Reviewerly/exHarmony huggingface dataset:
An End-to-End Trainable Video Panoptic Segmentation Method usingTransformers

In this paper, we present an algorithm to tackle a video panoptic segmentation problem, a newly emerging area of research. The video panoptic segmentation is a task that unifies the typical task of panoptic segmentation and multi-object tracking. In other words, it requires generating the instance tracking IDs along with panoptic segmentation results across video sequences. Our proposed video panoptic segmentation algorithm uses the transformer and it can be trained in end-to-end with an input of multiple video frames. We test our method on the STEP dataset and report its performance with recently proposed STQ metric. The method archived 57.81\% on the KITTI-STEP dataset and 31.8\% on the MOTChallenge-STEP dataset.
Learning Not to Reconstruct Anomalies

Video anomaly detection is often seen as one-class classification (OCC) problem due to the limited availability of anomaly examples. Typically, to tackle this problem, an autoencoder (AE) is trained to reconstruct the input with training set consisting only of normal data. At test time, the AE is then expected to well reconstruct the normal data while poorly reconstructing the anomalous data. However, several studies have shown that, even with only normal data training, AEs can often start reconstructing anomalies as well which depletes the anomaly detection performance. To mitigate this problem, we propose a novel methodology to train AEs with the objective of reconstructing only normal data, regardless of the input (i.e., normal or abnormal). Since no real anomalies are available in the OCC settings, the training is assisted by pseudo anomalies that are generated by manipulating normal data to simulate the out-of-normal-data distribution. We additionally propose two ways to generate pseudo anomalies: patch and skip frame based. Extensive experiments on three challenging video anomaly datasets demonstrate the effectiveness of our method in improving conventional AEs, achieving state-of-the-art performance.
The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person Pose Estimation

We introduce CenterGroup, an attention-based framework to estimate human poses from a set of identity-agnostic keypoints and person center predictions in an image. Our approach uses a transformer to obtain context-aware embeddings for all detected keypoints and centers and then applies multi-head attention to directly group joints into their corresponding person centers. While most bottom-up methods rely on non-learnable clustering at inference, CenterGroup uses a fully differentiable attention mechanism that we train end-to-end together with our keypoint detector. As a result, our method obtains state-of-the-art performance with up to 2.5x faster inference time than competing bottom-up methods. Our code is available at this https URL .
Why Out-of-distribution Detection in CNNs Does Not Like Mahalanobis -- and What to Use Instead

Convolutional neural networks applied for real-world classification tasks need to recognize inputs that are far or out-of-distribution (OoD) with respect to the known or training data. To achieve this, many methods estimate class-conditional posterior probabilities and use confidence scores obtained from the posterior distributions. Recent works propose to use multivariate Gaussian distributions as models of posterior distributions at different layers of the CNN (i.e., for low- and upper-level features), which leads to the confidence scores based on the Mahalanobis distance. However, this procedure involves estimating probability density in high dimensional data using the insufficient number of observations (e.g. the dimensionality of features at the last two layers in the ResNet-101 model are 2048 and 1024, with ca. 1000 observations per class used to estimate density). In this work, we want to address this problem. We show that in many OoD studies in high-dimensional data, LOF-based (Local Outlierness-Factor) methods outperform the parametric, Mahalanobis distance-based methods. This motivates us to propose the nonparametric, LOF-based method of generating the confidence scores for CNNs. We performed several feasibility studies involving ResNet-101 and EffcientNet-B3, based on CIFAR-10 and ImageNet (as known data), and CIFAR-100, SVHN, ImageNet2010, Places365, or ImageNet-O (as outliers). We demonstrated that nonparametric LOF-based confidence estimation can improve current Mahalanobis-based SOTA or obtain similar performance in a simpler way.
The Impact of Humor as A Teaching And Learning Strategy in The Literacy Skills Program on Increasing The Number of Visits to The Library of Universitas Sumatera Utara

The purpose of this article is to determine the effect of using humor as a teaching and learning approach in the Literacy skills program on the number of visitors to the University of North Sumatra's Library. The descriptive quantitative approach was utilized in this study, with a sample size of 100 participants, mainly new students who participated in the Literacy Skill program and visited the USU Library. Data collection was accomplished through the use of questionnaires, interviews, and a documentation study. The findings indicated that using humor as a teaching and learning approach in the Literacy skills program had a substantial beneficial effect on increasing library visits at the University of North Sumatra. The use of real-life examples of humor in literacy programs demonstrates that instructors have successfully integrated a set of best practices and methods into library instruction sessions, e.g., humorous examples, animations/cartoons, and narrative, with minimum risk. Based on a sense of humor, this strategy will assist in introducing more, promoting the library atmosphere, and encouraging visits and usage of library resources.
A Serverless Distributed Ledger for Enterprises

Enterprises have been attracted by the capability of blockchains to provide a single source of truth for workloads that span companies, geographies, and clouds while retaining the independence of each party's IT operations. However, so far production applications have remained rare, stymied by technical limitations of existing blockchain technologies and challenges with their integration into enterprises' IT systems. In this paper, we collect enterprises' requirements on distributed ledgers for data sharing and integration from a technical perspective, argue that they are not sufficiently addressed by available blockchain frameworks, and propose a novel distributed ledger design that is "serverless", i.e., built on cloud-native resources. We evaluate its qualitative and quantitative properties and give evidence that enterprises already heavily reliant on cloud service providers would consider such an approach acceptable, particularly if it offers ease of deployment, low transactional cost structure, and a combination of latency and scalability aligned with real-time IT application needs.
Robustness of non-Gaussian quantum nondemolition measurement induced entanglement between Bose-Einstein condensates

We study the robustness of quantum nondemolition (QND) measurement induced entanglement between Bose-Einstein Condensates (BECs). We consider an experimental scheme where two BECs are placed in the paths of a Mach-Zehnder interferometer, and a QND interaction creates entanglement between coherent light and the atoms. We analyze the two dominant channels of decoherence, atomic dephasing and photon loss on the entangled states produced by this scheme. We calculate the effect of dephasing on the variance and expectation values of the spin operators, entanglement, and correlation criteria. Our analysis does not use the Holstein-Primakoff approximation and is capable of modeling long light-atom interaction times, producing non-Gaussian states beyond the two-mode squeezed states. In the presence of dephasing, the entangled states are robust in the macroscopic limit as long as the dimensionless interaction time is less than $ 1/\sqrt{N}$, where $ N $ is the number of atoms in the BEC. For photon loss, the entangled states generated by long interaction times show a remarkable robustness that makes the scheme promising for various quantum information applications.
Learning to Learn a Cold-start Sequential Recommender

The cold-start recommendation is an urgent problem in contemporary online applications. It aims to provide users whose behaviors are literally sparse with as accurate recommendations as possible. Many data-driven algorithms, such as the widely used matrix factorization, underperform because of data sparseness. This work adopts the idea of meta-learning to solve the user's cold-start recommendation problem. We propose a meta-learning based cold-start sequential recommendation framework called metaCSR, including three main components: Diffusion Representer for learning better user/item embedding through information diffusion on the interaction graph; Sequential Recommender for capturing temporal dependencies of behavior sequences; Meta Learner for extracting and propagating transferable knowledge of prior users and learning a good initialization for new users. metaCSR holds the ability to learn the common patterns from regular users' behaviors and optimize the initialization so that the model can quickly adapt to new users after one or a few gradient updates to achieve optimal performance. The extensive quantitative experiments on three widely-used datasets show the remarkable performance of metaCSR in dealing with user cold-start problem. Meanwhile, a series of qualitative analysis demonstrates that the proposed metaCSR has good generalization.
Hiding Images into Images with Real-world Robustness

The existing image embedding networks are basically vulnerable to malicious attacks such as JPEG compression and noise adding, not applicable for real-world copyright protection tasks. To solve this problem, we introduce a generative deep network based method for hiding images into images while assuring high-quality extraction from the destructive synthesized images. An embedding network is sequentially concatenated with an attack layer, a decoupling network and an image extraction network. The addition of decoupling network learns to extract the embedded watermark from the attacked image. We also pinpoint the weaknesses of the adversarial training for robustness in previous works and build our improved real-world attack simulator. Experimental results demonstrate the superiority of the proposed method against typical digital attacks by a large margin, as well as the performance boost of the recovered images with the aid of progressive recovery strategy. Besides, we are the first to robustly hide three secret images.
A Semantic Question Answering through Heterogeneous Data Source in the Domain of Smart Factory

Manufacturing technologies have evolved with advancements of Industry 4.0 about big data systems, generation system for linked data from unstructured data sources, and streaming data pools. A heterogeneous data source is still problematic for restricted domain question answering due to the nature of unstructured data in manufacturing companies and data-intensive applications in smart factories. Smart factories have emerged with data-intensive operations that are occurring from manufacturing monitoring systems, hand terminals, mobile tablets, and assembly line controllers. Today, human operators experience an increased complexity of the data-intensive applications in the smart factory. Fetching data from various data sources brings a necessity of decreasing data size and derive an idea regarding what is happening by inductive reasoning at the smart factory. Heterogeneous data source occurs adversity in converting linked data to get answers asked questions by human operators, experts, workers through question answering systems. When dealing with a large amount of linked data, we need to design and implement a software solution that should enhance human operators’ and experts’ capabilities. In this study, we propose a semantic question answering that connects with heterogeneous data sources from different areas and devices of a smart factory. In the end, we will perform qualitative and quantitative evaluation regarding the semantic question answering that exploits heterogeneous data sources, as well as findings and conclude the main points concerning our research questions.
Effective and Innovative Online Teaching

There is no doubt that online learning and the development of online courses is proliferating.The convenience of working online has proven to be very attractive to students and instructors alike.Despite its popularity, online learning is not without its challenges.As instructors are finding their way in the online environment and paying more attention to good course design and delivery, they are discovering that traditional forms of assessment of student work such as tests and quizzes that served them well in the face to face classroom may not work quite as well online.Milam, Voorhees, and Bedard -Voorhees ( 2004) note, "The online paradigm holds that learning itself may be different in the online environment and, if that is true, then the methodology for measuring it should also be different or should measure those things that are, in fact, different" effective assessment has come increased concern about academic honesty, raising questions like these:• How do I, as the instructor, know that students really understand and can apply the material I' m teaching if I can' t sees them?• How can I ensure that students won' t cheat on exams or other assessments in my online course?In these fields, assessment criteria cannot be too precise or rigid, as building arguments that provide new insights is the benchmark of good performance.Jones, Voorhees, and Paulson (2002) define competencies as "the combination of skills, abilities, and knowledge needed to perform a specific task".Competencies support the dynamic link between knowing and expressing.Once competencies are established, the next level of focus should be the development of course outcomes.Course outcomes are important in that they help students learn more effectively and make it clear what students can expect to gain from taking the course.Collaborative effort helps learners achieve a deeper level of knowledge generation while moving from independence to interdependence, thus strengthening the foundation of an online learning community, which we believe is the vehicle through which the course should be delivered.By learning together in a learning community, students have the opportunity to extend and deepen their learning experience, test out new ideas by sharing them with a supportive group, and receive critical and constructive feedback.The Teacher Accurately Demonstrates Knowledge of the Content Area and Approved Curriculum.
A Channel Coding Benchmark for Meta-Learning

Meta-learning provides a popular and effective family of methods for data-efficient learning of new tasks. However, several important issues in meta-learning have proven hard to study thus far. For example, performance degrades in real-world settings where meta-learners must learn from a wide and potentially multi-modal distribution of training tasks; and when distribution shift exists between meta-train and meta-test task distributions. These issues are typically hard to study since the shape of task distributions, and shift between them are not straightforward to measure or control in standard benchmarks. We propose the channel coding problem as a benchmark for meta-learning. Channel coding is an important practical application where task distributions naturally arise, and fast adaptation to new tasks is practically valuable. We use our MetaCC benchmark to study several aspects of meta-learning, including the impact of task distribution breadth and shift, which can be controlled in the coding problem. Going forward, MetaCC provides a tool for the community to study the capabilities and limitations of meta-learning, and to drive research on practically robust and effective meta-learners.
Self-adaptive Multi-task Particle Swarm Optimization

Multi-task optimization (MTO) studies how to simultaneously solve multiple optimization problems for the purpose of obtaining better performance on each problem. Over the past few years, evolutionary MTO (EMTO) was proposed to handle MTO problems via evolutionary algorithms. So far, many EMTO algorithms have been developed and demonstrated well performance on solving real-world problems. However, there remain many works to do in adapting knowledge transfer to task relatedness in EMTO. Different from the existing works, we develop a self-adaptive multi-task particle swarm optimization (SaMTPSO) through the developed knowledge transfer adaptation strategy, the focus search strategy and the knowledge incorporation strategy. In the knowledge transfer adaptation strategy, each task has a knowledge source pool that consists of all knowledge sources. Each source (task) outputs knowledge to the task. And knowledge transfer adapts to task relatedness via individuals' choice on different sources of a pool, where the chosen probabilities for different sources are computed respectively according to task's success rate in generating improved solutions via these sources. In the focus search strategy, if there is no knowledge source benefit the optimization of a task, then all knowledge sources in the task's pool are forbidden to be utilized except the task, which helps to improve the performance of the proposed algorithm. Note that the task itself is as a knowledge source of its own. In the knowledge incorporation strategy, two different forms are developed to help the SaMTPSO explore and exploit the transferred knowledge from a chosen source, each leading to a version of the SaMTPSO. Several experiments are conducted on two test suites. The results of the SaMTPSO are comparing to that of 3 popular EMTO algorithms and a particle swarm algorithm, which demonstrates the superiority of the SaMTPSO.
Subject Review: Image Encryption Based on Multi Techniques

The enormous advancements in communication network, particularly the internet networks, that employed to allow numerous individuals to exchange various forms of data, Security of data has become a big issue.As a result, the usage of cipherment and decipherment ways is becoming more popular.Sundry cipherment means have been created to ensure data security, the chaos encryption system being one of the most widely employed in recent years.In this research, a review of diverse image cipherment algorithms have been reviewed, which researchers can gain insight into the most efficient techniques to employ.
Talking Detection In Collaborative Learning Environments

We study the problem of detecting talking activities in collaborative learning videos. Our approach uses head detection and projections of the log-magnitude of optical flow vectors to reduce the problem to a simple classification of small projection images without the need for training complex, 3-D activity classification systems. The small projection images are then easily classified using a simple majority vote of standard classifiers. For talking detection, our proposed approach is shown to significantly outperform single activity systems. We have an overall accuracy of 59% compared to 42% for Temporal Segment Network (TSN) and 45% for Convolutional 3D (C3D). In addition, our method is able to detect multiple talking instances from multiple speakers, while also detecting the speakers themselves.
Research Guides: A Guide to Library Research: Library vs Internet

Step-by-step guide with tutorials on the basics of research. Learn about library and online resources.
Adaptive Differentially Private Empirical Risk Minimization

We propose an adaptive (stochastic) gradient perturbation method for differentially private empirical risk minimization. At each iteration, the random noise added to the gradient is optimally adapted to the stepsize; we name this process adaptive differentially private (ADP) learning. Given the same privacy budget, we prove that the ADP method considerably improves the utility guarantee compared to the standard differentially private method in which vanilla random noise is added. Our method is particularly useful for gradient-based algorithms with time-varying learning rates, including variants of AdaGrad (Duchi et al., 2011). We provide extensive numerical experiments to demonstrate the effectiveness of the proposed adaptive differentially private algorithm.
MultiHead MultiModal Deep Interest Recommendation Network

With the development of information technology, human beings are constantly producing a large amount of information at all times. How to obtain the information that users are interested in from the large amount of information has become an issue of great concern to users and even business managers. In order to solve this problem, from traditional machine learning to deep learning recommendation systems, researchers continue to improve optimization models and explore solutions. Because researchers have optimized more on the recommendation model network structure, they have less research on enriching recommendation model features, and there is still room for in-depth recommendation model optimization. Based on the DIN\cite{Authors01} model, this paper adds multi-head and multi-modal modules, which enriches the feature sets that the model can use, and at the same time strengthens the cross-combination and fitting capabilities of the model. Experiments show that the multi-head multi-modal DIN improves the recommendation prediction effect, and outperforms current state-of-the-art methods on various comprehensive indicators.
The Effect of Green Information Technology on Enhancing Value of Information Technology Business: A Pilot Study in Iraqi Firms

This study aims to identify the effect of green information technology in enhancing the value of the information technology business.To achieve this goal, a study model was proposed based on a set of measures derived from the literature for this field, where the measures of green information technology were (position, policy, practice, technology, governance, and environmental effectiveness), while the measures related to the value of information technology business were (strategic dimension, informatics, sales and marketing support, customer relations).The descriptive approach was adopted to conduct the research and using the questionnaire form as a tool to collect data from a sample of ( 106) respondents, for data analysis, a structured equation model was used through (Smart PLS v3).The research reached a set of results, the most important of which is that green information technology has a significant impact on the value of the information technology business.
An Augmented Reality Platform for Introducing Reinforcement Learning to K-12 Students with Robots

Interactive reinforcement learning, where humans actively assist during an agent's learning process, has the promise to alleviate the sample complexity challenges of practical algorithms. However, the inner workings and state of the robot are typically hidden from the teacher when humans provide feedback. To create a common ground between the human and the learning robot, in this paper, we propose an Augmented Reality (AR) system that reveals the hidden state of the learning to the human users. This paper describes our system's design and implementation and concludes with a discussion on two directions for future work which we are pursuing: 1) use of our system in AI education activities at the K-12 level; and 2) development of a framework for an AR-based human-in-the-loop reinforcement learning, where the human teacher can see sensory and cognitive representations of the robot overlaid in the real world.