Reviewerly/exHarmony huggingface dataset:
A deep learning based neuro-fuzzy approach for solving classification problems

Techniques involved artificial intelligence and machine learning offers various classification methods in order to deal with daily life problems. Among these methods, Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Deep Neural Network (DNN) are the most commonly used classifiers. Since ANFIS is not suitable for high-dimensional data, therefore DNN was introduced to overcome this problem faced by conventional methods. However, due to the optimization of millions of parameters in their deep architecture, the decision made by DNN faced the criticism of being non-transparent. To overcome this problem, recently, various researchers are coming up with the idea of using fuzzy logic with DNN. Therefore, this study also proposed a Deep Neuro-Fuzzy Classifier (DNFC) with a cooperative based structure for solving classification problems, particularly. The performance of the proposed DNFC was evaluated with ANFIS and DNN classifier, where overall results show that the performance of ANFIS classifier decreased when input size increased. While the performance of the proposed model demonstrated nearly similar or slightly higher accuracy as compared to DNN.
Pre-service teachers’ perception of technology competencies based on the new ISTE technology standards

With a plethora of technology available to support teaching and learning, preservice teachers are expected to become well-versed in technology literacy and competencies through their teacher education programs. This study examined preservice teachers’ perceptions of technology competencies, based on newly issued International Society for Technology in Education (ISTE) Standards for Educators. A mixed-methods design was used to collect data at one of the national universities of education in South Korea. The preservice teachers viewed their current technology education courses as deficient: neither tailored to their technology competency levels, nor strategically aligned with each other. This study suggested that teacher education curricula should be redesigned to offer more and better opportunities for teachers to improve teaching technology skills that can be readily applied to classrooms.
Realizing Petabyte Scale Acoustic Modeling

Large scale machine learning (ML) systems such as the Alexa automatic speech recognition (ASR) system continue to improve with increasing amounts of manually transcribed training data. Instead of scaling manual transcription to impractical levels, we utilize semi-supervised learning (SSL) to learn acoustic models (AM) from the vast firehose of untranscribed audio data. Learning an AM from 1 Million hours of audio presents unique ML and system design challenges. We present the design and evaluation of a highly scalable and resource efficient SSL system for AM. Employing the student/teacher learning paradigm, we focus on the student learning subsystem: a scalable and robust data pipeline that generates features and targets from raw audio, and an efficient model pipeline, including the distributed trainer, that builds a student model. Our evaluations show that, even without extensive hyper-parameter tuning, we obtain relative accuracy improvements in the 10 to 20$\%$ range, with higher gains in noisier conditions. The end-to-end processing time of this SSL system was 12 days, and several components in this system can trivially scale linearly with more compute resources.
S-money: virtual tokens for a relativistic economy

We propose definitions and implementations of ‘S-money’—virtual tokens designed for high-value fast transactions on networks with relativistic or other trusted signalling constraints, defined by inputs that in general are made at many network points, some or all of which may be space-like separated. We argue that one significant way of characterizing types of money in space–time is via the ‘summoning’ tasks they can solve: that is, how flexibly the money can be propagated to a desired space–time point in response to relevant information received at various space–time points. We show that S-money is more flexible than standard quantum or classical money in the sense that it can solve deterministic summoning tasks that they cannot. It requires the issuer and user to have networks of agents with classical data storage and communication, but no long-term quantum state storage, and is feasible with current technology. User privacy can be incorporated by secure bit commitment and zero-knowledge proof protocols. The level of privacy feasible in given scenarios depends on efficiency and composable security questions that remain to be systematically addressed.
Runtime Safety Assurance Using Reinforcement Learning

The airworthiness and safety of a non-pedigreed autopilot must be verified, but the cost to formally do so can be prohibitive. We can bypass formal verification of non-pedigreed components by incorporating Runtime Safety Assurance (RTSA) as mechanism to ensure safety. RTSA consists of a meta-controller that observes the inputs and outputs of a non-pedigreed component and verifies formally specified behavior as the system operates. When the system is triggered, a verified recovery controller is deployed. Recovery controllers are designed to be safe but very likely disruptive to the operational objective of the system, and thus RTSA systems must balance safety and efficiency. The objective of this paper is to design a meta-controller capable of identifying unsafe situations with high accuracy. High dimensional and non-linear dynamics in which modern controllers are deployed along with the black-box nature of the nominal controllers make this a difficult problem. Current approaches rely heavily on domain expertise and human engineering. We frame the design of RTSA with the Markov decision process (MDP) framework and use reinforcement learning (RL) to solve it. Our learned meta-controller consistently exhibits superior performance in our experiments compared to our baseline, human engineered approach.
Adversarially Robust Streaming Algorithms via Differential Privacy

A streaming algorithm is said to be adversarially robust if its accuracy guarantees are maintained even when the data stream is chosen maliciously, by an adaptive adversary . We establish a connection between adversarial robustness of streaming algorithms and the notion of differential privacy . This connection allows us to design new adversarially robust streaming algorithms that outperform the current state-of-the-art constructions for many interesting regimes of parameters.
Regular subgroups with large intersection

In this paper we study the relationships between the elementary abelian regular subgroups and the Sylow $2$-subgroups of their normalisers in the symmetric group $\mathrm{Sym}(\mathbb{F}_2^n)$, in view of the interest that they have recently raised for their applications in symmetric cryptography.
Quantum state smoothing: why the types of observed and unobserved measurements matter

Abstract We investigate the estimation technique called quantum state smoothing introduced in (Guevara and Wiseman 2015 Phys. Rev. Lett. 115 180407), which offers a valid quantum state estimate for a partially monitored system, conditioned on the observed record both prior and posterior to an estimation time. The technique was shown to give a better estimate of the underlying true quantum states than the usual quantum filtering approach. However, the improvement in estimation fidelity, originally examined for a resonantly driven qubit coupled to two vacuum baths, was also shown to vary depending on the types of detection used for the qubit’s fluorescence. In this work, we analyse this variation in a systematic way for the first time. We first define smoothing power using an average purity recovery and a relative average purity recovery, of smoothing over filtering. Then, we explore the power for various combinations of fluorescence detection for both observed and unobserved channels. We next propose a method to explain the variation of the smoothing power, based on multi-time correlation strength between fluorescence detection records. The method gives a prediction of smoothing power for different combinations, which is remarkably successful in comparison with numerically simulated qubit trajectories.
Digital Otoscopy Videos Versus Composite Images: A Reader Study to Compare the Accuracy of <scp>ENT</scp> Physicians

With the increasing emphasis on developing effective telemedicine approaches in Otolaryngology, this study explored whether a single composite image stitched from a digital otoscopy video provides acceptable diagnostic information to make an accurate diagnosis, as compared with that provided by the full video.Diagnostic survey analysis.Five Ear, Nose, and Throat (ENT) physicians reviewed the same set of 78 digital otoscope eardrum videos from four eardrum conditions: normal, effusion, retraction, and tympanosclerosis, along with the composite images generated by a SelectStitch method that selectively uses video frames with computer-assisted selection, as well as a Stitch method that incorporates all the video frames. Participants provided a diagnosis for each item along with a rating of diagnostic confidence. Diagnostic accuracy for each pathology of SelectStitch was compared with accuracy when reviewing the entire video clip and when reviewing the Stitch image.There were no significant differences in diagnostic accuracy for physicians reviewing SelectStitch images and full video clips, but both provided better diagnostic accuracy than Stitch images. The inter-reader agreement was moderate.Equal to using full video clips, composite images of eardrums generated by SelectStitch provided sufficient information for ENTs to make the correct diagnoses for most pathologies. These findings suggest that use of a composite eardrum image may be sufficient for telemedicine approaches to ear diagnosis, eliminating the need for storage and transmission of large video files, along with future applications for improved documentation in electronic medical record systems, patient/family counseling, and clinical training.3 Laryngoscope, 131:E1668-E1676, 2021.
ConvAix: An Application-Specific Instruction-Set Processor for the Efficient Acceleration of CNNs

ConvAix is an application-specific instruction-set processor (ASIP) that enables the energy-efficient processing of convolutional neural networks (CNNs) while retaining substantial flexibility through its instruction-set architecture (ISA) based design. By utilizing a combination of data-level parallelism (DLP), instruction-level parallelism (ILP), and subword parallelism, the proposed design offers sufficient processing power for the execution of state-of-the-art CNNs in real-time. ConvAix's arithmetic logic units (ALUs) are C-programmable, thereby offering the degree of flexibility required to implement many different convolution layer types, e.g., depthwise-separable convolutions and residual blocks, as well as fully-connected and pooling layers. It comprises a total of 256 ALUs and leverages low-precision computations down to 4 bits. Furthermore, it exploits sparsity in feature maps and weights via zero-guarding of redundant computations to maximize its energy efficiency. The processor was implemented in a modern 28 nm CMOS technology operating at 1V supply voltage with a resulting clock frequency of 513 MHz. The final design offers a precision-dependent peak throughput between 263 GOP/s (int16) and 1.1 TOP/s (int4), while consuming between 972mW and 340mW of power, resulting in effective energy-efficiencies ranging from 176 GOP/s/W to 2 TOP/s/W. Well-known CNNs, such as AlexNet, MobileNet, and ResNet-18, are simulated based on the placed and routed netlist, achieving between 233 (AlexNet) and 69 (ResNet-18) frames-per-second for a batch-size of 1, including times for off-chip transfers.
On cloaking behaviors of malicious websites

Malicious websites often mimic top brands to host malware and launch social engineering attacks, e.g., to collect user credentials. Some such sites often attempt to hide malicious content from search engine crawlers (e.g., Googlebot), but show harmful content to users/client browsers—a technique known as cloaking. Past studies uncovered various aspects of cloaking, using selected categories of websites (e.g., mimicking specific types of malicious sites). We focus on understanding cloaking behaviors using a broader set of websites. As a way forward, we built a crawler to automatically browse and analyze content from 100000 squatting (mostly) malicious domains—domains that are generated through typo-squatting and combo-squatting of 2883 popular websites. We use a headless Chrome browser and a search-engine crawler with user-agent modifications to identify cloaking behaviors—a challenging task due to dynamic content, served at random; e.g., consecutive requests serve very different malicious or benign content. Most malicious sites (e.g., phishing and malware) go undetected by current blacklists; only a fraction of cloaked sites (127, 3.3%) are flagged as malicious by VirusTotal. In contrast, we identify 80% cloaked sites as malicious, via a semi-automated process implemented by extending the content categorization functionality of Symantec’s SiteReview tool. Even after 3 months of observation, nearly a half (1024, 45.4%) of the cloaked sites remained active, and only a few (31, 3%) of them are flagged by VirusTotal. This clearly indicate that existing blacklists are ineffective against cloaked malicious sites. Our techniques can serve as a starting point for more effective and scalable early detection of cloaked malicious sites.
A Slice Tour for Finding Hollowness in High-Dimensional Data

Taking projections of high-dimensional data is a common analytical and visualization technique in statistics for working with high-dimensional problems. Sectioning, or slicing, through high dimensions is less common, but can be useful for visualizing data with concavities, or nonlinear structure. It is associated with conditional distributions in statistics, and also linked brushing between plots in interactive data visualization. This short technical note describes a simple approach for slicing in the orthogonal space of projections obtained when running a tour, thus presenting the viewer with an interpolated sequence of sliced projections. The method has been implemented in R as an extension to the tourr package, and can be used to explore for concave and nonlinear structures in multivariate distributions. Supplementary materials for this article are available online.
Fuzzy Cloud Ranking Model based on QoS and Trust

Cloud computing is one of the emerging domains of information technology as most of the applications are moving towards Cloud because of its features like availability, performance, security, cost, and maintenance. In recent advancement due to the rapid growth of Cloud computing technology, a vast number of Cloud service providers are available to fulfill the needs of Cloud customers. So, it is quite difficult for a customer to choose a Cloud service provider that will satisfy his needs. This paper has reviewed popular Cloud ranking models to prioritize the ranking of Cloud services based on different parameters of Cloud and proposed a fuzzy trust model for the ranking of different cloud service providers using three basic parameters capacity, cost and performance.
Thinking aloud about confusing code: a qualitative investigation of program comprehension and atoms of confusion

Atoms of confusion are small patterns of code that have been empirically validated to be difficult to hand-evaluate by programmers. Previous research focused on defining and quantifying this phenomenon, but not on explaining or critiquing it. In this work, we address core omissions to the body of work on atoms of confusion, focusing on the ‘how’ and ‘why’ of programmer misunderstanding.
Three-dimensional weights of evidence modelling of a deep-seated porphyry Cu deposit

Given the challenges in data acquisition and modeling at the stage of detailed exploration, it is difficult to develop a prospectivity model, particularly for disseminated ore deposits.Recently, the weights of evidence (WofE) method has demonstrated a high efficiency for modeling such deposits.In this study, we propose a framework for creating a three-dimensional (3D) weights of evidence-based prospectivity model of the Nochoun porphyry Cu deposit in the Urmia-Dokhtar magmatic arc of Iran.The input data include qualitative geological and quantitative geochemical information obtained from boreholes and field observations.We combine ordinary and fuzzy weights of evidence for integrating qualitative and quantitative exploration criteria in a 3D space constrained by a metallogenic model of the study area for identifying a deep-seated ore body.Ordinary weights of evidence are determined for geological data, including lithology, alteration, rock type, and structure.Moreover, we determine the fuzzy weight of evidence for each class of continuous geochemical models created based on Fe, Mo, and Zn concentration values derived from boreholes.We integrate the input
A combined multivariate approach analyzing geochemical data for knowledge discovery: The Vazante – Paracatu Zinc District, Minas Gerais, Brazil

The Vazante Group comprises a Proterozoic sequence of carbonate and siliciclastic rocks located in Minas Gerais, Brazil. It is the host of the Vazante-Paracatu Zinc District, including world-class hypogene zinc silicate deposits, in the southern part, and several Pb – Zn sulfide deposits in the northern part, all hosted in dolomitic rocks. A recent study revealed the occurrence of base metal sulfide mineralization that formed prior to the Brasiliano orogenic event in the siliciclastic rocks (Serra do Garrote Formation) that underlie these dolomite-hosted silicates and sulfide deposits. These siliciclastic rocks were considered as potential sources of elements for the hydrothermal fluids that formed the dolomite-hosted deposits, however there was little evidence of depletion of the source rocks during the orogenic event. In this paper, Random Forest is used in unsupervised mode along with t-distributed Stochastic Neighbor Embedding (t-SNE) and principal component analysis (PCA) over a lithogeochemical dataset of samples of the Serra do Garrote siliciclastic rocks collected throughout the basin to provide insights about processes related to the mineralizing system at the Vazante-Paracatu District. Multivariate analysis reveals that the Serra do Garrote Formation geochemical signature typical of pre-orogenic mineralization. This is characterized by PC1+ with association of Zn, Cd, Cu, Hg, In, V ± Sb, Se, Mo, Re, which corresponds to nearly 60% of the variance in the data. This suggest that these elements are related to a widespread and/or, long-living hydrothermal activity without an efficient mechanism to focus the metal-bearing fluids, causing basin-scale sub-economical Zn and related metal enrichment. Furthermore, PC2 and PC6 distinguish the zones with signatures interpreted to be related to depletion. PC2+ separates Cd-poor sphalerite, which is typical of the generation of sphalerite that occur in zones with textural evidence of remobilization. PC6+ with association of As, organic carbon and S, identified the zones where sphalerite was leached from the pyrite-rich layers. Areas with multivariate signatures of both pre-orogenic enrichment and syn-orogenic depletion in the source siliciclastic rocks include the Vazante-North Extension and Varginha zinc silicate deposits/occurrence, Ambrósia silicate zinc deposit and the Engenho Velho prospect. Preliminary exploration in this prospect revealed hydrothermal alteration typical of the zinc silicate deposits. This study is pioneering in applying these numerical models in possible source rocks to assist in identifying targets for exploration in basins.
Using Character-Level and Entity-Level Representations to Enhance Bidirectional Encoder Representation From Transformers-Based Clinical Semantic Textual Similarity Model: ClinicalSTS Modeling Study

Background With the popularity of electronic health records (EHRs), the quality of health care has been improved. However, there are also some problems caused by EHRs, such as the growing use of copy-and-paste and templates, resulting in EHRs of low quality in content. In order to minimize data redundancy in different documents, Harvard Medical School and Mayo Clinic organized a national natural language processing (NLP) clinical challenge (n2c2) on clinical semantic textual similarity (ClinicalSTS) in 2019. The task of this challenge is to compute the semantic similarity among clinical text snippets. Objective In this study, we aim to investigate novel methods to model ClinicalSTS and analyze the results. Methods We propose a semantically enhanced text matching model for the 2019 n2c2/Open Health NLP (OHNLP) challenge on ClinicalSTS. The model includes 3 representation modules to encode clinical text snippet pairs at different levels: (1) character-level representation module based on convolutional neural network (CNN) to tackle the out-of-vocabulary problem in NLP; (2) sentence-level representation module that adopts a pretrained language model bidirectional encoder representation from transformers (BERT) to encode clinical text snippet pairs; and (3) entity-level representation module to model clinical entity information in clinical text snippets. In the case of entity-level representation, we compare 2 methods. One encodes entities by the entity-type label sequence corresponding to text snippet (called entity I), whereas the other encodes entities by their representation in MeSH, a knowledge graph in the medical domain (called entity II). Results We conduct experiments on the ClinicalSTS corpus of the 2019 n2c2/OHNLP challenge for model performance evaluation. The model only using BERT for text snippet pair encoding achieved a Pearson correlation coefficient (PCC) of 0.848. When character-level representation and entity-level representation are individually added into our model, the PCC increased to 0.857 and 0.854 (entity I)/0.859 (entity II), respectively. When both character-level representation and entity-level representation are added into our model, the PCC further increased to 0.861 (entity I) and 0.868 (entity II). Conclusions Experimental results show that both character-level information and entity-level information can effectively enhance the BERT-based STS model.
Image Inpainting based Compact Hash Code Learning using Modified U-Net

As a result of the widespread use of imaging devices, many images are recorded every second. Access to these recorded images becomes more difficult as the number increases. Various content-based image retrieval (CBIR) methods are suggested to solve this problem, but more is still needed. Unlike retrieval methods, which require intense hardware power and require long periods, the proposed study is very practical. The proposed framework produces hash codes of the desired length using a modified U-net model without the need for any label information. The proposed framework first randomly deletes 30% of the pixels of the images in the data. These pixel-deleted images are used as the input of the modified U-net, and the original images as the output. The purpose of this process is to learn all the features of the image without the need for the label value. In the second phase of the proposed framework, the traditional U-net architecture is modified for inpainting. The modification is done using reconstruction loss and adversarial loss. Finally, the hash code acquisition procedure is applied by taking the features from the modified U-net middle layer. The proposed framework has been tested in both natural and medical images. Gray level and color experiments are carried out with the images in these two data. The results obtained are highly promising and competitive with other state-of-the-art methods.