Reviewerly/exHarmony huggingface dataset:
Class-Discriminative CNN Compression

Compressing convolutional neural networks (CNNs) by pruning and distillation has received ever-increasing focus. In particular, designing a class-discrimination based approach would be desired as it fits seamlessly with the CNNs training objective. In this paper, we propose class-discriminative compression (CDC), which injects class discrimination in both pruning and distillation to facilitate the CNNs training goal. We first study the effectiveness of a group of discriminant functions for channel pruning, where we include well-known single-variate binary-class statistics like Student’s T-Test in our study via an intuitive generalization. We then propose a novel layer-adaptive hierarchical pruning approach, where we use a coarse class discrimination scheme for early layers and a fine one for later layers. This method naturally accords with the fact that CNNs process coarse semantics in the early layers and extract fine concepts at the later. Moreover, we leverage discriminant component analysis (DCA) to distill knowledge of intermediate representations in a subspace with rich discriminative information, which enhances hidden layers’ linear separability and classification accuracy of the student. Combining pruning and distillation, CDC is evaluated on CIFAR and ILSVRC-2012, where we consistently outperform the state-of-the-art results.
Salt and pepper noise removal method based on stationary Framelet  transform with non-convex sparsity regularization

Salt and pepper noise removal is a common inverse problem in image processing. Traditional denoising methods have two limitations. First, noise characteristics are often not described accurately. For example, the noise location information is often ignored and the sparsity of the salt and pepper noise is often described by L1 norm, which cannot illustrate the sparse variables clearly. Second, conventional methods separate the contaminated image into a recovered image and a noise part, thus resulting in recovering an image with unsatisfied smooth parts and detail parts. In this study, we introduce a noise detection strategy to determine the position of the noise, and a non-convex sparsity regularization depicted by Lp quasi-norm is employed to describe the sparsity of the noise, thereby addressing the first limitation. The morphological component analysis framework with stationary Framelet transform is adopted to decompose the processed image into cartoon, texture, and noise parts to resolve the second limitation. Then, the alternating direction method of multipliers (ADMM) is employed to solve the proposed model. Finally, experiments are conducted to verify the proposed method and compare it with some current state-of-the-art denoising methods. The experimental results show that the proposed method can remove salt and pepper noise while preserving the details of the processed image.
Learning disentangled representation for classical models

Finding disentangled representation plays a predominant role in the success of modern deep learning applications, but the results lack a straightforward explanation. Here we apply the information bottleneck method and its $\ensuremath{\beta}$-variational-autoencoder (VAE) implementation to find the disentangled low-dimensional representation of classical models. For the Ising model, our results reveal a deep connection between the disentangled features and the physical order parameters, and the widely used Bernoulli decoder is found to be learning a mean-field Hamiltonian at fixed temperature. This analogy motivates us to extend the application of $\ensuremath{\beta}$-VAE to more complex classical models with nonbinary variables using a different decoder neural network and propose a modified architecture ${\ensuremath{\beta}}^{2}$-VAE to enforce thermal fluctuations in generated samples. Our work provides a way to design a physics-informed algorithm that can yield learned features in potential correspondence with real physical properties.
Quantum pixel representations and compression for $N$-dimensional images

We introduce a novel and uniform framework for quantum pixel representations that overarches many of the most popular representations proposed in the recent literature, such as (I)FRQI, (I)NEQR, MCRQI, and (I)NCQI. The proposed QPIXL framework results in more efficient circuit implementations and significantly reduces the gate complexity for all considered quantum pixel representations. Our method only requires a linear number of gates in terms of the number of pixels and does not use ancilla qubits. Furthermore, the circuits only consist of Ry gates and CNOT gates making them practical in the NISQ era. Additionally, we propose a circuit and image compression algorithm that is shown to be highly effective, being able to reduce the necessary gates to prepare an FRQI state for example scientific images by up to 90% without sacrificing image quality. Our algorithms are made publicly available as part of QPIXL++, a Quantum Image Pixel Library.
Joint learn: A python package for task-specific weight sharing for sequence classification

Transfer Learning has enabled cutting-edge improvements in Deep Learning to attain state-of-the-art outcomes, notably in the domain of Natural Language Processing. Despite this, neural networks trained on the low-resource text classification corpora still face challenges because of the lack of pre-trained model checkpoints. In this paper, we introduce Joint Learn which is a PyTorch based comprehensive toolkit for weight sharing for text classification that leverages task-specific weight sharing to train a joint neural network for several sequence classification tasks and aids in the development of more generalized models while potentially eliminating the transfer learning issues that low-resource corpora encounter.
Clone-Writer: An effective editor for developing code by using code clones

“Code completion” is an important feature of code editors. Such editors usually provide recommendations in terms of next likely code token(s) of fixed length on the basis of code written so far. Code clones (similar code fragments), which play an important role in rapid development, are traditionally neglected as part of code completion.To facilitate code clone reuse, we propose an automated software development tool, called Clone-Writer. The tool helps develop code swiftly by recommending code clones on the basis of code written so far. Moreover, developers can perform code clone search based on a search query written either as source code terms, or as natural language. The tool also contains different visualization features supporting code completion. We have evaluated our tool by considering the classification framework of visualization methods along with user goals and information needs.
Sample Entropy Signatures: A new way to interpret SampEn values

We present the Sample Entropy Signatures method, which provides a new way to represent images via Sample entropy (SampEn). The proposed method defines texture signatures from multiple SampEn values obtained from combinations of the parameters m and r. Here, m signatures were determined (m ranging from 1 to 4), with r points each (r ranging from 0.06 to 0.4), and the behavior of each signature was determined by the metrics: area under the curve, skewness, maximum entropy value, and area ratio. This approach aimed to improve the quantitative capacity of SampEn and provide a new interpretation of its values.
Trans-Encoder: Unsupervised sentence-pair modelling through self- and  mutual-distillations

In NLP, a large volume of tasks involve pairwise comparison between two sequences (e.g. sentence similarity and paraphrase identification). Predominantly, two formulations are used for sentence-pair tasks: bi-encoders and cross-encoders. Bi-encoders produce fixed-dimensional sentence representations and are computationally efficient, however, they usually underperform cross-encoders. Cross-encoders can leverage their attention heads to exploit inter-sentence interactions for better performance but they require task fine-tuning and are computationally more expensive. In this paper, we present a completely unsupervised sentence representation model termed as Trans-Encoder that combines the two learning paradigms into an iterative joint framework to simultaneously learn enhanced bi- and cross-encoders. Specifically, on top of a pre-trained Language Model (PLM), we start with converting it to an unsupervised bi-encoder, and then alternate between the bi- and cross-encoder task formulations. In each alternation, one task formulation will produce pseudo-labels which are used as learning signals for the other task formulation. We then propose an extension to conduct such self-distillation approach on multiple PLMs in parallel and use the average of their pseudo-labels for mutual-distillation. Trans-Encoder creates, to the best of our knowledge, the first completely unsupervised cross-encoder and also a state-of-the-art unsupervised bi-encoder for sentence similarity. Both the bi-encoder and cross-encoder formulations of Trans-Encoder outperform recently proposed state-of-the-art unsupervised sentence encoders such as Mirror-BERT and SimCSE by up to 5% on the sentence similarity benchmarks.
Foreground object structure transfer for unsupervised domain adaptation

Unsupervised domain adaptation aims to train a classification model from the labeled source domain for the unlabeled target domain. Since the data distribution of the two domains are different, the model often performs poorly on the target domain. The existing methods align the global features of the source domain and the target domain, and learn the domain invariant features to improve the performance of the model, which ignores the difference between the foreground features and the background features, and does not consider the structural information in the image foreground object. Therefore we proposed a method called foreground object structure transfer (FOST), it avoids the problem of ignoring differences in the structure information of foreground features and background features, exploits foreground feature enhancement from source-to-target transfer during adaptation and structural contrast loss to drive the domain alignment process. FOST relies on prior knowledge to distinguish foreground and background features, and considers the structural information of the object, which makes the intra-class spatial distribution more compact, the interclass spatial distribution more separated, improves the transferability and improves the classification efficiency. Extensive experimental results on various benchmarks under different domain adaptation settings illustrated that our FOST compares favorably against the state-of-the-art domain adaptation methods, we achieved the accuracies of 95.3%, 91.3%, 76.6%, and 87.55% on the ImageCLEF-DA, Office-31, Office-Home, and Visda-2017 data sets, respectively.
(Retracted) Efficient intelligent compression and recognition system-based vision computing for computed tomography COVID images

Computed tomography (CT) image-based medical recognition is extensively used for COVID recognition as it improves recognition and scanning rate. A method for intelligent compression and recognition system-based vision computing for CT COVID (ICRS-VC-COVID) was developed. The proposed system first preprocesses lung CT COVID images. Segmentation is then used to split the image into two regions: nonregion of interest (NROI) with fractal lossy compression and region of interest with context tree weighting lossless. Subsequently, a fast discrete curvelet transform (FDCT) is applied. Finally, vector quantization is implemented through the encoder, channel, and decoder. Two experiments were conducted to test the proposed ICRS-VC-COVID. The first evaluated the segmentation compression, FDCT, wavelet transform, and discrete curvelet transform (DCT). The second evaluated the FDCT, wavelet transform, and DCT with segmentation. It demonstrates a significant improvement in performance parameters, such as mean square error, peak signal-to-noise ratio, and compression ratio. At similar computational complexity, the proposed ICRS-VC-COVID is superior to some existing techniques. Moreover, at the same bit rate, it significantly improves the quality of the image. Thus, the proposed method can enable lung CT COVID images to be applied for disease recognition with low computational power and space.
The Protection of Data Sharing for Privacy in Financial Vision

The primary motivation is to address difficulties in data interpretation or a reduction in model accuracy. Although differential privacy can provide data privacy guarantees, it also creates problems. Thus, we need to consider the noise setting for differential privacy is currently inconclusive. This paper’s main contribution is finding a balance between privacy and accuracy. The training data of deep learning models may contain private or sensitive corporate information. These may be dangerous to attacks, leading to privacy data leakage for data sharing. Many strategies are for privacy protection, and differential privacy is the most widely applied one. Google proposed a federated learning technology to solve the problem of data silos in 2016. The technology can share information without exchanging original data and has made significant progress in the medical field. However, there is still the risk of data leakage in federated learning; thus, many models are now used with differential privacy mechanisms to minimize the risk. The data in the financial field are similar to medical data, which contains a substantial amount of personal data. The leakage may cause uncontrollable consequences, making data exchange and sharing difficult. Let us suppose that differential privacy applies to the financial field. Financial institutions can provide customers with higher value and personalized services and automate credit scoring and risk management. Unfortunately, the economic area rarely applies differential privacy and attains no consensus on parameter settings. This study compares data security with non-private and differential privacy financial visual models. The paper finds a balance between privacy protection with model accuracy. The results show that when the privacy loss parameter ϵ is between 12.62 and 5.41, the privacy models can protect training data, and the accuracy does not decrease too much.
Improving Policy of Bunker Quality Management System in Korean Ports

우리나라 항만의 벙커링은 품질과 수량 측면에서 큰 문제를 안고 있다. 벙커의 품질은 우수하다고 평가받고 있으나, 벙커링 절차측면의 품질에는 허점이 많이 발생하고 있다. 특히 저유기지에서 벙커를 급유바지에 선적하고, 본선으로 운반하며, 본선으로 공급하는 과정의 공급망에 있어서는 품질이 확보되고 있지 않다, 벙커수량 측면에서는 문제가 더욱 심각하다. 벙커수량 분쟁이 매우 빈번하고, 잔량발생 및 도유현상까지 일상화되어 있다. 여기에는 벙커링 운송료가 낮다는 것이 주요 원인으로 작용하고 있음을 부인할 수 없다. 그러나 운송료 문제가 해소된다고 하여 벙커량 문제가 완벽하게 해결된다고 보기 힘들다. 이에 따라 이 연구는 현행 벙커링 공급과 관련된 품질 미확보 문제와 수량분쟁을 해결하여 벙커 선진항만으로 발전할 수 있는 방안을 모색·제시하였다. 이 연구에서는 벙커링이 이루어지는 현장 위주의 분석을 통해 현황 및 문제점을 면밀하게 분석하고, 벙커 선진항만인 싱가포르의 사례를 심도깊게 분석, 시사점을 활용하였다. 분석 결과 벙커공급절차에 있어서는 공급망의 품질강화제도를 도입하고, 벙커수량 측면에서는 현행 사운딩 방식에서 질량유량계 시스템으로 계측방식의 전환이라는 혁신적인 방안을 제시하였다. Korean ports have some problems in the aspect of quality & quantity in the bunkering process. Quality of bunker is assessed as more higher than competing ports. However, quality of bunkering procedure is assessed as lower. Especially, supply chain from loading of bunker to the bunker barge at oil terminal, transport it, and supply it to the ship has not been secured. Furthermore, aspect of quantity of bunker is more serious rather than that of quality of bunkering process. Disputes on the quantity of bunker between seller and buyer occur frequently, and residue & theft of bunker is also popularized issue and serious problem. Low bunkering fee is recognized as major reason of that problem, however, though low fee can be solved, it can not be necessary secured that problem could be solved, Therefore, this paper investigates and suggests the scheme to solve the quality problems of bunker supplying procedure, and develop solution toward advanced bunkering ports through removal of the quantity disputes. Concretely, this paper suggests introduction of quality system of bunker supply chain in the aspect of bunker supply procedure, and diversion from conventional sounding method to innovative Mass Flow Metering System in the aspect of bunker measuring. These two innovative solutions contribute to the removal and improvement of current structural problems in bunkering procedure.
DEVELOPING ELEMENTARY SCHOOL’S SOCIAL STUDIES LEARNING USING THE MONOPOLY GAME

This type of research uses Classroom Action Research (CAR), this study aims to improve student learning outcomes in Social Science (IPS) learners by using monopoly game media in elementary schools. Learning media is one of the important things needed by teachers and students in the learning process, in accordance with curriculum 13 which is expected to use learning media to improve student learning outcomes, using learning using monopoly game media is very effective and can be an option for used as a medium for the teaching and learning process in the classroom, by learning using the monopoly game media students can learn while playing so that they can improve student learning outcomes by proving the results at the first meeting, namely with the highest score with a value of 70, the lowest score with a value of 40, many students who completed as many as 2 people, while many students who did not complete as many as 18 people, while the percentage of student completeness was 20%, while the second meeting was the highest score with a value of 95, the lowest score with 65 results, many students who completed as many as 17 people, while many students 3 students who did not complete, while the percentage of students' completeness was 80%.
Binary Sequences With Length <i>n</i> and Nonlinear Complexity Not Less Than <i>n</i>/2

In this paper, the construction of finite-length binary sequences whose nonlinear complexity is not less than half of the length is investigated. By characterizing the structure of the sequences, an algorithm is proposed to generate all binary sequences with length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> and nonlinear complexity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$c\geq n/2$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is an integer larger than 2. Furthermore, a formula is established to calculate the exact number of these sequences. The distribution of nonlinear complexity for these sequences is thus completely determined.
SketchLattice: Latticed Representation for Sketch Manipulation

The key challenge in designing a sketch representation lies with handling the abstract and iconic nature of sketches. Existing work predominantly utilizes either, (i) a pixelative format that treats sketches as natural images employing off-the-shelf CNN-based networks, or (ii) an elaborately designed vector format that leverages the structural information of drawing orders using sequential RNN-based methods. While the pixelative format lacks intuitive exploitation of structural cues, sketches in vector format are absent in most cases limiting their practical usage. Hence, in this paper, we propose a lattice structured sketch representation that not only removes the bottleneck of requiring vector data but also preserves the structural cues that vector data provides. Essentially, sketch lattice is a set of points sampled from the pixelative format of the sketch using a lattice graph. We show that our lattice structure is particularly amenable to structural changes that largely benefits sketch abstraction modeling for generation tasks. Our lattice representation could be effectively encoded using a graph model, that uses significantly fewer model parameters (13.5 times lesser) than existing state-of-the-art. Extensive experiments demonstrate the effectiveness of sketch lattice for sketch manipulation, including sketch healing and image-to-sketch synthesis.
An Effective System for Multi-format Information Extraction

The multi-format information extraction task in the 2021 Language and Intelligence Challenge is designed to comprehensively evaluate information extraction from different dimensions. It consists of an multiple slots relation extraction subtask and two event extraction subtasks that extract events from both sentence-level and document-level. Here we describe our system for this multi-format information extraction competition task. Specifically, for the relation extraction subtask, we convert it to a traditional triple extraction task and design a voting based method that makes full use of existing models. For the sentence-level event extraction subtask, we convert it to a NER task and use a pointer labeling based method for extraction. Furthermore, considering the annotated trigger information may be helpful for event extraction, we design an auxiliary trigger recognition model and use the multi-task learning mechanism to integrate the trigger features into the event extraction model. For the document-level event extraction subtask, we design an Encoder-Decoder based method and propose a Transformer-alike decoder. Finally,our system ranks No.4 on the test set leader-board of this multi-format information extraction task, and its F1 scores for the subtasks of relation extraction, event extractions of sentence-level and document-level are 79.887%, 85.179%, and 70.828% respectively. The codes of our model are available at {https://github.com/neukg/MultiIE}.
Distance-aware Quantization

We address the problem of network quantization, that is, reducing bit-widths of weights and/or activations to lighten network architectures. Quantization methods use a rounding function to map full-precision values to the nearest quantized ones, but this operation is not differentiable. There are mainly two approaches to training quantized networks with gradient-based optimizers. First, a straight-through estimator (STE) replaces the zero derivative of the rounding with that of an identity function, which causes a gradient mismatch problem. Second, soft quantizers approximate the rounding with continuous functions at training time, and exploit the rounding for quantization at test time. This alleviates the gradient mismatch, but causes a quantizer gap problem. We alleviate both problems in a unified framework. To this end, we introduce a novel quantizer, dubbed a distance-aware quantizer (DAQ), that mainly consists of a distance-aware soft rounding (DASR) and a temperature controller. To alleviate the gradient mismatch problem, DASR approximates the discrete rounding with the kernel soft argmax, which is based on our insight that the quantization can be formulated as a distance-based assignment problem between full-precision values and quantized ones. The controller adjusts the temperature parameter in DASR adaptively according to the input, addressing the quantizer gap problem. Experimental results on standard benchmarks show that DAQ outperforms the state of the art significantly for various bit-widths without bells and whistles.