Reviewerly/exHarmony huggingface dataset:
Unsupervised Abstract Reasoning for Raven’s Problem Matrices

Raven's Progressive Matrices (RPM) is highly correlated with human intelligence, and it has been widely used to measure the abstract reasoning ability of humans. In this paper, to study the abstract reasoning capability of deep neural networks, we propose the first unsupervised learning method for solving RPM problems. Since the ground truth labels are not allowed, we design a pseudo target based on the prior constraints of the RPM formulation to approximate the ground-truth label, which effectively converts the unsupervised learning strategy into a supervised one. However, the correct answer is wrongly labelled by the pseudo target, and thus the noisy contrast will lead to inaccurate model training. To alleviate this issue, we propose to improve the model performance with negative answers. Moreover, we develop a decentralization method to adapt the feature representation to different RPM problems. Extensive experiments on three datasets demonstrate that our method even outperforms some of the supervised approaches. Our code is available at https://github.com/visiontao/ncd.
Scene Adaptive Online Surveillance Video Synopsis via Dynamic Tube Rearrangement Using Octree

Visual surveillance produces a significant amount of raw video data that can be time consuming to browse and analyze. In this work, we present a video synopsis methodology called “scene adaptive online video synopsis via dynamic tube rearrangement using octree (SSOcT)" that can effectively condense input surveillance videos. Our method entailed summarizing the input video by analyzing scene characteristics and determining an effective spatio-temporal 3D structure for video synopsis. For this purpose, we first analyzed the attributes of each extracted tube with respect to scene geometry and complexity. Then, we adaptively grouped the tubes using an online grouping algorithm that exploits these scene characteristics. Finally, the tube groups were dynamically rearranged using the proposed octree-based algorithm that efficiently inserted and refined tubes containing high spatio-temporal movements in real time. Extensive video synopsis experimental results are provided, demonstrating the effectiveness and efficiency of our method in summarizing real-world surveillance videos with diverse scene characteristics.
An Overview of Quantum Information Science Courses at US Institutions

As the field of Quantum Information Science (QIS) continues to advance, there is an increased need for a quantum-smart workforce to address the needs of the growing quantum industry.As institutions begin to expand their course offerings, there is a unique opportunity for discipline-based education researchers to have an impact on the curricular and pedagogical choices being made in these courses.As a first step, it is necessary for education researchers to have a representative picture of what QIS education currently looks like.We reviewed recent course catalogues from a large sample of institutions in the United States looking for courses focused on QIS content.Our conservative analysis reveals that roughly a quarter of the institutions we reviewed offer QIS courses.While encouraging for such an emerging field, we found disparities in the types of institutions offering these courses as the vast majority were Doctoral-granting institutions.Additionally, we found that some classifications of minority serving institutions were much less likely to offer a QIS course (for example Historically Black Colleges and Universities or Predominantly Black Institutions), while Asian American and Native American Pacific Islander serving institutions were more likely than the national average to offer a QIS course.These disparities may lead to further racial, socioeconomic, and geographic disparity in the future quantum workforce.We also found that there was no single department that offered a majority of the QIS courses, indicating that the best efforts to improve QIS education will need to consider the multi-disciplinary nature of the field of quantum information science.
Establishment and Evaluation of a Large Contact-Tracing and Case Investigation Virtual Training Academy

During the COVID-19 pandemic, the Virtual Training Academy (VTA) was established to rapidly develop a contact-tracing workforce for California. Through June 2021, more than 10 000 trainees enrolled in a contact-tracing or case investigation course at the VTA. To evaluate program effectiveness, we analyzed trainee pre- and postassessment results using the Wilcoxon signed-rank test. There was a statistically significant (P < .001) improvement in knowledge and self-perceived skills after course completion, indicating success in training a competent contact-tracing workforce. (Am J Public Health. 2021;111(11):1934-1938. https://doi.org/10.2105/AJPH.2021.306468).
Deep Learning Approaches and Applications in Toxicologic Histopathology: Current Status and Future Perspectives

Whole slide imaging enables the use of a wide array of digital image analysis tools that are revolutionizing pathology. Recent advances in digital pathology and deep convolutional neural networks have created an enormous opportunity to improve workflow efficiency, provide more quantitative, objective, and consistent assessments of pathology datasets, and develop decision support systems. Such innovations are already making their way into clinical practice. However, the progress of machine learning - in particular, deep learning (DL) - has been rather slower in nonclinical toxicology studies. Histopathology data from toxicology studies are critical during the drug development process that is required by regulatory bodies to assess drug-related toxicity in laboratory animals and its impact on human safety in clinical trials. Due to the high volume of slides routinely evaluated, low-throughput, or narrowly performing DL methods that may work well in small-scale diagnostic studies or for the identification of a single abnormality are tedious and impractical for toxicologic pathology. Furthermore, regulatory requirements around good laboratory practice are a major hurdle for the adoption of DL in toxicologic pathology. This paper reviews the major DL concepts, emerging applications, and examples of DL in toxicologic pathology image analysis. We end with a discussion of specific challenges and directions for future research.
An improved sparrow search algorithm based on levy flight and opposition-based learning

Purpose Sparrow search algorithm (SSA) is a novel global optimization method, but it is easy to fall into local optimization, which leads to its poor search accuracy and stability. The purpose of this study is to propose an improved SSA algorithm, called levy flight and opposition-based learning (LOSSA), based on LOSSA strategy. The LOSSA shows better search accuracy, faster convergence speed and stronger stability. Design/methodology/approach To further enhance the optimization performance of the algorithm, The Levy flight operation is introduced into the producers search process of the original SSA to enhance the ability of the algorithm to jump out of the local optimum. The opposition-based learning strategy generates better solutions for SSA, which is beneficial to accelerate the convergence speed of the algorithm. On the one hand, the performance of the LOSSA is evaluated by a set of numerical experiments based on classical benchmark functions. On the other hand, the hyper-parameter optimization problem of the Support Vector Machine (SVM) is also used to test the ability of LOSSA to solve practical problems. Findings First of all, the effectiveness of the two improved methods is verified by Wilcoxon signed rank test. Second, the statistical results of the numerical experiment show the significant improvement of the LOSSA compared with the original algorithm and other natural heuristic algorithms. Finally, the feasibility and effectiveness of the LOSSA in solving the hyper-parameter optimization problem of machine learning algorithms are demonstrated. Originality/value An improved SSA based on LOSSA is proposed in this paper. The experimental results show that the overall performance of the LOSSA is satisfactory. Compared with the SSA and other natural heuristic algorithms, the LOSSA shows better search accuracy, faster convergence speed and stronger stability. Moreover, the LOSSA also showed great optimization performance in the hyper-parameter optimization of the SVM model.
Oscillator-to-Oscillator Codes Do Not Have a Threshold

It is known that continuous variable quantum information cannot be protected against naturally occurring noise using Gaussian states and operations only.Noh et al. (PRL 125:080503, 2020) proposed bosonic oscillator-to-oscillator codes relying on non-Gaussian resource states as an alternative, and showed that these encodings can lead to a reduction of the effective error strength at the logical level as measured by the variance of the classical displacement noise channel.An oscillator-to-oscillator code embeds K logical bosonic modes (in an arbitrary state) into N physical modes by means of a Gaussian N -mode unitary and N -K auxiliary one-mode Gottesman-Kitaev-Preskill-states.Here we ask if -in analogy to qubit error-correcting codes -there are families of oscillator-to-oscillator codes with the following threshold property: They allow to convert physical displacement noise with variance below some threshold value to logical noise with variance upper bounded by any (arbitrary) constant.We find that this is not the case if encoding unitaries involving a constant amount of squeezing and maximum likelihood error decoding are used.We show a general lower bound on the logical error probability which is only a function of the amount of squeezing and independent of the number of modes.As a consequence, any physically implementable family of oscillator-to-oscillator codes combined with maximum likelihood error decoding does not admit a threshold.
Data Integrity Time Optimization of a Blockchain IoT Smart Home Network Using Different Consensus and Hash Algorithms

Data security is a major issue for smart home networks. Yet, different existing tools and techniques have not been proven highly effective for home networks’ data security. Blockchain is a promising technology because of the distributed computing infrastructure network that makes it difficult for hackers to intrude into the systems through the use of cryptographic signatures and smart contracts. In this paper, an architecture for smart home networks that could guarantee data integrity, robust security, and the ability to protect the validity of the blockchain transactions has been investigated. The system model is tested using various sizes of realistic datasets (30, 3 k, and 30 k to represent a small, medium, and large number of transactions, respectively). Four different consensus algorithms were considered, the conventional schemes concatenated hash transactions (CHT) and Merkle hash tree (MHT), as well as the newly proposed odd and even modified MHT (O&amp;E MHT) and modified MHT (MMHT). Moreover, 15 hash functions were also examined and compared to understand the effects of each consensus algorithms on the data integrity verification check execution time and the time optimization provided by the proposed MMHT algorithm. The results show that even though the CHT algorithm gives the lowest execution time, it is impractical for a blockchain implementation due to the requirement to copy the entire blockchain ledger in real time. Meanwhile, the O&amp;E MHT does not give any tangible benefit in the execution time. However, the proposed MMHT offers a minimum of 30% gain in time optimization than the conventional MHT algorithm typically used in blockchains. This work shows that the proposed MMHT consensus algorithm not only can identify malicious codes but has an improved data integrity check performance in smart homes, all while ensuring network stability.
Multilayer Batch Learning Growing Neural Gas for Learning Multiscale Topologies

Hierarchical topological structure learning methods are expected to be developed in the field of data mining for extracting multiscale topological structures from an unknown dataset. However, most methods require user-defined parameters, and it is difficult for users to determine these parameters and effectively utilize the method. In this paper, we propose a new parameter-less hierarchical topological structure learning method based on growing neural gas (GNG). First, we propose batch learning GNG (BL-GNG) to improve the learning convergence and reduce the user-designed parameters in GNG. BL-GNG uses an objective function based on fuzzy C-means to improve the learning convergence. Next, we propose multilayer BL-GNG (MBL-GNG), which is a parameter-less unsupervised learning algorithm based on hierarchical topological structure learning. In MBL-GNG, the input data of each layer uses parent nodes to learn more abstract topological structures from the dataset. Furthermore, MBL-GNG can automatically determine the number of nodes and layers according to the data distribution. Finally, we conducted several experiments to evaluate our proposed method by comparing it with other hierarchical approaches and discuss the effectiveness of our proposed method.
VOXEL

Delivering videos under less-than-ideal network conditions without compromising end-users' quality of experiences is a hard problem. Virtually all prior work follow a piecemeal approach---either "tweaking" the fully reliable transport layer or making the client "smarter." We propose VOXEL, a cross-layer optimization system for video streaming. We use VOXEL to demonstrate how to combine application-provided "insights" with a partially reliable protocol for optimizing video streaming. To this end, we present a novel ABR algorithm that explicitly trades off losses for improving end-users' video-watching experiences.
Automatic Search of Cubes for Attacking Stream Ciphers

Cube attack was proposed by Dinur and Shamir, and it has become an important tool for analyzing stream ciphers. As the problem that how to recover the superpolys accurately was resolved by Hao et al. in EUROCRYPT 2020, another important problem is how to find “good” superpolys, which is equivalent to finding “good” cubes. However, there are two difficulties in finding “good” cubes. Firstly, the number of candidate cubes is enormous and most of the cubes are not “good”. Secondly, it is costly to evaluate whether a cube is “good”.In this paper, we present a new algorithm to search for a kind of “good” cubes, called valuable cubes. A cube is called valuable, if its superpoly has (at least) a balanced secret variable. A valuable cube is “good”, because its superpoly brings in 1 bit of information about the key. More importantly, the superpolys of valuable cubes could be used in both theoretical and practical analyses. To search for valuable cubes, instead of testing a set of cubes one by one, the new algorithm deals with the set of cubes together, such that the common computations can be done only once for all candidate cubes and duplicated computations are avoided. Besides, the new algorithm uses a heuristic method to reject useless cubes efficiently. This heuristic method is based on the divide-and-conquer strategy as well as an observation.For verifications of this new algorithm, we applied it to Trivium and Kreyvium, and obtained three improvements. Firstly, we found two valuable cubes for 843-round Trivium, such that we proposed, as far as we know, the first theoretical key-recovery attack against 843-round Trivium, while the previous highest round of Trivium that can be attacked was 842, given by Hao et al. in EUROCRYPT 2020. Secondly, by finding many small valuable cubes, we presented practical attacks against 806- and 808-round Trivium for the first time, while the previous highest round of Trivium that can be attacked practically was 805. Thirdly, based on the cube used to attack 892-round Kreyvium in EUROCRYPT 2020, we found more valuable cubes and mounted the key-recovery attacks against Kreyvium to 893-round.
Context Matters: Recovering Human Semantic Structure from Machine Learning Analysis of Large‐Scale Text Corpora

Abstract Applying machine learning algorithms to automatically infer relationships between concepts from large‐scale collections of documents presents a unique opportunity to investigate at scale how human semantic knowledge is organized, how people use it to make fundamental judgments (“How similar are cats and bears?”), and how these judgments depend on the features that describe concepts (e.g., size, furriness). However, efforts to date have exhibited a substantial discrepancy between algorithm predictions and human empirical judgments. Here, we introduce a novel approach to generating embeddings for this purpose motivated by the idea that semantic context plays a critical role in human judgment. We leverage this idea by constraining the topic or domain from which documents used for generating embeddings are drawn (e.g., referring to the natural world vs. transportation apparatus). Specifically, we trained state‐of‐the‐art machine learning algorithms using contextually‐constrained text corpora (domain‐specific subsets of Wikipedia articles, 50+ million words each) and showed that this procedure greatly improved predictions of empirical similarity judgments and feature ratings of contextually relevant concepts. Furthermore, we describe a novel, computationally tractable method for improving predictions of contextually‐unconstrained embedding models based on dimensionality reduction of their internal representation to a small number of contextually relevant semantic features. By improving the correspondence between predictions derived automatically by machine learning methods using vast amounts of data and more limited, but direct empirical measurements of human judgments, our approach may help leverage the availability of online corpora to better understand the structure of human semantic representations and how people make judgments based on those.
Cross-domain knowledge graph chiasmal embedding for multi-domain item-item recommendation

Recommender system can provide users with the required information accurately and efficiently, playing a very important role in improving users' life experience. Although knowledge graph-based recommender system can solve the sparsity and cold start problems faced by traditional recommender system, it cannot handle the cross-domain cold start problem and cannot provide multi-domain recommendations. Therefore, this paper focuses on multi-domain item-item (I2I) recommendation based on cross-domain knowledge graph embedding by analyzing the association between items of the same domain and the interaction between items of diverse domains with the aid of knowledge graph that contains rich information. Firstly, a cross-domain knowledge graph chiasmal embedding approach is proposed to efficiently interact all items in multiple domains. To help achieve both homo-domain embedding and hetero-domain embedding of items, a binding rule is put forward. Secondly, a multi-domain I2I recommendation method is presented to efficiently recommend items in multiple domains, which is a recommendation method based on link prediction of knowledge graph. Finally, the proposed methods are compared and analyzed with some benchmark methods using two datasets. The experimental results show that the proposed methods achieve better link prediction results and multi-domain recommendation results.
Market basket analysis of administrative patterns data of consumer purchases using data mining technology

Food is the ingredient that enables people to grow, develop, and achieve. For this reason, food quality and types of food must be considered so that they are safe for consumption and managed. Some plant-based foodstuffs are often processed and consumed by the community, even the most needed in food processing. In this case, the research was carried out using data mining with market basket analysis algorithms to obtain very valuable information to decide the inventory of the type of material needed. Market Based Analysis method is used to analyze all data and create patterns for each data. One method of Market Based Analysis in question is the association rule with a priori algorithm. This algorithm produces sales transactions with strong associations between items in the transaction which are used as sales recommendations that help users (owners) get recommendations when users see details of the itemset purchased. From the results of the trials in this study, it was found that the greater the minimum support (minsup) and minimum confidence (minconf), the less time it takes to produce recommendations and the fewer recommendations are given, but the recommendations given come from transactions that often appear.
An Overview of Current Solutions for Privacy in the Internet of Things

As the Internet of Things (IoT) applications have been introduced into daily life, privacy issues have become significant concerns to users, network service providers, device producers, and related roles. This study provides a high-level introduction of current privacy-preserving solutions in IoT systems within the three phases of data collection, transmission, and storage. In these three phases, the following aspects were examined: (1). security protocols at the physical and data link layers; (2). network solutions; and (3). data storage and sharing approaches. Real-world implementations often involve more than one phase, and numerous technologies are combined to ensure privacy. Thus, an understanding of all phases and their technologies can be helpful for IoT research, design, development, and operation.
Enhancing the accuracy of forging measurement using silhouettes in images

In this study, a new passive camera system for heavy cylindrical forging measurement, based on silhouettes in images, is developed. New methods for making such a system more resistant to the negative effects of the industrial environment have been proposed. This includes weighted edge filtering based on complementary information about the edge quality in an image. The recorded measurement median errors were ±0.12 mm and ±0.14 mm. Moreover, the 95% confidence intervals were ±0.5 mm and ±1 mm for the forging axis and diameter measurements, respectively. Both results were achieved in a measurement volume of 6 × 6 × 2 m during the measurement of glowing hot forgings in industrial conditions. The results surpass those of the state-of-the-art method, mainly in the case of axis straightness measurement, by approximately 50%. The measurement is fast, and it provides feedback about the axis straightness for its subsequent correction.