Reviewerly/exHarmony huggingface dataset:
Text-independent speaker identification system using discrete wavelet transform with linear prediction coding

Abstract One of the key problems of the modern day is the presentation of an identity verification system that can perform sufficient accuracy in identity verification, is resilient to assaults and noises, and can be recorded in the simplest possible method. In this study, a new speaker feature extraction which based on discrete wavelet transform (DWT) and linear prediction coding (LPC) algorithm (WLPCA) are investigated. This paper's primary objective is to evidence the performance of the new method for speaker identification by a Gaussian mixture model (GMM). The proposed method improves the recognition rate over the Mel-frequency cepstral coefficient (MFCC). Experimental evaluation of the process performance is performed on two speech databases; our recorded database and the publicly available TIMIT database. We show that the speech features derived by the newly proposed method are more suitable for GMM (91.53%), in terms of the time-consumed, by requiring less Gaussian mixtures than MFCC (85.77%). For testing the presented method in a noisy environment, Additive white Gaussian noise (AWGN) was added to the TIMIT database, where a slight improvement in the performance of the presented method (60.02%) over the MFCC (59.89%) was observed.
Fuzzy system of automatic tuning of electromagnetic excitation system of vibration-frequency liquid density meter

Abstract A fuzzy system for setting the location of electromagnetic exciter of vibration-frequency density meter is presented, taking into account the possible nonlinearities of the object. This paper proposes a system and algorithm for tuning the location of electromagnetic exciter of vibration-frequency density meter using fuzzy set theory. The use of fuzzy control methods allowed us to obtain a system for tuning the location of the electromagnetic exciter of the vibration-frequency density meter without the use of cumbersome computational procedures characteristic of the classical control method.
A multiparameter mathematical model for the problem of nonlinear filtration of fluids in two-layer media

Abstract In the realm of fluid dynamics and porous media, the nonlinear filtration of fluids in two-layer media presents a complex and intriguing challenge that demands a comprehensive mathematical framework for accurate representation and analysis. This paper introduces a multiparameter mathematical model designed to elucidate the intricacies of fluid filtration in heterogeneous environments, specifically focusing on two-layer media. The authors research the construction of a multiparameter mathematical model and numerical solution of nonlinear problems of the theory of filtration of unstructured and structured fluids in hydrodynamically connected multilayer formations with different characteristics. The developed models and solution algorithms were tested on hypothetical data.
Should Decisions in QCDCL Follow Prefix Order?

Abstract Quantified conflict-driven clause learning (QCDCL) is one of the main solving approaches for quantified Boolean formulas (QBF). One of the differences between QCDCL and propositional CDCL is that QCDCL typically follows the prefix order of the QBF for making decisions. We investigate an alternative model for QCDCL solving where decisions can be made in arbitrary order. The resulting system $$\textsf{QCDCL}^\textsf {{A\tiny {\MakeUppercase {ny}}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>QCDCL</mml:mi> <mml:mrow> <mml:mi>A</mml:mi> <mml:mi>NY</mml:mi> </mml:mrow> </mml:msup> </mml:math> is still sound and terminating, but does not necessarily allow to always learn asserting clauses or cubes. To address this potential drawback, we additionally introduce two subsystems that guarantee to always learn asserting clauses ( $$\textsf{QCDCL}^\textsf {{U\tiny {\MakeUppercase {ni}}-A\tiny {\MakeUppercase {ny}}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>QCDCL</mml:mi> <mml:mrow> <mml:mi>U</mml:mi> <mml:mi>NI</mml:mi> <mml:mo>-</mml:mo> <mml:mi>A</mml:mi> <mml:mi>NY</mml:mi> </mml:mrow> </mml:msup> </mml:math> ) and asserting cubes ( $$\textsf{QCDCL}^\textsf {{E\tiny {\MakeUppercase {xi}}-A\tiny {\MakeUppercase {ny}}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>QCDCL</mml:mi> <mml:mrow> <mml:mi>E</mml:mi> <mml:mi>XI</mml:mi> <mml:mo>-</mml:mo> <mml:mi>A</mml:mi> <mml:mi>NY</mml:mi> </mml:mrow> </mml:msup> </mml:math> ), respectively. We model all four approaches by formal proof systems and show that $$\textsf{QCDCL}^\textsf {{U\tiny {\MakeUppercase {ni}}-A\tiny {\MakeUppercase {ny}}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>QCDCL</mml:mi> <mml:mrow> <mml:mi>U</mml:mi> <mml:mi>NI</mml:mi> <mml:mo>-</mml:mo> <mml:mi>A</mml:mi> <mml:mi>NY</mml:mi> </mml:mrow> </mml:msup> </mml:math> is exponentially better than $$\mathsf{{QCDCL}} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>QCDCL</mml:mi> </mml:math> on false formulas, whereas $$\textsf{QCDCL}^\textsf {{E\tiny {\MakeUppercase {xi}}-A\tiny {\MakeUppercase {ny}}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>QCDCL</mml:mi> <mml:mrow> <mml:mi>E</mml:mi> <mml:mi>XI</mml:mi> <mml:mo>-</mml:mo> <mml:mi>A</mml:mi> <mml:mi>NY</mml:mi> </mml:mrow> </mml:msup> </mml:math> is exponentially better than $$\mathsf{{QCDCL}} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>QCDCL</mml:mi> </mml:math> on true QBFs. Technically, this involves constructing specific QBF families and showing lower and upper bounds in the respective proof systems. We complement our theoretical study with some initial experiments that confirm our theoretical findings.
Relative Norm Alignment for Tackling Domain Shift in Deep Multi-modal Classification

Abstract Multi-modal learning has gained significant attention due to its ability to enhance machine learning algorithms. However, it brings challenges related to modality heterogeneity and domain shift. In this work, we address these challenges by proposing a new approach called Relative Norm Alignment (RNA) loss. RNA loss exploits the observation that variations in marginal distributions between modalities manifest as discrepancies in their mean feature norms, and rebalances feature norms across domains, modalities, and classes. This rebalancing improves the accuracy of models on test data from unseen (“target”) distributions. In the context of Unsupervised Domain Adaptation (UDA), we use unlabeled target data to enhance feature transferability. We achieve this by combining RNA loss with an adversarial domain loss and an Information Maximization term that regularizes predictions on target data. We present a comprehensive analysis and ablation of our method for both Domain Generalization and UDA settings, testing our approach on different modalities for tasks such as first and third person action recognition, object recognition, and fatigue detection. Experimental results show that our approach achieves competitive or state-of-the-art performance on the proposed benchmarks, showing the versatility and effectiveness of our method in a wide range of applications.
Revisiting Bag of Words Document Representations for Efficient Ranking with Transformers

Modern transformer-based information retrieval models achieve state-of-the-art performance across various benchmarks. The self-attention of the transformer models is a powerful mechanism to contextualize terms over the whole input but quickly becomes prohibitively expensive for long input as required in document retrieval. Instead of focusing on the model itself to improve efficiency, this paper explores different bag of words document representations that encode full documents by only a fraction of their characteristic terms, allowing us to control and reduce the input length. We experiment with various models for document retrieval on MS MARCO data, as well as zero-shot document retrieval on Robust04, and show large gains in efficiency while retaining reasonable effectiveness. Inference time efficiency gains are both lowering the time and memory complexity in a controllable way, allowing for further trading off memory footprint and query latency. More generally, this line of research connects traditional IR models with neural “NLP” models and offers novel ways to explore the space between (efficient, but less effective) traditional rankers and (effective, but less efficient) neural rankers elegantly.
Grid-like entorhinal representation of an abstract value space during prospective decision making

Abstract How valuable a choice option is often changes over time, making the prediction of value changes an important challenge for decision making. Prior studies identified a cognitive map in the hippocampal-entorhinal system that encodes relationships between states and enables prediction of future states, but does not inherently convey value during prospective decision making. In this fMRI study, participants predicted changing values of choice options in a sequence, forming a trajectory through an abstract two-dimensional value space. During this task, the entorhinal cortex exhibited a grid-like representation with an orientation aligned to the axis through the value space most informative for choices. A network of brain regions, including ventromedial prefrontal cortex, tracked the prospective value difference between options. These findings suggest that the entorhinal grid system supports the prediction of future values by representing a cognitive map, which might be used to generate lower-dimensional value signals to guide prospective decision making.
TransMI: a transfer-learning method for generalized map information evaluation

Information Theory of Cartography plays an important role in guiding map design, generalization, and evaluation, and measurement of map information is the most basic topic of this theory. However, there are many problems in current measurement methods, and there is a long way to go to form a theoretically rigorous algorithm that can effectively depict spatial information and comprehensively consider the feeling of map readers. Luckily, we can now propose an evaluation metric that exhibits a certain correlation with map information based on deep learning to benefit actual cartography, and we term it as generalized map information evaluation to demonstrate differentiation. Specifically, this paper first constructs a subjective data set to support the supervised learning paradigm. Also, considering the difficulty of large-scale subjective data set collection, this paper proposes a Transfer-learning method for generalized Map Information evaluation (TransMI). Technically, a Siamese Network is pre-trained to explicitly acquire prior knowledge about the reasons for changes to mapped information. On this basis, one branch of the network is extracted and fine-tuned on the subjective data set to achieve the goal of predicting the quality of generalized map information. The results and the analysis of ablation studies prove the feasibility of our method.
Joint controllers in large research consortia: a funnel model to distinguish controllers in the sense of the GDPR from other partners in the consortium

<ns3:p>Large European research consortia in the health sciences face challenges regarding the governance of personal data collected, generated and/or shared during their collective research. A controller in the sense of the GDPR is the entity which decides about purposes and means of the data processing. Case law of the Court of Justice of the European Union (CJEU) and Guidelines of the European Data Protection Board (EDPB) indicate that all partners in the consortium would be joint controllers. This paper summarises the case law, the Guidelines and literature on joint controllership, gives a brief account of a webinar organised on the issue by Lygature and the MLC Foundation. Participants at the webinar agreed in large majority that it would be extreme if all partners in the consortium would become joint controllers. There was less agreement how to disentangle partners who are controllers of a study from those who are not. In order to disentangle responsibilities, we propose a funnel model with consecutive steps acting as sieves in the funnel. It differentiates between two types of partners: all partners who are involved in shaping the project as a whole versus those specific partners who are more closely involved in a sub-study following from the DoA i or the use of the data Platform. If the role of the partner would be comparable to that of an outside advisor, that partner would not be a data controller even though the partner is part of the consortium. We propose further nuances for the disentanglement which takes place in various steps. Uncertainty about formal controllership under the GDPR can stifle collaboration in consortia due to concerns over (shared) responsibility and liability. Data subjects’ ability to exercise their right can also be affected by this. The funnel model proposes a way out of this conundrum.</ns3:p>
Fast Model Selection and Hyperparameter Tuning for Generative Models

Generative models have gained significant attention in recent years. They are increasingly used to estimate the underlying structure of high-dimensional data and artificially generate various kinds of data similar to those from the real world. The performance of generative models depends critically on a good set of hyperparameters. Yet, finding the right hyperparameter configuration can be an extremely time-consuming task. In this paper, we focus on speeding up the hyperparameter search through adaptive resource allocation, early stopping underperforming candidates quickly and allocating more computational resources to promising ones by comparing their intermediate performance. The hyperparameter search is formulated as a non-stochastic best-arm identification problem where resources like iterations or training time constrained by some predetermined budget are allocated to different hyperparameter configurations. A procedure which uses hypothesis testing coupled with Successive Halving is proposed to make the resource allocation and early stopping decisions and compares the intermediate performance of generative models by their exponentially weighted Maximum Means Discrepancy (MMD). The experimental results show that the proposed method selects hyperparameter configurations that lead to a significant improvement in the model performance compared to Successive Halving for a wide range of budgets across several real-world applications.
A risk-oriented ontological model for supporting management decision-making in a cargo port based on multi-level goal setting

In today's unstable market environment, transport logistics enterprises face the task of predicting possible risk situations in order to avoid them, as well as developing tools that allow them to form management decisions, evaluate the effectiveness of each of them and choose the most appropriate one, taking into account specific tasks and various influencing factors. Situations of failure to achieve the goals of a cargo port, as well as cases of failure by indicators to assess these goals of normative values, are considered as risks. The goals and indicators are outlined and described in the previously built Balanced Scorecard (MTSP). A set of links between goals and indicators of their assessment with risks at various levels of management is presented in the form of a developed multi-level hybrid logical-probabilistic (LV) risk model of a cargo port, as well as in detailed scenario models of this LV model. The integrated use of SSP and LV modeling technologies allowed us to build a general concept of multi-level goal setting, the main advantage of which lies in the detailed elaboration of the company's goals subordinate to the main strategic one, which allows, if necessary, to influence operational events and obtain a positive result in the longer term, in tactical and strategic plans. The ontological model contains all the information about the interaction of elements that affect the occurrence of risky events, and allows you to select options for getting out of a risky situation in accordance with various specified conditions. The results of queries to the risk-oriented ontological model are variants of management decisions aimed at reducing risks in the cargo port.
Blockchain Enabled Smart Contracts for Digital Assets

The diverse applications and benefits of blockchain technology and smart contracts across various sectors are explored in this overview. Decentralized Finance (DeFi) projects, supply chain management, tokenization of assets, NFT platforms, and identity verification stand out as prominent examples. Smart contracts are lauded for their cost efficiency and security, underpinned by blockchain's cryptographic features that fortify system integrity. Blockchain's global accessibility facilitates cross-border transactions, while decentralized exchanges (DEX) mitigate censorship and counterparty risks, empowering users with asset control. Moreover, blockchain expands investment opportunities through asset tokenization, encompassing diverse classes such as real estate and intellectual property, bolstering security and transparency through immutable ownership records. Oracles play a vital role in integrating real-time data into DeFi ecosystems, informing decision-making by linking off-chain data with blockchain networks.Diverse token standards like ERC-20 and ERC-721 are pivotal, catering to fungible and non-fungible token ecosystems respectively. Lastly, the advantages of proof of stake (PoS) over proof of work (PoW) are highlighted, emphasizing PoS's energy efficiency, transaction throughput, and scalability
Comparing Remote and Proximal Sensing of Agrometeorological Parameters across Different Agricultural Regions in Croatia: A Case Study Using ERA5-Land, Agri4Cast, and In Situ Stations during the Period 2019–2021

The paper evaluates the usability of remote satellite-based and proximal ground-based agrometeorological data sources for precision agriculture and crop production in Croatia. The compared agrometeorological datasets stem from the open-access data sources Copernicus CDS and the Agri4Cast portal, and commercial in situ agrometeorological stations (PinovaMeteo) which monitor environmental parameters relevant to the physiological state of crops. The study compares relevant parameters for 10 different locations in Croatia for three consecutive years (2019, 2020, and 2021) to investigate whether model-based data from ERA5-Land and Agri4Cast are well-correlated with ground measurements from independent in situ stations (PinovaMeteo) for specific agrometeorological parameters (air and soil temperature, and precipitation). Our results indicate the following: both the ERA5-Land and Agri4Cast datasets show mostly strong positive correlations with ground observations for air temperature, modest correlations for soil temperature, but modest or even low correlations for precipitation. Analysis of the residuals indicates higher overall residual values, especially in areas with complex topography and near large bodies of water or the sea, and deviations of residuals that may limit the usability of satellite- and model-based data for decision-making in agriculture.
Autonomous Quadcopter for Surveillance

The use of drone technology is becoming increasingly popular. Finding applications, across various industries. In agriculture drones are employed for tasks such as applying pesticides and fertilizers monitoring crops and creating maps. The construction sector utilizes drones to inspect job sites and track progress. Additionally, the film industry benefits from photography using drones while search and rescue operations rely on them to reach areas. Overall drone technology has the potential to revolutionize industries by offering solutions to previously challenging problems. We propose the development of an AI based autonomous drone system that can be deployed in areas for applications including surveillance and warehouse management. This system incorporates a PI CAM V2 camera mounted on a quadcopter equipped with GPS technology. The drone operates autonomously without the need for a pilot thanks to software, like Robot Operating System (ROS) other cutting-edge technologies
Applicability of Neural Combinatorial Optimization: A Critical View

Neural Combinatorial Optimization has emerged as a new paradigm in the optimization area. It attempts to solve optimization problems by means of neural networks and reinforcement learning. In the last few years, due to their novelty and presumably good performance, many research papers have been published introducing new neural architectures for a variety of combinatorial problems. However, the incorporation of such models in the conventional optimization portfolio raises many questions related to their performance compared to other existing methods, such as exact algorithms, heuristics or metaheuristics. This paper aims to present a critical view of these new proposals, discussing their benefits and drawbacks with respect to the tools and algorithms already present in the optimization field. For this purpose, a comprehensive study is carried out to analyse the fundamental aspects of such methods, including performance, computational cost, transferability and reusability of the trained model. Moreover, this discussion is accompanied by the design and validation of a new neural combinatorial optimization algorithm on two well-known combinatorial problems, the Linear Ordering Problem and the Permutation Flowshop Scheduling Problem. Finally, new directions for future work in the area of Neural Combinatorial Optimization algorithms are suggested.
A Security Information Transmission Method Based on DHR for Seafloor Observation Network

A seafloor observation network (SON) consists of a large number of heterogeneous devices that monitor the deep sea and communicate with onshore data centers. Due to the long-distance information transmission and the risk of malicious attacks, ensuring the integrity of data in transit is essential. A cryptographically secure frame check sequence (FCS) has shown great advantages in protecting data integrity. However, the commonly used FCS has a collision possibility, which poses a security risk; furthermore, reducing the encryption calculation cost is a challenge. In this paper, we propose a secure, lightweight encryption scheme for transmitted data inspired by mimic defense from dynamic heterogeneous redundancy theory. Specifically, we use dynamic keys to encrypt a data block and generate multiple encrypted heterogeneous blocks for transmission. These continuously changing encrypted data blocks increase the confusion regarding the original encoded data, making it challenging for attackers to interpret and modify the data blocks. Additionally, the redundant information from the multiple blocks can identify and recover tampered data. Our proposed scheme is suitable for resource-constrained environments where lightweight encryption is crucial. Through experimental demonstrations and analysis methods, we determine the effectiveness of our encryption scheme in reducing computational costs and improving security performance to protect data integrity.