Reviewerly/exHarmony huggingface dataset:
Weakly-supervised anomaly detection with a Sub-Max strategy

We study weakly-supervised anomaly detection where only video-level “anomalous”/“normal” labels are available in training, while anomaly events should be temporally localized in testing. For this task, a commonly used framework is multiple instance learning (MIL), where clip instances are sampled from individual videos to form video-level bags. This sampling process arguably is a bottleneck of MIL. If too many instances are sampled, we not only encounter high computational overheads but also have many noisy instances in the bag. On the other hand, when too few instances are used, e.g., through enlarged grids, much background noise may be included in the anomaly instances. To resolve this dilemma, we propose a simple yet effective method named Sub-Max. In partitioned image regions, it identifies instances that are most probable candidates for anomaly events by selecting cuboids that have high optical flow magnitudes. We show that our method effectively brings down the computational cost of the baseline MIL and at the same time significantly filters out the influence of noise. Albeit simple, this strategy is shown to facilitate the learning of discriminative features and thus improve event classification and localization performance. For example, after annotating the event location ground truths of the UCF-Crime test set, we report very competitive accuracy compared with the state of the art on both frame-level and pixel-level metrics, corresponding to classification and localization, respectively.
Highly Crowd Detection and Counting Based on Curriculum Learning

Highly crowd counting is a rapidly growing field, driven by the increasing demand for accurate and real-time crowd monitoring. Within this context, in this paper we formulate the problem in terms of point detection and we propose a novel training strategy, especially devised for point detection networks. The baseline architecture we use is Point to Point Network (P2PNet), that have shown impressing accuracy results in both localization and crowd counting task. In order to be able to deal with both sparse and very dense scenarios, and to well generalize both indoor and outdoor, we propose a brain-inspired training strategy based on curriculum learning, combined with a customized data augmentation technique. The main idea is that the neural network has to mimic human learning by initially taking into account the easy samples (sparse scenes) and then moving on to the more challenging ones (the ones with thousands of persons). The experimentation has shown impressive results. Indeed, with respect to the baseline solution, we obtain an improvement of 59%, 62% and 48% over the three indices we have considered, respectively MAE, MSE and nAP. An example of the proposed system in action is shown at the following link: https://youtu.be/yAHe7CI60hE .
Why You Should (or Shouldn't) Build an API Client

These days web Application Programming Interfaces (APIs) are everywhere (scientific data sources, your system for Customer relationship management, cat facts API…). Do you need to write some R code wrapping a web resource such as an API? Packaging it up might be useful to you or your team for the same reason as any code.
Evaluating GitHub Activity for Contributors

Say you have a bug report or feature request to make to a package.How can you use information on GitHub to manage your expectations (will there be a quick fix) and actions (should you go ahead and fork the repository)?In this post, we shall go over sources of information and explain how they can be used.In the end, there is no magical recipe, except perhaps graciousness, as software is made by humans.
Few-Shot Semantic Segmentation with Dynamic Weights Transfer Network

Few-shot semantic segmentation (FSS) has been getting noticed by its can segment unseen object classes of a few support samples. Most existing algorithms use some method to obtain the correlation between the support set and the query set to calculate the prediction mask, such as cosine distance or global mean pooling (GAP). However, it is difficult for these algorithms to achieve dynamic interaction between support set characteristics and query set characteristics. Therefore, we proposed a Dynamic Weights Transfer Network (DWTN), to dynamically implement the desirable interaction of the support set with query set. In DWTN, we propose three modules, Weight Generation Module (WGM), Inner Loop Module (ILM), and Outer Loop Module (OLM). WGM generates the dynamic weights from the query set images. ILM trains the weights. OLM obtains the prediction mask. Experiments on PASCAL-5i at 1-shot and 5-shot settings show that our proposed DWTN has simpler meta-learning and superior performance as a new solution with few-shot semantic segmentation.
Improved marine predator algorithm based on multiple strategies

This paper proposes a multi-strategy Marine Predator Algorithm (MSMPA) based on the shortcomings of the Marine Predator Algorithm (MPA), such as slow convergence speed, low solving quality, and an easy fall into local optima. Firstly, during the initialization of the algorithm, Latin hypercube sampling is used to update the initialization position of the population. This method can make the position distribution of the entire population more uniform and increase the diversity of the population. Secondly, since the three optimization stages of the MPA algorithm are determined by the number of iterations, resulting in a slow convergence speed, using the elite grouping method to execute the three optimization stages of the MPA algorithm can effectively accelerate the convergence speed. Finally, in view of the fact that the algorithm is easy to fall into local optimum and low solution accuracy, the survival crisis awareness mechanism of the predator is proposed, and the algorithm modeling is conducted to change the search mode of the MPA algorithm and improve the overall quality of the algorithm. In this paper, the simulation results of 10 benchmark test functions and five other meta-heuristic algorithms are compared. Experimental results show that the above three improvements contribute to improving the performance of the algorithm.
Research on Zero-Shot Stance Detection: Using Dual-Module adversarial training

Stance detection is to automatically determine the stance of a given topic from the document published by the user. Because stance detection is based on the particularity of the topic, it is not practical to collect all topics. Therefore, zero-shot stance detection is of great significance. The existing zero-shot stance detection models can not combine the topic-invariant features while maintaining the stance features between topics and documents. In this study we propose a zero-shot stance detection model that used dual-module step-by-step training, which can combine the topic-invariant features while maintaining the stance features between topics and documents. Our model has better classification performance than the baseline model in the SemEval2016 dataset.
Visual group target tracking algorithm based on MeanShift-PCA-PF

In recent years, target tracking has been widely studied and applied in intelligent video surveillance, human-machine interaction, radar early warning and tracking, and other fields. With the emergence of the new combat concept of unmanned aerial vehicle (UAV) swarm operations, and the maturity of large-area, high-resolution visible light COMS imaging device technology, the imaging detection and visualization of UAV swarm targets in large observation fields will greatly improve the efficiency of command systems. Therefore, the research on the group target detection, tracking, and recognition based on video image is very urgent and crucial. Group targets are collections of densely packed multiple objects with certain group characteristics that meet the given distance criterion. Due to the large number of targets, limited image resolution, and waste of communication resources, multiple target tracking techniques are challenging to implement effectively. In this paper, based on the characteristics of group targets, we propose a group target tracking framework based on optical measurement equipment. By dividing groups according to the density feature of group targets, we obtain the structural properties and spatial geometric features of the group and implement tracking using a particle filter. Finally, we validate the effectiveness of the group target tracking technique through simulation experiments in group target tracking.
Work-in-Progress-A Large-Scale Workload Forecasting Model for Containers

As application migration to the cloud becomes the mainstream way of application deployment, application runtime management presents a significant need for large-scale workload prediction technology. However, existing large-scale workload forecasting models focus more on improving model accuracy and ignore the models’ storage, training time, and testing time, which leads to colossal overhead. Therefore, this paper proposes a large-scale workload forecasting model for containers. First, based on the workload value features and waveform features, a feature-enhanced workload similarity calculation algorithm is proposed to determine the grouping of containers with similar workload patterns in real time by analyzing the historical similarity and recent similarity of workloads among different containers; second, we employ Transformer as the base model to design position encoding and attention mask based on the real-time workload similarity relationship and achieve forecasting model parallelized training based on the multi-head self-attention mechanism, which balances the workload prediction accuracy and model overhead. Finally, we will validate the comprehensive advantages of our model in terms of accuracy and overhead based on public datasets and verify the effectiveness of each subpart of our model through ablation experiments.
Siamese Tracker with Multi-template and Asymmetric Convolution

Recently, Siamese architecture-based trackers have achieved promising progress in the visual object tracking field. However, most Siamese trackers only adopt an initial frame as an object template, and cross-correlation operation is not learnable. In this paper, we propose a Siamese tracker with multi-template and asymmetric convolution based on SiamCAR. Our tracker introduces an asymmetric convolution module and proposes a multiple-template memory. The asymmetric convolution module embeds the template, the search region, and the bounding box information for offline training. The multiple template memory uses an initial template and filters some frames as dynamic templates during the tracking progress. The evaluation results on GOT-10k and OTB100 show that the proposed tracker achieves competitive tracking performance in AUC and Precision.
Digital Audio Steganography using FLT and Low-bit Encoding for Secure Storage and Transmission of Confidential Information

Confidentiality of data remains vital for securing critical knowledge and secure communication of information. One possible method of secure communication is encryption where data is obscured in such a format that makes little sense. The other method is steganography where information is hidden within a cover medium so as to remain undiscoverable and hence secured. Data hiding techniques are as old as the civilization itself and research is still going on to develop improved steganographic methods that can securely hide confidential data within different cover media. Digital images being easily available and offering a high level of redundancy, are considered as popular cover media. However, in this age of high-speed Internet, audio is not only abundantly available but is also heavily shared between various peers. It is also part of the video files. Therefore, audio can be a good cover for hiding text, images, audio, or video data. In this paper, we have suggested a novel audio steganography model that securely hides a high volume of data. The method is proved to be robust against various statistical and cryptanalytic attacks.
Dynamic Communications Network Linking Prediction by Disseminating Event Embedding

Communication networks represent communication between entities like social networks and microservice call graphs of microservice systems. Link prediction is useful in various communication network service systems such as predicting the relation between two services. Continuous-time dynamic graph (CTDG) is one form of representing temporal information in communication networks that treats them as a set of events occurring over time. Dynamic graph embedding for CTDG handles these events and disseminates event information to other nodes to get node embedding. Though dynamic graph embedding is suitable for making link prediction in communication networks, graph embedding for CTDG faces challenges such as how to model the event information dissemination process including information decaying over distance and influence of time information. To cope with this issue, we propose a CTDG-based dynamic graph embedding framework for dynamic communication networks link prediction called CTDGNN (Continuous-Time Dynamic Graph Neural Networks). In particular, we propose a self-adaptive information dissemination strategy based on node importance to update node embedding by disseminating event information. Finally, extensive numerical experiments on three real-world communication network datasets validate the effectiveness of our proposed model compared to other related methods.
ERROR CORRECTION USING QUANTUM COMPUTING

Quantum Error Correction (QEC) is an important technique for protecting quantum information against decoherence and errors. This involves the design and implementation of algorithms and techniques to minimize error rates and increase the stability of quantum circuits. One of the key parameters in QEC is the distance of the error- correcting code, which determines the number of errors that can be corrected. Another important parameter is the error probability, which quantifies the likelihood of errors occurring in the quantum system. In this context, the goal of a simulation sweep like the one performed in the code is to study the performance of the QEC code for different values of the distance and error probability, and to optimize the code for maximum accuracy. By varying these parameters and observing the performance of the code, researchers can gain insights into how to design better codes and improve the reliability of quantum computing systems. We also discuss the challenges that need to be addressed for quantum computing to realize its potential in solving practical Error-correction problems.
What Does It Mean to Maintain a Package?

Part of what we aim to do at rOpenSci is nurture a community of package maintainers who help each other.In addition to support during package maintenance, we also want to support maintainers who wish to move on. Situations can change, and there may come a time when a maintainer is looking to pass maintenance on to another.
Docking Control Algorithm for Autonomous Mobile Robot Through Sensor Fusion

We developed a methodology to achieve position estimation, path planning and control for autonomous docking systems of autonomous mobile robots (AMRs). For autonomous docking, the relative position between the AMR and the docking station must be accurately estimated. The relative position determined using a camera and lidar sensors is inaccurate, and the position update rate is insufficient. To solve this problem, we propose a Kalman filter that uses an inertial measurement unit and information from a wheel encoder sensor in combination. The position estimated by the Kalman filter has a smaller root mean square error and variance than those obtained from the camera and lidar sensors, and the position is updated every 25 ms. The control system for path planning and docking was implemented in the Robot Operating System, and the algorithm was verified through Gazebo simulation. Finally, the developed algorithm was verified in real environments. The experimental results yielded a position error of less than 1 cm and an angle error of less than 1°.
Kajian Teori Behavioristik pada Media Pembelajaran Flipbook

In this study the aim was to examine a behavioristic theory on flipbook learning media. The data collection technique used is a library research method by collecting primary and secondary data. Primary data here uses the theory of several behaviorists and secondary data in the form of scientific articles and books from various sources. From several studies and literature that have been processed by researchers, it can be concluded that another impact of behavioristic theory includes the birth of mastery learning formulas, which emphasize thorough learning. Complete learning has implications for the development of learning media, good learning media is one that can provide completeness in learning, meaning how learning is conditioned in such a way from the beginning to the end of learning, with clear and structured stages and steps, therefore in development the media needs to have a flowchart as an illustration in the development of learning media. The use of flowcharts is part of the influence of behaviorism learning theory. Another form of influence from the theory of behaviorism is programmed learning and individual teaching. Learning media is a highly programmed and individual form of learning. That's why learning design is very important as part of learning conditioning efforts. Likewise the presentation of material, giving summaries, questions and feedback are also part of the effort to provide stimulus, condition, know the response in learning.
Case Study on the Change of Body Composition and Physical Fitness of Female College Students through Six-Week Home Training

Purpose: The purpose of this study is to investigate the effects of home training(HT) and gym training(GT) on body composition and basic physical fitness in female college students.&#x0D; Method: In this study, 39 female first-year physical education students enrolled in K University in D city were selected as the subjects of the home training group(HT, n=15), gym training group(GT, n=12), and control group(Con, n=12). The subjects were fully informed of the purpose of the study and gave their voluntary consent to participate in the study. Body composition was measured by Weight, Body Mass Index, Percent Body Fat, Body Fat Mass, Lean Body Mass, and Skeletal Muscle Mass. Physical fitness was measured by grip strength, sit-ups, vertical jump, sitting trunk flexion, and standing on one leg with eyes closed. The exercise program consisted of a warm-up, main exercises(upper body, core, and lower body), and a cool-down, with the core exercises being the same for both programs. Data processing for this study was performed using the SPSS 27.0 statistical program to calculate the mean and standard deviation for each measure, and a two-way analysis of variance with repeated measures was performed to examine the effect of each item by group and time of measurement. If the interaction was significant, a paired samples t-test was performed, and a one-way analysis of variance was performed to analyze the difference in the mean of the dependent variable between groups at the same time point. Statistical significance was considered at p&lt;.05 for all measures.&#x0D; Results: The main effects of body composition period were significant(p&lt;.05) for body weight, LBM, and skeletal muscle mass, with no significant group interaction. Post hoc tests of the main effect of time revealed a significant main effect for body weight, but no significant post hoc differences from pre to post for any group. The main effects of physical fitness period were significant(p&lt;.01) for the vertical jump, grip strength(left), grip strength(right), sit-ups, and standing on one leg with eyes closed, but the group interaction was not significant for all items. Post hoc tests of the main effect of time showed that the HT group significantly(p&lt;.05) increased from pre to post in the standing vertical jump, grip strength(left) was significantly(p&lt;.05) lower in the GT group from pre to post, and sit-ups were significantly(p&lt;.01) higher in the HT group from pre to post.&#x0D; Conclusion: Gathering the results above, the home training group showed an increase in sit-ups and vertical jump in place from the pre- to post-training, it is possible to see some effectiveness of the training, but it is difficult to clear conclusions. Therefore, further research is needed to determine the effectiveness of home training.
Image Encryption Based On Chaotic Scrambling And DNA Sequence Multi-rule Coding

An image encryption algorithm based on chaotic scrambling and DNA sequence multi-rule coding is proposed. Through the research of related algorithms, the original image and the chaotic sequence generated by Logistic chaos are encoded, fused and decoded by using the multi-rule coding principle of DNA sequence, and the pixel value is changed to encrypt. Then, the PWLCM algorithm is used to generate new chaos to scramble the pixel blocks of the image, so as to obtain the encrypted image. The experimental results show that the algorithm not only improves the encryption efficiency of the traditional encryption algorithm, increases the key space, but also resists the exhaustive and statistical attacks.
Collaborative Filtering Hybrid Recommendation Algorithm Based on Improved Score Similarity

Collaborative filtering recommendation algorithm is one of the most widely used recommendation algorithms in the recommendation system, but the traditional similarity calculation method only considers the user-item rating data, and there are still great defects in the face of data sparsity and cold start, which leads to inaccurate similarity calculation results and low accuracy of recommendation results. In order to solve the above problems, this paper proposes a collaborative filtering hybrid recommendation algorithm based on improved rating similarity (ISSH-CF). In the calculation of traditional similarity, the hot item penalty factor and the active user penalty factor are introduced, and the similarity of user and item attribute characteristics is combined respectively, which alleviates the user cold start problem, the item cold start problem and the data sparsity problem. For old users, finally, the rating prediction of UserCF and ItemCF were calculated respectively, and the weighted hybrid recommendation was performed to obtain the final rating prediction result. Compared with the original UserCF and ItemCF, the MAE values are reduced by 2.81%and 2.78%respectively on the MovieLens 100k data set, which proves that the proposed algorithm can effectively improve the accuracy of recommendation results.
Digital and IoT Forensic: Recent Trends, Methods and Challenges

Digitalization contains a diverse set of information that plays a vital role in investigations, from a forensic stand point. Forensic investigations in the IoT/digital paradigms will need to develop and mature in order to meet the characteristics of IoT. In this paper the authors have investigated digital forensics and have addressed the method of processing the sources of evidences in the digital/IoT ecosystem. The authors have further outlined the guidelines, procedures, current trends and a few criteria and roadblocks in the process of digital investigation. This study lays open ways for investigating strategies and methodologies which support the implementation of digital forensics in the dynamic digital systems. This provides a deep and detailed comprehensive understanding of digital forensics and IoT forensics. Further, current problems and issues are highlighted which will inspire and motivate researchers for further research.