Reviewerly/exHarmony huggingface dataset:
Implicit Continuous Authentication Model Based on Mobile Terminal Touch Behavior

Most existing identity authentication technologies rely on some ways for the first login authentication, such as personal identification number (PIN), track, or biological characteristics. However, these ways exist plenty of security risks, which make people face password guessing attacks, trace attacks, and shoulder surfing attacks for a long time. Once the illegal users forge identity to complete authentication or bypass first login authentication, their subsequent behavior will become out of control. To solve the above problems, we propose an implicit continuous authentication model based on the touch behavior of the mobile terminal. The model uses the data collected by the accelerometer, gyroscope, and magnetometer to generate feature vectors and extracts the feature vectors containing macroscopic features, microscopic features, and joint features. And we design a convolutional bidirectional recurrent neural network model to distinguish the sensor feature vectors. On this basis, we perform various experiments on a large dataset Hand Movement, Orientation, and Grasp (HMOG) with different sensor characteristics. Compared with the most advanced models proposed recently, the results show that our model achieves an equal error rate (EER) of 0.53%, which significantly improves authentication accuracy.
Algorithms to Speed up Contour Tracing in Real Time Image Processing Systems

Contour tracing is an important pre-processing step in many image processing applications such as feature recognition, biomedical imaging, security and surveillance. As single processor architectures reach their performance limits, parallel processing architectures offer energy efficient and high performance solutions for real time applications. Parallel processing architectures, are thus used for several real time image processing applications. Among the several interconnection schemes available, Cayley graph based interconnections offer easy routing and symmetric implementation capabilities. For parallel processing systems with a Cayley graph based interconnection scheme, torus, we developed three accelerated algorithms corresponding to three existing families of contour tracing algorithms. We simulated these algorithms on a parallel processing framework to quantify the normalized speed-up possible in any torus connected parallel processing system. We also compared our best performing algorithm with the existing parallel processing implementations for Nvidia GPUs. We observed a speed-up of up to 468 times using our algorithms on a parallel processing architecture in comparison to the corresponding algorithm on a single processor architecture. We evaluated a speed up of 194 (and 47) compared to the existing parallel processing contour tracing implementation on Tesla K40c (and Quadro RTX 5000 GPU hardware respectively). We observe that for torus connected parallel processing architectures used for image processing, our algorithms can be used to speed up contour tracing, without any hardware modification.
Unsupervised Domain Adaptive Object Detection Based on Frequency Domain Adjustment and Pixel-Level Feature Fusion

It is very risky to apply the knowledge learned by the model on the labeled dataset directly to a completely new dataset, especially the unlabeled data, as the gap between these two domains can lead to a severe drop in model performance, However, the difference in illumination, texture and background of the image is one of the difficult problems to solve. In this paper, We proposed a Frequency domain Adjustment module (FA) and Pixel-Level feature Fusion (PLF) block for unsupervised domain adaptation of different types of features. From a frequency domain perspective, we first transfer the style of unlabeled target domain data to labeled source domain data before training using a Fourier transform. The benefit of this strategy can narrow the illumination gap between the source domain and the target domain. Secondly, due to the lack of labels in the target domain data, the feature extraction effect is poor. We consider proposing an improved multi-scale feature fusion mechanism to integrate information. Our method is experimentally validated on multiple public adaptive public datasets, The experimental results show that this method can more accurately detect and recognize images in domain adaptive scenes, and it outperforms the mainstream methods of domain adaptive object detection.
Learning in Bi-level Markov Games

Although multi-agent reinforcement learning (MARL) has demonstrated remarkable progress in tackling sophisticated cooperative tasks, the assumption that agents take simultaneous actions still limits the applicability of MARL for many real-world problems. In this work, we relax the assumption by proposing the framework of the bi-level Markov game (BMG). BMG breaks the simultaneity by assigning two players with a leader-follower relationship in which the leader considers the policy of the follower who is taking the best response based on the leader's actions. We propose two provably convergent algorithms to solve BMG: BMG-1 and BMG-2. The former uses the standard Q-learning, while the latter relieves solving the local Stackelberg equilibrium in BMG-1 with the further two-step transition to estimate the state value. For both methods, we consider temporal difference learning techniques with both tabular and neural network representations. To verify the effectiveness of our BMG framework, we test on a series of games, including Seeker, Cooperative Navigation, and Football, that are challenging to existing MARL solvers find challenging to solve: Seeker, Cooperative Navigation, and Football. Experimental results show that our BMG methods achieve competitive advantages in terms of better performance and lower variance.
Measuring Students’ Critical Thinking in Online Environment: Methodology, Conceptual Framework and Tasks Typology

Today critical thinking is one of the key competencies of the modern world. The abundance and availability of various information ( in particular due to the spread of electronic devices and the Internet) suggest that people, regardless of age, need to be able to effectively navigate and evaluate information, draw their own conclusions and use arguments when making decisions. Research of critical thinking in terms of the possibilities of its evaluation and development began more than 60 years ago and has a wide and heterogeneous field of theoretical approaches. In this article, we explore the theoretical possibility of measuring the complex latent construct “critical thinking in an online environment” using the Evidence- Centered Design (ECD) methodology. It allows one to build a chain of arguments that substantiate the conclusion about the level of critical thinking development in the respondent and thereby ensure the fairness of the assessment process. The measurement goes from theoretical assumptions about the nature of the construct to the search for empirical evidence - observable actions in the testing process, allowing to draw reasonable conclusions about the respondents. The result of the work is a theoretical framework for assessing critical thinking in an online environment for university students and its operationalization through the relevant observed behavior of the target audience, which allows obtaining valid data on the severity of critical thinking. This is the first step to create an instrument in Russian with a confirmed psychometric quality. The key feature of the tool is that the student does not work in a simulated environment where information sources are selected by developers, but in an open online environment, therefore, he can use any available materials to solve the task.
Distributed Semi-Supervised Learning with Positive and Unlabeled Data

Due to the prejudicial interest on positive aspect and/or absent of negative training data on some applications, positive and unlabeled learning (PU learning) has attracted much attention. However, all of the existing PU learning algorithms belong to centralized processing. Yet, in many real-world scenarios, data are usually collected and stored over multiple nodes with restriction of geographic factors or memory space. Considering this, in this paper, we focus on distributed classification with positive and unlabeled data, and propose a distributed PU learning algorithm based on Semi-Supervised Support Vector Machine (dPU-S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> VM). To exploit the underlying information in the unlabeled data, the manifold regularization is incorporated into the design of the cost function. Furthermore, the random feature map is used for mapping the original data to high dimensional kernel space such that the global optimization problem can be decentralized without the direct transmission of the original data. The convergence performance of the proposed dPU-S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> VM algorithm is analyzed theoretically. Experimental results show that the proposed algorithm has better classification performance than the state-of-the-art PU learning algorithms on various synthetic and real-world datasets especially when the portion of labeled positive examples is small.
Comparison of Breast Biopsy Pathology Reporting with Breast Imaging Reporting and Data System (Bi-Rads) Categories-An Institutional Study

Objective: To determine concordance and discordance between radiological and pathological findings of palpable breast lesions.&#x0D; Study Design: Comparative cross-sectional study.&#x0D; Place and Duration of Study: Department of Histopathology, Armed Forces Institute of Pathology, Rawalpindi Pakistan, from Oct 2018 to Mar 2019.&#x0D; Methodology: Imaging and histopathologic reports of a total of 170 female patients with breast lumps were analyzed. Concordance and discordance rates were estimated by comparing histopathology and imaging findings. All the reports were divided into four categories. Malignant concordant lesions showed malignant features and benign concordant lesions showed benign features on both imaging and histopathology. Malignant discordant lesions were those lesions, which were reported benign on imaging but proved to be malignant on histopathology and benign discordant lesions were those showing malignant features on imaging but turned out to be benign on histopathology.&#x0D; Results: Overall concordance was observed in 138 (81.2%) cases with a discordance rate of 18.8% (15.3% benign discordant and 3.5% malignant discordant). All malignant discordant cases (n=6, 3.5%) were reported as Breast Imaging Reporting and Data System category III on imaging and all came out to be invasive ductal carcinoma on histopathology. All benign discordant cases (n=26, 15.3%) were reported as Breast Imaging Reporting and Data System category IV on imaging.&#x0D; Conclusion: A higher discordance rate between imaging and histopathologic findings was observed in the present study as compared to what cited in the literature. Discordant cases should be followed by repeat biopsy for confirmation of diagnosis
Aligning before Aggregating: Enabling Cross-domain Federated Learning via Consistent Feature Extraction

Federated learning (FL) is an emerging machine learning paradigm where multiple distributed clients collaboratively train a model without centrally collecting their raw data. In FL setting, it is a common case that the data on local clients come from different domains, e.g., photos taken by different mobile phones can vary in intensity and contrast due to the difference of imaging parameters. In such a cross-domain case, features extracted from data of different clients deviate from each other in the feature space, leading to the so-called feature shift. The feature shift can reduce the discrimination of features and degrade the performance of the learned model. However, most existing FL methods are not particularly designed for cross-domain setting. In this paper, we propose a novel cross-domain FL method, named AlignFed. In AlignFed, the model on each client is separated to a personalized feature extractor and a shared classifier. The former extracts consistent features among clients by aligning features of different clients to some specific points in the feature space. The latter aggregates the knowledge across clients over the consistent feature space, which can mitigate the performance degradation caused by the feature shift in cross-domain FL. We conduct experiments on common-used multi-domain datasets, including Digits-Five, Office-Caltech10, and DomainNet. The experimental results demonstrate that AlignFed can outperform the state-of-art FL methods.
AN ANALYSIS OF CODE MIXING AND CODE SWITCHING USED BY THE STUDENT OF NURUL HUDA ISLAMIC BOARDING SCHOOL

This research focuses on code mixing and code switching. This research uses descriptive qualitative, where this research wants to explain the data with real results. The results of this study have two discussions. First, the form of code mixing from Javanese to Indonesian, from Indonesian to Javanese and from Javanese to English. Then, the form of transfer found by the researcher is from Javanese to Indonesian and from Indonesian to Javanese. Second, the factors that cause code mixing are: a). Santri understand the language spoken by the interlocutor but cannot express it, b). happens intentionally or unintentionally, c). because they are used to using code mixing when speaking, d). as a form so that they can talk using code mixing. In addition, the factors that cause code switching are: a). Speaker, b). interlocutor, c). the presence of other people.
The Power of Reformulation: From Validation to Planning in PDDL+

PDDL+ allows the formal specification of systems representing mixed discrete-continuous representation, under both discrete and continuous dynamics; this expressiveness is pivotal in real-world applications. An important aspect is the capability of validating plans obtained by planning systems, and assessing their compliance against the domain's model. Unfortunately, a very limited number of validation tools are capable of dealing with PDDL+ tasks. To overcome this problem, in this work we propose an approach that allows exploiting any domain-independent PDDL+ or PDDL2.1 planning engine for validating PDDL+ plans. We introduce a set of translations that, given a PDDL+ plan and the corresponding PDDL+ task, generate a new PDDL+ or PDDL2.1 whose solvability is bound to the validity of the considered plan. We empirically evaluate the usefulness of the proposed approach on a range of PDDL+ benchmarks under an interpretation of time that can be either continuous (through a PDDL+ translation) or discrete (through a PDDL+ or a PDDL2.1 translation).
The Design of Goods Data Storage Application based on Android using Barcode Scanner

Currently, technology and information system undergoes developing process rapidly. The meeting of information need is also very important, especially since the information produced contains useful, fast, and accurate value. Therefore, anyone who uses this information can deal with various occurring problems. An application is a very important thing to support various issues, one of which is in terms of goods data information systems. The use of data information system application is not only an automation process for accessing information, but also provides speed, accuracy, and completeness of an application system. The reality in the real life, especially for the middle to lower class entrepreneurs, shows that there are lacks of efficient and accurate data information system application. A store that still doesn't have the application is Toko Irawan. Toko Irawan is a department store that sells various kinds of home needs. This shop is still doing a manual recording, namely by recording the name of the item and its price in a notebook. Moreover, when the large number of items was being sold, they sometimes forgot the price of certain items. Therefore, an Android-based goods data storage application was designed using a Barcode Scanner to simplify checking names and prices of goods process easily and quickly. This application is designed based on Android using the App Inventor, so that the shop owner can input goods data and prices easily via a smartphone.
Robust Human Motion Forecasting using Transformer-based Model

Comprehending human motion is a fundamental challenge for developing Human-Robot Collaborative applications. Computer vision researchers have addressed this field by only focusing on reducing error in predictions, but not taking into account the requirements to facilitate its implementation in robots. In this paper, we propose a new model based on Transformer that simultaneously deals with the real time 3D human motion forecasting in the short and long term. Our 2-Channel Transformer (2CH-TR) is able to efficiently exploit the spatio-temporal information of a shortly observed sequence (400ms) and generates a competitive accuracy against the current state-of-the-art. 2CH-TR stands out for the efficient performance of the Transformer, being lighter and faster than its competitors. In addition, our model is tested in conditions where the human motion is severely occluded, demonstrating its robustness in reconstructing and predicting 3D human motion in a highly noisy environment. Our experiment results show that the proposed 2CH-TR outperforms the ST-Transformer, which is another state-of-the-art model based on the Transformer, in terms of reconstruction and prediction under the same conditions of input prefix. Our model reduces in 8.89% the mean squared error of ST-Transformer in short-term prediction, and 2.57% in long-term prediction in Human3.6M dataset with 400ms input prefix. Webpage: https://evm7.github.io/2CHTR-page/
Looking Beyond Corners: Contrastive Learning of Visual Representations for Keypoint Detection and Description Extraction

Learnable keypoint detectors and descriptors are beginning to outperform classical hand-crafted feature extraction methods. Recent studies on self-supervised learning of visual representations have driven the increasing performance of learnable models based on deep networks. By leveraging traditional data augmentations and homography transformations, these networks learn to detect corners under adverse conditions such as extreme illumination changes. However, their generalization capabilities are limited to corner-like features detected a priori by classical methods or synthetically generated data. In this paper, we propose the Correspondence Network (CorrNet) that learns to detect repeatable keypoints and extract discriminative descriptions via unsupervised contrastive learning under spatial constraints. Our experiments show that CorrNet is not only able to detect low-level features such as corners, but also high-level features that represent similar objects present in a pair of input images through our proposed joint guided backpropagation of their latent space. Our approach obtains competitive results under viewpoint changes and achieves state-of-the-art performance under illumination changes.
Parallel Generalized Hebbian Algorithm for Large Scale Data Analytics

In order to store and analyse large amounts of data on a parallel cluster, Big Data Systems such as Hadoop and DBMSs require a complex configuration and tuning procedure. This is mostly the result of static partitioning occurring whenever data sets are imported into the file system or transferred into it. Following that, parallel processing is carried out in a distributed fashion, with the objective of achieving balanced parallel execution among nodes. The system is notoriously difficult to configure, particularly in the areas of node synchronisation, data redistribution, and distributed caching in main memory. The extended Hebbian algorithm, abbreviated as GHA, is a linear feedforward neural network model for unsupervised learning that finds the majority of its applications in principle components analysis. Sanger's rule is another name for the GHA that may be found in the academic literature. Its formulation and stability, with the additional feature that it may be used to networks that have more than one output. A unique hardware architecture for principal component analysis is presented here in the form of a paper. The Generalized Hebbian Algorithm (GHA) was chosen as the foundation for the design because to the fact that it is both straightforward and efficient. The architecture may be broken down into three distinct parts: the memory unit, the weight vector updating unit, and the primary computing unit. Within the weight vector updating unit, the computation of various synaptic weight vectors uses the same circuit in order to cut down on the area expenses. This is done in order to save space. The GHA architecture incorporates a versatile multi-computer framework that is based on mpi. Therefore, GHA may be efficiently executed on platforms that utilise either sequential processing or parallel processing. When the data set is studied for a short period of time or when a dynamic number of virtual processors is selected at runtime, we predict that our architecture will be able to profit from parallel processing on the cloud. In this research, a parallel implementation of a variety of machine learning algorithms that are built on top of the MapReduce paradigm is presented with the purpose of improving processing speed and saving time.
Designing and developing a situation-based instructional model for an online Thai course for foreigners

This research aims to develop and evaluate a situation-based instructional model for an online course of Thai as a foreign language (TFL) and to evaluate the effectiveness of the instructional model.There were two experiments (35 and 43 participants, respectively) to explore the impacts of I4PAT between non-Thai major and Thai major students.The experimental groups were taught with I4PAT model (introduc-tion, phonemic training, present, practice, production, assessment, and technology) through an online Thai language instruction, whereas the control groups were taught with the paper-based Thai language instruction.For each experiment, we compare the effects of using I4PAT model through an online Thai language instruction and paper-based Thai language instruction.The results showed that 1) the efficien-cy of the I4PAT model was 93.84/99.70 for the first experiment and 94.07/97.95for the second experi-ment which are higher than the criterion at 90/90, 2) learners' achievement after taking the course of Thai language among first and second experiments was significantly improved, and 3) learners' achievement after learning with I4PAT model through an online Thai language instruction among first and second experiments was significantly higher than learning with the paper-based Thai language in-struction.The findings indicate the effectiveness of the I4PAT model for an online Thai language in-struction.Moreover, the I4PAT model could be applied for the instruction of other languages, both in online and offline modes.