Reviewerly/exHarmony huggingface dataset:
Expanding variational quantum eigensolvers to larger systems by dividing the calculations between classical and quantum hardware

We present a hybrid classical-quantum algorithm for efficiently solving the eigenvalue problem of many-particle Hamiltonians on quantum computers with limited resources by splitting the workload between classical and quantum processors. This algorithm reduces the needed number of qubits at the expense of an increased number of quantum evaluations. We demonstrate the method for the Hubbard model and show how the conservation of the $z$ component of the total spin allows the spin-up and spin-down configurations to be computed on classical and quantum hardware, respectively. Other symmetries can be exploited in a similar manner.
Deep Learning-Based Image Geolocation for Travel Recommendation via Multi-Task Learning

Localizing images by visual information is a very challenging task in image-based travel recommendations. Travelers take a large number of pictures every day and share them on social networks (Facebook, Sina Weibo, Yelp, etc.). Many of these images are associated with the location where they are taken. But for images that do not associate with geographic location information, how to estimate where they are taken? With the rapid development of social media, the increasing number of shared geographic-labeled images brings an opportunity to address this problem. Using geographic-labeled images to estimate the location of unlabeled images is a popular approach. In this paper, we propose an image geographic location estimation model via multi-task learning (GLML). It combines the classification task and retrieval task to calculate the similarity between the query image and dataset images. Additionally, it fuses multi-global features through multiple global pooling techniques to enhance feature extraction. Each part of the proposed GLML model is flexible and extensible. Experiments on seven public datasets show the effectiveness of the proposed model.
An Integrated Optimization Model for Medicine Order Distribution and Delivery Problem of Online Pharmacy Based on the Optimal Supply Chain Strategy

The issue of health and medicine has always been very important. The volume of demand in the field of medicine is very high and is considered as one of the sensitive and vital products, so the accuracy in using the medicine and the accuracy of time of its use and access are among the strict requirements in the field of drug circulation. As we know, in order to achieve the best therapeutic effect and cure the disease, several drugs for a specific disease are usually used simultaneously. Therefore, an order which contains different types of medicines must be delivered on time, together and at the same time. By focusing on the supply chain, companies are experiencing significant reductions in healthcare costs. The purpose of this article is to provide a model and solution for online pharmacies through which chain costs can be optimized. In this regard, it is assumed that after the orders are received through the protocol in each period, a third-party logistics organization will collect the medicines required for each order from the physical pharmacies that have these medicines in their stock and transfer them to pharmacies that have the ability to send the medicines to the customer and a shipping fleet so that, considering logistical costs, orders are delivered to customers on time.
An Improved Algorithm for The <i>k</i>-Dyck Edit Distance Problem

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)An Improved Algorithm for The k-Dyck Edit Distance ProblemDvir Fried, Shay Golan, Tomasz Kociumaka, Tsvi Kopelowitz, Ely Porat, and Tatiana StarikovskayaDvir Fried, Shay Golan, Tomasz Kociumaka, Tsvi Kopelowitz, Ely Porat, and Tatiana Starikovskayapp.3650 - 3669Chapter DOI:https://doi.org/10.1137/1.9781611977073.144PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract A Dyck sequence is a sequence of opening and closing parentheses (of various types) that is balanced. The Dyck edit distance of a given sequence of parentheses S is the smallest number of edit operations (insertions, deletions, and substitutions) needed to transform S into a Dyck sequence. We consider the threshold Dyck edit distance problem, where the input is a sequence of parentheses S and a positive integer k, and the goal is to compute the Dyck edit distance of S only if the distance is at most k, and otherwise report that the distance is larger than k. Backurs and Onak [PODS'16] showed that the threshold Dyck edit distance problem can be solved in O(n + k16) time. In this work, we design new algorithms for the threshold Dyck edit distance problem which costs O(n + k4.782036) time with high probability or O(n + k4.853059) deterministically. Our algorithms combine several new structural properties of the Dyck edit distance problem, a refined algorithm for fast (min, +) matrix product, and a careful modification of ideas used in Valiant's parsing algorithm. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-707-3 https://doi.org/10.1137/1.9781611977073Book Series Name:ProceedingsBook Code:PRDA22Book Pages:xvii + 3771
Gaussian Process Regression With Interpretable Sample-Wise Feature Weights

Gaussian process regression (GPR) is a fundamental model used in machine learning (ML). Due to its accurate prediction with uncertainty and versatility in handling various data structures via kernels, GPR has been successfully used in various applications. However, in GPR, how the features of an input contribute to its prediction cannot be interpreted. Here, we propose GPR with local explanation, which reveals the feature contributions to the prediction of each sample while maintaining the predictive performance of GPR. In the proposed model, both the prediction and explanation for each sample are performed using an easy-to-interpret locally linear model. The weight vector of the locally linear model is assumed to be generated from multivariate Gaussian process priors. The hyperparameters of the proposed models are estimated by maximizing the marginal likelihood. For a new test sample, the proposed model can predict the values of its target variable and weight vector, as well as their uncertainties, in a closed form. Experimental results on various benchmark datasets verify that the proposed model can achieve predictive performance comparable to those of GPR and superior to that of existing interpretable models and can achieve higher interpretability than them, both quantitatively and qualitatively.
ExGenNet: Learning to Generate Robotic Facial Expression Using Facial Expression Recognition

The ability of a robot to generate appropriate facial expressions is a key aspect of perceived sociability in human-robot interaction. Yet many existing approaches rely on the use of a set of fixed, preprogrammed joint configurations for expression generation. Automating this process provides potential advantages to scale better to different robot types and various expressions. To this end, we introduce ExGenNet, a novel deep generative approach for facial expressions on humanoid robots. ExGenNets connect a generator network to reconstruct simplified facial images from robot joint configurations with a classifier network for state-of-the-art facial expression recognition. The robots’ joint configurations are optimized for various expressions by backpropagating the loss between the predicted expression and intended expression through the classification network and the generator network. To improve the transfer between human training images and images of different robots, we propose to use extracted features in the classifier as well as in the generator network. Unlike most studies on facial expression generation, ExGenNets can produce multiple configurations for each facial expression and be transferred between robots. Experimental evaluations on two robots with highly human-like faces, Alfie (Furhat Robot) and the android robot Elenoide, show that ExGenNet can successfully generate sets of joint configurations for predefined facial expressions on both robots. This ability of ExGenNet to generate realistic facial expressions was further validated in a pilot study where the majority of human subjects could accurately recognize most of the generated facial expressions on both the robots.
Unsupervised Clothing Change Adaptive Person ReID

Clothing changes and lack of data labels are both crucial challenges in person ReID. For the former challenge, people may occur multiple times at different locations wearing different clothing. However, most of the current person ReID research works focus on the benchmarks in which a person's clothing is kept the same all the time. For the last challenge, some researchers try to make model learn information from a labeled dataset as a source to an unlabeled dataset. Whereas purely unsupervised training is less used. In this paper, we aim to solve both problems at the same time. We design a novel unsupervised model, Sync-Person-Cloud ReID, to solve the unsupervised clothing change person ReID problem. We developer a purely unsupervised clothing change person ReID pipeline with person sync augmentation operation and same person feature restriction. The person sync augmentation is to supply additional same person resources. These same person's resources can be used as part supervised input by same person feature restriction. The extensive experiments on clothing change ReID datasets show the out-performance of our methods.
Corruption, Credit Risk, and Bank Profitability

The economies of African countries are generally characterized by inefficient management of resources, strong heterogeneity in the rate of economic growth, as well as high levels of corruption and embezzlement of public funds, clearly highlighting the need to consider the role of government in the performance of the economic environment. Corruption is characterized by three key behaviors—bribery, embezzlement, and nepotism—characteristics that can influence the performance of any financial system. The objective is to examine the effect of corruption on credit risk in Angola. The result of the feasible generalized least squares (FGLS) estimation suggests that corruption increases non-performing loans in the Angolan economy; additionally, the authors find that the larger the bank's assets (bank size), the more averse to credit risk they become, and the smaller the state's stake in the banking system, the lower the non-performing loans.
Efficient and Secure TSA for the Tangle

The Tangle is the data structure used to store transactions in the IOTA cryptocurrency. In the Tangle, each block has two parents. As a result, the blocks do not form a chain, but a directed acyclic graph. In traditional Blockchain, a new block is appended to the heaviest chain in case of fork. In the Tangle, the parent selection is done by the Tip Selection Algorithm (TSA). In this paper, we make some important observations about the security of existing TSAs. We then propose a new TSA that has low complexity and is more secure than previous TSAs.
Neural Human Deformation Transfer

We consider the problem of human deformation transfer, where the goal is to retarget poses between different characters. Traditional methods that tackle this problem assume a human pose model to be available and transfer poses between characters using this model. In this work, we take a different approach and transform the identity of a character into a new identity without modifying the character’s pose. This offers the advantage of not having to define equivalences between 3D human poses, which is not straightforward as poses tend to change depending on the identity of the character performing them, and as their meaning is highly contextual. To achieve the deformation transfer, we propose a neural encoder-decoder architecture where only identity information is encoded and where the decoder is conditioned on the pose. We use pose independent representations, such as isometry-invariant shape characteristics, to represent identity features. Our model uses these features to supervise the prediction of offsets from the deformed pose to the result of the transfer. We show experimentally that our method outperforms state-of-the-art methods both quantitatively and qualitatively, and generalises better to poses not seen during training. We also introduce a fine-tuning step that allows to obtain competitive results for extreme identities, and allows to transfer simple clothing.
Personalized content recommendation in online health communities

Purpose Recommending suitable content for users of online health communities (OHCs) is critical for overcoming information overload problem and facilitate medical decision making, but remains not fully investigated. This study aims to provide a content recommendation approach to automatically match valuable health-related information for OHC members. Design/methodology/approach A framework of health-related content recommendation was proposed by leveraging rich social information in online communities. The authors constructed user influence relationship (UIR) utilizing users' interaction records, user profiles and user-generated content. The initial user rating matrix and the user post matching matrix were then created by analyzing text content of posts. Finally, the user rating matrix and the recommended content were generated for community members. Datasets were collected from an OHC to evaluate the effectiveness of the proposed approach. Findings The experimental results revealed that the proposed method statistically outperformed baseline models in content recommendation for users of OHCs. Research limitations/implications The incorporation of social information can significantly enhance the performance of content recommendation in OHCs. The user post matching degree based on text analysis can improve the effectiveness of recommendation. Practical implications This study potentially contributes to the social support exchange and medical decision making of community members and the sustainable prosperity of OHCs. Originality/value This study proposes a novel social content recommendation method for online health consumers based on UIRs by leveraging social information in OHCs. The results indicate the significance of social information in content recommendation of healthcare social media.
OFFENSIVE AI: UNIFICATION OF EMAIL GENERATION THROUGH GPT-2 MODEL WITH A GAME-THEORETIC APPROACH FOR SPEAR-PHISHING ATTACKS

A rising digital economy implies more cybersecurity challenges. As organisations continue with their digital transformations, they need to implement pervasive cyber defense measures to comply with the corresponding severe security threats. The number of organisations and individuals falling victim to targeted attacks such as spear-phishing attacks is growing rapidly. Regardless of substantial exploration in mitigation systems, attackers today are becoming more sophisticated as they cultivate their techniques, employing advanced natural language (NL) capabilities to deceive email security systems. Game theory approaches based on cybersecurity are mostly concentrated on proposing defence algorithms against attacks. This work is comprehensively centred on the role of the attacker in spear-phishing attacks, using OpenAI text generating model Generative Pre-trained Transformer 2 (GPT-2) to generate emails with various malicious content. Attackers use those emails to attack a target and attempt to deceive the defence system. Considering the lack of theoretic analysis from the attacker&apos;s perspective, a non-cooperative zero-sum spear-phishing game model is proposed that allows an attacker to choose an optimal strategy for maximising payoff. Moreover, we calculated the Nash equilibrium (NE) in mixed strategies for the attacker-defender game and provided a reasonable scheme for an attacker to gain an advantage over the target.
PPDL - Privacy Preserving Deep Learning Using Homomorphic Encryption

Deep Learning Models such as Convolution Neural Networks (CNNs) have shown great potential in various applications. However, these techniques will face regulatory compliance challenges related to privacy of user data, especially when they are deployed as a service on a cloud platform. Such concerns can be mitigated by using privacy preserving machine learning techniques. The purpose of our work is to explore a class of privacy preserving machine learning technique called Fully Homomorphic Encryption in enabling CNN inference on encrypted real-world dataset. Fully homomorphic encryption face the limitation of computational depth. They are also resource intensive operations. We run our experiments on MNIST dataset to understand the challenges and identify the optimization techniques. We used these insights to achieve the end goal of enabling encrypted inference for binary classification on melanoma dataset using Cheon-Kim-Kim-Song (CKKS) encryption scheme available in the open-source HElib library.
Penerapan Algoritma Support Vector Machine (SVM) dengan TF-IDF N-Gram untuk Text Classification

Syntax Journal of Informatics is an information system that contains a collection of scientific articles managed by the Informatics Study Program of Singaperbangsa Karawang University. Currently, Syntax Journal of Informatics does not have a feature for categorizing scientific articles based on their focus and scope. The research is conducted to classify scientific articles into categories according to focus and scope contained on Syntax Journal of Informatics' page automatically by utilizing the text mining process. Text mining is a process that aims to get important information from the text. The method used in the research is Knowledge Discovery in Database (KDD) with stages of data selection, preprocessing, transformation, modeling and evaluation. This study will compare the classifications based on the title of the article. The algorithm used is the Support Vector Machine (SVM) using four SVM kernels, including the linear kernel, polynomial kernel, sigmoid kernel and RBF kernel. Data are divided into four scenarios by using traintestsplit, namely 60:40, 70:30, 80:30 and 90:10. The results of the study after testing the model are measured by of Accuracy, Precision, Recall and F-measure. The best results are accuracy of 70%, precision of 75%, recall of 69% and f-measure of 71% in the 90:10 comparison scenario and linear kernel.
Ancient mural segmentation based on a deep separable convolution network

Abstract Traditional methods for ancient mural segmentation have drawbacks, including fuzzy target boundaries and low efficiency. Targeting these problems, this study proposes a pyramid scene parsing MobileNetV2 network (PSP-M) by fusing a deep separable convolution-based lightweight neural network with a multiscale image segmentation model. In this model, deep separable convolution-fused MobileNetV2, as the backbone network, is embedded in the image segmentation model, PSPNet. The pyramid scene parsing structure, particularly owned by the two models, is used to process the background features of images, which aims to reduce feature loss and to improve the efficiency of image feature extraction. In the meantime, atrous convolution is utilized to expand the perceptive field, aiming to ensure the integrity of image semantic information without changing the number of parameters. Compared with traditional image segmentation models, PSP-M increases the average training accuracy by 2%, increases the peak signal-to-noise ratio by 1–2 dB and improves the structural similarity index by 0.1–0.2.
Diagnosis of Malaria Infected Blood Cell Digital Images using Deep Convolutional Neural Networks

Automated medical diagnosis is an important topic, especially in detection and classification of diseases. Malaria is one of the most widespread diseases, with more than 200 million cases, according to the 2016 WHO report. Malaria is usually diagnosed using thin and thick blood smears under a microscope. However, proper diagnosis is difficult, especially in poor countries where the disease is most widespread. Therefore, automatic diagnostics helps in identifying the disease through images of red blood cells, with the use of machine learning techniques and digital image processing. This paper presents an accurate model using a Deep Convolutional Neural Network build from scratch. The paper also proposed three CNN models each one trained on the Malaria RBC dataset with different architectures for handling the classification tasks. Furthermore, disadvantage of the traditional method of using transfer learning, and how to control model complexity to achieve better performance was discussed. The dropout regularization technique was used to avoid overfitting problems and minimize validation loss. Applying Data Augmentation technique to avoid the problem of small data in training of proposed models, which is a very common problem in medical dataset. Finally, removing noise in Malaria images using a Median blur filter, and studying how effects of that on training CNN models. According to the classification results, the proposed model achieved better classification results at accuracy 99.22 on the original Malaria RBCs dataset, and it has the best performance comparing with related work.
A Likelihood Ratio Detector for QTMS Radar and Noise Radar

We derive a detector function for quantum two-mode squeezing (QTMS) radars and noise radars that is based on the use of a likelihood ratio (LR) test for distinguishing between the presence and absence of a target. In addition to an explicit expression for the LR detector, we derive a detector function which approximates the LR detector in the limit where the target is small, far away, or otherwise difficult to detect. When the number of integrated samples is large, we derive a theoretical expression for the receiver operating characteristic (ROC) curve of the radar when the LR detector is used. When the number of samples is small, we use simulations to understand the ROC curve behavior of the detector. One interesting finding is there exists a parameter regime in which a previously-studied detector outperforms the LR detector, contrary to the intuition that LR tests are optimal. This is because neither the Neyman-Pearson lemma, nor the Karlin-Rubin theorem which generalizes the lemma, hold in this particular problem. However, the LR detector remains a good choice for target detection.
Antisemitic Narratives on YouTube and Telegram as Part of Conspiracy Beliefs about COVID-19

In the context of the COVID-19 pandemic, a vocal protest scene emerged in Germany. Comparisons to Nazi-Germany, conspiracy topoi, and antisemitic narratives were its prominent companions – on the streets as well as on social media. One former vegan chef and b-lister became a well-known agitator, culminating in multiple threats to people in public life and blatant, Holocaust-denying antisemitism. This chapter analyzes the development of Attila Hildmann’s conspiracist and antisemitic escalation toward a “superconspiracy” (Barkun) using a mixed-methods approach. This antisemitic escalation begins with conspiracist narratives and a personalized concept of enemy (predominantly Bill Gates) and moves through prevalent conspiracist themes with antisemitic implications to outright promotion of violence. In the process, implicitly antisemitic references are replaced with open antisemitism. The results confirm that video hosting services and the messenger Telegram are not only important for mobilizing protests, but are also an important resource for analyzing antisemitism in its various forms. This work also proves that a combination of a hermeneutical approach and frequency analysis on multiple platforms is useful for analyzing enormous amounts of diverse data, and for contextualizing the characteristics of narrative structures of conspiracy beliefs.