Reviewerly/exHarmony huggingface dataset:
Considering user dynamic preferences for mitigating negative effects of  long tail in recommender systems

Nowadays, with the increase in the amount of information generated in the webspace, many web service providers try to use recommender systems to personalize their services and make accessing the content convenient. Recommender systems that only try to increase the accuracy (i.e., the similarity of items to users' interest) will face the long tail problem. It means that popular items called short heads appear in the recommendation lists more than others since they have many ratings. However, unpopular items called long-tail items are used less than popular ones as they reduce accuracy. Other studies that solve the long-tail problem consider users' interests constant while their preferences change over time. We suggest that users' dynamic preferences should be taken into account to prevent the loss of accuracy when we use long-tail items in recommendation lists. This study shows that the two reasons lie in the following: 1) Users rate for different proportions of popular and unpopular items over time. 2) Users of all ages have various interests in popular and unpopular items. As a result, recommendation lists can be created over time with a different portion of long-tail and short-head items. Besides, we predict the age of users based on item ratings to use more long-tail items. The results show that by considering these two reasons, the accuracy of recommendation lists reaches 91%. At the same time, the long tail problem is better improved than other related research and provides better diversity in recommendation lists in the long run.
Staatliche Steuerungsmöglichkeiten zur Förderung des Teilens von Forschungsdaten - Government control options to promote the sharing of research data

The open science movement is increasingly demanding that researchers provide access to the underlying data of published research results.While the exchange of such data is already widespread, data sharing is used in many areas of research still in its infancy.In order for Germany to stay "competitive", as a research location European as well as transnationally the potential of available and interlinkable data have to be exploited.There are still only a few requirements for data sharing that go beyond non-binding recommendations.There is a lot of catching up to do.
Distance Learning in Primary School During the COVID 19 Pandemic: Results of the "SMART KIDS" Experiment

The paper analyzes the results of the introduction of the distance learning form (DLF) using electronic educational resources (EER) and the teacher's virtual classroom in primary school. The experiment took place within the framework of the "Smart Kids" All-Ukrainian project during the long quarantine caused by the COVID-19 pandemic. The educational process took place both synchronously and asynchronously. The present paper substantiates the model of organization of distance learning of primary school students using EER and outlines its three main components: the organization of learning, conducting online classes (explaining new material or practicing skills by students) and monitoring the quality of students' independent performance of tasks. The results of the experiment prove that it is necessary to provide teachers and students with computer equipment, Internet access, digital resources for teaching and assessment to implement DLF. It has been established that EER in distance learning can be used both on a regular basis - in each class, and periodically - to explain new material or train skills, the quality of tasks performed by students can be monitored in the virtual office of the teacher and shape an individual trajectory of students' development. The teachers identified the following main problems of DLF implementation: internet interruptions, problems with providing new computer equipment to students and some teachers; lack of state aid in providing EER to all participants in the educational process; limited access to students' computers during complete isolation due to online work of parents. Despite the outlined problems, the quality of distance learning of primary school students during the pandemic using EER was positively and highly assessed by teachers.
Design and Implementation of Low-quality Article Review System Based on Deep Learning

In the era of information Internet, We Media is becoming increasingly mature, and users’ demand for We Media information is also increasing, promoting the rapid development of diversified We Media platforms. We Media platforms gradually expand from the simple release of news consulting content to life, entertainment, social commentary, finance and economics and other aspects of content. Compared with traditional media platforms, We Media is more flexible and independent and relatively free in speech. Therefore, the quality of articles in “We media” is on various level. If it is not screened, it is likely to cause the wrong direction of public opinion. However, the traditional manual review method has the disadvantages of low efficiency, high cost of human resources, error-prone, and no guarantee of timeliness. Therefore, the adoption of machine review is the key method to solve the above problems. In this paper, a low-quality article review system based on deep learning is designed to improve the reading environment by automatically reviewing politically sensitive, vulgar pornography, and advertising and marketing content.
Watermarking Images in Self-Supervised Latent Spaces

We revisit watermarking techniques based on pre-trained deep networks, in the light of self-supervised approaches. We present a way to embed both marks and binary messages into their latent spaces, leveraging data augmentation at marking time. Our method can operate at any resolution and creates watermarks robust to a broad range of transformations (rotations, crops, JPEG, contrast, etc). It significantly outperforms the previous zero-bit methods, and its performance on multi-bit watermarking is on par with state-of-the-art encoder-decoder architectures trained end-to-end for watermarking. The code is available at github.com/facebookresearch/ssl_watermarking
Approximate Inference for Stochastic Planning in Factored Spaces

Stochastic planning can be reduced to probabilistic inference in large discrete graphical models, but hardness of inference requires approximation schemes to be used. In this paper we argue that such applications can be disentangled along two dimensions. The first is the direction of information flow in the idealized exact optimization objective, i.e., forward vs backward inference. The second is the type of approximation used to compute this objective, e.g., Belief Propagation (BP) vs mean field variational inference (MFVI). This new categorization allows us to unify a large amount of isolated efforts in prior work explaining their connections and differences as well as potential improvements. An extensive experimental evaluation over large stochastic planning problems shows the advantage of forward BP over several algorithms based on MFVI. An analysis of practical limitations of MFVI motivates a novel algorithm, collapsed state variational inference (CSVI), which provides a tighter approximation and achieves comparable planning performance with forward BP.
DKM: Dense Kernelized Feature Matching for Geometry Estimation

Feature matching is a challenging computer vision task that involves finding correspondences between two images of a 3D scene. In this paper we consider the dense approach instead of the more common sparse paradigm, thus striving to find all correspondences. Perhaps counter-intuitively, dense methods have previously shown inferior performance to their sparse and semi-sparse counterparts for estimation of two-view geometry. This changes with our novel dense method, which outperforms both dense and sparse methods on geometry estimation. The novelty is threefold: First, we propose a kernel regression global matcher. Secondly, we propose warp refinement through stacked feature maps and depthwise convolution kernels. Thirdly, we propose learning dense confidence through consistent depth and a balanced sampling approach for dense confidence maps. Through extensive experiments we confirm that our proposed dense method, \textbf{D}ense \textbf{K}ernelized Feature \textbf{M}atching, sets a new state-of-the-art on multiple geometry estimation benchmarks. In particular, we achieve an improvement on MegaDepth-1500 of +4.9 and +8.9 AUC$@5^{\circ}$ compared to the best previous sparse method and dense method respectively. Our code is provided at https://github.com/Parskatt/dkm
A feast for trolls -- Engagement analysis of counternarratives against  online toxicity

This report provides an engagement analysis of counternarratives against online toxicity. Between February 2020 and July 2021, we observed over 15 million toxic messages on social media identified by our fine-grained, multilingual detection AI. Over 1,000 dashboard users responded to toxic messages with combinations of visual memes, text, or AI-generated text, or they reported content. This leads to new, real-life insights on self-regulatory approaches for the mitigation of online hate.
Extracting Label-specific Key Input Features for Neural Code Intelligence Models

The code intelligence (CI) models are often black-box and do not offer any insights on the input features that they learn for making correct predictions. This opacity may lead to distrust in their prediction and hamper their wider adoption in safety-critical applications. In recent, the program reduction technique is widely being used to identify key input features in order to explain the prediction of CI models. The approach removes irrelevant parts from an input program and keeps the minimal snippets that a CI model needs to maintain its prediction. However, the state-of-the-art approaches mainly use a syntax-unaware program reduction technique that does not follow the syntax of programs, which adds significant overhead to the reduction of input programs and explainability of models. In this paper, we apply a syntax-guided program reduction technique that follows the syntax of input programs during reduction. Our experiments on multiple models across different types of input programs show that the syntax-guided program reduction technique significantly outperforms the syntax-unaware program reduction technique in reducing the size of input programs. Extracting key input features from reduced programs reveals that the syntax-guided reduced programs contain more label-specific key input features and are more vulnerable to adversarial transformation when renaming the key tokens in programs. These label-specific key input features may help to understand the reasoning of models' prediction from different perspectives and increase the trustworthiness to correct classification given by CI models.
Kernel Density Estimation by Genetic Algorithm

This study proposes a data condensation method for multivariate kernel density estimation by genetic algorithm. First, our proposed algorithm generates multiple subsamples of a given size with replacement from the original sample. The subsamples and their constituting data points are regarded as $\it{chromosome}$ and $\it{gene}$, respectively, in the terminology of genetic algorithm. Second, each pair of subsamples breeds two new subsamples, where each data point faces either $\it{crossover}$, $\it{mutation}$, or $\it{reproduction}$ with a certain probability. The dominant subsamples in terms of fitness values are inherited by the next generation. This process is repeated generation by generation and brings the sparse representation of kernel density estimator in its completion. We confirmed from simulation studies that the resulting estimator can perform better than other well-known density estimators.
Mining of High Utility Itemsets with Negative Utility values for Incremental Datasets

Recent advances in knowledge discovery have led to the design of efficient methods of extracting meaningful patterns from datasets. Most of the algorithms are designed under the assumption that datasets are inherently static in nature and that all items hold only positive utility value. However, for industrial applications some of the items can have negative utility values to indicate discounts. Also, these databases are likely to evolve with time as new data is inserted. Traditional mining algorithms incur high execution times and demand large memory requirements to process new data by running the algorithms in a batch manner. Therefore, in this study the problem of mining itemsets with high utility, from incremental datasets is addressed, where the items can have negative utility values. Further, a detailed experimental analysis are undertaken to validate the efficacy of the proposed algorithm.
A unified framework to improve the interoperability between HPC and Big  Data languages and programming models

One of the most important issues in the path to the convergence of HPC and Big Data is caused by the differences in their software stacks. Despite some research efforts, the interoperability between their programming models and languages is still limited. To deal with this problem we introduce a new computing framework called IgnisHPC, whose main objective is to unify the execution of Big Data and HPC workloads in the same framework. IgnisHPC has native support for multi-language applications using JVM and non-JVM-based languages. Since MPI was used as its backbone technology, IgnisHPC takes advantage of many communication models and network architectures. Moreover, MPI applications can be directly executed in a efficient way in the framework. The main consequence is that users could combine in the same multi-language code HPC tasks (using MPI) with Big Data tasks (using MapReduce operations). The experimental evaluation demonstrates the benefits of our proposal in terms of performance and productivity with respect to other frameworks such as Apache Spark. IgnisHPC is publicly available for the Big Data and HPC research community.
Educational Activity Suggestion System of Children With Pervasive Developmental Disorder for Guiding Education and Training Staff Activities

Individuals with pervasive developmental disorders should be supported with special education programs that are planned according to the type and degree of the disorder, age, characteristics, and needs of the individual. Search over internet resources may provide suitable educational material and methods (and associated activity/game). However, syntactic search in today's static-based internet is insufficient to offer desired relevant results. An intelligent system able to identify the needed educational methods and material with the help of semantic web-based agents will not only contribute to the development of individuals with disorders, and support education specialists in this process, but also be extremely useful for the families of these individuals in assisting and monitoring their child's developmental progress. In this chapter, an agent-based educational activity suggestion system of children with pervasive developmental disorder for guiding education and training staff activities is proposed.
A Survey on Dynamic Neural Networks for Natural Language Processing

Effectively scaling large Transformer models is a main driver of recent advances in natural language processing. Dynamic neural networks, as an emerging research direction, are capable of scaling up neural networks with sub-linear increases in computation and time by dynamically adjusting their computational path based on the input. Dynamic neural networks could be a promising solution to the growing parameter numbers of pretrained language models, allowing both model pretraining with trillions of parameters and faster inference on mobile devices. In this survey, we summarize progress of three types of dynamic neural networks in NLP: skimming, mixture of experts, and early exit. We also highlight current challenges in dynamic neural networks and directions for future research.
Translation-equivariant Image Quantizer for Bi-directional Image-Text  Generation

Recently, vector-quantized image modeling has demonstrated impressive performance on generation tasks such as text-to-image generation. However, we discover that the current image quantizers do not satisfy translation equivariance in the quantized space due to aliasing, degrading performance in the downstream text-to-image generation and image-to-text generation, even in simple experimental setups. Instead of focusing on anti-aliasing, we take a direct approach to encourage translation equivariance in the quantized space. In particular, we explore a desirable property of image quantizers, called 'Translation Equivariance in the Quantized Space' and propose a simple but effective way to achieve translation equivariance by regularizing orthogonality in the codebook embedding vectors. Using this method, we improve accuracy by +22% in text-to-image generation and +26% in image-to-text generation, outperforming the VQGAN.
Context-based navigation for ground mobile robot in a semi-structured  indoor environment

There is a growing demand for mobile robots to operate in more variable environments, where guaranteeing safe robot navigation is a priority, in addition to time performance. To achieve this, current solutions for local planning use a specific configuration tuned to the characteristics of the application environment. In this paper, we present an approach for developing quality models that can be used by a self-adaptation framework to adapt the local planner configuration at run-time based on the perceived environment. We contribute a definition of a safety model that predicts the safety of a navigation configuration given the perceived environment. Experiments have been performed in a realistic navigation scenario for a retail application to validate the obtained models and demonstrate their integration in a self-adaptation framework.
A Secure and Effective Image Retrieval Based on Robust Features

The most typical approaches are content-based image retrieval systems. Content-based picture retrieval may be the only one in all the image retrieval techniques that uses user visual options of an image like color, form, and texture. The objective is to retrieve the set of pictures quickly and economically by supported color and texture options. Color is the foremost authoritative and utilized visual option that is invariant to image dimension and adjustment. Color car correlogram includes the special correlation and figures the mean color of all components of intensity about a distance k-th of a pixel of intensity the picture. Next, the feel feature may be a powerful region-based descriptor to provide a life of attributes like smoothness, coarseness, and regularity. Block distinction probabilities and block variation of native correlation features are analysed to speed up the retrieval method. BDIP may be a block-based approach to extract color and intensity features and live native brightness variation from the photographs.
Dynamic ConvNets on Tiny Devices via Nested Sparsity

This work introduces a new training and compression pipeline to build Nested Sparse ConvNets, a class of dynamic Convolutional Neural Networks (ConvNets) suited for inference tasks deployed on resource-constrained devices at the edge of the Internet-of-Things. A Nested Sparse ConvNet consists of a single ConvNet architecture containing N sparse sub-networks with nested weights subsets, like a Matryoshka doll, and can trade accuracy for latency at run time, using the model sparsity as a dynamic knob. To attain high accuracy at training time, we propose a gradient masking technique that optimally routes the learning signals across the nested weights subsets. To minimize the storage footprint and efficiently process the obtained models at inference time, we introduce a new sparse matrix compression format with dedicated compute kernels that fruitfully exploit the characteristic of the nested weights subsets. Tested on image classification and object detection tasks on an off-the-shelf ARM-M7 Micro Controller Unit (MCU), Nested Sparse ConvNets outperform variable-latency solutions naively built assembling single sparse models trained as stand-alone instances, achieving (i) comparable accuracy, (ii) remarkable storage savings, and (iii) high performance. Moreover, when compared to state-of-the-art dynamic strategies, like dynamic pruning and layer width scaling, Nested Sparse ConvNets turn out to be Pareto optimal in the accuracy vs. latency space.
Learning Cross-Scale Weighted Prediction for Efficient Neural Video Compression

Neural video codecs have demonstrated great potential in video transmission and storage applications. Existing neural hybrid video coding approaches rely on optical flow or Gaussian-scale flow for prediction, which cannot support fine-grained adaptation to diverse motion content. Towards more content-adaptive prediction, we propose a novel cross-scale prediction module that achieves more effective motion compensation. Specifically, on the one hand, we produce a reference feature pyramid as prediction sources and then transmit cross-scale flows that leverage the feature scale to control the precision of prediction. On the other hand, for the first time, a weighted prediction mechanism is introduced even if only a single reference frame is available, which can help synthesize a fine prediction result by transmitting cross-scale weight maps. In addition to the cross-scale prediction module, we further propose a multi-stage quantization strategy, which improves the rate-distortion performance with no extra computational penalty during inference. We show the encouraging performance of our efficient neural video codec (ENVC) on several benchmark datasets. In particular, the proposed ENVC can compete with the latest coding standard H.266/VVC in terms of sRGB PSNR on UVG dataset for the low-latency mode. We also analyze in detail the effectiveness of the cross-scale prediction module in handling various video content, and provide a comprehensive ablation study to analyze those important components. Test code is available at https://github.com/USTC-IMCL/ENVC .
Implicit Motion Handling for Video Camouflaged Object Detection

We propose a new video camouflaged object detection (VCOD) framework that can exploit both short-term dynamics and long-term temporal consistency to detect camouflaged objects from video frames. An essential property of camouflaged objects is that they usually exhibit patterns similar to the background and thus make them hard to identify from still images. Therefore, effectively handling temporal dynamics in videos becomes the key for the VCOD task as the camouflaged objects will be noticeable when they move. However, current VCOD methods often leverage homography or optical flows to represent motions, where the detection error may accumulate from both the motion estimation error and the segmentation error. On the other hand, our method unifies motion estimation and object segmentation within a single optimization framework. Specifically, we build a dense correlation volume to implicitly capture motions between neighbouring frames and utilize the final segmentation supervision to optimize the implicit motion estimation and segmentation jointly. Furthermore, to enforce temporal consistency within a video sequence, we jointly utilize a spatio-temporal transformer to refine the short-term predictions. Extensive experiments on VCOD benchmarks demonstrate the architectural effectiveness of our approach. We also provide a large-scale VCOD dataset named MoCA-Mask with pixel-level handcrafted ground-truth masks and construct a comprehensive VCOD benchmark with previous methods to facilitate research in this direction. Dataset Link: https://xueliancheng.github.io/SLT-Net-project.