Reviewerly/exHarmony huggingface dataset:
Defending Label Inference and Backdoor Attacks in Vertical Federated  Learning

In collaborative learning settings like federated learning, curious parities might be honest but are attempting to infer other parties' private data through inference attacks while malicious parties might manipulate the learning process for their own purposes through backdoor attacks. However, most existing works only consider the federated learning scenario where data are partitioned by samples (HFL). The feature-partitioned federated learning (VFL) can be another important scenario in many real-world applications. Attacks and defenses in such scenarios are especially challenging when the attackers and the defenders are not able to access the features or model parameters of other participants. Previous works have only shown that private labels can be reconstructed from per-sample gradients. In this paper, we first show that private labels can be reconstructed when only batch-averaged gradients are revealed, which is against the common presumption. In addition, we show that a passive party in VFL can even replace its corresponding labels in the active party with a target label through a gradient-replacement attack. To defend against the first attack, we introduce a novel technique termed confusional autoencoder (CoAE), based on autoencoder and entropy regularization. We demonstrate that label inference attacks can be successfully blocked by this technique while hurting less main task accuracy compared to existing methods. Our CoAE technique is also effective in defending the gradient-replacement backdoor attack, making it an universal and practical defense strategy with no change to the original VFL protocol. We demonstrate the effectiveness of our approaches under both two-party and multi-party VFL settings. To the best of our knowledge, this is the first systematic study to deal with label inference and backdoor attacks in the feature-partitioned federated learning framework.
Graph-based Generative Face Anonymisation with Pose Preservation

We propose AnonyGAN, a GAN-based solution for face anonymisation which replaces the visual information corresponding to a source identity with a condition identity provided as any single image. With the goal to maintain the geometric attributes of the source face, i.e., the facial pose and expression, and to promote more natural face generation, we propose to exploit a Bipartite Graph to explicitly model the relations between the facial landmarks of the source identity and the ones of the condition identity through a deep model. We further propose a landmark attention model to relax the manual selection of facial landmarks, allowing the network to weight the landmarks for the best visual naturalness and pose preservation. Finally, to facilitate the appearance learning, we propose a hybrid training strategy to address the challenge caused by the lack of direct pixel-level supervision. We evaluate our method and its variants on two public datasets, CelebA and LFW, in terms of visual naturalness, facial pose preservation and of its impacts on face detection and re-identification. We prove that AnonyGAN significantly outperforms the state-of-the-art methods in terms of visual naturalness, face detection and pose preservation.
Improving the Robustness of Reinforcement Learning Policies with  $\mathcal{L}_{1}$ Adaptive Control

A reinforcement learning (RL) control policy trained in a nominal environment could fail in a new/perturbed environment due to the existence of dynamic variations. For controlling systems with continuous state and action spaces, we propose an add-on approach to robustifying a pre-trained RLpolicy by augmenting it with an $\mathcal{L}_{1}$ adaptive controller ($ \mathcal{L}_{1}$AC). Leveraging the capability of an $\mathcal{L}_{1}$AC for fast estimation and active compensation of dynamic variations, the proposed approach can improve the robustness of an RL policy which is trained either in a simulator or in the real world without consideration of a broad class of dynamic variations. Numerical and real-world experiments empirically demonstrate the efficacy of the proposed approach in robustifying RL policies trained using both model-free and model-based methods. A video for the experiments on a real Pendubot setup is availableathttps://youtu.be/xgOB9vpyUgE.
The R Package knnwtsim: Nonparametric Forecasting With a Tailored  Similarity Measure

The R package knnwtsim provides functions to implement k nearest neighbors (KNN) forecasting using a similarity metric tailored to the forecasting problem of predicting future observations of a response series where recent observations, seasonal or periodic patterns, and the values of one or more exogenous predictors all have predictive value in forecasting new response points. This paper will introduce the similarity measure of interest, and the functions in knnwtsim used to calculate, tune, and ultimately utilize it in KNN forecasting. This package may be of particular value in forecasting problems where the functional relationships between response and predictors are non-constant or piece-wise and thus can violate the assumptions of popular alternatives. In addition both real world and simulated time series datasets used in the development and testing of this approach have been made available with the package.
Mpsn: Motion-Aware Pseudo Siamese Network for Indoor Video Head Detection

Head detection in the indoor video is an essential component of many real-world applications. While deep models have achieved remarkable progress in general object detection, they are not satisfying enough in complex indoor scenes. The indoor surveillance video often includes cluttered background objects, among which heads have small scales and diverse poses. In this paper, we propose Motion-aware Pseudo Siamese Network (MPSN), an end-to-end approach that leverages head motion information to guide the deep model to extract effective head features in indoor scenarios. By taking the pixel-wise difference of adjacent frames as the auxiliary input, MPSN effectively enhances human head motion information and removes the irrelevant objects in the background. Compared with prior methods, it achieves superior performance on the two indoor video datasets. Our experiments show that MPSN successfully suppresses static background objects and highlights the moving instances, especially human heads in indoor videos. We also compare different methods to capture head motion, which demonstrates the simplicity and flexibility of MPSN. Finally, to validate the robustness of MPSN, we conduct adversarial experiments with a mathematical solution of small perturbations for robust model selection. Code is available at https://github.com/pl-share/MPSN.
Learning Reinforced Dynamic Representations for Sequential  Recommendation

Recently, sequential recommendation systems are important in solving the information overload in many online services. Current methods in sequential recommendation focus on learning a fixed number of representations for each user at any time, with a single representation or multi-interest representations for the user. However, when a user is exploring items on an e-commerce recommendation system, the number of this user's interests may change overtime (e.g. increase/reduce one interest), affected by the user's evolving self needs. Moreover, different users may have various number of interests. In this paper, we argue that it is meaningful to explore a personalized dynamic number of user interests, and learn a dynamic group of user interest representations accordingly. We propose a Reinforced sequential model with dynamic number of interest representations for recommendation systems (RDRSR). Specifically, RDRSR is composed of a dynamic interest discriminator (DID) module and a dynamic interest allocator (DIA) module. The DID module explores the number of a user's interests by learning the overall sequential characteristics with bi-directional self-attention and Gumbel-Softmax. The DIA module allocates the historical clicked items into a group of sub-sequences and constructs user's dynamic interest representations. We formalize the allocation problem in the form of Markov Decision Process(MDP), and sample an action from policy pi for each item to determine which sub-sequence it belongs to. Additionally, experiments on the real-world datasets demonstrates our model's effectiveness.
Black-Box Quantum State Preparation with Inverse Coefficients

Black-box quantum state preparation is a fundamental building block for many higher-level quantum algorithms, which is applied to transduce the data from computational basis into amplitude. Here we present a new algorithm for performing black-box state preparation with inverse coefficients based on the technique of inequality test. This algorithm can be used as a subroutine to perform the controlled rotation stage of the Harrow-Hassidim-Lloyd (HHL) algorithm and the associated matrix inversion algorithms with exceedingly low cost. Furthermore, we extend this approach to address the general black-box state preparation problem where the transduced coefficient is a general non-linear function. The present algorithm greatly relieves the need to do arithmetic and the error is only resulted from the truncated error of binary string. It is expected that our algorithm will find wide usage both in the NISQ and fault-tolerant quantum algorithms.
Projection Mapping of a Liver 3D Model for a Laparoscopic Surgery Support System

In laparoscopic surgery, current surgical navigation systems use preoperative medical images. The actual shape of organs and the navigation image are different from each other due to the deformation of the organ during the operation. To solve this problem, our research group is developing an endoscopic surgery support system that can present intraoperative organ deformation. In this study, for this system, an organ 3D model is constructed from medical images. The 3D model is projected by projection mapping on the surface of a human body to present the organ that is inside the body. In addition, a full-scale liver was made from soft resin using a 3D model of an organ, and the usefulness of projection mapping was verified.
Future Path Presentation to the Passenger of an Autonomous Wheelchair Using Haptic Apparent Motion

While autonomous wheelchairs reduce the burden on the passenger, the automation of the operation may make it difficult for the passenger to understand the path the wheelchair will take in the future. In this case, passengers may feel anxious or uncomfortable due to unexpected movements of the wheelchair. To reduce passengers' anxiety and discomfort, the authors it is important to present information to passengers about the future path of the wheelchair when multiple people approach from the front. In previous studies, only the direction of the wheelchair's turn was presented. We propose a comfortable and easy-to-understand path presentation method that presents the direction and width of the wheelchair's turn by changing the length of the haptic apparent motion according to the width of the wheelchair's turn. Preliminary simulated wheelchair driving experiments have confirmed the potential of the proposed method to improve passenger comfort.
Math

Programming languages are the offspring of mathematics, but they have different capabilities to translate mathematical language and expressions to code. By implementing operators using their Unicode glyph, you will find that Raku code is closer to math than it is to other languages. By its functional nature, Raku functions can also work as pure functions (applications) and thus the data flow can be seen more clearly in Raku code.
Structuring Two-Dimensional Space

Perceptual space is not at all uniform. The most powerful pattern processing machinery of the brain is devoted to processing information in the image plane (up-down and sideways). Patterns in depth (the towards away direction) are less easy to perceive and less useful for displaying information. The brain processes patterns in a hierarchical fashion; first the visual image is broken down in to millions of tiny pattern elements, such as oriented edges and lines. Further up the visual system more complex features are processed and objects are the most complex patterns. Understanding how the brain pulls out patterns in the image is critical to effective visualization design. The various ways that lines, and simple shapes can carry meaning about data entities and relationships between entities are illustrated and discussed.
Edge Learning Architecture Design for System Scalability

With the growth of model complexity and computational overhead, modern ML applications are usually handled by the distributed systems, where the training procedure is conducted in parallel. Basically, the datasets and models are partitioned to different workers in data parallelism and model parallelism, respectively.
Upgrading a Hybrid Computing Algorithm

Researchers outline a protocol for performing a popular quantum-classical machine-learning algorithm with a so-called measurement-based quantum computer, which could allow for more resource-efficient calculations.
Klotzsche Receives 2020 Near-Surface Geophysics Early Career Achievement Award

Anja Klotzsche received the inaugural Near-Surface Geophysics Early Career Achievement Award at AGU’s virtual Fall Meeting 2020. The award is given in recognition of “significant contributions to the field of near-surface geophysics by an early-career researcher.”
Object Delineation by Iterative Dynamic Trees

While semantic segmentation networks can approximate well the shape of objects (e.g., people, chairs, tables) in images, boundary adherence is still inaccurate. We present an unsupervised image segmentation approach, named Iterative Dynamic Trees (IDT), for improved object delineation. We intend to combine IDT and semantic segmentation networks in future work towards improving object segmentation. For a given number of objects in an image graph, the IDT algorithm (i) estimates one seed per object (and background), (ii) delineates each object as one optimum-path tree, (iii) improves seed estimation and repeats steps (ii)–(iii) for a preset number of iterations or until the seed set convergence is achieved. Then, (iv) the optimum-path forest found with the lowest total cost in the loop (ii)–(iii) is selected as final segmentation. The IDT algorithm is a new method based on the Iterative Spanning Forest (ISF) framework, in which the number of superpixels is drastically reduced to the number of objects. It adds step (iv) and exploits, for the first time, dynamic arc-weight estimation in ISF for unsupervised object segmentation. We show that IDT can outperform its counterparts in two image datasets – a result that motivates its combination with semantic segmentation networks in future work.
Aspect based Sentiment Analysis for Urdu

Aspect based sentiment analysis (ABSA) is an important task that aims to determine the polarities of the given aspects in its context. Most of the study on ABSA mainly focuses on resource-rich languages, such as English and Chinese, there is little relevant research for the Urdu language. In this paper, we establish a fine-grained aspect-based sentiment analysis dataset for Urdu. This dataset contains Urdu reviews of restaurants and laptops with specific aspect words and polarities. Further, we conduct different experiments on the dataset to build an ABSA system using state-of-the-art algorithms. Our work aims to build reliable baselines for ABSA in Urdu.
Moral-Driven Planning in Simple Automated Narrative Generator

A moral is a vital part of the story and is vital for both the author and the audience. However, moral-based story planning is relatively underexplored. This paper proposes a planning-based Simple Automated Narrative Generator framework that explicitly considers the moral input and plans the story events around it. To achieve that, we determine the character's emotional arc based on the polarity of the input moral and then plan the events that adhere to the emotional arc. In the experiment of 103 human evaluators and 35 generated narratives for 7 morals, we find encouraging normalized confidence of 0.86 for intended moral being conveyed by the narratives and reasonable confidence of 0.71 for selecting the narratives with morals versus without morals.
Research on adaptive target tracking algorithm

Recently, many tracking algorithms based on correlation filtering are proposed, and good performance is obtained.. Although these algorithms have extremely high accuracy, they cannot solve the tracking failure caused by cumulative errors and scene changes. So how to design a robust tracker is very import. An adaptive target tracking algorithm is proposed to achieve fast and robust tracking. For feature selection, we propose a hybrid feature based on HoG, CN, and Haar-like to express the tracked target from multiple angles such as color, edge, and detail. In order to solve the speed reduction caused by complex fusion features, we preset a simple classifier in the front end of the algorithm. The role of the classifier is to distinguish the difference between the target and the background, and to determine whether to use fusion features for tracking. Finally, we use simple mobile prediction to reduce the impact of accumulated errors. To evaluate the proposed algorithm, a wide range of experiments are performed using some of the OTB benchmarks.
Gesture Decoder for Communication Using Deep Learning

The project focuses on building a Deep Learning/Machine Learning model that can interpret the sign language, i.e., the hand and finger gestures of a person. The model will convert the signs into corresponding words and characters. So that an easy communication can be developed between the people who are not able to speak or hear properly. The model will be trained on images consisting of the hand gestures for different characters used by deaf and dumb persons to communicate. The model will be tested on the image dataset as well as on real world images. For the model, we have considered various Deep Learning approaches such as ResNet-50, VGG-16, Inception Network, Transfer Learning and compared their accuracy on the dataset. Using Transfer Learning on the VGG-16 architecture by training it on the dataset containing the images of the hand signs, we obtained highest accuracy, so we used this approach. The pre-training of the VGG-16 Network is done on various different real world images which helps the model to understand different patterns of the image easily.
Archiving of Records -Latvian style

Today, information and communication technologies have become an integral part of our daily lives and basic information for the performance of their functions and for decision-making institutions accumulate in databases/information systems. The diversity of electronic information is extensive and continues to grow every year, creating ever-new challenges for Archives. The article presents the cooperation of the National Archives of Latvia with record creators, regulations regarding records and archives management and responsibility of both sides. At the moment, the priority of the National Archives of Latvia is appraisal, description and acquisition of electronic records and information system data sets. The article presents the approach from different perspectives. An overview of the Unified State Archives Information System is given and the process of acquisition of electronic records is described: standards, tools and file formats; record types created by institutions; current situation in institutions and cooperation with institution in order to transfer electronic records from institutions to the National Archives of Latvia according to regulations and standards.
Semi-automatic Ontology Learning for Twitter Messages Based on Semantic Feature Extraction

The enormous spreading of social network media like Twitter is speeding up the process of sharing information and expressing opinions about global health crises, and important events.Due to the use of different terms for expressing the same topic in a Twitter post, it becomes difficult to build applications such as retrieval of information by following previous mining methods to find a match between words or sentences. In order to solve this problem, it requires providing the knowledge source that collects many terms which reflect a single meaning, Such as ontology. Ontology is the process of representing the concepts of a specific field such as finance or epidemics, with their characteristics and relationships by dealing with the heterogeneity and complexity of terms.In this paper, the domain ontology for Twitter’s Covid-19 post will be developed by following the notion of semantic web layer cake and discuss the depth of terms and relationships extracted in this domain through a set of measurements, the ontology contains more than 900 single concepts and more than 180 multi-word concepts, which are the most concepts used in Twitter posts with the hashtag Corona epidemic which can be used to find semantic similarities between words and sentences.
Transferring Quantum Information in Hybrid Quantum Systems Consisting of a Quantum System with Limited Control and a Quantum Computer

We consider a hybrid quantum system consisting of a qubit system continuously evolving according to its fixed own Hamiltonian and a quantum computer. The qubit system couples to a quantum computer through a fixed interaction Hamiltonian, which can only be switched on and off. We present quantum algorithms to approximately transfer quantum information between the qubit system with limited control and the quantum computer under this setting. Our algorithms are programmed by the gate sequences in a closed formula for a given interface interaction Hamiltonian.
Teknik Interpolasi Bikubik untuk Memperbesar Gambar Hasil Pemetaan Suhu dari Sensor MLX90640 pada Praktikum Mikrokontroller

The occurrence of various disruptions and damage to equipment and electrical installations begins with a significant temperature increase in it. The temperature increasing in electrical equipment and installations will also increase the consumption of energy or power required. Previous technology used a temperature sensor and current sensor which had to be installed in the environment to be monitored. The temperature measurement system without touching the surface which is measured using infrared rays is a new breakthrough that really helps maintenance and repair. Also with the existence of a temperature sensor arranged in an array, it can map the existing temperature so that it can provide a better picture for analyzing the symptoms that occur, compared to using a temperature sensor that measures one point only. A temperature imaging camera is currently very expensive, making procurement a bit difficult for laboratories dealing with electrical equipment and installations. In this research, we will try to make an inexpensive temperature imaging camera using the MLX90640 sensor and the ESP32 microcontroller. Bicubic interpolation is used to enlarge the resulting image obtained by the MLX90640 sensor. The MATLAB program is used to simulate and create an overall temperature imaging camera system. Tested and compared with two other interpolation methods to ensure that bicubic interpolation can produce better magnified images.