Reviewerly/exHarmony huggingface dataset:
Adaptive Multi-Modality Residual Network for Compression Distorted Multi-View Depth Video Enhancement

Compression distorted multi-view video plus depth (MVD) should be enhanced at the receiver side without the original signals, especially the depth maps because they describe the positioning information in 3D space and they are important for subsequent virtual view synthesis.However, challenge arises from how to exploit the contribution from multi-modality priors from neighboring viewpoints, and how to handle the gradient vanishing when textureless depth maps are involved.In this paper, we propose a multi-modality residual network to enhance the quality of compressed multi-view depth video.Taking advantage from high correlation among different viewpoints, depth maps from adjacent views are exploited as guidance for the enhancement of depth video in target view.Color frames in target view are also involved to offer the information object contours, obtaining multi-modality guidance.The proposed network is organized a deep residual network to well eliminate distortion and restore details.Because above multi-modality guidance have different correlations with target depth video and not all information can contribute to the enhancement, an adaptive skip structure is designed to further exploit the contribution from different priors appropriately.Experimental results show that our scheme outperforms other benchmarks and achieves an average 1.935 dB and 0.0227 gains on PSNR and SSIM over all test sequences, respectively.All results on objective, subjective and 3D reconstruction suggest that our method is able to provide superiority performance in practical applications.
Neural Contract Element Extraction Revisited

We investigate contract element extraction. We show that LSTM-based encoders perform better than dilated CNNs, Transformers, and BERT in this task. We also find that domain-specific WORD2VEC embeddings outperform generic pre-trained GLOVE embeddings. Morpho-syntactic features in the form of POS tag and token shape embeddings, as well as context-aware ELMO embeddings do not improve performance. Several of these observations contradict choices or findings of previous work on contract element extraction and generic sequence labeling tasks, indicating that contract element extraction requires careful task-specific choices. Analyzing the results of (i) plain TRANSFORMER-based and (ii) BERT-based models, we find that in the examined task, where the entities are highly context-sensitive, the lack of recurrency in TRANSFORMERs greatly affects their performance.
The Algorithm of Voiceprint Recognition Model based DNN-RELIANCE

The traditional voiceprint recognition model (Gaussian hybrid model, hidden Markov model) is very dependent on the scale of speaker speech. Higher recognition performance often requires a large number of trained speaker speech to achieve, and has such shortcomings as environmental noise sensitivity and long training time, besides, it is difficult in convergence, the application of voiceprint recognition in practice is limited accordingly. The deep neural network has the ability of autonomous learning, and it can extract the deep speaker speech features that are not sensitive to environmental noise according to the target, and the extracted speaker speech features can be well classified and identified with the powerful pattern classification ability. So the deep neural network (DNN) is used as the voiceprint recognition model in this paper. However, the experiment shows that the DNN-based voiceprint recognition system still has a low accuracy rate of rejection of counterfeiters. For this reason, the trust degree and label distance are introduced, and the two-order judgment structure based on DNN-RLIANCE algorithm is proposed. Experiments show that the algorithm has short training and recognition time but high recognition accuracy, especially the system's correct rejection rate for impostors is improved.
Link-Sign Prediction in Signed Directed Networks from No Link Perspective

Predicting future sign of connections in a network is an important task for online systems such as social networks, e-commerce and other services. Several research studies have been presented since the early of this century to predict either the existence of a link in the future or the property of the link. In this study we present a new approach that combine both families by using machine learning techniques. Instead of focusing on the established links, we follow a new research approach that focusing on no-link relationship. We aim to understand the move between two states of no-link and link. We evaluate our methods in popular real-world signed networks datasets. We believe that the new approach by understanding the no-link relation has a lot of potential improvement in the future.
Australia’s COVIDSafe Experiment, Phase III: Legislation for Trust in Contact Tracing

The joint Australian governments’ coronavirus contact tracing app, marketed as ‘COVIDSafe’, was released on 26 April 2020 for public download by the federal government, together with an emergency Determination under the Biosecurity Act to govern its operation. In a brief federal Parliamentary sitting from 12-14 May, the Parliament enacted the Privacy Amendment (Public Health Contact Information) Act 2020 (Cth) (‘the COVIDSafe Act’) on 14 May 2020.The COVIDSafe app is more toward the centralised than decentralised end of the spectrum in the design of such apps, but its use is voluntary, and the government claims that will continue to be the case.The Act aims to create sufficient public confidence in the privacy protections surrounding the COVIDSafe app to result in downloads and use by a sufficient percentage of the Australian mobile-phone-owning population, for it to have a significant effect on the tracing of persons infected with the COVID19 virus. In the first two and a half weeks since its launch over 5.5 million Australian’s have downloaded the app, about 25% of those possible, and 20% of the population. Public trust must become more widespread, before success in uptake is likely to follow.Now that the Bill has been enacted, the purpose of this article is to provide a reasonably comprehensive explanation of the provisions of the COVIDSafe Act and important aspects of their Australian context. Significant deficiencies in both the extent of transparency around the introduction of the COVIDSafe app, and the privacy-protective provisions of the Act, are identified and improvements suggested. These extensive suggestions are made because debate over the app and the Act is not over, and opportunities to obtain improvements may arise, particularly through the operation of the two Parliamentary committees examining Australia’s COVID-19 response, and the human rights implications of the Act.Many other countries are developing contact tracing apps. Australia’s experiment is further advanced than most that are attempting to build a system based on voluntary uptake, protected by legislation. The results of its experiment will be of interest to many.
Fast 3D measurement based on improved optical flow for dynamic objects

High resolution, real-time three-dimensional (3D) measurement plays an important role in many fields. In this paper, a multi-directional dynamic real-time phase measurement profilometry based on improved optical flow is proposed. In a five-step phase shifting dynamic measurement, pixel matching is needed to make the pixels one-to-one corresponding in five patterns. However, in the frequently-used pixel matching method at present, it is necessary to calculate the correlation and traverse the whole deformed pattern for the motion information of the measured object. The huge amount of computation caused by correlation computation takes up most of the time in the process of the entire 3D reconstruction, so it can not meet the requirement of real-time dynamic measurement. In order to solve the problem, the improved optical flow algorithm is introduced to replace correlation calculation in pixel matching. In one measurement, five captured patterns need to be dealt with, and the optical flow between each two adjacent frames is calculated. Then four two-dimensional vector matrices can be obtained. The vector matrices contain the complete motion information of the measured object. Experiments and simulations prove that this method can improve the efficiency of pixel matching by 42 times and 3D reconstruction by 32 times on the premise of ensuring the accuracy.
Research data management literacy amongst lecturers at Strathmore University, Kenya

Purpose The purpose of this study was to analyse the skills required by lecturers to be able to support research data management effectively; assess the research data management literacy levels amongst lecturers at Strathmore University; and suggest how research data management capacity can be strengthened to mitigate the knowledge gaps identified. Design/methodology/approach This study was conducted as a mixed methods research. Explanatory sequential mixed methods approach was used to collect, analyse and interpret quantitative and qualitative data from lecturers at Strathmore University in Nairobi, Kenya. Quantitative data was collected using questionnaires while qualitative data was collected through focus group discussions. Quantitative data was analysed using SPSS while qualitative data was analysed thematically. Findings The findings of this study indicate varied levels of research data management literacy amongst lecturers at Strathmore University. Lecturers understand the need of having literacy skills in managing research data. They also participate in data creation, collection, processing, validation, dissemination, sharing and archiving. This is a clear indication of good research data management. However, the study also revealed gaps in research data management skills amongst the lecturers in areas such as sharing of research data on open access journals, data legislation and securing research data. Research limitations/implications The study has been conducted in one university in Kenya. However, the findings have been contextualised in the global landscape through suitable references. Practical implications The findings of this study may be used to attract the attention of lecturers and librarians to research data management. The findings may also be used to develop institutional policies on research data management at Strathmore University and beyond. The suggested ways of research data capacity strengthening can be adopted or adapted by other universities to enhance research data management. Originality/value This is an original study.
Applications of Artificial Intelligence to Cryptography

This paper considers some recent advances in the field of Cryptography using Artificial Intelligence (AI) It specifically considers the applications of Machine Learning (ML) and Evolutionary Computing (EC) concepts used to generate ciphers. A short overview is given on Artificial Neural Networks (ANNs) and the principles of Deep Learning (DL) using Deep ANNs. In this context, the paper considers: (i) the implementation of EC and ANNs to generate unique and unclonable ciphers; (ii) ML strategies for detecting the genuine randomness (or otherwise) of binary streams for applications in Cryptanalysis. The paper aims to provide an overview on how AI can be applied for encrypting data and undertaking cryptanalysis of such data and other encrypted data classes in order to assess the cryptographic strength of an encryption algorithm. For example, to detect patterns of intercepted data streams that are signatures of encrypted data. An application is presented which includes authentication of high-value documents such as bank notes, using smartphones. Using an antenna of a smartphone to read (in the near field) an embedded flexible integrate circuit with a non-programmable coprocessor, ultra-strong encrypted information can be used on-line for validation.
On Second-Order Group Influence Functions for Black-Box Predictions

With the rapid adoption of machine learning systems in sensitive applications, there is an increasing need to make black-box models explainable. Often we want to identify an influential group of training samples in a particular test prediction for a given machine learning model. Existing influence functions tackle this problem by using first-order approximations of the effect of removing a sample from the training set on model parameters. To compute the influence of a group of training samples (rather than an individual point) in model predictions, the change in optimal model parameters after removing that group from the training set can be large. Thus, in such cases, the first-order approximation can be loose. In this paper, we address this issue and propose second-order influence functions for identifying influential groups in test-time predictions. For linear models, across different sizes and types of groups, we show that using the proposed second-order influence function improves the correlation between the computed influence values and the ground truth ones. We also show that second-order influence functions could be used with optimization techniques to improve the selection of the most influential group for a test-sample.
Multi-Resolution Fusion Convolutional Neural Networks for Intrapulse Modulation LPI Radar Waveforms Recognition

Low Probability of Intercept (LPI) radar waveform has complex and diverse modulation schemes, which cannot be easily identified by the traditional methods. The research on intrapulse modulation LPI radar waveform recognition has received increasing attention. In this paper, we propose an automatic LPI radar waveform recognition algorithm that uses a multi-resolution fusion convolutional neural network. First, signals embedded within the noise are processed using Choi-William Distribution (CWD) to obtain time-frequency feature images. Then, the images are resized by interpolation and sent to the proposed network for training and identification. The network takes a dual-channel CNN structure to obtain features at different resolutions and makes features fusion by using the concatenation and Inception module. Extensive simulations are carried out on twelve types of LPI radar waveforms, including BPSK, Costas, Frank, LFM, P1~P4, and T1~T4, corrupted with additive white Gaussian noise of SNR from 10dB to -8dB. The results show that the overall recognition rate of the proposed algorithm reaches 95.1% when the SNR is -6dB. We also try various sample selection methods related to the recognition task of the system. The conclusion is that reducing the samples with SNR above 2dB or below -8dB can effectively improve the training speed of the network while maintaining recognition accuracy.
When Differential Privacy Meets Graph Neural Networks.

Graph Neural Networks have demonstrated superior performance in learning graph representations for several subsequent downstream inference tasks. However, learning over graph data types can raise privacy concerns when nodes represent people or human-related variables that involve personal information about individuals. Previous works have presented various techniques for privacy-preserving deep learning over non-relational data, such as image, audio, video, and text, but there is less work addressing the privacy issues involved in applying deep learning algorithms on graphs. As a result and for the first time, in this paper, we develop a privacy-preserving learning algorithm with formal privacy guarantees for Graph Convolutional Networks (GCNs) based on Local Differential Privacy (LDP) to tackle the problem of node-level privacy, where graph nodes have potentially sensitive features that need to be kept private, but they could be beneficial for learning rich node representations in a centralized learning setting. Specifically, we propose an LDP algorithm in which a central server can communicate with graph nodes to privately collect their data and estimate the graph convolution layer of a GCN. We then analyze the theoretical characteristics of the method and compare it with state-of-the-art mechanisms. Experimental results over real-world graph datasets demonstrate the effectiveness of the proposed method for both privacy-preserving node classification and link prediction tasks and verify our theoretical findings.
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions

Off-policy evaluation in reinforcement learning offers the chance of using observational data to improve future outcomes in domains such as healthcare and education, but safe deployment in high stakes settings requires ways of assessing its validity. Traditional measures such as confidence intervals may be insufficient due to noise, limited data and confounding. In this paper we develop a method that could serve as a hybrid human-AI system, to enable human experts to analyze the validity of policy evaluation estimates. This is accomplished by highlighting observations in the data whose removal will have a large effect on the OPE estimate, and formulating a set of rules for choosing which ones to present to domain experts for validation. We develop methods to compute exactly the influence functions for fitted Q-evaluation with two different function classes: kernel-based and linear least squares, as well as importance sampling methods. Experiments on medical simulations and real-world intensive care unit data demonstrate that our method can be used to identify limitations in the evaluation process and make evaluation more robust.
Scaling Genomics Data Processing with Memory-Driven Computing to Accelerate Computational Biology

Abstract Research is increasingly becoming data-driven, and natural sciences are not an exception. In both biology and medicine, we are observing an exponential growth of structured data collections from experiments and population studies, enabling us to gain novel insights that would otherwise not be possible. However, these growing data sets pose a challenge for existing compute infrastructures since data is outgrowing limits within compute. In this work, we present the application of a novel approach, Memory-Driven Computing (MDC), in the life sciences. MDC proposes a data-centric approach that has been designed for growing data sizes and provides a composable infrastructure for changing workloads. In particular, we show how a typical pipeline for genomics data processing can be accelerated, and application modifications required to exploit this novel architecture. Furthermore, we demonstrate how the isolated evaluation of individual tasks misses significant overheads of typical pipelines in genomics data processing.
A Multi-classifier Framework for Detecting Spam and Fake Spam Messages in Twitter

Social media plays vital role among the user communities for social gathering, entertainment, communication, sharing knowledge so on. Twitter is one such network to connect millions of users to share information. Nowadays, there are humpteen numbers of users using social media for social engagements. Due to the fact that wide publicity of individuals and products get viral in social media, everyone wish to use social media as a platform to promote their product. Furthermore, large number of people relies on social media contents to take decisions. Twitter is one of the social media platforms to post the media contents by the user. Spammers are illegal users intrude the twitter account and send the duplicate messages to promote advertisement, phishing, scam and personal blogs etc. In this paper, a novel spam detection mechanism is introduced to detect the suspicious users on twitter. The system has been designed such a way that it initially set with semi-supervised at the tweet level and update into supervised level for learning the input tweets to detect the spammers. The proposed system will also identify the type of spammers and will also remove duplicate tweets. We have applied with multi-classifier algorithms like naïve Bayesian, K-Nearest neighbor and Random forest into twitter data set and the performance is compared. The experimental result shows very promising results.
Hololens Used for Precise Position Tracking of the Third Party Devices - Autonomous Vehicles

A completely new area of HoloLens usage is proposed. The Hololens is an augmented reality device, which provides the high precision location information. Such an information is normally used to accurately position holograms within the real space with respect to the viewer (user of HoloLens). The information is precise enough to use it for reporting the position for the purpose of autonomous driving. Several experiments have been executed in vast areas (20 m x 40 m) in order to find out the potential error coming from vibrations or other effects when moving the HoloLens. The results show that the technology can be used for spaces, which are previously known by the system - pre-scan of the space is needed. The big advantage of the system is its readiness for indoor positioning applications with no additional infrastructure needed, simultaneous localization and mapping, complex space mapping and reached precision. The disadvantage is mainly the costs.
Risk and Advantages of Federated Learning for Health Care Data Collaboration

This paper explores the problem of data collaboration in health care, which is the one of the critical infrastructure sectors designated by the Department of Home Security. Limitations to data sharing in health care obstruct the development of a new generation of medical technology powered by artificial intelligence (AI). Collaborative machine learning helps to overcome these limitations through training models on distributed data sets without data sharing. Among other approaches to collaborative machine learning, federated learning in recent years has demonstrated multiple advantages. However, it had been developed and tested in a highly distributed data environment, which is different from the typical cases of health care data collaboration. The objective of this paper is to validate the known advantages of federated learning and to assess possible risks in a small multiparty setting. The experiments show that federated learning can be successfully applied in a multiparty collaboration setting. However, with a small number of parties, it becomes easier to overfit to each local data so that the averaging steps have to occur more frequently. In addition, for the first time, the risks of a membership inference attack were assessed for different methods of collaborative machine learning.
Heimdall, a Computer Program for Electronic Health Records Data Visualization.

Electronic health records (EHR) comprehend structured and unstructured data, that are usually time dependent, enabling the use of timelines. However, it is often difficult to display all data without inducing information overload. In both clinical usual care and medical research, users should be able to quickly find relevant information, with minimal cognitive overhead. Our goal was to devise simple visualization techniques for handling medical data in both contexts.An abstraction layer for structured EHR data was devised after an informal literature review and discussions between authors. The "Heimdall" prototype was developed. Two experts evaluated the tool by answering 5 questions on 24 clinical cases.Temporal data was abstracted in three simple types: events, states and measures, with appropriate visual representations for each type. Heimdall can load and display complex heterogeneous structured temporal data in a straightforward way. The main view can display events, states and measures along a shared timeline. Users can summarize data using temporal, hierarchical compression and filters. Default and custom views can be used to work in problem- oriented ways. The evaluation found conclusive results.The "Heimdall" prototype provides a comprehensive and efficient graphical interface for EHR data visualization. It is open source, can be used with an R package, and is available at https://koromix.dev/files/R.