Reviewerly/exHarmony huggingface dataset:
Graph Attentive Feature Aggregation for Text-Independent Speaker Verification

The objective of this paper is to combine multiple frame-level features into a single utterance-level representation considering pair-wise relationships. For this purpose, we propose a novel graph attentive feature aggregation module by interpreting each frame-level feature as a node of a graph. The inter-relationship between all possible pairs of features, typically exploited indirectly, can be directly modeled using a graph. The module comprises a graph attention layer and a graph pooling layer followed by a readout operation. The graph attention layer first models the non-Euclidean data manifold between different nodes. Then, the graph pooling layer discards less informative nodes considering the significance of the nodes. Finally, the readout operation combines the remaining nodes into a single representation. We employ two recent systems, SE-ResNet and RawNet2, with different input features and architectures and demonstrate that the proposed feature aggregation module consistently shows a relative improvement over 10%, compared to the baseline.
Visual Representation Learning with Self-Supervised Attention for Low-Label High-Data Regime

Self-supervision has shown outstanding results for natural language processing, and more recently, for image recognition. Simultaneously, vision transformers and its variants have emerged as a promising and scalable alternative to convolutions on various computer vision tasks. In this paper, we are the first to question if self-supervised vision transformers (SSL-ViTs) can be adapted to two important computer vision tasks in the low-label, high-data regime: few-shot image classification and zero-shot image retrieval. The motivation is to reduce the number of manual annotations required to train a visual embedder, and to produce generalizable and semantically meaningful embeddings. For few-shot image classification we train SSL-ViTs without any supervision, on external data, and use this trained embedder to adapt quickly to novel classes with limited number of labels. For zero-shot image retrieval, we use SSL-ViTs pre-trained on a large dataset without any labels and fine-tune them with several metric learning objectives. Our self-supervised attention representations outperforms the state-of-the-art on several public benchmarks for both tasks, namely miniImageNet and CUB200 for few-shot image classification by up-to 6%-10%, and Stanford Online Products, Cars196 and CUB200 for zero-shot image retrieval by up-to 4%-11%. Code is available at https://github.com/AutoVision-cloud/SSL-ViT-lowlabel-highdata.
Comparison of Boundary Artifact Removal Methods in Coding of Generalized Cubemap Projection Using VVC

Virtual reality applications use 360-degree videos and head mount displays with stereoscopic capabilities to provide full immersion experience. Among existing projection format, Cubemap projection provides and improved compression performance, but similar to other projects, it suffers from visual artifacts in the rendered viewport because of discontinuity of the content in different regions. In this work, we investigated the effect of different methods in Versatile Video Coding standard and other pre- and post-processing algorithms for removing boundary artifacts by introducing a new objective quality metric for systematic comparison. We further improve the current methods by aligning the GCMP’s face boundaries to coding unit boundaries. The observation is that the combination of these method with existing methods in VVC offers the best result.
TED Talk Teaser Generation with Pre-Trained Models

While we have seen significant advances in automatic summarization for text, research on speech summarization is still limited. In this work, we address the challenge of automatically generating teasers for TED talks. In the first step, we create a corpus for automatic summarization of TED and TEDx talks consisting of the talks' recording, their transcripts and their descriptions. The corpus is used to build a speech summarization system for the task. We adapt and combine pre-trained models for automatic speech recognition (ASR) and text summarization using the collected data. This initial work shows that is more important to adapt the summarization model to the ASR transcripts than to adapt the ASR model to the talks.
Mixed Knowledge Relation Transformer for Image Captioning

Internal relationship of image objects has contributed significantly to the development of image captioning, especially when combined with Transformer architecture. Most of these methods only calculate the relationship between entities and ignore the information between entities and background. Besides, the way of exploring the relational information inside the image can also be extended. In this paper, we continually explore the relationship between objects from both internal and external perspectives, and embed the vital image global information into the internal relationship module. To validate the effectiveness of our model, we conduct extensive experiments on the most popular MSCOCO dataset, and achieve state-of-the-art performance on both online and offline test sets.
Compressing Transformer-Based ASR Model by Task-Driven Loss and Attention-Based Multi-Level Feature Distillation

The current popular knowledge distillation (KD) methods effectively compress the transformer-based end-to-end speech recognition model. However, existing methods fail to utilize complete information of the teacher model, and they distill only a limited number of blocks of the teacher model. In this study, we first integrate a task-driven loss function into the decoder’s intermediate blocks to generate task-related feature representations. Then, we propose an attention-based multi-level feature distillation to automatically learn the feature representation summarized by all blocks of the teacher model. Under the 1.1M parameters model, the experimental results on the Wall Street Journal dataset reveal that our approach achieves a 12.1% WER reduction compared with the baseline system.
Anomaly credit data detection based on enhanced Isolation Forest

In view of the real-world problem of falsity and errors credit data, and the performance degradation of the credit evaluation model caused by these problems, we proposed an outlier detection algorithm, which considered two characteristics of class-imbalance and cost-sensitive in credit data. We use an anomaly detection model called EIF to optimize the credit evaluation models. EIF uses the EasyEnsemble algorithm to construct balanced data sets, and train an Isolation Forest model for anomaly detection by the balanced datasets with different disturbances. On the one hand, the balanced dataset ensures that the class-imbalance problem is solved by undersampling, on the other hand, each sub-model learns from the overall minority class samples in order to solve the cost-sensitive problem. Experiments were performed on UCI German dataset, and the test set with fake data was constructed by correlation. Compared with other anomaly detection algorithms in common credit evaluation models, the EIF-optimized model has a higher F1 score and a lower cost-sensitive error rate. In conclusion, the EIF model is effective in enhancing the performance of the credit evaluation model for forged credit datasets.
The Impact of Corporate Capital Structure on Financial Performance Based on Convolutional Neural Network

Capital structure is an important indicator to measure the source, composition, and proportion of a company's equity and debit capital. It is not only related to the internal operating environment of listed companies but also related to the rights and obligations of shareholders and is closely related to the company's future development direction, decision-making bodies, and changes in governance structure. This study aims to study the impact of corporate capital structure on financial performance based on convolutional neural network. Based on the relevant theories of capital structure, by constructing a convolutional neural network model, taking a listed company as the research object, this study analyzes the company's capital structure, liabilities, and other financial conditions. Finally, it is concluded that short-term liabilities can meet the company's sustainable development and enhance the competitiveness of the industry, thereby increasing the company's operating income. However, a poor capital structure can negatively impact a company's finances. By improving the corporate governance structure of listed companies, strengthening the adjustment of the financing structure of listed companies, and strengthening the management of listed company's operating risks, the company's capital structure can be improved so that the company's financial situation can be sustainable and healthy.
Towards Controllable and Physical Interpretable Underwater Scene Simulation

The realistic simulation of underwater scenes has important significance for many researches related to underwater vision, such as underwater image restoration, underwater moving object monitoring, etc. To date, however, the existing underwater scene simulation pipelines are either too complicated due to the continuous spectra and camera parameters involved, or difficult to control since the empirically controlled distance based fog effect is usually used by them. In this paper, we try to fill in this research gap by proposing an Underwater Scene Simulation approach, namely USSim, which especially focuses on the influence of ocean water. In USSim, Jerlov water type and depth are regarded as main variables to control the simulation effects. In addition, the spectra of the incident light is decomposed into three primary components and their attenuations are modeled separately, and finally the simulated scene is generated via the hybrid underwater imaging model proposed by us. USSim greatly reduces the computational complexity and enables the fog effect to be controlled by variables with explicit physical meanings. The controllability, physical interpretability and simulation effects of our USSim under different conditions have been verified by extensive experiments. To make our results reproducible, the source code is made online available at https://cslinzhang.github.io/USSim/.
Cyber Patrolling using Machine Learning

Crime examination and prediction is a strong method for knowing crime. The framework can anticipate spaces of high danger and comprehend spaces of high danger. Wrongdoing influences public prosperity, monetary development, and exposure. To shield society from wrongdoing, there is a requirement for better techniques and new strategies for further developing wrongdoing examination to ensure people in general. We give a framework that can examine, recognize and anticipate different wrongdoings in a specific district.
Open-Domain Dialogue Generation Grounded with Dynamic Multi-form Knowledge Fusion

Open-domain multi-turn conversations normally face the challenges of how to enrich and expand the content of the conversation. Recently, many approaches based on external knowledge are proposed to generate rich semantic and information conversation. Two types of knowledge have been studied for knowledge-aware open-domain dialogue generation: structured triples from knowledge graphs and unstructured texts from documents. To take both advantages of abundant unstructured latent knowledge in the documents and the information expansion capabilities of the structured knowledge graph, this paper presents a new dialogue generation model, Dynamic Multi-form Knowledge Fusion based Open-domain Chatting Machine (DMKCM). In particular, DMKCM applies an indexed text (a virtual Knowledge Base) to locate relevant documents as 1st hop and then expands the content of the dialogue and its 1st hop using a commonsense knowledge graph to get apposite triples as 2nd hop. To merge these two forms of knowledge into the dialogue effectively, we design a dynamic virtual knowledge selector and a controller that help to enrich and expand knowledge space. Moreover, DMKCM adopts a novel dynamic knowledge memory module that effectively uses historical reasoning knowledge to generate better responses. Experimental results indicate the effectiveness of our method in terms of dialogue coherence and informativeness.
OnSRAM: Efficient Inter-Node On-Chip Scratchpad Management in Deep Learning Accelerators

Hardware acceleration of Artificial Intelligence (AI) workloads has gained widespread popularity with its potential to deliver unprecedented performance and efficiency. An important challenge remains in how AI accelerators are programmed to sustain high utilization without impacting end-user productivity. Prior software optimizations start with an input graph and focus on node-level optimizations, viz. dataflows and hierarchical tiling, and graph-level optimizations such as operation fusion. However, little effort has been devoted to inter-node on-chip scratchpad memory (SPM) management in Deep Learning (DL) accelerators, whose significance is bolstered by the recent trends in complex network topologies and the emergence of eager execution in DL frameworks. We characterize and show that there exists up to a 5.2× performance gap in DL inference to be bridged using SPM management and propose OnSRAM, a novel SPM management framework integrated with the compiler runtime of a DL accelerator. We develop two variants, viz. OnSRAM-Static, which works on static graphs to identify data structures that can be lucratively held on-chip based on their size, liveness and significance, and OnSRAM-Eager, which targets an eager execution model (no graph) and uses a history-based speculative scheme to hold/discard data structures. We integrate OnSRAM with TensorFlow and analyze it on multiple accelerator configurations. Across a suite of 12 images, objects, and language networks, on a 3 TFLOP system with a 2 MB SPM and 32 GBps external memory bandwidth, OnSRAM-Static and OnSRAM-Eager achieve 1.02–4.8× and 1.02–3.1× reduction in inference latency (batch size of 1), over a baseline with no SPM management. In terms of energy savings, we observe average reductions of 1.51× (up to 4.1×) and 1.23× (up to 2.9×) for the static and eager execution scenarios, respectively.
Integrating Dependency Tree into Self-Attention for Sentence Representation

Recent progress on parse tree encoder for sentence representation learning is notable. However, these works mainly en-code tree structures recursively, which is not conducive to parallelization. On the other hand, these works rarely take into account the labels of arcs in dependency trees. To address both issues, we propose Dependency-Transformer, which applies a relation-attention mechanism that works in concert with the self-attention mechanism. This mechanism aims to encode the dependency and the spatial positional relations between nodes in the dependency tree of sentences. By a score-based method, we successfully inject the syntax information without affecting Transformer’s parallelizability. Our model outperforms or is comparable to the state-of-the-art methods on four tasks for sentence representation and has obvious advantages in computational efficiency.
Learning-to-Rank at the Speed of Sampling

Plackett-Luce gradient estimation enables the optimization of stochastic ranking models within feasible time constraints through sampling techniques. Unfortunately, the computational complexity of existing methods does not scale well with the length of the rankings, i.e. the ranking cutoff, nor with the item collection size. In this paper, we introduce the novel PL-Rank-3 algorithm that performs unbiased gradient estimation with a computational complexity comparable to the best sorting algorithms. As a result, our novel learning-to-rank method is applicable in any scenario where standard sorting is feasible in reasonable time. Our experimental results indicate large gains in the time required for optimization, without any loss in performance. For the field, our contribution could potentially allow state-of-the-art learning-to-rank methods to be applied to much larger scales than previously feasible.
High Throughput JPEG 2000 for Video Content Production and Delivery Over IP Networks

ITU-T Rec T.814 | IS 15444-15, known as High Throughput JPEG 2000, or simply HTJ2K, is Part-15 in the JPEG 2000 series of standards, published in 2019 by the ITU and ISO/IEC. JPEG 2000 Part-1 has long been used as a key component in the production, archival and distribution of video content, as the distribution format for Digital Cinema, and an Interoperable Master Format from which streaming video services are commonly derived. JPEG 2000 has one of the richest feature sets of any coding standard, including scalability, region-of-interest accessibility and non-iterative optimal rate control. HTJ2K addresses a long-standing limitation of the original JPEG 2000 family of standards: relatively low throughput on CPU and GPU platforms. HTJ2K introduces an alternative block coding algorithm that allows extremely high processing throughputs, while preserving all other aspects of the JPEG 2000 framework and offering truly reversible transcoding with the original block coded representation. This paper demonstrates the benefits that HTJ2K brings to video content production and delivery, including cloud-based processing workflows and low latency video content streaming over IP networks, considering CPU, GPU and FPGA-based platforms. For non-iterative optimal rate control, HTJ2K encoders with the highest throughputs and lowest hardware encoding footprints need a strategy for constraining the number of so-called HT-Sets that are generated ahead of the classic Post-Compression Rate-Distortion optimization (PCRD-opt) process. This paper describes such a strategy, known as CPLEX, that involves a second (virtual) rate-control process. The novel combination of this virtual (CPLEX) and actual (PCRD-opt) processes has many benefits, especially for hardware encoders, where memory size and memory bandwidth are key indicators of complexity.
Realistic frontal face reconstruction using coupled complementarity of far-near-sighted face images

There is still a huge gap in the accuracy of face recognition in public video surveillance scenarios. The far-sighted low-resolution (LR) frontal faces have holistic facial profiles but lack sufficient clearness, while the near-sighted high-resolution (HR) tilted faces show rich facial details yet incomplete facial structure suffering from the overhead self-occlusion of the head blocking the face. Following this observation, this paper proposes a dual-branch HR frontal face reconstruction network to explicitly exploit such coupled complementarity hidden in the far-near face images of the same subject, where one branch performs super-resolution (SR) of the LR frontal face and the other branch performs detail fusion and holistic compensation between multiple HR tilted faces as well as the super-resolved frontal result. In particular, we propose a secondary relevance attention mechanism to enhance the embedding of key features, which sequentially performs rough and precise feature matching and embedding, thus enabling coarse-to-fine progressive compensation. Further, scale-entangled densely connected blocks (SEDCB) are used to gradually integrate the relevance information at different scales (due to the different sighting distances) to promote the information interaction between the features of tilted faces. Besides, we also propose a ternary coupled sample pair (LR far-sighted frontal face, HR near-sighted tilted face, normal ground truth clear face) training scheme to supervise the network optimization. Extensive experimental results on two real-world tilt-view face datasets show that our method can not only reconstruct more realistic HR frontal faces but also facilitate the down-stream face identification task compared with the competing counterparts.
THE LOGIC OF HYPERLOGIC. PART A: FOUNDATIONS

Abstract Hyperlogic is a hyperintensional system designed to regiment metalogical claims (e.g., “Intuitionistic logic is correct” or “The law of excluded middle holds”) into the object language, including within embedded environments such as attitude reports and counterfactuals. This paper is the first of a two-part series exploring the logic of hyperlogic. This part presents a minimal logic of hyperlogic and proves its completeness. It consists of two interdefined axiomatic systems: one for classical consequence (truth preservation under a classical interpretation of the connectives) and one for “universal” consequence (truth preservation under any interpretation). The sequel to this paper explores stronger logics that are sound and complete over various restricted classes of models as well as languages with hyperintensional operators.
Optimization of University Financial Services Using the Blockchain Technology

At present, the financial work of most colleges and universities is facing the dilemma of low intelligence level. The financial sharing mode of colleges and universities using blockchain technology can effectively supervise their financial management while solving the problems of high costs related to financial sharing services, hidden dangers of data security, and low efficiency. Therefore, this paper makes an in-depth study on the application of blockchain technology and the financial sharing service mode of colleges and universities. Firstly, the University Financial Sharing Service Center platform based on blockchain technology is constructed. Furthermore, each module of the service center platform is described. Combined with the constructed platform, university finance is deeply mined by using the mining algorithm based on certain association rules, which is convenient for effective management. Finally, an artificial intelligence algorithm is used to adaptively optimize financial management. Similarly, the planning model and fitting algorithm of the least square method are used to control the risk of the financial management so as to realize the optimization of the financial sharing service mode. The experiment results demonstrate that the proposed method can effectively improve the efficiency of the financial sharing service and reduce the potential safety problems and costs of financial management.
Clinical Trial Classification of SNS24 Calls with Neural Networks

SNS24, the Portuguese National Health Contact Center, is a telephone and digital public service that provides clinical services. SNS24 plays an important role in the identification of users’ clinical situations according to their symptoms. Currently, there are a number of possible clinical algorithms defined, and selecting the appropriate clinical algorithm is very important in each telephone triage episode. Decreasing the duration of the phone calls and allowing a faster interaction between citizens and SNS24 service can further improve the performance of the telephone triage service. In this paper, we present a study using deep learning approaches to build classification models, aiming to support the nurses with the clinical algorithm’s choice. Three different deep learning architectures, namely convolutional neural network (CNN), recurrent neural network (RNN), and transformers-based approaches are applied across a total number of 269,654 call records belonging to 51 classes. The CNN, RNN, and transformers-based model each achieve an accuracy of 76.56%, 75.88%, and 78.15% over the test set in the preliminary experiments. Models using the transformers-based architecture are further fine-tuned, achieving an accuracy of 79.67% with Adam and 79.72% with SGD after learning rate fine-tuning; an accuracy of 79.96% with Adam and 79.76% with SGD after epochs fine-tuning; an accuracy of 80.57% with Adam after the batch size fine-tuning. Analysis of similar clinical symptoms is carried out using the fine-tuned neural network model. Comparisons are done over the labels predicted by the neural network model, the support vector machines model, and the original labels from SNS24. These results suggest that using deep learning is an effective and promising approach to aid the clinical triage of the SNS24 phone call services.