Reviewerly/exHarmony huggingface dataset:
A Digital Transformation Lab for Developing Countries and Small to Medium Enterprises

Digital transformation of small towns (population less than 100,000), small enterprises (employees less than 100) and small countries (population less than 10 million) especially in the developing nations is a challenging task. Besides financial and skillset shortages, a wide range of digital solutions are needed that are location and topic specific and must comply with regional government policies and industry guidelines. In addition, these solutions need to be produced quickly and at large scales to satisfy the needs of more than half of the world population. The needed solutions cannot be handcrafted individually and manully for such diverse populations. Based on the work with more thab 40 United Nations projects, a factory model has been developed that generates highly customized portals rapidly to meet the local requirements. Besides supporting UN Projects, this eFactory is being used to support graduate education in the Information Systems Engineering and Management Program at Harrisburg University. This paper explains how this eFactory can fuel a Digital Transformation Lab to support numerous humanitarian initiatives that may include some Metaverse experiments.
Enabling Customizable Workflows for Industrial AR Applications

Augmented Reality (AR) is increasingly considered for use in real industrial applications [8]. In our industrial research lab at Siemens Technology, we are continuously discussing suitable AR scenarios in business sectors involving power plants, wind turbine plants, and oil and gas factories. We have developed a company-internal AR system architecture, Hololayer, which provides AR facilities in a reusable manner to development engineers such that they can build their own AR applications for their specialized use cases. The integration of AR technology into industrial processes encounters many complex issues: we need to adhere to many safety, security and quality guarantees while also adapting quickly to the rapidly changing state of the art of AR devices (HMDs, tablets). To increase flexibility, it might be good to integrate Hololayer with one of the open frameworks that have recently been proposed [11], [29], [38]. Yet, care must be taken when converting an already existing, large company-owned framework like Hololayer to such platforms. Some of the proposed standardized APIs and communication protocols may be difficult to abide by, without requiring significant rewriting efforts or even violating company-internal regulations. In this paper, we report on our efforts to combine Hololayer with one such platform, Ubi-Interact [38]. This is a collaboration between Siemens technology and the FAR group at TU Munich. After exemplary descriptions of typical application scenarios, we present the underlying principles of Hololayer to support this range of applications. We then describe rudimentary concepts of Ubi-Interact, followed by an elaboration on the efforts to combine both systems for a selected application scenario and a discussion of the results achieved thus far.
Hierarchical Contrastive Inconsistency Learning for Deepfake Video Detection

With the rapid development of Deepfake techniques, the capacity of generating hyper-realistic faces has aroused public concerns in recent years. The temporal inconsistency which derives from the contrast of facial movements between pristine and forged videos can serve as an efficient cue in identifying Deepfakes. However, most existing approaches tend to impose binary supervision to model it, which restricts them to only focusing on the category-level discrepancies. In this paper, we propose a novel Hierarchical Contrastive Inconsistency Learning framework (HCIL) with a two-level contrastive paradigm. Specially, sampling multiply snippets to form the input, HCIL performs contrastive learning from both local and global perspectives to capture more general and intrinsical temporal inconsistency between real and fake videos. Moreover, we also incorporate a region-adaptive module for intra-snippet inconsistency mining and an inter-snippet fusion module for cross-snippet information fusion, which further facilitates the inconsistency learning. Extensive experiments and visualizations demonstrate the effectiveness of our method against SOTA competitors on four Deepfake video datasets, i.e., FaceForensics++, Celeb-DF, DFDC, and Wild-Deepfake.
Soft Upper-bound Support Vector Machines

The minimum complexity L1 SVM (ML1 SVM) is the fusion of the minimal complexity machine (MCM) and the standard support vector machine (L1 SVM) to realize high generalization ability over the L1 SVM. The fusion is realized by minimizing the upper bound on the decision function values (i.e., minimizing the maximum margin as well as maximizing the minimum margin) in the L1 SVM. In this paper, to avoid worsening the generalization ability by minimizing the upper bound, we introduce the soft upper bound on the decision function values. This is realized by allowing some of the training data outputs to exceed the upper bound. We call this architecture soft upper-bound L1 SVM (SL1 SVM). By introducing the soft upper-bound, the number of hyperparameters increases by one. We explain how to cancel the increase by setting the hyperparameter value properly, and clarify the characteristics of the SL1 SVM. Finally, we compare the SL1 SVM with ML1 SVMs using several benchmark data sets and show that overfitting occurs for the SL1 SVM more frequently than for the ML1 SVMs.
IRINS: Implementing a Research Information Management System in Indian Higher Education Institutions

An increasing number of organizations are encouraging and accepting the research information management system in the institute level to assess the research progress of the organization and bring out a report on scholarly communication activities. Open and commercial product on the Current Research Information System (CRIS) is emerging in the last decade. The Directory of Current Research Information system listed 25 CRIS system across European countries. The European Current Research Information System (euroCRIS) support the CRIS thought the European countries through the conference, working group and pioneer in the advocacy since 2002. This paper considers the recent development in the CRIS related activities around the world and recent progress in India. This paper describes the various challenges to setting up the RIM for the higher education institutions including identification of nodal officers, collecting faculty profile information, retrieving scholarly communication activities from internal and external sources, etc. Also, discuss the training programme to the nodal officer level and institute level to promote the academic identities and their importance. The paper also discusses the impact of the IRINS in the higher education system in India and its use case with different stakeholders such as students, scholars, faculty members, scientists, research administrators, funding agencies and policymakers, etc.
Automatic Check-Out via Prototype-Based Classifier Learning from Single-Product Exemplars

Automatic Check-Out (ACO) aims to accurately predict the presence and count of each category of products in check-out images, where a major challenge is the significant domain gap between training data (single-product exemplars) and test data (check-out images). To mitigate the gap, we propose a method, termed as PSP, to perform Prototype-based classifier learning from Single-Product exemplars. In PSP, by revealing the advantages of representing category semantics, the prototype representation of each product category is firstly obtained from single-product exemplars. Based on the prototypes, it then generates categorical classifiers with a background classifier to not only recognize fine-grained product categories but also distinguish background upon product proposals derived from check-out images. To further improve the ACO accuracy, we develop discriminative re-ranking to both adjust the predicted scores of product proposals for bringing more discriminative ability in classifier learning and provide a reasonable sorting possibility by considering the fine-grained nature. Moreover, a multi-label recognition loss is also equipped for modeling co-occurrence of products in check-out images. Experiments are conducted on the large-scale RPC dataset for evaluations. Our ACO result achieves 86.69%, by 6.18% improvements over state-of-the-arts, which demonstrates the superiority of PSP. Our codes are available at https://github.com/Hao-Chen-NJUST/PSP .
Hybrid Recommendation System with Graph based and Collaborative Filtering Recommendation Systems

Every online platform on the internet needs to have some kind of recommendation system. A recommendation system will suggest products or services on a platform to a user, based on their preference and history. This increases user engagement on the platform, as they help the user in their choices. Hence, there is a need for a simple recommendation system solution. Recommendation system works by creating relations between products or the users; and by evaluation of those relations, it can suggest relevant products related to any product. Hybrid Recommendation combines the predictions of two or more recommendation techniques. This system combines the predictions from product - based predictions; implemented using a Graph based recommendation system, and users - based predictions; implemented using Collaborative Filtering recommendation system.
Improving Code Completion by Sequence Features and Structural Features

Code completion is essential in integrated development environments (IDEs). It has also shown intelligence in helping developers to product. Recently, neural network-based models have helped improve code completion by capturing code information from the abstract syntax tree (AST). However, these methods suffer from several issues. First, the code sequence features are not fully exploited. Second, the sequence features are not effectively combined and utilized with structural features. In this paper, we explore the effectiveness of code sequence features using relative position encoding at first. Then we combine the sequence features with structural features using an extended attention mechanism to enhance performance. We evaluate the proposed approach in two real-world datasets and find that sequence features are practically crucial for code completion. The sequence features combined with structural features enhance the code completion performance. Also, we employ Byte-Pair Encoding (BPE) to mitigate the out-of-vocabulary (OOV) issue in this task. Our best model has a 10% improvement for the mean reciprocal rank (MRR) metric compared to previous researches.
VaderLogRest Algorithm: An Ensemble Learning Approach for Sentiment Analysis on Vaccination Tweets

Analyzing the emotions about the vaccines and vaccination will help to successfully carry forward the vaccination trials and government policies towards epidemic control. The tweets featured information on the most common immunizations has recently been available all around the world. The method of natural language processing is the successful tool to investigate the reactions of the people to various immunizations. This paper proposes a ensemble learning model making use of the VADER lexicon, logistic regression, and random forest algorithm for sentiment analysis to understand and interpret the people's sentiments through the tweets. We utilize a collection of tweets in April to May 2021 to extract inferences about public views on vaccinations as they become more widely available during the COVID-19 pandemic. The classification output of the VADER algorithm is used as one more feature that helps to achieve better accuracy using the random forest algorithm. One more feature is added with the available features using logistic regression. Hence, the classification outputs of VADER and logistic regression improve the classification accuracy to 88% for positive-negative outputs and 84% for positive, neutral, and negative outputs.
TACS: Taxonomy Adaptive Cross-Domain Semantic Segmentation

Traditional domain adaptive semantic segmentation addresses the task of adapting a model to a novel target domain under limited or no additional supervision. While tackling the input domain gap, the standard domain adaptation settings assume no domain change in the output space. In semantic prediction tasks, different datasets are often labeled according to different semantic taxonomies. In many real-world settings, the target domain task requires a different taxonomy than the one imposed by the source domain. We therefore introduce the more general taxonomy adaptive cross-domain semantic segmentation (TACS) problem, allowing for inconsistent taxonomies between the two domains. We further propose an approach that jointly addresses the image-level and label-level domain adaptation. On the label-level, we employ a bilateral mixed sampling strategy to augment the target domain, and a relabelling method to unify and align the label spaces. We address the image-level domain gap by proposing an uncertainty-rectified contrastive learning method, leading to more domain-invariant and class-discriminative features. We extensively evaluate the effectiveness of our framework under different TACS settings: open taxonomy, coarse-to-fine taxonomy, and implicitly-overlapping taxonomy. Our approach outperforms the previous state-of-the-art by a large margin, while being capable of adapting to target taxonomies. Our implementation is publicly available at https://github.com/ETHRuiGong/TADA .
The Communication Value of a Quantum Channel

There are various ways to quantify the communication capabilities of a quantum channel. In this work we study the communication value (cv) of channel, which describes the optimal success probability of transmitting a randomly selected classical message over the channel. The cv also offers a dual interpretation as the classical communication cost for zero-error channel simulation using non-signaling resources. We first provide an entropic characterization of the cv as a generalized conditional min-entropy over the cone of separable operators. Additionally, the logarithm of a channel's cv is shown to be equivalent to its max-Holevo information, which can further be related to channel capacity. We evaluate the cv exactly for all qubit channels and the Werner-Holevo family of channels. While all classical channels are multiplicative under tensor product, this is no longer true for quantum channels in general. We provide a family of qutrit channels for which the cv is non-multiplicative. On the other hand, we prove that any pair of qubit channels have multiplicative cv when used in parallel. Even stronger, all entanglement-breaking channels and the partially depolarizing channel are shown to have multiplicative cv when used in parallel with any channel. We then turn to the entanglement-assisted cv and prove that it is equivalent to the conditional min-entropy of the Choi matrix of the channel. A final component of this work investigates relaxations of the channel cv to other cones such as the set of operators having a positive partial transpose (PPT). The PPT cv is analytically and numerically investigated for well-known channels such as the Werner-Holevo family and the dephrasure family of channels.
Performance Prediction Under Dataset Shift

ML models deployed in production often have to face unknown domain changes, fundamentally different from their training settings. Performance prediction models carry out the crucial task of measuring the impact of these changes on model performance. We study the generalization capabilities of various performance prediction models to new domains by learning on generated synthetic perturbations. Empirical validation on a benchmark of ten tabular datasets shows that models based upon state-of-the-art shift detection metrics are not expressive enough to generalize to unseen domains, while Error Predictors bring a consistent improvement in performance prediction under shift. We additionally propose a natural and effortless uncertainty estimation of the predicted accuracy that ensures reliable use of performance predictors. Our implementation is available at https://github.com/dataiku-research/performance_prediction_under_shift.
PGDP5K: A Diagram Parsing Dataset for Plane Geometry Problems

Diagram parsing is an important foundation for geometry problem solving, attracting increasing attention in the field of intelligent education and document image understanding. Due to the complex layout and between-primitive relationship, plane geometry diagram parsing (PGDP) is still a challenging task deserving further research and exploration. An appropriate dataset is critical for the research of PGDP. Although some datasets with rough annotations have been proposed to solve geometric problems, they are either small in scale or not publicly available. The rough annotations also make them not very useful. Thus, we propose a new large-scale geometry diagram dataset named PGDP5K and a novel annotation method. Our dataset consists of 5000 diagram samples composed of 16 shapes, covering 5 positional relations, 22 symbol types and 6 text types. Different from previous datasets, our PGDP5K dataset is labeled with more fine-grained annotations at primitive level, including primitive classes, locations and relationships. What is more, combined with above annotations and geometric prior knowledge, it can generate intelligible geometric propositions automatically and uniquely. We performed experiments on PGDP5K and IMP-Geometry3K datasets reveal that the state-of-the-art (SOTA) method achieves only 66.07% F1 value. This shows that PGDP5K presents a challenge for future research. Our dataset is available at http://www.nlpr.ia.ac.cn/databases/CASIA-PGDP5K/.
Photon-Limited Blind Deconvolution Using Unsupervised Iterative Kernel Estimation

Blind deconvolution is a challenging problem, but in low-light it is even more difficult. Existing algorithms, both classical and deep-learning based, are not designed for this condition. When the photon shot noise is strong, conventional deconvolution methods fail because (1) the image does not have enough signal-to-noise ratio to perform the blur estimation; (2) While deep neural networks are powerful, many of them do not consider the forward process. When the noise is strong, these networks fail to simultaneously deblur and denoise; (3) While iterative schemes are known to be robust in the classical frameworks, they are seldom considered in deep neural networks because it requires a differentiable non-blind solver. This paper addresses the above challenges by presenting an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unsupervised</i> blind deconvolution method. At the core of this method is a reformulation of the general blind deconvolution framework from the conventional image-kernel alternating minimization to a purely kernel-based minimization. This kernel-based minimization leads to a new iterative scheme that backpropagates an unsupervised loss through a pre-trained non-blind solver to update the blur kernel. Experimental results show that the proposed framework achieves superior results than state-of-the-art blind deconvolution algorithms in low-light conditions.
L-CoDer: Language-Based Colorization with Color-Object Decoupling Transformer

Language-based colorization requires the colorized image to be consistent with the user-provided language caption. A most recent work proposes to decouple the language into color and object conditions in solving the problem. Though decent progress has been made, its performance is limited by three key issues. (i) The large gap between vision and language modalities using independent feature extractors makes it difficult to fully understand the language. (ii) The inaccurate language features are never refined by the image features such that the language may fail to colorize the image precisely. (iii) The local region does not perceive the whole image, producing global inconsistent colors. In this work, we introduce transformer into language-based colorization to tackle the aforementioned issues while keeping the language decoupling property. Our method unifies the modalities of image and language, and further performs color conditions evolving with image features in a coarse-to-fine manner. In addition, thanks to the global receptive field, our method is robust to the strong local variation. Extensive experiments demonstrate our method is able to produce realistic colorization and outperforms prior arts in terms of consistency with the caption.
Max Pooling with Vision Transformers Reconciles Class and Shape in Weakly Supervised Semantic Segmentation

Weakly Supervised Semantic Segmentation (WSSS) research has explored many directions to improve the typical pipeline CNN plus class activation maps (CAM) plus refinements, given the image-class label as the only supervision. Though the gap with the fully supervised methods is reduced, further abating the spread seems unlikely within this framework. On the other hand, WSSS methods based on Vision Transformers (ViT) have not yet explored valid alternatives to CAM. ViT features have been shown to retain a scene layout, and object boundaries in self-supervised learning. To confirm these findings, we prove that the advantages of transformers in self-supervised methods are further strengthened by Global Max Pooling (GMP), which can leverage patch features to negotiate pixel-label probability with class probability. This work proposes a new WSSS method dubbed ViT-PCM (ViT Patch-Class Mapping), not based on CAM. The end-to-end presented network learns with a single optimization process, refined shape and proper localization for segmentation masks. Our model outperforms the state-of-the-art on baseline pseudo-masks (BPM), where we achieve 69.3% mIoU on PascalVOC 2012 val set. We show that our approach has the least set of parameters, though obtaining higher accuracy than all other approaches. In a sentence, quantitative and qualitative results of our method reveal that ViT-PCM is an excellent alternative to CNN-CAM based architectures.
Monitoring the digital educational environment in the context of education transformation

Monitoring is one of the tools for information support of education management. The transformation of the education system in Russia presupposes changes in management tools. The digitalization of education sets the vector for the transformation of the educational environment for all lifelong learning practices in the direction of digitalization. Based on the analysis of modern trends in the development of the education system for children, taking into account the tasks of the state project "Digital educational environment", the relevance of designing a system for monitoring the development of the digital educational environment for the practice of additional education for children has been determined. The aim of the study is to identify significant components of the digital educational environment for an updated model of its monitoring, taking into account the key markers and trends affecting the development of education. The article proposes the basis for a model for monitoring the development of a digital educational environment, substantiates an approach to the planned research of tools for studying the attitude of participants in the educational process to the identified components of the development of a digital educational environment using the example of a digital educational environment for additional education for children.