Reviewerly/exHarmony huggingface dataset:
There’s a Strong Wind a’Blowing re Security Disclosures: Lessons Learned from SolarWinds — An analysis of the significance of the SolarWinds case for companies with making security statements on websites and in public filings

Abstract For years now, we have been advising clients to be cautious about including overly broad, or overly detailed, statements in their online privacy policies, noting that the FTC has in the past interpreted such statements as being representations to the market - such that false or misleading statements made therein could constitute misrepresentations. The Securities Exchange Commission complaint last year filed against SolarWinds Corp. and Timothy G. Brown (between July 2017 and December 2020, the Vice President of Security and Architecture and head of the Information Security group, and as of January 2021, the Chief Information Security Officer, for SolarWinds Corp.) has now made it clear that the same risk applies to public statements regarding the state of security of an organization. The SEC complaint raises additional related issues, however: namely, to what extent are organizations required to detail instances of non-compliance with their security policies in their public security disclosures; and to what extent should software companies be held to a higher standard of cybersecurity disclosure. This article proves the background to the SEC complaint (I.); provides a overview of the SEC complaint’s critique of the online security statement published by SolarWinds, and the security disclosures in the securities filings of SolarWinds (II.); outlines the challenges posed by the SEC approach in the SEC complaint and the resulting blowback, in particular from chief information security officers (III.); highlights three key lessons learned from the SEC complaint (IV.); and concludes (V.) with a warning as to what this means for organizations publishing online security statements.
A Study on College Students’ Learning Attitude During Synchronous and Asynchronous Video-Based Group Activities for English Presentation — A Secondary Publication

The purpose of the study is to explore college students’ perceptions/attitudes during synchronous and asynchronous group activities. The survey comprised a 10-item, 5-point Likert-type questionnaire and open-ended questions. In the study, two groups, a synchronous group and an asynchronous group, participated in four activities during one semester. For the data collection, the researcher surveyed 116 students who are in their 1st year of college and collected their final tests, an oral presentation. The findings are as follows. Firstly, between the two groups, the synchronous group showed higher mean scores for online group participation. Secondly, the synchronous group also indicated higher mean scores for English learning, speaking confidence, and interest in learning English. Lastly, both groups mentioned communication with team members, improvement in English speaking and pronunciation, increased self-confidence, and other elements as advantages of group work. In contrast, disadvantages included technology problems, decreased self-confidence, inconveniences when meeting with team members online, and others. The implications for the study are: firstly, online group activity can reduce isolation and increase interaction; secondly, the online video-based platform, Flipgrid, can help students improve English speaking skills by observing their own or peers’ videos; lastly, instructors can selectively use the synchronous and asynchronous group activities to suit their educational needs.
A Probe into the Integration Concept of Blended Teaching Mode of College English in Application-Oriented Universities

Blended teaching is the product of the organic combination of classroom teaching and online learning. The practice of college English teaching in application-oriented colleges shows that the construction of the integration concept of blended teaching mode will play a good role in promoting the quality of college English teaching. The organization and implementation of the blended teaching mode of college English should pay more attention to the integration of all elements of college English core literacy and online teaching resources, and establish a sense of integration in teaching concept, content, mode, and evaluation, so as to further promote the course reform of college English and realize the goal of cultivating English talents with emphasis on application practice.
A Study on Perception Survey of Preservice Elementary Teachers on Teaching Methods in Astronomy — A Secondary Publication

This study was conducted on 115 students from four classes of 2nd-year intensive courses at B University of Education. Preservice elementary teachers were surveyed on what teaching methods they were aware of regarding the subject of elementary science astronomy. Recognition data of 80 people from whom the questionnaire was collected were analyzed. For this study, a 5-day survey was conducted. Preservice elementary teachers complained of difficulties in teaching methods in the astronomy area of the science department. The purpose of this study was to find out what difficulties preservice elementary teachers have in teaching elementary science astronomy topics and to explore more efficient teaching methods for teaching astronomy topics. The topic of the survey was set by discussing with the preservice elementary teachers about what kind of survey to use in teaching the subject of elementary science astronomy. There are many topics for elementary science astronomy, but two questionnaires were prepared focusing on the unit on the Earth and the moon: “What does the Earth look like?” in Unit 4 (1/10) of the 3rd year, 1st semester; in Unit 2 (1/11) of the 1st semester of the 6th grade, it was set as “What does the moon look like?” The surveys candidly described how to teach the subject of astronomy to elementary school students by mobilizing all the background knowledge of preservice elementary teachers. The results of these surveys were visualized and displayed using NetMiner as a language analysis method, and the contents of the responses to the actual surveys by preservice elementary teachers were described and interpreted. Based on these results, preservice elementary teachers tried to suggest a more efficient teaching method for the subject of elementary science astronomy. In addition, basic procedures and methods for lecturing on the subject of elementary science astronomy were presented. A more efficient teaching method for teaching elementary science astronomy subjects to preservice elementary teachers was suggested.
A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation

Few-shot segmentation focuses on the generalization of models to segment unseen object with limited annotated samples. However, existing approaches still face two main challenges. First, huge feature distinction between support and query images causes knowledge transferring barrier, which harms the segmentation performance. Second, limited support prototypes cannot adequately represent features of support objects, hard to guide high-quality query segmentation. To deal with the above two issues, we propose self-distillation embedded supervised affinity attention model to improve the performance of few-shot segmentation task. Specifically, the self-distillation guided prototype module uses self-distillation to align the features of support and query. The supervised affinity attention module generates high-quality query attention map to provide sufficient object information. Extensive experiments prove that our model significantly improves the performance compared to existing methods. Comprehensive ablation experiments and visualization studies also show the significant effect of our method on few-shot segmentation task. On COCO-20i dataset, we achieve new state-of-the-art results. Training code and pretrained models are available at https://github.com/cv516Buaa/SD-AANet.
Clustering longitudinal ordinal data via finite mixture of matrix-variate distributions

In social sciences, studies are often based on questionnaires asking participants to express ordered responses several times over a study period. We present a model-based clustering algorithm for such longitudinal ordinal data. Assuming that an ordinal variable is the discretization of a underlying latent continuous variable, the model relies on a mixture of matrix-variate normal distributions, accounting simultaneously for within- and between-time dependence structures. The model is thus able to concurrently model the heterogeneity, the association among the responses and the temporal dependence structure. An EM algorithm is developed and presented for parameters estimation. An evaluation of the model through synthetic data shows its estimation abilities and its advantages when compared to competitors. A real-world application concerning changes in eating behaviours during the Covid-19 pandemic period in France will be presented.
Importance of GWAS Risk Loci and Clinical Data in Predicting Asthma Using Machine-learning Approaches

To understand the risk factors of asthma, we combined genome-wide association study (GWAS) risk loci and clinical data in predicting asthma using machine-learning approaches.A case-control study with 123 asthmatics and 100 controls was conducted in the Zhuang population in Guangxi. GWAS risk loci were detected using polymerase chain reaction, and clinical data were collected. Machine-learning approaches were used to identify the major factors that contribute to asthma.A total of 14 GWAS risk loci with clinical data were analyzed on the basis of 10 times the 10-fold cross-validation for all machine-learning models. Using GWAS risk loci or clinical data, the best performances exhibited area under the curve (AUC) values of 64.3% and 71.4%, respectively. Combining GWAS risk loci and clinical data, the XGBoost established the best model with an AUC of 79.7%, indicating that the combination of genetics and clinical data can enable improved performance. We then sorted the importance of features and found the top six risk factors for predicting asthma to be rs3117098, rs7775228, family history, rs2305480, rs4833095, and body mass index.Asthma-prediction models based on GWAS risk loci and clinical data can accurately predict asthma, and thus provide insights into the disease pathogenesis.
Joint Sparse Locality Preserving Regression for Discriminative Learning

Ridge Regression (RR) is a classical method that is widely used in multiple regression analysis. However, traditional RR does not take the local geometric structure of data into consideration for discriminative learning and it is sensitive to outliers as it is based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{2}$</tex-math></inline-formula> -norm. To address this problem, this article proposes a novel method called Joint Sparse Locality Preserving Regression (JSLPR) for discriminative learning. JSLPR not only applies <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{2,1}$</tex-math></inline-formula> -norm on both loss function and regularization term but also takes the local geometric structure of the data into consideration. The use of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{2,1}$</tex-math></inline-formula> -norm can guarantee the robustness to outliers or noises and the joint sparsity for effective feature selection. Taking the local geometric structure into consideration can improve the performance of the feature extraction and selection method when the data lie on a manifold. To solve the optimization problem of JSLPR, an iterative algorithm is proposed and the convergence of the algorithm is also proven. Experiments on four famous face databases are conducted and the results show the merit of the proposed JSLPR on feature extraction and selection.
Joint Optimization of QoE and Fairness for Adaptive Video Streaming in Heterogeneous Mobile Environments

The rapid growth of mobile video traffic and user demand poses a more stringent requirement for efficient bandwidth allocation in mobile networks where multiple users may share a bottleneck link. This provides content providers an opportunity to jointly optimize multiple users’ experiences but users often suffer short connection durations and frequent handoffs because of their high mobility. In this paper, we propose an end-to-end scheme, VSiM, for supporting mobile video streaming applications in heterogeneous wireless networks. The key idea is allocating bottleneck bandwidth among multiple users based on their mobility profiles and Quality of Experience (QoE)-related knowledge to achieve max-min QoE fairness. Besides, the QoE of buffer-sensitive clients is further improved by the novel server push strategy based on HTTP/3 protocol without affecting the existing bandwidth allocation approach or sacrificing other clients’ view quality. VSiM is lightweight and easy to deploy in the real world without touching the underlying network infrastructure. We evaluated VSiM experimentally in both simulations and a lab testbed on top of the HTTP/3 protocol. We find that the clients’ QoE fairness of VSiM achieves more than 40% improvement compared with state-of-the-art solutions, i.e., the viewing quality of clients in VSiM can be improved from 720p to 1080p in resolution. Meanwhile, VSiM provides about 20% improvement of average QoE.
pFedEff: An Efficient and Personalized Federated Cognitive Learning Framework in Multiagent Systems

With the increase in data volume and environment complexity, real-world problems require more advanced algorithms to acquire useful information for further analysis or decision-making. Cognitive Learning effectively handles incomplete information, and multi-agent systems can provide enough data for analysis. Inspired by Distributed Machine Learning, Federated Learning has become an efficient framework for implementing Cognitive Learning algorithms in multi-agent systems while preserving user privacy. However, traditional communication optimizations on the federated learning framework suffer from either large communication volumes or large accuracy loss. In this paper, we propose pFedEff, a personalized federated learning framework with efficient communication that can reduce communication volume and preserve training accuracy. pFedEff uses two magnitude masks, two importance masks, and a personalized aggregation method to reduce the model and update size while maintaining the training accuracy. Specifically, we use a pre-training magnitude mask for approximated regularization to reduce the magnitude of ineffective parameters during training. We also use a post-training magnitude mask to eliminate the low-magnitude parameters after training. Furthermore, we use uploading and downloading importance masks to reduce the communication volume in both upload and download streams. Our experimental results show that pFedEff reduces up to 94% communication volume with only a 1% accuracy loss over other state-of-the-art federated learning algorithms. In addition, we conduct multiple ablation studies to evaluate the influence of hyperparameters in pFedEff, which shows the flexibility of pFedEff and its applicability in different scenarios.
Cryptosystem for IoT Devices With Feedback Operation Modes Based on Shared Buffer and Unrolled-Pipeline Techniques

This paper presents a multichannel cryptosystem based on the proposed shared buffer techniques which enhance security for several Internet of Things (IoT) sensors and devices by using block cipher with feedback operation modes. Information security and its performance are both important characteristics for modern networking devices. In cryptography, the block cipher, for example, advanced encryption standard (AES), using feedback operation modes can enhance the security level but, unfortunately, the encryption cannot be performed in parallel owing to data dependency. Therefore, the pipeline and unrolling techniques are not applicable to increase the throughput of hardware designs. Under this situation, the round-based architecture is popular and regarded as an area-efficient solution. However, this method inherently limits its throughput. The proposed cryptosystem aims to provide many resource-constrained IoT devices with high-speed centralized encryption service to enhance their security levels, which are applicable for various scenarios, such as vehicular network, home network and network function virtualization (NFV)/software-defined networking (SDN) IoT. Beyond that, the proposed design introduces the shared buffer technique based on linked lists and presents a novel queuing structure to enhance the memory utilization so that it can reduce 72.9% memory requirement of the naïve implementation while achieving the same speedup. According to the implementation result, an aggregate throughput of 130.91 Gbps for encrypting 10 IoT devices in cipher block chaining (CBC), cipher feedback (CFB), and output feedback (OFB) modes can be achieved on TSMC 40nm. The area efficiency of this work significantly outperforms the state-of-the-art works.
Does Qubit Connectivity Impact Quantum Circuit Complexity?

Some physical implementation schemes of quantum computing can apply two-qubit gates only on certain pairs of qubits. These connectivity constraints are commonly viewed as a significant disadvantage. For example, compiling an unrestricted n-qubit quantum circuit to one with poor qubit connectivity, such as a 1D chain, usually results in a blowup of depth by O(n2) and size by O(n). It is appealing to conjecture that this overhead is unavoidable—a random circuit on n qubits has Θ(n) two-qubit gates in each layer and a constant fraction of them act on qubits separated by distance Θ(n). While it is known that almost all n-qubit unitary operations need quantum circuits of Ω(4n/n) depth and Ω(4n) size to realize with all-to-all qubit connectivity, in this paper, we show that all n-qubit unitary operations can be implemented by quantum circuits of O(4n/n) depth and O(4n) size even under 1D chain qubit connectivity constraint. We extend this result and investigate qubit connectivity in three directions. First, we consider more general connectivity graphs and show that the circuit size can always be made O(4n) as long as the graph is connected. For circuit depth, we study d-dimensional grids, complete d-ary trees and expander graphs, and show results similar to the 1D chain. Second, we consider the case when ancillary qubits are available. We show that, with ancilla, the circuit depth can be made polynomial, and the space-depth trade-off is not impaired by connectivity constraints unless we have exponentially many ancillary qubits. Third, we obtain nearly optimal results on special families of unitaries, including diagonal unitaries, 2-by-2 block diagonal unitaries, and Quantum State Preparation (QSP) unitaries, the last being a fundamental task used in many quantum algorithms for machine learning and linear algebra.
ATRC: An Anonymous Traceable and Revocable Credential System Using Blockchain for VANETs

With the number of smart vehicle drivers increasing rapidly, privacy-preserving identity management methods in Vehicular Ad-hoc Networks (VANETs) become more imperative and receive much attention from researchers. Since identity leakage or a single point of failure may result in serious consequences in the VANET, the decentralized anonymous credential (DAC) could be a potential approach to construct a robust network. However, the supervision under decentrilized environment could be a necessary and troublesome in the VANETs. In this paper, we present an anonymous traceable and revocable credential system using blockchain, called ATRC, built over a generalized group signature. The underlying group signature not only meets the anonymity requirement but also makes the users master their identities. What's more, to tackle the privacy leakage in the revocation process, we employ the Merkle tree to construct a whitelist, which trades off the efficiency and the privacy. Finally, we gives the comparison and experiment performance to show our scheme holds lightweight on the user side and has fewer computation costs in the show and revocation phase.
Learning from Incorrectness: Active Learning with Negative Pre-training and Curriculum Querying for Histological Tissue Classification

Patch-level histological tissue classification is an effective pre-processing method for histological slide analysis. However, the classification of tissue with deep learning requires expensive annotation costs. To alleviate the limitations of annotation budgets, the application of active learning (AL) to histological tissue classification is a promising solution. Nevertheless, there is a large imbalance in performance between categories during application, and the tissue corresponding to the categories with relatively insufficient performance are equally important for cancer diagnosis. In this paper, we propose an active learning framework called ICAL, which contains Incorrectness Negative Pre-training (INP) and Category-wise Curriculum Querying (CCQ) to address the above problem from the perspective of category-to-category and from the perspective of categories themselves, respectively. In particular, INP incorporates the unique mechanism of active learning to treat the incorrect prediction results that obtained from CCQ as complementary labels for negative pre-training, in order to better distinguish similar categories during the training process. CCQ adjusts the query weights based on the learning status on each category by the model trained by INP, and utilizes uncertainty to evaluate and compensate for query bias caused by inadequate category performance. Experimental results on two histological tissue classification datasets demonstrate that ICAL achieves performance approaching that of fully supervised learning with less than 16% of the labeled data. In comparison to the state-of-the-art active learning algorithms, ICAL achieved better and more balanced performance in all categories and maintained robustness with extremely low annotation budgets. The source code will be released at https://github.com/LactorHwt/ICAL.