Reviewerly/exHarmony huggingface dataset:
General Approach to Poset and Additive Metrics

Let P = ([n], ≤ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</sub> ) be a poset on [n] = {1, 2, <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">⋯</sub> , n}, F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> be the linear space of n-tuples over a finite field Fq and w be a weight on F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> . In this paper we consider metrics on F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> which are induced by posets over [n] and weights over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> . Such family of metrics extend both the additive metric induced by the weight w (when the poset is an anti-chain) and the poset metrics (when the weight is the Hamming weight). Furthermore, the pomset metrics is also a particular case of our construction, consequently, we provide a simpler approach to these metrics without using the multiset structure originally proposed. For the general case, we provide a complete description of the groups of linear isometries of these metric spaces in terms of a semi-direct product, which turns out to be similar to the case of poset metric spaces. In particular, we (re)obtain the groups of linear isometries of the poset, pomset and additive metric spaces. When considering a chain order, we develop, for codes on these spaces, several of the invariants and properties found in the classical coding theory. Our construction of metrics based on partial orders and any weight over the base field, highlights the dependence of the poset metric over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> with the Hamming metric on F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> and the additive property of its extension on F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> .
Multi-criteria online frame-subset selection for autonomous vehicle videos

Data Subset selection for training learning models for a variety of tasks, has been widely studied in the literature of batch mode active learning. Recent works attempt to utilize the model specific signals in the deep learning context for computer vision tasks. Companies, in their bid to create safe autonomous driving models, train and test their models on billions of miles of driving data; not all of which may be valuable for a training task. In this paper, we study the problem of frame-subset selection from autonomous vehicle driving data, for the problem of semantic segmentation - which is a crucial component of the perception module in an autonomous driving system. We find that state of the art methods for deep active learning do not utilize pairwise similarity between incoming and existing frames. We explore both active learning settings, where labels for incoming points are not available, as well as frame selection settings and find that our method selects more valuable frames than only score-based frame subset selection, or frame subset selection without label information. We demonstrate the effectiveness of our method using DeeplabV3+ model on both benchmark as well as datasets generated by driving simulators. Our generated dataset and code will be made publicly available.
Robust, efficient and privacy-preserving violent activity recognition in videos

Human activity recognition is an extensively researched topic in the field of computer vision. However, some specific events like aggressive behavior or fights have been relatively less investigated. The automatic recognition of such tasks is particularly important in video surveillance scenarios like prisons, railway stations, psychiatric wards, as well as filtering violent contents on-line. In this paper, we attempt to make a violent activity recognition system using deep learning paradigm, which is not only more accurate, but also can be deployed in real-time video surveillance systems. First, multiple approximate dynamic images (ADI) are computed from the input video sequence. An efficient convolutional neural network (CNN) called MobileNet is then used to extract short-term spatio-temporal features from these ADIs. These features are stacked together and fed to a gated recurrent unit (GRU) network, which enables modeling the long-term dynamics of the video sequence. In addition, we also introduce a privacy protection scheme based on randomization of pixel values. The proposed framework is evaluated on three violence recognition benchmark datasets, and the results obtained shows the superiority of our method both in terms of accuracy and memory requirement than the current state-of-the-art.
A high payload separable reversible data hiding in cipher image with good decipher image quality

Reversible Data Hiding (RDH) is a technique that is used to protect the secret by using digital cover media to hide it and to retrieve the cover after extracting the secret. The Reversible Data Hiding In an Encrypted Image (RDHIEI) protects the privacy of secret information and also the cover by hi ding confidential information in a cipher image. Some of the algorithms in RDHIEI can extract the information if and only if the cipher image is already decrypted and some algorithms can decrypt the image if only if the data has already been extracted from the cipher image. In those algorithms, the extraction and decryption cannot be separated. But some applications like healthcare and army image processing require that image recovery and information extraction to be separate processes; this new technique is called Separable Reversible Data Hiding In an Encrypted Image (SRDHIEI). In this paper, a novel SRDHIEI is suggested with high payload and good quality decipher image by embedding information in a cipher image on two levels. In the first level data is embedded by the Least Significant Bit(LSB) substitution method and in the second level data is embedded by using Pixel Expansion (PE) method. For image confidentiality, the cover image is encrypted by using an additive homomorphism technique. The benefits of the proposed method is to transfer the cover image in an extremely secure manner with PSNR of 8.6813 dB, – 0.0077 correlation and 7.998 entropy. The average embedding capacity of the proposed method is 217579 bits, and the decrypted image PSNR is 29.5 dB. 100% restoration of the host image and 100% lossless secret information extraction can be achieved.
Node proximity preserved dynamic network embedding via matrix perturbation

In recent years, network embedding has attracted extensive interests, which aims at representing nodes of an original network in a low-dimensional vector space while preserving the inherent topological structures of the network. Despite the remarkable advantages of complex networks, most existing network embedding methods are mainly focused on static networks while ignoring the evolving characteristic, which is proved to be an essential property of real-world networks. In this paper, we propose Node Proximity Preserved Dynamic Network Embedding via Matrix Perturbation (NPDNE) to tackle the dilemma. Specifically, our method implements a low-rank transformation on the normalized Laplacian matrix of the given networks and then derives the embedding vectors through generalized SVD. Subsequently, the node proximities are preserved in the embedding vectors by exploiting the eigen-decomposition reweighing theorem, which reveals the intrinsic relationship among different-order proximities. Moreover, a generalized eigen perturbation is adopted to update the embedding vectors so that the evolution of given networks can be captured over time. Finally, we conduct experiments of multi-label classification, link prediction, and visualization on several real-world datasets. The experimental results demonstrate the superiority of the proposed NPDNE model compared with state-of-the-art baselines.
A Randomized Reputation System in the Presence of Unfair Ratings

With the increasing popularity of online shopping markets, a significant number of consumers rely on these venues to meet their demands while choosing different products based on the ratings provided by others. Simultaneously, consumers feel confident in expressing their opinions through ratings. As a result, millions of ratings are generated on the web for different products, services, and dealers. Nonetheless, a noticeable number of users post unfair feedback. Recent studies have shown that reputation escalation is emerging as a new service, by which dealers pay to receive good feedback and escalate their ratings in online shopping markets. Therefore, finding robust and reliable ways to distinguish between fake and trustworthy ratings from users is a crucial task for every online shopping market. Moreover, with the dramatic increase in the number of ratings provided by consumers, scalability has arisen as another significant issue in the existing methods of reputation systems. To tackle these issues, we propose a randomized algorithm that calculates the reputation based on a random sample of the ratings. Since the randomly selected sample has a logarithmic size, it guarantees feasible scalability for large-scale online review systems. In addition, the randomness nature of the algorithm makes it robust against unfair ratings. We provide a thorough theoretical analysis of the proposed algorithm and validate its effectiveness through extensive empirical evaluation using real-world and synthetically generated datasets. Our experimental results show that the proposed method provides a high accuracy while running much faster than the existing iterative filtering approaches.
How EvoStreets Are Observed in Three-Dimensional and Virtual Reality Environments

When analyzing software systems, a large amount of data accumulates. In order to assist developers in the preparation, evaluation, and understanding of findings, different visualization techniques have been developed. Due to recent progress in immersive virtual reality, existing visualization tools were ported to this environment. However, three-dimensional and virtual reality environments have different advantages and disadvantages, and by transferring concepts, such as layout algorithms and user interaction mechanisms, more or less one-to-one, the characteristics of these environments are neglected. In order to develop techniques adapting to the circumstance of a particular environment, more research in this field is necessary. In previously conducted case studies, we compared EvoStreets deployed in three different environments: 2D, 2.5D, and virtual reality. We found evidence that movement patterns—path length, average speed, and occupied volume—differ significantly between the 2.5D and virtual reality environments for some of the tasks that had to be solved by 34 participants in a controlled experiment. In this paper, we analyze the results of this experiment in more details, to study if not only movement is affected by these environments, but also the way how EvoStreets are observed. Although we could not find enough evidence that the number of viewpoints and their duration differ significantly, we found indications that in virtual reality viewpoints are located closer to the EvoStreets and that the distance between viewpoints is shorter. Based on our previous results and the findings of this paper, we present visualization and user interaction concepts specific to the kind of environment.
Statistics Pooling Time Delay Neural Network Based on X-Vector for Speaker Verification

This paper aims to improve speaker embedding representation based on x-vector for extracting more detailed information for speaker verification. We propose a statistics pooling time delay neural network (TDNN), in which the TDNN structure integrates statistics pooling for each layer, to consider the variation of temporal context in frame-level transformation. The proposed feature vector, named as statsvector, are compared with the baseline x-vector features on the VoxCeleb dataset and the Speakers in the Wild (SITW) dataset for speaker verification. The experimental results showed that the proposed stats-vector with score fusion achieved the best performance on VoxCeleb1 dataset. Furthermore, considering the interference from other speakers in the recordings, we found that the proposed statsvector efficiently reduced the interference and improved the speaker verification performance on the SITW dataset.
Deep Fusion for 3D Gaze Estimation From Natural Face Images Using Multi-Stream CNNs

Over the last few decades, eye gaze estimation techniques have been thoroughly investigated by many researchers. However, predicting a 3D gaze from a 2D natural image remains challenging because it has to deal with several issues such as diverse head positions, face shape transformation, illumination variations, and subject individuality. Many previous studies employ convolutional neural networks (CNNs) for this task, and yet the accuracy needs improvement for its practical use. In this paper, we propose a 3D gaze estimation framework based on the data science perspective: First, a novel neural network architecture is designed to exploit every possible visual attribute such as the states of both eyes and the head position, including several augmentations; secondly, the data fusion method is utilized by incorporating multiple gaze datasets. Extensive experiments were carried out using two standard eye gaze datasets, including comparative analysis. The experimental results suggest that our method outperforms state-of-the-art with 2.8 degrees for MPIIGaze and 3.05 degrees for EYEDIAP dataset, respectively, indicating that it has a potential for real applications.
Double Competitive Constraints-Based Collaborative Representation for Pattern Classification

Representation-based classification (RBC) has attracted much attention in pattern recognition. As a linear representative RBC method, collaborative representation-based classification (CRC) is very promising for classification. Although many extensions of CRC have been developed recently, the discriminative and competitive representations of different classes for favorable classification has not yet been fully explored. To design the discriminative and competitive collaborative representations for enhancing the power of pattern discrimination, we propose a novel double competitive constraints-based collaborative representation for classification (DCCRC). In the proposed DCCRC, one competitive constraint is the l2-norm regularization of residuals between each query sample and the class-specific representations, the other one is the l2-norm regularization of the representations of all the classes excluding any one class. In two competitive constraints, the class discrimination information is employed to generate competitive representations. Moreover, the proposed method integrates both the representation learning and classification into the unified model. We study the effectiveness and robustness of the proposed method by comparing it with the state-of-the-art CRC methods on six face databases and twelve UCI data sets. The experimental results demonstrate the promising classification performance of the proposed method.
A Discriminative Condition-Aware Backend for Speaker Verification

We present a scoring approach for speaker verification that mimics the standard PLDA-based backend process used in most current speaker verification systems. However, unlike the standard backends, all parameters of the model are jointly trained to optimize the binary cross-entropy for the speaker verification task. We further integrate the calibration stage inside the model, making the parameters of this stage depend on metadata vectors that represent the conditions of the signals. We show that the proposed backend has excellent outof-the-box calibration performance on most of our test sets, making it an ideal approach for cases in which the test conditions are not known and development data is not available for training a domainspecific calibration model.
Implement a Lean UX Model: Integrating Students' Academic Monitoring through a mobile app

This study explores how a Lean method collaborates with the UX characteristics to implement a single sign on application for the “Y Generation” age group to monitor their daily academic life. With a personalized mobile application, students will be able to monitor their academic and/or nonacademic activities that scheduled every day. Lean UX will support the development phase by creating a minimum viable product (MVP) fast as possible during several steps or iterations. The UI / UX experiment results generated through two iterations with Think Aloud Interview Testing and Software Usability Testing. The results obtained are used to represent the user acceptance during User Experience testing with increasing SUS score to 72.85 and decreasing average time completion, which indicates an increasing point of cognitive process of the users as 80% while interacting with the application.
Deep Clusteringwith Concrete K-Means

We address the problem of simultaneously learning a k-means clustering and deep feature representation from unlabelled data, which is of interest due to the potential for deep k-means to outperform traditional two-step feature extraction and shallow clustering strategies. We achieve this by developing a gradient estimator for the non-differentiable k-means objective via the Gumbel-Softmax reparameterisation trick. In contrast to previous attempts at deep clustering, our concrete k-means model can be optimised with respect to the canonical k-means objective and is easily trained end-to-end without resorting to time consuming alternating optimisation techniques. We demonstrate the efficacy of our method on standard clustering benchmarks.
Forensic Authorship Analysis of Microblogging Texts Using N-Grams and Stylometric Features

In recent years, messages and text posted on the Internet are used in criminal investigations. Unfortunately, the authorship of many of them remains unknown. In some channels, the problem of establishing authorship may be even harder, since the length of digital texts is limited to a certain number of characters. In this work, we aim at identifying authors of tweet messages, which are limited to 280 characters. We evaluate popular features employed traditionally in authorship attribution which capture properties of the writing style at different levels. We use for our experiments a self-captured database of 40 users, with 120 to 200 tweets per user. Results using this small set are promising, with the different features providing a classification accuracy between 92% and 98.5%. These results are competitive in comparison to existing studies which employ short texts such as tweets or SMS.
A New Perspective for Flexible Feature Gathering in Scene Text Recognition Via Character Anchor Pooling

Irregular scene text recognition has attracted much attention from the research community, mainly due to the complexity of shapes of text in natural scene. However, recent methods either rely on shape-sensitive modules such as bounding box regression, or discard sequence learning. To tackle these issues, we propose a pair of coupling modules, termed as Character Anchoring Module (CAM) and Anchor Pooling Module (APM), to extract high-level semantics from two-dimensional space to form feature sequences. The proposed CAM localizes the text in a shape-insensitive way by design by anchoring characters individually. APM then interpolates and gathers features flexibly along the character anchors which enables sequence learning. The complementary modules realize a harmonic unification of spatial information and sequence learning. With the proposed modules, our recognition system surpasses previous state-of-the-art scores on irregular and perspective text datasets, including, ICDAR 2015, CUTE, and Total-Text, while paralleling state-of-the-art performance on regular text datasets.
dMazeRunner: Optimizing Convolutions on Dataflow Accelerators

Convolution neural networks (CNNs) can be efficiently executed on dataflow accelerators. However, the vast space of executing convolutions on computational and memory resources of accelerators makes difficult for programmers to automatically and efficiently accelerate the convolutions and for architects to achieve efficient accelerator designs. We propose dMazeRunner framework, which allows users to optimize execution methods for accelerating convolution and matrix multiplication on a given architecture and to explore dataflow accelerator designs for efficiently executing CNN models. dMazeRunner determines efficient dataflows tailored for CNN layers and achieves efficient execution methods for CNN models within several seconds.
Keyword extraction and structuralization of medical reports

In recent years, patients usually accept more accurate and detailed examinations because of the rapid advances in medical technology. Many of the examination reports are not represented in numerical data, but text documents written by the medical examiners based on the observations from the instruments and biochemical tests. If the above-mentioned unstructured data can be organized as a report in a structured form, it will help doctors to understand a patient's status of the various examinations more efficiently. Besides, further association analysis on the structuralized data can be performed to identify potential factors that affect a disease.In this paper, from the pathology examination reports of renal diseases, we applied the POS tagging results of natural language analysis to automatically extract the keyword phrases. Then a medical dictionary for various examination items in an examination report is established, which is used as the basic information for retrieving the terms to construct a structured form of the report. Moreover, a topical probability modeling method is applied to automatically discover the candidate keyword phrases of the examination items from the reports. Finally, a system is implemented to generate the structured form for the various examination items in a report according to the constructed medical dictionary.The results of the experiments showed that the methods proposed in this paper can effectively construct a structural form of examination reports. Furthermore, the keywords of the popular examination items can be extracted correctly. The above techniques will help automatic processing and analysis of medical text reports.