Reviewerly/exHarmony huggingface dataset:
Optimizing Hospital Room Layout to Reduce the Risk of Patient Falls

Despite years of research into patient falls in hospital rooms, falls and related injuries remain a serious concern to patient safety. In this work, we formulate a gradient-free constrained optimization problem to generate and reconfigure the hospital room interior layout to minimize the risk of falls. We define a cost function built on a hospital room fall model that takes into account the supportive or hazardous effect of the patient's surrounding objects, as well as simulated patient trajectories inside the room. We define a constraint set that ensures the functionality of the generated room layouts in addition to conforming to architectural guidelines. We solve this problem efficiently using a variant of simulated annealing. We present results for two real-world hospital room types and demonstrate a significant improvement of 18% on average in patient fall risk when compared with a traditional hospital room layout and 41% when compared with randomly generated layouts.
CD2CR: Co-reference Resolution Across Documents and Domains

Cross-document co-reference resolution (CDCR) is the task of identifying and linking mentions to entities and concepts across many text documents. Current state-of-the-art models for this task assume that all documents are of the same type (e.g. news articles) or fall under the same theme. However, it is also desirable to perform CDCR across different domains (type or theme). A particular use case we focus on in this paper is the resolution of entities mentioned across scientific work and newspaper articles that discuss them. Identifying the same entities and corresponding concepts in both scientific articles and news can help scientists understand how their work is represented in mainstream media. We propose a new task and English language dataset for cross-document cross-domain co-reference resolution (CD$^2$CR). The task aims to identify links between entities across heterogeneous document types. We show that in this cross-domain, cross-document setting, existing CDCR models do not perform well and we provide a baseline model that outperforms current state-of-the-art CDCR models on CD$^2$CR. Our data set, annotation tool and guidelines as well as our model for cross-document cross-domain co-reference are all supplied as open access open source resources.
Benchmarking Quantum Chemistry Computations with Variational, Imaginary Time Evolution, and Krylov Space Solver Algorithms

The rapid progress of noisy intermediate-scale quantum (NISQ) computing underscores the need to test and evaluate new devices and applications. Quantum chemistry is a key application area for these devices, and therefore serves as an important benchmark for current and future quantum computer performance. Previous benchmarks in this field have focused on variational methods for computing ground and excited states of various molecules, including a benchmarking suite focused on performance of computing ground states for alkali-hydrides under an array of error mitigation methods. Here, we outline state of the art methods to reach chemical accuracy in hybrid quantum-classical electronic structure calculations of alkali hydride molecules on NISQ devices from IBM. We demonstrate how to extend the reach of variational eigensolvers with new symmetry preserving Ansatze. Next, we outline how to use quantum imaginary time evolution and Lanczos as a complementary method to variational techniques, highlighting the advantages of each approach. Finally, we demonstrate a new error mitigation method which uses systematic error cancellation via hidden inverse gate constructions, improving the performance of typical variational algorithms. These results show that electronic structure calculations have advanced rapidly, to routine chemical accuracy for simple molecules, from their inception on quantum computers a few short years ago, and they point to further rapid progress to larger molecules as the power of NISQ devices grows.
Soft measurements of qualitative integral indicators for monitoring quantitative dataset

Abstract In many areas, the criteria for the adoption of certain decisions are not quantitative, but qualitative assessments of some crucial criteria. This applies, in particular, to the integral assessment of the health status of patients, or the assessment of the ecological state of the environment, etc. Therefore, an integral qualitative assessment of an object, based on the known numerical states of its individual elements, is an urgent non-trivial task. Currently, there are methods for calculating the quantitative integral assessment, which is a kind of “code” for the qualitative assessment. A direct verbal evaluation is obtained, as a rule, by ranking the possible numerical values of the resulting integral code, and assigning to each given interval a certain qualitative definition. However, well-known methods for constructing analytical computational integrated assessment are not effective. As an alternative, the authors of the article propose an approach consisting in a comprehensive assessment of the state of objects based on the allocation of “similar” groups, and analysis of the basic general properties of objects in the group. Such problems can be solved by cluster analysis methods. Cluster analysis allows you to group (decompose) data that has the property of “similarity” in a given sense and to separate them from “dissimilar” data. By analyzing data in groups, experts can give a high-quality interpretation of homogeneous data in a cluster. However, when analyzing the obtained groupings by domain experts, a situation often arises when the data in some groups have a sufficient degree of homogeneity in order to give them a qualitative assessment, and in others the mathematical method of clustering does not allow to separate heterogeneous data from each other, and experts cannot give rating. So the idea to apply step-by-step clusterization when in each step it is decided whether every cluster has a sufficient degree of homogeneity or not. If not, all non-homogeneous clusters should be further decomposed into two or more clusters. It remained to decide which clustering method to choose for the data decomposition. To address this problem, reconnaissance experiments were conducted. As a result, the Kohonen Self-Organizing Map (SOM-cards) method has been proved to be the best. The proposed algorithm for multilevel clustering was called the Cascade Neural Network Filtering Data. Its effectiveness was confirmed by numerical experiments.
12 Detection of breast cancer using deep neural networks with transfer learning on histopathological images

Breast cancer remains as the second leading cause of cancer death in women worldwide. The basal-like carcinoma is defined as an important breast cancer subtype and associated with its protein and gene expression profile. There are five main breast subtypes, namely, normal breast-like subtype, luminal subtype A, HER-2 overexpression subtype, luminal subtype B and basal-like subtype. Breast cancer includes five molecularly defined subclasses, one of which is basal-like tumors. In recent years, basal-like tumors have taken great attention of researchers due to limited therapy opportunities they offer. This tumor subtype tends to occur more frequently in younger (<50) patients and almost 15% of all breast cancers are caused by this tumor subtype. Basal-like tumors are expressed by their distinctive immunophenotypic, morphologic, clinical and genetic features. However, no widely accepted consensus could be reached on definition and common clinical identification of the subtype of breast cancer. In addition, there is no widely accepted way on how to systematically classify this complex group of tumors. As a cost-effective approach, immunohistochemical markers have been used to classify the basal-like breast cancer (BLBC). Unfortunately, these markers only have a 60% accuracy. In this study, in order to improve the accuracy of BLBC detection on histopathological images, the results of performance tests using a deep-learning-based approach on multiple GPUs are presented. Experimental studies are performed with the help of five different pretrained deep neural networks such as ResNet18, ResNet50, AlexNet, SqueezeNet and InceptionV3 using global histopathological image dataset breast histopathology images. Parallel computing performances are also evaluated using GPUs during the experimental tests. As a result, 70.18%, 95.13%, 77.55%, 75.89% and 96.94% accuracy rates are achieved for each pretrained network, respectively.
An Improved Prediction of Software Enhancement Effort using Correlation-Based Feature Selection and M5P ML Algorithm

Software enhancement must be carefully planned and quantified to satisfy customer change requests, such as adding a new functionality, or deleting or changing an existing one. This paper investigates the use of M5P Machine Learning (ML) algorithm on predicting software enhancement effort. This M5P ML algorithm is trained and tested with 302 software enhancement projects obtained from the ISBSG dataset. The correlation-based feature selection (CFS) algorithm is used to achieve efficient data reduction. Thus, the selected ML techniques are trained on a dataset with relevant features that lead to improve the accuracy of their estimates. The Performance of the M5P using CFS is compared with the three Machine Learning Regression Methods (MLRM): Gradient Boosting Regression (GBRegr), Linear Support Vector Regression (LinearSVR), and Random Forest Regression (RFR). Results show that the prediction of Software Enhancement Effort using Correlation-based Feature Selection and M5P is improved in terms of MAE (Mean Absolute Error) = 0.0612 and Root Mean Square Error (RMSE) = 0.2514.
Decentralized Multi-authority Anonymous Credential System with Bundled Languages on Identifiers

We propose an anonymous credential system equipped with independent decentralized authorities who issue credentials. In our system, the number of authorities can dynamically increase or decrease. A credential is a private secret key issued by an authority, and it is given to an entity distinguished by an identifier. In the issuing phase, an authority only has to sign identifiers. In the proving phase, under a principle of “commit-to-id”, an entity proves to a verifier the knowledge of his/her identifier and private secret keys by generating a unified proof. The verifier should resist against collusion attacks executed by adversaries who bring together the private secret keys issued to different identifiers. To construct our system, we employ two building blocks; the structure-preserving signature scheme and the Groth-Sahai non-interactive proof system. Both blocks work in the setting of bilinear groups. To attain the collusion resistance, we propose a notion of “bundled language” that is abstraction of simultaneous pairing-product equations which include an identifier as a variable.
Chapter 1. A History of Satisfiability

This chapter traces the links between the notion of Satisfiability and the attempts by mathematicians, philosophers, engineers, and scientists over the last 2300 years to develop effective processes for emulating human reasoning and scientific discovery, and for assisting in the development of electronic computers and other electronic components. Satisfiability was present implicitly in the development of ancient logics such as Aristotle’s syllogistic logic, its extentions by the Stoics, and Lull’s diagrammatic logic of the medieval period. From the renaissance to Boole algebraic approaches to effective process replaced the logics of the ancients and all but enunciated the meaning of Satisfiability for propositional logic. Clarification of the concept is credited to Tarski in working out necessary and sufficient conditions for “p is true” for any formula p in first-order syntax. At about the same time, the study of effective process increased in importance with the resulting development of lambda calculus, recursive function theory, and Turing machines, all of which became the foundations of computer science and are linked to the notion of Satisfiability. Shannon provided the link to the computer age and Davis and Putnam directly linked Satisfiability to automated reasoning via an algorithm which is the backbone of most modern SAT solvers. These events propelled the study of Satisfiability for the next several decades, reaching “epidemic proportions” in the 1990s and 2000s, and the chapter concludes with a brief history of each of the major Satisfiability-related research tracks that developed during that period.
Measurement of Information Security Awareness Level: A Case Study of Online Transportation Users

The rise of misuse of online transportation accounts causes a person to be aware to protect their personal information. A person's awareness is an important factor for safeguarding his personal information assets and reducing crime loopholes. This research was conducted to measure the security awareness of online transportation users. This research was conducted by distributing questionnaires to 215 respondents who use online transportation applications in Indonesia. The framework used to measure the level of information security awareness is the Knowledge-Attitude-Behavior (KAB) model and Human Aspects of Information Security Questionnaire (HAIS-Q). Analytic Hierarchy Process (AHP) will be used as a data processing method to assess information security awareness level of online transportation users. The result shows that information security awareness level of online transportation users is at the level of “good”. But some focus areas of users indicate the score at the level of “average”, which the score is not enough to protect personal assets so need certain improvements in the application and user aspects that can increase the level of information security awareness of online transportation users.
Prospectivity mapping of critical raw material at the continental scale- a part of the FRAME project

Summary The prime focus of mineral prospectivity mapping (MPM) is the identification, delineation and level of potential for an area to host feasible mineral resources. In general, GIS-based MPM can be either data driven or knowledge-driven. Methods of data-driven MPM, which involve quantitative analysis of spatial relationships among anomalies (i.e., indicators of mineralization) and existing occurrences of mineral deposits of the type sought, is suitable for “brownfields” or well-explored regions, wherein the objective is to define additional targets for exploration. One of the main objectives of WP3 in the FRAME project (www.frame.lneg.pt) is to produce a map of Strategic and Critical Raw Materials (SCRM) for Europe. Another objective is to produce predictive targeting based on GIS exploration tools at continental scale. in this presentation we will review the state of the art for the MPM and show example of the favourability maps using CBA method and hybride fuzzy weight of evidence produced in the FRAME project. This methodology represents different development stages, scales and progress of economic geology surveys which could be a tool to improve effectiveness and efficiency of future investments in exploration. Improved understanding and support for a balance management of competitive land-usage interests is an additional benefit.
How to Code Data Integrity Verification Secure Against Single-Spot-Laser-Induced Instruction Manipulation Attacks

Fault injection attacks (FIA), which cause information leakage by injecting intentional faults into the data or operations of devices, are one of the most powerful methods compromising the security of confidential data stored on these devices. Previous studies related to FIA on software report that attackers can skip instructions running on many kinds of devices through many means of fault injection. Most existing anti-FIA countermeasures on software are designed to secure against instruction skip (IS). On the other hand, recent studies report that attackers can use laser fault injection to manipulate instructions running on devices as they want. Although the previous studies show that instruction manipulation (IM) can attack the existing countermeasures against IS, no effective countermeasures against IM have been proposed yet to the best of our knowledge. In this paper, we define an attacker's model under the assumption that the attacker injects IM using a single-spot laser, and propose a coding method to verify data integrity secure against the assumed IM model in ARM 16-bit Thumb instruction set.
Emulation of dynamic multi–output simulator of risk of type-2 diabetes

We have recently developed and validated multilevel patient-specific model able to integrate metabolic, nutritional and lifestyle data for the prediction of the metabolic and inflammatory processes underlying the development of type-2 diabetes in the absence of familiarity. Given the incidence of type-2 diabete, which accounts for 85-90% of all cases of diabetes in the world, the implementation of this predictive model on mobile devices could provide a useful instrument to assess the risk of type-2 diabete by informed and aware individuals. However, give the high computational cost of this model, being a mixture of agent-based and ordinary differential equations and providing a dynamic multivariate output, it can run only on powerful workstations but not on mobile devices. The aim of the present paper it to construct an emulator model, using a machine learning approach, with reduced computational cost so to run on mobile devices to provide real time self-monitoring.
Applications of Artificial Intelligence in Mammography from a Development and Validation Perspective

Mammography is the primary imaging modality for breast cancer detection; however, a high level of expertise is needed for its interpretation. To overcome this difficulty, artificial intelligence (AI) algorithms for breast cancer detection have recently been investigated. In this review, we describe the characteristics of AI algorithms compared to conventional computer-aided diagnosis software and share our thoughts on the best methods to develop and validate the algorithms. Additionally, several AI algorithms have introduced for triaging screening mammograms, breast density assessment, and prediction of breast cancer risk have been introduced. Finally, we emphasize the need for interest and guidance from radiologists regarding AI research in mammography, considering the possibility that AI will be introduced shortly into clinical practice.
Design of Memory Access Module for YOLO v2 Neural Network Accelerator Based on FPGA

In recent years, deep learning has flourished, and significant progress has been made in computer vision fields such as image classification, target detection, and image semantic segmentation. Target detection based on deep learning has also made remarkable achievements. However, most of the existing target detection methods are difficult to apply to mobile platforms with small size and low power consumption, and it is difficult to meet the requirements of real-time detection. The simple structure and fast speed of YOLO v2 opens up new ideas for real-time target detection. FPGA provides a lot of design resources, and uses the idea of parallel computing to accelerate the target detection algorithm, so that it can be applied on a small-scale, low-power embedded platform. The main purpose of this article is to realize the acceleration of YOLO v2 algorithm on FPGA platform by designing accelerator memory access module. Experiments have proved that compared with similar research results, this design can better balance speed and accuracy. The parallel read and write design of accelerator memory access module greatly improves the parallelism of the system and has better overall performance.
Compact CNN Training Accelerator with Variable Floating-Point Datapath

This paper presents a compact architecture of CNN training accelerator targeted for mobile devices. Accuracy was verified using python in the CNN structure, and accuracy was compared by applying several data types to find optimized data types. In addition, floating-point operations are used in the computation of the CNN structure, and to implemented them, we have created and verified the addition, subtraction, and multiplication circuits of floating-point. The CNN architecture was verified using python, the floating point operation was verified using Vivado, and Area was verified TSMC 180nm.
Quality Estimation without Human-labeled Data

Quality estimation aims to measure the quality of translated content without access to a reference translation. This is crucial for machine translation systems in real-world scenarios where high-quality translation is needed. While many approaches exist for quality estimation, they are based on supervised machine learning requiring costly human labelled data. As an alternative, we propose a technique that does not rely on examples from human-annotators and instead uses synthetic training data. We train off-the-shelf architectures for supervised quality estimation on our synthetic data and show that the resulting models achieve comparable performance to models trained on human-annotated data, both for sentence and word-level prediction.
AI-driven Visualizations for Performance Monitoring and Anomaly Detection in Robots

Smart robotics is one of the fields that are been greatly enriched by the development of Cyber-Physical Systems (CPS). Under this frame, the software facet of robots is as important as the physical one. As it is widely known, the ever-increasing complexity of robots usually implies a parallel increase in the number of failures of such systems. Due to this, system monitoring and anomaly detection play a key role in the implementation of smart robotics and Artificial Intelligence (AI) can significantly contribute to this task. Accordingly, some Exploratory Projection Pursuit techniques, mainly implemented through neural networks, are applied an compared in the present paper to monitor the performance of a component-based robotic software. Thanks to the intuitive projections obtained by these techniques, anomaly detection can be also visually carried out. The visualizations are validated on an open and up-to-date dataset containing information about software anomalies that affect the middleware (a crucial component in CPS) of the analysed robot.
Web service creation in SOA based on the gSOAP

This paper expounds the concepts and thoughts of SOA, and analyzes the conceivable methods of implementing the SOA, and points out the possibility of using the WEB services to implement the SOA. On the question of how to develop the WEB services, this paper uses the open source development tool gSOAP to achieve it.
Booth Fusion: Efficient Bit Fusion Multiplier with Booth Encoding

Recently, several attempts have been made to optimize Deep Neural Networks (DNNs) through various hardware acceleration methods. Among them, Bit Fusion, the dynamic bit-level fusion/decomposition hardware architecture, was noted. We introduce a new model structure, Booth Fusion, which makes dynamic bit-level operations more efficient by implementing Bit Fusion with booth encoding. Our design shows improvements in 16.4% for the number of LUT and 14.2% for throughput.