Reviewerly/exHarmony huggingface dataset:
Language Education Optimization: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

In this paper, based on the concept of the NFL theorem, that there is no unique algorithm that has the best performance for all optimization problems, a new human-based metaheuristic algorithm called Language Education Optimization (LEO) is introduced, which is used to solve optimization problems.LEO is inspired by the foreign language education process in which a language teacher trains the students of language schools in the desired language skills and rules.LEO is mathematically modeled in three phases: (i) students selecting their teacher, (ii) students learning from each other, and (iii) individual practice, considering exploration in local search and exploitation in local search.The performance of LEO in optimization tasks has been challenged against fifty-two benchmark functions of a variety of unimodal, multimodal types and the CEC 2017 test suite.The optimization results show that LEO, with its acceptable ability in exploration, exploitation, and maintaining a balance between them, has efficient performance in optimization applications and solution presentation.LEO efficiency in optimization tasks is compared with ten well-known metaheuristic algorithms.Analyses of the simulation results show that LEO has effective performance in dealing with optimization tasks and is significantly superior and more competitive in combating the compared algorithms.The implementation results of the proposed approach to four engineering design problems show the effectiveness of LEO in solving real-world optimization applications.
Faber: A Hardware/SoftWare Toolchain for Image Registration

Image registration is a well-defined computation paradigm widely applied to align one or more images to a target image. This paradigm, which builds upon three main components, is particularly compute-intensive and represents many image processing pipelines’ bottlenecks. State-of-the-art solutions leverage hardware acceleration to speed up image registration, but they are usually limited to implementing a single component. We present Faber, an open-source HW/SW CAD toolchain tailored to image registration. The Faber toolchain comprises HW/SW highly-tunable registration components, supports users with different expertise in building custom pipelines, and automates the design process. In this direction, Faber provides both default settings for entry-level users and latency and resource models to guide HW experts in customizing the different components. Finally, Faber achieves from 1.5× to 54× in speedup and from 2× to 177× in energy efficiency against state-of-the-art tools on a Xeon Gold.
Self-supervised deep partial adversarial network for micro-video multimodal classification

Micro-videos have gained popularity on various social media platforms because they provide a great medium for real-time storytelling. Although micro-videos can be naturally characterized by several modalities, for situations with uncertain missing modalities, a flexible multimodal representation learning framework that integrates complementary and consistent information has been difficult to develop. To better deal with the issue regarding incomplete modalities in multimodal micro-video classification, in this paper, we propose a self-supervised deep multimodal adversarial network (SDMAN) to learn comprehensive and robust micro-video representations. Specifically, we first consider a parallel multi-head attention (MHA) encoding module that simultaneously learns the representations of complete and incomplete modality groupings. We then present a multimodal self-supervised cycle generative adversarial network module, in which multiple generative adversarial networks are explored to transfer the information obtained from the complete modality grouping to the incomplete modality groupings. As a result, complementarity and consistency are mutually promoted among the modalities. Furthermore, experiments conducted on a large-scale micro-video dataset demonstrate that the SDMAN performs better than the state-of-the-art methods.
Reproducibility of deep learning in digital pathology whole slide image analysis

For a method to be widely adopted in medical research or clinical practice, it needs to be reproducible so that clinicians and regulators can have confidence in its use. Machine learning and deep learning have a particular set of challenges around reproducibility. Small differences in the settings or the data used for training a model can lead to large differences in the outcomes of experiments. In this work, three top-performing algorithms from the Camelyon grand challenges are reproduced using only information presented in the associated papers and the results are then compared to those reported. Seemingly minor details were found to be critical to performance and yet their importance is difficult to appreciate until the actual reproduction is attempted. We observed that authors generally describe the key technical aspects of their models well but fail to maintain the same reporting standards when it comes to data preprocessing which is essential to reproducibility. As an important contribution of the present study and its findings, we introduce a reproducibility checklist that tabulates information that needs to be reported in histopathology ML-based work in order to make it reproducible.
Experimental Analysis of a Quantum Encoder in Various Quantum Systems

Quantum computer performs operations by adopting the principles of quantum physics and quantum mechanics. With these principles, it performs operations exponentially faster compared to classical computers. The major problem observed in quantum computation is noise and decoherence. The noise and decoherence generate errors while performing the operations on quantum states. As a solution to this Quantum error correction(QEC) methods are introduced. Encoding plays a key role in QEC. In the encoding process, the logical qubits are encoded into physical qubits by appending extra qubits to them. With this, the logical qubit will be strengthened and can be transferred safely. Initially, the experimental results of quantum computation are theoretical or mathematical. But with the existence of quantum computers, it is possible to develop and run new quantum architectures on publicly available quantum computers. Thus in this paper, we developed an efficient algorithm for encoding quantum information using various quantum gates. The developed algorithm is executed on various quantum systems and the performance is analyzed in terms of frequency, run time, error rate, number of qubits, and quantum volume. This analysis helps the researchers to opt an efficient quantum system to perform the experiments.
Predicting hypertension onset from longitudinal electronic health records with deep learning

Hypertension has long been recognized as one of the most important predisposing factors for cardiovascular diseases and mortality. In recent years, machine learning methods have shown potential in diagnostic and predictive approaches in chronic diseases. Electronic health records (EHRs) have emerged as a reliable source of longitudinal data. The aim of this study is to predict the onset of hypertension using modern deep learning (DL) architectures, specifically long short-term memory (LSTM) networks, and longitudinal EHRs.We compare this approach to the best performing models reported from previous works, particularly XGboost, applied to aggregated features. Our work is based on data from 233 895 adult patients from a large health system in the United States. We divided our population into 2 distinct longitudinal datasets based on the diagnosis date. To ensure generalization to unseen data, we trained our models on the first dataset (dataset A "train and validation") using cross-validation, and then applied the models to a second dataset (dataset B "test") to assess their performance. We also experimented with 2 different time-windows before the onset of hypertension and evaluated the impact on model performance.With the LSTM network, we were able to achieve an area under the receiver operating characteristic curve value of 0.98 in the "train and validation" dataset A and 0.94 in the "test" dataset B for a prediction time window of 1 year. Lipid disorders, type 2 diabetes, and renal disorders are found to be associated with incident hypertension.These findings show that DL models based on temporal EHR data can improve the identification of patients at high risk of hypertension and corresponding driving factors. In the long term, this work may support identifying individuals who are at high risk for developing hypertension and facilitate earlier intervention to prevent the future development of hypertension.
Cluster Alignment With Target Knowledge Mining for Unsupervised Domain Adaptation Semantic Segmentation

Unsupervised domain adaptation (UDA) carries out knowledge transfer from the labeled source domain to the unlabeled target domain. Existing feature alignment methods in UDA semantic segmentation achieve this goal by aligning the feature distribution between domains. However, these feature alignment methods ignore the domain-specific knowledge of the target domain. In consequence, 1) the correlation among pixels of the target domain is not explored; and 2) the classifier is not explicitly designed for the target domain distribution. To conquer these obstacles, we propose a novel cluster alignment framework, which mines the domain-specific knowledge when performing the alignment. Specifically, we design a multi-prototype clustering strategy to make the pixel features within the same class tightly distributed for the target domain. Subsequently, a contrastive strategy is developed to align the distributions between domains, with the clustered structure maintained. After that, a novel affinity-based normalized cut loss is devised to learn task-specific decision boundaries. Our method enhances the model's adaptability in the target domain, and can be used as a pre-adaptation for self-training to boost its performance. Sufficient experiments prove the effectiveness of our method against existing state-of-the-art methods on representative UDA benchmarks.
Speeding up Heterogeneous Federated Learning with Sequentially Trained Superclients

Federated Learning (FL) allows training machine learning models in privacy-constrained scenarios by enabling the cooperation of edge devices without requiring local data sharing. This approach raises several challenges due to the different statistical distribution of the local datasets and the clients’ computational heterogeneity. In particular, the presence of highly non-i.i.d. data severely impairs both the performance of the trained neural network and its convergence rate, increasing the number of communication rounds requested to reach a performance comparable to that of the centralized scenario. As a solution, we propose FedSeq, a novel framework leveraging the sequential training of subgroups of heterogeneous clients, i.e. superclients, to emulate the centralized paradigm in a privacy-compliant way. Given a fixed budget of communication rounds, we show that FedSeq outperforms or match several state-of-the-art federated algorithms in terms of final performance and speed of convergence. Finally, our method can be easily integrated with other approaches available in the literature. Empirical results show that combining existing algorithms with FedSeq further improves its final performance and convergence speed. We test our method on CIFAR-10 and CIFAR-100 and prove its effectiveness in both i.i.d. and non-i.i.d. scenarios. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Enhancing item-based collaborative filtering by users’ similarities injection and low-quality data handling

With the increasing amount of the commercial items (movies, music, books, cars, etc.) produced each day by companies, it becomes very difficult for customers to find the suitable products satisfying their needs. Generally, Recommendation Systems (RSs) were used to fit this necessary requirement by solving the problem of information overload, specially on the web. Indeed, RS are designed to provide relevant resources to a client using certain information about users and resources. To the best of our knowledge, RS remains providing modest performances in many domains. In this context, we proposed a new model named CSWMC that combines two different techniques: item-based and user-based Collaborative Filtering. In fact, our proposed algorithm starts with the estimation of the suitable number of the user's neighbors that offers to the Recommender System the optimal efficiency. Then, the system integrates this knowledge about users in the 'Mean Centered' aggregation method. Also, we proposed a simple method for handling the cold start and data sparsity problems that used mean value of the training datasets. The proposed models were validated through an experimental study on three standards datasets and compared with six well-known models. The obtained results demonstrated that our proposed model (in its two versions: with and without cold start handling) outperforms all the other models in terms of three evaluation metrics: RMSE, MAE and R2.
Secret image sharing by using multi-prime modular arithmetic

All secret image sharing (SIS) schemes are based on a finite field, on which the generation and reconstruction can be accomplished correctly. To achieve distortionless secret images, a Galois field GF(2m) would be adopted with increased computational complexity. Simple modular arithmetic, on the other hand, is frequently utilized to improve computational efficiency. However, the SIS using simple modular arithmetic is not a completely distortion-free scheme. In this paper, simple modular arithmetic will continue to be investigated to reserve its utility of reducing computational complexity. Multi-prime modular arithmetic is explored for building SIS with multi-prime (referred to as SISw/M) rather than using single-prime modular arithmetic. When dealing with the N-bit frames in a secret image, the prime employed in SISw/M would be closer to the 2N value, resulting in improved image quality. In addition, with Chinese Remainder Theorem, progressive recovery is provided. Experimental results and comparisons are demonstrated to show the merits (enhanced image quality and progressive recovery) of the proposed approach.
A relative granular ratio-based outlier detection method in heterogeneous data

Outlier detection is the discovery of some objects that are significantly different from many objects in data, and it is widely used in important fields. Most existing methods are based on prior knowledge, while few methods are suitable for heterogeneous data. In this paper, we detect outliers based on neighborhood rough set, which can process heterogeneous data and reduce some hyper-parameters. Considering the few characters of outliers, a relative granular ratio factor is consequently created to measure the size of a neighborhood in which an object belongs. Since outliers always differ from the majority of objects, a granule-based majority set is defined. Then, a valid outlier factor is determined by the feature of a negative region to measure the difference between outliers and the majority set. Finally, a ratio and negative region detection factor (RNRD) is constructed by combining the above factors under a wide range of relations. In addition, the RNRD-based outlier detection (RNROD) algorithm is designed. And experiments show the superiority of RNROD by comparing with seven existing detection algorithms on sixteen heterogeneous datasets.
Visual Question Answering based on multimodal triplet knowledge accumuation

Knowledge-based visual Question Answer needs to be associated with external knowledge to better understand. Existing solutions generally acquire relevant knowledge from pure text knowledge bases, but these knowledge bases only contain some facts for simple questions and answers, lacking visual content for deep understanding. This paper proposes an explicit triplet to represent multimodal knowledge, which associates visual objects and factual answers with implicit relationships. The header is obtained by calculating the Euclidean distance between the regional feature and the text token. To narrow the heterogeneity gap, we propose three target losses of learning triplet representation from the perspective of complementarity, using TransH, topological relations, and semantic space to calculate loss respectively. By adopting unique learning strategies, we gradually accumulate multimodal knowledge in specific fields and predict answers. On two knowledge-based datasets: ok-vqa and kr-vqa, the performance is higher than that of the latest method respectively. The experimental results prove the validity of the proposed model.
Research on Improving the Quality of Talent Training in Higher Vocational Colleges in China

According to the action plan for improving the quality of vocational education (2020-2023), the vocational school evaluation system includes professional ethics, professional quality, technical skills, employment quality, and entrepreneurial ability as important content areas for assessing the quality of talent training that should be strengthened. This plan was released by the Ministry of Education and nine other government departments. An example higher vocational college in Guangdong is used to illustrate the importance of quality talent training in the new era, and a method for improving talent training quality in vocational colleges is proposed. This chapter is intended to serve as a model for other higher vocational colleges seeking to improve the quality of talent training while also promoting the modernization and internationalization of high-quality vocational education.
Hyperspectral measurement technique based rapid determination of coal quality parameters of Jharia and Raniganj basin coal

Hyperspectral technology has a striking ability to detect the quality of materials, and consequently, research on coal quality using this technique has increased many folds in recent years. For the purpose of assessing the quality of coal, hyperspectral signatures, proximate analysis, and gross calorific value of 78 coal specimens from different parts of the Jharia and Raniganj basins (India) were studied. All the coal samples demonstrate well-defined absorption features at different wavelengths. Based on the spectral responses, seven wavelength regions, namely 400–900 nm, 950–1050 nm, 1350–1500 nm, 1840–1950 nm, 2120–2250 nm, 2260–2350 nm, and 2450–2500 nm, were chosen for this study. Three techniques were applied to capture the relationship between coal spectral absorption features and coal proximate parameters, i.e., XGBoost, Random Forest, and Partial-least-square. It has been concluded that the result obtained for random-forest-based regression work best for predicting ash, moisture, and GCV. In comparison, the PLS regression model shows the most accurate predictions for the volatile matter.
Stepping Closer to Pulsed Single Microwave Photon Detectors for Axions Search

Axions detection requires the ultimate sensitivity down to the single photon limit. In the microwave region this corresponds to energies in the yJ range. This extreme sensitivity has to be combined with an extremely low dark count rate, since the probability of axions conversion into microwave photons is supposed to be very low. To face this complicated task, we followed two promising approaches that both rely on the use of superconducting devices based on the Josephson effect. The first one is to use a single Josephson junction (JJ) as a switching detector (i.e. exploiting the superconducting to normal state transition in presence of microwave photons). We designed a device composed of a coplanar waveguide terminated on a current biased Josephson junction. We tested its efficiency to pulsed (pulse duration 10 ns) microwave signals, since this configuration is closer to an actual axions search experiment. We show how our device is able to reach detection capability of the order of 10 photons with frequency 8 GHz. The second approach is based on an intrinsically quantum device formed by two resonators coupled only via a superconducting qubit network (SQN). This approach relies on quantum nondemolition measurements of the resonator photons. We show that injecting RF power into the resonator, the frequency position of the resonant drop in the transmission coefficient (S21) can be modulated up to 4 MHz. We anticipate that, once optimized, both the devices have the potential to reach single photon sensitivity.
Deep Inside Tor: Exploring Website Fingerprinting Attacks on Tor Traffic in Realistic Settings

In recent years, with the new advances in the areas of machine learning, Tor’s advertised anonymity has been widely threatened. Despite all the protection mechanisms employed by Tor, attackers can now draw inferences on the online activities of a Tor user. Although such an study is critical for users of Tor, most of the existing works in this regard are based on unrealistic assumptions and settings. In this work, we explore the effectiveness of fingerprinting attacks under realistic settings. We focus on identifying the target websites and applications visited or used by a Tor user, through analyzing the heavily encrypted traffic that any local eavesdropper can also see. Unlike existing works, we focus on small groups of consecutive packets, which allows us to study more complex user behavior. By modifying our Tor client to label the Tor cells with the name of the destined application or website name, could label the packets even when multiple websites and applications were simultaneously using the Tor proxy. To label the network packets, the byte sequence of a labeled cell was located inside the packets, which, to the best of our knowledge, is the most accurate way for labeling the packets. In this way, we accomplished to gather an extensive dataset of Tor traffic, corresponding to different types of user’s behavior. Finally, we proposed several deep neural network structures to classify the packets of different websites and analyzed the effectiveness of Tor’s encryption methods in realistic settings by achieving an accuracy of 3% in classifying 100 different websites. The results show the effectiveness of Tor’s multi-layer encryption scheme.