised mammography images as negative or non-negative using models built on 55,890 pre-processed training images and applied to 15,364 unseen test images. A small image representation from the fitted training model is returned to evaluate the portion of the loss function gradient with respect to the image that maximizes the classification probability. This gradient is then re-mapped back to the original images, highlighting the areas of the original image that are most influential for classification (perhaps masses or boundary areas). (3) Results: initial classification results were 97% accurate, 99% specific, and 83% sensitive. Gradient techniques for unsupervised region of interest mapping identified areas most associated with the classification results clearly on positive mammograms and might be used to support clinician analysis. (4) Conclusions: deep vision techniques hold promise for addressing the overdiagnoses and treatment, underdiagnoses, and automated region of interest identification on mammography.
Multicriticality and quantum fluctuation in a generalized Dicke model

Quantum many-body systems that support high-order quantum phase transitions are quite rare. Here we consider an important generalization of the Dicke model in which an ensemble of multilevel atoms, instead of two-level atoms as in the conventional Dicke model, interact with a single photonic mode, and show that this generalized Dicke model can become multicritical. For a subclass of experimentally realizable schemes, multicritical conditions of arbitrary order can be expressed analytically in compact forms. As such, experiments can be readily designed to achieve quantum phase transition of desired order. We also calculate the atom-photon entanglement entropy for both critical and noncritical cases and find that the order of the criticality strongly affects the critical entanglement entropy: higher order yields stronger entanglement. Our work provides deep insight into quantum phase transitions and multicriticality.
Probabilistic hierarchically controlled teleportation of anarbitrary &amp;lt;italic&amp;gt;m&amp;lt;/italic&amp;gt;-qudit state with a pure entangledquantum channel

<p indent=0mm>Quantum teleportation is a unique quantum information processing method in quantum communication. Researchers have shown considerable interest in hierarchically controlled teleportation by using quantum entangled states as the quantum channel. Protocols for the hierarchically controlled teleportation of an arbitrary single-qubit via a four-qubit χ state and a four-qubit cluster state have been proposed. We present a protocol for the hierarchically controlled teleportation of an arbitrary <italic>m</italic>-qudit (<italic>d</italic>-dimensional quantum system) state by using a partially entangled quantum state as the quantum channel. The sender performs <italic>m</italic> generalized Bell-state measurements on her <sc>2<italic>m</italic></sc> particles. To reconstruct the original state, the upper-grade receiver only needs to apply a unitary operation in accordance with one of the receivers’ measurement results, and the lower-grade receivers need to perform a unitary operation according to all the other receivers’ measurement results. The receivers can reconstruct the original state probabilistically by introducing an auxiliary qubit and performing a collective unitary transformation. The protocol has the advantage of having high channel capacity by using a high-dimensional entangled state as the quantum channel.
E.T.

Transformer-based deep learning models have become a ubiquitous vehicle to drive a variety of Natural Language Processing (NLP) related tasks beyond their accuracy ceiling. However, these models also suffer from two pronounced challenges, that is, gigantic model size and prolonged turnaround time. To this end, we introduce ET. that r<u>E</u>-thinks self-attention computation for <u>T</u>ransformer models on GPUs with the following contributions: First, we introduce a novel self-attention architecture, which encompasses two tailored self-attention operators with corresponding sequence length-aware optimizations, and operation reordering optimizations. Second, we present an attention-aware pruning design which judiciously uses various pruning algorithms to reduce more computations hence achieves significantly shorter turnaround time. For the pruning algorithms, we not only revamp the existing pruning algorithms, but also tailor new ones for transformer models. Taken together, we evaluate E.T. across a variety of benchmarks for Transformer, BERTBASE and DistilBERT, where E.T. presents superior performance over the mainstream projects, including the popular Nvidia Enterprise solutions, i.e., TensorRT and FasterTransformer.
The Accidental Origins, Underappreciated Limits, and Enduring Promises of Platform Transparency Reporting about Information Operations

&#x0D; &#x0D; &#x0D; Intense public and regulatory pressure following revelations of Russian interference in the US 2016 election led social media platforms to develop new policies to demonstrate how they had addressed the troll-shaped blind spot in their content moderation practices. This moment also gave rise to new transparency regimes that endure to this day and have unique characteristics, notably: the release of regular public reports of enforcement measures; the provision of underlying data to external stakeholders and, sometimes, the public; and collaboration across industry and with government. Despite these positive features, platform policies and transparency regimes related to information operations remain poorly understood. Underappreciated ambiguities and inconsistencies in platforms’ work in this area create perverse incentives for enforcement and distort public understanding of information operations. Highlighting these weaknesses is important as platforms expand content moderation practices in ways that build on the methods used in this domain. As platforms expand these practices, they are not continuing to invest in their transparency regimes, and the early promise and momentum behind the creation of these pockets of transparency are being lost as public and regulatory focus turns to other areas of content moderation.&#x0D; &#x0D; &#x0D;
The Emergence of a Third Wave of Open Data: How To Accelerate the Re-Use of Data for Public Interest Purposes While Ensuring Data Rights and Community Flourishing

We live, as we are so often told, in an era of data abundance. Yet we also live during a period marked by tremendous inequities and asymmetries when it comes to data access. These contradictory phenomena represent one of the great paradoxes of our times, reflective of more general socio-economic and political trends. Even as ever greater amounts of data are generated and stored, the capability to actually access and re-use this data to spur positive social change remains stunted. This discrepancy occurs despite the success of the open data movement, which built on earlier Freedom of Information (FOI) legislation in pushing at the boundaries of transparency and accessibility.In this piece, we argue these (and many other) limitations necessitate additional improvements to accelerate the re-use of data to unleash the public good potential of the digital era. We reflect on the potential of what we call the “Third Wave of Open Data.” We discuss both what we see as the emerging elements of this wave and the actions several players have taken that may contribute to its realization. In doing so, we propose actions for how policy makers and data practitioners might address the limitations of previous waves and outline a set of actions that could accelerate data re-use and collaboration.
Constrained Generative Adversarial Learning for Dimensionality Reduction

Emerging data-driven technologies and big data analytics generate and deal with high-dimensional data. Transformation of such data into a low-dimensional feature space brings about numerous benefits, such as a more discriminant feature space, performance enhancement, less computational burden, and facilitating data visualization. This paper proposes a novel dimensionality reduction algorithm based on generative adversarial networks to tackle the issues related to high-dimensional data and common challenges in dimensionality reduction. To this aim, two constraints are defined to preserve the characteristics of the original data while rectifying the data distribution upon transformation. Formulating the transformation as sequential projections, the proposed Constrained Adversarial Dimensionality Reduction (CADR) method finds a set of sequential projection vectors that lead to a feature space in which between-class separability and within-class integrity are satisfied. This is while the transformed data perfectly comply with the pairwise affinity correlation in the original feature space. To evaluate the proposed method, nine advanced dimensionality reduction techniques are employed to enable a comparative study. The experiments are performed on several real-world benchmark datasets in terms of classification accuracy, F-measure, and G-mean. The obtained results show that the CADR could yield classification performance at a satisfactory level and outperforms the other competitors.
Spectator View: Enabling Asymmetric Interaction between HMD Wearers and Spectators with a Large Display

In this paper, we present a system that allows a user with a head-mounted display (HMD) to communicate and collaborate with spectators outside of the headset. We evaluate its impact on task performance, immersion, and collaborative interaction. Our solution targets scenarios like live presentations or multi-user collaborative systems, where it is not convenient to develop a VR multiplayer experience and supply each user (and spectator) with an HMD. The spectator views the virtual world on a large-scale tiled video wall and is given the ability to control the orientation of their own virtual camera. This allows spectators to stay focused on the immersed user's point of view or freely look around the environment. To improve collaboration between users, we implemented a pointing system where a spectator can point at objects on the screen, which maps an indicator directly onto the objects in the virtual world. We conducted a user study to investigate the influence of rotational camera decoupling and pointing gestures in the context of HMD-immersed and non-immersed users utilizing a large-scale display. Our results indicate that camera decoupling and pointing positively impacts collaboration. A decoupled view is preferable in situations where both users need to indicate objects of interest in the scene, such as presentations and joint-task scenarios, as it requires a shared reference space. A coupled view, on the other hand, is preferable in synchronous interactions such as remote-assistant scenarios.
The Application of Ultrasound Image in Cancer Diagnosis

In order to solve the problem of low accuracy, high cost, and difficult detection of traditional algorithms, a new algorithm based on ultrasound imaging is proposed in this paper. The algorithm is based on fuzzy clustering to diagnose the disease and effectively improve the accuracy of the algorithm. By testing the actual scene, it effectively proves the effectiveness and accuracy of the algorithm, which can find and treat diseases in time and can be widely used and popularized in clinical research in China.
GPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles

GPS sensors are widely used to know a vehicle’s location and to track its route. Although GPS sensor technology is advancing, they present systematic failures depending on the environmental conditions to which they are subjected. To tackle this problem, we propose an intelligent system based on fuzzy logic, which takes the information from the sensors and correct the vehicle’s absolute position according to its latitude and longitude. This correction is performed by two fuzzy systems, one to correct the latitude and the other to correct the longitude, which are trained using the MATLAB ANFIS tool. The positioning correction system is trained and tested with two different datasets. One of them collected with a Pmod GPS sensor and the other a public dataset, which was taken from routes in Brazil. To compare our proposal, an unscented Kalman filter (UKF) was implemented. The main finding is that the proposed fuzzy systems achieve a performance of 69.2% higher than the UKF. Furthermore, fuzzy systems are suitable to implement in an embedded system such as the Raspberry Pi 4. Another finding is that the logical operations facilitate the creation of non-linear functions because of the ‘if else’ structure. Finally, the existence justification of each fuzzy system section is easy to understand.
100+ FPS detector of personal protective equipment for worker safety: A deep learning approach for green edge computing

In industrial production, personal protective equipment (PPE) protects workers from accidental injuries. However, wearing PPE is not strictly enforced among workers due to all kinds of reasons. To enhance the monitoring of workers and thus avoid safety accidents, it is essential to design an automatic detection method for PPE. In this paper, we constructed a dataset called FZU-PPE for our study, which contains four types of PPE (helmet, safety vest, mask, and gloves). To reduce the model size and resource consumption, we propose a lightweight object detection method based on deep learning for superfast detection of whether workers are wearing PPE or not. We use two lightweight methods to optimize the network structure of the object detection algorithm to reduce the computational effort and parameters of the detection model by 32% and 25%, respectively, with minimal accuracy loss. We propose a channel pruning algorithm based on the BN layer scaling factor γ to further reduce the size of the detection model. Experiments show that the automatic detection of PPE using our lightweight object detection method takes only 9.5 ms to detect a single video frame and achieves a detection speed of 105 FPS. Our detection model has a minimum size of 1.82 MB and a model size compression rate of 86.7%, which can meet the strict requirements of memory occupation and computational resources for embedded and mobile devices. Our approach is a superfast detection method for green edge computing.
Comparative Opinion Quintuple Extraction from Product Reviews

As an important task in opinion mining, comparative opinion mining aims to identify comparative sentences from product reviews, extract the comparative elements, and obtain the corresponding comparative opinion tuples.However, most previous studies simply regarded comparative tuple extraction as comparative element extraction, which ignored the fact that many comparative sentences may contain multiple comparisons.The comparative opinion tuples defined in these studies also failed to explicitly provide comparative preferences.To address these issues, in this work we first introduce a new Comparative Opinion Quintuple Extraction (COQE) task, to identify comparative sentences from product reviews and extract all comparative opinion quintuples (Subject, Object, Comparative Aspect, Comparative Opinion, Comparative Preference).Secondly, based on the existing comparative opinion mining corpora, we make supplementary annotations and construct three datasets for the COQE task.Finally, we benchmark the COQE task by proposing a new multi-stage neural network approach which significantly outperforms the baseline systems extended from previous comparative opinion mining methods.The datasets and source
Applying Declarative Analysis to Software Product Line Models: An Industrial Study

Software Product Lines (SPLs) are families of related software products developed from a common set of artifacts. Most existing analysis tools can be applied to a single product at a time, but not to an entire SPL. Some tools have been redesigned/re-implemented to support the kind of variability exhibited in SPLs, but this usually takes a lot of effort, and is error-prone. Declarative analyses written in languages like Datalog have been collectively lifted to SPLs in prior work [1], which makes the process of applying an existing declarative analysis to a product line more straightforward. In this paper, we take an existing declarative analysis (behaviour alteration) and apply it to a set of automotive software product lines from General Motors. We discuss the design of the analysis pipeline used in this process, present its scalability results, and provide a means to visualize the analysis results for a subset of products filtered by feature expression. We also reflect on some of the lessons learned throughout this project.
Natural Disaster on Twitter: Role of Feature Extraction Method of Word2Vec and Lexicon Based for Determining Direct Eyewitness

Researchers have collected Twitter data to study a wide range of topics, one of which is a natural disaster. A social network sensor was developed in existing research to filter natural disaster information from direct eyewitnesses, none eyewitnesses, and non-natural disaster information. It can be used as a tool for early warning or monitoring when natural disasters occur. The main component of the social network sensor is the text tweet classification. Similar to text classification research in general, the challenge is the feature extraction method to convert Twitter text into structured data. The strategy commonly used is vector space representation. However, it has the potential to produce high dimension data. This research focuses on the feature extraction method to resolve high dimension data issues. We propose a hybrid approach of word2vec-based and lexicon-based feature extraction to produce new features. The Experiment result shows that the proposed method has fewer features and improves classification performance with an average AUC value of 0.84, and the number of features is 150. The value is obtained by using only the word2vec-based method. In the end, this research shows that lexicon-based did not influence the improvement in the performance of social network sensor predictions in natural disasters.&#x0D; HIGHLIGHTS&#x0D; &#x0D; Implementation of text classification is generally only used to perform sentiment analysis, it is still rare to use it to perform text classification for use in determining direct eyewitnesses in cases of natural disasters&#x0D; One of the common problems in text mining research is the extracted features from the vector space representation method generate high dimension data&#x0D; A hybrid approach of word2vec-based and lexicon-based feature extraction experiment was conducted in order to find a method that can generate new features with low dimensions and also improve the classification performance&#x0D; &#x0D; GRAPHICAL ABSTRACT
Automatic extraction of 12 cardiovascular concepts from German discharge letters using pre-trained language models

Objective A vast amount of medical data is still stored in unstructured text documents. We present an automated method of information extraction from German unstructured clinical routine data from the cardiology domain enabling their usage in state-of-the-art data-driven deep learning projects. Methods We evaluated pre-trained language models to extract a set of 12 cardiovascular concepts in German discharge letters. We compared three bidirectional encoder representations from transformers pre-trained on different corpora and fine-tuned them on the task of cardiovascular concept extraction using 204 discharge letters manually annotated by cardiologists at the University Hospital Heidelberg. We compared our results with traditional machine learning methods based on a long short-term memory network and a conditional random field. Results Our best performing model, based on publicly available German pre-trained bidirectional encoder representations from the transformer model, achieved a token-wise micro-average F1-score of 86% and outperformed the baseline by at least 6%. Moreover, this approach achieved the best trade-off between precision (positive predictive value) and recall (sensitivity). Conclusion Our results show the applicability of state-of-the-art deep learning methods using pre-trained language models for the task of cardiovascular concept extraction using limited training data. This minimizes annotation efforts, which are currently the bottleneck of any application of data-driven deep learning projects in the clinical domain for German and many other European languages.
Energy-Efficient Topology Construction via Power Allocation for Decentralized Learning via Smart Devices With Edge Computing

Data privacy preservation has drawn much attention in emerging machine learning applications. Decentralized learning among smart devices over wireless networks is thus developed to guarantee data security and eliminate the involvement of parameter servers to avoid transmission bottlenecks. However, the previous research focuses on data compression and exchange rules of model parameters among smart devices. Still, it neglects the interplay between link cardinality, transmission power consumption, and collision in transmission. To jointly optimize these issues, in this paper, we first set collision and interference aside and formulate a new optimization problem, named GreenDL, and then extend GreenDL to be collision-aware, namely, GreenDL-CA, by restricting the maximum degree of each smart devices. We prove their hardness and propose two approximation algorithms dubbed as CoTRAIN and CoTRAIN-CA for GreenDL and GreenDL-CA, respectively. Experiment and simulation results manifest that both CoTRAIN and CoTRAIN-CA reduce more than 20% power compared with the other heuristics without sacrificing the convergence rate in the decentralized learning practices.
Prevalence of virtual reality (VR) games found through mental health categories on STEAM: a first look at VR on commercial platforms as tools for therapy

Immersive virtual reality (VR) games are increasingly becoming part of everyday life. Several studies support immersive VR technology as a treatment method for mental health problems. There is however little insight into the prevalence of commercially available VR games for treatment of mental health problems on commercial platforms such as STEAM, and to what extent they can be used as tools for treatment or add-on treatment of mental health problems.The aim of this study was to take a first look at the prevalence and overview of content of commercially available games. The games were found using search words related to psychiatric diagnosis or care.We performed a search for keywords related to symptoms, diagnosis, and treatment strategies of mental health problems. The search was performed November 2020 on STEAM. A scheme was created for elimination and inclusion of games, eliminating those games which were irrelevant to mental health or had triggering elements such as violence, excessive movement which could trigger nausea, horror and pornographic imagery.In total, 735 hits were found, 565 unique games. After the games were reviewed for content via trailers, descriptions and screenshots, 32%, i.e. 182 games passed the inclusion criteria. Majority of the games which were excluded were either not connected to mental health, contained violence, adult content or were in other ways irrelevant or inappropriate.Commercial platforms are only at the beginning of the development towards therapeutic content. Currently the quality and usability for therapeutic use is scarce but holds great potential.