Reviewerly/exHarmony huggingface dataset:
A Survey on Differentially Private Machine Learning [Review Article]

Recent years have witnessed remarkable successes of machine learning in various applications. However, machine learning models suffer from a potential risk of leaking private information contained in training data, which have attracted increasing research attention. As one of the mainstream privacy- preserving techniques, differential privacy provides a promising way to prevent the leaking of individual-level privacy in training data while preserving the quality of training data for model building. This work provides a comprehensive survey on the existing works that incorporate differential privacy with machine learning, so- called differentially private machine learning and categorizes them into two broad categories as per different differential privacy mechanisms: the Laplace/ Gaussian/exponential mechanism and the output/objective perturbation mechanism. In the former, a calibrated amount of noise is added to the non-private model and in the latter, the output or the objective function is perturbed by random noise. Particularly, the survey covers the techniques of differentially private deep learning to alleviate the recent concerns about the privacy of big data contributors. In addition, the research challenges in terms of model utility, privacy level and applications are discussed. To tackle these challenges, several potential future research directions for differentially private machine learning are pointed out.
Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor-Infiltrating Lymphocytes in Invasive Breast Cancer

Quantitative assessment of spatial relations between tumor and tumor-infiltrating lymphocytes (TIL) is increasingly important in both basic science and clinical aspects of breast cancer research. We have developed and evaluated convolutional neural network analysis pipelines to generate combined maps of cancer regions and TILs in routine diagnostic breast cancer whole slide tissue images. The combined maps provide insight about the structural patterns and spatial distribution of lymphocytic infiltrates and facilitate improved quantification of TILs. Both tumor and TIL analyses were evaluated by using three convolutional neural network networks (34-layer ResNet, 16-layer VGG, and Inception v4); the results compared favorably with those obtained by using the best published methods. We have produced open-source tools and a public data set consisting of tumor/TIL maps for 1090 invasive breast cancer images from The Cancer Genome Atlas. The maps can be downloaded for further downstream analyses.
Experimental free-space quantum secure direct communication and its security analysis

We report an experimental implementation of free-space quantum secure direct communication based on single photons. The quantum communication scheme uses phase encoding, and the asymmetric Mach-Zehnder interferometer is optimized so as to automatically compensate phase drift of the photons during their transitions over the free-space medium. An information transmission rate of 500 bps over a 10-meter free space with a mean quantum bit error rate of 0.49%$\pm$0.27% is achieved. The security is analyzed under the scenario that Eve performs collective attack and photon number splitting collective attack. Our results show that quantum secure direct communication is feasible in free space.
Privacy-Aware Blockchain Innovation for 6G: Challenges and Opportunities

6G wireless networks improve on 5G by further increasing reliability, speeding up the networks and increasing the available bandwidth. These evolutionary enhancements, together with a number of revolutionary improvements such as high-precision 3D localization, ultra-high reliability and extreme mobility, introduce a new generation of 6G-native applications. Such application can be based on, for example, distributed, ubiquitous Artificial Intelligence (AI) and ultra-reliable, low-latency Internet of Things (IoT). Along with the enhanced connectivity and novel applications, privacy and security of the networks and the applications must be ensured. Distributed ledger technologies such as blockchain provide one solution for application security and privacy, but introduce their own set of security and privacy risks. In this work, we discuss the opportunities and challenges related to blockchain usage in 6G, and map out possible directions for overtaking the challenges.
FAIR data sharing: The roles of common data elements and harmonization

The value of robust and responsible data sharing in clinical research and healthcare is recognized by patients, patient advocacy groups, researchers, journal editors, and the healthcare industry globally. Privacy and security concerns acknowledged, the act of exchanging data (interoperability) along with its meaning (semantic interoperability) across studies and between partners has been difficult, if not elusive. For shared data to retain its value, a recommendation has been made to follow the Findable, Accessible, Interoperable, Reusable (FAIR) principles. Without applying appropriate data exchange standards with domain-relevant content standards and accessible rich metadata that uses applicable terminologies, interoperability is burdened by the need for transformation and/or mapping. These obstacles to interoperability limit the findability, accessibility and reusability of data, thus diminishing its value and making it impossible to adhere to FAIR principles. One effort to standardize data collection has been through common data elements (CDEs). CDEs are data collection units comprising one or more questions together with a set of valid values. Some CDEs contain standardized terminology concepts that define the meaning of the data, and others include links to unique terminology concept identifiers and unique identifiers for each CDE; however, usually CDEs are defined for specific projects or collaborations and lack traceable or machine readable semantics. While the name implies that these are 'common', this has not necessarily been a requirement, and many CDEs have not been commonly used. The National Institutes of Health (NIH) CDEs are, in fact, a conglomerate of CDEs developed in silos by various NIH institutes. Therefore, CDEs have not brought the anticipated benefit to the industry through widescale interoperability, nor is there widespread reuse of CDEs. Certain institutes in the NIH recommend, albeit do not enforce, institute-specific preferred CDEs; however, at the NIH level a preponderance of choice and a lack of any overarching harmonization of CDEs or consistency in linking them to controlled terminology or common identifiers create confusion for researchers in their efforts to identify the best CDEs for their protocol. The problem of comparing data among studies is exacerbated when researchers select different CDEs for the same variable or data collection field. This manuscript explores reasons for the disappointingly low adoption of CDEs and the inability of CDEs or other clinical research standards to broadly solve the interoperability and data sharing problems. Recommendations are offered for rectifying this situation to enable responsible data sharing that will help in adherence to FAIR principles and the realization of Learning Health Systems for the sake of all of us as patients.
word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings of Structured Data

Vector representations of graphs and relational structures, whether hand-crafted feature vectors or learned representations, enable us to apply standard data analysis and machine learning techniques to the structures. A wide range of methods for generating such embeddings have been studied in the machine learning and knowledge representation literature. However, vector embeddings have received relatively little attention from a theoretical point of view. Starting with a survey of embedding techniques that have been used in practice, in this paper we propose two theoretical approaches that we see as central for understanding the foundations of vector embeddings. We draw connections between the various approaches and suggest directions for future research.
Detection of Hate Speech Text in Hindi-English Code-mixed Data

Social media sites like Twitter, Facebook, being user-friendly and a free source, provide opportunities to people to air their voice. People, irrespective of the age group, use these sites to share every moment of their life making these sites flooded with data. Apart from these commendable features, these sites have down side as well. Due to lack of restrictions set by these sites for its users to express their views as they like, anybody can make adverse and unrealistic comments in abusive language against anybody with an ulterior motive to tarnish one’s image and status in the society. So it became a huge responsibility for the Government and these sites to identify this hate content before it disseminates to mass. Automatic hate speech detection faces quite a lot of challenges due to the non-standard variations in spelling and grammar. Especially for a country like India with huge multilingual and bilingual population, this hate content would be in code-mixed form which makes the task demanding. So our paper projects a machine learning model to detect hate speech in Hindi-English code-mixed social media text. The methodology makes use of Facebook’s pre-trained word embedding library, fastText to represent 10000 data samples collected from different sources as hate and non-hate. The performance of the proposed methodology is compared with word2vec and doc2vec features and it is observed that fastText features gave better feature representation with Support Vector Machine (SVM)-Radial Basis Funcrion (RBF) classifier. The paper also provides an insight to the researchers working in the field of code-mixed data that character level features provide best result for code-mixed data.
An Efficient PointLSTM for Point Clouds Based Gesture Recognition

Point clouds contain rich spatial information, which provides complementary cues for gesture recognition. In this paper, we formulate gesture recognition as an irregular sequence recognition problem and aim to capture long-term spatial correlations across point cloud sequences. A novel and effective PointLSTM is proposed to propagate information from past to future while preserving the spatial structure. The proposed PointLSTM combines state information from neighboring points in the past with current features to update the current states by a weight-shared LSTM layer. This method can be integrated into many other sequence learning approaches. In the task of gesture recognition, the proposed PointLSTM achieves state-of-the-art results on two challenging datasets (NVGesture and SHREC'17) and outperforms previous skeleton-based methods. To show its advantages in generalization, we evaluate our method on MSR Action3D dataset, and it produces competitive results with previous skeleton-based methods.
Learning the Redundancy-Free Features for Generalized Zero-Shot Object Recognition

Zero-shot object recognition or zero-shot learning aims to transfer the object recognition ability among the semantically related categories, such as fine-grained animal or bird species. However, the images of different fine-grained objects tend to merely exhibit subtle differences in appearance, which will severely deteriorate zero-shot object recognition. To reduce the superfluous information in the fine-grained objects, in this paper, we propose to learn the redundancy-free features for generalized zero-shot learning. We achieve our motivation by projecting the original visual features into a new (redundancy-free) feature space and then restricting the statistical dependence between these two feature spaces. Furthermore, we require the projected features to keep and even strengthen the category relationship in the redundancy-free feature space. In this way, we can remove the redundant information from the visual features without losing the discriminative information. We extensively evaluate the performance on four benchmark datasets. The results show that our redundancy-free feature based generalized zero-shot learning (RFF-GZSL) approach can outperform the state-of-the-arts often by a large margin.
Erasing Integrated Learning: A Simple Yet Effective Approach for Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) aims to localize object with only weak supervision like image-level labels. However, a long-standing problem for available techniques based on the classification network is that they often result in highlighting the most discriminative parts rather than the entire extent of object. Nevertheless, trying to explore the integral extent of the object could degrade the performance of image classification on the contrary. To remedy this, we propose a simple yet powerful approach by introducing a novel adversarial erasing technique, erasing integrated learning (EIL). By integrating discriminative region mining and adversarial erasing in a single forward-backward propagation in a vanilla CNN, the proposed EIL explores the high response class-specific area and the less discriminative region simultaneously, thus could maintain high performance in classification and jointly discover the full extent of the object. Furthermore, we apply multiple EIL (MEIL) modules at different levels of the network in a sequential manner, which for the first time integrates semantic features of multiple levels and multiple scales through adversarial erasing learning. In particular, the proposed EIL and advanced MEIL both achieve a new state-of-the-art performance in CUB-200-2011 and ILSVRC 2016 benchmark, making significant improvement in localization while advancing high performance in image classification.
Expanding Participation to Design with More-Than-Human Concerns

Participatory Design's focus on people comes from a social democratic vision. However, as climate and existential crises press us to consider wellbeing beyond humans alone, we ask what a pluriversal design agenda might include and what could be articulated as ‘participatory’? Necessarily, this inquiry has limits, as participation usually implies human voice, rights, representation and structures of decision-making. This paper commits to these concerns while asking ethical, political and onto-epistemological questions regarding how worlds and futures are shaped when more-than-human entities – plants, animals, rocks, rivers and spirits – participate in our becoming? We offer a meeting of feminist techno-science with practices and philosophies from Japan and beyond to offer thought experiments in engaging with difference and plurality. And we give several examples of practice situated at ontological boundaries to offer some novel thoughts on ‘participation otherwise’, always-participating-with-many and the futures this could usher in.
Enhanced 3DTV Regularization and Its Applications on HSI Denoising and Compressed Sensing

The total variation (TV) is a powerful regularization term encoding the local smoothness prior structure underlying images. By combining the TV regularization term with low rank prior, the 3D total variation (3DTV) regularizer has achieved advanced performance in general hyperspectral image (HSI) processing tasks. Intrinsically, 3DTV assumes i.i.d. sparsity structures on all bands of the gradient maps calculated along the spectrum and space of an HSI. This, however, largely deviates from the real-world cases, where the gradient maps generally have different while correlated gradient map structures across all bands. To alleviate this issue, we propose an enhanced 3DTV (E-3DTV) regularization term beyond the conventional. Instead of imposing sparsity on gradient maps themselves, the new term calculates sparsity on the subspace bases on gradient maps along all bands of an HSI, which naturally encodes the correlation and difference among all these bands, and thus more faithfully reflects the insightful configurations of an HSI. The E-3DTV term can easily replace the conventional 3DTV term and be embedded into an HSI processing model to ameliorate its performance. We made such attempts on two typical related tasks: HSI denoising and compressed sensing. The superiority of our proposed method is substantiated by extensive experiments on synthetic and real HSI data, visually and quantitatively on both tasks, as compared with current state-of-the-arts. The code of our algorithm is released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/andrew-pengjj/Enhanced-3DTV.git</uri> .
A Novel Outlier-Robust Kalman Filtering Framework Based on Statistical Similarity Measure

In this article, a statistical similarity measure is introduced to quantify the similarity between two random vectors. The measure is, then, employed to develop a novel outlier-robust Kalman filtering framework. The approximation errors and the stability of the proposed filter are analyzed and discussed. To implement the filter, a fixed-point iterative algorithm and a separate iterative algorithm are given, and their local convergent conditions are also provided, and their comparisons have been made. In addition, selection of the similarity function is considered, and four exemplary similarity functions are established, from which the relations between our new method and existing outlier-robust Kalman filters are revealed. Simulation examples are used to illustrate the effectiveness and potential of the new filtering scheme.
Blockchain as a Driver for Smart City Development: Application Fields and a Comprehensive Research Agenda

The term “Smart City” denotes a comprehensive concept to alleviate pending problems of modern urban areas which have developed into an important work field for practitioners and scholars alike. However, the question remains as to how cities can become “smart”. The application of information technology is generally considered a key driver in the “smartization” of cities. Detailed frameworks and procedures are therefore needed to guide, operationalize, and measure the implementation process as well as the impact of the respective technologies. In this paper, we discuss blockchain technology, a novel driver of technological transformation that comprises a multitude of underlying technologies and protocols, and its potential impact on smart cities. We specifically address the question of how blockchain technology may benefit the development of urban areas. Based on a comprehensive literature review, we present a framework and research propositions. We identify nine application fields of blockchain technology in the smartization of cities: (1) healthcare, (2) logistics and supply chains, (3) mobility, (4) energy, (5) administration and services, (6) e-voting, (7) factory, (8) home and (9) education. We discuss current developments in these fields, illustrate how they are affected by blockchain technology and derive propositions to guide future research endeavors.
RGBD Salient Object Detection via Disentangled Cross-Modal Fusion

Depth is beneficial for salient object detection (SOD) for its additional saliency cues. Existing RGBD SOD methods focus on tailoring complicated cross-modal fusion topologies, which although achieve encouraging performance, are with a high risk of over-fitting and ambiguous in studying cross-modal complementarity. Different from these conventional approaches combining cross-modal features entirely without differentiating, we concentrate our attention on decoupling the diverse cross-modal complements to simplify the fusion process and enhance the fusion sufficiency. We argue that if cross-modal heterogeneous representations can be disentangled explicitly, the cross-modal fusion process can hold less uncertainty, while enjoying better adaptability. To this end, we design a disentangled cross-modal fusion network to expose structural and content representations from both modalities by cross-modal reconstruction. For different scenes, the disentangled representations allow the fusion module to easily identify, and incorporate desired complements for informative multi-modal fusion. Extensive experiments show the effectiveness of our designs and a large outperformance over state-of-the-art methods.
Unfolding quantum computer readout noise

Abstract In the current era of noisy intermediate-scale quantum computers, noisy qubits can result in biased results for early quantum algorithm applications. This is a significant challenge for interpreting results from quantum computer simulations for quantum chemistry, nuclear physics, high energy physics (HEP), and other emerging scientific applications. An important class of qubit errors are readout errors. The most basic method to correct readout errors is matrix inversion, using a response matrix built from simple operations to probe the rate of transitions from known initial quantum states to readout outcomes. One challenge with inverting matrices with large off-diagonal components is that the results are sensitive to statistical fluctuations. This challenge is familiar to HEP, where prior-independent regularized matrix inversion techniques (“unfolding”) have been developed for years to correct for acceptance and detector effects, when performing differential cross section measurements. We study one such method, known as iterative Bayesian unfolding, as a potential tool for correcting readout errors from universal gate-based quantum computers. This method is shown to avoid pathologies from commonly used matrix inversion and least squares methods.
Ascon v1.2: Lightweight Authenticated Encryption and Hashing

Abstract Authenticated encryption satisfies the basic need for authenticity and confidentiality in our information infrastructure. In this paper, we provide the specification of Ascon -128 and Ascon -128a. Both authenticated encryption algorithms provide efficient authenticated encryption on resource-constrained devices and on high-end CPUs. Furthermore, they have been selected as the “primary choice” for lightweight authenticated encryption in the final portfolio of the CAESAR competition. In addition, we specify the hash function Ascon-Hash , and the extendable output function Ascon-Xof . Moreover, we complement the specification by providing a detailed overview of existing cryptanalysis and implementation results.
The Path Planning of Mobile Robot by Neural Networks and Hierarchical Reinforcement Learning

Existing mobile robots cannot complete some functions. To solve these problems, which include autonomous learning in path planning, the slow convergence of path planning, and planned paths that are not smooth, it is possible to utilize neural networks to enable to the robot to perceive the environment and perform feature extraction, which enables them to have a fitness of environment to state action function. By mapping the current state of these actions through Hierarchical Reinforcement Learning (HRL), the needs of mobile robots are met. It is possible to construct a path planning model for mobile robots based on neural networks and HRL. In this article, the proposed algorithm is compared with different algorithms in path planning. It underwent a performance evaluation to obtain an optimal learning algorithm system. The optimal algorithm system was tested in different environments and scenarios to obtain optimal learning conditions, thereby verifying the effectiveness of the proposed algorithm. Deep Deterministic Policy Gradient (DDPG), a path planning algorithm for mobile robots based on neural networks and hierarchical reinforcement learning, performed better in all aspects than other algorithms. Specifically, when compared with Double Deep Q-Learning (DDQN), DDPG has a shorter path planning time and a reduced number of path steps. When introducing an influence value, this algorithm shortens the convergence time by 91% compared with the Q-learning algorithm and improves the smoothness of the planned path by 79%. The algorithm has a good generalization effect in different scenarios. These results have significance for research on guiding, the precise positioning, and path planning of mobile robots.