Reviewerly/exHarmony huggingface dataset:
PRECEPT-4-Justice: A bias-neutralising framework for digital forensics investigations

Software invisibly permeates our everyday lives: operating devices in our physical world (traffic lights and cars), effecting our business transactions and powering the vast World Wide Web. We have come to rely on such software to work correctly and efficiently. The generally accepted narrative is that any software errors that do occur can be traced back to a human operator's mistakes. Software engineers know that this is merely a comforting illusion. Software sometimes has bugs, which might lead to erratic performance: intermittently generating errors. The software, hardware and communication infrastructure can all introduce errors, which are often challenging to isolate and correct. Anomalies that manifest are certainly not always due to an operator's actions. When the general public and the courts believe the opposite, that errors are usually attributable to some human operator's actions, it is entirely possible for some hapless innocent individual to be blamed for anomalies and discrepancies whose actual source is a software malfunction. This is what occurred in the Post Office Horizon IT case, where unquestioning belief in the veracity of software-generated evidence led to a decade of wrongful convictions. We will use this case as a vehicle to demonstrate the way biases can influence investigations, and to inform the development of a framework to guide and inform objective digital forensics investigations. This framework, if used, could go some way towards neutralising biases and preventing similar miscarriages of justice in the future.
Spoofed Voice Detection using Dense Features of STFT and MDCT Spectrograms

Attestation of audio signals for recognition of forgery in voice is challenging task. In this research work, a deep convolutional neural network (CNN) is utilized to detect audio operations i.e. pitch shifted and amplitude varied signals. Short-time Fourier transform (STFT) and Modified Discrete Cosine Transform (MDCT) features are chosen for audio processing and their plotted patterns are fed to CNN. Experimental results show that our model can successfully distinguish tampered signals to facilitate the audio authentication on TIMIT dataset. Proposed CNN architecture can distinguish spoofed voices of shifting pitch with accuracy of 97.55% and of varying amplitude with accuracy of 98.85%.
Inverse-Based Approach to Explaining and Visualizing Convolutional Neural Networks

This article presents a new method for understanding and visualizing convolutional neural networks (CNNs). Most existing approaches to this problem focus on a global score and evaluate the pixelwise contribution of inputs to the score. The analysis of CNNs for multilabeled outputs or regression has not yet been considered in the literature, despite their success on image classification tasks with well-defined global scores. To address this problem, we propose a new inverse-based approach that computes the inverse of a feedforward pass to identify activations of interest in lower layers. We developed a layerwise inverse procedure based on two observations: 1) inverse results should have consistent internal activations to the original forward pass and 2) a small amount of activation in inverse results is desirable for human interpretability. Experimental results show that the proposed method allows us to analyze CNNs for classification and regression in the same framework. We demonstrated that our method successfully finds attributions in the inputs for image classification with comparable performance to state-of-the-art methods. To visualize the tradeoff between various methods, we developed a novel plot that shows the tradeoff between the amount of activations and the rate of class reidentification. In the case of regression, our method showed that conventional CNNs for single image super-resolution overlook a portion of frequency bands that may result in performance degradation.
Modelling non-Markovian dynamics in photonic crystals with recurrent neural networks

We develop a recurrent neural network framework to model the non-Markovian dynamics exhibited by two-level atoms interacting with the radiation reservoir of a photonic crystal. Despite the strong non-Markovianity of the atomic dynamics induced by the rapid spectral variation in photonic density of states of the photonic reservoir, our recurrent neural network approach is able to capture precise details in the atomic evolution, including the fractional steady-state atomic population inversion and spectral splitting of the atomic transition. We demonstrate the robustness of the recurrent neural network setup against reduced data sets and its effectiveness to deal with systems of increased complexity.
Action recognition for sports video analysis using part-attention spatio-temporal graph convolutional network

Action recognition makes significant contributions to sports video analysis, especially for athletes’ training evaluations. For sports video analysis, the action information is mainly conveyed by human body parts’ temporal movement, and each of the parts has a unique importance to the action representation. Aiming to involve this point in action recognition, we propose a part-attention spatio-temporal graph convolutional network (PSGCN) to exploit the dynamic spatio-temporal information in a sports video; it learns the importance of different parts to emphasize the contribution on the task of action recognition. Specifically, PSGCN first divides the human body into six parts and extracts their convolutional neural network (CNN) features, as well as concatenating the global feature of the whole frame; it then utilizes a cross-part and cross-frame graph building module to formulate the graph correlation of the parts from different frames. Inspired by the larger temporal variation of the same part containing more action information, we further propose a part-attention (PA) learning module to estimate the importance of each part, which can strengthen the graph correlation to support a PA graph. Finally, PSGCN conducts a graph convolutional network on the learned PA spatio-temporal graph with the learned part CNN features, which can obtain the action representation for the given sports video. In addition, the whole network is optimized by two losses of PA and action classification. To perform the superiority of PSGCN, we carry out extensive experiments of our model compared with several state-of-the-art methods on widely used action recognition datasets, especially for sports action. The results reflect the advantages of the proposed PSGCN on sports video analysis.
Globally Optimized Matchmaking in Online Games

As one of the core components of online games, matchmaking is the process of arranging multiple players into matches, where the quality of matchmaking systems directly determines player satisfaction and further affects the life cycle of game products. With the number of candidate players increases, the number of possible match combinations grows exponentially, which makes the current implementation for multiplayer matchmaking can only obtain locally optimal arrangement in an inefficient fashion. In this paper, we focus on the globally optimized matchmaking problem, in which the objective is to decide an optimal matching sequence for the queuing players. To tackle this challenging problem, we propose a novel data-driven matchmaking framework, called GloMatch, based on machine learning principles. Through transforming the matchmaking problem into a sequential decision problem, we solve it with the help of an effective policy-based deep reinforcement learning algorithm. Quantitative experiments on simulation and online game environments demonstrate the effectiveness of the presented framework.
Real-time tracking of organ-shape and vessel-locations for surgical navigation using MEMS tri-axis magnetic sensors

Laparoscopic surgery is less invasive to patients; however, fatal bleeding occurs when a surgeon misinterprets the anatomical location of the blood vessels. Therefore, we have proposed a location tracking system by generating an artificial magnetic field around a patient and attaching MEMS magnetic sensor nodes to certain locations of the patient's organs for real-time tracking of the organ shape and vessel locations. This paper presents the detailed system design and configuration. The results suggest that a high spatial resolution of 1-2 mm may be achieved by static and ultralow-frequency magnetic fields for rotation recognition of each sensor node and noise cancelation of the entire system. The algorithm for creating the navigation 'map' has been investigated from both efficiency and accuracy perspectives, which is essential for practical applications of the above system in surgical navigation.
Health issue identification in social media based on multi-task hierarchical neural networks with topic attention

Health issue identification in social media is to predict whether the writers have a disease based on their posts. Numerous posts and comments are shared on social media by users. Certain posts may reflect writers' health condition, which can be employed for health issue identification. Usually, the health issue identification problem is formulated as a classification task. In this paper, we propose novel multi-task hierarchical neural networks with topic attention for identifying health issue based on posts collected from the social media platforms. Specifically, the model incorporates the hierarchical relationship among the document, sentences, and words via bidirectional gated recurrent units (BiGRUs). The global topic information shared across posts is incorporated with the hidden states of BiGRUs to obtain the topic-enhanced attention weights for words. In addition, tasks of predicting whether the writers suffer from a disease (health issue identification) and predicting the specific domain of the posts (domain category classification) are learned jointly in multi-task mechanism. The proposed method is evaluated on two datasets: dementia issue dataset and depression issue dataset. The proposed approach achieves 98.03% and 88.28% F-1 score on two datasets, outperforming the state-of-the-art approach by 0.73% and 0.4% respectively. Further experimental analysis shows the effectiveness of incorporating both the multi-task learning framework and topic attention mechanism.
A Software Toolbox for Deploying Deep Learning Decision Support Systems with XAI Capabilities

We describe the software architecture of a toolbox of reusable components for the configuration of convolutional neural networks (CNNs) for classification and labeling problems. The toolbox architecture has been designed to maximize the reuse of established algorithms and to include domain experts in the development and evaluation process across different projects and challenges. In addition, we implemented easy-to-edit input formats and modules for XAI (eXplainable AI) through visual inspection capabilities. The toolbox is available for the research community to implement applied artificial intelligence projects.
Anonymous Multireceiver Identity-Based Encryption against Chosen-Ciphertext Attacks with Tight Reduction in the Standard Model

Multireceiver identity-based encryption is a cryptographic primitive, which allows a sender to encrypt a message for multiple receivers efficiently and securely. In some applications, the receivers may not want their identities to be revealed. Motivated by this issue, in 2010, Fan et al. first proposed the concept of anonymous multireceiver identity-based encryption (AMRIBE). Since then, lots of literature studies in this field have been proposed. After surveying the existing works, however, we found that most of them fail to achieve provable anonymity with tight reduction. A security proof with tight reduction means better quality of security and better efficiency of implementation. In this paper, we focus on solving the open problem in this field that is to achieve the ANON-IND-CCA security with tight reduction by giving an AMRIBE scheme. The proposed scheme is proven to be IND-MID-CCA and ANON-MID-CCA secure with tight reduction under a variant of the DBDH assumption. To the best of our knowledge, this is the first scheme proven with tight reducible full CCA security in the standard model.
MLMG: Multi-Local and Multi-Global Model Aggregation for Federated Learning

Federated learning has attracted much interest and attention as a solution to collaboratively learn a prediction model without sharing the training data of users. Existing federated learning approaches usually develop a single independent local model for each client to train their privacy-sensitive data, afterward adopt a single centralized global model to exchange the trained parameters of clients that participate in federated training. However, given the diverse characteristics of local data and the heterogeneity across participating clients, the conventional federated learning paradigm may not achieve uniformly good performance over all users. In this work, we propose a novel federated learning mechanism which suggests using a Multi-Local and Multi-Global (MLMG) model aggregation to train the non-IID user data with clustering methods. Then a Matching algorithm is introduced to derive the appropriate exchanges between local models and global models. The new federated learning mechanism helps separate the data and user with different characteristics, thus makes it easier to capture the heterogeneity of data distributions across the users. We choose the latest on-device neural network for anomaly detection to evaluate the proposal, and experimental results based on several benchmark datasets demonstrate better detection accuracy (up to 2.83% accuracy improvement) of the novel paradigm compared with a conventional federated learning approach.
Continual Learning Using Data Regeneration for Remote Sensing Scene Classification

When building a model with learning capability, the usual hypothesis is that the data of all the possible situations or tasks are available. Nevertheless, dealing with a massive number of tasks in a sequential manner necessitates preserving the previous tasks data and then retrain the model on it, which is infeasible. Another solution is to retrain the model only on the new task data, but, in this case, the model dramatically collapses when tested on the old tasks data due to the phenomenon known as catastrophic forgetting. To overcome this shortcoming, we propose a novel continual learning technique based on the learned tasks’ data auto-generation subnetworks. We sequentially train the proposed model on a set of classification tasks, where each task includes a certain number of remote sensing scenes or classes. The proposed neural network architecture encapsulates two trainable subnetworks. The first module adapts its weights by minimizing the discrimination error between the land-cover classes during the new task learning. In parallel, the second module learns how to reproduce data of the previous tasks by discovering the latent data structure of the new task dataset. Experiments are conducted on two scene datasets (Merced and Optimal31). The experimental results confirm the outperformance and robustness of the proposed model.
Information-Theoretic Segmentation by Inpainting Error Maximization

We study image segmentation from an information-theoretic perspective, proposing a novel adversarial method that performs unsupervised segmentation by partitioning images into maximally independent sets. More specifically, we group image pixels into foreground and background, with the goal of minimizing predictability of one set from the other. An easily computed loss drives a greedy search process to maximize inpainting error over these partitions. Our method does not involve training deep networks, is computationally cheap, class-agnostic, and even applicable in isolation to a single unlabeled image. Experiments demonstrate that it achieves a new state-of-the-art in unsupervised segmentation quality, while being substantially faster and more general than competing approaches. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Efficient Face Detection in the Fisheye Image Domain

Significant progress has been made for face detection from normal images in recent years; however, accurate and fast face detection from fisheye images remains a challenging issue because of serious fisheye distortion in the peripheral region of the image. To improve face detection accuracy, we propose a light-weight location-aware network to distinguish the peripheral region from the central region in the feature learning stage. To match the face detector, the shape and scale of the anchor (bounding box) is made location dependent. The overall face detection system performs directly in the fisheye image domain without rectification and calibration and hence is agnostic of the fisheye projection parameters. Experiments on Wider-360 and real-world fisheye images using a single CPU core indeed show that our method is superior to the state-of-the-art real-time face detector RFB Net.
Conventional displays of structures in data compared with interactive projection-based clustering (IPBC)

Abstract Clustering is an important task in knowledge discovery with the goal to identify structures of similar data points in a dataset. Here, the focus lies on methods that use a human-in-the-loop, i.e., incorporate user decisions into the clustering process through 2D and 3D displays of the structures in the data. Some of these interactive approaches fall into the category of visual analytics and emphasize the power of such displays to identify the structures interactively in various types of datasets or to verify the results of clustering algorithms. This work presents a new method called interactive projection-based clustering (IPBC). IPBC is an open-source and parameter-free method using a human-in-the-loop for an interactive 2.5D display and identification of structures in data based on the user’s choice of a dimensionality reduction method. The IPBC approach is systematically compared with accessible visual analytics methods for the display and identification of cluster structures using twelve clustering benchmark datasets and one additional natural dataset. Qualitative comparison of 2D, 2.5D and 3D displays of structures and empirical evaluation of the identified cluster structures show that IPBC outperforms comparable methods. Additionally, IPBC assists in identifying structures previously unknown to domain experts in an application.
Maneuvering Target Tracking using the Autoencoder-Interacting Multiple Model Filter

This paper considers the problem of tracking and predicting the state of a dynamic system with stochastic dynamics and multiple modes of operation. A well-known approach to this problem is the "interacting multiple model" (IMM) estimator, which uses knowledge of the different modes of operation to update a bank of Kalman Filters (each optimal for a given mode of operation). The IMM combines estimates according to the posterior probability of the different modes. Despite their popularity, IMMs are known to sometimes be slow to detect mode switching, however, which can result in large state estimation errors. This paper addresses this problem by developing an Autoencoder-Interacting Multiple Model (AEIMM) algorithm. The AEIMM effectively embeds an IMM within an autoencoder framework to create a hybrid approach using both deep learning and classical tracking frameworks. The motivation for this approach is that the neural network can perform nonlinear transformations on the measurements to help the IMM more quickly identify mode changes. The effectiveness of the AEIMM is demonstrated in a maneuvering target tracking scenario. Numerical results show that the AEIMM outperforms classical tracking techniques as well as hybrid techniques and a Long Short-Term Memory network in this scenario.
Performance Analysis of Encryption Techniques Based on Avalanche effect and Strict Avalanche Criterion

To obtain high efficiency in encryption techniques many factors plays vital role. Avalanche effect is one of the most important factor which is calculated as number of flipped bits by small change in key, or plaintext or initialization vector. This paper provides performance analysis of encryption techniques considering avalanche effect as a key role. One more property ciphers should exhibit is completeness property. The combination of avalanche effect and completeness property gives a new criterion termed as strict avalanche criterion (SAC).
On the Sheffer stroke operation in fuzzy logic

From the beginnings of fuzzy logic, the Sheffer stroke operation has been overlooked and the efforts of the researchers have been devoted to other logical connectives. In this paper, the Sheffer stroke operation is introduced in fuzzy logic generalizing the classical operation when the truth values are restricted to {0,1}2. Similar to what happens in Boolean logic, the fuzzy Sheffer stroke is functionally complete and it can be used to generate any other fuzzy logical connective by combinations of itself. Two construction methods are presented and the close connection of this operation with a pair of fuzzy conjunction and negation is analysed.
Neural Text Classification and Stacked Heterogeneous Embeddings for Named Entity Recognition in SMM4H 2021

This paper presents our findings from participating in the SMM4H Shared Task 2021.We addressed Named Entity Recognition (NER) and Text Classification.To address NER we explored BiLSTM-CRF with Stacked Heterogeneous Embeddings and linguistic features.We investigated various machine learning algorithms (logistic regression, Support Vector Machine (SVM) and Neural Networks) to address text classification.Our proposed approaches can be generalized to different languages and we have shown its effectiveness for English and Spanish.Our text classification submissions (team:MIC-NLP) have achieved competitive performance with F1-score of 0.46 and 0.90 on ADE Classification (Task 1a) and Profession Classification (Task 7a) respectively.In the case of NER, our submissions scored F1score of 0.50 and 0.82 on ADE Span Detection (Task 1b) and Profession Span detection (Task 7b) respectively.
Multi-Pretext Attention Network For Few-Shot Learning With Self-Supervision

Few-shot learning is an interesting and challenging study, which enables machines to learn from few samples like humans. Existing studies rarely exploit auxiliary information from large amount of unlabeled data. Self-supervised learning is emerged as an efficient method to utilize unlabeled data. Existing self-supervised learning methods always rely on the combination of geometric transformations for the single sample by augmentation, while seriously neglect the endogenous correlation information among different samples that is the same important for the task. In this work, we propose a Graph-driven Clustering (GC), a novel augmentation-free method for self-supervised learning, which does not rely on any auxiliary sample and utilizes the endogenous correlation information among input samples. Besides, we propose Multi-pretext Attention Network (MAN), which exploits a specific attention mechanism to combine the traditional augmentation-relied methods and our GC, adaptively learning their optimized weights to improve the performance and enabling the feature extractor to obtain more universal representations. We evaluate our MAN extensively on miniImageNet and tieredImageNet datasets and the results demonstrate that the proposed method outperforms the state-of-the-art (SOTA) relevant methods. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>