Reviewerly/exHarmony huggingface dataset:
Diagnosis of Thyroid Nodules Based on Lightweight Residual Network

Thyroid cancer is extremely common in the population and its incidence has gradually increased in recent years. In order to help doctors diagnose thyroid nodules, many computer diagnosis algorithms have been proposed, but most of these algorithms only focus on improving the accuracy of recognition of benign and malignant thyroid nodules, the neural network model established is relatively complex, which will lead to longer inference time. In addition, due to the limitation of hardware, the model with a large number of parameters can easily make the relevant equipment unable to function normally. To solve the above problems, this paper proposes a lightweight residual network, EDSResNet, by improving ResNet-34. In the experimental part, Vgg-16, AlexNet, MobileNet_v2, ResNet, ShuffleNet_v2_x2.0, and DenseNet-121 are introduced for comparison, and the results show that the proposed EDSResNet improves 1.1% in accuracy compared with the ResNet-34, and the parameter quantity of EDSResNet is only 6.6% of that before the improvement, the number of floating point operations (Flops) is 10% of that before the improvement. Compared with another lightweight network, MobileNet_v2, EDSResNet has $8\times 10^{5}$ fewer parameters, and the accuracy, sensitivity and specificity are higher by 1.7%, 2.0% and 0.8%, respectively. After comparing with all networks, it can be found that the EDSResNet proposed in this paper has an excellent classification effect on the ultrasound image dataset of thyroid nodules while having the smallest number of parameters.
Second‐Order Autocorrelation Function of Spectrally Filtered Light From an Incoherently Pumped Two‐Level System

Abstract Light with zero second‐order autocorrelation function, g (2) (0), emitted by single‐photon source (SPS), for example, two‐level system (TLS), is usually considered as being in a Fock state with one photon in a certain electromagnetic mode of free space. However, real SPSs have finite linewidths and excite all modes lying within the linewidth. It is shown that the zero value of g (2) (0) of light emitted by an incoherently pumped TLS is a consequence of the quantum interference of electromagnetic modes lying within the linewidth. Applying the quantum regression theorem for out‐of‐time‐ordered correlation functions, an analytical expression for g (2) (0) is obtained for the light emitted by a TLS and passed through a spectral filter. It is shown that narrowing the spectral width of the band‐pass filter inevitably leads to an increase in g (2) (0). is found and it is demonstrated that certain spectral filters lead to its nonmonotonic time dependence. These results open up the possibility of controlling the statistics of the emitted light using a spectral filter, which can find applications in quantum communication and cryptography.
NetSHa: In-Network Acceleration of LSH-Based Distributed Search

Locality Sensitive Hashing (LSH) is widely adopted to index similar data in high-dimensional space for approximate nearest neighbor search. Demanding applications (e.g. web search) mean that LSH must exhibit low response times and high throughput. To achieve this, they tend to load balance between multiple machines. However, as the scale of concurrent queries and the volume of data grow, large numbers of index messages are required. Hence, the network is a key bottleneck. To address this gap, we propose NetSHa, which exploits the computational capacity of programmable switches. Specifically, we introduce a heuristic sort-reduce approach to drop potentially poor candidate answers while preserving search quality. Then, NetSHa aggregates good candidate answers from different index messages when transmitting them. Through this, it reduces the network communication cost. Furthermore, we introduce a best-effort replacement mechanism to improve its concurrency. We implement NetSHa on a Barefoot Tofino programmable switch and evaluate it using 7 real-world datasets. The experimental results show that NetSHa reduces the packet volume by <inline-formula><tex-math notation="LaTeX">$4\sim 10$</tex-math></inline-formula> times and improves the search efficiency by least 3× in comparison with typical LSH-based distributed search frameworks.
Cost Minimizing Reservation and Scheduling Algorithms for Public Clouds

Cloud Service Providers offer various pricing schemes to charge for their computational resources. Cloud Service Users can opt for <i>on-demand</i> , <i>reserved</i> , or <i>spot</i> instances for their requirements. The overall cost of an application depends on the instances chosen to execute the job. Finding the optimal reservation amount is a significant research problem, and it is more challenging when one uses spot instances with unpredictable prices. We have proposed two algorithms to determine the reservation amount, and for both algorithms, the future spot prices are unknown. But the first algorithm assumes that future demands are known. The second algorithm does not make any such assumption and yet can ensure that the cost of reservation and usage of cloud resources is within a factor <inline-formula><tex-math notation="LaTeX">$2-\frac{u_h}{e_c}$</tex-math></inline-formula> of the optimal cost where <inline-formula><tex-math notation="LaTeX">$u_h$</tex-math></inline-formula> is the usage cost per hour of the reserved instance and <inline-formula><tex-math notation="LaTeX">$e_c$</tex-math></inline-formula> is the average cost per hour for unreserved instances. We have compared our findings with that of some recent works in the literature. We have also given an <i>Integer Linear Programming</i> (ILP) formulation of the problem. Experimental results show that our algorithm differs in cost from ILP by less than 21%.
Deep Learning-based Wireless Signal Classification in the IoT Environment

With the development of the Internet of Things (IoT), diverse wireless devices are increasing rapidly. Those devices have different wireless interfaces that generate incompatible wireless signals. Each signal has its own physical characteristics with signal modulation and demodulation scheme. When there exist different wireless devices, they can suffer from severe Cross-Technology Interferences (CTI). To reduce the communication overhead due to the CTI in the real IoT environment, a central coordinator can be able to detect and identify wireless signals existing in the same communication areas. This paper investigates how to classify various radio signals using Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and attention mechanism. CNN can reduce the amount of computation by reducing weights by using convolution, and LSTM belonging to RNN models can alleviate the long-term dependence problem. Furthermore, attention mechanism can reduce the short-term memory problem of RNNs by re-examining the data output from the decoder and the entire data entered into the encoder at every point in time. To accurately classify radio signals according to their weights, we design a model based on CNN, LSTM, and attention mechanism. As a result, we propose a model CLARINet that can classify original data by minimizing the loss and detects changes in sequences. In a case of the real IoT environment with Wi-Fi, Bluetooth and ZigBee devices, we can normally obtain wireless signals from 10 to 20 dB. The accuracy of CLARINet's radio signal classification with CNN-LSTM and attention mechanism can be seen that signal-to-noise ratio (SNR) exhibits high accuracy at 16 dB to about 92.03%.
Online education and surveillance during COVID-19 pandemic in Palestinian universities

This article employs Foucault’s concepts of docile bodies, Panopticism, surveillance and the paradigm of resistance, subversion and containment to delineate the issues of power and control that shored up in online Higher education in Palestinian universities during the outbreak of the pandemic. We propose that the online mode of education perpetuates the traditional way of teaching literature, impeding both students’ and teachers’ innovation. Topics related to sexuality, politics, misogyny, and religion in literary works have been repressed in staff members’ discussion due to multilayered surveillance from the students, parents and online learning centralized management offices who, based on the testimonies of the literature teachers, are often attentive to online class meetings. Surveillance shatters the illusion of liberation many literacy educators thought they have gained in online education; indeed, the instructors’ testimonies highlight their internalization of panoptic surveillance that derails the liberating purpose of education.
Efficient and Distributed Temporal Pattern Mining

The widespread deployment of IoT systems in the real world today has enabled the generation and collection of an enormous amount of sensor times series. One of the important mining techniques to extract patterns from time series is temporal pattern mining (TPM). Unlike the sequential pattern mining, TPM adds an additional temporal dimension, i.e., time intervals, into extracted patterns, making them more informative. However, adding the extra temporal dimension into patterns results in an additional exponential factor to the growth of the search space, and thus, significantly increases the mining complexity. Current TPM approaches work sequentially, therefore, cannot scale to large datasets. In this paper, we propose Distributed Hierarchical Pattern Graph TPM (DHPG-TPM), the first distributed solution that supports large-scale TPM using the leading distributed platform Apache Spark. Moreover, DHPG-TPM employs efficient data structures, distributed bitmap and distributed Hierarchical Pattern Graph that are carefully designed to work efficiently in a distributed environment to enable fast computations of support and confidence. To address the exponential search space of TPM, we design effective distributed pruning techniques based on the Apriori principle and the transitivity property of temporal relations to reduce the search space while minimizing the communication overhead between the cluster nodes. We conduct extensive experiments on real-world and synthetic datasets, showing that DHPG-TPM outperforms the sequential baselines and scales to very large datasets.
Virolinguistics: Introduction to the Study of the Coronavirus Language

Abstract This paper undertakes the introduction to virolinguistics, a new linguistic discipline that investigates the virus language (virolect) based on the Hungarian linguistic material drawn from the scientific literature and our own collection. The goal of this work is to evaluate the effect of the pandemic on certain aspects of the Hungarian language: genres, vocabulary, communication, the linguistic landscape, and social media. The linguistic materials of these various areas play an important role in our society: they have a warning, entertaining, or stress-relieving function. Due to the restrictions, most studies have moved to the Internet. The methodological paradox of virolinguistics can be identified in the fact that it disregards certain scientific standards in order to assist linguists in collecting their valuable linguistic and visual materials.
An approach for solving the three-objective arc welding robot path planning problem

Multi-objective welding robot path planning is becoming an important problem owing to the developing requirements of industrial production intelligence. An approach with path planning and optimization is proposed for solving the problem of arc welding. A rapidly exploring random tree* (RRT*)-based local path search algorithm is applied to generate a set of collision-free paths between any two welding seams, and the global search algorithm based on the decomposition-based multi-objective evolutionary algorithm (MOEA/D) framework is introduced to improve welding production efficiency by optimizing the requested contradictory objectives, namely, path length, trajectory smoothness and energy consumption. Both proposed algorithms are tested in different instances and on an actual welding workpiece, and the results prove that the proposed method could be useful in industrial production.
A cost-aware logical framework

We present calf , a c ost- a ware l ogical f ramework for studying quantitative aspects of functional programs. Taking inspiration from recent work that reconstructs traditional aspects of programming languages in terms of a modal account of phase distinctions , we argue that the cost structure of programs motivates a phase distinction between intension and extension . Armed with this technology, we contribute a synthetic account of cost structure as a computational effect in which cost-aware programs enjoy an internal noninterference property: input/output behavior cannot depend on cost. As a full-spectrum dependent type theory, calf presents a unified language for programming and specification of both cost and behavior that can be integrated smoothly with existing mathematical libraries available in type theoretic proof assistants. We evaluate calf as a general framework for cost analysis by implementing two fundamental techniques for algorithm analysis: the method of recurrence relations and physicist’s method for amortized analysis . We deploy these techniques on a variety of case studies: we prove a tight, closed bound for Euclid’s algorithm, verify the amortized complexity of batched queues, and derive tight, closed bounds for the sequential and parallel complexity of merge sort, all fully mechanized in the Agda proof assistant. Lastly we substantiate the soundness of quantitative reasoning in calf by means of a model construction.
A Focally Discriminative Loss for Unsupervised Domain Adaptation

The maximum mean discrepancy (MMD) as a representative distribution metric between source domain and target domain has been widely applied in unsupervised domain adaptation (UDA), where both domains follow different distributions, and the labels from source domain are merely available. However, MMD and its class-wise variants possibly ignore the intra-class compactness, thus canceling out discriminability of feature representation. In this paper, we endeavor to improve the discriminative ability of MMD from two aspects: 1) we re-design the weights for MMD in order to align the distribution of relatively hard classes across domains; 2) we explore a focally contrastive loss to trade-off the positive sample pairs and negative ones for better discrimination. The intergration of both losses makes the intra-class features close as well as push away the inter-class features far from each other. Moreover, the improved loss is simple yet effective. Our model shows state-of-the-art compared to the most domain adaptation methods.
The stochastic test collection problem: Models, exact and heuristic solution approaches

The classic test collection problem (TCP) selects a minimal set of binary tests needed to classify the state of a system correctly. The TCP has applications in various domains, such as the design of monitoring systems in engineering, communication, and healthcare. In this paper, we define the stochastic test collection problem (STCP) that generalizes the TCP. While the TCP assumes that the tests' results can be deterministically mapped into classes, in the STCP, the results are mapped to probability distributions over the classes. Moreover, each test and each type of classification error is associated with some cost. A solution of the STCP is a subset of tests and a mapping of their results to classes. The objective is to minimize the weighted sum of the tests' costs and the expected cost of the classification errors. We present an integer linear programming formulation of the problem and solve it using a commercial solver. To solve larger instances, we apply three metaheuristics for the STCP, namely, Tabu Search (TS), Cross-Entropy (CE), and Binary Gravitational Search Algorithm (BGSA). These methods are tested on publicly available datasets and shown to deliver nearly optimal solutions in a fraction of the time required for the exact solution.
Improved Adaptive Particle Swarm Optimization Algorithm with a Two-Way Learning Method

Traditional particle swarm optimization algorithm has some disadvantages, such as slow convergence speed and easy to fall into local extremes. In order to improve the performance, an improved adaptive particle swarm optimization algorithm with a two-way learning method is proposed. First, the algorithm adaptively adjusts the algorithm according to the iteration periods of the optimization process. Specifically, the inertia weight and the value of the learning factor are changed nonlinearly, so as to better balance the search behavior of the particles in the group; Second, the idea of beetle search is introduced into the particle swarm algorithm to form a new two-way learning mechanism, which overcomes the limitations of the traditional particle swarm algorithm and help to increase the diversity of the population. In this way, the search scope is expanded, and the search accuracy of the algorithm is enhanced. Finally, the simulation is carried out on several multi-dimensional functions, and compared with other two related algorithms. The experimental results show that under the same experimental conditions, the improved algorithm has obvious advantages in optimization ability and convergence speed.
OCLC library holdings: assessing availability of academic books in libraries in print and electronic compared to citations and altmetrics

Abstract Although library holding data is constructed upon work format of books, it is less known how much print and electronic books in libraries contribute to the library holding counts. In response, this research is an attempt to explore the distribution of library holding data across work formats and investigate the availability of library print holdings and library electronic holdings for books as constituents of the library holding metric across fields and over time and compared with other book metrics. ISBNs, titles and author names of 119,794 Scopus-indexed book titles across 26 fields were examined for fourteen variables including OCLC Library Holdings, Scopus Citations, Google Books Citations, Goodreads engagements, and Altmetric indicators. There are three major findings: (a) library holdings are a more comprehensively available metric for books (over 97%) than any other metric and could be useful after short time after first edition publication, followed by Google Books, Goodreads and Scopus, respectively; (b) on average electronic holdings are seven times (median three times) more numerous than print holdings and their ratio is growing considerably for more recent books; (c) there is consistent downward trend in average print book holdings, suggesting that library print holding data are cumulative in nature and statistically comparable to formal citations; however, acquisition of electronic books in libraries is inconsistent in distribution plot as well as over time. In sum, the differences between print and electronic holding data are broad making them distinct metrics, suggesting that further research is needed for understanding their implications for book impact assessment.
Explaining transformer-based models for automatic short answer grading

Over recent years, advances in natural language processing have brought ever more advanced and expressive language models to the world. With open-source implementations and model registries, these state-of-the-art models are freely available to anyone, and the successful application of transfer learning has meant benchmarks on previously difficult tasks can be beaten with relative ease.
Complex-Valued Random Fourier Geometric Algebra Adaptive Filtering

The complex-valued fixed dimensional adaptive filters (CFDAFs) can improve the performance and computational efficiency of complex-valued kernel adaptive filters (CKAFs), but have the challenge of over-coupling for the real and imaginary parts of complex-valued signals. To this end, this brief proposes a novel fixed dimensional adaptive filter for complex-valued signals named complex-valued random Fourier geometric algebra least mean square (CRFGALMS). On the basis of the framework of geometric algebraic adaptive filtering, the real and imaginary parts of complex-valued signals are mapped into random Fourier features space (RFFS) to improve the efficiency of the nonlinear mapping for complex-valued signals, respectively. The proposed random Fourier feature mapping based on the geometric algebra is endowed with superior presentation abilities in complex-valued domain as it can decouple the nonlinear mapping of the real and imaginary parts of complex-valued signals, efficiently. Simulations on nonlinear channel equalization are used to illustrate the superiority of the proposed CRFGALMS algorithm in terms of dimensionality and filtering performance.
From Fairness to Full Security in Multiparty Computation

In the setting of secure multiparty computation (MPC), a set of mutually distrusting parties wish to jointly compute a function, while guaranteeing the privacy of their inputs and the correctness of the output. An MPC protocol is called fully secure if no adversary can prevent the honest parties from obtaining their outputs. A protocol is called fair if an adversary can prematurely abort the computation, however, only before learning any new information. We present efficient transformations from fair computations to fully secure computations, assuming a constant fraction of honest parties (e.g., $$1\%$$ of the parties are honest). Compared to previous transformations that require linear invocations (in the number of parties) of the fair computation, our transformations require super-logarithmic, and sometimes even super-constant, such invocations. The main idea is to delegate the computation to random committees that invoke the fair computation. Apart from the benefit of uplifting security, the reduction in the number of parties is also useful, since only committee members are required to work, whereas the remaining parties simply “listen” to the computation over a broadcast channel. One application of these transformations is a new $$\delta $$ -bias coin-flipping protocol, whose round complexity has a super-logarithmic dependency on the number of parties, improving over the linear-dependency protocol of Beimel, Omri, and Orlov (Crypto 2010). A second application is a new fully secure protocol for computing the Boolean OR function, with a super-constant round complexity, improving over the protocol of Gordon and Katz (TCC 2009) whose round complexity is linear in the number of parties. Finally, we show that our positive results are in a sense optimal, by proving that for some functionalities, a super-constant number of (sequential) invocations of the fair computation is necessary for computing the functionality in a fully secure manner.
Robust Linearly Constrained Kalman Filter for General Mismatched Linear State-Space Models

It is well known that Wiener filter and Kalman filter (KF) like techniques are sensitive to misspecified covariances, uncertainties in the system matrices, filter initialization, or unwanted system behaviors. A possible solution to robustify these estimation techniques is to impose linear constraints (LCs). In this article: 1) we introduce a general class of linearly constrained KF (LCKF), where a set of nonstationary LCs can be set at every time step; 2) explore the use of such LCs to mitigate modeling errors in general mismatched linear discrete state-space models; and 3) provide the theoretical formulation to show that the gain-constrained KF is a particular instance of the proposed LCKF. Because such LCs can be taken into account in any KF generalization, this sets the basis for a new robust filtering framework. An illustrative example is provided to support the discussion.