Reviewerly/exHarmony huggingface dataset:
Multimodal Attention-Based Learning for Imbalanced Corporate Documents Classification

The corporate document classification process may rely on the use of textual approach considered separately of image features. On the opposite, some methods only use the visual content of documents while ignoring the semantic information. This semantic corresponds to an important part of corporate documents which make some classes of document impossible to distinguish effectively. The recent state-of-the-art deep learning methods propose to combine the textual content and the visual features within a multi-modal approach. In addition, corporate document classification processes offer a particular challenge for deep learning-based systems with an imbalanced corpus. Indeed the neural network performances strongly depend on the corpus used to train the network, and an imbalanced set generally entails bad final system performances. This paper proposes a multi-modal deep convolutional network with an attention model designed to classify a large variety of imbalanced corporate documents. Our proposed approach is compared to several state-of-the-art methods designed for document classification task using the textual content, the visual content and some multi-modal approaches. We obtained higher performances on our two testing datasets with an improvement of 2% on our private dataset and a 3% on the public RVL-CDIP dataset.
Identifying learners' behavior from videos affects teaching methods of lecturers in Universities

Automated learning behavior analysis of learners is becoming an essential topic in the field of education, where effective systems are needed to monitor learning progress and provide necessary feedback to instructor activities. teach. Recent advances in the application of computer vision allow automatic monitoring of behavior and attention status of learners at different levels, from elementary school students to college students.The goal of this study is to develop an automated system that allows schools to capture and summarize student behavior in the classroom as part of data collection for decision-making. . The system records the entire session and determines when students pay attention in the classroom, then reports it to the facility. Our experiments show that assessing the behavior of learners in the class from the video combined with a number of algorithms brings high accuracy. On that basis, adjust the content and teaching methods suitable for each learner. One of the ways to detect student attitudes is through students' gestures and postures in the classroom.
Lyndon Words Accelerate Suffix Sorting.

Suffix sorting is arguably the most fundamental building block in string algorithmics, like regular sorting in the broader field of algorithms. It is thus not surprising that the literature is full of algorithms for suffix sorting, in particular focusing on their practicality. However, the advances on practical suffix sorting stalled with the emergence of the DivSufSort algorithm more than 10 years ago, which, up to date, has remained the fastest suffix sorter. This article shows how properties of Lyndon words can be exploited algorithmically to accelerate suffix sorting again. Our new algorithm is 6-19% faster than DivSufSort on real-world texts, and up to three times as fast on artificial repetitive texts. It can also be parallelized, where similar speedups can be observed. Thus, we make the first advances in practical suffix sorting after more than a decade of standstill.
An Approach to Modeling and Analyzing Reliability for Microservice-Oriented Cloud Applications

Microservice architecture is a cloud-native architectural style, which has attracted extensive attention from the scientific research and industry communities to benefit independent development and deployment. However, due to the complexity of cloud-based platforms, the design of fault-tolerant strategies for microservice-oriented cloud applications becomes challenging. In order to improve the quality of service, it is essential to focus on the microservice with more criticality and maximize the reliability of the entire cloud application. This paper studies the modeling and analysis of service reliability in the cloud environment. Firstly, a formal description language is defined to model microservice, user request, and container accurately. Secondly, the reliability analysis is conducted to measure a critical microservice’s fluctuation and vibration attributes within a period, and the related properties of the constructed model are analyzed. Thirdly, a fault-tolerant strategy with redundancy operation has been proposed to optimize cloud application reliability. Finally, the effectiveness of the method is verified by experiments. The simulation results show that the algorithm obtains the maximum benefits and has high performance through several experiments.
Idiosyncratic but not Arbitrary: Learning Idiolects in Online Registers Reveals Distinctive yet Consistent Individual Styles

An individual's variation in writing style is often a function of both social and personal attributes.While structured social variation has been extensively studied, e.g., gender based variation, far less is known about how to characterize individual styles due to their idiosyncratic nature.We introduce a new approach to studying idiolects through a massive crossauthor comparison to identify and encode stylistic features.The neural model achieves strong performance at authorship identification on short texts and through an analogybased probing task, showing that the learned representations exhibit surprising regularities that encode qualitative and quantitative shifts of idiolectal styles.Through text perturbation, we quantify the relative contributions of different linguistic elements to idiolectal variation.Furthermore, we provide a description of idiolects through measuring inter-and intraauthor variation, showing that variation in idiolects is often distinctive yet consistent.
Feature Learning Improved by Location Guidance and Supervision for Object Detection

In recent years, the single-stage detectors have been developed rapidly; however, compared with the multi-stage detectors, their detection precision is still relatively low. Single-stage detectors and multi-stage detectors are analyzes and compared in detail in this paper, which reveals that single-stage detectors suffer from some problems, including feature loss and inaccurate feature extraction. Therefore, this paper proposes a novel detection model, dubbed Optimized Network (OptNet), to alleviate these deficiencies. OptNet consists of three modules: pyramid of attention features, feature alignment and consistency supervision (CS). The pyramid of attention features, based on feature pyramid networks (FPNs), introduces a novel branch named attention FPN (AtFPN), which aggregates the multi-layer features of the backbone network and optimizes the object features by using lightweight attention modules. AtFPN alleviates the loss of the feature pyramid information and the blocking of feature transmission between adjacent layers. Meanwhile, it provides global information for the model. The feature alignment module aligns the anchor box to the feature by using the object location information to guide the network to extract precise object features. Finally, CS accelerates network optimization and reduces semantic differences between the features on different layers. In the detection stage, OptNet optimizes the prediction of the model with the first detection result to improve the accuracy. Experiments on the MS COCO 2017 dataset demonstrate that OptNet yields significant improvement in the detection precision.
Discovering Reliable Causal Rules

We study the problem of deriving policies, or rules, that when enacted on a complex system, cause a desired outcome. Absent the ability to perform controlled experiments, such rules have to be inferred from past observations of the system's behaviour. This is a challenging problem for two reasons: First, observational effects are often unrepresentative of the underlying causal effect because they are skewed by the presence of confounding factors. Second, naive empirical estimations of a rule's effect have a high variance, and, hence, their maximisation can lead to random results. To address these issues, first we measure the causal effect of a rule from observational data---adjusting for the effect of potential confounders. Importantly, we provide a graphical criteria under which causal rule discovery is possible. Moreover, to discover reliable causal rules from a sample, we propose a conservative and consistent estimator of the causal effect, and derive an efficient and exact algorithm that maximises the estimator. On synthetic data, the proposed estimator converges faster to the ground truth than the naive estimator and recovers relevant causal rules even at small sample sizes. Extensive experiments on a variety of real-world datasets show that the proposed algorithm is efficient and discovers meaningful rules.
Structure-Enhanced Graph Representation Learning for Link Prediction in Signed Networks

Link prediction in signed networks has attracted widespread attention from researchers recently. Existing studies usually learn a representation vector for each node, which is used for link prediction tasks, by aggregating the features of neighbour nodes in the network. However, how to incorporate structural features, e.g., community structure and degree distribution, into graph representation learning remains a difficult challenge. To this end, we propose a novel Structure-enhanced Graph Representation Learning method called SGRL for link prediction in signed networks, which enables the incorporation of structural features into a unified representation. Specifically, the feature of community structure is described by introducing two latent variables to submit to Bernoulli distribution and Gaussian distribution. Moreover, the degree distribution of each node is described by a hidden variable that submits to the Dirichlet distribution by using the community feature as the parameter. Finally, the unified representation obtained from the Dirichlet distribution is further employed for the link prediction based on similarity computation. The effectiveness of the SGRL is demonstrated using benchmark datasets against the state-of-the-art methods in terms of signed link prediction, ablation study, and robustness analysis.
An improved data leakage detection system in a cloud computing environment

An Improved Data Leakage Detection System is designed to mitigate the leakage of crucial and sensitive data in a cloud computing environment. Generally, leakage of data in computing system has caused a lot of irreparable damage or catastrophe to various institutions or organizations worldwide. Therefore, this research aims at detecting and preventing any intentional or non-intentional data leakages using dynamic password or key for data decryption security mechanisms. To achieve this the OOADM methodology was adopted. The new system was implemented using ASP.net MVC and Microsoft SQL Server Management Studio as the backend. And by incorporating an Audit trail/Transaction log mechanism, the new system monitors the activities within and outside the computing environment with date and time stamp. Hence, the system can be applied in any environment for the prevention and detection of any data leakage.
Adaptive Matrix Pattern Steganography on RGB Images

Almost all spatial domain image steganography methods rely on modifying the Least Significant Bits (LSB) of each pixel to minimize the visual distortions. However, these methods are susceptible to LSB blind attacks and quantitative steganalyses. This paper presents an adaptive spatial domain image steganography algorithm for hiding digital media based on matrix patterns, named “Adaptive Matrix Pattern” (AMP). The AMP method increases the security of the steganography scheme of largely hidden messages since it adaptively generates a unique codebook matrix pattern for each ASCII character in each image block. Therefore, each ASCII character gets a different codebook matrix pattern even in different regions of the same image. Moreover, it uses a preprocessing algorithm to identify the most suitable image blocks for hiding purposes. The resulting stego-images are robust against LSB blind attacks since the middle bits of green and blue channels generate matrix patterns and hiding secrets, respectively. Experimental results show that AMP is robust against quantitative steganalyses. Additionally, the quality of stego-images, based on the peak signal-to-noise ratio metric, remains high in both stego-RGB-image and in the stego-blue-channel. Finally, the AMP method provides a high hiding capacity, up to 1.33 bits per pixel.
Khaos: Dynamically Optimizing Checkpointing for Dependable Distributed Stream Processing

Distributed Stream Processing systems are becoming an increasingly essential part of Big Data processing platforms as users grow ever more reliant on their ability to provide fast access to new results.As such, making timely decisions based on these results is dependent on a system's ability to tolerate failure.Typically, these systems achieve fault tolerance and the ability to recover automatically from partial failures by implementing checkpoint and rollback recovery.However, owing to the statistical probability of partial failures occurring in these distributed environments and the variability of workloads upon which jobs are expected to operate, static configurations will often not meet Quality of Service constraints with low overhead.In this paper we present Khaos, a new approach which utilizes the parallel processing capabilities of cloud orchestration technologies for the automatic runtime optimization of fault tolerance configurations in Distributed Stream Processing jobs.Our approach employs three subsequent phases which borrows from the principles of Chaos Engineering: establish the steadystate processing conditions, conduct experiments to better understand how the system performs under failure, and use this knowledge to continuously minimize Quality of Service violations.We implemented Khaos prototypically together with Apache Flink and demonstrate its usefulness experimentally.
Indirect Domain Shift for Single Image Dehazing

Despite their remarkable expressibility, convolution neural networks (CNNs) still fall short of delivering satisfactory results on single image dehazing, especially in terms of faithful recovery of fine texture details. In this paper, we argue that the inadequacy of conventional CNN-based dehazing methods can be attributed to the fact that the domain of hazy images is too far away from that of clear images, rendering it difficult to train a CNN for learning direct domain shift through an end-to-end manner and recovering texture details simultaneously. To address this issue, we propose to add explicit constraints inside a deep CNN model to guide the restoration process. In contrast to direct learning, the proposed mechanism shifts and narrows the candidate region for the estimation output via multiple confident neighborhoods. Therefore, it is capable of consolidating the expressibility of different architectures, resulting in a more accurate indirect domain shift (IDS) from the hazy images to that of clear images. We also propose two different training schemes, including hard IDS and soft IDS, which further reveal the effectiveness of the proposed method. Our extensive experimental results indicate that the dehazing method based on this mechanism dramatically outperforms the state-of-the-arts.
PEMBELAJARAN SASTRA DI SEKOLAH: SEBELUM, SELAMA, DAN SESUDAH PANDEMI (Literature Learning In School: Before, During, And After Pandemic)

This article aims to review some discussions on literature learning in Indonesian schools before the pandemic, highlight the real problems, and propose a new model of learning during and after the pandemic. This is a qualitative narrative article. This article interconnects between the references and the writer’s personal experience in tackling the problems and proposing solutions. Based on the findings, literature learning in Indonesian schools needs to be made more challenging and fun for students by refreshing the benefit of literature learning within Indonesian literature teachers’ minds which is expected to change the teachers’ perspective on literature learning into something positive and beneficial in literature learning as well as the use of the internet and digital information technology which serves as students’ new learning environment in developing Higher-Order Thinking Skills and 4C skills (critical thinking &amp; problem solving, communication, collaboration, dan creativity &amp; innovation). Meanwhile, the introduction of basic knowledge on the sociology of literature, reader-response criticism, and intertextuality to students will give them a certain framework and direction needed in a critical and challenging reading of literary work. This can be applied in literature learning during and after the pandemic.
Efficient Two-Party Integer Comparison With Block Vectorization Mechanism

Private integer comparison has been an essential computation function for many applications, including online auction, credential identification, data mining, and joint bidding. In the setting of two-party computation, two parties with private inputs (x and y) want to jointly compare them without revealing the value of those inputs to others (also known as the Millionaires’ problem) while the output should ensure correctness and preserve data privacy. The private inputs only can be revealed if they are equal, i.e., x = y. Many related works have been proposed to solve the integer comparison problem in various settings, focusing on different properties such as round and computation complexity. Most solutions decompose integers into bitwise representation and then securely evaluate the function in a Boolean circuit on encrypted bits. However, this type of solution is costly (especially for large integers) as each bit requires encryption and decryption. In this paper, we transform the private integer comparison into a block comparison problem. In particular, we employ a block vectorization mechanism to encode the private inputs into blocks. We show the security of our two-party protocol in the semi-honest model. Also, we implement the protocol to demonstrate its efficiency using block vectorization mechanism and homomorphic encryption. The experimental result proves that our proposed solution achieves high efficiency, particularly for large integer comparisons.
Never Query Alone: A distributed strategy to protect Internet users from DNS fingerprinting attacks

The Domain Name System (DNS) plays an essential role in everyday Internet activities. However, unauthorized access to DNS-generated traffic also poses some serious privacy concerns. For instance, DNS traffic traces can be processed by third parties to identify an Internet user by means of behavioral analysis (i.e., a technique that employs machine learning classifiers to link multiple pieces of traffic belonging to the same person). In general, the more sessions an attacker can link, the more he or she will learn about the interests of an individual, and the more likely that the identity of this user will be revealed. The development of such methods of user identification has been the focus of several pieces of research, and currently, there are several strategies to obtain behavioral fingerprints from DNS traces. However, only a few works have proposed countermeasures to protect users against this privacy threat on the Internet. Furthermore, new technologies such as DNS-over-TLS, DNS-over-HTTPS, or DNS over QUIC can potentially render available countermeasures ineffective. This paper proposes Never Query Alone (NQA), a strategy that allows a set of nodes to modify their DNS query patterns to mitigate the risk of being tracked by DNS resolvers. In NQA, users forward their DNS queries through their neighbors in such a way that the identification accuracy achieved by the attackers is proportionally reduced as the number of participant nodes is increased. A second strategy, called NQA-SA, is also proposed. NQA-SA decreases the accuracy of the attackers to nearly 1 %, independently of the number of participant nodes. Both proposed countermeasures reduce the accuracy of the classifiers at the cost of increasing the delay of the DNS query resolution process. Thus, a trade-off between privacy and delay arises, which is theoretically studied in this work by means of queueing analysis. Experimental results with real networks demonstrate that the proposed countermeasures can significantly degrade the accuracy of commonly used machine learning classifiers, thus increasing the privacy protection of individuals on the Internet.
Image Captioning using CNN and LSTM

Abstract: In the model we proposed, we examine the deep neural networks-based image caption generation technique. We give image as input to the model, the technique give output in three different forms i.e., sentence in three different languages describing the image, mp3 audio file and an image file is also generated. In this model, we use the techniques of both computer vision and natural language processing. We are aiming to develop a model using the techniques of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to build a model to generate a Caption. Target image is compared with the training images, we have a large dataset containing the training images, this is done by convolutional neural network. This model generates a decent description utilizing the trained data. To extract features from images we need encoder, we use CNN as encoder. To decode the description of image generated we use LSTM. To evaluate the accuracy of generated caption we use BLEU metric algorithm. It grades the quality of content generated. Performance is calculated by the standard calculation matrices. Keywords: CNN, RNN, LSTM, BLEU score, encoder, decoder, captions, image description.
Information scrambling in chaotic systems with dissipation

Chaotic dynamics in closed local quantum systems scrambles quantum information, which is manifested quantitatively in the decay of the out-of-time-ordered correlators (OTOC) of local operators. How is information scrambling affected when the system is coupled to the environment and suffers from dissipation? In this paper, we address this question by defining a dissipative version of OTOC and numerically study its behavior in a prototypical chaotic quantum chain in the presence of dissipation. We find that dissipation leads to not only the overall decay of the scrambled information due to leaking but also structural changes so that the ‘information light cone’ can only reach a finite distance even when the effect of overall decay is removed. Based on this observation we conjecture a modified version of the Lieb-Robinson bound in dissipative systems.