Reviewerly/exHarmony huggingface dataset:
Principal Component Wavelet Networks for Solving Linear Inverse Problems

In this paper we propose a novel learning-based wavelet transform and demonstrate its utility as a representation in solving a number of linear inverse problems—these are asymmetric problems, where the forward problem is easy to solve, but the inverse is difficult and often ill-posed. The wavelet decomposition is comprised of the application of an invertible 2D wavelet filter-bank comprising symmetric and anti-symmetric filters, in combination with a set of 1×1 convolution filters learnt from Principal Component Analysis (PCA). The 1×1 filters are needed to control the size of the decomposition. We show that the application of PCA across wavelet subbands in this way produces an architecture equivalent to a separable Convolutional Neural Network (CNN), with the principal components forming the 1×1 filters and the subtraction of the mean forming the bias terms. The use of an invertible filter bank and (approximately) invertible PCA allows us to create a deep autoencoder very simply, and avoids issues of overfitting. We investigate the construction and learning of such networks, and their application to linear inverse problems via the Alternating Direction of Multipliers Method (ADMM). We use our network as a drop-in replacement for traditional discrete wavelet transform, using wavelet shrinkage as the projection operator. The results show good potential on a number of inverse problems such as compressive sensing, in-painting, denoising and super-resolution, and significantly close the performance gap with Generative Adversarial Network (GAN)-based methods.
Promotion Scenario Based Sales Prediction on E-Retail Groceries Using Data Mining

Mostly in many business cases, sales prediction plays an important role. Production planning is a good example. One aspect which affecting sales forecasting is promotion schedule. Since using promotion is commonly done nowadays, especially in internet business, it is hardly seen a day without promotion in Indonesian e-commerce. Thus, this study discusses about forecasting future sales based on promotion scenario data with main objective is to discover the best machine learning algorithm and model to forecast future sales. Promotion mechanism which employed in this study are price cut, buy 1-get 1, and product bundling. We use 577 data from January 2018 to July 2019 as dataset. We compare kNN, GLM, and SVR as the model predictor to forecast number of transactions in a day. From the experiment k-NN yielded the highest performance ability with squared correlation of 0.938. the worst model predictor for this case is GLM with squared correlation of 0.507. We also determine the best parameter input for each parameter using grid optimization method. We discover 2 is the best k value of kNN and Manhattan distance is the best distance calculation for this case
Improvement of channel coding gain of chaos modulation using logistic maps

A chaos modulation is a Gaussian modulation that achieves coding gains and physical layer securities. In the conventional method, a Bernoulli shift map, which is a chaos equation, is used for the modulation. However, the coding gain is limited to avoid an increase in the modulation complexity. In this paper, we propose a chaos modulation using a logistic map. Numerical results demonstrated that the proposed method increased the coding gain with the same amount of computation as that of the existing method.
European strategy for determining the level of competence in the field of digital technologies: a framework for digital competence for citizens

The Digital Competence Framework for Citizens (DigComp 2.0: Digital Competence Framework for Citizens), presented by the European Commission in 2016 and modernized in 2017 (DigComp 2.1) is supplemented with examples of application in different European countries, is described. Today, the Digital Competence Framework for Citizens 2.0 and 2.1 is one of the latest modern European strategic documents developed by the European community of countries that create educational standards and educational technologies. The need to have digital competence as a teacher is today a personal, professional need and challenge in a rapidly evolving technology. It is important for modern educational reforms to meet this need through a system of professional development, taking into account the need for lifelong learning. Acquaintance with the achievements of the European community, with the Digital Competence Framework for Citizens will contribute to a holistic vision of this category and progress towards the formation of this important key human capacity. Descriptors of learning outcomes in the field of digital competence are described. Prospects for further research are identified, which are the use of the Digital Competence Framework for citizens in the field of educational policy and in practice in educational institutions.
Hierarchical Task Learning from Language Instructions with Unified Transformers and Self-Monitoring

Despite recent progress, learning new tasks through language instructions remains an extremely challenging problem. On the ALFRED benchmark for task learning, the published state-of-the-art system only achieves a task success rate of less than 10% in an unseen environment, compared to the human performance of over 90%. To address this issue, this paper takes a closer look at task learning. In a departure from a widely applied end-to-end architecture, we decomposed task learning into three sub-problems: sub-goal planning, scene navigation, and object manipulation; and developed a model HiTUT (stands for Hierarchical Tasks via Unified Transformers) that addresses each sub-problem in a unified manner to learn a hierarchical task structure. On the ALFRED benchmark, HiTUT has achieved the best performance with a remarkably higher generalization ability. In the unseen environment, HiTUT achieves over 160% performance gain in success rate compared to the previous state of the art. The explicit representation of task structures also enables an in-depth understanding of the nature of the problem and the ability of the agent, which provides insight for future benchmark development and evaluation.
Beyond Fine-Tuning: Transferring Behavior in Reinforcement Learning

Designing agents that acquire knowledge autonomously and use it to solve new tasks efficiently is an important challenge in reinforcement learning. Knowledge acquired during an unsupervised pre-training phase is often transferred by fine-tuning neural network weights once rewards are exposed, as is common practice in supervised domains. Given the nature of the reinforcement learning problem, we argue that standard fine-tuning strategies alone are not enough for efficient transfer in challenging domains. We introduce Behavior Transfer (BT), a technique that leverages pre-trained policies for exploration and that is complementary to transferring neural network weights. Our experiments show that, when combined with large-scale pre-training in the absence of rewards, existing intrinsic motivation objectives can lead to the emergence of complex behaviors. These pre-trained policies can then be leveraged by BT to discover better solutions than without pre-training, and combining BT with standard fine-tuning strategies results in additional benefits. The largest gains are generally observed in domains requiring structured exploration, including settings where the behavior of the pre-trained policies is misaligned with the downstream task.
SCIFFS: Enabling Secure Third-Party Security Analytics using Serverless Computing

Third-party security analytics allow companies to outsource threat monitoring tasks to teams of experts and avoid the costs of in-house security operations centers. By analyzing telemetry data from many clients these services are able to offer enhanced insights, identifying global trends and spotting threats before they reach most customers. Unfortunately, the aggregation that drives these insights simultaneously risks exposing sensitive client data if it is not properly sanitized and tracked. In this work, we present SCIFFS, an automated information flow monitoring framework for preventing sensitive data exposure in third-party security analytics platforms. SCIFFS performs decentralized information flow control over customer data it in a serverless setting, leveraging the innate polyinstantiated nature of serverless functions to assure precise and lightweight tracking of data flows. Evaluating SCIFFS against a proof-of-concept security analytics framework on the widely-used OpenFaaS platform, we demonstrate that our solution supports common analyst workflows data ingestion, custom dashboards, threat hunting) while imposing just 3.87% runtime overhead on event ingestion and the overhead on aggregation queries grows linearly with the number of records in the database (e.g., 18.75% for 50,000 records and 104.27% for 500,000 records) as compared to an insecure baseline. Thus, SCIFFS not only establishes a privacy-respecting model for third-party security analytics, but also highlights the opportunities for security-sensitive applications in the serverless computing model.
Predicting high-dimensional heterogeneous time series employing generalized local states

We generalize the concept of local states (LS) for the prediction of high-dimensional, potentially mixed chaotic systems. The construction of generalized local states (GLS) relies on defining distances between time series on the basis of their (non-)linear correlations. We demonstrate the prediction capabilities of our approach based on the reservoir computing (RC) paradigm using the Kuramoto-Sivashinsky (KS), the Lorenz-96 (L96) and a combination of both systems. In the mixed system a separation of the time series belonging to the two different systems is made possible with GLS. More importantly, prediction remains possible with GLS, where the LS approach must naturally fail. Applications for the prediction of very heterogeneous time series with GLSs are briefly outlined.
Data-driven thinking for measuring the human experience in the built environment

This article introduces a methodology to implement Data-driven Thinking in the context of urban design. We present the results of a case study based on a 7-day workshop with 10 participants with landscape design and architecture background. The goal of the workshop was to expose participants to Data-driven Thinking through experimental design, multi-sensor data collection, data analysis, visualization, and insight generation. We evaluate their learning experience in designing an experimental setup, collecting real-time immediate environmental and physiological body reactions data. Our results from the workshop show that participants increased their knowledge about measuring, visualizing and understanding data of the surrounding built environment.
MM-AVS: A Full-Scale Dataset for Multi-modal Summarization

Multimodal summarization becomes increasingly significant as it is the basis for question answering, Web search, and many other downstream tasks.However, its learning materials have been lacking a holistic organization by integrating resources from various modalities, thereby lagging behind the research progress of this field.In this study, we present a full-scale multimodal dataset comprehensively gathering documents, summaries, images, captions, videos, audios, transcripts, and titles in English from CNN and Daily Mail.To our best knowledge, this is the first collection that spans all modalities and nearly comprises all types of materials available in this community.In addition, we devise a baseline model based on the novel dataset, which employs a newly proposed Jump-Attention mechanism based on transcripts.The experimental results validate the important assistance role of the external information for multimodal summarization.
Recurrent Graph Convolutional Autoencoder for Unsupervised Skeleton-Based Action Recognition

Skeleton-based action recognition is a significant task in computer vision due to its robustness and wide application. Most unsupervised methods do not employ topological information of skeleton graphs, which actually ignore the spatial dependencies of action sequences. In this paper, we introduce a Recurrent Graph Convolutional Autoencoder (RGCA) for unsupervised action recognition from skeleton data. Our method explicitly exploits the spatial relationships among every frame’s joints while preserving the long-term temporal dynamics in whole sequences. Moreover, a Spatial Joints Attention Module is employed to measure the importance of joints in the input sequence automatically. We conduct experiments on three datasets (NTU RGB+D 60, NW-UCLA, and UWA3D) and exceed the state-of-the-art performance.
Firmware Security Module

Abstract New services such as autonomous driving, the connectivity of the traffic infrastructure and the tight coupling of user operated smart devices with the vehicle have significantly increased the demand for cryptographic protection in the automobile. To provide a secure environment for the calculation and verification of cryptographic material, automotive microcontrollers now frequently integrate Hardware Security Modules (HSM), special well-protected co-processors, which are protected against manipulation and external access. HSMs use special hardware accelerators to provide the required cryptographic services. While these accelerators provide good performance, they limit flexibility and updatability. In addition, as more services require cryptographic protection, the amount of key material that needs to be managed by the HSM also increases, turning the limited protected memory of the HSM into a bottleneck. This paper presents a framework that uses the safety mechanisms of a microcontroller to achieve both HSM-equivalent security through software solutions, while providing an equivalent level of functionality. Furthermore, the proposed framework provides crypto-agility, as the security stack can be updated if desired. In order to verify the functionality, the presented framework is implemented and evaluated on an Infineon AURIX TC399 and compared with the integrated HSM.
Image Association to URLs across CMS Websites with Unique Watermark Signatures to Identify Who Owns the Camera

Internet is the world's network of connected computer networks. Internet means an interconnected network. It is a network of connected web servers. Internet helps data and people across the globe. Internet of things refers to network-connected things with embedded computer chips. Things on the internet would include devices enabled for internet access. IoT association of images on content management websites with unique watermark signature to account for Royal to the owner of the picture will help against piracy, copyright infringement, and misuse of photos registered with unique identification keys. This will make content management easier. It will generate revenue for the person who takes the copyrighted picture. A watermark is an embedded signature in a thing. It could be embedded in a video, image, and other file types for distinction and marking for ownership. It could be visible or invisible. It also provides a means to trace a product to the owner. This work looks into how images with watermark can be connected to the IoT for tracking and fighting piracy.
Multi-Scale Feature Aggregation by Cross-Scale Pixel-to-Region Relation Operation for Semantic Segmentation

Exploiting multi-scale features has shown great potential in tackling semantic segmentation problems. The aggregation is commonly done with sum or concatenation (concat) followed by convolutional (conv) layers. However, it fully passes down the high-level context to the following hierarchy without considering their interrelation. In this work, we aim to enable the low-level feature to aggregate the complementary context from adjacent high-level feature maps by a cross-scale pixel-to-region relation operation. We leverage cross-scale context propagation to make the long-range dependency capturable even by the high-resolution low-level features. To this end, we employ an efficient feature pyramid network to obtain multi-scale features. We propose a Relational Semantics Extractor (RSE) and Relational Semantics Propagator (RSP) for context extraction and propagation respectively. Then we stack several RSP into an RSP head to achieve the progressive top-down distribution of the context. Experiment results on two challenging datasets Cityscapes and COCO demonstrate that the RSP head performs competitively on both semantic segmentation and panoptic segmentation with high efficiency. It outperforms DeeplabV3 [1] by 0.7% with 75% fewer FLOPs (multiply-adds) in the semantic segmentation task.
FLIN: A Flexible Natural Language Interface for Web Navigation

AI assistants can now carry out tasks for users by directly interacting with website UIs.Current semantic parsing and slot-filling techniques cannot flexibly adapt to many different websites without being constantly re-trained.We propose FLIN, a natural language interface for web navigation that maps user commands to concept-level actions (rather than low-level UI actions), thus being able to flexibly adapt to different websites and handle their transient nature.We frame this as a ranking problem: given a user command and a webpage, FLIN learns to score the most relevant navigation instruction (involving action and parameter values).To train and evaluate FLIN, we collect a dataset using nine popular websites from three domains.Our results show that FLIN was able to adapt to new websites in a given domain.
Barriers to Effective Interaction of Russian Universities and Companies

Today universities become drivers of technology and business development all over the world. Besides fulfilling traditional tasks of creating new knowledge and competence, higher education institutions play a significant role in developing innovative ecosystems and entrepreneurship. In Russia, however, the interaction of universities and business is forming not as quickly as in developed economies. This article uses qualitative interviews and case analysis to identify and discuss the barriers to effective university-enterprise interaction in Russia. The research is based on expert interviews with representatives of universities and businesses, as well as on the case study of PJSC «LUKOIL» and HEIs interaction. The study showed that the development of university-enterprise interaction meets several barriers rooted in the mismatch of both sides’ expectations of each other. The results of the research also show promising directions for university-business cooperation in Russia.
Unified Interpretation of Softmax Cross-Entropy and Negative Sampling: With Case Study for Knowledge Graph Embedding

In knowledge graph embedding, the theoretical relationship between the softmax cross-entropy and negative sampling loss functions has not been investigated. This makes it difficult to fairly compare the results of the two different loss functions. We attempted to solve this problem by using the Bregman divergence to provide a unified interpretation of the softmax cross-entropy and negative sampling loss functions. Under this interpretation, we can derive theoretical findings for fair comparison. Experimental results on the FB15k-237 and WN18RR datasets show that the theoretical findings are valid in practical settings.
Siame-se(3): regression in se(3) for end-to-end visual servoing

In this paper we propose a deep architecture and the associated learning strategy for end-to-end direct visual servoing. The considered approach allows to sequentially predict, in se(3), the velocity of a camera mounted on the robot&#x2019;s end-effector for positioning tasks. Positioning is achieved with high precision despite large initial errors in both cartesian and image spaces. Training is fully done in simulation, alleviating the burden of data collection. We demonstrate the efficiency of our method in experiments in both simulated and real-world environments. We also show that the proposed approach is able to handle multiple scenes.
Using a Low Code Development Environment to Teach the Agile Methodology

Abstract The Agile development methodology is soaring in popularity in the business world. Companies are turning to Agile to develop products quickly and to achieve digital transformation of their organization. Because of this push, companies need employees who understand Agile. Therefore, higher education is obligated to provide an understanding of Agile to students as they enter the workplace. Providing Agile experience to students who are new to programming is difficult because they are so worried about the coding aspects of the assignment, they cannot take time to think about the methodology they are using. The coding crowds out the time needed to get an understanding of how Agile actually works. One remedy for this is to use a low or no-code development platform. With this type of platform students spend less time learning to create apps, freeing them to experience the rituals and roles of Agile. This study examines using the Agile methodology along with the Microsoft Power Apps platform to provide an Agile experience to students. Two course sections were surveyed to learn if students perceived that they acquired a better understanding of Agile and to learn their perceptions of a no-code platform experience. The students completed surveys to ascertain their comfort with the Agile methodology and whether the no-code environment increased their comfort level. The results showed students perceived the no-code platform increased their comfort with using the Agile methodology. The implication is that no-code platforms can be used broadly to help students to gain experience with Agile.
Defense against Membership Inference Attack Applying Domain Adaptation with Addictive Noise

Deep learning can train models from a dataset to solve tasks.Although deep learning has attracted much interest owing to the excellent performance, security issues are gradually exposed.Deep learning may be prone to the membership inference attack, where the attacker can determine the membership of a given sample.In this paper, we propose a new defense mechanism against membership inference: NoiseDA.In our proposal, a model is not directly trained on a sensitive dataset to alleviate the threat of membership inference attack by leveraging domain adaptation.Besides, a module called Feature Crafter has been designed to reduce the necessary training dataset from 2 to 1, which creates features for domain adaptation training using noise addictive mechanisms.Our experiments have shown that, with the noises properly added by Feature Crafter, our proposal can reduce the success of membership inference with a controllable utility loss.
Multi Label Toxic Comment Classification using Machine Learning Algorithms

Toxic comments are the comments found in the online forums that are rude, offensive, or unfair and usually cause many users to exit the conversation. The threat of bullying and abuse on the internet obstructs the free exchange of ideas by limiting people’s opposing viewpoints. Most of the Websites fail to successfully facilitate healthy conversations, leading them to either restrict or disable user comments entirely. This paper would explore the scope of online abuse and categorize them into different labels to assess the toxicity as accurately as possible using machine learning algorithms.