Reviewerly/exHarmony huggingface dataset:
Two-channel 3D range geometry compression with primitive depth modification

Modern computing and imaging technologies have allowed for many recent advances to be made in the field of 3D range imaging: range data can now be acquired at speeds much faster than real-time, with sub-millimeter precision. However, these benefits come at the cost of an increased quantity of data being generated by 3D range imaging systems, potentially limiting the number of applications that can take advantage of this technology. One common approach to the compression of 3D range data is to encode it within the three color channels of a traditional 24-bit RGB image. This paper presents a novel method for the modification and compression of 3D range data such that the original depth information can be stored within, and recovered from, only two channels of a traditional 2D RGB image. Storage within a traditional image format allows for further compression to be realized via lossless or lossy image compression techniques. For example, when JPEG 80 is used to store the encoded output image, this method achieves an 18.2% reduction in file size when compared to a similar three-channel, image-base compression method, with only a corresponding 0.17% reduction in global reconstruction accuracy.
Towards cost-effective API deprecation: A win–win strategy for API developers and API users

API deprecation, which enables API developers to assist API users in migration tasks, has been widely employed in API removal management. However, mismanaged API deprecation will cause unnecessary cost and bring negligible benefit to API users. Cost-effective investments in API deprecation become challenges for API developers. In this work, an iterative model for cost-effective investments in API deprecation is developed. The model provides a data-driven mechanism for API developers to iteratively make investments in API deprecation. A tool named AWARE (A Win–win Assistant for API REmoval management) is also developed for API developers to accurately assess the benefit from the perspective of API usage statistics. Based on the prioritized benefit, API developers can allocate appropriate resources on API deprecation. A case study is performed to evaluate the effectiveness of the iterative model with AWARE. The evaluation result shows that the cost paid by API developers can be reduced significantly while the benefit brought to API users can be increased. A win–win strategy for API deprecation can be achieved.
An Interactive Strategy for the Training Set Definition Based on Active Self-Paced Learning Implemented on a Cloud-Computing Platform

Supervised classification of remote sensing data requires a large number of high-quality annotated samples. At the operational level, the definition of a large training set by photograph interpretation is costly and time-consuming. The manual annotation activity is typically supported by high-resolution satellite data. Therefore, when working at country or continental scale, it is necessary to efficiently access large archives of remotely sensed data. To address these issues, this letter presents an interactive strategy implemented in a cloud-computing platform for defining effective training sets with significantly reduced human effort. This is achieved by combining active learning (AL) and self-paced learning (SPL) techniques. First, an initial training set is used to classify the pool of unlabeled samples. Then, the method progressively adds high-confidence samples, selected through an SPL strategy, and low-confidence samples selected considering an AL strategy. While the high-confidence sample labels are self-paced, the low-confidence ones are manually assigned. The cloud-computing platform allows the: 1) definition of a complete training set in a fast and efficient way and 2) access to a multipetabyte catalog of satellite imagery. Experiments carried out on the Google Earth Engine (GEE) Platform demonstrate the effectiveness of the proposed strategy compared to the standard manual annotation.
Network Traffic Characteristics of Machine Learning Frameworks Under the Microscope

High computational demands of complex deep learning models led to workload distribution across multiple machines. Many frameworks for distributed machine learning (DML) have been developed and are employed in practice for orchestrating workload distribution. In this paper, we analyze and compare network behaviors of three widely used state-of-the-art DML frameworks. The study reveals that traffic can largely vary across the frameworks. While some frameworks exhibit well predictable patterns, others are less structured. We further explore whether and how it is possible to relate the network traffic to the DML jobs' attributes, and present a multiple linear regression model accordingly. Our results can inform the networking community about traffic characteristics and contribute toward the generation of realistic DML traffic for simulation studies.
Resynthesis of logic circuits using machine learning and reconvergent paths

Boolean network scoping represents a common approach incorporated in conventional synthesis tools for maintaining good scalability of the synthesis process. Recently, an approach to the local resynthesis based on combination of evolutionary optimization with the principle of Boolean network scoping has been proposed. Local resynthesis is an iterative process based on the extraction of smaller sub-circuits from a complex circuit that are optimized locally and implanted back to the original circuit. The main advantage of the local resynthesis is that it can mitigate the problem of scalability of representation which is typical to the evolutionary algorithms as the efficiency of the evolutionary optimization applied at the global level deteriorates with the increasing circuit complexity. Unfortunately, the efficiency of local resynthesis depends on the efficiency of the sub-circuit extraction process. We propose an alternative method, based on the reconvergent paths. The evaluation is performed on a set of highly optimized benchmark problems representing various real-world controllers, logic and arithmetic circuits. The method provides better results compared to the state-of-the-art logic synthesis tool and evolutionary optimization techniques operating locally and globally. A substantially higher number of redundant gates was removed in more than 70% cases, while keeping the computational effort at the same level. A huge improvement was achieved especially for the controllers. On average, the proposed method was able to remove more than 14.3% of gates. The highest achieved gate reduction was more than 45% of gates.
An Analysis of the Effectiveness of Machine Learning Theory in the Evaluation of Education and Teaching

Artificial intelligence was first proposed in the 1950s, when it was only a forward-looking concept. If machines can have the same learning ability as human beings and the computing power of computers themselves, this concept has been placed high hopes. Until about 2010, with the explosion of data volume and the improvement of computer performance, machine learning has become a leader in breaking through the bottleneck of artificial intelligence. Research on machine learning in education and teaching has attracted much attention. From the above research status, we can see that in the current period of the vigorous development of machine learning, many applications are still not perfect and ordinary education and teaching evaluation is difficult to meet people’s requirements, so how to gradually improve its effectiveness is a significant goal with research significance and practical interests. However, in the environment of colleges and universities, prediction information and evaluation methods have important application value and development space in education and teaching. In this context, according to the theory of machine science, the effectiveness of several conventional prediction and evaluation methods is analyzed. In this paper, machine learning theory is used to study college students’ performance prediction and credit evaluation, as well as teaching quality evaluation and comprehensive ability evaluation in colleges and universities. Questionnaire survey is used to investigate and analyze the results. The effectiveness of machine theory in teaching is analyzed. It is found that machine learning has great advantages in education and teaching evaluation. It builds models in complex computing environment and is not affected by human factors; the effectiveness of prediction and evaluation is significant.
An automatic method for matching salient structures in optical remote sensing images

Feature matching in optical remote sensing images usually suffers from significant intensity differences and background variations. In this paper, an effective image matching approach is proposed to match salient structures in optical remote sensing images. The proposed method consists of three main steps. In the first step, EDLines algorithm is implemented to extract straight lines from two corresponding images, and these lines are further processed and divided into line segments for feature matching. In the second step, a robust descriptor namely line segment context (LSC) descriptor is computed for each line segment. An iterative procedure that combines line segment description and feature matching is presented for accurate matching. Finally, the consistency check is implemented to further refine the matching results. We conduct the experiments on pairs of optical remote sensing images, and experimental results show that the proposed method can automatically and robustly generate line segment features and achieve favourable performance compared to existing image matching approaches.
Prediction of heart conditions by consensus<i>K</i>-nearest neighbor algorithm and convolution neural network

In recent days, health problems are becoming more prevalent because of changes in lifestyles and inherited factors. Heart disease (HD), in particular, has become increasingly widespread in recent years, putting people’s lives in jeopardy. Blood pressure, cholesterol, and pulse rate are all varied for each person. Normal blood pressure should be 120/90, cholesterol should be 100–129[Formula: see text]mg/dL, pulse rate should be 72, fasting blood sugar level should be 100[Formula: see text]mg/dL, heart rate should be 60–100[Formula: see text]bpm, and ECG should be normal, according to medically established data. The aorta is 25[Formula: see text]mm (1 inch) wide, whereas capillaries are only 8[Formula: see text][Formula: see text]m wide, which signifies HD. This paper is based on a public health dataset that also contains a cardiac dataset. In our previous work, a new conventional neural network (CNN) architecture was used to extract and categorize histopathological images using the [Formula: see text]-means consensus clustering. We achieved good results with the cardiac dataset compared to the existing results. The outcome of the proposed work achieves a precision rate of 97%. In this paper, a novel conventional neural network (CNN) architecture was utilized to identify and characterize histopathology pictures with the help of using the consensus [Formula: see text]-nearest neighbor algorithm (CKNN). The usage of a deep neural network, as well as the selection and recovery of functionality, is an essential step until the dataset is classified. As a result, the training process has pre-formed the dataset. The system evaluates the person’s symptoms as input and provides the disease’s possibility as an output. The suggested approach is linked to temporal data modeling and makes use of a prior HD CNN prediction. In comparison to previous outcomes, we had good outcomes with the current cardiac dataset. The proposed model’s conclusion has brought out accuracy of 99.1%.
A Word-Level Method for Generating Adversarial Examples Using Whole-Sentence Information

Adversarial examples mislead the deep neural networks (DNNs) by adding slight human-imperceptible perturbations to the input, they reveal the vulnerability of DNNs and can be applied to improve the robustness of the model. Recent work generates adversarial examples by performing word-level substitutions. However, these methods can lead to contextually inappropriate or semantically deviant substitutions because they do not take full advantage of the whole-sentence information and are inefficient in searching. The aim of this study is to improve current methods to enhance the effectiveness of adversarial examples. This study proposes an adversarial example generation method based on an improved application of the masked language model exemplified by BERT. The method injects fuzzy target word information into BERT to predict substitutes by regularizing its token embedding, which empowers BERT to integrate whole-sentence information, and then searches for adversarial examples within the substitute space using beam search with the guidance of word importance. Exhaustive experiments show that it not only significantly outperforms state-of-the-art attack methods, but also has high application value as it can generate fluent and natural samples with minimal perturbation. The work indicates that the method proved to be both effective and efficient in generating adversarial examples.
Geometry-Aware Unsupervised Domain Adaptation for Stereo Matching

Recently proposed DNN-based stereo matching methods that learn priors directly from data are known to suffer a drastic drop in accuracy in new environments. Although supervised approaches with ground truth disparity maps often work well, collecting them in each deployment environment is cumbersome and costly. For this reason, many unsupervised domain adaptation methods based on image-to-image translation have been proposed, but these methods do not preserve the geometric structure of a stereo image pair because the image-to-image translation is applied to each view separately. To address this problem, in this paper, we propose an attention mechanism that aggregates features in the left and right views, called Stereoscopic Cross Attention (SCA). Incorporating SCA to an image-to-image translation network makes it possible to preserve the geometric structure of a stereo image pair in the process of the image-to-image translation. We empirically demonstrate the effectiveness of the proposed unsupervised domain adaptation based on the image-to-image translation with SCA.
Deep Neural Networks for Multicarrier Waveforms Classification in UAV Networks

During the last decade, the adoption of Unmanned aerial vehicles (UAV) for wireless networks has been rapidly growing for both civilian and military applications, due to their flexibility and mobility. In this paper, we consider their use as an aerial base station, then investigate the task of automatic signal recognition (ASR) through a deep learning (DL) approach, while considering spectrally efficient filtered multicarrier waveforms, including UFMC, F-OFDM, FBMC and GFDM compared to the legacy OFDM. This study has been done through series of simulations; in each one, different filter sizes, number of filters and number of convolutional layers have been applied. The best performance, with an overall accuracy of 0.93 at 2 dB signal to noise ratio (SNR), has been obtained when using smaller networks, with smaller filter sizes and higher number of filters.
An Improved TCN-Based Network Combining RLS for Bearings-Only Target Tracking

Traditional approaches like extended Kalman filter may suffer performance degradation in bearings-only target tracking, because of the non-linear relationship between the measured angle and target position. In this paper, a temporal convolutional network-based target position estimation method is proposed, which has strong capability of fitting any linear or non-linear mapping. The recursive least squares is combined to further improve the accuracy and ability of target tracking in an iterative way, as well as estimate the velocity of target. Simulation results show that the proposed method achieves a higher accuracy compared to traditional approaches during the whole tracking process.
An Automatic Approach for Combinational Problems on a Hybrid Quantum Architecture

Quantum computing has shown great potential and advantages in solving integer factorization and disordered database search. However, it is not easy to solve specific problems with quantum computing device efficiently and widely, because a lot of professional background knowledge is required. In order to solve this problem, we propose an optimization problem’s automatic hybird quantum framework (OpAQ) for solving user-specified problems on a hybrid computing architecture including both quantum and classical computing resources. Such a solver can allow nonprofessionals who are not familiar with quantum physics and quantum computing to use quantum computing device to solve some classically difficult problems easily. Combinatorial optimization problem is one of the most important problems in both academic and industry. In this paper, we mainly focus on these problems and solve them with OpAQ, which is based on quantum approximation optimization algorithm (QAOA). We evaluate the performance of our approach in solving Graph Coloring, Max-cut, Traveling Salesman and Knapsack Problem. The experimental results show that quantum solver can achieve almost the same optimal solutions with the classical.
Design of Digital Library Data Search Engine Based on Cloud Computing in Big Data Era

In the big data environment, people's acquisition of knowledge is no longer limited to the contents of books, but more data information from the Internet. Intelligent search engine implements information retrieval based on knowledge level, with strong natural language understanding and knowledge processing ability, showing good personalized information service characteristics. he theory of information ecology uses the theory, viewpoint and method of ecology to study the constituent factors of information ecosystem. Excess information and meeting the needs of readers have become the main contradictions in library services. As an architecture model suitable for library application, cloud computing can integrate scattered internet information data, realize the intensification of digital libraries, and provide a new solution for building and sharing digital resources and intelligent search engines. This paper discusses the construction mode of digital library service platform based on cloud computing technology, and analyzes the application and design method of intelligent search engine of cloud service digital library under the background of big data.
Shapley-Value Data Valuation for Semi-supervised Learning

Semi-supervised learning aims at training accurate prediction models on labeled and unlabeled data. Its realization strongly depends on selecting pseudo-labeled data. The standard approach is to select instances based on the pseudo-label confidence values that they receive from the prediction models. In this paper we argue that this is an indirect approach w.r.t. the main goal of semi-supervised learning. Instead, we propose a direct approach that selects the pseudo-labeled instances based on their individual contributions for the performance of the prediction models. The individual instance contributions are computed as Shapley values w.r.t. characteristic functions related to the model performance. Experiments show that our approach outperforms the standard one when used in semi-supervised wrappers.
Multimodal Asymmetric Dual Learning for Unsupervised Eyeglasses Removal

Glasses removal is a challenging task due to the diversity of glasses species and the difficulty of obtaining paired datasets. Most existing methods need to build different models for different glasses or expensive paired datasets for supervised training, which lacks universality. In this paper, we propose a multimodal asymmetric dual learning method for unsupervised glasses removal. This method uses large-scale face images with and without glasses for dual feature learning, which does not require intensive manual marking of the glasses. Given a face image with glasses, we aim to generate a glasses-free image preserving the person identity. Thus, in order to make up for the lack of semantic features in the glasses region, we introduce the text description of the target image into the task, and propose a text-guided multimodal feature fusion method. We adaptively select the glasses-free image closest to the target one for better dual feature learning. We also propose a exchange residual loss to generate more precise mask of glasses. Extensive experiments prove that our method can generate real glasses-free images, and better retain the person identity, which can be useful for face recognition.
NLP-guided Video Thin-slicing for Automated Scoring of Non-Cognitive, Behavioral Performance Tasks

We propose a novel and practical approach to model non-cognitive, behavioral performance tasks, by using natural language processing (NLP) to guide the selection of “thin-slices” that are representative of holistic video performances and demonstrate its promise.
iBalancer: Load-Aware in-Server Flow Scheduling for Sub-Millisecond Tail Latency

Achieving microsecond-scale tail latency poses an extreme challenge to the conventional architecture of “NIC-OS-Application” in the face of high concurrent requests. Existing kernel-bypass network systems improve this situation significantly. Still, they cannot achieve load-aware in-server requests distribution, which in turn not only harms resource efficiency but, more importantly, beats the goal of squeezing tail latency. This paper proposes iBalancer, an in-server proactive load balancer for the kernel-bypass system, which aggressively handles NIC-side flow scheduling according to the load of threads on the processor-side. Furthermore, we propose a novel metric, “polling time interval (PTI),” to quantify the load of worker threads, which not only indicates utilization of the core bound to the worker thread but also reflects the differences in the processing time of different flows. By scheduling flows according to the metric PTI, iBalancer tends to average the queueing latencies of different flows, such as Set & Get operations for an in-memory key-value store. In addition, by decoupling flow scheduling from packet steering, iBalancer achieves a tail latency aware flow-to-core binding and preserves hardware-based request distribution among cores. The proposed system is evaluated and compared to mTCP and Shenango using two representative microsecond-scale network applications: Memcached KVS and a real-time deep-learning-based financial fraud identification application. Experimental results show that iBalancer can process up to 4.75 <inline-formula><tex-math notation="LaTeX">$ \times $</tex-math></inline-formula> and 1.55 <inline-formula><tex-math notation="LaTeX">$ \times \ $</tex-math></inline-formula> higher load over mTCP and Shenango under 500μs 99 <sup>th</sup> percentile tail latency limit on Memcached. For the financial fraud identification application, iBalancer is able to process 4.56 <inline-formula><tex-math notation="LaTeX">$ \times $</tex-math></inline-formula> and 1.16 <inline-formula><tex-math notation="LaTeX">$ \times $</tex-math></inline-formula> higher load than mTCP and Shenango considering 900μs tail latency.