Reviewerly/exHarmony huggingface dataset:
The Covering Radius of the Reed–Muller Code <i>RM</i>(<i>m</i> – 4, <i>m</i>) in <i>RM</i>(<i>m</i> – 3, <i>m</i>)

We present methods for computing the distance from a Boolean polynomial on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> variables of degree <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m-3$ </tex-math></inline-formula> (i.e., a member of the Reed–Muller code <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$RM(m-3, m)$ </tex-math></inline-formula> ) to the space of lower-degree polynomials ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$RM(m-4, m)$ </tex-math></inline-formula> ). The methods give verifiable certificates for both the lower and upper bounds on this distance. By applying these methods to representative lists of polynomials, we show that the covering radius of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$RM(4,8)$ </tex-math></inline-formula> in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$RM(5,8)$ </tex-math></inline-formula> is 26 and the covering radius of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$RM(5,9)$ </tex-math></inline-formula> in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$RM(6,9)$ </tex-math></inline-formula> is between 28 and 32 inclusive, and we get improved lower bounds for higher <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> . We also apply our methods to various polynomials in the literature, thereby improving the known bounds on the distance from 2-resilient polynomials to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$RM(m-4, m)$ </tex-math></inline-formula> .
Information geometry approach to parameter estimation in hidden Markov model

We consider the estimation of the transition matrix of a hidden Markovian process by using information geometry with respect to transition matrices. In this paper, only the histogram of k-memory data is used for the estimation. To establish our method, we focus on a partial observation model with the Markovian process and we propose an efficient estimator whose asymptotic estimation error is given as the inverse of projective Fisher information of transition matrices. This estimator is applied to the estimation of the transition matrix of the hidden Markovian process. In this application, we carefully discuss the equivalence problem for hidden Markovian process on the tangent space.
Semantic Description of Explainable Machine Learning Workflows for Improving Trust

Explainable Machine Learning comprises methods and techniques that enable users to better understand the machine learning functioning and results. This work proposes an ontology that represents explainable machine learning experiments, allowing data scientists and developers to have a holistic view, a better understanding of the explainable machine learning process, and to build trust. We developed the ontology by reusing an existing domain-specific ontology (ML-SCHEMA) and grounding it in the Unified Foundational Ontology (UFO), aiming at achieving interoperability. The proposed ontology is structured in three modules: (1) the general module, (2) the specific module, and (3) the explanation module. The ontology was evaluated using a case study in the scenario of the COVID-19 pandemic using healthcare data from patients, which are sensitive data. In the case study, we trained a Support Vector Machine to predict mortality of patients infected with COVID-19 and applied existing explanation methods to generate explanations from the trained model. Based on the case study, we populated the ontology and queried it to ensure that it fulfills its intended purpose and to demonstrate its suitability.
DTKT:An Improved Deep Temporal Convolutional Network for Knowledge Tracing

In adaptive education, knowledge tracking predicts the future performance of students based on their historical learning trajectory, the modeling of students&#x2019; knowledge state is achieved. Through knowledge tracking, personalized learning can be carried out well to promote the future level of education. At present, lots of Knowledge Tracking models can promote the accuracy of prediction by simulating the complex learning process of human beings. However, when faced with intricate data containing a large number of problem types, it cannot perform satisfactorily, and this is exactly the situation that will always be encountered in our real life. Therefore, we introduced Time Convolution Network into the field of knowledge tracking for the first time, which is expert in processing time series prediction tasks. DTKT completes the parallel processing of data through the characteristics of convolutional network, which greatly enhances the computing power. concurrently, and our model uses a novel data processing method, which takes more into account the authenticity and universality of the input data, which can handle more intricate data better than existing models. Eventually, extensive experimental results on various datasets show that our model performance is better than the latest knowledge tracking model, and it also achieves surpassing in speed.
Computation of a Database of Trajectories and Primitives for Decision-Based Contingency Management of UAVs over Congested Areas

We present a novel approach to enable provably safe contingency path planning in complex high-risk environments within a strict regulatory framework. We decompose the flight planning task between a network of vertiports into an offline and an online phase. This work focuses on the offline phase covering the flight planning, which can be validated to fulfill regulatory boundary conditions and target safety levels before flight. In the online phase the planning task is collapsed to a trajectory selection problem, which is low dimensional and provides only solutions with guaranteed feasibility. The proposed method computes a tree of trajectories with constant time separation of branches and leading to both the destination and diversion vertiports from each fork point. We further extend the planning tree with motion primitives to enable transitions between flight levels and local holding patterns for temporal deconfliction without a change of the lateral flight profile. We demonstrate the method in a realistic urban scenario and provide metrics for memory and time complexity of the algorithm.
BATDIV: A Blockchain-based Approach for Tourism Data Insertion and Visualization

In this work, we introduce BATDIV, a Blockchain-based Approach for Tourism Data Insertion and Visualization, focusing mainly on the European Tourism Indicators System (ETIS). Tourism development programs, promoted at both national and international levels, use measurable indicators to monitor and manage the sustainable development of territories. The adoption of blockchain technology (BCT) allows lowering some typical technological barriers encountered when dealing with this kind of data collections, such as data management process, data integrity, time delay for information retrieval, data accessibility, storage costs, data ownership rights management, data centralization, backup procedures management. The BCT natively supports decentralization by sharing and replicating databases to ensure the same standards for data definition, and provides access control, data integrity, and immutability through cryptographic credentials. We developed a demonstrator using BigchainDB, a permissioned blockchain, writing the infrastructure code in JavaScript, both for the back-end and the front-end, to provide data insertion and visualization via a web browser. We finally tested our architecture on a small-scale testbed, deploying multiple BigchainDB nodes, performing throughput measurements, and showing transactions per second variation when increasing the number of validator nodes.
The butterfly effect in the price of agricultural products: A multidimensional spatial-temporal association mining

With the advent of the era of big data, data mining methods show their powerful information mining ability in various fields, seeking the association information hidden in the data, which is convenient for people to make scientific decisions.This paper analyses the butterfly effect in the agricultural product industry chain from the perspective of producer and consumer by using multidimensional time and space theory and proposes a new price forecasting method.We consider that the price change of agricultural products is not only affected by the balance of market supply and demand but also by the factors of time and space.Taking the pig industry chain of Sichuan Province as an example, this paper explores and excavates the data from 2010 to 2020 in the time dimension.Interestingly, we found that the price changes in pork in the market are generally highly correlated with the prices of slaughtered pigs, piglets a few weeks ago and the prices of multiple feed a few months ago.Based on the precise time-space factors, we improved the price forecasting model, greatly improved the accuracy of price prediction, and proved the effectiveness of multidimensional spatiotemporal association mining.The research in this paper is helpful to establish a brand-new agricultural product price prediction theory, which is of great significance to the development of the agricultural economy and global poverty alleviation.
STEMUR: An Automated Word Conflation Algorithm for the Urdu Language

Stemming is a common word conflation method that perceives stems embedded in the words and decreases them to their stem (root) by conflating all the morphologically related terms into a single term, without doing a complete morphological analysis. This article presents STEMUR, an enhanced stemming algorithm for automatic word conflation for Urdu language. In addition to handling words with prefixes and suffixes, STEMUR also handles words with infixes. Rather than using a totally unsupervised approach, we utilized the linguistic knowledge to develop a collection of patterns for Urdu infixes to enhance the accuracy of the stems and affixes acquired during the training process. Additionally, STEMUR also handles English loan words and can handle words with more than one affix. STEMUR is compared with four existing Urdu stemmers including Assas-Band and the template-based stemmer that are also implemented in this study. Results are processed on two corpora containing 89,437 and 30,907 words separately. Results show clear improvements regarding strength and accuracy of STEMUR. The use of maximum possible infix rules boosted our stemmer's accuracy up to 93.1% and helped us achieve a precision of 98.9%.
Fundy: A Scalable and Extensible Resource Manager for Cloud Resources

Scalability is an important property for a resource manager in the cloud. It is challenging to manage large-scale cluster resources while serving a large number of requests. Moreover, cloud-based applications and new technologies result in changeable requirements, so a cloud resource manager needs to continuously evolve. This paper presents a novel architecture design for a scalable and extensible resource manager. Our resource manager named Fundy employs a microservices architecture based on an in-memory data grid. The in-memory data grid accelerates data processing and enables Fundy to easily scale out. The data grid facilitates Fundy's microservices-based architecture. Because of the architecture, it is possible to rapidly deploy new features in Fundy. Fundy also enables multiple schedulers to schedule different workloads on shared infrastructure. In addition, we introduce a new packing algorithm to improve the allocation quality of our scheduler and a tensor scheduling algorithm to speed up parallel processing of requests by orders of magnitude. Fundy has been deployed in our production public cloud. This paper includes evaluation based on real-world traces and the results highlight the advantages of Fundy.
Attentional Transfer is All You Need: Technology-aware Layout Pattern Generation

Having a set of comprehensive VLSI layout patterns is important in researches and applications of design for manufacturability (DFM). However, due to the complexity of the manufacturing process, a large and diverse layout pattern library is usually not available, especially during the early stages of the next technology generation, and this will slow down the technology node development. Many previous pattern generation methods rely on complex rule-based manual guidance or a massive number of existing patterns in the new technology node for learning, which are both costly and with limited availability. Instead of requiring these expensive resources, we propose an attentional transfer-based framework, named CUP-EUV, learning to reuse knowledge from previous technology nodes that should have reserved an enormous amount of layout resources. With the guidance of transferred knowledge, a pattern generation model can be trained by only a small number of patterns from the expensive EUV designs. Experiments show that our model can generate new patterns with much higher performance than the state-of-the-art approaches.
Shift-Invariant Segmentation in Breast Ultrasound Images

While accuracy is an evident criterion for ultrasound (US) image segmentation, output consistency across different tests is equally crucial for tracking changes in regions of interest in applications such as monitoring the patients' response to treatment, measuring the progression or regression of the disease, reaching a diagnosis, or treatment planning. Convolutional neural networks (CNNs) have attracted rapidly growing interest in automatic US image segmentation recently. However, CNNs are not shift-equivariant, meaning that if the input translates, e.g., in the lateral direction by one pixel, the output segmentation may drastically change. To the best of our knowledge, this problem has not been studied in US image segmentation or even more broadly in any application in US. Herein, we investigate and quantify the shift-variance problem of CNNs in this application and further evaluate the performance of a recently published technique, called BlurPooling, for addressing the problem. Source code is available at https://git.io/pbpunet and http://code.sonography.ai.
Two-Round Maliciously Secure Computation with Super-Polynomial Simulation

We propose the first maliciously secure multi-party computation (MPC) protocol for general functionalities in two rounds, without any trusted setup. Since polynomial-time simulation is impossible in two rounds, we achieve the relaxed notion of superpolynomial-time simulation security [Pass, EUROCRYPT 2003]. Prior to our work, no such maliciously secure protocols were known even in the two-party setting for functionalities where both parties receive outputs. Our protocol is based on the sub-exponential security of standard assumptions plus a special type of non-interactive non-malleable commitment.
On the Potential of Execution Traces for Batch Processing Workload Optimization in Public Clouds

With the growing amount of data, data processing workloads and the management of their resource usage becomes increasingly important. Since managing a dedicated infrastructure is in many situations infeasible or uneconomical, users progressively execute their respective workloads in the cloud. As the configuration of workloads and resources is often challenging, various methods have been proposed that either quickly profile towards a good configuration or determine one based on data from previous runs. Still, performance data to train such methods is often lacking and must be costly collected.In this paper, we propose a collaborative approach for sharing anonymized workload execution traces among users, mining them for general patterns, and exploiting clusters of historical workloads for future optimizations. We evaluate our prototype implementation for mining workload execution graphs on a publicly available trace dataset and demonstrate the predictive value of workload clusters determined through traces only.
SEALing Neural Network Models in Encrypted Deep Learning Accelerators

Deep learning (DL) accelerators suffer from a new security problem, i.e., being vulnerable to physical access based attacks. An adversary can easily obtain the entire neural network (NN) model by physically snooping the memory bus that connects the accelerator chip with DRAM memory. Therefore, memory encryption becomes important for DL accelerators to improve their security. Nevertheless, we observe that traditional memory encryption techniques that have been efficiently used in CPU systems cause significant performance degradation when directly used in DL accelerators, due to the big bandwidth gap between the memory bus and the encryption engine. To address this problem, our paper proposes SEAL, a Secure and Efficient Accelerator scheme for deep Learning to enhance the performance of encrypted DL accelerators by improving the data access bandwidth. Specifically, SEAL leverages a criticality-aware smart encryption scheme that identifies partial data having no impact on the security of NN models and allows them to bypass the encryption engine, thus reducing the amount of data to be encrypted without affecting security. Extensive experimental results demonstrate that, compared with existing memory encryption techniques, SEAL achieves 1.34 – 1.4× overall performance improvement.
An Explainable Artificial Intelligence (xAI) Framework for Improving Trust in Automated ATM Tools

With the increased use of intelligent Decision Support Tools in Air Traffic Management (ATM) and inclusion of non-traditional entities, regulators and end users need assurance that new technologies such as Artificial Intelligence (AI) and Machine Learning (ML) are trustworthy and safe. Although there is a wide amount of research on the technologies themselves, there seem to be a gap between research projects and practical implementation due to different regulatory and practical challenges including the need for transparency and explainability of solutions. In order to help address these challenges, a novel framework to enable trust on AI-based automated solutions is presented based on current guidelines and end user feedback. Finally, recommendations are provided to bridge the gap between research and implementation of AI and ML-based solutions using our framework as a mechanism to aid advances of AI technology within ATM.
Sit-to-Stand Test for Neurodegenerative Diseases Video Classification

In this paper, an automatic video diagnosis system for dementia classification is presented. Starting from video recordings of patients and control subjects, performing sit-to-stand test, the designed system is capable of extracting relevant patterns for binary discern patients with dementia from healthy subjects. The proposed system achieves an accuracy 0.808 by using the rigorous inter-patient separation scheme especially suited for medical purposes. This separation scheme provides the use of some people for training and others, different, people for testing. This work is an original and pioneering work on sit-to-stand video classification for neurodegenerative diseases, thus the novelty in this study is both on phases segmentation and experimental setup.
Generative Adversarial Networks for anomaly detection on decentralised data

Generative Adversarial Networks (GANs) have seen great research interest in recent years, due to both their ability to represent structure in data and generate novel samples. Anomaly detection, which discerns novel samples or patterns, is a well-known problem that can be studied using GANs with a fresh perspective, especially in novel application domains such as wireless communication networks. For these models to achieve an accurate representation of the underlying data distribution, significant volumes of data are required. If this data source is not centralised (e.g. stored at multiple hosts or data centres), non-standard training methods are required to achieve comparable performance to the centralised case. This paper presents the key collaborative training methods that have emerged in recent years that draw on the GAN’s modular structure to achieve high performance while balancing computation, storage, and communication requirements and demonstrates their application to the task of anomaly detection using cognitive radios.
STCM-Net: A symmetrical one-stage network for temporal language localization in videos

The task of temporal language localization in the video is to locate a video segment through natural language description for an untrimmed video. Compared with the general video localization task, it is more flexible and complex, which can accurately locate various scenes described by any natural language without making video labels in advance. It can be widely used for the field such as video retrieval and robot intelligent cognition. The main challenges of this task are the extraction of sentence semantics and the integration of contextual information in videos. Among them, contextual video integration can be optimized through the two-dimensional temporal adjacent network. Therefore, complete extraction of the potential information in the query sentence is necessary to solve the task more granularly. At the same time, we found a large amount of time-related information in the query sentence, which helps improve the localization accuracy. Thus, in this paper, we first define the time concept in a sentence and then propose a Sentence Time Concept Mining Network (STCM-Net), an symmetrical one-stage network. Can effectively extract the time concept contained in the query sentence, it can optimize the process of target localization and improve the localization performance. We also evaluate the proposed STCM-Net on three challenging public benchmarks: Charades-STA, ActivityNet Captions, and TACoS. Our STCM-Net gets encouraging improvements compared with the state-of-the-art approaches.
ICHV: A New Compression Approach for Industrial Images

The rapid development of the Industrial Internet of Things and artificial intelligence has brought new pressures to the compression and storage of image information. Thus, efficient image compression approaches are important in the fourth industrial revolution (Industry 4.0), which can save storage resources, accelerate transmission speed, and improve imaging fidelity. In this article, a new approach of image compression based on Haar-V functions (ICHV) is investigated, which has good performance in industrial image compression. The Haar-V functions form a discontinuous orthogonal function system composed of piecewise polynomials. The construction and properties of Haar-V functions are introduced at first. Then, 2-D discrete orthogonal matrices of Haar-V functions and the corresponding image transform method are presented successfully. Finally, a fast computation of ICHV is proposed to enhance the computation speed of image compression. The experimental results indicate that compared with related algorithms, the new approach can effectively improve the quality of compressed images and consume less computational cost.