Reviewerly/exHarmony huggingface dataset:
Heterogeneous fairness algorithm based on federated learning in intelligent transportation system

With the development of the Intelligent Transportation System, various distributed sensors (including GPS, radar, infrared sensors) process massive data and make decisions for emergencies. Federated learning is a new distributed machine learning paradigm, in which system heterogeneity is the difficulty of fairness design. This paper designs a system heterogeneous fair federated learning algorithm (SHFF). SHFF introduces the equipment influence factor I into the optimization target and dynamically adjusts the equipment proportion with other performance. By changing the global fairness parameter θ, the algorithm can control fairness according to the actual needs. Experimental results show that, compared with the popular q-FedAvg algorithm, the SHFF algorithm proposed in this paper improves the average accuracy of the Worst 10% by 26% and reduces the variance by 61%.
On the Performance of Pipelined HotStuff

HotStuff is a state-of-the-art Byzantine fault-tolerant consensus protocol. It can be pipelined to build large-scale blockchains. One of its variants called LibraBFT is adopted in Facebook's Libra blockchain. Although it is well known that pipelined HotStuff is secure against up to 1/3 of Byzantine nodes, its performance in terms of throughput and delay is still under-explored. In this paper, we develop a multi-metric evaluation framework to quantitatively analyze pipelined HotStuff's performance with respect to its chain growth rate, chain quality, and latency. We then propose several attack strategies and evaluate their effects on the performance of pipelined HotStuff. Our analysis shows that the chain growth rate (resp, chain quality) of pipelined HotStuff under our attacks can drop to as low as 4/9 (resp, 12/17) of that without attacks when 1/3 nodes are Byzantine. As another application, we use our framework to evaluate certain engineering optimizations adopted by LibraBFT. We find that these optimizations make the system more vulnerable to our attacks than the original pipelined HotStuff. Finally, we provide two countermeasures to thwart these attacks. We hope that our studies can shed light on the rigorous understanding of the state-of-the-art pipelined HotStuff protocol as well as its variants.
Research on medical system based on blockchain technology

Abstract Medical data sharing, anti-tampering, and leakage prevention have always been severe problems that plagued the pharmaceutical industry. When a patient is referred, he often cannot provide information about previous visits because the medical information of each hospital cannot be shared in most cases, but only through Medical records, test sheets, and other easily lost paper information are used to share some medical information. At the same time, patient medical information is easily leaked, and the medical information provided in the event of a medical dispute cannot guarantee authenticity and impartiality. This article designs a consortium medical blockchain system based on a Possible Byzantine Fault Tolerance algorithm. This system is a medical system that is maintained and shared by multiple nodes and can prevent medical data from being tampered with or leaked. It can be used to solve these medical problems. Compared with the existing medical blockchain system, this system has certain advantages and better applicability.
Knowledge Graph for China's Genealogy

Genealogical knowledge graphs depict the relationships of family networks and the development of family histories. They can help researchers to analyze and understand genealogical data, search for genealogical descendant paths, and explore the origins of a family more easily. However, the heterogenous, autonomous, complex, and evolving natures of genealogical data bring challenges to the development of contemporary genealogical knowledge graph models. Applying existing methods to genealogical data may be improper because general knowledge graph models lack in-depth domain knowledge. In this paper, we propose a genealogical knowledge graph model named Huapu-KG that combines HAO intelligence (human intelligence + artificial intelligence + organizational intelligence) to implement the construction and applications of genealogical knowledge graphs. Furthermore, challenges in constructing genealogical knowledge graphs are demonstrated, and experiments conducted on real-world genealogical datasets verify the feasibility and effectiveness of our proposed model.
Learning Gaussian–Bernoulli RBMs Using Difference of Convex Functions Optimization

The Gaussian-Bernoulli restricted Boltzmann machine (GB-RBM) is a useful generative model that captures meaningful features from the given n -dimensional continuous data. The difficulties associated with learning GB-RBM are reported extensively in earlier studies. They indicate that the training of the GB-RBM using the current standard algorithms, namely contrastive divergence (CD) and persistent contrastive divergence (PCD), needs a carefully chosen small learning rate to avoid divergence which, in turn, results in slow learning. In this work, we alleviate such difficulties by showing that the negative log-likelihood for a GB-RBM can be expressed as a difference of convex functions if we keep the variance of the conditional distribution of visible units (given hidden unit states) and the biases of the visible units, constant. Using this, we propose a stochastic difference of convex (DC) functions programming (S-DCP) algorithm for learning the GB-RBM. We present extensive empirical studies on several benchmark data sets to validate the performance of this S-DCP algorithm. It is seen that S-DCP is better than the CD and PCD algorithms in terms of speed of learning and the quality of the generative model learned.
Using QKD in MACsec for secure Ethernet networks

Media access control security (MACsec) is an IEEE 802.1AE standard for secure communication on Ethernet links. MACsec ensures the confidentiality, integrity and origin authenticity of Ethernet frames. The secrecy of MACsec stems from a root key that is either configured as a pre-shared key or derived from a mutual authentication protocol. However, both methods are not ideal because such a root key may be disclosed due to human errors or broken by quantum attacks. Here, the authors investigate the quantum key distribution (QKD) as an alternative source of trust for MACsec. QKD can be used as either a root key provider or a session key generator. The authors develop a new key exchange protocol based on QKD for Ethernet networks. Furthermore, it is verified by the experiment that QKD could be well integrated into MACsec without performance degradation.
Hierarchical Dependence-aware Evaluation Measures for Conversational Search

Conversational agents are drawing a lot of attention in the information retrieval (IR) community also thanks to the advancements in language understanding enabled by large contextualized language models. IR researchers have long ago recognized the importance o fa sound evaluation of new approaches. Yet, the development of evaluation techniques for conversational search is still an underlooked problem. Currently, most evaluation approaches rely on procedures directly drawn from ad-hoc search evaluation, treating utterances in a conversation as independent events, as if they were just separate topics, instead of accounting for the conversation context. We overcome this issue by proposing a framework for defining evaluation measures that are aware of the conversation context and the utterance semantic dependencies. In particular, we model the conversations as Direct Acyclic Graphs (DAG), where self-explanatory utterances are root nodes, while anaphoric utterances are linked to sentences that contain their missing semantic information. Then,we propose a family of hierarchical dependence-aware aggregations of the evaluation metrics driven by the conversational graph. In our experiments, we show that utterances from the same conversation are 20% more correlated than utterances from different conversations. Thanks to the proposed framework, we are able to include such correlation in our aggregations, and be more accurate when determining which pairs of conversational systems are deemed significantly different.
Face Recognition Based Activity Detection for Security Application

Many applications including defence applications require identifying a person along with the kind of activities he is performing. Some of the applications include surveillance, security, healthcare and monitoring training activities etc. Many of these applications especially in defence require use of noncontact methods for recognizing person and the kind of activities he is performing. In this paper we have proposed an innovative approach that combines both the person recognition as well as the kind of activities he/she is performing using a camera based non-contact method. This innovative concept has been validated on multiple persons conducting multiple activities in an indoor environment.
Optimal calibration of gates in trapped-ion quantum computers

Abstract To harness the power of quantum computing, it is essential that a quantum computer provide maximal possible fidelity for a quantum circuit. To this end, much work has been done in the context of qubit routing or embedding, i.e., mapping circuit qubits to physical qubits based on gate performance metrics to optimize the fidelity of execution. Here, we take an alternative approach that leverages a unique capability of a trapped-ion quantum computer, i.e., the all-to-all qubit connectivity. We develop a method to determine a fixed number (budget) of quantum gates that, when calibrated, will maximize the fidelity of a batch of input quantum programs. This dynamic allocation of calibration resources on randomly accessible gates, determined using our heuristics, increases, for a wide range of calibration budget, the average fidelity from 70% or lower to 90% or higher for a typical batch of jobs on an 11-qubit device, in which the fidelity of calibrated and uncalibrated gates are taken to be 99% and 90%, respectively. Our heuristics are scalable, more than 2.5 orders of magnitude faster than a randomized method for synthetic benchmark circuits generated based on real-world use cases.
Understanding the Impact of Encrypted DNS on Internet Censorship

DNS traffic is transmitted in plaintext, resulting in privacy leakage. To combat this problem, secure protocols have been used to encrypt DNS messages. Existing studies have investigated the performance overhead and privacy benefits of encrypted DNS communications, yet little has been done from the perspective of censorship. In this paper, we study the impact of the encrypted DNS on Internet censorship in two aspects. On one hand, we explore the severity of DNS manipulation, which could be leveraged for Internet censorship, given the use of encrypted DNS resolvers. In particular, we perform 7.4 million DNS lookup measurements on 3,813 DoT and 75 DoH resolvers and identify that 1.66% of DoT responses and 1.42% of DoH responses undergo DNS manipulation. More importantly, we observe that more than two-thirds of the DoT and DoH resolvers manipulate DNS responses from at least one domain, indicating that the DNS manipulation is prevalent in encrypted DNS, which can be further exploited for enhancing Internet censorship. On the other hand, we evaluate the effectiveness of using encrypted DNS resolvers for censorship circumvention. Specifically, we first discover those vantage points that involve DNS manipulation through on-path devices, and then we apply encrypted DNS resolvers at these vantage points to access the censored domains. We reveal that 37% of the domains are accessible from the vantage points in China, but none of the domains is accessible from the vantage points in Iran, indicating that the censorship circumvention of using encrypted DNS resolvers varies from country to country. Moreover, for a vantage point, using a different encrypted DNS resolver does not lead to a noticeable difference in accessing the censored domains.
Controllable and Diverse Text Generation in E-commerce

In E-commerce, a key challenge in text generation is to find a good trade-off between word diversity and accuracy (relevance) in order to make generated text appear more natural and human-like. In order to improve the relevance of generated results, conditional text generators were developed that use input keywords or attributes to produce the corresponding text. Prior work, however, do not finely control the diversity of automatically generated sentences. For example, it does not control the order of keywords to put more relevant ones first. Moreover, it does not explicitly control the balance between diversity and accuracy. To remedy these problems, we propose a fine-grained controllable generative model, called Apex, that uses an algorithm borrowed from automatic control (namely, a variant of the proportional, integral, and derivative (PID) controller) to precisely manipulate the diversity/accuracy trade-off of generated text. The algorithm is injected into a Conditional Variational Autoencoder (CVAE), allowing Apex to control both (i) the order of keywords in the generated sentences (conditioned on the input keywords and their order), and (ii) the trade-off between diversity and accuracy. Evaluation results on real world datasets 1 show that the proposed method outperforms existing generative models in terms of diversity and relevance. Moreover, it achieves about 97% accuracy in the control of the order of keywords.
Instance weighting through data imprecisiation

In machine learning, instance weighting is commonly used to control the influence of individual data points in a learning process. The general idea is to improve results (e.g., the accuracy of a predictor) by restricting the influence of training examples that do not appear to be representative and may bias the learner in an undesirable way. The simplest and most common approach is to modulate the influence of each data point through multiplicative scaling. In this paper, we elaborate on the idea of instance weighting through data imprecisiation as a viable alternative to existing methods, and formalize this approach within the framework of superset learning. Roughly speaking, the idea is to reduce the influence of training examples by turning a precise data point into an imprecise observation. Within the framework of optimistic superset learning, a generic approach to superset learning, this effectively comes down to modifying an underlying loss function on a per-instance basis. We illustrate our approach for the case of binary classification with support vector machines, showing that it compares favorably with existing approaches to instance weighting in support vector machines. In a further case study, we demonstrate the usefulness of instance weighting through data imprecisiation for the practical problem of depth estimation in monocular images.
A semiautomatic annotation approach for sentiment analysis

Sentiment analysis (SA) aims to extract users’ opinions automatically from their posts and comments. Almost all prior works have used machine learning algorithms. Recently, SA research has shown promising performance in using the deep learning approach. However, deep learning is greedy and requires large datasets to learn, so it takes more time for data annotation. In this research, we proposed a semiautomatic approach using Naïve Bayes (NB) to annotate a new dataset in order to reduce the human effort and time spent on the annotation process. We created a dataset for the purpose of training and testing the classifier by collecting Saudi dialect tweets. The dataset produced from the semiautomatic model was then used to train and test deep learning classifiers to perform Saudi dialect SA. The accuracy achieved by the NB classifier was 83%. The trained semiautomatic model was used to annotate the new dataset before it was fed into the deep learning classifiers. The three deep learning classifiers tested in this research were convolutional neural network (CNN), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM). Support vector machine (SVM) was used as the baseline for comparison. Overall, the performance of the deep learning classifiers exceeded that of SVM. The results showed that CNN reported the highest performance. On one hand, the performance of Bi-LSTM was higher than that of LSTM and SVM, and, on the other hand, the performance of LSTM was higher than that of SVM. The proposed semiautomatic annotation approach is usable and promising to increase speed and save time and effort in the annotation process.
Bayesian Critiquing with Keyphrase Activation Vectors for VAE-based Recommender Systems

Critiquing is a method for conversational recommendation that incrementally adapts recommendations in response to user preference feedback. Recent advances in critiquing have leveraged the power of VAE-CF recommendation in a critiquable-explainable (CE-VAE) framework that updates latent user preference embeddings based on their critiques of keyphrase-based explanations. However, the CE-VAE has two key drawbacks: (i) it uses a second VAE head to facilitate explanations and critiquing, which can sacrifice recommendation performance of the first VAE head due to multiobjective training, and (ii) it requires iterating an inverse decoding-encoding loop for multi-step critiquing that yields poor performance. To address these deficiencies, we propose a novel Bayesian Keyphrase critiquing VAE (BK-VAE) framework that builds on the strengths of VAE-CF, but avoids the problematic second head of CE-VAE. Instead, the BK-VAE uses a Concept Activation Vector (CAV) inspired approach to determine the alignment of item keyphrase properties with latent user preferences in VAE-CF. BK-VAE leverages this alignment in a Bayesian framework to model uncertainty in a user's latent preferences and to perform posterior updates to these preference beliefs after each critique --- essentially achieving CE-VAE's explanation and critique inversion through a simple application of Bayes rule. Our empirical evaluation on two datasets demonstrates that BK-VAE matches or dominates CE-VAE in both recommendation and multi-step critiquing performance.
Equipment detection and recognition in electric power room based on faster R-CNN

Faster RCNN is a popular and very efficient object detection algorithm, it has good performance in image recognition in various fields. In this paper, we applied the Faster RCNN algorithm to the equipment recognition and status detection tasks in the electric power room, then proposed a complete technical implementation scheme. First of all, after site investigation and shooting, we collected a total of 5600 pictures in 100 categories of electrical equipment. Then, we achieved the labeled pictures for all categories by employing an open source software named Labelimg. In the next step, the annotated data was randomly shuffled, 70% of which was used as the training set for model training, 10% of which was used as the validation set for model tuning, and the remaining data was used as the test set for model testing. We get less than 0.01% loss in training step and get 91.3% mAP for all test pictures in testing step. Besides, the test for video received a great performance. The well performance on the test pictures and videos showed the effectiveness and implementability of our technical solutions.
Effective preprocessed thin blood smear images to improve malaria parasite detection using deep learning

Abstract Malaria can be difficult to detect from thin blood smears. Image recognition methods such as convolutional neural network can be used to detect malaria, but the training process takes a long time. Previous research created a new architecture and compares it to several other architectures such as VGG-16 and ResNet. The effect of preprocessing is analyzed in this research. VGG-16, ResNet, and the custom architecture created by the previous research are being used in this study. The preprocessing methods being analyzed in this research include gray-world normalization and comprehensive normalization. The highest accuracy improvement per epoch (0.5256% using ResNet-50 and 0.0352% using custom architecture) is achieved through gray-world normalization, that also improves final accuracy (90.1% using ResNet-50 and 93.1% using custom architecture) when compared to other methods with the same epochs for ResNet and custom architecture.
A New T-S Model Based Fuzzy Logic Approach For Mobile Robots Path Planning

An important expectation from a mobile robot is to be able to explore in an unstructured path. This paper proposes a Takagi-Sugeno(T-S) type fuzzy inference system with grid partition technique for mobile robot path planning. The sensors extracted information from the robot is plugged into the proposed model to calculate the steering direction for the robot. The simulation results are showed considering various scenarios using V-REP mobile simulator. It is proved that using the proposed model the robot can rove in any kinds of terrains. The simulation results are let-out that T-S type fuzzy inference system path planning model showed the best results in all aspects. Finally, experimentation is carried out using a physical robot to validate the V-REP simulations and it is observed that our proposed approach has outperformed current exciting navigation methods.
Efficient propagation of sparse edits on 360<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msup><mml:mrow /><mml:mo>∘</mml:mo></mml:msup></mml:math> panoramas

We present an efficient method to propagate sparse user edits indicated by strokes on 360∘ panoramas. Our algorithm first projects each equirectangular pixel to its corresponding position on a 3D unit sphere, so each pixel can be characterized by a feature vector consisting of its 3D coordinates and RGB color values. We formulate edit propagation as an optimization problem that aims to satisfy the user edit constraints while preserving the manifold structure of the image at the same time. To solve the problem using a linear system efficiently, we first construct the K-D tree structure in the feature space to cluster pixels. Then we optimize the manifold structure where both the number of nearest neighbors and their corresponding weights are determined by the feature distributions. We further apply a multiresolution strategy to speedup the edit propagation. Our method is the first to perform interactive edit propagation on 360∘ panoramas. Experiments show that our method is able to generate seam-free and visually pleasing results, and users can receive instant feedback during interactive editing.