Reviewerly/exHarmony huggingface dataset:
NJU MCG - Sensetime Team Submission to Pre-training for Video Understanding Challenge Track II

This paper presents the method that underlies our submission to the Pre-training for Video Understanding Challenge Track II. We follow the basic pipeline of temporal segment networks [20] and further improve its performance in several aspects. Specifically, we use the latest transformer-based architectures, e.g., Swin Transformer, DeiT, CLIP-ViT, to enhance the representation power. We analyze different pre-training proxy tasks on the official pre-training datasets and other open-source video datasets. With these techniques, we derive an ensemble of deep models to attain a high classification accuracy (Top-1 accuracy 62.28%) on the testing set and secures first place in Track II of this challenge.
TKB$$^2$$ert: Two-Stage Knowledge Infused Behavioral Fine-Tuned BERT

Machine Reading Comprehension (MRC) is a technique to make machine answer questions corresponding to the given passage. However, the existing MRC systems confront challenges to deal with the complex real-world fine-grained question answering tasks. To address the issue, we propose a novel method named TKB\(^2\)ert that generates prior knowledge data and utilizes it in behavioral fine-tuning at the first stage and trains itself through the dual attentive streams at the second stage. Subsequently, we take part in the MRC track of 2021 Language and Intelligence Challenge and win the first place. The result on the newly-built fine-grained MRC competition dataset validates the effectiveness of TKB\(^2\)ert.
Decentralized Payment Clearing using Blockchain and Optimal Bidding

In this paper, we construct a decentralized clearing mechanism which endogenously and automatically provides a claims resolution procedure. This mechanism can be used to clear a network of obligations through blockchain. In particular, we investigate default contagion in a network of smart contracts cleared through blockchain. In so doing, we provide an algorithm which constructs the blockchain so as to guarantee the payments can be verified and the miners earn a fee. We, additionally, consider the special case in which the blocks have unbounded capacity to provide a simple equilibrium clearing condition for the terminal net worths; existence and uniqueness are proven for this system. Finally, we consider the optimal bidding strategies for each firm in the network so that all firms are utility maximizers with respect to their terminal wealths. We first look for a mixed Nash equilibrium bidding strategies, and then also consider Pareto optimal bidding strategies. The implications of these strategies, and more broadly blockchain, on systemic risk are considered.
Large-scale Multi-Modality Pretrained Models

In this talk, we present our experiences and applications of large-scale multi-modality pretrained models, developed at Alibaba and Ant Group. We first present a cross-modal pretraining method called M6 (Multi-Modality to Multi-Modality Multitask Mega-transformer) [1], for unified pretraining on the data of multiple modalities. We scale the model size up to 1 trillion parameters [2], and build the largest pretrained model in Chinese. We apply the model to a series of downstream applications, and demonstrate its outstanding performance in comparison with strong baselines. Furthermore, we specifically design a downstream task of text-guided image generation [3], and show that the finetuned M6 can create high-quality images with high resolution and fidelity.
Estimation Of Fields Using Binary Measurements From A Mobile Agent

In this paper we consider the problem of field estimation using a mobile autonomous agent. The agent is equipped with a sensor which can make noisy binary measurements of the field. We model the field as a sum of radial basis functions, whose parameters are then estimated using sequential Monte Carlo (SMC) techniques. We also devise an active sensing mechanism for the agent to adaptively choose its next measurement location, given the information currently collected. Simulation studies illustrate the performance of the proposed algorithms.
Dancing with the sound in edge computing environments

Abstract Conventional motion predictions have achieved promising performance. However, the length of the predicted motion sequences of most literatures are short, and the rhythm of the generated pose sequence has rarely been explored. To pursue high quality, rhythmic, and long-term pose sequence prediction, this paper explores a novel dancing with the sound task, which is appealing and challenging in computer vision field. To tackle this problem, a novel model is proposed, which takes the sound as an indicator input and outputs the dancing pose sequence. Specifically, our model is based on the variational autoencoder (VAE) framework, which encodes the continuity and rhythm of the sound information into the hidden space to generate a coherent, diverse, rhythmic and long-term pose video. Extensive experiments validated the effectiveness of audio cues in the generation of dancing pose sequences. Concurrently, a novel dataset of audiovisual multimodal sequence generation has been released to promote the development of this field.
MGIC: Multigrid-in-Channels Neural Network Architectures

We present a multigrid-in-channels (MGIC) approach that tackles the quadratic growth of the number of parameters with respect to the number of channels in standard convolutional neural networks (CNNs). Thereby our approach addresses the redundancy in CNNs that is also exposed by the recent success of lightweight CNNs. Lightweight CNNs can achieve comparable accuracy to standard CNNs with fewer parameters; however, the number of weights still scales quadratically with the CNN’s width. Our MGIC architectures replace each CNN block with an MGIC counterpart that utilizes a hierarchy of nested grouped convolutions of small group size to address this. Hence, our proposed architectures scale linearly with respect to the network’s width while retaining full coupling of the channels as in standard CNNs. Our extensive experiments on image classification, segmentation, and point cloud classification show that applying this strategy to different architectures like ResNet and MobileNetV3 reduces the number of parameters while obtaining similar or better accuracy.
Approaches for Designing Handheld Augmented Reality Learning Experiences for Mathematics Classrooms

Augmented Reality Learning Experiences (ARLEs) for classrooms provide student-centered learning. In recent years, there has been an increase in HCI research on various handheld AR learning applications and the authoring tools to design them. However, there is a lack of studies exploring the design decisions required to create ARLEs, specific to the classroom context. To analyze the same, we conducted a design workshop with 32 participants forming 8 groups to investigate approaches for designing classroom-based ARLEs. Each group consisted of an AR developer, an interaction designer, an education researcher, and a middle-grade Math teacher. The groups designed ARLEs based on a given Mathematics topic for a classroom scenario. Though the groups used varied approaches for generating design prototypes, we observed similarities between their techniques. We report the key design approaches and decisions that were adopted by these groups. The findings are articulated through the lenses of content, context and design. Based on the analysis, we discuss the design approaches relevant for the novice designers while conceptualizing the design of a handheld ARLE for classrooms.
Early Stage Fault Prediction via Inter-Project Rule Transfer

Software fault protection can be first achieved by fault prediction. The earlier the fault prediction can be done in the software development life-cycle, the lower the damage and repair costs caused by the defects that will occur. Machine learning is one well-known method for the decision-making part of automatic software fault prediction. However, the applicability of machine learning methods is low due to the lack of data in the early stages of development processes. In this study, the data needed in the design of rule-base was obtained from counterpart projects, and the fault prediction problem was evaluated by the fuzzy rule-based systems’ point of view since these systems have portability utility which allows rule transfer between different problems with similar goals in the same domain. Briefly, this study aims to show that early-stage fault prediction is possible with the portability characteristics of fuzzy systems sourced from the inter-project rule transfer. Several experiments have been performed by using the software metrics datasets of 5 software projects to support this idea. Fuzzy systems obtained from several combinations of these datasets were evaluated by their prediction accuracy. The results show that more accurate rules can be obtained from previously completed software projects, and the use of rule bases gathered from those projects’ software metrics repositories can be transfered to predict the faulty modules of the current software project.
Computer Modeling as a Means of Implementing Project-Based Activities in STEM-Education

The issues of application of projects implemented with the help of computer modeling within the framework of STEM-education, which is fundamentally different from traditional teaching, are considered.This system creates conditions for development and improvement of analytical and creative abilities of students, gives them an opportunity to try themselves in teamwork, and develops their independence in acquiring new knowledge.This type of activity contributes to the integration of academic subjects, the formation of students' skills of using modern technologies and modeling in their activities.Independent creation of models gives students the opportunity to better understand the basic properties of objects, phenomena, processes, their studied components, and the relationship between them.The study of models develops in children the skills of analysis, synthesis, critical thinking, and methodical approach to content realization by means of computer modeling within the framework of project-based activities.The results of the research into the use of computer modeling as a means of implementing project activities in STEM-education are presented.The conception of the research was cross-cutting skills common for all key competencies would be formed among those who receive education after the STEM-projects implementation.Cross-cutting skills are formed in all subjects and are common to the key competencies.In the course of the study we have come to the conclusion that after the implementation of STEM-projects the distribution of cross-curricular skills common to all key competencies of education applicants has changed.
Keywords-Based Dam Defect Image Caption Generation

China has the largest number of dams in the world. The aging of the dam and various factors such as environment and disasters have led to the formation of defects, which threatening the overall structural safety of the dam. In this paper, we focus on the defect image caption, which is one of the core functions of automated inspection equipment. Various real defect images from dams are selected as the training datasets to extract the multi-modal features of defects with the text generation base on the image defects keywords. Factorization Machine in Seq2Seq model is proposed to take the keywords of defect images as input to generate image caption. It can establish the causal relationship of keywords and improve the capability of model expression through the factorization machine. Meanwhile, the attention mechanism and LSTM model are applied to encoder-decoder framework in order to control the text semantics as well as avoid the gradient disappearance during the model training. The experimental results on the real dataset demonstrate that the proposed FMSeq2Seq model can exhibit good performance on the causal relationship of keywords. Compared with the original Seq2Seq model, the FMSeq2Seq model can increase the recall rate of image caption about 34% and precision rate about 25%.
The Pen as Camera

The photographic effect of overexposure is analogous to Michael Finnissy’s technique of selective musical borrowing. Just as a photographer uses the camera to allow an overabundance of light to wash out pictorial details, Finnissy uses his transcriptive pen to allow an overabundance of silence to alter and fragment his borrowed sources. Case studies demonstrate Finnissy’s borrowing of cadential phrases by J. S. Bach, Beethoven, and Bruckner in his solo piano works Wenn wir in höchsten Nöthen sind (1992) and The History of Photography in Sound (1995–2001). Comparing original sources, unpublished sketches, and published autographs reveals the composer’s precise transcriptive mechanisms. Measuring the alteration of tonal function enacted by specific harmonic and rhythmic distortions illuminates Finnissy’s pre-compositional practice while celebrating the sonic experience of his music on its own terms.
On the power of lookahead in single-player PuyoPuyo

PuyoPuyo is one of the Tetris-type games, which is dealt with as a single-player game in this paper. The player has a winning strategy if the player can keep playing the game infinitely on a gameboard of a constant height. In this paper, we consider how lookahead of input pieces affects the existence of winning strategies in PuyoPuyo, and show conditions that the player cannot win even with lookahead. First, we show the number of colors sufficient to make the player lose on the gameboard of width w when the number of lookahead pieces is m. Next, we show that ten and twenty-six colors are sufficient to make the player lose on the gameboards of width two and three, respectively, no matter how large the number of lookahead pieces is.
Detection of Swan by a New YOLO Model

YOLOV3 algorithm, which has both speed and detection accuracy. Inspired by the SPP, we propose a new algorithm model called the Preciser-YOLOV3 based on the YOLOV3 algorithm and the actual application scenarios of wild swan in the Yellow River Basin. Experimental results show that Preciser-YOLOV3 improves the detection accuracy of wild swan from 0.65 to 0.92 of wild swan in the actual scenes, which meets the need of real-time detection and industrialization. Meanwhile, it promote the construction of ecological civilization in the Yellow River Basin.
Linear Maximum Rank Distance Codes of Exceptional Type

Scattered polynomials of a given index over finite fields are intriguing rare objects with many connections within mathematics. Of particular interest are the exceptional ones, as defined in 2018 by the first author and Zhou, for which partial classification results are known. In this paper we propose a unified algebraic description of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {F}_{q^{n}}$ </tex-math></inline-formula> -linear maximum rank distance codes, introducing the notion of exceptional linear maximum rank distance codes of a given index. Such a connection naturally extends the notion of exceptionality for a scattered polynomial in the rank metric framework and provides a generalization of Moore sets in the monomial MRD context. We move towards the classification of exceptional linear MRD codes, by showing that the ones of index zero are generalized Gabidulin codes and proving that in the positive index case the code contains an exceptional scattered polynomial of the same index.
Testing homogeneity in contaminated mixture models

Contaminated mixture models (CMMs) have wide applications in the real world. Testing homogeneity in CMMs is an important and fundamental problem. In this article, we develop an EM‐test for homogeneity in the general framework of the CMM with unbounded likelihood. The null limiting distribution of the test is shown to be a shifted mixture of chi‐square distributions. Simulation studies demonstrate that the EM‐test has excellent finite‐sample performance. Two real‐data examples illustrate the applications of the proposed method.
Spatial Graph Regularized Multi-kernel Subtask Cross-correlation Tracker

Some impressing multi-kernel or multi-task correlation filter trackers only focus on boosting the discrimination of multi-channel features, or exploiting the interdependence among different tasks. However, the cooperation and complementary of both technologies are missed, and the spatial structure among or inside target regions is also ignored. Therefore, this paper proposes a spatial graph regularized hierarchical subtask multi-kernel cross-correlation tracker (GHMK) via Gaussian process regression view, which enjoys the merits of multi-subtask multi-kernel learning and Gaussian process regression to jointly learn kernel cross-correlation filters, and makes them complement and boost each other. The interdependence and discrimination among multi-kernel multi-subtask are jointly exploited by group structure sparsity, which is also used to evaluate spatial feature selection. The spatial graph is constructed via cross similarity to maintain the geometric structure among or inside hierarchy subtasks. Besides, the developed model is general, and provides a unified solution from GPR for CF trackers without boundary effect. Comprehensive experiments demonstrate its favorable and competitive performance against the state-of-the-art trackers.
Waste Heat Recovery from Servers Using an Air to Water Heat Pump

The analysis of advisability and profitability of using an air to water heat pump for the purpose of waste heat recovery from servers being used as cryptocurrency mining rigs, was performed. To carry out such an analysis, the cooling unit of the computing server was connected to the heat pump, and the entire system was adequately equipped with devices measuring parameters of the process. Performed experiments proves that the heat pump coefficient of performance (COP) reaches satisfactory values (i.e., an average of 4.21), what is the result of stable and high-temperature source of heat at the pump inlet (i.e., in the range of 29.9-34.1). Economic analysis shows a significant reduction in the cost of heating domestic hot water (by nearly 59-61%). The main conclusion which can be drawn from the paper, is that in a case of having a waste heat source in a form of a server or similar, it is advisable to consider the purchase of air-to-water heat pump for the purpose of domestic hot water heating.
RegGuard: Leveraging CPU registers for mitigation of control- and data-oriented attacks

CPU registers are small discrete storage units that are used to store temporary data and instructions within the CPU. Registers are not addressable in the same way memory is, which makes them immune to memory attacks and manipulation by other means. In this paper, we take advantage of this to protect critical program data with integrity guarantees that cover register spills. This protection effectively addresses control- and data-oriented attacks targeting the stack, even by adversaries with the full knowledge of program memory. Our solution RegGuard is a software-based mitigation technique that uses existing CPU registers and cryptographic primitives to protect critical variables with hardware-level assurance. Unlike conventional register allocation methods, RegGuard prioritises the security significance of a register candidate over its expected performance gain. Our scheme also deals effectively with saved registers to the stack, i.e., when the compiler frees registers to make room for the variables of a new call. With RegGuard, register values saved to the stack are protected, including strong adversaries with arbitrary read and write access capabilities. While our primary design focus is on security, performance is important for a scheme to be adopted in practice. RegGuard is still benefiting from the performance gain normally associated with register allocations and provides practical protection. Despite being adaptable to different CPU architectures, we showcase the performance of RegGuard using different benchmark programs and the C library on the ARM64 architecture as a proof-of-concept.
The Risk Assessment of False Disclosure of financial Statements Based on the Mining of Vague Cross Checking——Take the Pharmaceutical Industry for Example

From the perspective of the history of financial fraud at home and abroad, when the temptation of economic interests is large enough and the regulatory system is not perfect enough, there are listed companies that obtain benefits through financial fraud. From the “Blue Ocean bubble” in early Britain to the silver Guangsha incident in China, financial fraud has been continuing and fraudulent practices are constantly changing. At home and abroad, the research on financial fraud mainly focuses on the transformation and update of detection methods, and rarely excavates the business logic and cross checking relationship contained in financial statements, as well as the investment value and risk assessment behind them, which leads to the blank and lack of research in the field of financial fraud and becomes a new innovation.
Rotated Hybrid Task Cascade Network for Remote Sensing Aircraft Target Recognition

Automatic aircraft target recognition, including direction detection and fine-grained classification, is an important but challenging problem. Multi-directional densely arranged targets and the tiny differences between classes cause difficulties in recognition and direction prediction. To overcome the aforementioned problems, a rotated hybrid task cascade (RHTC) network is proposed. Specifically, RHTC cascades the segmentation branch and the bounding-box (bbox) branch to fuse the semantic feature in a coarse- to- fine manner. In addition, a new oriented bounding box regressor (OBBR) is proposed to predict the direction of target, and a new directionalloss function is added to further optimize the regressor. Moreover, we design fine masks in preprocessing to achieve improved recognition performance. The experimental results evaluated on the datasets collected from Google Earth show that RHTC can achieve the state-of-the-art performance on self-defined direction precision (DP) and mean average precision (mAP).
BurstLink: Techniques for Energy-Efficient Video Display for Conventional and Virtual Reality Systems

Conventional planar video streaming is the most popular application in mobile systems. The rapid growth of 360° video content and virtual reality (VR) devices is accelerating the adoption of VR video streaming. Unfortunately, video streaming consumes significant system energy due to high power consumption of major system components (e.g., DRAM, display interfaces, and display panel) involved in the video streaming process. For example, in conventional planar video streaming, the video decoder (in the processor) decodes video frames and stores them in the DRAM main memory before the display controller (in the processor) transfers decoded frames from DRAM to the display panel. This system architecture causes large amount of data movement to/from DRAM as well as high DRAM bandwidth usage. As a result, DRAM by itself consumes more than 30% of the video streaming energy.