Reviewerly/exHarmony huggingface dataset:
Detecting and ranking pornographic content in videos

The detection and ranking of pornographic material is a challenging task, especially when it comes to videos, due to factors such as the definition of what is pornographic and its severity level, the volumes of data that need to be processed, as well as temporal ambiguities between the benign and pornographic portions of a video. In this paper we propose a video-based pornographic detection system consisting of a convolutional neural network (CNN) for automatic feature extraction, followed by a recurrent neural network (RNN) in order to exploit the temporal information present in videos. We describe how our system can be used for both video-level labelling as well as for localising pornographic content within videos. Given pornographic video segments, we describe an efficient method for finding sexual objects within the segments, and how the types of detected sexual objects can be used to generate an estimate of the severity (‘harmfulness’) of the pornographic content. This estimate is then utilised for ranking videos based on their severity, a common requirement of law enforcement agencies (LEAs) when it comes to categorising pornographic content. We evaluate our proposed system against a benchmark dataset, achieving results on par with the state of the art, while providing additional benefits such as ranking videos according to their severity level, something which to the best of our knowledge has not been attempted before. We perform further investigations into model generalisability by performing an out-of-distribution (o.o.d.) test, investigate whether our model is making use of shortcut learning, and address the issue of explainability. The results obtained indicate that our model is using strong learning, thus further validating our proposed approach and the results obtained.
Normal Assisted Pixel-Visibility Learning With Cost Aggregation for Multiview Stereo

Multiple-View Stereo (MVS) aims to reconstruct the dense 3D representations of scenes. MVS has potential applications in the fields of autonomous driving (unstructured environment construction) and robotic navigation (visual-inertial navigation). To mitigate the error of depth estimation in low-textured or occluded regions, this work proposes a two-stage multi-view stereo network for fast and accurate depth estimation. The improvements of this work over the state of the art are as follows: 1) Sparse costs are constructed to jointly predict the initial depth map and surface normal by cost regularization, which proves that the surface normals can be estimated in this way with low memory consumption. 2) A new edge refinement block is developed to refine the coarse surface normal to obtain a fine-grained surface normal map. 3) Instead of using the general variance-based metric to equally aggregate cost, a new content-adaptive cost aggregation mechanism based on the similarity of the neighboring surface normal is designed for reliable cost aggregation. To the best of our knowledge, the proposed work is the first trainable network that leverages surface normal as guidance to capture neighboring pixel-visibility, which is an effective supplement to existing depth/normal estimation frameworks. Experimental results indicate that our method can not only achieve accurate depth estimation for scene perception but also make no concession to the real-time performance and limited memory bottleblock. Multiple-view stereo (MVS) aims to reconstruct the dense 3D representations of scenes. It is widely used in the fields of industrial measurement, autonomous driving, and robotic navigation. To mitigate the error of depth estimation in challenging scenarios, this work proposes a two-stage multi-view stereo network for fast and accurate depth estimation. Our method is the first trainable network that leverages surface normal as pixel-visibility guidance to aggregate reliable cost, which could achieve accurate depth estimation and provide the perception ability for the robot. The proposed method has great potential in the fields of 3D reconstruction, industrial measurement, and robotic navigation to estimate real-time and accurate depth with limited memory consumption.
Constructions of MDS, Near MDS and Almost MDS Codes From Cyclic Subgroups of F*<i>q</i> <sup>2</sup>

Linear codes achieving or nearly achieving the Singleton bound are interesting in both theory and practice. The objective of this paper is to construct several infinite families of MDS, near MDS and almost MDS codes from some special cyclic subgroups of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb {F}}_{q^{2}}^{*}$ </tex-math></inline-formula> . To this end, the augmentation and extension techniques are used. The codes in this paper have flexible parameters and their lengths could be large. The minimum linear locality of the codes constructed in this paper is also studied. Some infinite families of optimal linearly locally recoverable codes are obtained. Besides, some codes in this paper are proved to be proper for error detection.
A Method to Automatic Create Dataset for Training Object Detection Neural Networks

Numerous high-accuracy neural networks for object detection have been proposed in recent years. Creating a specific training dataset seems to be the main obstacle in putting them into practice. This paper aims to overcome this obstacle and proposes an automatic dataset creation method. This method first extracts objects from the source images and then combines them as synthetic images. These synthetic images are automatic annotated using the data flow in the process and can be used directly for training. To the best of our knowledge, this is the first automatic dataset creation method for object detection tasks. In addition, the adaptive object extraction method and created natural synthetic images make the proposed method maintain strong adaptation and generalization ability. To validate the feasibility, a dataset that includes 44 categories of objects is created for the object detection task in a vending supermarket. Under the strictest metric <i>AP<sub>75</sub></i>, the trained EfficientDet achieves 98.7% accuracy on the common difficulty testing set and 93.1% accuracy on the high difficulty testing set.
Security-Aware and Efficient Data Deduplication for Edge-Assisted Cloud Storage Systems

Data deduplication at the network edge significantly improves communication efficiency in edge-assisted cloud storage systems. With the increasing concern about data privacy, secure deduplication has been proposed to provide data security while supporting deduplication. Since conventional secure deduplication schemes are mainly based on deterministic encryption, they are vulnerable to frequency analysis attacks. Some recent research has focused on this problem, where several works studied the trade-off between deduplication efficiency and resistance to frequency analysis attacks. However, no existing work can provide different deduplication efficiency and protection for different data chunks. In this paper, we propose a security-aware and efficient data deduplication scheme for edge-assisted cloud storage systems. It not only improves the efficiency of deduplication but also reduces information leakage caused by frequency analysis attacks. In particular, we first define the security level for chunks to measure the security needs of users. Then, we develop an encryption scheme with multiple levels of security for deduplication. It provides higher security protection for chunks with higher security levels, while sacrificing security to achieve higher deduplication efficiency for chunks with lower security levels. We also analyze the security of our proposed scheme. Evaluations on the real-world datasets show the efficiency of our design.
Edge Aware Domain Transformation for Face Sketch Synthesis

With the development of generative adversarial networks (GAN), the field of face sketch synthesis has received extensive attention. Face sketch synthesis (FSS) has promising prospects in the fields of entertainment and law enforcement, where it plays an increasingly important role. We propose a novel generative adversarial network for synthesizing sketches with similar shapes and rich details to photos. This problem is challenging because it involves the transition between the sketch domain and the photo domain. Many methods have been used for face sketch synthesis in recent years, but existing methods cannot fully exploit the semantic information between different domains. To this end, we use a cross-domain face sketch synthesis framework based on edge-preserving filters to make the boundaries of different semantics in semantic layouts have a smooth transition. We further propose a new spatially adaptive denormalization module named edge-aware enhancement Spatially Adaptive DEnormalization (eaeSPADE), which can make full use of the semantic information in the semantic layout of faces and improve the details of the synthesized face images. Extensive experiments demonstrate that our method outperforms existing face sketch synthesis methods.
Introducing Parallel Computing in a Second CS Course

The ubiquity of multicore processors, cloud computing, and hardware accelerators have elevated parallel and distributed computing (PDC) topics into fundamental building blocks of the undergraduate CS curriculum. Therefore, it is increasingly important for students to learn a common core of introductory PDC topics and develop parallel thinking skills early in their CS studies. We present the curricular design, pedagogy, and goals of an introductory-level course on com-puter systems that introduces parallel computing to students who have only a CS1 background. Our course focuses on three curricular goals that serve to integrate the ACM-IEEE TCPP guidelines throughout: a vertical slice through the computer of how it runs a program; evaluating system costs associated with running a program; and taking advantage of the power of parallel computing. We elaborate on the goals and details of our course's key modules, and we discuss our pedagogical approach that includes active-learning techniques. We find that the PDC foundation gained through early exposure in this course helps students gain confidence in their ability to expand and apply their understanding of PDC concepts throughout their CS education.
Comparing of Modern Methods Used to Assess the Quality of Video Sequences During Signal Streaming with and Without Human Perception

Nowadays there is a trend of streaming video taking up more and more of the bandwidth of the Internet. Evaluating the quality of compression is an important area of research. Modern information coding technologies imply a trade-off between resources and visual quality. Subjective video quality assessments are time-consuming to experiment with and impossible in real-time applications. Therefore, the interest in developing models for objective video quality estimation has a high growth trend in recent years. In this paper, we present an analysis of the current categories of objective quality metrics used to assess the quality of video sequences in streaming signal transmission. We also, present comparing of the performance of reduce-referenced metrics and full-reference metrics with metric, which correlates with human perception. This work will be useful for researchers in the field of video compression, and will also be useful for creating additional test materials, planning future experiments, and improving adaptive codecs.
A Hospital Information Management System With Habit-Change Features and Medial Analytical Support for Decision Making

A hospital information management system (Doctive) with habit-change features and medial analytical support for decision making is developed in this study to reduce the risks of heart diseases. Doctive is targeted for hospital authorities to monitor patients’ habits and to prescribe medication and advice accordingly. Furthermore, this system provides emergency assistance for patients based on their current location. The proposed system would be beneficial for monitoring and organizing patients’ information to ease data entry, data management, data access, data retrieval and finally decision making. Doctive is tested and evaluated by 41 people who are either medical experts or professionals in the field of data analytics and visualization. The results indicate a high acceptance rate towards using Doctive system in hospitals and very good usability of the system. Doctive can be useful for healthcare providers and developers to track users’ habits for reducing the risk of heart disease. In the future.
Attribute based spatio-temporal person retrieval in video surveillance

Many venues, such as airports, railway stations, and shopping malls, have video surveillance systems for security and monitoring. However, searching for and retrieving people based on attribute descriptions in a large number of videos is difficult, particularly with weather variations and crowded places. Most of the existing attribute-based person retrieval systems consist of two main modules: object detection and person attribute recognition. The common drawbacks of object detection in the existing methods are false-positive, missing detection, and multi bounding boxes for the same object. Moreover, attribute recognition algorithms suffer from low accuracy for a single attribute classifier, while attributes error spread in the cascading multi-attribute classifier. This paper overcomes these issues by applying the ByteTrack algorithm instead of object detection to exploit the person's spatio-temporal information and generate a tube that maintains all the boxes that include the objects and associates high and low score boxes of the objects without raising false positive detection. Also, linking each person bounding boxes together results in more accurate attributes recognition than defining the attributes of each bounding box separately. Moreover, the proposed algorithm merges between selected predictions of two attribute recognition algorithms to improve the recognition performance. An extensive empirical evaluation was carried out on the SoftBioSearch database. The simulation results reveal that the proposed retrieval algorithm provides effective retrieval performance that exceeds the best conventional method by 14%.
Boolean logic gates implemented by a single photonic neuron based on a semiconductor Fano laser

We propose and numerically demonstrate a high-speed photonic neuron model using a single semiconductor Fano laser (FL) under electrical modulation that can mimic the Class 1 and Class 3 neuronal dynamics. In this model, the unwanted relaxation oscillation found in the spiking dynamics of conventional semiconductor lasers can be suppressed by the formation of a Fano mirror. The Boolean logic operations are tested based on the dynamics of the photonic neuron combined with the all-or-none law and time encoding method, which are the key properties of biological neurons. The results show that the Boolean logic gates can be successfully implemented in the proposed photonic neuron. Importantly, as a unique type of microscopic laser, the FL has the potential to develop integrated neuromorphic photonic computing systems.
Entropic associative memory for manuscript symbols

Manuscript symbols can be stored, recognized and retrieved from an entropic digital memory that is associative and distributed but yet declarative; memory retrieval is a constructive operation, memory cues to objects not contained in the memory are rejected directly without search, and memory operations can be performed through parallel computations. Manuscript symbols, both letters and numerals, are represented in Associative Memory Registers that have an associated entropy. The memory recognition operation obeys an entropy trade-off between precision and recall, and the entropy level impacts on the quality of the objects recovered through the memory retrieval operation. The present proposal is contrasted in several dimensions with neural networks models of associative memory. We discuss the operational characteristics of the entropic associative memory for retrieving objects with both complete and incomplete information, such as severe occlusions. The experiments reported in this paper add evidence on the potential of this framework for developing practical applications and computational models of natural memory.
A formal approach to good practices in Pseudo-Labeling for Unsupervised Domain Adaptive Re-Identification

The use of pseudo-labels prevails to tackle Unsupervised Domain Adaptive (UDA) Re-Identification (re-ID) with the best performance. Indeed, this family of approaches has given rise to several UDA re-identification-specific frameworks, which are effective. In these works, research directions to improve Pseudo-Labeling UDA re-ID performance are varied and primarily based on intuition and experiments: refining pseudo-labels, reducing the impact of errors in pseudo-labels... It can be hard to deduce from them general good practices that we can implement in any Pseudo-Labeling method to improve its performance consistently. We propose a new theoretical view on Pseudo-Labeling UDA re-ID to address this fundamental question. The contributions are threefold: (i) A novel theoretical framework for Pseudo-Labeling UDA re-ID, formalized through a new general learning upper bound on the UDA re-ID performance. (ii) General good practices for Pseudo-Labeling are directly deduced from the interpretation of the proposed theoretical framework to improve the target re-ID performance. (iii) Extensive experiments on challenging person and vehicle cross-dataset re-ID tasks, showing consistent performance improvements for various state-of-the-art methods and various proposed implementations of good practices.
neoDBMS: In-situ Snapshots for Multi-Version DBMS on Native Computational Storage

Multi-versioning and MVCC are the foundations of many modern DBMSs. Under mixed workloads and large datasets, the creation of the transactional snapshot can become very expensive, as long-running analytical transactions may request old versions, residing on cold storage, for reasons of transactional consistency. Furthermore, analytical queries operate on cold data, stored on slow persistent storage. Due to the poor data locality, snapshot creation may cause massive data transfers and thus lower performance. Given the current trend towards computational storage and near-data processing, it has become viable to perform such operations in-storage to reduce data transfers and improve scalability. neoDBMS is a DBMS designed for near-data processing and computational storage. In this paper, we demonstrate how neoDBMS performs snapshot computation in-situ. We showcase different interactive scenarios, where neoDBMS outperforms PostgreSQL 12 by up to 5×.
ExSample: Efficient Searches on Video Repositories through Adaptive Sampling

Capturing and processing video is increasingly common as cameras become cheaper to deploy. At the same time, rich video understanding methods have progressed greatly in the last decade. As a result, many organizations now have massive repositories of video data, with applications in mapping, navigation, autonomous driving, and other areas. Because state-of-the-art object detection methods are slow and expensive, our ability to process even simple ad-hoc object search queries ('find 100 traffic lights in dashcam video') over this accumulated data lags far behind our ability to collect it. Processing video at reduced sampling rates is a reasonable default strategy for these types of queries, however, the ideal sampling rate is both data and query dependent. We introduce ExSample, a low cost framework for object search over unindexed video that quickly processes search queries by adapting the amount and location of sampled frames to the particular data and query being processed. ExSample prioritizes the processing of frames in a video repository so that processing is focused in portions of video that most likely contain objects of interest. It continually re-prioritizes processing based on feedback from previously processed frames. On large, real-world datasets, ExSample reduces processing time by up to 6x over an efficient random sampling baseline and by several orders of magnitude over state-of-the-art methods that train specialized per-query surrogate models. ExSample is thus a key component in building cost-efficient video data management systems.
Effective Few-Shot Named Entity Linking by Meta-Learning

Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base, which is significant and fundamental for various downstream applications, e.g., knowledge base completion, question answering, and information extraction. While great efforts have been devoted to this task, most of these studies follow the assumption that large-scale labeled data is available. However, when the labeled data is insufficient for specific domains due to labor-intensive annotation work, the performance of existing algorithms will suffer an intolerable decline. In this paper, we endeavor to solve the problem of few-shot entity linking, which only requires a minimal amount of in-domain labeled data and is more practical in real situations. Specifically, we firstly propose a novel weak supervision strategy to generate non-trivial synthetic entity-mention pairs based on mention rewriting. Since the quality of the synthetic data has a critical impact on effective model training, we further design a meta-learning mechanism to assign different weights to each synthetic entity-mention pair automatically. Through this way, we can profoundly exploit rich and precious semantic information to derive a well-trained entity linking model under the few-shot setting. The experiments on real-world datasets show that the proposed method can extensively improve the state-of-the-art few-shot entity linking model and achieve impressive performance when only a small amount of labeled data is available. Moreover, we also demonstrate the outstanding ability of the model's transferability. Our code and models will be open-sourced.
Method to design a neural network with minimal number of neurons for approximation problems

The widespread use of neural networks to model complex processes requires that a parsimonious model of the process be obtained. One of the main variables in neural networks is the number of neurons in the hidden layer. Selecting an inappropriate number of neurons can lead to over- or underfitting. Therefore, a method is required which determines the appropriate number of neurons in order to approximate a defined system response or time function. This paper presents a proposition to determine the appropriate number of neurons in a feedforward neural network, based on the number of inflection points included in the system response or the time function. The results show that the proposed method has marginal approximation errors (no underfitting) and overfitting can never occur because the minimal number of neurons for the approximation problem is used. To verify the effectiveness of this method, simulations were carried out on a second-order system with and without noise, the Lotka-Volterra equations, and the Runge function.