Reviewerly/exHarmony huggingface dataset:
Bitcoin: an analysis of the accounting and finance research published in international journals

This paper consists of a literature review and a survey of scientific research published in journals about the theme cryptocurrencies. In a descriptive study, we used bibliometrics and the content analysis of articles that address the central theme, bitcoin. The study analysed 337 articles. The number of studies has been increased in the period from 2013 to 2018. The findings corroborate with the law of Bradford (many journals with few articles) and will meet with the Lotka's law (80% of the authors are logged only one study on the theme). The majority of researches conducted, in short, comparative studies with indexes and mathematical properties, using statistical tools such as analysis of variance and regression. It generally noted the lack of research on the topic in Brazilian journals, which enables opportunities for future studies.
Privacy-Preserving and Truthful Auction for Task Assignment in Outsourced Cloud Environments

Due to high fairness and allocation efficiency, the task assignment problem of mobile applications via auctions has become a promising approach to motivate bidders to provide their mobile device resources effectively. However, most of existing works focus on the auction mechanism under the plaintexts, and ignore the problems caused by information leakage. In this paper, we study the problem of the privacy-preserving auction for task assignment in outsourced cloud environments without leaking any private information to anyone. Specifically, we use Yao’s garbled circuits and homomorphic encryption system as underlying tools. Along with several elaborately designed secure arithmetic subroutines, we propose a privacy-preserving and truthful auction framework for task assignment in outsourced cloud environments. Theoretically, we analyze the complexity of our scheme in detail and prove the security in the presence of semi-honest adversaries. Finally, we evaluate the performance and feasibility of our scheme through a large number of simulation experiments.
Semi-supervised active learning hypothesis verification for improved geometric expression in three-dimensional object recognition

Efficient three-dimensional (3D) object recognition plays an important role in the 3D reconstruction of light-field displays. However, presently, the error rate of 3D implicit shape object recognition remains high, because the local features are sparse in the geometric expression of 3D reconstruction. To address this issue, a hypothesis verification method based on semi-supervised active learning-based K-means++ combined with 3D feature extraction is proposed. The proposed approach consists of the offline and online phases. The algorithm time complexity is O(n) and O(n2), respectively. The offline phase includes keypoint detection, normal estimation, fast point feature histograms (FPFH) descriptor extraction, geometric word weight saving, and indexing structure construction. In addition to the FPFH extraction, the online phase includes nearest geometric word searching, corresponding direction and center voting, and non-maximum suppression. Comparative experiments were conducted in which the models and scenes were tested on the 3D datasets Mian and Tosca that is high-resolution. The experimental results demonstrate that the proposed method resolves the low recognition rate problem of 3D implicit objects, with the highest 3D intersection over union (IoU) reaching 88.89%.
Some thoughts on comprehensive Quality Evaluation -- Based on Cite space knowledge graph Analysis

This paper makes full use of Cite Space software to analyze the knowledge graph of comprehensive quality evaluation, analyzes the hot spots of comprehensive quality evaluation since the 21st century, and then puts forward the development trend of this research.The research shows that the current research hot spots include the theoretical exploration of "quality", the method research of "evaluation", the application research of "comprehensive quality evaluation" and the education strategy research of "improving comprehensive quality".Researchers can grasp the future research trend from four aspects: strengthening cooperation, changing direction, focusing on policy and international perspective.
The Effect of Digital Marketing Strategy on Customer and Organizational Outcomes

This study aims to test the impact of digital marketing strategy as an antecedent on customer outcomes (customer satisfaction, customer engagement, and customer loyalty) and the impact of these three outcomes on organizational outcomes. Twenty-five items are used to measure research variables. These items are developed referring to previous related works: digital marketing strategy, customer satisfaction, customer engagement, customer loyalty, and organizational outcomes. According to the research budget and time constraints, a convenience sampling method was implemented in the data collection process. This study applied structural equation modeling (SEM) to test the research hypotheses through AMOS software.The authors conducted a questionnaire to collect data from a sample of marketing practitioners selected from 10 organizations with a total number of 200 respondents. The total number of returned questionnaires is 187 responses. Eleven questionnaires are excluded due to invalid responses. Thus, 176 questionnaires are used to carry out data analysis. The results underline the significant effects of customer satisfaction, engagement, and loyalty on organizational outcomes. The authors noted that customer satisfaction is the most affected outcome by digital marketing strategy, followed by customer engagement and customer loyalty. Additionally, customer loyalty has the greatest effect on organizational outcomes, followed by customer satisfaction and customer loyalty. The findings showed that enhancing organizational outcomes requires marketing managers consider customer outcomes such as customer satisfaction, customer engagement, and customer loyalty, which are affected by marketing strategies. Marketing managers are requested to pay great attention to their marketing strategy, considering its effects on customer satisfaction and other customer outcomes, like customer engagement and loyalty. Likewise, scholars are required to re-test the current model to identify the mediating role of the outcomes mentioned above in the effect of marketing strategy on organizational outcomes.
Digital Audio Effects

ModernAudio effectsDigital audio effects Digital audio effects|( processing and circuit technology has made available a number of methods for processing the acoustic signal covering various requirements. Among the different methods, the term effect generally refers to the processing of an existing sound in order to make it more suggestive.
Vibrating Systems

The generation of sound is commonly associated with the vibration of solid objects, and sound itself is often referred to as “air vibration.” The words sound and vibration are, in fact, commonly connected to each other. In musical instruments, for example, some sound generators are vibrating strings as in piano, guitar, violin, etc.; vibration of bars like in the xylophone, vibraphone; vibration of membranes as in drums and banjo; vibration columns of air in pipes as in pipe organ, brass, wood, etc.
Multi-rate Audio Processing and Wavelet Transform

The hearing organ is capable of perceiving sounds in a very wide frequency range, covering about 4 decades: nominally from 20 to 20 kHz. Most common audio signal sampling frequencies, although they ensure adequate acquisition of higher frequencies, may not be necessary for signals in the lower frequency range. For example, for processing a signal to be sent to a subwoofer, (or LFE, low-frequency effect), which typically covers the band from 3 to 120 Hz, a sampling frequency of 44.1 kHz or higher is completely redundant.
Memory-Tight Multi-challenge Security of Public-Key Encryption

We give the first examples of public-key encryption schemes which can be proven to achieve multi-challenge, multi-user CCA security via reductions that are tight in time, advantage, and memory. Our constructions are obtained by applying the KEM-DEM paradigm to variants of Hashed ElGamal and the Fujisaki-Okamoto transformation that are augmented by adding uniformly random strings to their ciphertexts. The reductions carefully combine recent proof techniques introduced by Bhattacharyya’20 and Ghoshal-Ghosal-Jaeger-Tessaro’22. Our proofs for the augmented ECIES version of Hashed-ElGamal make use of a new computational Diffie-Hellman assumption wherein the adversary is given access to a pairing to a random group, which we believe may be of independent interest.
Knowledge Encryption and Its Applications to Simulatable Protocols with Low Round-Complexity

We introduce a new notion of public key encryption, knowledge encryption, for which its ciphertexts can be reduced to the public-key, i.e., any algorithm that can break the ciphertext indistinguishability can be used to extract the (partial) secret key. We show that knowledge encryption can be built solely on any two-round oblivious transfer with game-based security, which are known based on various standard (polynomial-hardness) assumptions, such as the DDH, the Quadratic( $$N^{th}$$ ) Residuosity or the LWE assumption. We use knowledge encryption to construct the first three-round (weakly) simulatable oblivious transfer. This protocol satisfies (fully) simulatable security for the receiver, and weakly simulatable security ( $$(T,\epsilon )$$ -simulatability) for the sender in the following sense: for any polynomial T and any inverse polynomial $$\epsilon $$ , there exists an efficient simulator such that the distinguishing gap of any distinguisher of size less than T is at most $$\epsilon $$ . Equipped with these tools, we construct a variety of fundamental cryptographic protocols with low round-complexity, assuming only the existence of two-round oblivious transfer with game-based security. These protocols include three-round delayed-input weak zero knowledge argument, three-round weakly secure two-party computation, three-round concurrent weak zero knowledge in the BPK model, and a two-round commitment with weak security under selective opening attack. These results improve upon the assumptions required by the previous constructions. Furthermore, all our protocols enjoy the above $$(T,\epsilon )$$ -simulatability (stronger than the distinguisher-dependent simulatability), and are quasi-polynomial time simulatable under the same (polynomial hardness) assumption.
An Adaptive Ensembled Neural Network-Based Approach to IoT Device Identification

The Internet of Things (IoT) has developed rapidly in recent years and has been widely used in our daily life. An online report claimed that the connected IoT devices will reach the scale of 14.4 billion globally at the end of 2022. With the rapid and large-scale deployment of such devices, however, some severe security problems and challenges arised as well, especially in the field of IoT device management. Device identification is a prerequisite procedure to mitigate the above issues. Therefore, accurately identifying the deployed IoT devices plays a vital role in network management and cyber security. In this work, we come up with a spatio-temporal-based method that characterizes IoT device behaviors by leveraging the packet sequence features of IoT traffic, which is able to automatically extract the high-level features from raw IoT traffic. The further evaluation indicates that our method is capable of identifying diverse IoT devices with satisfactory accuracy.
Circuits and Algorithms for Physical Modeling

Sound and acoustic wave propagation are very complex phenomena, and for the generation or numerical manipulation of audio signals, it is of primary importance to have both theoretical and practical tools to model this complexity in systematic terms.
Sound Synthesis

The possibility of generating sounds from computers has had an extraordinary impact on the way in which the musical message is created and enjoyed. In 1962 Mathews et al. conclude their article Musical Sounds from Digital Computers [1], by stating that.
Spatial Data Analysis Using Gaussian Markov Random Fields and Gaussian Processes

We will learn about a Gaussian Markov random filed (GMRF), which can be considered as an extension of the state space model, and how to use it to analyze spatial data. It has a wide range of application and can be applied to one-dimensional data, two-dimensional grid data type, and geospatial map data. Later, we will see how a Gaussian process (GP) can be considered as a generalization of a GMRF. A GP can represent smooth functions, and usually gives high prediction performance.
Physical Modeling

One of the most important issues in sound synthesis techniques, as we saw in the previous chapter, is the dynamic adjustment of parameters related to the perceived acoustic quality or acousticity of the produced sound. Any musician considers important not only the timbre, but also the so-called playability of the instrument.
Finite state transducer based light-weight cryptosystem for data confidentiality in cloud computing

Cloud computing is derived from parallel, cluster, grid and distributed computing and is becoming an advanced and growing technology. With the rapid growth of Internet technology and its speed, the number of users for cloud is growing enormously and also huge amount of data is also being generated. With the growth of data in cloud, the security and safety of data, like data confidentiality and privacy' is paramount issue because data plays vital role in the current trend. This paper proposes new type of cryptosystem based on the finite state transducer to provide data confidentiality for cloud computing. The paper presents the protocol communication process and gives an insight into security analysis on the proposed scheme. The scheme proves that, it is stronger and more secure than the existing schemes that can be derived from results as proof of concept.
Mobile Application Code Generation Approaches: A Survey

With the extensive usage of mobile applications in daily life, it has become crucial for the companies of software to develop applications for the most popular platforms such as Android and iOS in the shortest possible time and at the lowest possible cost. However, ensuring consistent UIs and functionalities among cross-platform versions can be challenging and costly since different platforms have their own UI controls and programming languages. Also, when cross-platform tools are used, it is always time consuming to learn a new language. Many solutions were proposed to achieve the native performance. In this paper, two categories are surveyed: Code generation and code mapping which is also known as code-to-code conversion. The code generation approach is concerned with generating user interfaces for applications utilizing deep learning and image processing techniques. Meanwhile, the code-to-code mapping method maps native code to the appropriate platform. In addition, proposals for further investigations are made.
Refined-Para Forming Question Generation System Using Lamma

The task of producing questions from a given material is known as Question Generation (QG). Its goal is to produce natural and meaningful queries. Existing QG techniques, on the other hand, frequently overlook the complex text structure that may complement the simple word sequence. Meanwhile, exposure bias and inconsistency between train and test measurement are well-known drawbacks of cross-entropy-based training. To solve the concerns, we offer a unique approach that integrates a Semantics classification discriminator into the informative para-formation process, allowing for better exploitation of passage information and a better comprehension of the passage's internal structure. We put our QGWVSL model to the test on a well-known QG Dataset to see how it performs. Extensive experimental results showed that when compared to existing models based on the public Dataset, the proposed QGWVSL may achieve a large increase in BLEU score, and it consistently outperformed all evaluated baseline models, including state-of-the-art (SOTA) approaches.
Intelligent Evaluation Framework of English Composition Based on Intelligent Algorithm

The English composition intelligent evaluation system is a tool for automatic writing scoring using computer and natural language processing technology. At present, more and more practical English composition intelligent evaluation systems have been put into use, which can reduce a large number of teachers and evaluation agencies. Manual repetition of labor realizes the scale of composition correction. This paper aims to study the intelligent evaluation framework of English composition based on intelligent algorithm. This paper researches and integrates the features of word vector, paragraph vector, part-of-speech vector, and LDA. This paper researches and integrates the characteristics of word vector, paragraph vector, part-of-speech vector, and LDA, and uses the kNN semantic algorithm similar to the model to obtain the abstract tag label; The regression model of XGBoost calculates the evaluation scores of English compositions; and uses the summaries of 900 English students as samples to construct an intelligent composition evaluation system. Experiments show that the user satisfaction of the English composition intelligent evaluation framework constructed in this paper has reached 100%, and the mean square error is about 10, the error is small, which meets the needs of intelligent evaluation system.
EFFICIENT REMOVAL OF HEAVY METALS BY PHYTOREMEDIATION TECHNIQUES

Direct and indirect disposal of different wastes either due to some accidental spillage or due to practicing of sewage sludge to agricultural fields for diverse reasons in the water reservoirs contributes towards contamination of our ecosystem. Physical removal or immobilization is required for making soil and water contaminant-free from such kind of heavy metals. The Earth crust is mainly composed of these heavy metals and as they are non-degradable in nature, so there is a greater risk of their entrance into the food web and lead to various health hazards. Phytoremediation is an innovative, environment friendly, cost-effective, and aesthetically pleasing approach to remove/immobilize heavy metals. Processes mainly involves the detoxification, removal, or stabilization of retentive pollutants via utilization of vegetation and is a green environmental-friendly tool for cleaning polluted soils. It is a broad-spectrum remediation mechanism in which several processes are involved as mentioned here includes phyto-stabilization, rhizo-filtration, rhizo-degradation, phyto-degradation, phyto-extraction, and phyto-volatilization. Use of aromatic non-edible plants is sustainable and the best treatment approach for the elimination of toxic metals. These plants are not removed directly by humans or animals like non-aromatic edible crops such as cereals, pulses, or vegetables. This research is entirely based on qualitative analysis with descriptive approach. The crops like mustard plant, sunflower, rapeseed etc. are effectively put in an application at sites with superficial contamination of organic or inorganic contaminants via the above-mentioned six techniques. Phytoremediation is a reliable reclaiming treatment and hence the most demanding remediation technology in the world.
Differences of Mean Dependency Distances of English Essays Written by Learners of Different Proficiency Levels

This study investigates the differences in the mean dependency distances (MDDs) of the English essays in a learner corpus, focusing on the different proficiency levels of learners, and the different dependency types. This study is based on the following three assumptions. Firstly, the MDDs of learners' production increase as proficiency levels increase. Secondly, there is an upper limit over which MDDs do not exceed, as predicted by the Dependency Distance Minimization principle. Finally, different types of dependencies show different tendencies across learners of different proficiency levels. This study attempts to verify these assumptions with substantial learner corpus data, categorized into subcorpora according to learner proficiency. Corpus analyses yield results that support these assumptions. These results are expected to constitute a prerequisite for employing the MDD of an individual learner's production to evaluate his or her proficiency level.
Marine geophysical research in the first Ukrainian Antarctic expeditions: historical outline and some results

The article is devoted of the 25th anniversary of the First Ukrainian Marine Antarctic Expedition (1997). The short history of development the marine geophysical study of the deep structures of the World Ocean and Antarctica in the S. I. Subbotin Institute of Geophysics (National Academy of sciences of Ukraine) is presented. Main focus is on the results obtained during the implementation of the «State research programs in Antarctica». The main patterns of geophysical anomalies and their spatiotemporal distributions in West Antarctica were investigated. The lithosphere deep structured geophysical models well corresponds with the evolution processes of the continental margin of the West Antarctica. The built-depth sections record the large-scale processes of the formation of the Drake Passage and the western part of the Scotia Sea as a result of the primary crust fragments’ transformation and the geodynamic evolution of the region located between South America and West Antarctica. Some results for the assessment of mineral resources and hydrocarbon potential of the continental margin structures of the NW area of the Antarctic Peninsula are presented.
Thwart and Safeguard of Cyber Crime &amp; Cyber Attack in Computer Networks

The Cyber is the term closely relating to or characteristic of the culture of computers or computer networks, information technology, and virtual reality. The Internet is a global network of billions of computers and other electronic devices using standardized communication rules or protocols. A total of 5.07 billion people around the world use the internet today – equivalent to 63.5 percent of the world's total population. Internet users continue to grow too, as of 2022, China had over one billion internet users, more than any other country in the world. India ranked second, as close to 933 million Indians accessed the internet via any device. Cybercrime is criminal activity that either targets or uses a computer, a computer network or a networked device. The slogans used to address data privacy are: “Data privacy belongs to you.” Lock it down, protect it, and block hackers. This paper reveals the issues related to categories &amp; impact of cyber crime, basic cyber laws, security threats and protection which are a warning alarm to internet users.
Randomized block-coordinate adaptive algorithms for nonconvex optimization problems

Nonconvex optimization problems have always been one focus in deep learning, in which many fast adaptive algorithms based on momentum are applied. However, the full gradient computation of high-dimensional feature vector in the above tasks become prohibitive. To reduce the computation cost for optimizers on nonconvex optimization problems typically seen in deep learning, this work proposes a randomized block-coordinate adaptive optimization algorithm, named RAda, which randomly picks a block from the full coordinates of the parameter vector and then sparsely computes its gradient. We prove that RAda converges to a δ-accurate solution with the stochastic first-order complexity of O(1/δ2), where δ is the upper bound of the gradient’s square, under nonconvex cases. Experiments on public datasets including CIFAR-10, CIFAR-100, and Penn TreeBank, verify that RAda outperforms the other compared algorithms in terms of the computational cost.