Reviewerly/exHarmony huggingface dataset:
Risk-averse optimization of reward-based coherent risk measures

In real-world problems such as robotics, finance and healthcare, randomness is always present, thus, it is important to take risk into consideration in order to limit the chance of rare but dangerous events. The literature on risk-averse reinforcement learning has produced many different approaches to tackle the problem, but they either struggle to scale up to complex instances, or they exhibit irrational behaviors. Here we present two novel risk-averse objectives that are both coherent and easy to optimize: the reward-based mean-mean absolute deviation (Mean-RMAD) and the reward-based conditional value at risk (RCVaR). Instead of reducing the return risk, these measures minimize the per-step reward one. We prove that these risk measures bound the corresponding return-based risk measures, so that they can be also used as proxies for their return-based versions. We develop safe algorithms for these risk measures with guaranteed monotonic improvement, and their practical trust-region versions. Furthermore, we propose a decomposition for the RCVaR optimization problem into a sequence of risk-neutral problems. Finally, we conduct an empirical analysis on the introduced approaches, demonstrating their effectiveness in retrieving a variety of risk-averse behaviors on both toy problems and more challenging ones, such as a simulated trading environment and robotic locomotion tasks.
Federated multi-objective reinforcement learning

Multi-objective reinforcement Learning (MORL) has significant potential for solving complex decision problems with conflicting objectives. Desiring sufficient training samples, it is promising to achieve federated MORL in large-scale distributed settings. However, itstill suffers from poor efficiency and high privacy risks. To mitigate the inefficiency issue, we first propose a novel probablistic algorithm PMORL that can seek an optimal policy via the expectation maximization (EM) algorithm with high efficiency. To extend PMORL to distributed settings with privacy protection, we then present the first federated MORL algorithm Fed-PMORL with client-level differential privacy (DP). In Fed-PMORL, personalized actors are trained and maintained at local clients whereas critics are aggregated and sanitized at the central server. Extensive experimental results in benchmark MORL environments demonstrate that Fed-PMORL under DP guarantees can achieve superior performance with high efficiency. In particular, compared with the state-of-the-art methods, PMORL and Fed-PMORL can save up to 50% training episodes for achieving the same model utility. With a sufficient number of clients (e.g., 1000 clients), Fed-PMORL with a formal DP guarantee shows utility comparable to that of the non-private algorithm.
Distance geometry for word representations and applications

Many machine learning methods used for the treatment of sequential data often rely on the construction of vector representations of unitary entities (e.g. words in natural language processing, or k-mers in bioinformatics). Traditionally, these representations are constructed with optimization formulations arising from co-occurrence based models. In this work, we propose a new method to embed these entities based on the Distance Geometry Problem: find object positions based on a subset of their pairwise distances or inner products. Considering the empirical Pointwise Mutual Information as a surrogate for the inner product, we discuss two Distance Geometry based algorithms to obtain word vector representations. The main advantage of such algorithms is their significantly lower computational complexity in comparison with state-of-the-art word embedding methods, which allows us to obtain word vector representations much faster. Furthermore, numerical experiments indicate that our word vectors behave quite well on text classification tasks in natural language processing as well as regression tasks in bioinformatics.
A New Global Measure to Simultaneously Evaluate Data Utility and Privacy Risk

Measuring data utility and privacy risk embedded in synthetic or other de-identified datasets is an increasingly important research area. Existing measures in the data privacy literature however are one-sided in that they either measure utility or privacy risk only. In this paper we propose a new measure that can evaluate both data utility and privacy, a well-known trade-off relationship in data synthesis. The proposed measure employs the notion of relative distance between the synthetic and original datasets at the dataset level, and can identify the optimally balanced position of the synthetic data in terms of both utility and privacy. In addition, we devise a graphical tool that visually reveals the current utility-privacy trade-off position of the synthetic data. Numerical studies show our new measure consistently performs better and offers richer interpretations than other existing global data utility measures, for both simulated and real datasets, confirming its distinctive advantages.
CNNFlow: Memory-driven Data Flow Optimization for Convolutional Neural Networks

Convolution Neural Networks (CNNs) are widely deployed in computer vision applications. The datasets are large, and the data reuse across different parts is heavily interleaved. Given that memory access (SRAM and especially DRAM) is more expensive in both performance and energy than computation, maximizing data reuse to reduce data movement across the memory hierarchy is critical to improving execution efficiency. This is even more important for the common use case of CNNs on mobile devices where computing/memory resources are limited. We propose CNNFlow, a memory-driven dataflow optimization framework to automatically schedule CNN computation on a given CNN architecture to maximize data reuse at each level of the memory hierarchy. We provide a mathematical calculation for data reuses in terms of parameters including loop ordering, blocking, and memory-bank allocation for tensors in CNN. We then present a series of techniques that help prune the large search space and reduce the cost of the exploration. This provides, for the first time, an exact and practical search algorithm for optimal solutions to minimize memory access cost for CNN. The efficacy is demonstrated for two widely used CNN algorithms: AlexNet and VGG16 with 5 and 13 convolution layers, respectively. CNNFlow finds the optimal solution for each layer within tens of minutes of compute time. Its solution requires about 20% fewer DRAM accesses and 40%–80% fewer SRAM accesses compared to state-of-the-art algorithms in the literature.
Robust numerical solution to the Levi-Civita wavefront coupling problem via level set computation of the point characteristic function

The Levi-Civita wavefront coupling problem consists of, given two prescribed wavefronts, obtaining a refractive or reflective surface coupling them. We propose a robust numerical method to solve Levi-Civita's problem, whose rationale is to consider that Levi-Civita's solutions are level surfaces of the point characteristic function established between points of incoming and outgoing wavefronts. The method obtains both surface data points and their normals, enabling a more robust surface reconstruction. We carry out a detailed error analysis of our method by means of comparing the surface data estimation with nominal surfaces obtained in reference tests offering analytical solutions to Levi-Civita's problem. The method offers, in computer simulations, highly accurate results with moderate computational cost.
User Reputation on E-Commerce: Blockchain-Based Approaches

User trust is a fundamental issue in e-commerce. To address this problem, recommendation systems have been widely used in different application domains including social media healthcare, e-commerce, and others. In this paper, we present a systematic review of the literature in the area of blockchain-based reputation models and we discuss the obtained results, answering the initial research questions. These findings lead us to conclude that the existing systems are based on a trusted third party (TTP) to collect and store reputation data, which does not provide transparency on users’ reputation scores. In the recent literature, on the one hand, blockchain-based reputation systems have been highlighted as possible solutions to effectively provide the necessary transparency, as well as effective identity management. On the other hand, new challenges are posed in terms of user privacy and performance, due to the specific characteristics of the blockchain. According to the literature, two major approaches have been proposed based on public and permissioned blockchains. Each approach applies adjusted models for calculating reputation scores. Despite the undoubted advantages added by a blockchain, the problem is only partially solved since there is no effective way to prevent blockchain oracles from feeding the chain with false, unfair, or biased data. In our future work, we intend to explore the two approaches discussed in the literature in order to propose a new blockchain-based model for deriving user reputation scores.
Image Generation from Text Using StackGAN with Consistency Regularization

Image generation from natural language is one of the most exciting areas of image and language multimodal research in recent years. The Stacked Generative Adversarial Networks (StackGAN) model generates images from text; however, although this model has been successful in generating high-resolution images, it has some problems. Generated images can be unintelligible, and there are cases of mode collapse. Therefore, this study attempts to solve these two StackGAN problems and aims to generate more accurate images. We propose incorporating balanced consistency regularization (bCR) into StackGAN. The bCR method uses data augmentation to learn the meaning of data by making the identification results consistent. Additionally, bCR can stabilize learning in adversarial networks. Our experiments show that the Inception Score of StackGAN with bCR was 7% better than StackGAN alone. In addition, mode collapse was eliminated.
Dictionary data structure for a text analysis task using cross-references

In this research, the dictionary structure of data for storing text data is considered. The main characteristics of the analysis of textual information by thematic classification are defined. The use of an improved dictionary data structure using tags and nesting levels is proposed. The advantages of using levels of data nesting for access on the selected topic are noted using the example of an intelligent chatbot.
IT strategies for the development of higher educational institutions

The article contains the concept of building an IT strategy for the development of a higher educational institution, its information electronic systems, and the corresponding electronic information educational environment. The IT strategy can be presented as a separate document for the development of a higher educational institution or/and as part of its general strategy in terms of digitalization. The development of the electronic university is related to its target areas. Among them are the educational process, scientific research, management processes, information technologies, etc. These directions should be combined into a single integrated system. For the effective operation of such a system and the reduction of the payback paradox, it is necessary to determine not only the strategic directions but also the steps of their implementation. The basis for the implementation of such a strategy is the definition of the university’s development specifics and scenarios for IT strategy implementation. Research and practical implementation of learning management systems in higher education institutions of Vinnytsia allowed the development of a general concept of forming IT strategy development by using contours of the interaction of its agents. Each of its contours corresponds to the definite target of the development direction. It was determined that the digitalization implementation processes correspond to the expected strategic results. It is recommended to choose implementation scenarios by using the method of hierarchies which is based on the results of expert evaluation. The research results will make it possible to formulate recommendations for building and implementing an IT strategy for a higher educational institution. These recommendations are based on the determined performance indicators of the IT strategy and the results of the practical implementation of digitalization systems of higher education institutions in Vinnytsia.
Trends in the Development of Online Platforms for Distance Learning

The article examines modern trends in the use of online} platforms in distance learning. The prerequisites for the transition to distance education in the conditions of the COVID-19 pandemic are outlined. Features of the most popular online learning platforms, such as Google Classroom, Microsoft Teams, Zoom and Moodle, are reviewed. A comparison of services and services offered to users of educational platforms was carried out. The performance indicators of the Zoom Video Communications company were considered. Prospects for the development of platforms for distance learning in the future are outlined.
Evolution of StyleGAN3

NVIDA launched StyleGAN3 with powerful image generation capability, and its generation of a set of images combined to create a smooth and realistic transition, leading to its hot topic on Twitter some time ago. StyleGAN3 was first developed from ProGAN. The interesting thing is that each generation of it is designed and modified by the same team. For this reason, this paper intends to introduce the development history of StyleGAN3 from the origin of ProGAN, and introduce the important technical details of each stage, especially the part that plays a role in the whole development process, and finally summarize.
Attention-based Image Compression in Sensor Assembly

The rapid development of the Industrial Internet of Things (IIoT) has led to an explosion in the amount of data to be transmitted and stored in the industrial sector. The investigated images are used in particular for quality control after individual process steps as part of Automated Optical Inspections (AOI). Image processing systems are among the key technologies here. Nevertheless, the storage and transmission of image data is still a major challenge for many companies. The use of AI-based image compression methods with higher quality performance compared to conventional methods becomes inevitable. Convolutional Neural Networks (CNNs) or Transformers are currently gaining ground in this area. However, the efficiency of these architectures has not yet been demonstrated in an industrial scenario. In this work, both a CNN- and a Transformer-based model with built-in Attention Mechanisms are trained with 8 bits per pixel (BPP) grayscale images generated in an industrial ultrasonic sensor production line. Even in the first test phase, in which only 2,000 images were used for model training, a compression rate of 0.162 BPP is achieved, far exceeding the quality of comparable JPEG-compressed images. When training the models with 100,000 images, the lowest BPP rate of 0.009 is achieved by the Transformer-based algorithm, resulting in an image quality that is sufficient for most quality assessment use cases. Thus, our results demonstrate the applicability and superiority of AI-based compression algorithms in industry. Their use leads not only to more efficient data transmission, but also to huge savings in storage space and costs for companies compared to traditional image compression methods.
PAMS-DP: Building a Unified Open PAMS Human Movement Data Platform

Research on physical activity can reduce the probability of disease and improve people’s health. Due to the complexity and diversity of human movement, the lack of a unified and standardized human movement data has become a significant bottleneck in human movement research. Building a unified and standardized human movement data platform requires four key technologies: movement modeling, movement coding, movement data organization, and movement data storage. In this paper, we propose a solution to building a large-scale movement data platform by researching these four key technologies and realizing the construction of the PAMS human movement data platform (PAMS-DP). PAMS-DP can integrate massive movement data, storing and automatically analyzing movement relationships and movement properties, publicly providing available movement data files in a unified and standardized format, and enhancing people’s understanding of human movements. Consequently, redundant work associated with collecting and processing massive movement data is reduced.
Trajectory Guided Robust Visual Object Tracking With Selective Remedy

Siamese trackers have received a lot of attentions due to their promising performance and real-time high speed. However, the robustness is generally limited especially in challenging conditions, such as occlusion. Motivated by that tracking failure does not always occur and the target movement tends to follow certain patterns, in the paper, we propose a generic, fast and flexible approach to improve the robustness of Siamese trackers with two light-load novel modules: Trajectory Guidance Module (TGM) and Selective Refinement Module (SRM). Specifically, TGM encourages to pay a soft attention on possible target location based on short-term historical trajectory. SRM selectively remedies the tracking results at the risk of failure with little impact on the speed. The proposed algorithm can be easily establish upon state-of-the-art Siamese trackers and obtains better performance on seven benchmarks with high real-time tracking speed. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/TJUMMG/TGSR</uri> .
An assistant decision-making method for rare diseases based on RNNs model

Big data and artificial intelligence technologies have great potential for development in the process of disease diagnosis and treatment. However, existing methods tend to focus on common diseases, but rarely involve the prediction of rare diseases. In this paper, we propose a rare disease-assisted decision-making method based on RNNs enhanced graph network. The model inferred rare disease probabilities based on patient complaints. Specifically, firstly, the overall syntax of the text is captured using a BERT-based pre-trained model and the local semantics are modeled using convolutional neural networks to identify patient intent and word slots. Secondly, the RNNs model is designed to capture the multi-hop node information around rare diseases, and a random walk algorithm with weights is used, which is finally transformed into a language model problem for prediction. Finally, in the experiment, the model achieves the current optimal effect in the real disease graph, which proves the effectiveness and advancement of the model.
Collaborative Learning With Mobile Technologies in Teacher Education

The increasing use of mobile technologies in K-12 schools has challenged teacher educators to consider how to best provide preservice teachers with the opportunity to integrate mobile technologies into their teaching and learning. In this chapter, the author aims to highlight how mobile technologies that support collaboration in both asynchronous and synchronous learning can enhance preservice teachers' teaching and learning experiences. Drawing from the literature, this chapter focuses on facilitating collaborative learning using mobile technologies in teacher preparation. The affordances of mobile technologies, which promote a social constructivist approach to learning, are discussed in detail. These affordances include the ability to enhance computer-mediated learning conversations, co-construction of knowledge, and social interactions using social media. This chapter also provides information on the barriers to technology adoption that hinder preservice teachers from integrating mobile technologies into teaching and learning.
Semi-Supervised Cardiac Image Segmentation Using a Bicycle VAE with Cross Prior Attention

This paper proposes a semi-supervised learning framework for cardiac image segmentation based on a bicycle Variational Auto-Encoder (biVAE) architecture by embedding a Prior Transformer and a conditional generative network. Specifically, to enhance the presentation of extracted features, a Transformer with cross attention to prior is embedded in the encoder of our framework, where the global context and anatomical prior are modeled at each layer of the Prior Transformer’s decoder to fuse multi-scale features. A biVAE is proposed by employing a conditional generative network, which maps the latent space to the image and maps the image to the latent space again. It makes the encoder of biVAE serve as a good feature representation for cardiac image segmentation. Many experiments on public cardiac MR datasets show that by introducing prior to the Transformer, more powerful features can be extracted in terms of representation, improving our semi-supervised framework’s predictive performance. Furthermore, the proposed semi-supervised framework achieves competitive results with state-of-the-art supervised learning-based networks.
Forward Hand Gesture Spotting and Prediction Using HMM-DNN Model

Automatic key gesture detection and recognition are difficult tasks in Human–Computer Interaction due to the need to spot the start and the end points of the gesture of interest. By integrating Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs), the present research provides an autonomous technique that carries out hand gesture spotting and prediction simultaneously with no time delay. An HMM can be used to extract features, spot the meaning of gestures using a forward spotting mechanism with varying sliding window sizes, and then employ Deep Neural Networks to perform the recognition process. Therefore, a stochastic strategy for creating a non-gesture model using HMMs with no training data is suggested to accurately spot meaningful number gestures (0–9). The non-gesture model provides a confidence measure, which is utilized as an adaptive threshold to determine where meaningful gestures begin and stop in the input video stream. Furthermore, DNNs are extremely efficient and perform exceptionally well when it comes to real-time object detection. According to experimental results, the proposed method can successfully spot and predict significant motions with a reliability of 94.70%.
Design and Analysis of Neural-Network-based, Single-User Codes for Multiuser Channels

Inspired by its success in other fields, there have been many recent developments in the use of machine learning and neural networks to enable multiuser communication and to design efficient channel codes along with practical decoders. However, there has been little attempt to combine the results of these efforts. In this paper, for the first time, we present a neural network autoencoder architecture to jointly address both problems. The resulting codes designed by our simple and easy-to-train neural network can have arbitrary rates, are comparable to existing state-of-the-art neural network designed codes, and are directly applicable in a multiuser context. We analyze these single-user codes and characterize the design parameters which affect their performance. We then show that these same single-user codes can be used to operate close the maximum sum rate of a K-user Gaussian multiple access channel (MAC) under various SNR scenarios, without the need for retraining or learning a joint code. This improved performance is achieved by introducing a new iterative successive interference cancellation method (SIC) that outperforms traditional onion-peeling.