Reviewerly/exHarmony huggingface dataset:
Automated Whiteboard Lecture Video Summarization by Content Region Detection and Representation

Lecture videos are rapidly becoming an invaluable source of information for students across the globe. Given the large number of online courses currently available, it is important to condense the information within these videos into a compact yet representative summary that can be used for search-based applications. We propose a framework to summarize whiteboard lecture videos by finding feature representations of detected handwritten content regions to determine unique content. We investigate multi-scale histogram of gradients and embeddings from deep metric learning for feature representation. We explicitly handle occluded, growing and disappearing handwritten content. Our method is capable of producing two kinds of lecture video summaries - the unique regions themselves or so-called key content and keyframes (which contain all unique content in a video segment). We use weighted spatio-temporal conflict minimization to segment the lecture and produce keyframes from detected regions and features. We evaluate both types of summaries and find that we obtain state-of-the-art peformance in terms of number of summary keyframes while our unique content recall and precision are comparable to state-of-the-art.
A New Approach for Spear phishing Detection

Spear phishing is a malicious attack activity that occurs in e-mail sending communications. Spear phishing attacks aim to steal personal or confidential data with individual targets and are personal in nature. Several studies proposed the detection of spear phishing activities using a classification approach with a high false-positive value. In this paper, a new approach model was proposed for detecting spear phishing activity using the cosine-similarity. This research aims to detect host activity with a similar activity pattern to the spear phishing activity knowledge database. The proposed research's contribution is to develop a new detection model to detect spear phishing activities with a co-similarity approach based on network traffic data analysis that records the communication process of sending the e-mail on a computer network. The proposed model can detect spear phishing activity with a detection accuracy of 90.45%. This shows that the model has a good performance in detecting spear phishing activity in network traffic.
Bit-wise cryptanalysis on AND-RX permutation Friet-PC

This paper presents three attack vectors of bit-wise cryptanalysis including rotational, bit-wise differential, and zero-sum distinguishing attacks on the AND-RX permutation Friet-PC, which is implemented in a lightweight authenticated encryption scheme Friet. First, we propose a generic procedure for a rotational attack on AND-RX cipher with round constants. By applying the proposed attack to Friet-PC, we can construct an 8-round rotational distinguisher with a time complexity of 2102. Next, we explore single- and dual-bit differential biases, which are inspired by the existing study on Salsa and ChaCha, and observe the best bit-wise differential bias with 2−9.552. This bias allows us to practically construct a 9-round bit-wise differential distinguisher with a time complexity of 220.044. Finally, we construct 13-, 15-, and 17-round zero-sum distinguishers with time complexities of 231, 263, and 2127, respectively. To summarize our study, we apply three attack vectors of bit-wise cryptanalysis to Friet-PC and show their superiority as effective attacks on AND-RX ciphers.
Neighbour-transitive codes and partial spreads in generalised quadrangles

A code C in a generalised quadrangle $${\mathcal {Q}}$$ is defined to be a subset of the vertex set of the point-line incidence graph $${\Gamma }$$ of $${\mathcal {Q}}$$ . The minimum distance $$\delta $$ of C is the smallest distance between a pair of distinct elements of C. The graph metric gives rise to the distance partition $$\{C,C_1,\ldots ,C_\rho \}$$ , where $$\rho $$ is the maximum distance between any vertex of $${\Gamma }$$ and its nearest element of C. Since the diameter of $${\Gamma }$$ is 4, both $$\rho $$ and $$\delta $$ are at most 4. If $$\delta =4$$ then C is a partial ovoid or partial spread of $${\mathcal {Q}}$$ , and if, additionally, $$\rho =2$$ then C is an ovoid or a spread. A code C in $${\mathcal {Q}}$$ is neighbour-transitive if its automorphism group acts transitively on each of the sets C and $$C_1$$ . Our main results (i) classify all neighbour-transitive codes admitting an insoluble group of automorphisms in thick classical generalised quadrangles that correspond to ovoids or spreads, and (ii) give two infinite families and six sporadic examples of neighbour-transitive codes with minimum distance $$\delta =4$$ in the classical generalised quadrangle $${\mathsf {W}}_3(q)$$ that are not ovoids or spreads.
Image Coding with Neural Network-Based Colorization

Automatic colorization is a process with the objective of inferring the color of grayscale images. This process is frequently used for artistic purposes and to restore the color in old or damaged images. Motivated by the excellent results obtained with deep learning-based solutions in the area of automatic colorization, this paper proposes an image coding solution integrating a deep learning-based colorization process to estimate the chrominance components based on the decoded luminance which is regularly encoded with a conventional image coding standard. In this case, the chrominance components are not coded and transmitted as usual, notably after some subsampling, as only some color hints, i.e. chrominance values for specific pixel locations, may be sent to the decoder to help it creating more accurate colorizations. To boost the colorization and final compression performance, intelligent ways to select the color hints are proposed. Experimental results show performance improvements with the increased level of intelligence in the color hints extraction process and a good subjective quality of the final decoded (and colorized) images.
A Rule-Based AI Method for an Agent Playing Big Two

Big Two is a popular multiplayer card game in Asia. This research proposes a new method, named rule-based AI, for an agent playing Big Two. The novel method derives rules based on the number of cards the AI agent has left and the control position. The rules for two to four cards left are used to select the card combination to discard based on the number of cards remaining in the agent’s hand. The rules for more than four cards left conditionally prioritize discarding the card combination in the classified cards with lower priority. A winning strategy provides guidelines to guarantee that the AI agent will win when a win is achievable within three moves. We also design the rules for the AI agent without control for holding cards and splitting cards. The experimental results show that our proposed AI agent can play Big Two well and outperform randomized AI, conventional AI, and human players, presenting winning rates of 89.60%, 73.00%, and 55.05%, respectively, with the capability of maximizing the winning score and minimizing the number of cards left when the chance of winning is low.
Weakly Supervised Body Part Segmentation with Pose based Part Priors

Human body part segmentation refers to the task of predicting the semantic segmentation mask for each body part. Fully supervised body part segmentation methods achieve good performances but require an enormous amount of effort to annotate part masks for training. In contrast to high annotation costs needed for a limited number of part mask annotations, a large number of weak labels such as poses and full body masks already exist and contain relevant information. Motivated by the possibility of using existing weak labels, we propose the first weakly supervised body part segmentation framework. The core idea is first converting the sparse weak labels such as keypoints to the initial estimate of body part masks, and then iteratively refine the part mask predictions. We name the initial part masks estimated from poses the “part priors”. with sufficient extra weak labels, our weakly supervised framework achieves a comparable performance (62.0% mIoU) to the fully supervised method (63.6% mIoU) on the Pascal-Person-Part dataset. Furthermore, in the extended semi-supervised setting, the proposed framework outperforms the state-of-art methods. Moreover, we extend our proposed framework to other keypoint-supervised part segmentation tasks such as face parsing.
Completions, Perforations and Fillings

We introduce new local operations, perforations and fillings, which transform couples of objects (X, Y) such that \(X \subseteq Y\). We complement these two operations by collapses and anti-collapses of both X and Y. This set of operations may be seen as a generalization of transformations based solely on collapses, thus a generalization of simple homotopy. Though these transformations do not, in general, preserve homotopy, we make a link between perforations, fillings, and simple homotopy. We give a full characterization of the collection of couples of objects that is closed under the above set of operations. Also, we provide a full characterization of the collection of couples of objects that is closed under the single operation of collapse.
Task-based Focal Loss for Adversarially Robust Meta-Learning

Adversarial robustness of machine learning has been widely studied in recent years, and a series of effective methods are proposed to resist adversarial attacks. However, less attention is paid to few-shot meta-learners which are much more vulnerable due to the lack of training samples. In this paper, we propose Task-based Adversarial Focal Loss (TAFL) to handle this tough challenge on a typical meta-learner called MAML. More concretely, we regard few-shot classification tasks as normal samples in learning models and apply focal loss mechanism on them. Our proposed method focuses more on adversarially fragile tasks, leading to improvement on overall model robustness. Results of extensive experiments on several benchmarks demonstrate that TAFL can effectively promote the performance of the meta-learner on adversarial examples with elaborately designed perturbations.
Image Saliency Detection via Multi-Feature and Manifold-Space Ranking

In this paper, we propose an image saliency detection method by using multi-feature and manifold-space ranking. Basically, the proposed method extracts the color-histogram feature to obtain the fine information of the image, and the color-mean feature to obtain the coarse information respectively. To further improve the detection accuracy of the feature correlation between different image units, a manifold-space ranking method is used to calculate saliency values of image units to construct a saliency map on each feature-space. Specifically, we fuse the two saliency maps to obtain the final saliency map. Extensive experiments demonstrate that the proposed method not only outperforms the other methods, but also improves the accuracy and robustness of the saliency detection.
Combinatorics of minimal absent words for a sliding window

A string w is called a minimal absent word (MAW) for another string T if w does not occur in T but the proper substrings of w occur in T. For example, let Σ={a,b,c} be the alphabet. Then, the set of MAWs for string w=abaab is {aaa,aaba,bab,bb,c}. In this paper, we study combinatorial properties of MAWs in the sliding window model, namely, how the set of MAWs changes when a sliding window of fixed length d is shifted over the input string T of length n, where 1≤d<n. We present tight upper and lower bounds on the maximum number of changes in the set of MAWs for a sliding window over T, both in the cases of general alphabets and binary alphabets. Our bounds improve on the previously known best bounds [Crochemore et al., 2020].
Object detection method based on dense connection and feature fusion

Object detection is a basic task in the field of computer vision and is widely used in various fields. However, there is also low detection performance caused by object scale changes and low feature extraction capabilities of the network, which makes the utilization of multi-scale features low. Therefore, this paper proposes a method based on dense connection and feature fusion. In this method, a dense connection module is designed to improve the ability of the network to extract features and improve the utilization of multi-scale features; a feature fusion module is designed to integrate feature information. In addition, The loss function uses Focal loss as classification loss and GIoU as positioning loss. The Tensorflow deep learning framework is used to deploy the network, and experiments are conducted on the VOC2007 and 2012 data sets to verify the effectiveness of the proposed method and compare it with the current method.
K-expectiles clustering

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Challenges for Edge-AI Implementations of Text-To-Speech Synthesis

The purpose of this paper is to provide a brief review of the current state of the art in text to speech models and evaluate the feasibility of implementing these techniques on neural accelerators. This review is beneficial to anyone seeking to implement text to speech functionality inside devices on the edge.
Few-Shot Object Detection using Global Attention and Support Attention

General object detection methods in computer vision task typically require a lot of bounding box labeled data. In addition, after training on the training data of the specified category, only the existing categories in the training can be detected. In order to detect the new category, sufficient training data of the new category must be collected and the new category can only be detected after the network is trained again. In this paper, we design a few-shot object detection network, aiming to achieve object detection against unseen categories by using a small number of annotated sample data as support sets. Our network is based on Faster R-CNN, and using a Fixed-size Global Attention module on the features generated by the backbone so that the whole network has different attention weights for different positions of the image. A Support-Set Attention module is proposed which enables the network to focus the detection target on the categories provided by the support set. Finally, the relation detection module uses the similarity between the few-shot support set and the query set to give the matching score and the location regression. After training on the base class, our network can be directly used on N-way K-shot detection without fine-tuning. In our experiments, our network achieved equal or better results than others.
A Survey on Spatiotemporal Co-occurrence Pattern Mining Techniques

Spatiotemporal data mining (STDM) is the methodology for determining meaningful patterns from the information rich spatiotemporal data. The phenomenal growth of spatiotemporal data along with the emergence of IOT, smart devices, social media, and location-based services, etc., has accentuated the demand for efficient discovery of spatiotemporal knowledge. STDM has a widespread use in several real-life situations. STDM techniques are generally classified based on the spatiotemporal output patterns family types like spatiotemporal outlier detection, spatiotemporal associations, etc. Spatiotemporal association pattern is also referred as spatiotemporal frequent pattern mining or spatiotemporal co-occurrence pattern (STCOP) mining. Traditional transaction dataset, based approaches are not useful when it comes to obtaining knowledge or patterns from spatiotemporal datasets. This survey gives an overview of the recent spatiotemporal data mining techniques available, giving special focus to spatiotemporal co-occurrence pattern mining family.
Optimization-based Resource Allocation for Cloud Computing Environment

Cloud computing has become another age innovation that has colossal possibilities in undertakings and markets. Clouds can make it conceivable to get to applications and related information from anyplace. Organizations can lease resources from cloud for capacity and other computational purposes with the goal that their foundation cost can be diminished fundamentally. Further they can utilize expansive access to applications, in light of pay-more only as costs arise model. Henceforth, there is no requirement for getting licenses for singular items. Be that as it may, one of the significant traps in cloud computing is identified with upgrading the resources being dispensed. In view of the uniqueness of the model, resource allocation is performed with the goal of limiting the expenses related with it. Different difficulties of resource allocation are satisfying client needs and application necessities. In this paper, an optimization-based energy aware resource allocation is proposed for the allotting the resources in an improved way. An energy-aware particle swarm optimization method is proposed for the resource allocation in the cloud condition.
Social Media as a Design and Research Site in HCI: Mapping Out Opportunities and Envisioning Future Uses

In this workshop, we will explore the emergent methodological space of social media based HCI design and research. We will gather scholars and practitioners from different areas within HCI to discuss how social media platforms might support their practice. Through short presentations, open discussions, and design-led activities, we will examine the affordances of existing social media platforms and speculate future developments in this methodological space. The outcome of the workshop will be an interactive data visualization of existing social media platforms, their main characteristics, and their affordances for HCI design and research. Overall, we will begin to characterize the methodological space of social media based HCI design and research, setting the foundation for future developments in this space.
A Hybrid Model for Combining Neural Image Caption and k-Nearest Neighbor Approach for Image Captioning

A hybrid model is proposed that integrates two popular image captioning methods to generate a text-based summary describing the contents of the image. The two image captioning models are the Neural Image Caption (NIC) and the k-nearest neighbor approach. These are trained individually on the training set. We extract a set of five features, from the validation set, for evaluating the results of the two models that in turn is used to train a logistic regression classifier. The BLEU-4 scores of the two models are compared for generating the binary-value ground truth for the logistic regression classifier. For the test set, the input images are first passed separately through the two models to generate the individual captions. The five-dimensional feature set extracted from the two models is passed to the logistic regression classifier to take a decision regarding the final caption generated which is the best of two captions generated by the models. Our implementation of the k-nearest-neighbor model achieves a BLEU-4 score of 15.95 and the NIC model achieves a BLEU-4 score of 16.01, on the benchmark Flickr8k dataset. The proposed hybrid model is able to achieve a BLEU-4 score of 18.20 proving the validity of our approach.
Multimodal Deep Learning for Computer-Aided Detection and Diagnosis of Cancer: Theory and Applications

Cancer is a group of diseases caused by the abnormal and disorderly growth of cells, representing the second leading cause of deaths worldwide. The number of cancer cases is growing yearly, medical systems are an essential tool to speed up the diagnosis process and increase patient survival probabilities. Electronic health record systems store the patient’s health data, which can be of structured and unstructured types. Physicians use all the information available in these systems during a cancer diagnostic, regardless of its sort and modality. Deep learning is a machine learning sub-field that has algorithms able to process data end-to-end using deep architectures, often inspired by the brain’s synaptic model. Using more than one data source in these architectures is known as multimodal deep learning. Computer-aided detection and diagnosis systems developed using multimodal deep learning algorithms have been achieving promising results in diagnostic performance, which are often comparable to human specialists. E-health and telemedicine applications can be boosted with these high-performance detection and diagnosis systems, which can provide real-time analysis capabilities to the healthcare staff and improve the quality of the medical services. This chapter explores the theory and the applications of multimodal deep learning techniques to develop computer-aided detection and diagnosis systems for cancer, describing examples of the state-of-the-art in this area, looking for essential and innovative aspects of these systems. Heterogeneous and hybrid fusion strategies combining high-quality imaging, clinical attributes and genetic data in deep multimodal architectures are resulting in computer-aided systems for cancer with promising diagnosis performance.
A Secure Cloud-centric IoT Framework for Smart Device Registration

In this paper, a generic framework for IoT device registration is proposed. Unlike existing approaches, the proposed method is designed to provide a high level of compatibility, allowing it to work well with devices from different manufacturers. Furthermore, this framework requires only some commonly available technologies (like Bluetooth or BLE) to perform, making it highly applicable to most of the current generation of smart devices available in the market. With security and user-friendliness in mind, the developed registration protocol requires less user interaction while maintaining a considerable high level of security against various types of attacks, i.e., eavesdropping, replay attacks, modification, and man-in-the-middle attack.
‘Vital signs’: EEG wearables and the nervous system of the city

Human electroencephalography (EEG) has become a mobile, extra-laboratory technology capable of collecting human brain signals while wearers are on the move. Many of the wearable EEG projects that artists, academics, and companies have undertaken are based around the idea that the technology will grant us ‘instrumental intimacy’, or better access to our own bodies and minds. A related but divergent trend in EEG data projects uses the human as a mobile sensing device whose brainwave data can be collected and aggregated for the purpose of urban planning. Many of these neurogeographic projects encourage city developers to reconsider not only how urban planning data is gathered, but also which data is underrecognized and underutilised. In this essay, I examine two examples of EEG-based projects aimed at altering urban design: #wildcities, Dan Raven-Ellison’s ‘guerrilla’ EEG project that played a role in London’s National Park City status; and Multimer, a company invested in rendering city spaces legible to new kinds of consumerism based on brainwave data. In both examples, the environment (not the individual) is figured as increasingly understandable and malleable via aggregate EEG analysis, while humans are often reduced to instrumental antennae, producing, channelling, and collecting the brainwave data.