Reviewerly/exHarmony huggingface dataset:
Dialogues and Proofs; Yankov’s Contribution to Proof Theory

In the 1990s Yankov published two papers containing important contributions to proof theory and based on the application of a dialogical interpretation of proofs. In both cases the method is used for providing constructive proofs of important metalogical results concerning classical logic and fundamental mathematical theories. In the first paper it is shown that impredicative extensions of intuitionistic versions of arithmetic, analysis and set theory, enriched with suitable bar induction schemata, are sufficiently strong for proving the consistency of their classical counterparts. In the second paper the same method is applied to provide a constructive proof of the completeness theorem for classical logic. In both cases a version of a one-sided sequent calculus in Schütte-style is used and cut elimination is established in the second case. Although the obtained results are important, and the applied method is original and interesting, they have not received the attention they deserve from the wider community of researchers in proof theory. In this paper we briefly recall the content of both papers. We focus on essential features of Yankov’s approach and provide comparisons with other results of similar character.
Security Analysis of LDPC Code-Based Encryption

In the search for good alternatives to classical cryptosystems with number theory as their backbone, code-based systems have emerged as a strong competitor for “Post-Quantum cryptography”. In this paper we provide a comprehensive overview on quasi-cyclic (QC) low-density parity-check (LDPC) code-based public key encryption scheme known as LEDAcrypt. It is based on McEliece cryptosystem as the building block. A comparative study of security analysis based on different information-set decoding (ISD) and performance evaluation of the said system are also carried out.
Use of Cyberbullying Language on Instagram Social Media

This research is entitled Use of Cyberbullying Language on Instagram social media. This study aims to describe the use of cyberbullying language on social media Instagram. This research uses the descriptive qualitative method. The results of this study indicate that there are 94 comments using cyberbullying language which contains negative names (called named), 12 comments using cyberbullying language which threatens physical safety (Threatened Physical Harm), and Cyberbullying language use that contains degrading opinions (Opinion Slammed) as many as 96 comments.
Graph Representation Learning for Spatial Image Steganalysis

In this article, we introduce a novel graph representation learning architecture for spatial image steganalysis, which was motivated by the reasonable assumption that steganographic modifications will inevitably distort the statistical characteristics of the latent graph features determined from the cover images. In the architecture, we translate each image to a graph, in which the nodes represent the patches of the image and the edges between nodes indicate the local relationships between the corresponding patches. Each graph node is then associated with a feature vector determined from the corresponding patch by a shallow convolutional neural network (CNN). By feeding the graph containing node features to an attention network, the discriminative features can be learned for efficient spatial steganalysis. The experiments indicate that the reported architecture in this paper achieves a competitive detection performance compared to the benchmark CNN, which demonstrates the potential of graph neural network for steganalysis and may inspire us to develop advanced works.
MSNet: Multi-Resolution Synergistic Networks for Adaptive Inference

Adaptive inference with multiple networks has attracted much attention for resource-limited image classification. It assumes that a large portion of test samples can be correctly classified by small networks with fewer layers or channels, which poses a great challenge for them. In this paper, we argue that large networks have abilities to help the small ones address this challenge if fully explored. To this end, we propose a multi-resolution synergistic network (MSNet) using two different kinds of fusion modules. The first one is a cross-branch aggregation module, which aims to transfer the high-resolution features to the low-resolution ones between neighboring branches. The other one is an adaptive distillation module, whose purpose is feeding the discriminative ability of the large network to the other ones. Via these two modules, the small networks will be powerful enough to correctly classify large numbers of test samples, thus improving the classification accuracy and inference efficiency. We evaluate MSNet on three benchmark datasets: CIFAR-10, CIFAR-100, and ImageNet. Experimental results show that our network can obtain better results than several state-of-the-art networks in both anytime classification and budgeted batch classification settings. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/bigdata-qian/MSNet-Pytorch</uri> .
Where There is No CISO

Globally, health information security and associated topics have received considerable attention from both professionals and the academic community. The literature on the threats and mitigations when it comes to developing countries is scarce, and tends to focus on issues such as cryptographic techniques for secure safe data transmission or patients’ perceptions of data confidentiality. However, investigation of health information threats in relation to the local context has received less attention. In this paper we reflect on a long-term and global action research project that presents different perspectives on information security. Operating in environments of absent or obsolete relevant jurisdiction, poor institutional capacity for adherence and oversight, and limited awareness of appropriate security and confidentiality issues, we note unique security and confidentiality threats “where there is no CISO”. We reflect on mitigations adopted over the years to counter rising threats, and provide recommendations for practice and further research in this regard.
Decentralized Automatic Modulation Classification Method Based on Lightweight Neural Network

Due to the computing capability and memory limitations, it is difficult to apply the traditional deep learning (DL) models to the edge devices (EDs) for realizing automatic modulation classification (AMC). In this paper, a lightweight neural network for decentralized learning-based automatic modulation classification (DecentAMC) method is proposed. Specifically, group convolutional neural network (GCNN) is designed by replacing the standard convolution layer with the group convolution layer, replacing the flatten layer with the global average pooling (GAP) layer and removing part of fully connected layers. DecentAMC method is achieved by the cooperation in which multiple EDs update and upload the model weight to a central device (CD) for model aggregation to avoid the data privacy disclosure. Experimental results show that the proposed GCNN-based DecentAMC method can improve training efficiency to about 4 times and 57 times than that of GCNN-based centralized AMC (CentAMC) and CNN-based DecentAMC respectively. GCNN-based DecentAMC method can effectively reduce the communication cost and save storage of EDs when compared with CNN-based DecentAMC. Meanwhile, the time complexity and the space complexity of GCNN is significantly decreased when compared with CNN and SCNN, which is suitable to be deployed in EDs.
Partitioning Image Representation in Contrastive Learning

In contrastive learning in the image domain, the anchor and positive samples are forced to have as close representations as possible. However, forcing the two samples to have the same representation could be misleading because the data augmentation techniques make the two samples different. In this paper, we introduce a new representation, partitioned representation, which can learn both common and unique features of the anchor and positive samples in contrastive learning. The partitioned representation consists of two parts: content part and style part. The content part represents common features of the class, and the style part represents own features of each sample, which can lead to the representation of the data augmentation method. We can achieve the partitioned representation simply by decomposing a loss function of contrastive learning into two terms on the two separate representations, respectively. To evaluate our representation with two parts, we take two framework models: Variational AutoEncoder (VAE) and Bootstrap Your Own Latent (BYOL), to show the content and style’s separability and confirm the generalization ability in classification, respectively. Based on the experiments, we show that our approach can separate two types of information in the VAE framework and outperforms the conventional BYOL in the classification and a few-shot learning task as downstream tasks.
3D Imaging Systems for Optical Metrology

In recent years, new noncontact measurement devices known as 3D imaging systems have being introduced on the market. They combine one or more cameras, and most often, a controllable light source, which allows the acquisition of dense and accurate digital representations of the geometry of manufactured objects. Modern 3D imaging systems also require complex software stacks to extract useful information from the dense digital representation. Since such systems can be used to compute the deviation between as-built and as-designed objects, they tend to complement more traditional coordinate measurement machines. Due to their compactness, short acquisition time, and affordability, 3D imaging systems constitute an interesting tool for quality control operations, reverse engineering and metrology-assisted assembly, and/or manufacturing. These systems can thus act as important enabler for cyber-physical systems, which are critical to Industry 4.0.
Optimizing Federated Unsupervised Person Re-identification via Camera-aware Clustering

Person re-identification (ReID) is a critical computer vision problem which identifies individuals from non-overlapping cameras. Many recent works on person ReID achieve remarkable performance by extracting features from large amounts of data using deep neural networks. However, the growing awareness of privacy concerns limits the development of person ReID. Prior studies employ federated person ReID to learn from decentralized edges without sharing raw data, but they overlook the variation of identities in different camera views. Concerning this issue, we propose a federated unsupervised person ReID (FedUCA) that leverages camera information to improve learning from decentralized unlabeled data. Specifically, FedUCA jointly learns person ReID models by transmitting training updates instead of raw data. We generate pseudo-labels for unlabeled local datasets on edges by clustering them into multiple groups according to different cameras. We then introduce contrastive learning with an intra-camera loss and an inter-camera loss to enhance the discrimination ability. In extensive experiments on eight person ReID datasets, our proposed approach significantly outperforms the state-of-the-art federated learning based method. It improves performance by 6% to 32% on these datasets, and notably by over 25 % on large datasets. We hope this paper will shed light on optimizing federated learning across a broader range of multimedia applications.
Heterogenous Action Ensembling for Visual Recognition of Large-Scale Actions

Previous works achieved excellent performance on datasets with few action categories. However, the same works failed when the number increases. Hence, to overcome this scalability limitation, we propose a new action recognition paradigm labeled heterogeneous action ensembling. We divide the recognition task on light-weight classifiers; each specialized in a heterogeneous subset of actions. The network-wise results are later aggregated into a single final decision. The particularity of this design choice is that we attribute interfering action categories to different networks, using a pre-selector to decide the most appropriate network. We, therefore, obtain a more robust system scalability. This paradigm is validated on two challenging datasets, namely NTU RGB-D [12] and SBU Interaction [15].
A Question Embedding-based Method to Enrich Features for Knowledge Base Question Answering

Knowledge base question answering (KBQA) has become an important manner to access large-scale knowledge bases. A major challenge in this task is to obtain the answer in a large search space, whereas the dataset contains limited training data. Previous works mainly concentrated on narrowing down the search space but failed to deal with the limited training data well. To address this situation, we propose a novel method adopting few-shot learning to enable the KBQA model to infer more precisely with limited samples. First, we design a question embedding model to retrieve historical questions similar to the target question. Information of the golden path, which can obtain the right answers from the knowledge base, is adopted to train the retrieval model. Then, we utilize prior features from the retrieved questions in an effective way to make the KBQA model search answers more concisely. Experimental results on benchmark datasets have demonstrated the effectiveness of our approach.
Vehicle detection algorithm based on multi-scale features and normalization attention model

As the key technology of automatic driving perception module, vehicle detection in complex scenes requires real-time and accurate acquisition of the position and distance information of surrounding vehicles, so as to ensure the safety of passengers. Centernet algorithm performs well in vehicle detection, achieving a trade-off between accuracy and speed, but the network only extracts features of the target at the last layer of the feature map, leading to the problem of missed and false detections during detection. Therefore, this paper proposes a Vehicle-CenterNet detection model, which obtains more detailed information by modifying the original ResNet, constructing layered connections within a single residual block, and increasing the perceptual field size of each layer by stacking convolution operators. In addition, the Mish activation function is used instead of the ReLU activation function, and the smoothed activation function allows better information penetration into the neural network, resulting in better accuracy and generalization. The normalization-based attention module (NAM) is also incorporated to suppress non-target features and further improve the detection accuracy of the model. Experimental results on VOC dataset and KITTI dataset show that the mean average precision (mAP) and F1 Score of the proposed method are improved to different degrees, and the comprehensive performance is better than the original CenterNet algorithm.
Enhancing Paraphrase Question Generation With Prior Knowledge

Paraphrase question generation (PQG) aims to rewrite a given original question to a new paraphrase question, where the paraphrase question needs to have the same expressed meaning as the original question, but have a difference in expression form. Existing methods on PQG mainly focus on synonym substitution or word order adjustment based on the original question. However, rewriting based on the word-level may not guarantee the difference between paraphrase questions and original questions. In this paper, we propose a knowledge-aware paraphrase question generation model. Our model first employs a knowledge extractor to extract the prior knowledge related to the original question from the knowledge base. Then an attention mechanism and a gate mechanism are introduced in our model to selectively utilize the extracted prior knowledge for rewriting, which helps to expand the content of the generated question to maximize the difference. Additionally, we use a discriminator module to promote the generated paraphrase to be semantically close to the original question and the ground truth. Specifically, the loss function of the discriminator penalizes the excessive distance between the representation of the paraphrase question and the ground truth. Extensive experiments on the Quora dataset show that the proposed model outperforms the baselines. Further, our model is applied to the SQuAD dataset, which proves the generalization ability of our model in the existing QA dataset.
Safety Assessment: From Black-Box to White-Box

Safety assurance for Machine-Learning (ML) based applications such as object detection is a challenging task due to the black-box nature of many ML methods and the associated uncertainties of its output. To increase evidence in the safe behavior of such ML algorithms an explainable and/or interpretable introspective model can help to investigate the black-box prediction quality. For safety assessment this explainable model should be of reduced complexity and humanly comprehensible, so that any decision regarding safety can be traced back to known and comprehensible factors. We present an approach to create an explainable, introspective model (i.e., white-box) for a deep neural network (i.e., black-box) to determine how safety-relevant input features influence the prediction performance, in particular, for confidence and Bounding Box (BBox) regression. For this, Random Forest (RF) models are trained to predict a YOLOv5 object detector output, for specifically selected safety-relevant input features from the open context environment. The RF predicts the YOLOv5 output reliability for three safety related target variables, namely: softmax score, BBox center shift and BBox size shift. The results indicate that the RF prediction for softmax score are only reliable within certain constrains, while the RF prediction for BBox center/size shift are only reliable for small offsets.
Object Permanence in Object Detection Leveraging Temporal Priors at Inference Time

Object permanence is the concept that objects do not suddenly disappear in the physical world. Humans understand this concept at young ages and know that another person is still there, even though it is temporarily occluded. Neural networks currently often struggle with this challenge. Thus, we introduce explicit object permanence into two stage detection approaches drawing inspiration from particle filters. At the core, our detector uses the predictions of previous frames as additional proposals for the current one at inference time. Experiments confirm the feedback loop improving detection performance by a up to 10.3 mAP with little computational overhead.Our approach is suited to extend two-stage detectors for stabilized and reliable detections even under heavy occlusion. Additionally, the ability to apply our method without retraining an existing model promises wide application in real-world tasks.
Detection and Removal of Visible Watermarks Combined with Perceptual Attention

In view of the diversity of visible watermarks and the high cost of human intervention in the process of removing visible watermarks, a method of removing visible watermarks including detection and removal was proposed. Watermarking detection based on YOLO V3 model adopts model fusion method and improves the loss function to improve the generalization of watermarking detection. Based on Generative Adversarial Nets (GAN), the structure similarity is introduced into the loss function by combining perceptual attention mechanism. The network generates images prior to repair images by learning regions other than the watermark. Experimental results show that the proposed method can improve the objective evaluation indexes such as peak signal to noise ratio (PSNR) and structural similarity.
A Document Retrieval Method based on Knowledge Map

In view of the fact that the output of traditional document retrieval results is only a simple matching text presentation, and the retrieval results are static and independent, lack of interaction and association between information, and can not quickly carry out deeper query analysis according to a result and information point, this paper proposes a document retrieval method based on knowledge map. Through the deep learning model, the entity relationship is extracted to construct the knowledge map, so as to establish the relationship between documents, and then realize the network map search; The retrieval method based on knowledge map can actively present the knowledge information structure of documents to users, actively guide users to carry out retrieval interaction and result preview, and change the previous passive and tentative input search of users.
The Use of ICT in Times of Crisis: Evidence of the Earthquake of September 19, 2017

This document aims to describe the impact of the earthquake of September 19, 2017, on the academic performance of a sample of Public Accounting and Economics students from the Tecnologico de Monterrey Mexico City campus (TEC-CCM). The report highlights the support of information and communication technologies (ICT) to continue the teaching–learning process when the facilities are damaged. The research is based on a conceptual and descriptive analysis of virtual educational tools used after an earthquake's damage to physical facilities interfered with the institution's ability to continue providing its educational services. The findings suggest that the proactive strategy of TEC-CCM to transform traditional education into a hybrid learning environment, in which the student–teacher face-to-face contact was modified, allowed reestablishing the educational services. Likewise, the results indicated that the students’ academic performance was maintained and even improved with technological teaching tools. This study presents evidence of the significant challenges to the sustainability of educational services in the aftermath of an earthquake that must be faced by educational institutions, the government, and society.
Implementation of the canny edge detection algorithm on mobile cameras

The advances of photography technology on Android smartphone cameras to develop one of its features, namely edge detection. Edge Detection is a process that produces the edges of image objects, the goal is to improve blurred image details that occur due to errors or the effects of the image acquisition process. There are several methods to support the work of edge detection, namely the Canny operator. One of the best edge detection methods is canny edge detection which has a minimum error rate compared to other edge detection methods. Canny edge detection will produce the edge line of an object in real time on the android smartphone camera.
Sentiment Analysis Using Neural Network: Application and Future Direction

Sentiment analysis has garnered substantial attention in e-commerce and many other fields. The world Wide web gains a substantial amount of information in text form such product reviews which greatly help subsequent buyers. With booming texts numbers, it has become impossible for buyers to delve into all the reviews. This paper delineates its significance across all aspects of daily life and discusses its assistance to national governance. Humans are emotion-oriented creatures; therefore, research on emotions has substantial development potential. In this paper, three basic algorithms and numerous new improvements are reviewed with neural network in sentiment analysis. Most researchers combine different algorithms while other researchers focus on making slight amendments to an already existing structure. Finally, the shortcomings of existing algorithms and future research directions are discussed. This paper has sorted out numerous different algorithm enhancements conducive to the research tasks of academics.