A Weighted Voting Classifier Based on Differential Evolution

Ensemble learning is to employ multiple individual classifiers and combine their predictions, which could achieve better performance than a single classifier. Considering that different base classifier gives different contribution to the final classification result, this paper assigns greater weights to the classifiers with better performance and proposes a weighted voting approach based on differential evolution. After optimizing the weights of the base classifiers by differential evolution, the proposed method combines the results of each classifier according to the weighted voting combination rule. Experimental results show that the proposed method not only improves the classification accuracy, but also has a strong generalization ability and universality

Enhanced microwave dielectric tunability of Ba0.5Sr0.5TiO3 thin films grown with reduced strain on DyScO3 substrates by three-step technique

Tunable dielectric properties of epitaxial ferroelectric Ba0.5Sr0.5TiO3 (BST) thin films deposited on nearly lattice-matched DyScO3 substrates by radio frequency magnetron sputtering have been investigated at microwave frequencies and correlated with residual compressive strain. To reduce the residual strain of the BST films caused by substrate clamping and improve their microwave properties, a three-step deposition method was devised and employed. A high-temperature deposition at 1068 K of the nucleation layer was followed by a relatively low-temperature deposition (varied in the range of 673–873 K) of the BST interlayer and a high-temperature deposition at 1068 K of the top layer. Upon post-growth thermal treatment at 1298 K the films grown by the three-step method with the optimized interlayer deposition temperature of 873 K exhibited lower compressive strain compared to the control layer (−0.002 vs. −0.006). At 10 GHz, a high dielectric tunability of 47.9% at an applied electric field of 60 kV/cm was achieved for the optimized films. A large differential phase shift of 145°/cm and a figure of merit of 23°/dB were obtained using a simple coplanar waveguide phase shifter at 10 GHz. The low residual strain and improved dielectric properties of the films fabricated using the three-step deposition technique were attributed to reduced clamping of the BST films by the nearly lattice-matched substrate

A Weighted Voting Classifier Based on Differential Evolution

Ensemble learning is to employ multiple individual classifiers and combine their predictions, which could achieve better performance than a single classifier. Considering that different base classifier gives different contribution to the final classification result, this paper assigns greater weights to the classifiers with better performance and proposes a weighted voting approach based on differential evolution. After optimizing the weights of the base classifiers by differential evolution, the proposed method combines the results of each classifier according to the weighted voting combination rule. Experimental results show that the proposed method not only improves the classification accuracy, but also has a strong generalization ability and universality

Actively Secure Half-Gates with Minimum Overhead under Duplex Networks

Actively secure two-party computation (2PC) is one of the canonical building blocks in modern cryptography. One main goal for designing actively secure 2PC protocols is to reduce the communication overhead, compared to semi-honest 2PC protocols. In this paper, we propose a new actively secure constant-round 2PC protocol with one-way communication of $2\kappa+5$ bits per AND gate (for $\kappa$-bit computational security and any statistical security), essentially matching the one-way communication of semi-honest half-gates protocol. This is achieved by two new techniques: 1. The recent compression technique by Dittmer et al. (Crypto 2022) shows that a relaxed preprocessing is sufficient for authenticated garbling that does not reveal masked wire values to the garbler. We introduce a new form of authenticated bits and propose a new technique of generating authenticated AND triples to reduce the one-way communication of preprocessing from $5\rho+1$ bits to $2$ bits per AND gate for $\rho$-bit statistical security. 2. Unfortunately, the above compressing technique is only compatible with a less compact authenticated garbled circuit of size $2\kappa+3\rho$ bits per AND gate. We designed a new authenticated garbling that does not use information theoretic MACs but rather dual execution without leakage to authenticate wire values in the circuit. This allows us to use a more compact half-gates based authenticated garbled circuit of size $2\kappa+1$ bits per AND gate, and meanwhile keep compatible with the compression technique. Our new technique can achieve one-way communication of $2\kappa+5$ bits per AND gate. Our technique of yielding authenticated AND triples can also be used to optimize the two-way communication (i.e., the total communication) by combining it with the authenticated garbled circuits by Dittmer et al., which results in an actively secure 2PC protocol with two-way communication of $2\kappa+3\rho+4$ bits per AND gate

ReSolveD: Shorter Signatures from Regular Syndrome Decoding and VOLE-in-the-Head

We present ReSolveD, a new candidate post-quantum signature scheme under the regular syndrome decoding (RSD) assumption for random linear codes, which is a well-established variant of the well-known syndrome decoding (SD) assumption. Our signature scheme is obtained by designing a new zero-knowledge proof for proving knowledge of a solution to the RSD problem in the recent VOLE-in-the-head framework using a sketching scheme to verify that a vector has weight exactly one. We achieve a signature size of 3.99 KB with a signing time of 27.3 ms and a verification time of 23.1 ms on a single core of a standard desktop for a 128-bit security level. Compared to the state-of-the-art code-based signature schemes, our signature scheme achieves $1.5\times \sim 2\times$ improvement in terms of the common signature size + public-key size metric, while keeping the computational efficiency competitive

Estimating cancer incidence based on claims data from medical insurance systems in two areas lacking cancer registries in China.

BACKGROUND: We aimed to establish a Medical-Insurance-System-based Cancer Surveillance System (MIS-CASS) in China and evaluate the completeness and timeliness of this system through reporting cancer incidence rates using claims data in two regions in northern and southern China. METHODS: We extracted claims data from medical insurance systems in Hua County of Henan Province, and Shantou City in Guangdong Province in China from Jan 1, 2012 to Jun 30, 2019. These two regions have been considered to be high risk regions for oesophageal cancer. We developed a rigorous procedure to establish the MIS-CASS, which includes data extraction, cleaning, processing, case ascertainment, privacy protection, etc. Text-based diagnosis in conjunction with ICD-10 codes were used to determine cancer diagnosis. FINDINGS: In 2018, the overall age-standardised (Segi population) incidence rates (ASR World) of cancer in Hua County and Shantou City were 167·39/100,000 and 159·78/100,000 respectively. In both of these areas, lung cancer and breast cancer were the most common cancers in males and females respectively. Hua County is a high-risk region for oesophageal cancer (ASR World: 25·95/100,000), whereas Shantou City is not a high-risk region for oesophageal cancer (ASR World: 11·43/100,000). However, Nanao island had the highest incidence of oesophageal cancer among all districts and counties in Shantou (ASR World: 36·39/100,000). The age-standardised male-to-female ratio for oesophageal cancer was lower in Hua County than in Shantou (1·69 vs. 4·02). A six-month lag time was needed to report these cancer incidences for the MIS-CASS. INTERPRETATION: MIS-CASS efficiently reflects cancer burden in real-time, and has the potential to provide insight for improvement of cancer surveillance in China. FUNDING: The National Key R&D Program of China (2016YFC0901404), the Digestive Medical Coordinated Development Center of Beijing Municipal Administration of Hospitals (XXZ0204), the Sanming Project of Shenzhen (SZSM201612061), and the Shantou Science and Technology Bureau (190829105556145, 180918114960704)

Pest-YOLO: A model for large-scale multi-class dense and tiny pest detection and counting

Frequent outbreaks of agricultural pests can reduce crop production severely and restrict agricultural production. Therefore, automatic monitoring and precise recognition of crop pests have a high practical value in the process of agricultural planting. In recent years, pest recognition and detection have been rapidly improved with the development of deep learning-based methods. Although certain progress has been made in the research on pest detection and identification technology based on deep learning, there are still many problems in the production application in a field environment. This work presents a pest detector for multi-category dense and tiny pests named the Pest-YOLO. First, the idea of focal loss is introduced into the loss function using weight distribution to improve the attention of hard samples. In this way, the problems of hard samples arose from the uneven distribution of pest populations in a dataset and low discrimination features of small pests are relieved. Next, a non-Intersection over Union bounding box selection and suppression algorithm, the confluence strategy, is used. The confluence strategy can eliminate the errors and omissions of pest detection caused by occlusion, adhesion and unlabeling among tiny dense pest individuals to the greatest extent. The proposed Pest-YOLO model is verified on a large-scale pest image dataset, the Pest24, which includes more than 20k images with over 190k pests labeled by agricultural experts and categorized into 24 classes. Experimental results show that the Pest-YOLO can obtain 69.59% for mAP and 77.71% for mRecall on the 24-class pest dataset, which is 5.32% and 28.12% higher than the benchmark model YOLOv4. Meanwhile, our proposed model is superior to other several state-of-the-art methods, including the SSD, RetinaNet, Faster RCNN, YOLOv3, YOLOv4, YOLOv5s, YOLOv5m, YOLOX, DETR, TOOD, YOLOv3-W, and AF-RCNN detectors. The code of the proposed algorithm is available at: https://github.com/chr-secrect/Pest-YOLO