Reviewerly/exHarmony huggingface dataset:
A Secure and Robust Autoencoder-Based Perceptual Image Hashing for Image Authentication

With the advancement of technology, it has become easier to modify or tamper with digital data effortlessly. In recent times, the image hashing algorithm has gained popularity for image authentication applications. In this paper, a convolutional stacked denoising autoencoder (CSDAE) is utilized for producing hash codes that are robust against different content preserving operations (CPOs). The CSDAE algorithm comprises mapping high-dimensional input data into hash codes while maintaining their semantic similarities. This implies that the images having similar content should have similar hash codes. To demonstrate the effectiveness of the model, the correlation between hash codes of semantically similar images has been evaluated. Subsequently, tampered localization is done by comparing the decoder output of the manipulated image with the hash of the actual image. Then, the localization ability of the model was measured by computing the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>f</mi> <mn>1</mn> </math> scores between the predicted region and the original tampered region. Based on the comparative performance and receiver-operating characteristics (ROC) curve, we may conclude that the proposed hashing proposed algorithm provides improved performance compared to various state-of-the-art techniques.
A Survey of Current Machine Learning Approaches to Student Free-Text Evaluation for Intelligent Tutoring

Recent years have seen increased interests in applying the latest technological innovations, including artificial intelligence (AI) and machine learning (ML), to the field of education. One of the main areas of interest to researchers is the use of ML to assist teachers in assessing students' work on the one hand and to promote effective self-tutoring on the other hand. In this paper, we present a survey of the latest ML approaches to the automated evaluation of students' natural language free-text, including both short answers to questions and full essays. Existing systematic literature reviews on the subject often emphasise an exhaustive and methodical study selection process and do not provide much detail on individual studies or a technical background to the task. In contrast, we present an accessible survey of the current state-of-the-art in student free-text evaluation and target a wider audience that is not necessarily familiar with the task or with ML-based text analysis in natural language processing (NLP). We motivate and contextualise the task from an application perspective, illustrate popular feature-based and neural model architectures and present a selection of the latest work in the area. We also remark on trends and challenges in the field.
Dynamic 3D Scene Analysis by Point Cloud Accumulation

Multi-beam LiDAR sensors, as used on autonomous vehicles and mobile robots, acquire sequences of 3D range scans ("frames"). Each frame covers the scene sparsely, due to limited angular scanning resolution and occlusion. The sparsity restricts the performance of downstream processes like semantic segmentation or surface reconstruction. Luckily, when the sensor moves, frames are captured from a sequence of different viewpoints. This provides complementary information and, when accumulated in a common scene coordinate frame, yields a denser sampling and a more complete coverage of the underlying 3D scene. However, often the scanned scenes contain moving objects. Points on those objects are not correctly aligned by just undoing the scanner's ego-motion. In the present paper, we explore multi-frame point cloud accumulation as a mid-level representation of 3D scan sequences, and develop a method that exploits inductive biases of outdoor street scenes, including their geometric layout and object-level rigidity. Compared to state-of-the-art scene flow estimators, our proposed approach aims to align all 3D points in a common reference frame correctly accumulating the points on the individual objects. Our approach greatly reduces the alignment errors on several benchmark datasets. Moreover, the accumulated point clouds benefit high-level tasks like surface reconstruction. [ project page ]
Gaussian Fourier Pyramid for Local Laplacian Filter

Multi-scale processing is essential in image processing and computer graphics.Halos are a central issue in multi-scale processing.Several edge-preserving decompositions resolve halos, e.g., local Laplacian filtering (LLF), by extending the Laplacian pyramid to have an edge-preserving property.Its processing is costly; thus, an approximated acceleration of fast LLF was proposed to linearly interpolate multiple Laplacian pyramids.This paper further improves the accuracy by Fourier series expansion, named Fourier LLF.Our results showed that Fourier LLF has a higher accuracy for the same number of pyramids.Moreover, Fourier LLF exhibits parameter-adaptive property for contentadaptive filtering.
Secure data authentication and access control protocol for industrial healthcare system

Because of recent COVID-19 epidemic, the Internet-of-Medical-Things (IoMT) has acquired a significant impetus to diagnose patients remotely, regulate medical equipment, and track quarantined patients via smart electronic devices installed at the patient's end. Nevertheless, the IoMT confronts various security and privacy issues, such as entity authentication, confidentiality, and integrity of health-related data, among others, rendering this technology vulnerable to different attacks. To address these concerns, a number of security procedures based on traditional cryptographic approaches, such as discrete logarithm and integer factorization problems, have been developed. All of these protocols, however, are vulnerable to quantum attacks. This paper, in this context, presents a data authentication and access control protocol for IoMT systems that can withstand quantum attacks. A comprehensive formal security assessment demonstrates that the proposed algorithm can endure both current and future threats. In terms of data computing, transmission, and key storage overheads, it also surpasses other related techniques.
On the rise of fear speech in online social media

Recently, social media platforms are heavily moderated to prevent the spread of online hate speech, which is usually fertile in toxic words and is directed toward an individual or a community. Owing to such heavy moderation, newer and more subtle techniques are being deployed. One of the most striking among these is fear speech. Fear speech, as the name suggests, attempts to incite fear about a target community. Although subtle, it might be highly effective, often pushing communities toward a physical conflict. Therefore, understanding their prevalence in social media is of paramount importance. This article presents a large-scale study to understand the prevalence of 400K fear speech and over 700K hate speech posts collected from Gab.com. Remarkably, users posting a large number of fear speech accrue more followers and occupy more central positions in social networks than users posting a large number of hate speech. They can also reach out to benign users more effectively than hate speech users through replies, reposts, and mentions. This connects to the fact that, unlike hate speech, fear speech has almost zero toxic content, making it look plausible. Moreover, while fear speech topics mostly portray a community as a perpetrator using a (fake) chain of argumentation, hate speech topics hurl direct multitarget insults, thus pointing to why general users could be more gullible to fear speech. Our findings transcend even to other platforms (Twitter and Facebook) and thus necessitate using sophisticated moderation policies and mass awareness to combat fear speech.
Quantum Neural Network for Quantum Neural Computing

Neural networks have achieved impressive breakthroughs in both industry and academia. How to effectively develop neural networks on quantum computing devices is a challenging open problem. Here, we propose a new quantum neural network model for quantum neural computing using (classically-controlled) single-qubit operations and measurements on real-world quantum systems with naturally occurring environment-induced decoherence, which greatly reduces the difficulties of physical implementations. Our model circumvents the problem that the state-space size grows exponentially with the number of neurons, thereby greatly reducing memory requirements and allowing for fast optimization with traditional optimization algorithms. We benchmark our model for handwritten digit recognition and other nonlinear classification tasks. The results show that our model has an amazing nonlinear classification ability and robustness to noise. Furthermore, our model allows quantum computing to be applied in a wider context and inspires the earlier development of a quantum neural computer than standard quantum computers.
Discourse and technology

The paper discusses three aspects of the relation between discourse and technology. The first is the influence of new communication technologies on the agenda of linguistics and discourse analysis. The beginning of radio broadcasting led to new theories of intonation and the beginning of television created an interest in linguistically inspired approaches to non-verbal communication. The second is the use of technologies as tools for discourse analysis. The move from film to the tape recorder and the subsequent birth of conversation analysis diminished the earlier interest in non-verbal communication, while today corpus linguistics challenges approaches that focus on the analysis of single, whole texts. Finally, contemporary software not only builds in spelling and grammar rules, but also text structure rules (templates), and systematic options for the use of layout, typography, colour, animation and other non-linguistic means of expression, leading to a renewed interest in multimodality. The paper concludes with the view that considerations of technology should be integrated with discourse analysis and cites the work of Ron and Suzie Scollon as an early example of this approach.
A Novel Motion Detection Method Using 3D Discrete Wavelet Transform

Recently, there has been a considerable attention given to the motion detection problem due to the explosive growth of its applications in video analysis and surveillance systems. While the previous approaches can produce good results, an accurate detection of motion remains a challenging task due to the difficulties raised by illumination variations, occlusion, camouflage, burst physical motion, dynamic texture, and environmental changes such as those on climate changes, sunlight changes during a day, etc. In this paper, we propose a novel per-pixel motion descriptor for both motion detection and dynamic texture segmentation which outperforms the current methods in the literature particularly in severe scenarios. The proposed descriptor is based on two complementary three-dimensional-discrete wavelet transform (3D-DWT) and three-dimensional wavelet leader. In this approach, a feature vector is extracted for each pixel by applying a novel three dimensional wavelet-based motion descriptor. Then, the extracted features are clustered by a clustering method such as well-known k-means algorithm or Gaussian Mixture Model (GMM). The experimental results demonstrate the effectiveness of our proposed method compared to the other motion detection approaches from the literature. The application of the proposed method and additional experimental results for the different datasets are available at (http://dspl.ce.sharif.edu/motiondetector.html).
Comment on “Highly Efficient Linear Regression Outsourcing to a Cloud”

We show that Chen et al.'s schemes [IEEE TCC, 2(4), 2014, 499-508] for outsourcing linear regression computation to the cloud are not unquestioned. In scheme 1, the client has to generate an orthogonal matrix. Its computational complexity is almost equal to that of solving a linear regression problem locally. In such case, the client has no necessary to outsource the computations to the cloud. In scheme 2, it masks a matrix by multiplying two diagonal matrixes. The linear transformation is very vulnerable to statistical attack.
Estimation of atmospheric turbidity over Adrar city in Algeria

Under a cloudless sky atmospheric turbidity causes attenuation of solar radiation reaching the earth's surface. The Linke’s turbidity factor (TL) and the Ångström’s turbidity coefficients (β) are the most frequently used atmospheric turbidity indices. TL and β are a key input for several models that assess the solar radiation under clear skies. In this paper, the Linke and Ångström turbidities were obtained from radiometric and meteorological data recorded in the Sahara desert area of Adrar region, Algeria, during October 2012–May 2015. It is observed that there is a high correlation between atmospheric turbidity caused by aerosols and meteorological parameters. A comparison has also been carried out with results acquired in Ghardaïa and Tamanrasset regions. In addition to verify the appropriateness of the theoretical Linke turbidity factor formula presented in Algerian solar atlas, the monthly mean theoretical values are compared with the experimental monthly average values.
Cloud Resource Management for Analyzing Big Real-Time Visual Data from Network Cameras

Thousands of network cameras stream real-time visual data for different environments, such as streets, shopping malls, and natural scenes. The big visual data from these cameras can be useful for many applications, but analyzing the large quantities of data requires significant amounts of resources. These resources can be obtained from cloud vendors offering cloud instances (referred to as instances in this paper) with different capabilities and hourly costs. It is a challenging problem to manage cloud resources to reduce the cost for analyzing the big real-time visual data from network cameras while meeting the performance requirements. That is because the problem is affected by many factors related to the analysis programs, the cameras, and the instances. This paper proposes a cloud resource manager (referred to as manager in this paper) that aims at solving this problem. The manager estimates the resource requirements of analyzing the data stream from each camera, formulates the resource allocation problem as a 2D vector bin packing problem, and solves it using a heuristic algorithm. The resource manager monitors the allocated instances; it allocates more instances if needed and deallocates existing instances to reduce the cost if possible. The experiments show that the resource manager is able to reduce up to 60 percent of the overall cost. The experiments use multiple analysis programs, such as moving objects detection, feature tracking, and human detection. One experiment analyzes more than 97 million images (3.3 TB of data) from 5,310 cameras simultaneously over 24 hours using 15 Amazon EC2 instances costing $188.
Using Stacked Sparse Auto-Encoder and Superpixel CRF for Long-Term Visual Scene Understanding of UGVs

Multiple images have been widely used for scene understanding and navigation of unmanned ground vehicles in long term operations. However, as the amount of visual data in multiple images is huge, the cumulative error in many cases becomes untenable. This paper proposes a novel method that can extract features from a large dataset of multiple images efficiently. Then the membership K-means clustering is used for high dimensional features, and the large dataset is divided into N subdatasets to train N conditional random field (CRF) models based on superpixel. A Softmax subdataset selector is used to decide which one of the N CRF models is chosen as the prediction model for labeling images. Furthermore, some experiments are conducted to evaluate the feasibility and performance of the proposed approach.
Copy-Move Forgery Detection with Similar But Genuine Objects

Copy-Move Forgery Detection (CMFD) is a classic image forensics problem. However, CMFD with Similar but Genuine Objects (SGO) has received relatively less attention in the current literature. Recently, it has been observed that state-of-the-art CMFD techniques are not quite effective in solving this important yet less explored problem variant. We have addressed this specific problem by using Rotated Local Binary Pattern (RLBP) texture features, followed by feature matching by Generalized 2-Nearest Neighborhood (g2NN) technique, agglomerative hierarchical clustering, and geometric transformation estimation through homography computation among the clusters. Experimental results verify that our CMFD framework not only outperforms the state-of-the-art CMFD schemes for tampered images having similar but genuine objects, but also commensurates with the accuracy of state-of-the-art schemes for other copy-move forgery types. The proposed method has also been found to be robust with respect to several post-processing operations, e.g., compression and filtering.
Perceptual Quality Assessment of Medical Images

Today, healthcare professionals are viewing medical images in a variety of environments. The technologies and methodologies used to acquire, process, store, transmit, and display images vary, and consequently, the ultimate visual information received by the clinicians differs significantly in perceived quality. Visual signal distortions, such as various types of noise and artifacts arising in medical image acquisition, processing, compression, and transmission, affect the perceptual quality of images and potentially impact diagnoses. To optimize clinical practice, we need to understand human perception of medical image quality in practical settings and then use what is learned to develop useful solutions for improved image quality and better image-based diagnoses. This article focuses on the methodologies used to measure the perceptual quality of medical images using magnetic resonance image acquisition and computed tomography image compression as examples, where modern digital image processing technologies and statistical analysis approaches play important roles in helping with both subjective visual testing and objective quality predictions.
Multi-Layer Architecture for Efficient Steganalysis of UnderMp3Cover in Multi-Encoder Scenario

Mp3 is a very popular audio format and hence it can be a good host for carrying hidden messages. Therefore, different steganography methods have been proposed for mp3 hosts. But, current literature has only focused on steganalysis of mp3stego. In this paper we mention some of the limitations of mp3stego and argue that UnderMp3Cover (Ump3c) does not have those limitations. Ump3c makes subtle changes only to the global gain of bitstream and keeps the rest of bitstream intact. Therefore, its detection is much harder than mp3stego. To address this, joint distributions between global gain and other fields of mp3 bit stream are used. The changes are detected by measuring the mutual information from those joint distributions. Furthermore, we show that different mp3 encoders have dissimilar performances. Consequently, a novel multi-layer architecture for steganalysis of Ump3c is proposed. In this manner, the first layer detects the encoder and the second layer performs the steganalysis job. One of advantages of this architecture is that feature extraction and feature selection can be optimized for each encoder separately. We show this multi-layer architecture outperforms the conventional single-layer methods. Comparing results of the proposed method with other works shows an improvement of 20.4% in the accuracy of steganalysis.
Perceptual Experience Management

In automatically generated interactive narratives (INs) with strong story structure, there is often a tension between player choice and author constraints. This tension arises when the player contradicts a story the system is in the process of telling. When this happens there are two options available to an IN storyteller that preserve the author's intentions. First, called accommodation, finds a new story that matches the player's action while preserving author constraints. Second, called intervention, removes unwanted effects from the player's action. Neither of these alternatives are ideal. For accommodation, a new story is not always available. For intervention, the player could notice the inconsistent effects of their actions. In this paper, we present a new approach to mediate between player choice and authorial constraints, called perceptual experience management, which mitigates drawbacks of both accommodation and intervention by incorporating a model of player knowledge into the strong story experience management process. This model is used to widen the space of possible accommodations and constrain the space of possible interventions to better balance author control and player choice. These two strategies are implemented in the general mediation engine, an experience manager capable of procedurally generating and revising a game world.
Person re-identification with multiple similarity probabilities using deep metric learning for efficient smart security applications

Surveillance video analysis plays a vital role in the daily operations of smart cities, which increasingly relies on person re-identification technology to sustain smart security applications. However, research challenges of re-identification remain especially in terms of recognizing the different appearances of the same person in a harsh real-world environment: (1) the adaptability of the selected features to the dynamic environment cannot be guaranteed, and (2) existing methods rooted from metric learning aim to find a single metric function, and they lack the ability to measure the different appearances of the same person. To address these problems, this study proposes a multiple deep metric learning method empowered by the functionality of person similarity probability measurement. The proposed method exploits multiple stacked auto-encoder networks and classification networks to quantify pedestrians’ similarity relations. The stacked auto-encoder networks directly recognize persons from surveillance images at the pixel level. The classification networks are equipped with the Softmax regression models and produce multiple similarity probabilities to characterize different appearances belonging to the same person. An Adaboost-like model is designed to fuse the probabilities corresponding to multiple metrics, which ensures a high accuracy of recognition. Experimental results on two public datasets (VIPeR and CUHK-01) indicate that the proposed method outperforms existing algorithms by 2%–10% at rank 1. Based on the similarity probabilities learned by the proposed model, the algorithm for matching the person pair can achieve a time complexity as low as O(n), which can be deployed at a large scale on the distributed intelligent surveillance network, with each node maintaining limited computing capabilities.
Supervised Learning: Classification

Supervised learning is the task of building a model that is able to fit the available observations. In the area of supervised learning, classification is one of the most studied problems. Given a set of predefined class labels (two or more) and a set of available observations, the aim is to build a model based on the features of the observations that is able to assign each observation to the corresponding class. In Bioinformatics several problems can be formulated as a classification task. This article introduces several supervised learning techniques that are commonly used to address a classification problem, presenting the most used measures to evaluate the performance of a classification model.