Reviewerly/exHarmony huggingface dataset:
A Refined Hybrid Network for Object Detection in Aerial Images

Aerial object detection is a challenging task that needs to detect objects with large variations in scale and orientation. Previous dense object detectors rely on heuristic Non-Maximum Suppression (NMS) to filter out redundant detections. This may reduce the recall rate for objects with arbitraty orientations and large aspect ratios. Recently proposed sparse object detectors treat object detection as a set prediction task, effectively eliminating the need for hand-crafted components. However, applying this paradigm directly to aerial images achieves inferior performance. In this paper, we develop an effective refined hybrid network for object detection in aerial images. Our method combines the advantages of both dense and sparse detectors, achieving outstanding performance for aerial objects with large variations. Specifically, considering the highly diverse orientations of objects, we first apply a dynamic query generation module to produce high-quality oriented queries. These queries can effectively locate the foreground objects in an image, ensuring a high recall rate. Then, the object queries are sent to a query decoder for further refinement. This refinement stage adopts one-to-one matching to eliminate the negative impact caused by NMS. Moreover, an adaptive feature fusion module is designed to learn stronger modeling capabilities for rotated objects at different scales. In addition, we propose a practical mixed query sampling strategy that utilizes many-to-one assignment as an auxiliary scheme to help the detector training. Extensive experiments conducted on several aerial datasets demonstrate the superior performance of the proposed method in comparison with other state-of-the-art approaches.
CrossFuN: Multiview Joint Cross-Fusion Network for Time-Series Anomaly Detection

Time Series are often used to record the various states (i.e., metrics) of a system. Detecting the anomaly state is challenging because temporal dynamics and inter-metric dependencies need to be learned simultaneously, and anomaly types are diverse due to the complexity of the time series. Many anomaly detection models still leave some challenges unresolved. They mainly ignore the importance of information from frequency domain while concentrating on time domain modeling and further neglect the cross-domain effects of time and frequency domains. In this paper, we proposed a novel Multi-View Joint Cross Fusion Network (CrossFuN) to detect diverse types of anomaly, which has wide applicability to both univariate and multivariate time series. Particularly, based on the assumptions of Time-Frequency Heterogeneity and Time Frequency Coordination, a time-frequency joint cross fusion block is designed to simultaneously model the information of both the time and frequency domains, and captures the relationship between the time domain and the frequency domain. Moreover, taking advantage of the attention mechanism, CrossFuN can capture temporal dynamics and inter-metric dependencies. We conduct extensive experiments on seven real-world datasets to demonstrate the effectiveness of CrossFuN.
Fast Performance Prediction for Efficient Distributed DNN Training

Training large-scale DNN models requires parallel distributed training using hyper-scale systems. To make the best use of the numerous accelerators, it is essential to intelligently combine different parallelization schemes. However, as the size of DNN models increases, the possible combinations of schemes become enormous, and consequently, finding the optimal parallel plan becomes exceedingly expensive and practically unfeasible. In this paper, we introduce a novel cost model, the Markovian Performance Estimator (MPE). This model provides affordable estimates of the throughput of various parallel plans, promoting efficient and fast searches for the ideal parallel plan, even when resources are limited. Significantly, this work is pioneering in explaining the expensive nature of searching for an optimal plan and addressing it using intuitive performance estimations based on real device evaluations. Our experiments demonstrate the effectiveness of the MPE, revealing that it accelerates the optimization process up to 126x faster (36.4 on average) than the existing state-of-the-art baseline, Alpa.
SYSTEM TO FORM MORAL VALUES OF STUDENTS OF MILITARY TRAINING CENTERS IN GLOBAL INFORMATION SPACE

The information society is characterized by a new system of values. Formation of values as social guidelines for the existence of society and its development takes place in education. New education goals require to design modern systems for the formation of values. These systems should consider the influence of the factors of the information space on the value sphere of the personality. The students of military training centers are the potential defenders of the Motherland, therefore, it is crucial to educate their personal moral values. The article aims to design a system to form the moral values of military training centers’ students in the global information space. On the basis of the identified level of values’ formation of the students the author developed a system to form the moral values of military training centers’ students in the global information space; described the model’s and the information space basic characteristics. The system considers the values’ changes in modern global information space and is built in accordance with the goals to form the adjusted moral orientations of the students.
SOCIOCULTURAL SUPPORT OF INTERNATIONL STUDENTS AS ADAPTATION TOOL IN INTERCULTURAL ENVIRONMENT OF RUSSIAN UNIVERSITY (SUSU CASE STUDY)

Russian universities strive to implement strategies to enter the global educational space and increase the number of international students, as this is the crucial indicator of a university’s success. The implementation of these strategies involves not only the introduction of innovation in the educational process, but also the creation of favorable conditions for international students. However, academic migrants have to adapt to these new conditions, as they will differ from the conditions in the home country. Although many institutions put efforts to maximize students’ adaptation and acculturation, the desired outcome is yet to come and the search for effective practices is still a pressing challenge. The organization of socio-cultural support as an adaptation tool in the intercultural environment is of great importance. The research aims to examine the current conceptions and best practices of sociocultural support in the academic environment, reveals the major challenges of the international students, identifies the international student’ satisfaction rate and suggests the model of sociocultural support in SUSU. The authors employ qualitative research with the data obtained from the interviews, classroom observations, and satisfaction survey among 30 international students. Modern concepts and certain practices of sociocultural support for international students in the Russian academic environment are examined, their major challenges are revealed, the level of satisfaction of international students with the quality of sociocultural support at SUSU is determined, sociocultural pedagogical model for student support is elaborated. The implementation of the suggested model is supposed to contribute to more efficient adaptation of international students into Russian educational environment.
Background-detail restoration image deraining network based on convolutional dictionary network

The heavy rain streaks can seriously fade the color of images and destroy the background texture information. Although the existing deraining methods perform well in rain removal, we find that most of them tend to favor rain removal or restoration. In this paper, we propose a background-detail restoration image deraining network based on convolutional dictionary network to achieve effective deraining results with better-restored background details. Our framework includes two sub-networks, namely the rain-removal network and the background-restoration network. The former sub-network is constructed as the multi-stage architecture. In order to accurately maintain as much background detail as possible while removing rain streaks, we break down the overall rain removal process into more manageable steps. We introduce convolutional dictionary network and Multi-Scale Detail Restoration Block (MSDRB) to achieve the balance between the effectiveness of deraining and background restoration. The second sub-network is Residual-Unet with Squeeze-and-Excitation (RUSE) operation, which can utilize the output of every stage generated from the former sub-network to fine-tune the background details in rain-free images. Experiments on benchmark public synthetic datasets and real-world datasets demonstrate the effectiveness and generalization capability of the proposed method in rain streak removal and background restoration with different rain patterns.
Why Deep Neural Networks: Yet Another Explanation

One of the main motivations for using artificial neural networks was to speed up computations. From this viewpoint, the ideal configuration is when we have a single nonlinear layer: this configuration is computationally the fastest, and it already has the desired universal approximation property. However, the last decades have shown that for many problems, deep neural networks, with several nonlinear layers, are much more effective. How can we explain this puzzling fact? In this paper, we provide a possible explanation for this phenomena: that the universal approximation property is only true in the idealized setting, when we assume that all computations are exact. In reality, computations are never absolutely exact. It turns out that if take this non-exactness into account, then one-nonlinear-layer networks no longer have the universal approximation property, several nonlinear layers are needed–and several layers is exactly what deep networks are about.
Multiqubit State Tomography with Only a Few Pauli Measurements

In quantum information transformation and quantum computation, the most critical issues are security and accuracy. These features, therefore, stimulate research on quantum state characterization. A characterization tool, quantum state tomography, reconstructs the density matrix of an unknown quantum state. Theoretically, reconstructing an unknown state using this method can be arbitrarily accurate. However, this is not so practical owing to the huge burden of measurements and data processing for large numbers of qubits. Even comprising an efficient estimator and a precise algorithm, an optimal tomographic framework can also be overburdened owing to the exponential growth of the measurements. Moreover, the consequential postprocessing of huge amounts of data challenges the capacity of computers. Thus, it is crucial to build an efficient framework that requires fewer measurements but yields an expected accuracy. To this end, we built a tomography schema by which only a few Pauli measurements enable an accurate tomographic reconstruction. Subsequently, this schema was verified as efficient and accurate through numerical simulations on the tomography of multiqubit quantum states. Furthermore, it was proven to be robust through numerical simulations on a noisy superconducting qubit system. Therefore, the tomography schema paves an alternative effective way to reconstruct the density matrix of a quantum state owing to its efficiency and accuracy, which are essential for quantum state tomography.
How to Solve the Apportionment Paradox

In the ideal world, the number of seats that each region or each community gets in a representative body should be exactly proportional to the population of this region or community. However, since the number of seats allocated to each region or community is whole, we cannot maintain the exact proportionality. Not only this leads to a somewhat unfair situation, when residents of one region get more votes per person than residents of another one, it also leads to paradoxes—e.g., sometimes a region that gained the largest number of people loses a number of seats. To avoid this unfairness (and thus, to avoid the resulting paradoxes), we propose to assign, to each representative, a fractional number of votes, so that the overall number of votes allocated to a region will be exactly proportional to the region’s population. This approach resolves the fairness problem, but it raises a new problem: in a secret vote, if we—as it is usually done—disclose the overall numbers of those who voted For and those who voted Against, we may reveal who voted how. In this paper, we propose a way to avoid this disclosure.
Why Rectified Power (RePU) Activation Functions are Efficient in Deep Learning: A Theoretical Explanation

At present, the most efficient machine learning techniques is deep learning, with neurons using Rectified Linear (ReLU) activation function $$s(z)=\max (0,z)$$ , in many cases, the use of Rectified Power (RePU) activation functions $$(s(z))^p$$ —for some p—leads to better results. In this paper, we explain these results by proving that RePU functions (or their “leaky” versions) are optimal with respect that all reasonable optimality criteria.
What Is a Natural Probability Distribution on the Class of All Continuous Functions: Maximum Entropy Approach Leads to Wiener Measure

While many data processing techniques assume that we know the probability distributions, in practice, we often only have a partial information about these probabilities—so that several different distributions are consistent with our knowledge. Thus, to apply these data processing techniques, we need to select one of the possible probability distributions. There is a reasonable approach for such selection—the Maximum Entropy approach. This approach selects a uniform distribution if all we know is that the random variable if located in an interval; it selects a normal distribution if all we know is the mean and the variance. In this paper, we show that the Maximum Entropy approach can also be applied if what we do not know is a continuous function. It turns out that among all probability distributions on the class of such functions, this approach selects the Wiener measure—the probability distribution corresponding to Brownian motion.
Semantic-enhanced Contrastive Learning for Session-based Recommendation

Session-based recommendation aims to predict the next clicked item based on the short-term behavior sequence of an anonymous user, which is a challenging task owing to data sparsity. Although contrastive learning has been used extensively to address this problem, existing methods generally consider all other sessions in the mini-batch as negative samples, leading to a limited negative sample space and a failure to distinguish real negative samples from false negative samples. Thus, Semantic-enhanced Contrastive Learning for Session-based Recommendation (SCLRec) is proposed in this study. Specifically, a queue is designed to store session samples, and a momentum encoder is used to ensure high consistency in the large-capacity sample space. Furthermore, a novel semantic-enhanced mechanism is devised to filter out false negative samples and increase the weights of high-confidence negative samples according to semantic similarity scores between sessions, effectively reducing noise and enhancing contrastive learning. Moreover, the gated attention unit is used as the encoder to obtain excellent performance and efficiency compared with traditional attention networks. Extensive experiments on three real-world public datasets demonstrate that the proposed method achieves state-of-the-art performance with a relatively low time complexity.
Word Representation: Theoretical Explanation of an Empirical Fact

There is a reasonably accurate empirical formula that predicts, for two words i and j, the number $$X_{ij}$$ of times when the word i will appear in the vicinity of the word j. The parameters of this formula are determined by using the weighted least square approach. Empirically, the predictions are the most accurate if we use the weights proportional to a power of $$X_{ij}$$ . In this paper, we provide a theoretical explanation for this empirical fact.
One More Physics-Based Explanation for Rectified Linear Neurons

The main idea behind artificial neural networks is to simulate how data is processed in the data processing devoice that has been optimized by million-years natural selection–our brain. Such networks are indeed very successful, but interestingly, the most recent successes came when researchers replaces the original biology-motivated sigmoid activation function with a completely different one–known as rectified linear function. In this paper, we explain that this somewhat unexpected function actually naturally appears in physics-based data processing.
Anomaly Detection in Crowdsourcing: Why Midpoints in Interval-Valued Approach

In many practical situations—e.g., when preparing examples for a machine learning algorithm—we need to label a large number of images or speech recordings. One way to do it is to pay people around the world to perform this labeling; this is known as crowdsourcing. In many cases, crowd-workers generate not only answers, but also their degrees of confidence that the answer is correct. Some crowd-workers cheat: they produce almost random answers without bothering to spend time analyzing the corresponding image. Algorithms have been developed to detect such cheaters. The problem is that many crowd-workers cannot describe their degree of confidence by a single number, they are more comfortable providing an interval $$[\underline{x},\overline{x}]$$ of possible degrees. To apply anomaly-detecting algorithms to such interval data, we need to select a single number from each such interval. Empirical studies have shown that the most efficient selection is when we select the arithmetic average. In this paper, we explain this empirical result by showing that arithmetic average is the only selection that satisfies natural invariance requirements.
Data Processing Under Fuzzy Uncertainty: Towards More Accurate Algorithms

Data that we process comes either from measurements or from experts—or from the results of previous data processing that were also based on measurements and/or expert estimates. In both cases, the data is imprecise. To gauge the accuracy of the results of data processing, we need to take the corresponding data uncertainty into account. In this paper, we describe a new algorithm for taking fuzzy uncertainty into account, an algorithm that, for small number of inputs, leads to the same or even better accuracy than the previously proposed methods.
How to Detect (and Analyze) Independent Subsystems of a Black-Box (or Grey-Box) System

Often, we deal with black-box or grey-box systems where we can observe the overall system’s behavior, but we do not have access to the system’s internal structure. In many such situations, the system actually consists of two (or more) independent components: (a) how can we detect this based on observed system’s behavior? (b) what can we learn about the independent subsystems based on the observation of the system as a whole? In this paper, we provide (partial) answers to these questions.
Unexpected Economic Consequence of Cloud Computing: A Boost to Algorithmic Creativity

While theoreticians have been designing more and more efficient algorithms, in the past, practitioners were not very interested in this activity: if a company already owns computers that provide computations in required time, there is nothing to gain by using faster algorithms. We show the situation has drastically changed with the transition to cloud computing: many companies have not yet realized this, but with the transition to cloud computing, any algorithmic speed up leads to immediate financial gain. This also has serious consequences for the whole computing profession: there is a need for professionals better trained in subtle aspects of algorithmics.
AI-Powered Technologies Used in Online Fashion Retail for Sustainable Business

The business problem that arises from the topic of how AI-powered technologies used in online fashion retail is impacting Indian consumer buying behavior. Marketers need to be aware of the impact of artificial intelligence in digital marketing for online fashion retail as competition is intensifying, particularly in the fashion retail sector. To remain viable and provide a better customer experience, it is crucial to identify the areas that will prosper from the use of artificial intelligence-powered technology in marketing. This research aims to investigate the impact of AI-powered technologies on consumer buying behavior in online fashion retail in India. The study found that AI-powered technologies, such as recommender systems, virtual assistant, visual search, and chatbots, have positively impacted Indian consumer buying behavior. Online fashion retailers can leverage the benefits of AI-powered technologies to enhance the customer experience, increase sales, and gain a competitive edge in the market.
Finding top-k longest palindromes in substrings

Palindromes are strings that read the same forward and backward. Problems of computing palindromic structures in strings have been studied for many years with the motivation of their application to biology. The longest palindrome problem is one of the most important and classical problems regarding palindromic structures, that is, to compute the longest palindrome appearing in a string T of length n. The problem can be solved in O(n) time by the famous algorithm of Manacher (1975) [27]. This paper generalizes the longest palindrome problem to the problem of finding the top-k longest palindromes in an arbitrary substring, including the input string T itself. The internal top-k longest palindrome query is, given a substring T[i..j] of T and a positive integer k as a query, to compute the top-k longest palindromes appearing in T[i..j]. This paper proposes a linear-size data structure that can answer internal top-k longest palindromes query in optimal O(k) time. Also, given the input string T, our data structure can be constructed in O(nlog⁡n) time. For k=1, the construction time is reduced to O(n).
Transformed Schatten-1 penalty based full-rank latent label learning for incomplete multi-label classification

Incomplete multi-label learning is a challenging issue due to the difficulty of revealing low-rank structure of multi-labels. There is already much literature to tackle the challenge by imposing penalties of nuclear norm and matrix factorization. However, nuclear norm based methods treat all singular equally and deviate significantly from the approximation of rank of a matrix, whereas matrix factorization technique ignored class structure of latent labels. To address the two issues, in this paper, a transformed Schatten-1 penalty based full-rank latent label learning (TS1FRLL) method is proposed for incomplete multi-label classification. In this model, an improved transformed Schatten-1 regularization is proposed to approximate rank function in the circumstance of labels missing. To preserve the consistency of class structure of latent labels with that of original labels, both low-rank and row full-rank constraints are imposed on the multi-label matrix factorization. The c-block segmentation constraint and manifold learning are combined to characterize the global and local topological structure of latent labels. A classifier is collaboratively learnt to predict labels of unlabeled instances. Comparative experiments on lots of real-world benchmark datasets are conducted and experimental results show excellent performance of the proposed TS1FRLL compared to the state-of-the-art models of incomplete multi-label learning.