Reviewerly/exHarmony huggingface dataset:
Linearized Multi-Sampling for Differentiable Image Transformation

We propose a novel image sampling method for differentiable image transformation in deep neural networks. The sampling schemes currently used in deep learning, such as Spatial Transformer Networks, rely on bilinear interpolation, which performs poorly under severe scale changes, and more importantly, results in poor gradient propagation. This is due to their strict reliance on direct neighbors. Instead, we propose to generate random auxiliary samples in the vicinity of each pixel in the sampled image, and create a linear approximation with their intensity values. We then use this approximation as a differentiable formula for the transformed image. We demonstrate that our approach produces more representative gradients with a wider basin of convergence for image alignment, which leads to considerable performance improvements when training networks for registration and classification tasks. This is not only true under large downsampling, but also when there are no scale changes. We compare our approach with multi-scale sampling and show that we outperform it. We then demonstrate that our improvements to the sampler are compatible with other tangential improvements to Spatial Transformer Networks and that it further improves their performance.
Convex Shape Prior for Multi-Object Segmentation Using a Single Level Set Function

Many objects in real world have convex shapes. It is a difficult task to have representations for convex shapes with good and fast numerical solutions. This paper proposes a method to incorporate convex shape prior for multi-object segmentation using level set method. The relationship between the convexity of the segmented objects and the signed distance function corresponding to their union is analyzed theoretically. This result is combined with Gaussian mixture method for the multiple objects segmentation with convexity shape prior. Alternating direction method of multiplier (ADMM) is adopted to solve the proposed model. Special boundary conditions are also imposed to obtain efficient algorithms for 4th order partial differential equations in one step of ADMM algorithm. In addition, our method only needs one level set function regardless of the number of objects. So the increase in the number of objects does not result in the increase of model and algorithm complexity. Various numerical experiments are illustrated to show the performance and advantages of the proposed method.
Deep Hashing by Discriminating Hard Examples

This paper tackles a rarely explored but critical problem within learning to hash, i.e., to learn hash codes that effectively discriminate hard similar and dissimilar examples, to empower large-scale image retrieval. Hard similar examples refer to image pairs from the same semantic class that demonstrate some shared appearance but have different fine-grained appearance. Hard dissimilar examples are image pairs that come from different semantic classes but exhibit similar appearance. These hard examples generally have a small distance due to the shared appearance. Therefore, effective encoding of the hard examples can well discriminate the relevant images within a small Hamming distance, enabling more accurate retrieval in the top-ranked returned images. However, most existing hashing methods cannot capture this key information as their optimization is dominated byeasy examples, i.e., distant similar/dissimilar pairs that share no or limited appearance. To address this problem, we introduce a novel Gamma distribution-enabled and symmetric Kullback-Leibler divergence-based loss, which is dubbed dual hinge loss because it works similarly as imposing two smoothed hinge losses on the respective similar and dissimilar pairs. Specifically, the loss enforces exponentially variant penalization on the hard similar (dissimilar) examples to emphasize and learn their fine-grained difference. It meanwhile imposes a bounding penalization on easy similar (dissimilar) examples to prevent the dominance of the easy examples in the optimization while preserving the high-level similarity (dissimilarity). This enables our model to well encode the key information carried by both easy and hard examples. Extensive empirical results on three widely-used image retrieval datasets show that (i) our method consistently and substantially outperforms state-of-the-art competing methods using hash codes of the same length and (ii) our method can use significantly (e.g., 50%-75%) shorter hash codes to perform substantially better than, or comparably well to, the competing methods.
Object-Driven Multi-Layer Scene Decomposition From a Single Image

We present a method that tackles the challenge of predicting color and depth behind the visible content of an image. Our approach aims at building up a Layered Depth Image (LDI) from a single RGB input, which is an efficient representation that arranges the scene in layers, including originally occluded regions. Unlike previous work, we enable an adaptive scheme for the number of layers and incorporate semantic encoding for better hallucination of partly occluded objects. Additionally, our approach is object-driven, which especially boosts the accuracy for the occluded intermediate objects. The framework consists of two steps. First, we individually complete each object in terms of color and depth, while estimating the scene layout. Second, we rebuild the scene based on the regressed layers and enforce the recomposed image to resemble the structure of the original input. The learned representation enables various applications, such as 3D photography and diminished reality, all from a single RGB image.
Advance Management Education for Power-Engineering and Industry of the Future

The objectives of this study are to substantiate the principles of advance education and the requirements for the new generation of educational programs aimed at developing competences for the design, manufacture, operation and maintenance of complex industrial systems integrated into intellectual production, environments, and robotic systems. The study included formulating a hypothesis, refining the conceptual framework, creating the necessary empirical base, reviewing the cases of world leading universities, and presenting and substantiating the main features of advance management education that meet the current challenges. A flexible modular architecture of the educational process was developed which allows making timely adjustments to the content and teaching methods to include new knowledge. The experience of testing the results obtained in consulting practice, in teaching master’s degree students at Ural Federal University (Russia) and in educational projects for leaders and breakthrough teams of large energy corporations is described. The academic novelty of the results lies in the comprehensive examination of the issue of advance education, the conditions and the tools for its implementation within the framework of the proactive management methodology developed by the authors for sustainable business development in a revolutionary and changing industrial landscape.
Cryptographic system for data applications, in the context of internet of things

With each passing day, Internet of Things (IoT), has the potential to transform our society to a more digital way. In this paper, a cryptographic system is proposed, which has been designed and implemented, following the IoT optimized technologies. As the benefits of IoT are numerous, the need for a privacy platform is more than necessary to be developed. This work aims to demonstrate this by, firstly, implementing efficient and flexible, the fundamentals primitives of cryptography and privacy. Secondly, this is achieved, by introducing applied cryptography, in a more interactive and flexible approach. The proposed system and the incorporation of this platform is scrutinized. In the context of this work, an application of symmetric cryptography is introduced, based on the Advanced Encryption Standard (AES) in Electronic Code Book (ECB), Cipher Block Chaining (CBC) and Counter (CTR) modes of operation, for both encryption and decryption of texts, images and electronic data applications. In addition two other security schemes are supported by the proposed system: AES Galois/Counter Mode (GCM) and AES Galois Message Authentication Code (GMAC). The GCM proposed integration, in an authentication scheme, designed to provide authenticity and confidentiality, at the same time. On the other hand, GMAC, can be applied as message authentication code. Both operations, are optimized in sense of implementation resources, since the major cost is targeted to AES core. In addition, based on the integrated hardware modules, user registration and validation is proposed and implemented, with no additional cost, and with no performance penalty. Furthermore, two factor authentication has been designed and proposed, based on One Time Passwords (OTP), which can been produced with a random procedure. After these, a reference to the security levels, as regards to the communication between the IoT layers of the architecture, is presented. IoT hardware platforms are facing lack of security level and this brings the opportunity to use advanced security mechanisms. Implementation comparison results emphasize the importance of testing and measuring the performance of the alternative encryption algorithms, supported by hardware platforms.
Archaeological Analysis in the Information Age: Guidelines for Maximizing the Reach, Comprehensiveness, and Longevity of Data

ABSTRACT With the advent of the Web, increased emphasis on “research data management,” and innovations in reproducible research practices, scholars have more incentives and opportunities to document and disseminate their primary data. This article seeks to guide archaeologists in data sharing by highlighting recurring challenges in reusing archived data gleaned from observations on workflows and reanalysis efforts involving datasets published over the past 15 years by Open Context. Based on our findings, we propose specific guidelines to improve data management, documentation, and publishing practices so that primary data can be more efficiently discovered, understood, aggregated, and synthesized by wider research communities.
Crowd Counting via Multi-layer Regression

Crowd counting aims to estimate the number of persons in a crowd image--a challenge until this day--as congestion degree varies, people's appearances may seem different. To address this problem, we propose a novel crowd counting method named Multi-layer Regression Network (MRNet), which consists of a multi-layer recognition branch and several density regressors. In practice, the recognition branch recognizes the congestion degree of the regions in a crowd image, then disintegrates the image into background and several crowd regions layer by layer, each regions are assigned different congestion degrees. In each layer, the recognized crowd regions with the specific congestion degree are delivered to a regressor with the corresponding density prior for crowd density estimation. The generated density maps at all layers are integrated to obtain the final density map for crowd density estimation. To date, MRNet is the first method to estimate crowd densities on crowd regions with different regressors. We conduct a comprehensive evaluation of MRNet on four typical datasets in comparison with nine state-of-the-art methods. By using multi-layer regression, MRNet achieves significant improvement in crowd counting accuracy, and outperforms the state-of-the-art methods.
Meta-Learning for Fast Classifier Adaptation to New Users of Signature Verification Systems

Offline Handwritten Signature verification presents a challenging Pattern Recognition problem, where only knowledge of the positive class is available for training. While classifiers have access to a few genuine signatures for training, during generalization they also need to discriminate forgeries. This is particularly challenging for skilled forgeries, where a forger practices imitating the user's signature, and often is able to create forgeries visually close to the original signatures. Most work in the literature address this issue by training for a surrogate objective: discriminating genuine signatures of a user and random forgeries (signatures from other users). In this work, we propose a solution for this problem based on meta-learning, where there are two levels of learning: a task-level (where a task is to learn a classifier for a given user) and a meta-level (learning across tasks). In particular, the meta-learner guides the adaptation (learning) of a classifier for each user, which is a lightweight operation that only requires genuine signatures. The meta-learning procedure learns what is common for the classification across different users. In a scenario where skilled forgeries from a subset of users are available, the meta-learner can guide classifiers to be discriminative of skilled forgeries even if the classifiers themselves do not use skilled forgeries for learning. Experiments conducted on the GPDS-960 dataset show improved performance compared to Writer-Independent systems, and achieve results comparable to state-of-the-art Writer-Dependent systems in the regime of few samples per user (5 reference signatures).
Towards Understanding the Design of Playful Gustosonic Experiences with Ice Cream

There is an increasing trend that explores the convergence of digital play and eating to support a playful relationship with food. We note that interactive sound, although prevalent in digital game design, has only received limited attention in this trend. To contribute to an understanding of "playful gustosonic experiences", we present a design and study of a novel capacitive-sensing ice cream cone, "iScream!". In a study with 32 participants, the cone played four different sounds (a roaring, crunchy, giggling, and burping sound in order to explore fantasy facilitation, food congruency, anthropomorphism and bodily response) when eating ice cream. The results are two themes derived from six findings each, which detail how players explored the different auditory interaction possibilities with their eating actions while the sounds in turn modified those eating actions. Based on these findings, we present four design tactics for designers aiming to create playful gustosonic experiences to ultimately facilitate a more playful relationship with food.
Learning Disentangled Representation for Cross-Modal Retrieval with Deep Mutual Information Estimation

Cross-modal retrieval has become a hot research topic in recent years for its theoretical and practical significance. This paper proposes a new technique for learning such deep visual-semantic embedding that is more effective and interpretable for cross-modal retrieval. The proposed method employs a two-stage strategy to fulfill the task. In the first stage, deep mutual information estimation is incorporated into the objective to maximize the mutual information between the input data and its embedding. In the second stage, an expelling branch is added to the network to disentangle the modality-exclusive information from the learned representations. This helps to reduce the impact of modality-exclusive information to the common subspace representation as well as improve the interpretability of the learned feature. Extensive experiments on two large-scale benchmark datasets demonstrate that our method can learn better visual-semantic embedding and achieve state-of-the-art cross-modal retrieval results.
Long Short-Term Relation Networks for Video Action Detection

It has been well recognized that modeling human-object or object-object relations would be helpful for detection task. Nevertheless, the problem is not trivial especially when exploring the interactions between human actor, object and scene (collectively as human-context) to boost video action detectors. The difficulty originates from the aspect that reliable relations in a video should depend on not only short-term human-context relation in the present clip but also the temporal dynamics distilled over a long-range span of the video. This motivates us to capture both short-term and long-term relations in a video. In this paper, we present a new Long Short-Term Relation Networks, dubbed as LSTR, that novelly aggregates and propagates relation to augment features for video action detection. Technically, Region Proposal Networks (RPN) is remoulded to first produce 3D bounding boxes, i.e., tubelets, in each video clip. LSTR then models short-term human-context interactions within each clip through spatio-temporal attention mechanism and reasons long-term temporal dynamics across video clips via Graph Convolutional Networks (GCN) in a cascaded manner. Extensive experiments are conducted on four benchmark datasets, and superior results are reported when comparing to state-of-the-art methods.
Epistemic Uncertainty Sampling

Various strategies for active learning have been proposed in the machine learning literature. In uncertainty sampling, which is among the most popular approaches, the active learner sequentially queries the label of those instances for which its current prediction is maximally uncertain. The predictions as well as the measures used to quantify the degree of uncertainty, such as entropy, are almost exclusively of a probabilistic nature. In this paper, we advocate a distinction between two different types of uncertainty, referred to as epistemic and aleatoric, in the context of active learning. Roughly speaking, these notions capture the reducible and the irreducible part of the total uncertainty in a prediction, respectively. We conjecture that, in uncertainty sampling, the usefulness of an instance is better reflected by its epistemic than by its aleatoric uncertainty. This leads us to suggest the principle of "epistemic uncertainty sampling", which we instantiate by means of a concrete approach for measuring epistemic and aleatoric uncertainty. In experimental studies, epistemic uncertainty sampling does indeed show promising performance.
A Survey of Research on CAPTCHA Designing and Breaking Techniques

The Internet plays an increasingly important role in people's lives, but it also brings security problems. CAPTCHA, which stands for Completely Automated Public Turing Test to Tell Computers and Humans Apart, has been widely used as a security mechanism. This paper outlines the scientific and technological progress in both the design and attack of CAPTCHAs related to these three CAPTCHA categories. It first presents a comprehensive survey of recent developments for each CAPTCHA type in terms of usability, robustness and their weaknesses and strengths. Second, it summarizes the attack methods for each category. In addition, the differences between the three CAPTCHA categories and the attack methods will also be discussed. Lastly, this paper provides suggestions for future research and proposes some problems worthy of further study.
Performance of Three Slim Variants of The Long Short-Term Memory (LSTM) Layer

The Long Short-Term Memory (LSTM) layer is an important advancement in the field of neural networks and machine learning, allowing for effective training and impressive inference performance. LSTM-based neural networks have been successfully employed in various applications such as speech processing and language translation. The LSTM layer can be simplified by removing certain components, potentially speeding up training and runtime with limited change in performance. In particular, several recently introduced variants, called Slim LSTMs, have shown success in initial experiments to support this view. In this paper, we perform computational analysis of the validation accuracy of a convolutional plus recurrent neural network architecture designed to analyze sentiment, using comparatively the standard LSTM and three Slim LSTM layers. We found that some realizations of the Slim LSTM layers can potentially perform as well as the standard LSTM layer for our considered architecture targeted at sentiment analysis.
WC-PAD: Web Crawling based Phishing Attack Detection

Phishing is a criminal offense which involves theft of user's sensitive data. The phishing websites target individuals, organizations, the cloud storage hosting sites and government websites. Currently, hardware based approaches for anti-phishing is widely used but due to the cost and operational factors software based approaches are preferred. The existing phishing detection approaches fails to provide solution to problem like zero-day phishing website attacks. To overcome these issues and precisely detect phishing occurrence a three phase attack detection named as Web Crawler based Phishing Attack Detector(WC-PAD) has been proposed. It takes the web traffics, web content and Uniform Resource Locator(URL) as input features, based on these features classification of phishing and non phishing websites are done. The experimental analysis of the proposed WC-PAD is done with datasets collected from real phishing cases. From the experimental results, it is found that the proposed WC-PAD gives 98.9% accuracy in both phishing and zero-day phishing attack detection.
An infinite family of circulant graphs with perfect state transfer in discrete quantum walks

We study perfect state transfer in Kendon's model of discrete quantum walks. In particular, we give a characterization of perfect state transfer purely in terms of the graph spectra, and construct an infinite family of 4-regular circulant graphs that admit perfect state transfer. Prior to our work, the only known infinite families of examples were variants of cycles and diamond chains.
Task-Guided Pair Embedding in Heterogeneous Network

Many real-world tasks solved by heterogeneous network embedding methods can be cast as modeling the likelihood of a pairwise relationship between two nodes. For example, the goal of author identification task is to model the likelihood of a paper being written by an author (paper-author pairwise relationship). Existing taskguided embedding methods are node-centric in that they simply measure the similarity between the node embeddings to compute the likelihood of a pairwise relationship between two nodes. However, we claim that for task-guided embeddings, it is crucial to focus on directly modeling the pairwise relationship. In this paper, we propose a novel task-guided pair embedding framework in heterogeneous network, called TaPEm, that directly models the relationship between a pair of nodes that are related to a specific task (e.g., paper-author relationship in author identification). To this end, we 1) propose to learn a pair embedding under the guidance of its associated context path, i.e., a sequence of nodes between the pair, and 2) devise the pair validity classifier to distinguish whether the pair is valid with respect to the specific task at hand. By introducing pair embeddings that capture the semantics behind the pairwise relationships, we are able to learn the fine-grained pairwise relationship between two nodes, which is paramount for task-guided embedding methods. Extensive experiments on author identification task demonstrate that TaPEm outperforms the state-of-the-art methods, especially for authors with few publication records.