Extended differential privacy, a generalization of standard differential privacy (DP) using a general metric, has been widely studied to provide rigorous privacy guarantees while keeping high utility. However, existing works on extended DP are limited to few metrics, such as the Euclidean metric. Consequently, they have only a small number of applications, such as location-based services and document processing. In this paper, we propose a couple of mechanisms providing extended DP with a different metric: angular distance (or cosine distance). Our mechanisms are based on locality sensitive hashing (LSH), which can be applied to the angular distance and work well for personal data in a high-dimensional space. We theoretically analyze the privacy properties of our mechanisms, and prove extended DP for input data by taking into account that LSH preserves the original metric only approximately. We apply our mechanisms to friend matching based on high-dimensional personal data with angular distance in the local model, and evaluate our mechanisms using two real datasets. We show that LDP requires a very large privacy budget and that RAPPOR does not work in this application. Then we show that our mechanisms enable friend matching with high utility and rigorous privacy guarantees based on extended DP.

I'm also a mathematics lecturer at Cal State East Bay, and have been fortunate to be able to work with my mentor Prateek Jain as a Data Science Fellow at SharpestMinds. This project was selected as a way for me to practice writing a machine learning algorithm from scratch (no scikit-learn allowed!) and to therefore deeply learn and understand the k-nearest neighbors algorithm, or kNN. If you're not already familiar with kNN, it's a nice ML algorithm to make your first deep dive with, because it's relatively intuitive. Zip codes are frequently useful proxies for individuals because people who live in the same neighborhood often have similar economic backgrounds and educational attainment, and are therefore also likely to share values and politics (not a guarantee, though!). So if you wanted to predict whether a particular piece of legislation would pass in an area, you might poll some of the area's constituents and assume most of those constituents' neighbors will feel similarly about your bill as do the majority of those you polled.

At first glance, a recently granted South African patent relating to a food container based on fractal geometry seems fairly mundane. The innovation in question involves interlocking food containers that are easy for robots to grasp and stack. On closer inspection, the patent is anything but mundane. That's because the inventor is not a human being -- it is an artificial intelligence (AI) system called DABUS. DABUS (which stands for device for the autonomous bootstrapping of unified sentience) is an AI system created by Stephen Thaler, a pioneer in the field of AI and programming.

Researchers at the University of Sydney and quantum control startup Q-CTRL have developed a way to identify sources of error in quantum computers through machine learning, providing hardware developers the ability to pinpoint performance degradation with unprecedented accuracy. A joint scientific paper detailing the research, Quantum oscillator noise spectroscopy via displaced Cat states, was published in the Physical Review Letters, a physical science research journal and flagship publication of the American Physical Society. Focused on reducing errors caused by environmental "noise" – the Achilles' heel of quantum computing – the University of Sydney team developed a technique to detect the tiniest deviations from the precise conditions needed to execute quantum algorithms using trapped ion and superconducting quantum computing hardware. These are the core technologies used by industrial quantum computing efforts at IBM, Google, Honeywell and others. To pinpoint the source of the measured deviations, Q-CTRL scientists developed a new way to process the measurement results using custom machine learning algorithms.

Dialogue systems research is traditionally focused on dialogues between two interlocutors, largely ignoring group conversations. Moreover, most previous research is focused either on task-oriented dialogue (e.g.\ restaurant bookings) or user engagement (chatbots), while research on systems for collaborative dialogues is an under-explored area. To this end, we introduce the first publicly available dataset containing collaborative conversations on solving a cognitive task, consisting of 500 group dialogues and 14k utterances. Furthermore, we propose a novel annotation schema that captures deliberation cues and release 50 dialogues annotated with it. Finally, we demonstrate the usefulness of the annotated data in training classifiers to predict the constructiveness of a conversation. The data collection platform, dataset and annotated corpus are publicly available at https://delibot.xyz

We review practical challenges in building and deploying ethical AI at the scale of contemporary industrial and societal uses. Apart from the purely technical concerns that are the usual focus of academic research, the operational challenges of inconsistent regulatory pressures, conflicting business goals, data quality issues, development processes, systems integration practices, and the scale of deployment all conspire to create new ethical risks. Such ethical concerns arising from these practical considerations are not adequately addressed by existing research results. We argue that a holistic consideration of ethics in the development and deployment of AI systems is necessary for building ethical AI in practice, and exhort researchers to consider the full operational contexts of AI systems when assessing ethical risks.

Entity alignment (EA) aims to find the equivalent entities in different KGs, which is a crucial step in integrating multiple KGs. However, most existing EA methods have poor scalability and are unable to cope with large-scale datasets. We summarize three issues leading to such high time-space complexity in existing EA methods: (1) Inefficient graph encoders, (2) Dilemma of negative sampling, and (3) "Catastrophic forgetting" in semi-supervised learning. To address these challenges, we propose a novel EA method with three new components to enable high Performance, high Scalability, and high Robustness (PSR): (1) Simplified graph encoder with relational graph sampling, (2) Symmetric negative-free alignment loss, and (3) Incremental semi-supervised learning. Furthermore, we conduct detailed experiments on several public datasets to examine the effectiveness and efficiency of our proposed method. The experimental results show that PSR not only surpasses the previous SOTA in performance but also has impressive scalability and robustness.

Defeasible rules are used in providing computable representations of legal documents and, more recently, have been suggested as a basis for explainable AI. Such applications draw attention to the scalability of implementations. The defeasible logic $DL(\partial_{||})$ was introduced as a more scalable alternative to $DL(\partial)$, which is better known. In this paper we consider the use of (implementations of) $DL(\partial_{||})$ as a computational aid to computing conclusions in $DL(\partial)$ and other defeasible logics, rather than as an alternative to $DL(\partial)$. We identify conditions under which $DL(\partial_{||})$ can be substituted for $DL(\partial)$ with no change to the conclusions drawn, and conditions under which $DL(\partial_{||})$ can be used to draw some valid conclusions, leaving the remainder to be drawn by $DL(\partial)$.

We propose a new general Graph Adversarial Domain Adaptation (GADA) based on semantic knowledge reasoning of class structure for solving the problem of unsupervised domain adaptation (UDA) from the big data with non-shared and imbalanced classes to specified small and imbalanced applications (NI-UDA), where non-shared classes mean the label space out of the target domain. Our goal is to leverage priori hierarchy knowledge to enhance domain adversarial aligned feature representation with graph reasoning. In this paper, to address two challenges in NI-UDA, we equip adversarial domain adaptation with Hierarchy Graph Reasoning (HGR) layer and the Source Classifier Filter (SCF). For sparse classes transfer challenge, our HGR layer can aggregate local feature to hierarchy graph nodes by node prediction and enhance domain adversarial aligned feature with hierarchy graph reasoning for sparse classes. Our HGR contributes to learn direct semantic patterns for sparse classes by hierarchy attention in self-attention, non-linear mapping and graph normalization. our SCF is proposed for the challenge of knowledge sharing from non-shared data without negative transfer effect by filtering low-confidence non-shared data in HGR layer. Experiments on two benchmark datasets show our GADA methods consistently improve the state-of-the-art adversarial UDA algorithms, e.g. GADA(HGR) can greatly improve f1 of the MDD by \textbf{7.19\%} and GVB-GD by \textbf{7.89\%} respectively on imbalanced source task in Meal300 dataset. The code is available at https://gadatransfer.wixsite.com/gada.

The frequency-based method is the most commonly used method for measuring cable tension. However, the calculation formulas for the conventional frequency-based method are generally based on the ideally hinged or fixed boundary conditions without a comprehensive consideration of the inclination angle, sag-extensibility, and flexural stiffness of cables, leading to a significant error in cable tension identification. This study aimed to propose a frequency-based method of cable tension identification considering the complex boundary conditions at the two ends of cables using the particle swarm optimization (PSO) algorithm. First, the refined stay cable model was established considering the inclination angle, flexural stiffness, and sag-extensibility, as well as the rotational constraint stiffness and lateral support stiffness for the unknown boundaries of cables. The vibration mode equation of the stay cable model was discretized and solved using the finite difference method. Then, a multiparameter identification method based on the PSO algorithm was proposed. This method was able to identify the tension, flexural stiffness, axial stiffness, boundary rotational constraint stiffness, and boundary lateral support stiffness according to the measured multiorder frequencies in a synchronous manner. The feasibility and accuracy of this method were validated through numerical cases. Finally, the proposed approach was applied to the tension identification of the anchor span strands of a suspension bridge (Jindong Bridge) in China. The results of cable tension identification using the proposed method and the existing methods discussed in previous studies were compared with the on-site pressure ring measurement results. The comparison showed that the proposed approach had a high accuracy in cable tension identification.