Reviewerly/exHarmony huggingface dataset:
A Procedure for Tracing Chain of Custody in Digital Image Forensics: A Paradigm Based on Grey Hash and Blockchain

Digital evidence is critical in cybercrime investigations because it is used to connect individuals to illegal activity. Digital evidence is complicated, diffuse, volatile, and easily altered, and as such, it must be protected. The Chain of Custody (CoC) is a critical component of the digital evidence procedure. The aim of the CoC is to demonstrate that the evidence has not been tampered with at any point throughout the investigation. Because the uncertainty associated with digital evidence is not being assessed at the moment, it is impossible to determine the trustworthiness of CoC. As scientists, forensic examiners have a responsibility to reverse this tendency and officially confront the uncertainty inherent in any evidence upon which they base their judgments. To address these issues, this article proposes a new paradigm for ensuring the integrity of digital evidence (CoC documents). The new paradigm employs fuzzy hash within blockchain data structure to handle uncertainty introduced by error-prone tools when dealing with CoC documents. Traditional hashing techniques are designed to be sensitive to small input modifications and can only determine if the inputs are exactly the same or not. By comparing the similarity of two images, fuzzy hash functions can determine how different they are. With the symmetry idea at its core, the suggested framework effectively deals with random parameter probabilities, as shown in the development of the fuzzy hash segmentation function. We provide a case study for image forensics to illustrate the usefulness of this framework in introducing forensic preparedness to computer systems and enabling a more effective digital investigation procedure.
Interpretable Autonomous Flight Via Compact Visualizable Neural Circuit Policies

We learn interpretable end-to-end controllers based on Neural Circuit Policies (NCPs) to enable goal reaching and dynamic obstacle avoidance in flight domains. In addition to being able to learn high-quality control, NCP networks are designed with a small number of neurons. This property allows for the learned policies to be interpreted at the neuron level and interrogated, leading to more robust understanding of why the artificial agents make the decisions that they do. We also demonstrate transfer of the learned policy to physical flight hardware by deploying a small NCP (200 KB of memory) capable of real-time inference on a Raspberry Pi Zero controlling a DJI Tello drone. Designing interpretable artificial agents is crucial for building trustworthy AIs, both as fully autonomous systems and also for parallel autonomy, where humans and AIs work on collaboratively solving problems in the same environment.
Optimization-Based GenQSGD for Federated Edge Learning

Optimal algorithm design for federated learning (FL) remains an open problem. This paper explores the full potential of FL in practical edge computing systems where workers may have different computation and communication capabilities, and quantized intermediate model updates are sent between the server and workers. First, we present a general quantized parallel mini-batch stochastic gradient descent (SGD) algorithm for FL, namely GenQSGD, which is parameterized by the number of global iterations, the numbers of local iterations at all workers, and the mini-batch size. We also analyze its convergence error for any choice of the algorithm parameters. Then, we optimize the algorithm parameters to minimize the energy cost under the time constraint and convergence error constraint. The optimization problem is a challenging non-convex problem with non-differentiable constraint functions. We propose an iterative algorithm to obtain a KKT point using advanced optimization techniques. Numerical results demonstrate the significant gains of GenQSGD over existing FL algorithms and reveal the importance of optimally designing FL algorithms.
Cooperative Refinement Learning for domain adaptive person Re-identification

Domain adaptive person re-identification (re-ID) targets at identifying the same persons’ images in unlabeled target domain. Some existing domain adaptive Person re-ID methods assigned pseudo labels by clustering algorithms on the target domain, which tends to generate noisy labels and neglect samples with low confidence as outliers. These may hinder the retraining process, thereby limiting the model’s generalization ability. In order to overcome these problems, we propose a Cooperative Refinement Learning (CooRL) framework, which resists noisy labels and takes advantage of the outliers by developing a multi-branches structure with refinement mechanism. Specifically, a mean-attention guided network is leveraged to learn more complementary features from pure and noisy samples generated by clustering, which includes a mean network and two sub-branches. Meanwhile, to better optimize the neural networks, CooRL jointly refines and aligns the pseudo labels of sub-branches by progressively adjusting the predicted logits through the mean network. Comprehensive experimental results have demonstrated that our proposed method can achieve excellent performances on benchmark datasets.
TGP-PCQA: Texture and geometry projection based quality assessment for colored point clouds

Colored point cloud (PC) will inevitably encounter distortion during its acquisition, processing, coding and transmission, which may affect the visual quality of the colored PC. Therefore, it is necessary to design an effective tool for colored PC quality assessment (PCQA). In this paper, considering the mapping relationship of perception between the colored PC and its corresponding projection images, we propose a novel PCQA method based on texture and geometry projection (denoted as TGP-PCQA). The main idea of the proposed TGP-PCQA method is to obtain texture and geometry projection maps from different perspectives for evaluating the colored PC. Specifically, 4D tensor decomposition is used to obtain the combination and difference information between the reference and distorted texture projection maps for mainly characterizing texture distortion of colored PC. Meanwhile, the edge features of the geometry projection map are calculated to measure the global or local geometry distortion. All of the extracted features are combined to predict an overall quality of colored PC. In addition, this paper establishes a multi-distorted colored PC database named CPCD2.0 with compression distortions and Gaussian noise, which orients to the influence of both geometry and texture components in distortion. Experimental results on two open subjective evaluation databases (IRPC and SJTU-PCQA) and the self-built CPCD2.0 database show that the proposed TGP-PCQA method outperforms the state-of-the-art PCQA methods. We are also providing the self-built CPCD2.0 database free of charge at https://github.com/cherry0415/CPCD2.0.
Removal of fog from hazy images and their restoration

The prominent reason behind road accidents during the winter season is the presence of fog in the environment. Other important reasons for the degradation of visibility are haze, smog, cloud, and rain. In the process of developing automation of a vehicle on the road, visibility and contrast are the most affected parameters of the captured image or video. Road accidents can be prevented if images taken in foggy conditions are processed to improve their quality and legibility. There are different methods available to improve the quality of foggy images, like colour attenuation prior method, dark channel prior method and fog removal using region detection network. The atmospheric particles such as water droplets which cause absorption and scattering of light further produce attenuation and air-light. The present research work is based on Dark Channel Prior (DCP) method. DCP method needs to find the transmission map which gives the strength of the fog in the image. Major parts in this algorithm are the estimation of the dark channel, finding the transmission map, refining the transmission map, and reconstructing the image without haze. The proposed algorithm has also been implemented using Raspberry pi. This research work focuses on the improvement of the reconstructed de-hazed image using various filters. The results are compared based on Contrast Gain (CG) and Color Index (CI) parameters. Many times, this application needs the object detection phase, which uses various methods; however, the scope of this paper is limited to the reconstruction of the image after the removal of fog.
A Comparative Study of Deep Learning Methods for Hate Speech and Offensive Language Detection in Textual Data

The problem of hate speech on social network sites is very prevalent which is being faced by every major social media platform. Several methods have been explored for the purpose of intent-based text classification. Each method has its own pros and cons concerning the type of intent, size of data set, the maximum length of text, etc. Several approaches have been presented in the literature for the hate and offensive speech detection. The main objective of this work is to present a comparative study among select deep learning methods for hate speech and offensive language detection. These methods include recurrent neural network (RNN), convolutional neural network (CNN), long shortterm memory (LSTM) and bidirectional encoder representations from transformer (BERT). We have investigated the effect of class weighting technique on the performance of the deep learning methods. Our study finds that the pre-trained BERT model outperforms the other explored models in case of both unweighted and weighted hate speech classification. For offensive language classification, RNN and CNN model outperforms all other models in case of unweighted and weighted respectively. It came out that, the class weighting technique has considerably boost the classification performance of all four models for hate speech.
Modeling Sparse Data Using MLE with Applications to Microbiome Data

Modeling sparse data such as microbiome and transcriptomics (RNA-seq) data is very challenging due to the exceeded number of zeros and skewness of the distribution. Many probabilistic models have been used for modeling sparse data, including Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial models. One way to identify the most appropriate probabilistic models for zero-inflated or hurdle models is based on the p-value of the Kolmogorov–Smirnov test. The main challenge for identifying the probabilistic model is that the model parameters are typically unknown in practice. This paper derives the maximum likelihood estimator for a general class of zero-inflated and hurdle models. We also derive the corresponding Fisher information matrices for exploring the estimator's asymptotic properties. We include new probabilistic models such as zero-inflated beta binomial and zero-inflated beta negative binomial models. Our application to microbiome data shows that our new models are more appropriate for modeling microbiome data than commonly used models in the literature.
Adversarial examples for network intrusion detection systems

Machine learning-based network intrusion detection systems have demonstrated state-of-the-art accuracy in flagging malicious traffic. However, machine learning has been shown to be vulnerable to adversarial examples, particularly in domains such as image recognition. In many threat models, the adversary exploits the unconstrained nature of images–the adversary is free to select some arbitrary amount of pixels to perturb. However, it is not clear how these attacks translate to domains such as network intrusion detection as they contain domain constraints, which limit which and how features can be modified by the adversary. In this paper, we explore whether the constrained nature of networks offers additional robustness against adversarial examples versus the unconstrained nature of images. We do this by creating two algorithms: (1) the Adapative-JSMA, an augmented version of the popular JSMA which obeys domain constraints, and (2) the Histogram Sketch Generation which generates adversarial sketches: targeted universal perturbation vectors that encode feature saliency within the envelope of domain constraints. To assess how these algorithms perform, we evaluate them in a constrained network intrusion detection setting and an unconstrained image recognition setting. The results show that our approaches generate misclassification rates in network intrusion detection applications that were comparable to those of image recognition applications (greater than 95%). Our investigation shows that the constrained attack surface exposed by network intrusion detection systems is still sufficiently large to craft successful adversarial examples – and thus, network constraints do not appear to add robustness against adversarial examples. Indeed, even if a defender constrains an adversary to as little as five random features, generating adversarial examples is still possible.
Sentiment Analysis of COVID-19 Vaccine Tweets in Indonesia Using Recurrent Neural Network (RNN) Approach

The issue in social media Indonesia about the COVID-19 vaccine is unsafe to use caused concerns among the public. This issue has become a topic of discussion and contention. This work aims to conduct sentiment analysis on the COVID-19 vaccine issues in Indonesia using Recurrent Neural Networks (RNN) variant and traditional machine learning. Data collection is obtained using Tweet&#x0027;s data crawling from the Twitter API. Traditional Machine Learning Methods are used to make a comparison. Compared to other algorithms, the Support Vector Machine has the greatest accuracy, precision, and recall, with an RMSE value of 0.117. We tested types of Recurrent Neural Network (RNN) algorithms using LSTM approach such as simple RNN, Bidirectional LSTM, and Gated Recurrent Unit (GRU). LSTM, BLSTM, and GRU have the same good performance with 91&#x0025; Accuracy, 91&#x0025; Recall, 91&#x0025; Precision, and 0.085 RMSE. Dataset comparisons are also performed for each method. As a result, when more datasets are utilized, good results are produced. Furthermore, the World cloud Tweet reveals that the word &#x201C;sehat&#x201D; frequently appears in positive sentiment, whereas the word &#x201C;daftar&#x201D; frequently appears in negative sentiment.
Improving the Statistical Qualities of Pseudo Random Number Generators

Pseudo random and true random sequence generators are important components in many scientific and technical fields, playing a fundamental role in the application of the Monte Carlo methods and stochastic simulation. Unfortunately, the quality of the sequences produced by these generators are not always ideal in terms of randomness for many applications. We present a new nonlinear filter design that improves the output sequences of common pseudo random generators in terms of statistical randomness. Taking inspiration from techniques employed in symmetric ciphers, it is based on four seed-dependent substitution boxes, an evolving internal state register, and the combination of different types of operations with the aim of diffusing nonrandom patterns in the input sequence. For statistical analysis we employ a custom initial battery of tests and well-regarded comprehensive packages such as TestU01 and PractRand. Analysis results show that our proposal achieves excellent randomness characteristics and can even transform nonrandom sources (such as a simple counter generator) into perfectly usable pseudo random sequences. Furthermore, performance is excellent while storage consumption is moderate, enabling its implementation in embedded or low power computational platforms.
VR-enabled engineering consultation chatbot for integrated and intelligent manufacturing services

Industrial equipment manufacturing, such as electric power transformers, require highly customized design, assembly, installation and maintenance services for customers. Engineering consultation is an essential manufacturing service which requires deep domain knowledge. This research develops and implements the framework and the prototype of a Virtual Reality (VR) enabled intelligent engineering consultation chatbot system with natural language communication capabilities. The consultation chatbot case focuses on the power transformer knowledge domain. The system is constructed using a comprehensive knowledge base of Frequently Asked Questions (FAQs) and answers from a power transformer manufacturer. A total of 490,000 technical articles (with more than 1.1 billion words) from Wikipedia and a comprehensive domain-specific document set are used to train a word embedding model for retrieving questions and answers (QA) from the FAQ knowledge base. Immersive VR technology provides a highly interactive QA chatbot with realistic graphical views for informative engineering counselling. The case example demonstrates the accuracy of the proposed VR-enabled chatbot for transformer QAs exceeds 91%.
Hybrid Interacting Multiple Model Filtering for Improving the Reliability of Radar-Based Forward Collision Warning Systems

Automotive forward collision warning (FCW) systems based on radar sensors attracted widespread attention in recent years. To achieve a reliable FCW, it is essential to accurately estimate the position and velocity of a preceding vehicle. To this end, this study proposed a novel estimation algorithm, which is a hybrid of interacting multiple model probabilistic data association (IMM-PDA) and finite impulse response (FIR) filters. Although the IMM-PDA filter is one of the most successful algorithm for tracking a maneuvering target in clutters, it sometimes exhibits divergence owing to modeling errors. In this study, the divergent IMM-PDA filter in the novel algorithm was reset and recovered using an assisting FIR filter. Consequently, this enabled reliable estimation for FCW. The improved reliability of the proposed algorithm was demonstrated through the simulation of preceding vehicle tracking using automotive radars.
Multimodal Sentiment Analysis: Review, Application Domains and Future Directions

In this information age, opinion mining which is also known as sentiment analysis turns up to be the most important task in the field of natural language processing. Previous literature in area of sentiment analysis which mostly focused on single modality that is on textual data. Almost all the latest advancement in the sentiment analysis are using textual dataset and resources only. With the invent of internet which increases the use of social media, people are using vlogs, videos, pictures, audios, emojis and microblogs to represent their opinions on different web platforms. In this new media age, every day 720k hours of videos are uploaded on alone Youtube only. We have number of such platforms like YouTube. In the classical methods other modalities’ expressiveness is overlooked and thus these methods fail to generate accurate results. Numerous commercial applications used the aggregation of sentiments and opinions of individuals by anticipating large population. Thus, it is highly necessary that the diverse modalities from the raw data available from the internet should be utilized to mine opinions and identify sentiments. Varied data (i.e., text, speech, visual and code-mixed data) available over internet is integrated by Multimodal Sentiment Analysis. Multimodality refers to more than one modality like bimodal which uses any two modalities or trimodal which uses all the three modalities. Each modality offers its own exclusive features and can be collectively used to mine their positive or negative sentiments, opinions or responses about the entity. The latest development in multimodal sentiment analysis is that the diverse modalities i.e., audio, visual and textual are fused to generate better accuracy. Also, language and culture independent and speaker independent models can be generated. In this survey, we have defined various fusion techniques for sentiment analysis using multiple modalities, characteristics, features for multimodal sentiment analysis. This paper gives an outline of latest approaches used for multimodal sentiment analysis and various application domains in the field of multimodal Sentiment analysis using traditional methods as well as various deep learning methods. It also describes emerging areas of research in sentiment analysis using multimodal data.
Survey on English Entity Linking on Wikidata: Datasets and approaches

Wikidata is a frequently updated, community-driven, and multilingual knowledge graph. Hence, Wikidata is an attractive basis for Entity Linking, which is evident by the recent increase in published papers. This survey focuses on four subjects: (1) Which Wikidata Entity Linking datasets exist, how widely used are they and how are they constructed? (2) Do the characteristics of Wikidata matter for the design of Entity Linking datasets and if so, how? (3) How do current Entity Linking approaches exploit the specific characteristics of Wikidata? (4) Which Wikidata characteristics are unexploited by existing Entity Linking approaches? This survey reveals that current Wikidata-specific Entity Linking datasets do not differ in their annotation scheme from schemes for other knowledge graphs like DBpedia. Thus, the potential for multilingual and time-dependent datasets, naturally suited for Wikidata, is not lifted. Furthermore, we show that most Entity Linking approaches use Wikidata in the same way as any other knowledge graph missing the chance to leverage Wikidata-specific characteristics to increase quality. Almost all approaches employ specific properties like labels and sometimes descriptions but ignore characteristics such as the hyper-relational structure. Hence, there is still room for improvement, for example, by including hyper-relational graph embeddings or type information. Many approaches also include information from Wikipedia, which is easily combinable with Wikidata and provides valuable textual information, which Wikidata lacks.
Face Forgery Detection Based on the Improved Siamese Network

Face tampering is an intriguing task in video/image genuineness identification and has attracted significant amounts of attention in recent years. In this work, we propose a face forgery detection method that consists of preprocessing, an improved Siamese network-based feature extractor (including a feature alignment module), and postprocessing (a voting principle). Roughly speaking, our method extracts the features in the grey space of face/background image pairs and measures the difference to make decisions. Experiments on several standard databases prove the effectiveness of our method, and especially on the low-quality subdataset of the FaceForensics++ , our method achieves a competitive result.