Reviewerly/exHarmony huggingface dataset:
Source Camera Attribution from Videos

Abstract Photo-response non-uniformity (PRNU) is an intrinsic characteristic of a digital imaging sensor, which manifests as a unique and permanent pattern introduced to all media captured by the sensor. The PRNU of a sensor has been proven to be a viable identifier for source attribution and has been successfully utilized for identification and verification of the source of digital media.
Comfortable place for quantum walkers on finite path

We consider the stationary state of a quantum walk on the finite path, where the sink and source are set at the left and right boundaries. The quantum coin is uniformly placed at every vertex of the path graph. At every time step, a new quantum walker penetrates into the internal from the left boundary and also some existing quantum walkers in the internal goes out to the sinks located in the left and right boundaries. The square modulus of the stationary state at each vertex is regarded as the comfortability for a quantum walker to this vertex in this paper. We show the weak convergence theorem for the scaled limit distribution of the comfortability in the limit of the length of the path.
Intelligent Auxiliary System for Sports Public Service Information Platform Based on Communication and Computing Methods

In the recent time, the combination of the information system with computational models is the essential trend, hence, this paper studies the intelligent auxiliary system for the sports public service information platform based on communication and computing methods. Computer communication network security technology plays a very important role in the development and application of computer technology. The development of various new technologies also makes network applications more convenient and safer. With this integration, this paper adopts the novel information system to finalize the framework. The application scenario of the sports public service information platform is integrated. The test on the performance has shown the satisfactory performance.
Multi-View Document Representation Learning for Open-Domain Dense Retrieval

Dense retrieval has achieved impressive advances in first-stage retrieval from a large-scale document collection, which is built on bi-encoder architecture to produce single vector representation of query and document. However, a document can usually answer multiple potential queries from different views. So the single vector representation of a document is hard to match with multi-view queries, and faces a semantic mismatch problem. This paper proposes a multi-view document representation learning framework, aiming to produce multi-view embeddings to represent documents and enforce them to align with different queries. First, we propose a simple yet effective method of generating multiple embeddings through viewers. Second, to prevent multi-view embeddings from collapsing to the same one, we further propose a global-local loss with annealed temperature to encourage the multiple viewers to better align with different potential queries. Experiments show our method outperforms recent works and achieves state-of-the-art results.
Integral Basis and Discriminant

Discriminant whose notion is due to DedekindDedekind, Richard, is a basic invariant associated with an algebraic number field. Its computation is one of the most important problems in algebraic number theory. For an algebraic number field $$K=\mathbb Q(\theta )$$ with $$\theta $$ in the ring $$\mathcal {O}_K$$ of algebraic integers of K having f(X) as its minimal polynomial over the field $$\mathbb Q$$ of rational numbers, the discriminant $$d_K$$ of K and the discriminant of the polynomial f(X) are related by the formula $$ discr(f) = [\mathcal {O}_K: \mathbb {Z}[\theta ]]^2d_K.$$ So computation of $$d_K$$ is closely connected with that of the index of the group $$\mathbb {Z}[\theta ]$$ in $$\mathcal {O}_K$$ . It will be shown that $$\mathcal {O}_K$$ is a free abelian group of rank equal to the degree of the extension $$K/\mathbb Q$$ . A $$\mathbb {Z}$$ -basis of the group $$\mathcal {O}_K$$ is called an integral basis of K. We shall describe explicit integral basis for quadratic, pure cubic and cyclotomic extensions of $$\mathbb Q$$ . The problem of computation of discriminant as well as an integral basis of an infinite family of algebraic number fields which are defined over $$\mathbb Q$$ by certain types of irreducible polynomials has attracted the attention of several mathematicians. We shall cite some recent results in this direction and mention a few related open problems.
Automatic Encoding and Repair of Reactive High-Level Tasks with Learned Abstract Representations

We present a framework that, given a set of skills a robot can perform, abstracts sensor data into symbols that we use to automatically encode the robot's capabilities in Linear Temporal Logic. We specify reactive high-level tasks based on these capabilities, for which a strategy is automatically synthesized and executed on the robot, if the task is feasible. If a task is not feasible given the robot's capabilities, we present two methods, one enumeration-based and one synthesis-based, for automatically suggesting additional skills for the robot or modifications to existing skills that would make the task feasible. We demonstrate our framework on a Baxter robot manipulating blocks on a table, a Baxter robot manipulating plates on a table, and a Kinova arm manipulating vials, with multiple sensor modalities, including raw images.
Spatiotemporal Pyramid Convolution Action Recognition Network Based on Shared Convolution Strategy

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An Infinite, Two-parameter Family of Polynomials with Factorization  Similar to $X^m-1$

For a suitable irreducible \textit{base} polynomial $f(x)\in \mathbf{Z}[x]$ of degree $k$, a family of polynomials $F_m(x)$ depending on $f(x)$ is constructed with the properties: (i) there is exactly one irreducible factor $\Phi_{d,f}(x)$ for $F_m(x)$ for each divisor $d$ of $m$; (ii) deg $(\Phi_{d,f}(x))=\varphi(d)\cdot\mathrm{deg} (f)$ generalizing the factorization of $x^m-1$ into cyclotomic polynomials; (iii) when the base polynomial $f(x) = x-1$ this $F_m(x)$ coincides with $x^m-1$. As an application, irreducible polynomials of degree 12, 24, 24 are constructed having Galois groups of order matching their degrees and isomorphic to $S_3 \oplus C_2 , S_3 \oplus C_2\oplus C_2$ and $S_3 \oplus C_4$ respectively.
Patch-NetVLAD+: Learned patch descriptor and weighted matching strategy for place recognition

Visual Place Recognition (VPR) in areas with similar scenes such as urban or indoor scenarios is a major challenge. Existing VPR methods using global descriptors have difficulty capturing local specific regions (LSR) in the scene and are therefore prone to localization confusion in such scenarios. As a result, finding the LSR that are critical for location recognition becomes key. To address this challenge, we introduced Patch-NetVLAD+, which was inspired by patch-based VPR researches. Our method proposed a fine-tuning strategy with triplet loss to make NetVLAD suitable for extracting patch-level descriptors. Moreover, unlike existing methods that treat all patches in an image equally, our method extracts patches of LSR, which present less frequently throughout the dataset, and makes them play an important role in VPR by assigning proper weights to them. Experiments on Pittsburgh30k and Tokyo247 datasets show that our approach achieved up to 6.35\% performance improvement than existing patch-based methods.
Personalized Federated Learning of Driver Prediction Models for Autonomous Driving

Autonomous vehicles (AVs) must interact with a diverse set of human drivers in heterogeneous geographic areas. Ideally, fleets of AVs should share trajectory data to continually re-train and improve trajectory forecasting models from collective experience using cloud-based distributed learning. At the same time, these robots should ideally avoid uploading raw driver interaction data in order to protect proprietary policies (when sharing insights with other companies) or protect driver privacy from insurance companies. Federated learning (FL) is a popular mechanism to learn models in cloud servers from diverse users without divulging private local data. However, FL is often not robust -- it learns sub-optimal models when user data comes from highly heterogeneous distributions, which is a key hallmark of human-robot interactions. In this paper, we present a novel variant of personalized FL to specialize robust robot learning models to diverse user distributions. Our algorithm outperforms standard FL benchmarks by up to 2x in real user studies that we conducted where human-operated vehicles must gracefully merge lanes with simulated AVs in the standard CARLA and CARLO AV simulators.
Just-Noticeable-Difference Based Edge Map Quality Measure

The performance of an edge detector can be improved when assisted with an effective edge map quality measure. Several evaluation methods have been proposed resulting in different performance score for the same candidate edge map. However, an effective measure is the one that can be automated and which correlates with human judgement perceived quality of the edge map. Distance-based edge map measures are widely used for assessment of edge map quality. These methods consider distance and statistical properties of edge pixels to estimate a performance score. The existing methods can be automated; however, they lack perceptual features. This paper presents edge map quality measure based on Just-Noticeable-Difference (JND) feature of human visual system, to compensate the shortcomings of distance-based edge measures. For this purpose, we have designed constant stimulus experiment to measure the JND value for two spatial alternative. Experimental results show that JND based distance calculation outperforms existing distance-based measures according to subjective evaluation.
Parallel black-box optimization of expensive high-dimensional multimodal functions via magnitude

Building on the recently developed theory of magnitude, we introduce the optimization algorithm EXPLO2 and carefully benchmark it. EXPLO2 advances the state of the art for optimizing high-dimensional ($D \gtrapprox 40$) multimodal functions that are expensive to compute and for which derivatives are not available, such as arise in hyperparameter optimization or via simulations.
Configuration Path Control

Reinforcement learning methods often produce brittle policies -- policies that perform well during training, but generalize poorly beyond their direct training experience, thus becoming unstable under small disturbances. To address this issue, we propose a method for stabilizing a control policy in the space of configuration paths. It is applied post-training and relies purely on the data produced during training, as well as on an instantaneous control-matrix estimation. The approach is evaluated empirically on a planar bipedal walker subjected to a variety of perturbations. The control policies obtained via reinforcement learning are compared against their stabilized counterparts. Across different experiments, we find two- to four-fold increase in stability, when measured in terms of the perturbation amplitudes. We also provide a zero-dynamics interpretation of our approach.
NeuroView-RNN: It's About Time

Recurrent Neural Networks (RNNs) are important tools for processing sequential data such as time-series or video. Interpretability is defined as the ability to be understood by a person and is different from explainability, which is the ability to be explained in a mathematical formulation. A key interpretability issue with RNNs is that it is not clear how each hidden state per time step contributes to the decision-making process in a quantitative manner. We propose NeuroView-RNN as a family of new RNN architectures that explains how all the time steps are used for the decision-making process. Each member of the family is derived from a standard RNN architecture by concatenation of the hidden steps into a global linear classifier. The global linear classifier has all the hidden states as the input, so the weights of the classifier have a linear mapping to the hidden states. Hence, from the weights, NeuroView-RNN can quantify how important each time step is to a particular decision. As a bonus, NeuroView-RNN also offers higher accuracy in many cases compared to the RNNs and their variants. We showcase the benefits of NeuroView-RNN by evaluating on a multitude of diverse time-series datasets.
DigNet: Digging Clues from Local-Global Interactive Graph for Aspect-level Sentiment Classification

In aspect-level sentiment classification (ASC), state-of-the-art models encode either syntax graph or relation graph to capture the local syntactic information or global relational information. Despite the advantages of syntax and relation graphs, they have respective shortages which are neglected, limiting the representation power in the graph modeling process. To resolve their limitations, we design a novel local-global interactive graph, which marries their advantages by stitching the two graphs via interactive edges. To model this local-global interactive graph, we propose a novel neural network termed DigNet, whose core module is the stacked local-global interactive (LGI) layers performing two processes: intra-graph message passing and cross-graph message passing. In this way, the local syntactic and global relational information can be reconciled as a whole in understanding the aspect-level sentiment. Concretely, we design two variants of local-global interactive graphs with different kinds of interactive edges and three variants of LGI layers. We conduct experiments on several public benchmark datasets and the results show that we outperform previous best scores by 3\%, 2.32\%, and 6.33\% in terms of Macro-F1 on Lap14, Res14, and Res15 datasets, respectively, confirming the effectiveness and superiority of the proposed local-global interactive graph and DigNet.
Stage Classification of Lung Cancer using the Comparative Analysis of the Machine Learning Techniques

Stage classification today is widely used in the fields of biological sciences and engineering, The idea of the stage classification is to perform a clinical analysis of the spread of the disease . Lung cancer which is termed as Lung Carcinoma is a highly dangerous lung tumour which is defined by the uncontrolled growth of cells in the tissues of the lung. This growth of cells leading to the tumour identifies the different stages of cancer. The tumours are identified based on the probability density function. The goal is to design models for the stage classification of the of cancer patients . The description of the extent of a tumour consists of three components: T for extent of the primary tumour, N for involvement of lymph nodes, and M for distant metastases. Each T, N, and M component is divided into several categories (eg, T1, T2). This study proposes to build a classification system that can identify the stage classification using the lung cancer dataset for better accuracy. An “IQ-OTHNCCD” lung cancer dataset of 1190 images representing CT scan slices of 110 cases is used in this research.
Verifying quantum information scrambling dynamics in a fully controllable superconducting quantum simulator

Quantum simulation elucidates properties of quantum many-body systems by mapping its Hamiltonian to a better-controlled system. Being less stringent than a universal quantum computer, noisy small- and intermediate-scale quantum simulators have successfully demonstrated qualitative behavior such as phase transition, localization and thermalization which are insensitive to imperfections in the engineered Hamiltonian. For more complicated features like quantum information scrambling, higher controllability will be desired to simulate both the forward and the backward time evolutions and to diagnose experimental errors, which has only been achieved for discrete gates. Here, we study the verified scrambling in a 1D spin chain by an analogue superconducting quantum simulator with the signs and values of individual driving and coupling terms fully controllable. We measure the temporal and spatial patterns of out-of-time ordered correlators (OTOC) by engineering opposite Hamiltonians on two subsystems, with the Hamiltonian mismatch and the decoherence extracted quantitatively from the scrambling dynamics. Our work demonstrates the superconducting system as a powerful quantum simulator.
A Review of Augmented Reality Apps for an AR-Based STEM Education Framework

Within the past two decades, Augmented Reality (AR) applications have received increased attention. Augmented Reality is now widely used in the education sector at level K to 12. AR is expected to be generally adopted in two to three years in higher education and four to five years in K to 12. Applying AR technology in the education sector especially in STEM subjects, can result in having a smart campus. In adopting a SMART Campus strategy, education practitioners must address many intrinsic issues in science, technology, engineering, and mathematics (STEM) research. For example, in physics, there are expensive or insufficient laboratory systems, system faults, and difficulty simulating other experimental circumstances. In technology, many schools do not have enough computers. In engineering, there are only a few instructors who are knowledgeable in computer aided design (CAD). In mathematics, few teachers incorporate technology into their lessons often because they believe it is still better to teach through the traditional methods. Hence, In this paper we discuss how AR is being used now in different learning areas in STEM to open new doors to researchers and teachers as they transition their schools into SMART campuses with the use of AR apps. Aligned with this, a suggested framework for school administrators and policymakers is proposed based on a review of the positive benefits of different AR apps.
Data Efficient Language-supervised Zero-shot Recognition with Optimal Transport Distillation

Traditional computer vision models are trained to predict a fixed set of predefined categories. Recently, natural language has been shown to be a broader and richer source of supervision that provides finer descriptions to visual concepts than supervised "gold" labels. Previous works, such as CLIP, use InfoNCE loss to train a model to predict the pairing between images and text captions. CLIP, however, is data hungry and requires more than 400M image-text pairs for training. The inefficiency can be partially attributed to the fact that the image-text pairs are noisy. To address this, we propose OTTER (Optimal TransporT distillation for Efficient zero-shot Recognition), which uses online entropic optimal transport to find a soft image-text match as labels for contrastive learning. Based on pretrained image and text encoders, models trained with OTTER achieve strong performance with only 3M image text pairs. Compared with InfoNCE loss, label smoothing, and knowledge distillation, OTTER consistently outperforms these baselines in zero shot evaluation on Google Open Images (19,958 classes) and multi-labeled ImageNet 10K (10032 classes) from Tencent ML-Images. Over 42 evaluations on 7 different dataset/architecture settings x 6 metrics, OTTER outperforms (32) or ties (2) all baselines in 34 of them.
Design structure matrix for evaluation of critical success factors for software projects

The topic of critical success factors draws high attention of both academia and practitioners working in the software industry. Literature review shows that existing models of critical success factors for software projects are more concentrated on specific categories of success factors, neglecting the interaction that exists within these models between different groups of factors. The purpose of this paper is to present a new concept of interaction-based model for identification and evaluation of critical success factors for software projects. The approach is based on conjoint use of graph theory and Design Structure Matrices to highlight important interactions of a project system. The key result of the paper is the tool for evaluation of the interaction between critical success factors through quantification and the analysis of the possible propagation of specific factor within project success factor network, which will make possible the identification of the critical factors with the highest influence on the entire project system.
Analysis of differences between skilled and novice subjects for visual inspection by using eye trackers

The current state of visual inspection in the manufacturing industry is that there is a limit to human concentration in the work, and since concentration does not last for a long time, there is a problem of variation in accuracy depending on fatigue and physical condition.Therefore, there is such a thing as peripheral visual inspection, where visual inspection can be maintained stably over a long period of time by working with rhythmic movements.In this study, we propose an algorithm that can discriminate between skilled and novice subjects in visual inspection.In this algorithm, we used a gaze analysis system to detect differences in gaze direction (peripheral features for skilled and central features for novices) between skilled and novices.As a procedure, we built a database, extracted the necessary data, and then performed preprocessing.After that, the basic statistics of the features were calculated and SVM training was performed to build the model.As a result, we were able to classify novices and skilled with an accuracy of 93.3%
Analysis of the impact of the introduced restrictions caused by the new COVID-19 infection in the field of education on the results of the unified state exam in various constituent entities of the Russian Federation

Since the end of 2019, all countries of the world have faced a rapidly increasing level of disease caused by the new coronavirus infection COVID-19, which in turn has led to the introduction of a number of restrictions in various areas of life. Educational institutions in many countries were forced to transfer students to a remote mode of work, for which both sides were not ready. The difficulty of a quick transition to a distance learning mode was primarily associated with a number of technical limitations, since not every educational institution had the proper material and technical support, just like not every student had the opportunity to use electronic resources. The resulting situation temporarily made it impossible to carry out the educational process. As technical and other problems were solved, educational organizations sought to bridge the gap in the quality of distance and face-to-face training. This article provides statistical data on the results of the unified state exam in mathematics in various regions of Russia and analyzes the results of the exam.