Reviewerly/exHarmony huggingface dataset:
Research on Equalization Technology of Broadband Satellite Communication Channel

Abstract Satellite channel is the representative non-linear channel with limited frequency band. The random change of the channel causes the communication stability to be affected. With the development of satellite communication technology, the bandwidth of satellite communication has increased and the data rate has also increased significantly, resulting in more serious code interference, which has become a key factor affecting communication quality. Finding an efficient equalization technology to eliminate channel inter-code interference is the key to ensuring satellite broadband communications. This article first briefly discusses the principle of eliminating inter-symbol interference and common equalizer algorithms, and summarizes the main factors that affect the performance of channel equalization algorithms. On this basis, an improved LSTM blind equalization algorithm is proposed.
On the rank of Hankel matrices over finite fields

Given three nonnegative integers $p,q,r$ and a finite field $F$, how many Hankel matrices $\left( x_{i+j}\right) _{0\leq i\leq p,\ 0\leq j\leq q}$ over $F$ have rank $\leq r$ ? This question is classical, and the answer ($q^{2r}$ when $r\leq\min\left\{ p,q\right\} $) has been obtained independently by various authors using different tools (Daykin, Elkies, Garcia Armas, Ghorpade and Ram). In this note, we study a refinement of this result: We show that if we fix the first $k$ of the entries $x_{0},x_{1},\ldots,x_{k-1}$ for some $k\leq r\leq\min\left\{ p,q\right\} $, then the number of ways to choose the remaining $p+q-k+1$ entries $x_{k},x_{k+1},\ldots,x_{p+q}$ such that the resulting Hankel matrix $\left( x_{i+j}\right) _{0\leq i\leq p,\ 0\leq j\leq q}$ has rank $\leq r$ is $q^{2r-k}$. This is exactly the answer that one would expect if the first $k$ entries had no effect on the rank, but of course the situation is not this simple. The refined result generalizes (and provides an alternative proof of) a result by Anzis, Chen, Gao, Kim, Li and Patrias on evaluations of Jacobi-Trudi determinants over finite fields.
A contrastive perspective on French and Italian <i>wh-in situ</i> questions

Abstract This paper offers a qualitative and quantitative analysis of French and Italian wh-in situ questions based on spontaneous spoken data. A pragmatic analysis relying on two parameters, propositional activation and pragmatic functions, reveals that the licensing conditions and the use of this structure largely differ in the two languages. While French wh-in situ do not require an activated proposition and can introduce a discourse-new topic, Italian wh-in situ mostly require an activated proposition and, at least in the analyzed corpus data, do not introduce discourse-new topics. An examination of the context also reveals that the different licensing conditions influence the interactional uses of these questions. All in all, both French and Italian wh-in situ require a pragmatic condition, which is their ‘anchoring’ to given, or at least inferable, information in the linguistic context (as is typical of Italian) or to predictable situations in the extralinguistic context (such as expected discourse moves in social interactions, as is the case for French).
To protect and to serve: developing a road map for research data management services

Research Data Management (RDM) has become a major issue for universities over the last decade. This case study outlines the review of RDM services carried out at the University of Oxford in partnership with external consultants between November 2019 and November 2020. It aims to describe and discuss the processes in undertaking a university-wide review of services supporting RDM and developing a future road map for them, with a strong emphasis on the design processes, methodological approaches and infographics used. The future road map developed is a live document, which the consulting team handed over to the University at the end of the consultation process. It provides a suggested RDM action plan for the University that will continue to evolve and be iterated in the light of additional internal costings, available resources and reprioritization in the budget cycle for each academic year. It is hoped that the contents of this case study will be useful to other research-intensive universities with an interest in developing and planning RDM services to support their researchers.
On the Viability of Decision Trees for Learning Models of Systems

Abstract models of embedded systems are useful for various tasks, ranging from diagnosis, through testing to monitoring at run-time. However, deriving a model for an unknown system is difficult. Generic learners like decision trees can identify specific properties of systems and have been applied successfully, e.g., for anomaly detection and test case identification. We consider Decision Tree Learning (DTL) to derive a new type of model from given observations with bounded history for systems that have a Mealy machine representation. We prove theoretical limitations and evaluate the practical characteristics in an experimental validation.
Sports Video Panorama Synthesis Technology Based on Edge Computing and Video Shot Boundary Detection

The development requirements for sports panorama synthesis technology, which rely on modern network technology, abandon traditional basketball training forms, and make effective use of the application and development of video panorama technology in the actual training process, are not only an important response to the current characteristics of students’ physical education learning and physical and mental growth but also an important response to the current characteristics of students’ physical education learning and physical and mental growth. In sports video analysis technology, a sports video panorama is a technical tool that converts an action video into a static action panorama to achieve the effect of action freezing and make overall analysis and mastery of the action easier. Shot segmentation is the foundation of hierarchical video structure, and it necessitates the accurate detection of all types of complex edited shot boundaries, as well as the effective distinction of motion changes in shots, in order to avoid shot boundary recognition being hampered. The synthesis technology for sports video panorama is investigated in this paper using edge computing and video shot boundary detection. After obtaining the boundary feature that describes the video shot, a comparison of this feature with a predetermined threshold value can be used to determine whether there is shot shear.
An All-data-segment Radio Frequency Fingerprint Extraction Method Based on Cross-power Spectrum

Radio frequency fingerprint (RFF) is the inherent signal characteristic of wireless devices and has been employed for identification. Its stability and robustness against noise has brought the necessity of further research for practical use. This paper proposes a novel RFF extraction method based on cross-power spectrum. This method could extract RFF from all signal segments, i.e., the device could send arbitrary content in data segments for RFF extraction rather than repetitive training symbols. Experimental verifications used 50 CC2540 ZigBee modules for classification. The result shows that the proposed scheme can achieve a 97% accuracy rate at 20dB signal-to-noise-rate (SNR), and performs more efficiently than other anti-noise methods in low SNR scenarios.
Uncertainty Learning towards Unsupervised Deformable Medical Image Registration

Uncertainty estimation in medical image registration enables surgeons to evaluate the operative risk based on the trustworthiness of the registered image data thus of paramount importance for practical clinical applications. Despite the recent promising results obtained with deep unsupervised learning-based registration methods, reasoning about uncertainty of unsupervised registration models remains largely unexplored. In this work, we propose a predictive module to learn the registration and uncertainty in correspondence simultaneously. Our framework introduces empirical randomness and registration error based uncertainty prediction. We systematically assess the performances on two MRI datasets with different ensemble paradigms. Experimental results highlight that our proposed framework significantly improves the registration accuracy and uncertainty compared with the baseline.
Variance of the number of cycles of random <i>A</i>-permutation

Abstract We consider random permutations having uniform distribution on the set of all permutations of the n -element set with lengths of cycles belonging to a fixed set A (so-called A -permutations). For some class of sets A the asymptotic formula for the variance of the number of cycles of such permutations is obtained.
Flexible and Controllable Access Policy Update for Encrypted Data Sharing in the Cloud

Abstract As a promising service paradigm, cloud computing has attracted lots of enterprises and individuals to outsource big data to public cloud. To facilitate secure data using and sharing, ciphertext-policy attribute-based encryption (CP-ABE) is a suitable solution, which can provide fine-grained access control and encryption functionalities simultaneously. However, some serious challenges are still remaining toward achieving flexible and controllable access policy update in CP-ABE, which essentially impede the powerful access control ability of CP-ABE from long-term and large-scale deployment in real systems. In this work, we propose a novel scheme named policy updatable CP-ABE for encrypted data sharing scenes. The proposed scheme features the following achievements: (i) it supports fine-grained update algorithms with no restriction on update time; (ii) the cloud server can effectively verify update ciphertexts, so the integrity of original data would not be compromised intentionally or accidentally during the update and (iii) most operations of encryption and policy update are securely outsourced to cloud servers, leaving extremely low overheads for data owners and users. We formally define its security model and prove it is adaptively secure. Also, we implement the proposed scheme using the Charm framework. The experiment results demonstrate that it is efficient and practical.
Here we go again

Once a programmer knows one language, they can leverage concepts and knowledge already learned, and easily pick up another programming language. But is that always the case? To understand if programmers have difficulty learning additional programming languages, we conducted an empirical study of Stack Overflow questions across 18 different programming languages. We hypothesized that previous knowledge could potentially interfere with learning a new programming language. From our inspection of 450 Stack Overflow questions, we found 276 instances of interference that occurred due to faulty assumptions originating from knowledge about a different language. To understand why these difficulties occurred, we conducted semistructured interviews with 16 professional programmers. The interviews revealed that programmers make failed attempts to relate a new programming language with what they already know. Our findings inform design implications for technical authors, toolsmiths, and language designers, such as designing documentation and automated tools that reduce interference, anticipating uncommon language transitions during language design, and welcoming programmers not just into a language, but its entire ecosystem.
Feature-blind fairness in collaborative filtering recommender systems

Abstract Recommender systems were originally proposed for suggesting potentially relevant items to users, with the unique objective of providing accurate suggestions. These recommenders started being adopted in several domains, and were identified as generating biased results that could harm the data items being recommended. The exposure in generated rankings, for instance in a job candidate selection situation, is supposed to be fairly distributed among candidates, regardless of their sensitive attributes (gender, race, nationality, age) for promoting equal opportunities. It can happen, however, that no such sensitive information is available in the data applied for training the recommender, and in this case, there is still space for biases that can lead to unfair treatment, named Feature-Blind unfairness. In this work, we adopt Variational Autoencoders (VAE), considered as the state-of-the-art technique for Collaborative Filtering (CF) recommendations, and we present a framework for addressing fairness when having only access to information about user-item interactions. More specifically, we are interested in Position and Popularity Bias. VAE loss function combines two terms associated with accuracy and quality of representation; we introduce a new term for encouraging fairness, and demonstrate the effect of promoting fair results despite of a tolerable decrease in recommendation quality. In our best scenario, position bias is reduced by 42% despite a reduction of 26% in recall in the top 100 recommendation results, compared to the same situation without any fairness constraints.
MAPS: Multimodal Attention for Product Similarity

Learning to identify similar products in the e-commerce domain has widespread applications such as ensuring consistent grouping of the products in the catalog, avoiding duplicates in the search results, etc. Here, we address the problem of learning product similarity for highly challenging real-world data from the Amazon catalog. We define it as a metric learning problem, where similar products are projected close to each other and dissimilar ones are projected further apart. To this end, we propose a scalable end-to-end multimodal framework for product representation learning in a weakly supervised setting using raw data from the catalog. This includes product images as well as textual attributes like product title and category information. The model uses the image as the primary source of information, while the title helps the model focus on relevant regions in the image by ignoring the background clutter. To validate our approach, we created multimodal datasets covering three broad product categories, where we achieve up to 10% improvement in precision compared to state-of-the-art multimodal benchmark. Along with this, we also incorporate several effective heuristics for training data generation, which further complements the overall training. Additionally, we demonstrate that incorporating the product title makes the model scale effectively across multiple product categories.
Word storage and computation

The goal of this chapter is to assess some representative morphological theories with respect to their compatibility with results coming from psycholinguistic experiments.We will concentrate mainly on the question of whether inflected words are computationally built by the addition of discrete units, called 'morphemes', or have to be treated as undecomposable wholes which relate to each other through connections with other words.The issue is complicated by two problems: the nature of morphemes as abstract units that anchor phonological and semantic information or symbolic Saussurean units, and the problem of whether inflected forms are decomposable at some level of analysis or must be stored as fully built forms.
Learning Temporal Video Procedure Segmentation from an Automatically Collected Large Dataset

Temporal Video Segmentation (TVS) is a fundamental video understanding task and has been widely researched in recent years. There are two subtasks of TVS: Video Action Segmentation (VAS) and Video Procedure Segmentation (VPS): VAS aims to recognize what actions happen in-side the video while VPS aims to segment the video into a sequence of video clips as a procedure. The VAS task inevitably relies on pre-defined action labels and is thus hard to scale to various open-domain videos. To overcome this limitation, the VPS task tries to divide a video into several category-independent procedure segments. However, the existing dataset for the VPS task is small (2k videos) and lacks diversity (only cooking domain). To tackle these problems, we collect a large and diverse dataset called TIPS, specifically for the VPS task. TIPS contains 63k videos including more than 300k procedure segments from instructional videos on YouTube, which covers plenty of how-to areas such as cooking, health, beauty, parenting, gardening, etc. We then propose a multi-modal Transformer with Gaussian Boundary Detection (MT-GBD) model for VPS, with the backbone of the Transformer and Convolution. Furthermore, we propose a new EIOU metric for the VPS task, which helps better evaluate VPS quality in a more comprehensive way. Experimental results show the effectiveness of our proposed model and metric.
APM Bots: An Automated Presentation Maker for Tourists/Corporates Using NLP-Assisted Web Scraping Technique

Summarizing information and preparing presentation slides remain the biggest measure for corporates, or startup enthusiasts to augment clients. Actively involving clients or convincing tourists with appropriate stories that mimic their interests in a short span of time has been a challenging task for most of the sales communities or governmental agencies. This paper proposed an Automated Presentation Maker (APM) bots, which utilized Natural Language Processing (NLP) and web scraping techniques, to deliver conceivable contents within a limited time frame. APM automated the preparation processes of presentation slides considering the emotions of clients/tourists. Sentiment analyzer and Stochastic Gradient Descent (SGD) classifier with an accuracy of 84% were implemented in APM for organizing presentation slides. Experiments were carried out at the IoT Cloud Research laboratory with specific case studies. Additionally, the performance study of the proposed APM bots, while experimented with different websites, was studied. The proposed approach would be beneficial for business corporates or government agencies to quickly capture the minds of clients/citizens with the automated presentations of APM .
Dirt on photovoltaic modules and efficient energy generation in the Brazilian semiarid

ABSTRACT A photovoltaic electrical energy system is one of the least aggressive ways to affect the environment. Factors such as dirt influence the efficiency of these systems and can have a considerable impact in low rainfall regions, such as the Brazilian semiarid. In this respect, the aim of the present study was to analyze the effect of dirt on the efficiency of electricity generation in photovoltaic modules installed in the Brazilian semiarid. To that end, the output power of a periodically cleaned module was compared with a naturally dirty module; in addition, the percentage output power, dirt deposition density and relationship between these variables were estimated. The study was carried out at the University of Pernambuco, in the city of Petrolina, PE, Brazil, during dry and rainy periods. Data were collected via a data acquisition system and dirt using glass slides. The results indicate that more than 15 days without rain significantly reduces the efficiency of photovoltaic modules installed in semiarid areas, reaching an 18.72% decrease in output power after 70 days.
Network Structure Learning Under Uncertain Interventions

Gaussian Directed Acyclic Graphs (DAGs) represent a powerful tool for learning the network of dependencies among variables, a task which is of primary interest in many fields and specifically in biology. Different DAGs may encode equivalent conditional independence structures, implying limited ability, with observational data, to identify causal relations. In many contexts however, measurements are collected under heterogeneous settings where variables are subject to exogenous interventions. Interventional data can improve the structure learning process whenever the targets of an intervention are known. However, these are often uncertain or completely unknown, as in the context of drug target discovery. We propose a Bayesian method for learning dependence structures and intervention targets from data subject to interventions on unknown variables of the system. Selected features of our approach include a DAG-Wishart prior on the DAG parameters, and the use of variable selection priors to express uncertainty on the targets. We provide theoretical results on the correct asymptotic identification of intervention targets and derive sufficient conditions for Bayes factor and posterior ratio consistency of the graph structure. Our method is applied in simulations and real-data world settings, to analyze perturbed protein data and assess antiepileptic drug therapies. Details of the MCMC algorithm and proofs of propositions are provided in the supplementary materials, together with more extensive results on simulations and applied studies. Supplementary materials for this article are available online.
Maneuvering Target Tracking with Multi‐Model Based on the Adaptive Structure

In the “snake” maneuver tracking of anti‐ship missiles, the traditional interactive multi‐model algorithm has poor tracking accuracy and the computational complexity of the improved algorithm is large. An adaptive structure multi‐model (ASMM) algorithm is proposed to solve this problem. The multi‐model tracking algorithm is improved from two aspects: tracking accuracy and computational complexity. First, the fuzzy control idea is introduced to speed up the update of the model probability, which improves the convergence speed and tracking accuracy of the interacting multiple model (IMM) algorithm. Next, a tracking model with an adaptive structure is proposed, which can adjust the tracking model adaptively according to the motion form of the target, thereby reducing the calculation redundancy. The simulation experiment shows that the proposed algorithm has better tracking accuracy than the traditional multi‐model algorithm while maintaining the approximate computational complexity, and has smaller computational complexity than the traditional improved algorithm while maintaining the approximate tracking accuracy. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Evaluation of the performance of erasable marker pen ink for the development of indentations on documents upon surface charging by electrostatic detection device

Indented impressions can be left on the surface beneath a document when it is written on. In the absence of this document, electrostatic detection devices can be used to reveal the underneath previously written information. However, there are instances where the toner used to develop these indentations has to be substituted with alternative application, under unexpected circumstances, such as the supply chain disruption during the ongoing global pandemic. This study aimed to verify the use of erasable marker pen ink as an alternative application for the development of indentations. The procedure was optimized and evaluated, and its performance in deciphering indented impressions from 11 different underlying surfaces was compared to a conventional electrostatic detection device that applied toner to develop indentation. Electrostatic device with toner application using cascade developer method has successfully developed indented impressions from all surfaces, except for the coated glossy paper. In contrast, the application of erasable marker pens revealed indentation successfully from not only the coated glossy paper but also six other common writing surfaces. While the toner is a reliable application for deciphering indentations, the application of erasable marker ink pen can be used in the event when toners are unavailable but also on surfaces such as glossy paper, where application of toners to develop indentation may not provide satisfactory results.