Reviewerly/exHarmony huggingface dataset:
Komparasi Metode SMOTE dan ADASYN dalam Meningkatkan Performa Klasifikasi Herregistrasi Mahasiswa Baru

Universities annually accept new students at the beginning of the new school year. In the acceptance of prospective students on the Seleksi Prestasi Akademik Nasional Perguruan Tinggi Keagamaan Islam Negeri (SPAN PTKIN) di State Islamic University Of Sunan Ampel Surabaya, many prospective students who do not register will have an impact on income of the State Islamic University Of Sunan Ampel Surabaya institution. If the institution can find out early on the probability of a prospective student who will resign, then the management can take action to retain the prospective student. To overcome this, data mining classification can be carried out. The methods used in this classification are decision trees and naïve bayes. The number of students who did not re register compared to reregister resulted in the data being imbalanced. Data imbalances can affect the accuracy of the classification results. The imbalance of the data used can result in an unsuitable model. The solution to handle the data imbalance is to use the SMOTE and ADASYN oversampling methods. The purpose of this study was to compare performance of the SMOTE and ADASYN methods. The results show that the SMOTE method can balance the data in a balanced way compared to ADASYN. From the test results, the SMOTE method is more suitable to use than the ADASYN method because the ROCAUC SMOTE value is higher than ADASYN.
Comparative Analysis of Deep Learning Models for Vehicle Detection

Deep Learning techniques are now widely used instead of traditional Computer Vision. There are many Deep Learning model algorithms for each use case such as Object Detection has several models, including Faster R-CNN, SSD, and YOLO v3. The performance and results of each Deep Learning model have advantages and disadvantages. Therefore, we must determine which model is suitable for the use cases and datasets that we have so that we can make the best Deep Learning model. Based on this need, this paper will make a comparative analysis of the Deep Learning model for Vehicle Detection (the spesific of Object Detection) from the models mentioned, namely, Faster R-CNN, SSD (Single Shot Detector), and YOLO v3 (You Only Look Once) to see the advantages and the disadvantages and which ones are the best. And after a comparison, it was concluded that of the three models mentioned only YOLO v3 model is able to be used as real time detection because it has low latency due to YOLO v3 only performs single convolution process so that it makes the process simpler and faster without reduce the accuracy.
MemREIN: Rein the Domain Shift for Cross-Domain Few-Shot Learning

Few-shot learning aims to enable models generalize to new categories (query instances) with only limited labeled samples (support instances) from each category. Metric-based mechanism is a promising direction which compares feature embeddings via different metrics. However, it always fail to generalize to unseen domains due to the considerable domain gap challenge. In this paper, we propose a novel framework, MemREIN, which considers Memorized, Restitution, and Instance Normalization for cross-domain few-shot learning. Specifically, an instance normalization algorithm is explored to alleviate feature dissimilarity, which provides the initial model generalization ability. However, naively normalizing the feature would lose fine-grained discriminative knowledge between different classes. To this end, a memorized module is further proposed to separate the most refined knowledge and remember it. Then, a restitution module is utilized to restitute the discrimination ability from the learned knowledge. A novel reverse contrastive learning strategy is proposed to stabilize the distillation process. Extensive experiments on five popular benchmark datasets demonstrate that MemREIN well addresses the domain shift challenge, and significantly improves the performance up to 16.43% compared with state-of-the-art baselines.
OPOM: Customized Invisible Cloak Towards Face Privacy Protection

While convenient in daily life, face recognition technologies also raise privacy concerns for regular users on the social media since they could be used to analyze face images and videos, efficiently and surreptitiously without any security restrictions. In this paper, we investigate the face privacy protection from a technology standpoint based on a new type of customized cloak, which can be applied to all the images of a regular user, to prevent malicious face recognition systems from uncovering their identity. Specifically, we propose a new method, named one person one mask (OPOM), to generate person-specific (class-wise) universal masks by optimizing each training sample in the direction away from the feature subspace of the source identity. To make full use of the limited training images, we investigate several modeling methods, including affine hulls, class centers and convex hulls, to obtain a better description of the feature subspace of source identities. The effectiveness of the proposed method is evaluated on both common and celebrity datasets against black-box face recognition models with different loss functions and network architectures. In addition, we discuss the advantages and potential problems of the proposed method. In particular, we conduct an application study on the privacy protection of a video dataset, Sherlock, to demonstrate the potential practical usage of the proposed method.
Using Explainable Artificial Intelligence Models (ML) to Predict Suspected Diagnoses as Clinical Decision Support

The complexity of emergency cases and the number of emergency patients have increased dramatically. Due to a reduced or even missing specialist medical staff in the emergency departments (EDs), medical knowledge is often used without professional supervision for the diagnosis. The result is a failure in diagnosis and treatment, even death in the worst case. Secondary: high expenditure of time and high costs. Using accurate patient data from the German national registry of the medical emergency departments (AKTIN-registry, Home - Notaufnahmeregister (aktin.org)), the most 20 frequent diagnoses were selected for creating explainable artificial intelligence (XAI) models as part of the ENSURE project (ENSURE (umg.eu)). 137.152 samples and 51 features (vital signs and symptoms) were analyzed. The XAI models achieved a mean area under the curve (AUC) one-vs-rest of 0.98 for logistic regression (LR) and 0.99 for the random forest (RF), and predictive accuracies of 0.927 (LR) and 0.99 (RF). Based on its grade of explainability and performance, the best model will be incorporated into a portable CDSS to improve diagnoses and outcomes of ED treatment and reduce cost. The CDSS will be tested in a clinical pilot study at EDs of selected hospitals in Germany.
Efficient online motion planning method for the robotic arm to pick-up moving objects smoothly with temporal constraints

This paper proposes a new efficient online motion planning method for the manipulator to grasp moving objects smoothly. The algorithm framework consists of front-end pathfinding and back-end nonlinear trajectory optimization. The sample-based pathfinding algorithm can predict the intersection location and find a safe initial path in the dynamic environment. The gradient-based continuous-time trajectory optimization method converts the Euclidean Signed Distance Field information into the joint space, uses the B-spline curve to represent the joint trajectories, optimizes the initial path using convex hull characteristics of B-spline, and generates a smooth and kinematics feasible trajectory in joint space. Finally, we use an adjustment method of iterative trajectory fragment length to guarantee kinodynamic feasibility. Simulation comparisons and real-world experiments verify our planning framework’s high performance.
Probing for Passwords – Privacy Implications of SSIDs in Probe Requests

Probe requests help mobile devices discover active Wi-Fi networks. They often contain a multitude of data that can be used to identify and track devices and thereby their users. The past years have been a cat-and-mouse game of improving fingerprinting and introducing countermeasures against fingerprinting. This paper analyses the content of probe requests sent by mobile devices and operating systems in a field experiment. In it, we discover that users (probably by accident) input a wealth of data into the SSID field and find passwords, e-mail addresses, names and holiday locations. With these findings we underline that probe requests should be considered sensitive data and be well protected. To preserve user privacy, we suggest and evaluate a privacy-friendly hash-based construction of probe requests and improved user controls.
Frustrated Ising Model on D-wave Quantum Annealing Machine

We study the frustrated Ising model on the two-dimensional $L \times L$ square lattice with ferromagnetic (FM) nearest-neighbor and antiferromagnetic diagonal-neighbor interactions using the D-wave quantum annealing machine (D-QAM) with 5000+ qubits composed on structure of the Pegasus graph. As the former Monte Carlo and mean field results, we find the FM to stripe order phase transition, through observations of the magnetization $M$, energy, magnetic susceptibility and structure factor. We also analyze probability which occurs any $M$ at a given interaction for many quantum annealing shots to estimate the shape of objective function $f$. The only one value of $M$ with specific phase is observed in the regions far from phase transition for many quantum annealing shots, while several values of $M$ with different possibilities are appeared in the regimes of phase transition. We guess that $f$ in the regimes of phase transition retains the multi-modal structure with several local minimums, due to the strong degeneracies caused by frustrations. Finally, we discuss fail of the quantum annealing simulations, through analysis of the number of the chains, defined as the same variable with $N$-qubits, as a function of $L$.
Dual-Frequency Phase Unwrapping Method for anti-Jump Errors of Wrapped Phase

The accuracy of stripe structured light three-dimensional measurement is greatly improved with the development of hardware and image processing technology. However, the fault tolerance and anti-jamming ability of complex measured objects need to be improved. First, by aiming at the problem of large error caused via wavelength position jump in phase unwrapping, the shortcomings of single-frequency phase unwrapping method were analyzed, a dual-frequency phase unwrapping method was proposed, and its mathematical model was established. Second, the error tolerance was analyzed near the dual-frequency phase hopping point, and the maximum error tolerance value is 0.25δ. In the environment of interference and noninterference, the three-dimensional measurement experiment shows that the mathematical model of dual-frequency phase unwrapping and the theoretical correctness of error analysis. Its measurement accuracy and anti-interference ability are higher than those of single-frequency phase unwrapping. Also, it can suppress the gross error caused by wavelength dislocation effectively, and the operation is simple. Aiming at three-dimensional measurement and reconstruction of highly complex surface with self-occlusion, the local reconstructed has high accuracy, and the reconstructed surface is smooth. It corresponds to the real measured surface and has good anti-interference performance.
Complete analysis of the maximally hyperentangled state via the weak cross-Kerr nonlinearity

We present a simple method for the complete analysis of the maximally hyperentangled state in polarization and spatial-mode degrees of freedom assisted by the weak cross-Kerr nonlinearity. Our method can not only be used for two-photon hyperentangled Bell state analysis and three-photon hyperentangled Greenberger–Horne–Zeilinger (GHZ) state analysis, but is also suitable for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>N</mml:mi> </mml:math> -photon hyperentangled GHZ state analysis. In our protocols, the bit information of hyperentanglement is read out via the nonlinear interaction, and the phase information is obtained by using linear optical element and single photon detector. This approach is achievable with the current technology, and it will be useful for practical high-capacity quantum communication schemes.
AggEnhance: Aggregation Enhancement by Class Interior Points in Federated Learning with Non-IID Data

Federated learning (FL) is a privacy-preserving paradigm for multi-institutional collaborations, where the aggregation is an essential procedure after training on the local datasets. Conventional aggregation algorithms often apply a weighted averaging of the updates generated from distributed machines to update the global model. However, while the data distributions are non-IID, the large discrepancy between the local updates might lead to a poor averaged result and a lower convergence speed, i.e., more iterations required to achieve a certain performance. To solve this problem, this article proposes a novel method named AggEnhance for enhancing the aggregation, where we synthesize a group of reliable samples from the local models and tune the aggregated result on them. These samples, named class interior points (CIPs) in this work, bound the relevant decision boundaries that ensure the performance of aggregated result. To the best of our knowledge, this is the first work to explicitly design an enhancing method for the aggregation in prevailing FL pipelines. A series of experiments on real data demonstrate that our method has noticeable improvements of the convergence in non-IID scenarios. In particular, our approach reduces the iterations by 31.87% on average for the CIFAR10 dataset and 43.90% for the PASCAL VOC dataset. Since our method does not modify other procedures of FL pipelines, it is easy to apply to most existing FL frameworks. Furthermore, it does not require additional data transmitted from the local clients to the global server, thus holding the same security level as the original FL algorithms.
Argumentative Text Generation in Economic Domain

The development of large and super-large language models, such as GPT-3, T5, Switch Transformer, ERNIE, etc., has significantly improved the performance of text generation. One of the important research directions in this area is the generation of texts with arguments. The solution of this problem can be used in business meetings, political debates, dialogue systems, for preparation of student essays. One of the main domains for these applications is the economic sphere. The key problem of the argument text generation for the Russian language is the lack of annotated argumentation corpora. In this paper, we use translated versions of the Argumentative Microtext, Persuasive Essays and UKP Sentential corpora to fine-tune RuBERT model. Further, this model is used to annotate the corpus of economic news by argumentation. Then the annotated corpus is employed to fine-tune the ruGPT-3 model, which generates argument texts. The results show that this approach improves the accuracy of the argument generation by more than 20 percentage points (63.2\% vs. 42.5\%) compared to the original ruGPT-3 model.
Health monitoring catalogue based on human activity classification using machine learning

In recent times, fitness trackers and smartphones equipped with different sensors like gyroscopes, accelerometers, global positioning system sensors and programs are used for recognizing human activities. In this paper, the results collected from these devices are used to design a system that can assist an application in monitoring a person’s health. The proposed system takes the raw sensor signals as input, preprocesses it and using machine learning techniques outputs the state of the user with minimum error. The objective of this paper is to compare the performance of different algorithms logistic regression (LR), support vector machine (SVM), k-nearest neighbor (k-NN) and random forest (RF). The algorithms are trained and tested with an original number of features as well as with transformed number of features (using linear discriminant analysis). The data with a smaller number of features is then used to visualize the high dimensional data. In this paper, each data point is mapped in the high dimensional data to two-dimensional data using t-distributed stochastic neighbor embedding technique. Overall, the first high dimensional data is visualized and compared with model’s performance with different algorithms and different number of coordinates.
[White blood segmentation based on dual path and atrous spatial pyramid pooling].

The count and recognition of white blood cells in blood smear images play an important role in the diagnosis of blood diseases including leukemia. Traditional manual test results are easily disturbed by many factors. It is necessary to develop an automatic leukocyte analysis system to provide doctors with auxiliary diagnosis, and blood leukocyte segmentation is the basis of automatic analysis. In this paper, we improved the U-Net model and proposed a segmentation algorithm of leukocyte image based on dual path and atrous spatial pyramid pooling. Firstly, the dual path network was introduced into the feature encoder to extract multi-scale leukocyte features, and the atrous spatial pyramid pooling was used to enhance the feature extraction ability of the network. Then the feature decoder composed of convolution and deconvolution was used to restore the segmented target to the original image size to realize the pixel level segmentation of blood leukocytes. Finally, qualitative and quantitative experiments were carried out on three leukocyte data sets to verify the effectiveness of the algorithm. The results showed that compared with other representative algorithms, the proposed blood leukocyte segmentation algorithm had better segmentation results, and the mIoU value could reach more than 0.97. It is hoped that the method could be conducive to the automatic auxiliary diagnosis of blood diseases in the future.血涂片图像中白细胞的计数和识别对诊断包括白血病在内的血液疾病起着至关重要的作用。传统的人工检测结果容易受到多种因素的干扰，有必要开发白细胞自动分析系统为医生提供辅助诊断，而血液白细胞分割则是自动分析的基础。本文改进U-Net模型，提出一种基于双路径和空洞空间金字塔池化的血液白细胞分割算法。首先在特征编码器中引入双路径网络提取图像中白细胞的多尺度特征，并使用空洞空间金字塔池化模块强化网络的特征提取能力，再用卷积和反卷积组成特征解码器将分割目标恢复到原始图像大小，实现血液白细胞的像素级分割。最后在三个白细胞数据集上进行定性定量实验，验证本文算法的有效性。研究结果表明，提出的血液白细胞分割算法相对于其他典型方法具有更为优秀的分割结果，mIoU值能达到0.97以上，今后或有助于血液疾病的自动辅助诊断。.
Expert insights on digital contact tracing: interviews with contact tracing policy professionals in New Zealand

Abstract Digital contact tracing (DCT) is the application of digital tools to assist with identifying and informing close contacts of a COVID-19 case. DCT is a potential solution to capacity constraints of current manual contact tracing processes. Expert opinion from contact tracing professionals rarely informs public discourse on the benefits and limitations of DCT solutions. Three focus groups were undertaken in New Zealand to understand benefits and limitations of DCT solutions from contact tracing professionals. One was with the National Investigation and Tracing Centre (NITC) and two were with Public Health Units (PHUs). Participants highlighted four key themes including: (i) equity, (ii) privacy, (iii) communication and public perception and (iv) the operational model. Participants were concerned DCT solutions could exacerbate existing health inequities due to lack of access to, or familiarity with, technology. Poor communication and public understanding of DCT were seen as a major threat to both the efficacy of DCT solutions and the wider COVID-19 response. Most importantly, end-users were cautious of the operational model for DCT data that might: (i) attempt to replace manual processes that cannot or should not be automated by technology (case investigations, follow-ups); (ii) place undue burden on citizens and (iii) increase the workload for the current system beyond its capacity, for unproven or limited benefit. To be effective, contact tracing professionals believed DCT technologies must have strong privacy safeguards, a clear and simple communication strategy, interoperability with the existing contact tracing system and a foundation of health equity.
Tree-constrained Pointer Generator with Graph Neural Network Encodings for Contextual Speech Recognition

Incorporating biasing words obtained as contextual knowledge is critical for many automatic speech recognition (ASR) applications. This paper proposes the use of graph neural network (GNN) encodings in a tree-constrained pointer generator (TCPGen) component for end-to-end contextual ASR. By encoding the biasing words in the prefix-tree with a tree-based GNN, lookahead for future wordpieces in end-to-end ASR decoding is achieved at each tree node by incorporating information about all wordpieces on the tree branches rooted from it, which allows a more accurate prediction of the generation probability of the biasing words. Systems were evaluated on the Librispeech corpus using simulated biasing tasks, and on the AMI corpus by proposing a novel visual-grounded contextual ASR pipeline that extracts biasing words from slides alongside each meeting. Results showed that TCPGen with GNN encodings achieved about a further 15% relative WER reduction on the biasing words compared to the original TCPGen, with a negligible increase in the computation cost for decoding.
Area-Optimized Constant-Time Hardware Implementation for Polynomial Multiplication

This work presents a lightweight, FPGA-based hardware implementation for polynomial multiplication, which is the major bottleneck in the NTRU public-key cryptographic scheme. NTRU is a quantum-resilient, lattice-based key exchange cryptosystem, and is currently a finalist in the ongoing National Institute of Standards and Technology post-quantum cryptography standardization. It is challenging to fit these quantum-resilient schemes into Internet of Things (IoT) sensor nodes due to the strict resource constraints (smaller area, less memory, and lower energy budgets) and the limited computational capabilities in embedded devices. We undertake this compact implementation for polynomial multiplication with two motivations: 1) constant-time implementation ensuring inherent security against timing side-channel attacks and 2) optimized hardware consumption to make it suitable for IoT applications. A single-step multiplexer-based iterative architecture is proposed to achieve both goals simultaneously. Compared to the architectures presented in the literature, our proposed work eliminates the utilization of a modular arithmetic unit and replaces it with the correct selection of input followed by an accumulator, which can help to save substantial device resources. Experimental results with an FPGA show that our proposed architecture achieves an area reduction of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.86 \times $ </tex-math></inline-formula> and the throughput increase up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.23 \times $ </tex-math></inline-formula> compared to the state-of-the-art implementation strategies, providing comparable latency along with an inherent-timing attack resilience that is absent in several NTRU hardware implementation schemes.