Reviewerly/exHarmony huggingface dataset:
Supervised Learning Approach for Section Title Detection in PDF Scientific Articles

The majority of scientific articles is available in Portable Document Format (PDF). Although PDF format has the advantage of preserving layout across platforms it does not maintain the original metadata structure, making it difficult further text processing. Despite different layouts, depending on the applied template, articles have a hierarchical structure and are divided into sections, which represent topics of specific subjects, such as methodology and results. Hence, section segmentation serves as an important step for a contextualized text processing of scientific articles. Therefore, this work applies binary classification, a supervised learning task, for section title detection in PDF scientific articles. To train the classifiers, a large dataset (more than 5 millions samples from 7,302 articles) was created through an automated feature extraction approach, comprised by 17 features, where 4 were introduced in this work. Training and testing were made for ten different classifiers for which the best F1 score reached 0.94. Finally, we evaluated our results against CERMINE, an open-source system that extracts metadata from scientific articles, having an absolute improvement in section detection of 0.19 in F1 score.
Deep Double Center Hashing for Face Image Retrieval

Hashing is an effective and widely used technology for fast approximate nearest neighbor search in large-scale images. In recent years, it has been combined with a powerful feature learning model, convolutional neural network(CNN), to boost the efficiency of large-scale image retrieval. In this paper, we introduce a new Deep Double Center Hashing (DDCH) network to learn hash codes with higher discrimination between different people and compact hash codes between the same person for large-scale face image retrieval. Our method uses a deep neural network to learn image features as well as hash codes. We use a deep CNN to extract image features and a multi-layer neural network as the hash function. The whole model is trained end-to-end. In order to learn compact and discriminative hash codes, we impose a compact constraint on the codes to force lower intra-class variations of the codes. Our constraint is formulated as a center-loss over the learned codes, which encourages hash codes to be near the hash center of the same class. In addition, new discrete hashing modules and multi-scale fusion are designed to capture discriminative and multi-scale information. We conduct experiments on the most popular datasets, YouTubeFaces and FaceScrub, and demonstrates the efficient performance of DDCH over the state-of-the-art face image hashing methods.
Semantic Segmentation of Underwater Environments Using DeepLabv3+ and Transfer Learning

The semantic segmentation approach is essential in automated scene analysis, but its application in underwater environments is still limited. Datasets generally have insufficient labeled data, unbalanced data classes, and different lighting conditions, making it difficult to obtain optimal results. Currently, deep convolutional neural networks allow very good results in machine vision tasks, and one of the network architectures with good performance in semantic segmentation is DeepLabv3 + . This paper evaluates the performance of DeepLabv3 + and transfer learning based on pre-trained backend networks in ImageNet to study underwater scenes. The experimentation is carried out on a dataset available on the Internet with labels of eight classes. Experimental results show that DeepLabv3 + and transfer learning are effective for semantic segmentation of multiple underwater scene objects with insufficient tagged data and unbalanced classes.
Can DNN Detectors Compete Against Human Vision in Object Detection Task?

Object detection is the most fundamental problem in computer vision, which receives unprecedented attention in past decades. Although state-of-the-art detectors achieve promising results on public evaluation datasets, they may still suffer performance degradation problems dealing with some specific challenging factors. In contrast, evidences suggest that human vision system exhibits greater robustness and reliability compared with the deep neural networks. In this paper, we study and compare deep neural networks and human visual system on generic object detection task. The purpose of this paper is to evaluate the capability of DNN detectors benchmarked against human, with a proposed object detection Turing test. Experiments show that there still exists a tremendous performance gap between object detectors and human vision system. DNN detectors encounter more difficulties to effectively detect uncommon, camouflaged or sheltered objects compared with human. It's hoped that findings of this paper could facilitate studies on object detection. We also envision that human visual capability is expected to become a valuable reference to evaluate DNN detectors in the future.
On-line Firmware Updating and Fingerprint Generating for Solid State Disks

Virus and Rootkit may modify hard disk’s firmware to hide itself, while the traditional security software is not able to detect the modification of hard disk’s firmware. This paper relies on a USB analyzer to collect the protocol communication data of the JMUtility tool for a Solid State Disk, then unveils its internal protocol interface to dump the RAM content via the USB-SATA interface, and the firmware code is located in the RAM. By reverse engineering the firmware code, the protocol of writing to the RAM is also inferred to enable the modification of firmware code to change the device identification data. Meanwhile, the tool Firmware Extractor is developed to dump the firmware code for a specific Solid State Disk, and the possibility of on-line updating firmware and generating fingerprint is validated.
Video and Image Complexity in Human Action Recognition

We analyze the relation of video complexity with the performance of Human Action Recognition (HAR) algorithms. The rationale behind this is that variations in image conditions (e.g. occlusion, camera movement, resolution, and illumination), and image content (e.g. edge density, and number of objects), both depicting scene complexity increase the difficulty to recognize activities for a computing model. The HAR algorithms used in this work are improved Dense Trajectories (iDT) [], Motion-Augmented RGB Stream for Action Recognition (MARS) [], and SlowFast [] compared with the number of people and objects in the scene and to three statistical measures: entropy, number of regions and edge density. The results so far show a correlation between complexity and the classification performance. Mask-RCNN simulation for counting elements was carried in the supercomputer cluster of LSC-INAOE.
Controlling the Spectral Flow of Light in Non-Hermitian Photonic Time Crystals

Molding light scattering through artificially engineered materials has been the subject of intensive research so far. However, the majority of these studies have focused on the engineering of scattering in the spatial or wavevector domain, where, for instance, the scatterer has been designed to eliminate wave scattering in certain spatial directions. Different from these approaches, here we show that a complex time-periodic medium, namely a non-Hermitian photonic time crystal, offers a new degree of control over the scattering response in the frequency direction. Specifically, we show that the phase difference between real and imaginary parts of the temporal modulation can be controlled to induce spectral scattering predominantly in a specified frequency-direction. Such a unidirectional spectral scattering feature might find important applications, such as for optical isolation and cloaking of electrically large objects.
Exploring Generalization Ability of Pretrained Language Models on Arithmetic and Logical Reasoning

To quantitatively and intuitively explore the generalization ability of pre-trained language models (PLMs), we have designed several tasks of arithmetic and logical reasoning. We both analyse how well PLMs generalize when the test data is in the same distribution as the train data and when it is different, for the latter analysis, we have also designed a cross-distribution test set other than the in-distribution test set. We conduct experiments on one of the most advanced and publicly released generative PLM - BART. Our research finds that the PLMs can easily generalize when the distribution is the same, however, it is still difficult for them to generalize out of the distribution.
An End-to-End Video Content Encryption Module for HLS Video Streaming

For an individual or a small organization, protecting and securing content could be a new and challenging task. The existing options do not completely fulfill the demands for today's content consumption and security while providing a good customer experience. The authors came across this problem of content security as a small group while building an application and tried to find a simple solution to secure content for playback on Android, so that the end users would be able to stream seamlessly and without any hindrance caused due to the enhanced security. They explore the way of securing video content through AES and using HLS to enable streaming of those video files over the internet. At the client's end, they have used Google's exoplayer to decrypt the data and play it directly after authentication and authorization. They performed a comparative analysis of the current models with the given model of securing content. Overall, with the aim to create an end-to-end module, they show how all the elements interact and work together as a system to provide protection against external threats.
Towards a Formalisation of Justification and Justifiability

We introduce the logic QKSD which is a normal multi-modal logic over finitely many modalities that additionally supports bounded quantification of modalities. An important feature of this logic is that it allows to quantify over the information components of systems and, hence, can be used to derive justifications. We compare the proposed logic with Artemov's justification logic and also report on a prototypical implementation of a satisfiability solver of this logic and show some examples.
Multicolor Tunable Photonic Reservoir Computing

Reservoir computing (RC) is a bioinspired computational paradigm that employs nonlinear dynamical systems (i.e. reservoir) to increase the dimensionality of sequential data [1] . The reservoir is fixed and only the readout is trained with a simple method such as linear regression to map the states of the reservoir to a targeted pattern. A delay line together with a nonlinear node, constitutes the basic topology of a delay-feedback reservoir. The complex dynamics of such systems is engaged with time multiplexing to create a number of virtual time nodes over the delay time associated with the feedback loop. The delay time is usually harmonized to the time sequence of the input data. To create a sufficiently strong nonlinear mapping, the independent internal states of the reservoir must be increased by time multiplexing at a rate much faster than the delay time. The number of virtual nodes created by time multiplexing is thus limited by the speed at which the input data is masked (e.g. by modulating the sampled-and-hold input data). In most applications, hundreds of nodes are typically required. This necessitates multiplexing with the speeds hundreds of times faster than the input data stream. To overcome the inherent trade-off between the number of neurons and the processing time, parallelization schemes might be envisioned [2] , [3] .
Algorithm Research and Design of College Student Management Platform Based on Web Technology

The traditional educational administration management work content is complicated and the workload is large; the continuous improvement of work efficiency and intelligence requirements has made the shortcomings of the traditional educational administration management system increasingly prominent. Therefore, an intelligent educational administration management system is designed to improve the quality of education and teaching. The efficiency of education, teaching and office work has become the top priority for the informationization of teaching management. Web technology is the core of the design of the college student management platform. This article determines the technical core of the development through the analysis of the supply and demand of the student management system, then conducts the feasibility analysis and system architecture design of the system, and completes the database design. In the design process, the component design idea is used to compile and debug the system code. Through testing, it is found that the system meets the needs of intelligence in the educational administration management system.
Implementation of Security Technology of Cloud Computing Platform Based on Hadoop

In recent years, cloud computing has been widely researched and applied, and the key factor that affects the popularity or acceptance of cloud computing is the security of cloud computing. As an open source basic cloud computing framework, Hadoop has become more and more widely used in the corporate world. Research on the security technology of cloud computing platform based on Hadoop is of great significance for better research on cloud computing security and promotion of Hadoop. The purpose of this paper is to study the security technology of cloud computing platform based on Hadoop. This article first summarizes the basic theories of cloud computing security and the core framework of Hadoop, and then researches and designs the cloud computing file management system based on Hadoop, and conducts research and analysis on its security solutions. This paper systematically expounds the architecture design, function design and detailed design of the cloud computing file management system, and uses comparative analysis and observation methods to study the subject of this paper. Experimental research shows that the choice of k (nearest neighbor range) has little effect on the detection rate. It is inferred that the detection rate is mainly affected by the length of the subsequence. However, as the value of k increases, the detection rate decreases slightly, but the false detection rate is proportional to the value of k, that is, the detection effect is relatively reduced.
Aggregation as Unsupervised Learning and its Evaluation

Regression uses supervised machine learning to find a model that combines several independent variables to predict a dependent variable based on ground truth (labeled) data, i.e., tuples of independent and dependent variables (labels). Similarly, aggregation also combines several independent variables to a dependent variable. The dependent variable should preserve properties of the independent variables, e.g., the ranking or relative distance of the independent variable tuples, and/or represent a latent ground truth that is a function of these independent variables. However, ground truth data is not available for finding the aggregation model. Consequently, aggregation models are data agnostic or can only be derived with unsupervised machine learning approaches. We present an empirical evaluation framework that allows to evaluate aggregation approaches in a context of machine learning regression tasks with two relevant perspectives: (i) how well the aggregation output represents properties of the input tuples, and (ii) how well can aggregated output predict a latent ground truth. To this end, we use data sets for assessing supervised regression approaches that contain explicit ground truth labels. However, the ground truth is not used for deriving the aggregation models, but it allows for the assessment from a perspective (ii). More specifically, we use regression data sets from the UCI machine learning repository and benchmark several data-agnostic and unsupervised approaches for aggregation against unsupervised aggregation approach based on intrinsic properties of unlabeled training data, such as the cumulative probability distributions of the single independent variables and their mutual dependencies.
The Influence of Multimedia Input Mode Based on Complex System Discrete Model on College Students’ English Ability

With the rapid development of the Internet, the amount of data increases sharply. In this network age, data have been generated, such as multimedia, social networks, and search records. Due to the generation of data, complex system discrete model technology is also widely used in all walks of life. Multimedia learning is a popular teaching mode in the world. Through the discrete model technology of the complex system, this paper deeply studies the impact of multimedia input on college students’ English ability. Because the modeling and sampling of complex systems are abstract and not fixed, approximation and control methods are adopted in discrete time, which greatly improves the reliability of the final research results. Compared with the original discrete modeling technology, this paper adds data sampling compression and assimilation technology on the basis of the original discrete modeling technology of the complex system. The results show that, by adding the technical application of controllable and observable systems, the discrete model of the whole complex system can reduce the error of the final output data. At the same time, this paper compresses and simplifies the data nodes at the microlevel. Macroscopically, it reduces the scale, especially the number of node data compression. The final result also realizes the data scale and greatly shortens the calculation time. This study has greatly improved the accuracy of the impact of multimedia on Chinese college students’ English ability in practical application.
Research on orchard water and fertilizer automatic control system based on service composition

At present, integrated architecture is adopted by orchard fertigation management systems. The coupling of the system is too strong, which makes its operation and maintenance cost too high. To solve the problem, this article designs the architecture of the software platform under the cloud computing model. The resources related to the management of orchard fertigation are built into cloud services. These cloud services are described in categories and scheduled uniformly. These services are combined with the support of expert strategies. The corresponding business process(BP) is established to realize the intelligent operation of orchard fertigation.
Attitude Control of Spacecraft Formation under Directional Constraints

In order to solve the problem of large-angle and high-precision reorientation of spacecraft under external directional constraints, a spacecraft attitude control method is proposed. Firstly, a strictly lower convex logarithmic boundary potential function is designed based on the formation member’s attitude; then a new sliding mode surface is designed to meet the attitude and the angular velocity amplitude constraints, and the switching strategy of intermediate variables is proposed based on the existence condition of the repulsive potential function, and then the adaptive coordination tracking controller under the saturation of the actuator is designed. Finally, the effectiveness of the design is verified by simulation analysis.
A Real-Time Tracking Method for Satellite Video Based on Long-Term Tracking Framework

Tracking accuracy and frame rate are the two most important indexes of satellite video tracking. Now, the research of close-range tracking algorithm is gradually developing from short-term to long-term, and has been widely used. This paper refers to the idea of long-term tracking strategy and introduces it into satellite video target tracking. With a variety of features as precision guarantee, the redetection function is added to the tracker to ensure the robust tracking. At the same time, the feature dimension is properly reduced. The validity of the algorithm is verified by the video sequence obtained from Skybox-1 satellite. The results show that the tracking effect is good, and the performance of the dimension-reduced version has no significant decrease, and the frame rate is improved by 37.8%. It shows the high feasibility and wide application prospect of long-term tracking strategy in space-based video tracking.
Research on Model of Passive Bearing-Crossing Location

Passive crossing location is widely used in reconnaissance location without positive electromagnetic radiation. For passive crossing location, the existed model is built on the hypothesis that the direction-finding errors are all the same for the direction-finding stations, and then intersection point, fuzzy area and error probability are studied and analyzed. According to the signal-to-noise (SNR) performance of the direction-finding antenna employed by bearing-crossing direction-finding station, the SNR of one direction-finding antenna can be estimated by the SNR of the other direction-finding antenna and relative position of unknown emitter, and finally direction-finding errors can be calculated. In this paper, model of passive bearing-crossing location is studied, relative position between unknown emitter and direction-finding stations and SNR performance of direction-finding antenna are studied, and meanwhile simulations are conducted. Simulation results showed that the distances between signal source and direction-findings will influence SNRs received by antenna of direction-findings, and will result in different RMSE of direction-finding results. This work will contribute to further study of more accurate and efficient cross location method.
A Two-Pass Hybrid Mean and Median Framework for Eliminating Impulse Noise From a Grayscale Image

In a digital era, Image recuperation plays a vital role in the area of digital image processing. Image instauration offers more visualization on the quality of the image thereby eliminating noise. Elimination of Gaussian and impulse noise is a challenging problem in the area of image restoration. Rigorous research is pursued to restore salt-and-pepper (SAP) noise utilizing spatial filters. Mean and Median are two contributing spatial filters for eliminating impulse noise. This paper applies a two-pass hybrid mean and median framework on a corrupted grayscale image to replace salt and pepper noise. The hybrid framework is effectively restoring the image by abstracting the low, medium, and high-density impulse noise. The efficacy of the recommended strategy is evaluated by quantifying the peak signal to noise ratio and structural similarity index metric. The result obtained when compared with recent recuperation strategies outperforms to remove noise from grayscale images.
Group-Aware Disentangle Learning for Head Pose Estimation

In this paper, we propose a group-aware disentangle learning (GADL) approach for robust head pose estimation. Conventional methods usually utilize landmarks or depth information to recover the head poses, likely leading to bias prediction due to that the performance of head pose estimation highly relies on landmark detection accuracy. Instead of using external information, our GADL estimates the head poses directly from facial images in a group-supervised manner. Specifically, we first map a group of input images that share a common orientation onto a latent space, then swap the shared orientation subspaces across images to reorganize the representation. Next, we use the decoder to synthetic the original images via the reorganized representation. Thus, we explicitly disentangle the pose-relevant features shared by a group of facial images. Extensive experimental results on the challenging widely-evaluated datasets indicate the superiority of our GADL compared with the state of the arts.