Reviewerly/exHarmony huggingface dataset:
Animal Image Recognition Method Based on Two-pass Feature Fusion

- The identification and protection of wild animals are crucial to maintaining biodiversity and maintaining ecological balance. Based on the deep learning method and the characteristics of field monitoring images, this paper proposes a Dc_YOLOv5 (Deformable convolution YOLOv5) and NLBP dual-pass fusion network as a feature extraction module. First, due to the randomness of animal activities, the monitoring images may have complex background information. In order to improve the feature expression ability of complex image target detection, a deformable convolutional neural network is introduced in the CSP2_1 module of the YOLOv5 network; in the LBP pathway, the proposed Compared with the traditional LBP method, the NLBP extraction algorithm takes into account the changing relationship between the gray value of the pixel and its neighboring pixels, reducing the loss of feature information; finally, the CAFF-feature fusion module is introduced to reconstruct the feature and Obtain intermediate predictions, effectively focusing on important channels and suppressing irrelevant channels. The experimental results show that this method has excellent recognition effect.
Chinese Named Entity Recognition Method Based on Multi-Granularity Embedding

The most current Chinese named entity recognition models can be divided into the following two types: one is based on character unit and the other is based on word unit. Both of them have their disadvantages. The former lacks semantic and boundary information of words, while the latter depends on the accuracy of word segmentation. In addition, Chinese characters evolved from hieroglyphics, which contain rich semantic information. To address the above problems, this paper proposes a Chinese named entity recognition model with the multi-granularity embedding from Chinese strokes, characters, and words. The method first obtains the stroke list corresponding to Chinese characters in the training corpus, and uses a convolutional kernel with multi-window size to obtain the n-gram information of strokes. Secondly, a convolutional neural network is used to obtain the local information of the sequence and the representation of Chinese characters within the local area. Finally, considering the constraint relationship between the information of the context and the label, the BiLSTM network is used for context modeling, and the CRF network is used as the label decoding layer. The experimental results show that the F1-Score of this method achieves 62.41% and 95.28% on Weibo and MSRA datasets, respectively.
Sentiment Analysis and Recurrent Radial Basis Function Network for Bitcoin Price Prediction

The value of Bitcoin, and more generally the world of "crypto-currencies", has always been characterized by its volatile and unpredictable nature. The value of these assets, indeed, is established independently by the parties participating in the exchanges and cannot be influenced by any regulatory organ or central authority. In recent years, thanks to the Web, a world of virtual communities has been created around the Bitcoin phenomenon, whose discussions directly or indirectly influence the price of this digital asset. Through appropriate analysis of Big Data from the main Social Networks, it is possible to identify correlations between the general sentiment of the community and the value of the currency exchanged. The purpose, therefore, of this work is to go and identify the influences of the Bitcoin market through the analysis of the opinions and feelings of the large communities belonging to the Social Network Reddit to try to make short-term predictions about the price of the currency. Tools based on Machine Learning and Deep Learning techniques can help to identify these phenomena with a high degree of accuracy. The results show how an approach based on Recurrent Radial Basis Function Network (RRBFN) is effective to perform the prediction of a given digital asset starting from the analysis of sentiments contained in online discussions.
You Only Hear Once: Lightweight In-Network AI Design for Multi-object Anomaly Detection

With the rapid evolution of programmable network devices, the transformation of network infrastructure to a programmable computing platform has become a reality. As a result, such programmable networks make the deployment of in-network AI possible for achieving predictive maintenance, which in turn enables the much desired (sound-based) time-critical anomaly detection in modern smart factories. However, such abnormal sound detection systems face high computational complexity due to their high density and multi-source nature. In this paper, we present our novel You only hear once (Yoho) design for multi-target anomalous sound detection and end-to-end feature extraction. Yoho can detect anomalies directly from a mixed multi-source acoustic signal without recovering each source data. In addition, with a lightweight dual-path backbone design (Yoho-Dual-Path), we achieve low model complexity and high detection accuracy. Our experiments show that the proposed solution can reduce the inference time by 77% and improve the detection accuracy by 51%. This enables the integration of intelligent anomalous sound detection systems into the network.
Data Processing Using Edge Computing: A Case Study For The Remote Care Environment

Falling is one of the most common concerns among caregivers [1]. For people with dementia and the elderly in remote care and hospitals, immediately informing caregivers of abnormal behaviour such as a fall can improve their quality of life [2] [3]. Latency occurs when processing massive amounts of continuous data from the Internet of Things devices in the cloud. Network latency impacts latency-sensitive critical real-time applications, such as those used in the healthcare sector [4]. This study seeks to reduce latency and network bandwidth when sending continuous data from wearable sensors in a remote care environment in order to meet the latency requirements of health applications. To reduce latency and network bandwidth, a framework is proposed that deploys edge computing using a geo-distributed intermediate layer of intelligence in the middle of the sensor and cloud layers. It includes raw collected data processing, early sensor fusion, missing data, data reduction and conversion and data storage. The case study is a remote care environment focused on fall detection. The research focuses on fall detection and analysis of sensor data for human fall detection using various activity recognition techniques, threshold-based and Machine Learning algorithms. As a result, a fall activity recorded from the wearable device to the edge server could be processed, predicted, and reported to the caregiver in 294 milliseconds.
Feature Selection Based Approach for Handling Cold Start Problem in Collaborative Recommender Systems

Due to the widespread usage of Information and communication technology (ICT), nowadays people are getting a large number of options to choose a particular item or a service. So, in this scenario, the recommender system (RS) plays a very vital role to optimize their decisions. But cold start problem is one of the major challenges in RS for new users and items. In this paper, a novel method using feature selection and prediction has been proposed to address the cold start problem in Collaborative RS. The proposed approach has been validated using two data sets i.e Laptop Dataset and Red wine Quality dataset taking Mean Absolute Error (MAE) and Precision as the evaluation metrics.
Efficient Communication and The Organization of The Lexicon

Abstract This chapter applies a language design perspective to the lexicon. It reviews and synthesizes a body of work in cognitive science and linguistics that uses ideas from computer science, specifically information theory, to explore how structural features of lexicons can be explained by principles of efficient communication. It pays particular attention to four major properties of lexicons. The first is the structure of word frequency distributions, particularly the Zipfian structure of these distributions and the way that individual semantic spaces are carved up so as to be maximally efficient. The second is the relationship between word frequency and properties like word length and phonotactic probability. The third concerns lexical arbitrariness: the extent to which word forms contain information about their meanings. Finally, the chapter considers how lexicons are structured for child language learning.
Elements of Methodology of Precision Phonetic Analysis of Oral Phonograms

The study of the cornerstone of modern linguistics - the process of speech and textual interpersonal communication, given the size of the infosphere of the twenty-first century, is impossible without a sound and purposeful involvement of information technology from other fields of knowledge, including computer science. The resulting relatively young science, computational linguistics, aims to automatically analyze natural languages in all spectra of their implementations. Among the long list of topical issues actively studied in the paradigm of computational linguistics, we mention the automation of compilation and linguistic processing of language corpora, automated classification and abstracting of documents, creating accurate linguistic models of natural languages, extraction of factual information from informal linguistic data. An effective, strictly formalized methodology for computational phonetic analysis of linguistic information, especially speech information, is potentially a driving force for improving the results of solving these research problems. This thesis is fully consistent with the content of the article, which proves the relevance of the presented scientific and applied results. Accordingly, the paper presents elements of the methodology of precision phonetic analysis of phonograms of oral speech, taking into account the phenomenon of phonetic fusion. The mathematical apparatus of the created methods is based on the provisions of the theory of pattern recognition, information theory and acoustic theory of language formation. This basis provided the basis for a system of analytical formalization of the problem of multicriteria of the process of recognition of language units of human speech. As a result, a method for reliable clustering of personal phonetic alphabets of speakers is presented. A method for detecting potentially unreliable classified speech units and adjusting the results of the process of automated transcription of speech signals is also presented. A method for estimating the influence of the medium of propagation of the studied speech signals on the transcription result is also proposed.
On the Design of a New Stochastic Meta-Heuristic for Derivative-Free Optimization

Abstract Optimization problems are frequent in several fields, such as the different branches of Engineering. In some cases, the objective function exposes mathematically exploitable properties to find exact solutions. However, when it is not the case, heuristics are appreciated. This situation occurs when the objective function involves numerical simulations and sophisticated models of reality. Then, population-based meta-heuristics, such as genetic algorithms, are widely used because of being independent of the objective function. Unfortunately, they have multiple parameters and generally require numerous function evaluations to find competitive solutions stably. An attractive alternative is DIRECT, which handles the objective function as a black box like the previous meta-heuristics but is almost parameter-free and deterministic. Unfortunately, its rectangle division behavior is rigid, and it may require many function evaluations for degenerate cases. This work presents an optimizer that combines the lack of parameters and stochasticity for high exploration capabilities. This method, called Tangram, defines a self-adapted set of division rules for the search space yet relies on a stochastic hill-climber to perform local searches. This optimizer is expected to be effective for low-dimensional problems (less than 20 variables) and few function evaluations. According to the results achieved, Tangram outperforms Teaching-Learning-Based Optimization (TLBO), a widespread population-based method, and a plain multi-start configuration of the stochastic hill-climber used.
Transforming DIGROW into a Multi-attribute Digital Maturity Model. Formalization and Implementation of the Proposal

SMEs form the backbone of the economy of most countries all over the world. Increasing their performance is therefore essential to increase the well-being of peoples. The adoption of digital technologies is universally recognized an essential component to achieve this objective. Unfortunately, European SMEs are lagging behind in the transition to digital. The Digital Europe Programme has been started at the end of 2021 to speed up such a transition. As part of the Programme, a network of so-called European Digital Innovation Hubs (EDIHs) is under construction in order to lend a hand to SMEs in the process of increasing their digitalization awareness and capabilities. The availability of a digital maturity assessment tool would be of great help for EDIHs to accomplish their mission. This paper gives a contribution in such a direction. It starts from an already published framework suitable to assess the digital maturity level of SMEs, as well as the capabilities associated to each level that are necessary for promoting a digital enabled growth. Then, the framework is transformed into a qualitative multi-attribute digital maturity model and the latter is implemented by means of a freeware software. The proposed model returns transparent indications to SMEs about where they are on their digital transformation journey, together with eventual strengths and weaknesses. SMEs should take these indications into account before allocating the budget for future investments.
Scenarios as a Tool for Professional Training in Information Security Dialogues

This article presents scenarios designed to support abstract and reflective thinking necessary to inculcate information security awareness among IT service designers.Data for the study was obtained in empirical interventions and through an action research approach in cooperation with an IT company.The findings highlight the need for training that, in combination with traditional contents, also integrates organizational, business, and social aspects into information security awareness.Rethinking a strategy for training to be grounded in scenarios from day-to-day business activities is one implication of the study; another is the suggestion to frame the scenarios as dilemmas, that is, problematic and realistic situations having multiple solutions depending on interpretations and perspectives, and a final conclusion is the importance of enabling structured in-depth dialogues among employees.
The consistency of arithmetic, continued

Abstract By assigning ordinal notations to proofs in classical arithmetic it is possible to show that each step in the simplification process making up the consistency proof, the complexity of proofs, as measured by the associated ordinal notation, successively decreases. Since the system of ordinal notations is well-ordered, it is not possible to have an infinite decreasing sequence. The reduction process described in Chapter 7 therefore must bring down the ordinal notations assigned to the successive transformed proofs, and must ultimately end with a proof whose ordinal notation is less than ω, i.e. a “simple” proof. Since “simple” proofs cannot be proofs of a contradiction, this establishes the consistency of classical arithmetic.
The cut-elimination theorem

Abstract All the rules of the sequent calculus have the property that all the formulas that are present in the premises also occur in the conclusion. There is only one exception, the cut rule. In this chapter, it is shown using double induction that every theorem provable in Gentzen’s sequent calculi using the cut rule can also be proved without. One first proves that the mix rule is equivalent in proof-theoretic strength to the cut rule. Then one proves by induction on two complexity measures, the degree and the rank of a mix, that every application of mix can be eliminated. Several results follow from the cut-elimination theorem, including the so-called “mid-sequent” theorem and Herbrand’s theorem. In addition, one can prove the consistency of minimal, intuitionistic, and classical logic.
Axiomatic calculi

Abstract The first results in proof theory were obtained using axiomatic systems of logical deduction. The chapter develops propositional and predicate logic in such an axiomatic system. It shows how reasoning under assumption can be regained in an axiomatic system by proving the so-called “deduction theorem” propositional logic (and later, predicate logic as well). The deduction theorem provides the opportunity to introduce one of the main tools needed for proving properties of formal systems, namely, proof by induction on the natural numbers. The differences between minimal, intuitionistic, and classical systems of logic according to the properties of negation characterizing each of the systems are outlined. Independence of theorems such as the law of excluded middle are here given model-theoretically (in later chapters, these are also presented proof-theoretically). After presenting classical predicate logic, the systems of classical and intuitionistic arithmetic are presented. Gentzen’s first substantial result, the so-called Gödel-Gentzen translation, is discussed in the final section where it is proved that if intuitionistic arithmetic is consistent, so is classical arithmetic.
The sequent calculus

Abstract In addition to natural deduction, Gentzen developed a different calculus, called the sequent calculus. A sequent is a configuration presenting an arrow symbol (⇒) flanked on the left and on the right by finite sequences of formulas, possibly empty. The sequent calculus is developed, with examples of how to prove statements in the calculus, and a few results about transforming proofs through variable replacements are proved. Proofs in the intuitionistic sequent calculus can be translated into natural deductions, and vice versa (this system is obtained by restricting sequents to those that have at most one formula on the right hand side of the arrow).
Introduction

Abstract Proof theory arose as a mathematical solution to a problem in the philosophy of mathematics. In the first third of the twentieth century, debates raged among philosophers and mathematicians about the safety of mathematical reasoning. David Hilbert proposed to safeguard classical mathematics by formalizing it using the then-new tools of symbolic logic, and using philosophically uncontentious methods to prove that these formal systems are free from contradiction, i.e., consistent. To carry out this project, Gerhard Gentzen developed a number of formal systems in the 1930s, and proved the consistency of elementary number theory. The chapter surveys the historical background of the development of proof theory arising out of the foundational debate and Hilbert’s program, discusses the significance of Gentzen’s work, and outlines some developments in proof theory after Gentzen.
Normal deductions

Abstract The concept of a normal deduction is introduced as a way to capture the notion of a “direct” proof, i.e., one that makes no detours. The latter happens, e.g., when a formula is introduced first by a rule of introduction (say, starting from A and B to introduce the conjunction A ∧ B) and is followed immediately by an elimination of the same connective (yielding, for instance, A). A normalization theorem proves that if one has a proof with detours then there is a proof without detours. Proving such more advanced results in proof theory requires more complex proof methods than simple induction. A first step to more complex proofs is the use of double induction. A first application of double induction is the proof of normalization for a fragment of minimal logic containing conjunction, implication, negation, and the universal quantifier. The result is extended to full intuitionistic logic, followed by a discussion of the structure of normal deductions. Normal deductions have the sub-formula property, and so cannot be proofs of contradictions. This shows that natural deduction is consistent. Normalization can also be used to show that certain formulas cannot be proved, e.g., that the principle of excluded middle cannot be proved in intuitionistic logic. Finally, a full proof of normalization for full classical logic is given.
Image Inpainting for 3D Reconstruction Based on the Known Region Boundaries

Pointcloud is a collection of 3D object coordinate systems in 3D scene. Generally, point data in pointclouds represent the outer surface of an object. It is widely used in 3D reconstruction applications in various fields. When obtaining pointcloud data from RGB-D images, if part of the information in the RGB-D images is lost or damaged, the pointcloud data will be hollow or too sparse. Moreover, it is not conducive to the subsequent application of pointcloud data. Based on the boundary of the region to be repaired, we proposes to repair the damaged image and synthesize the complete pointcloud data after a series of preprocessing steps related to the image. Experiments show that the our method can effectively improve the restoration of the lost details of the pixel in the target area and that it will have the fuller pointcloud data after synthesizing the restored image.
MSDA-NMF: A Multilayer Complex System Model Integrating Deep Autoencoder and NMF

In essence, the network is a way of encoding the information of the underlying social management system. Ubiquitous social management systems rarely exist alone and have dynamic complexity. For complex social management systems, it is difficult to extract and represent multi-angle features of data only by using non-negative matrix factorization. Existing deep NMF models integrating multi-layer information struggle to explain the results obtained after mid-layer NMF. In this paper, NMF is introduced into the multi-layer NMF structure, and the feature representation of the input data is realized by using the complex hierarchical structure. By adding regularization constraints for each layer, the essential features of the data are obtained by characterizing the feature transformation layer-by-layer. Furthermore, the deep autoencoder and NMF are fused to construct the multi-layer NMF model MSDA-NMF that integrates the deep autoencoder. Through multiple data sets such as HEP-TH, OAG and HEP-TH, Pol blog, Orkut and Livejournal, compared with 8 popular NMF models, the Micro index of the better model increased by 1.83, NMI value increased by 12%, and link prediction performance improved by 13%. Furthermore, the robustness of the proposed model is verified.