Reviewerly/exHarmony huggingface dataset:
Actor-Centered Representations for Action Localization in Streaming Videos

Event perception tasks such as recognizing and localizing actions in streaming videos are essential for scaling to real-world application contexts. We tackle the problem of learning actor-centered representations through the notion of continual hierarchical predictive learning to localize actions in streaming videos without the need for training labels and outlines for the objects in the video. We propose a framework driven by the notion of hierarchical predictive learning to construct actor-centered features by attention-based contextualization. The key idea is that predictable features or objects do not attract attention and hence do not contribute to the action of interest. Experiments on three benchmark datasets show that the approach can learn robust representations for localizing actions using only one epoch of training, i.e., a single pass through the streaming video. We show that the proposed approach outperforms unsupervised and weakly supervised baselines while offering competitive performance to fully supervised approaches. Additionally, we extend the model to multi-actor settings to recognize group activities while localizing the multiple, plausible actors. We also show that it generalizes to out-of-domain data with limited performance degradation.
An Accurate Deep Learning Threat Image Detection Algorithm for X-Ray Baggage Dataset

Recently, the use of x-ray imaging has become widespread in public places like stadiums and airports to reduce the risk of terrorist attacks. The importance of baggage inspection using X-ray imaging comes from its ability to detect any possible threat objects like (explosives and guns) by classifying the images of the objects into positive or negative. Different approaches have been followed to improve the accuracy level of threat detection systems, especially in deep learning areas such as the convolution neural networks (CNNs) approach. Researchers obtained overall good detection performance results using publicly available datasets. However, accuracy improvement remains a vital concern in the security sector. In this paper, a deep learning algorithm has been proposed to enhance the performance of threat image projection systems. The core of this algorithm is based on the deep learning network (DenseNet), as well as the test time augmentation method trained on the GDXray dataset. As far as is known the authors, this is the first time that a DenseNet121 has been used to classify GDXray dataset. Moreover, the logistic regression model was added as a final stage in order to get the final prediction. The obtained results indicate the ability of the proposed algorithm to detect threat images with an accuracy of (97.68%), which outperforms the existing approaches using the same dataset.
Smart Agriculture Decision Making Scheme Using Quantum Annealing

Smart agriculture is introduced with the latest technologies to improve the overall agricultural production capacity and efficiency. This paper proposes a quantum-inspired decision making scheme for crop cultivation in smart agriculture. The goal of the scheme is to increase the farmers’ welfare by planting different types of crop on adjacent farmlands to achieve optimal balance of crop demand and production. The proposed scheme is modeled as a graph coloring problem, which is computationally hard to solve for large and complex graphs in general. By taking advantage of quantum-inspired metaheuristic approach, the graph coloring problem is formulated as Quadratic Unconstrained Binary Optimization (QUBO) model and solved by a quantum annealer. The experimental realization of the proposed method is investigated on D-Wave quantum annealing devices and its performance is compared with those of the brute-force and greedy algorithms. The experimental results show that the proposed decision making scheme achieves a higher success probability than the greedy algorithm and a much lower run time than the brute-force approach.
Watermarking Method Using Dual-Tree Complex Discrete Wavelet Transform and Quantization

In recent years, with the development of the information society, digital images can be easily uploaded and downloaded. Against this background, instances of plagiarism and tampering with images have been increasing. Therefore, it is necessary to develop robust watermarking for the copyright protection of digital images. In this study, we propose a blind digital image watermarking method based on dual-tree complex discrete wavelet transform and quantization. We apply this wavelet transform to the original image and embed two watermarked images of different sizes using quantization index modulation and relative modulation. Quantization index modulation is robust against geometric attacks. Relative modulation is robust against noise addition. The results of our experiments show that our method is robust against Gaussian noise, salt and pepper noise, cropping, and Jpeg compression. The experimental results show that the watermark can be extracted by Gaussian noise, salt and pepper noise, cropping, Jpeg compression. In addition, we visually check the extracted watermarked images. Furthermore, we compare three methods: one using dual-tree complex discrete wavelet transform and sliding window. The second method is based on discrete wavelet transform, discrete cosine transform, and singular value decomposition. The third method is based on Discrete Cosine Transform and RM, QIM. By comparing these three methods with the existing methods, we confirmed the significance and performance of our method.
Uncertainty Quantification of Bifacial Performance Modeling

Analysis on uncertainty in the annual energy of PV systems that can be attributed to parameters of particular importance to bifacial PV modules is presented. Monte Carlo simulations are used to evaluate the effect of uncertain module bifaciality factors, module transmission fractions, albedo values, and ground clearance. The analyses cover a wide spectrum of potential PV array archetypes through variation of installation parameters. The results of the Monte Carlo analysis reveal that the uncertainty is largely dependent on albedo uncertainty, but more simulations are needed to identify trends across system archetypes. The simulations are aimed at attributing an annual energy uncertainty factor for bifacial considerations that can be applied in post-processing of project probability of exceedance analysis.
Cross-Stage Class-Specific Attention for Image Semantic Segmentation

Recent backbones built on transformers capture the context within a significantly larger area than CNN, and greatly improve the performance on semantic segmentation. However, the fact, that the decoder utilizes features from different stages in the shallow layers, indicates that local context is still important. Instead of simply incorporating features from different stages, we propose a cross-stage class-specific attention mainly for transformer-based backbones. Specifically, given a coarse prediction, we first employ the final stage features to aggregate a class center within the whole image. Then high-resolution features from the earlier stage are used as queries to absorb the semantics from class centers. To eliminate the irrelevant classes within a local area, we build the context for each query position according to the classification score from coarse prediction, and remove the redundant classes. So only relevant classes provide keys and values in attention and participate the value routing. We validate the proposed scheme on different datasets including ADE20K, Pascal Context and COCO-Stuff, showing that the proposed model improves the performance compared with other works.
YOLOX-based dynamic vehicle target detection

With the widespread investment in intelligent traffic management, vehicle-related object detection and tracking are the most needed step in computer research. However, complex road conditions result in dense vehicles and small intervals between vehicles on the road, it is hard to identify and detect vehicle types quickly and accurately. To overcome this issue, this paper proposes a YOLOX object detection algorithm to be applied to traffic road vehicle detection. First, the CSPnet and Focus network structures are added to the Darknet network to improve target detection accuracy. Secondly, the decoupling head and SimOTA strategy are added to the prediction end to reduce the training time and further enhance the target detection efficiency. The pre-trained weights on the COCO dataset are fused together perform detection and identification to enhance the detection generalization and accuracy. The results of experiments demonstrate the detection of vehicles on the bus dataset constructed in this paper, vehicle detection of multiple targets and small targets in dense vehicle scenarios.
Cross-Guided Feature Fusion with Intra-Modality Reweighting for Multi-Spectral Pedestrian Detection

Multi-spectral pedestrian detection has gained extensive attention over the past decade. To alleviate the problem of modality imbalance in the multi-spectral tasks, a novel cross-guided feature fusion network based on the auto-encoder framework is proposed using RGB-thermal image pairs as inputs. To obtain the complementary features, a cross-guided loss is designed, so that the output images are balanced with both modalities in an unsupervised manner. An intra-modality reweighting module is implemented to filter the redundant features before the fusion. Finally, YOLOv3 is chosen as the detector fed by the fused features. The proposed method is verified using the public KAIST and VOT-RGBT datasets. Experimental results demonstrate that the proposed method can outperform the state-of-the-art methods, the miss rate of pedestrian detection reaches 48.57% and 4.52% using KAIST and VOT-RGBT datasets, respectively.
Deep Transfer Learning-Enabled Energy Management Strategy for Smart Home Sensor Networks

The applications of wireless sensor networks are extensively used to detect and control home residents’ activities in smart homes. However, the sensors are battery-powered, so keeping them in active mode consumes tremendous energy. In this regard, we propose a solution to activate the smart home sensors based on detecting the upcoming activities using a Deep Long-Short Term Memory (DLSTM) model. The pre-trained model is then transferred to the same and different Target Domains (TDs) to reduce the time for training. The proposed system applies to preprocess and feature mapping steps to both the source and target data to make grounds for efficient transfer. Further, applying the trained model to the TD may miss the essential activities. Therefore, a reinforcement learning model is applied in the TD. To handle unusual activities in real-time, guard sensors are appointed among the idle sensors. The performance evaluation shows that the proposed scheme detects the activities with an accuracy of 96.1%. Additionally, the proposed scheme outperforms the sentry and prediction-based schemes in energy consumption of the sensors and network lifetime.
eMoCo: Sentence Representation Learning With Enhanced Momentum Contrast

Sentence representation learning can transform sentences into fixed format vectors, and provides foundation for downstream tasks such as information retrieval, semantic similarity analysis, etc. With the popularity of contrastive learning, sentence representation learning has also been further developed. At the same time, contrastive learning method based on momentum has achieved great success in computer vision. It solves the coupling between negative samples and batch size. But its expected performance is not observed in natural language processing tasks because the combination of data augmentation strategies is weak, and it only utilizes the samples in the momentum queue as negatives while ignoring those generated in current batch. In this paper, we propose eMoCo: enhanced Momentum Contrast to solve the above issues. We formulate a set of data augmentation strategies for text, and present a novel Dual-Negative loss to make full use of all negative samples. Extensive experiments on STS (Semantic Text Similarity) datasets show that our method outperforms the current state-of-the-art models, indicating its advantages in sentence representation learning.
Distortion Localization and Restoration Method in Water Column Area of Side-scan Sonar Image

Side-scan sonar is equipped on two sides of AUV to obtain underwater information. Due to the interference of the changing marine environment and the limitation of the measurement principle of sonar equipment, there are obvious distortion, noise interference and contrast imbalance in the real side-scan sonar image, which needs to be geometric correction and preprocessed before being used. However, the distortion problem in the water column area due to the geometric correction process is present in all sonar image and contains many useless features. To solve this problem, firstly, this paper analyzes the distortion problem in the water column area, and then proposes a method to automatically determine the area to be restored of distortion problem. Finally, a method to restore the side-scan sonar image using its self-similarity is presented. Comparing experiment is given and the results show that the proposed method can restore side-scan sonar image and make the texture structure more coherent.
Image Defogging Based on Improved Residual Block Feature Fusion Network

In this paper, we propose an image defog network based on improved residual block feature fusion, which aims to reconstruct fog-free images. Conventional methods usually cannot handle different fog density features differentially, and are limited to only focus on processing local features. To address this, we design a new residual block to obtain the depth information of the foggy images. In addition, our model can make use of complementary features with different weights, which increases the receptive field, makes better use of global information, speeds up network convergence, and achieves more accurate results. Extensive experimental results on the RESTED dataset have shown the effectiveness of our proposed method for image defog.
Theoretical derivation of super-resolution sub-image stitching based on method of Lagrangian multipliers

Researchers found that different regions in low-resolution (LR) images present different restoration difficulties. For large or small scales, a unified super-resolution (SR) network (large/small) will either waste computing resources and hardware resources, or it will be difficult to obtain the desired reconstruction effect. For this reason, researchers began to apply different processing strategies to different parts of images. Kong et al., for instance, first cropped LR images into sub-images and then processed these images that are hard to super solve using large-scale SR networks. Medium-scale networks were employed to process sub-images that were more difficult to super solve, a small-scale SR network was used to process sub-images that were easy to super-solve, and subsequently, these images were stitched together using weighted average fusion. The major focus of the above research method was to crop different regions into LR images, and then reconstruct and merge those images into a complete SR image using the weighted average fusion strategy. Although this strategy helped achieve satisfactory results, these results could often be adjusted through various experiments, and the setting of weights lacks theoretical guidance. In this paper, we propose a Lagrangian function for SR sub-image fusion and optimize the function through the method of Lagrangian multipliers. The research results provide a theoretical reference for the weights of SR sub-image stitching.
Level-Ordered Morphology in Bētē Simple Nouns

Bētē (Different from Bete [byf] in Taraba State and Bété [bet] of Côte d’Ivoire (Ethnologue, 2021). The language is also written as ‘Bette’ or ‘Bette-Bendi’, associated with the language closest to the Bendi group, in some works and administrative documents.) (Iso: btt) is a Bantoid language spoken in southern Nigeria (Blench R. M. (2001). The Bendi languages: More lost Bantu languages? Paper presented at the 32nd Annual Conference on African Linguistics: Benue-Congo Workshop Berkeley, Cambridge.). It still faces a classification problem, as there is no universally accepted classification (We have chosen to work with the classification of (Blench in The Bendi languages: more lost Bantu languages? Paper presented at the 32nd Annual Conference on African Linguistics: Benue-Congo Workshop Berkeley, Cambridge, 2001.) which is the most recent classification of Bendi languages.) This Chapter presents the morphology of simple nouns in Bētē within the framework of Lexical Morphology theory. The aim is to examine simple noun formation in Bete. Results obtained show that simple nouns in Bētē undergo the process of derivation. This happens when the noun class is prefixed to the verb base. These prefixes have the structures V- and CV- and act as classifiers for these nouns. This study is therefore an elaborate discussion of the morphological process of derivation which falls under Level-One Nominal Morphology of Bētē.
Active Vision Control Policies for Face Recognition using Deep Reinforcement Learning

Robotic systems are capable of interacting with their environment in order to better sense their surroundings. This key ability of robotic systems is often ignored when developing Deep Learning models, since the later are usually trained using static datasets. This limits the ability of robots to perceive the environment in challenging scenarios. On the other hand, integrating perception and action in tightly coupled systems while operating on-the-edge, holds the credentials for deploying DL-enabled robots in such scenarios; Thus leading to more robust agents that can solve challenging tasks more accurately. In this work, we investigate whether active perception approaches can be employed and integrated into robotic systems in order to improve face recognition accuracy, as well as, study the effect of such an approach on the computational requirements for edge applications. To this end, we propose a DRL-based control approach for training agents that are able to identify task-relevant objects, as well as, issue the appropriate control commands to acquire better results. Through the conducted experimental evaluation, we demonstrate that the proposed method leads to significant improvements in face recognition over the rest of the evaluated approaches by providing accurate control commands.
PENERAPAN MODEL PEMBELAJARAN BERBASIS MASALAH UNTUK MENINGKATKAN TANGGUNG JAWAB DAN PRESTASI BELAJAR

This research was conducted with the aim to find out 1) The application of the Problem Learning can increase Responsibility student, 2) The application of the Problem Learning can improve student learning achievement in mathematics student in Class VIII in SMP Negeri 4 Kikim Selatan Kabupaten Lahat. The problem in this study is that the learning methods used are less varied, the low attitude of student obligation in learning. This study used 20 subjects in PTK research. Data collection techniques in this study used observation and tests. The results of this study indicate that The application 0f a Problem Learning can increase student Responsibility in Mathematics subjects. The application of a Problem Learning can improve student learning achievement in Mathematics subjects class VIII in SMP Negeri 4 Kikim selatan Kabupaten Lahat.
Retrieval-Efficiency Trade-Off of Unsupervised Keyword Extraction

Efficiently identifying keyphrases that represent a given document is a challenging task. In the last years, plethora of keyword detection approaches were proposed. These approaches can be based on statistical (frequency-based) properties of e.g., tokens, specialized neural language models, or a graph-based structure derived from a given document. The graph-based methods can be computationally amongst the most efficient ones, while maintaining the retrieval performance. One of the main properties, common to graph-based methods, is their immediate conversion of token space into graphs, followed by subsequent processing. In this paper, we explore a novel unsupervised approach which merges parts of a document in sequential form, prior to construction of the token graph. Further, by leveraging personalized PageRank, which considers frequencies of such sub-phrases alongside token lengths during node ranking, we demonstrate state-of-the-art retrieval capabilities while being up to two orders of magnitude faster than current state-of-the-art unsupervised detectors such as YAKE and MultiPartiteRank. The proposed method's scalability was also demonstrated by computing keyphrases for a biomedical corpus comprised of 14 million documents in less than a minute.
Application of Differential Privacy to the 2020 U.S. Census and American Community Survey: Researchers’ Understanding and Reactions

The U.S. Census and the related American Community Survey (ACS) are used for studies of many types by researchers in a variety of domains. The U.S. Census Bureau discovered that the methods intended to preserve the privacy of individuals used for the 2010 survey were not adequate. Thus, a decision was made to apply differential privacy (DP) to the data from the 2020 Census. DP methods introduce noise into the data set with the intention of allowing the group statistics still to be useful, while protecting the individual data by way of random perturbation. Researchers expressed concern as to how much this application of DP will impact research. We report results of a survey conducted of researchers who published studies based on the 2010 U.S. Census data or the ACS data to assess their understanding of and concerns about DP.
TEACHERS’ STRATEGIES IN CONDUCTING SPEAKING ASSESSMENTS FOR YOUNG ENGLISH LEARNERS AT MIS NURUL HUDA, TANJUNG MORAWA

ABSTRACT - The purpose of this study is to ascertain and pinpoint the teacher's comprehension of speaking, particularly for young students. Through the use of a qualitative research approach, this study tried to examine teachers' methods for administering speaking tests to young English learners. A questionnaire for expert lecturers and students, teacher interviews, and the outcomes of the most recent exam given to students make up the instrument. In MIS Nurul Huda interviews with teachers and teacher observations to evaluate young pupils' speaking abilities, a questionnaire for teachers was utilized as the testing tool. The results show that even though most teachers do not fully comprehend assessment concepts, they can nevertheless use these principles to evaluate assessments for their pupils. Despite the fact that they are unfamiliar with the assessment's terminology, their understanding of the procedure is in line with the theories advanced by the experts. The teacher employs speaking tactics for each student's speaking strengths and shortcomings throughout formative speaking assessments in the classroom. Speaking techniques include imitative speaking (repetition), intensive speaking (tasks requiring you to finish sentences and dialogues), responsive speaking (questioning and responding), interactive speaking (role playing, discussion, and conversation), and extensive speaking (illustrated discussions in stories).Keyword : Speaking Assesment, Young English, Strategies