Reviewerly/exHarmony huggingface dataset:
Fuzzy Neighbors and Deep Learning-Assisted Spark Model for Imbalanced Classification of Big Data

Big data is important in knowledge manipulation, assessment, and prediction. However, extracting and analyzing knowledge through big database are complex because of imbalance data distribution that leads to wrong decisions and biased classification outputs. Hence, an effective and optimal big data classification approach is designed using the proposed Bird Swarm Deer Hunting Optimization-Deep Belief Network (BSDHO-based DBN) algorithm based on spark architecture that follows the master and slave nodes. The proposed BSDHO is obtained by combining Deer Hunting Optimization algorithm and Bird Swarm Algorithm. The developed model poses two nodes, namely slave and master node. The training data is initially given to the master node in the spark architecture to perform transformation of data. Here, the transformation of data is done with an exponential log kernel, and then selection of feature is done with sequential forward selecting for choosing suitable features for enhanced processing. Consequently, oversampling process is performed with Fuzzy K-Nearest Neighbor (Fuzzy KNN) in the slave node using selected features to manage imbalance data. Then, in master node, classification is done with Deep belief Network, and trained using developed Bird swarm Deer Hunting Optimization (BSDHO) algorithm. On the other hand, the test data is taken as input, and is fed to the slave node to perform data transformation. Then, the transformed data is given to the master node for classification based on the proposed BSDHO. At last, the training data and testing data output produced the classified output. The proposed BSDHO-based DBN provided enhanced outcomes with highest specificity of 97.92%, accuracy of 96.92%, and sensitivity of 96.9%.
Evaluation of Coal Reserve Reliability in the Nui Beo Mine, Quang Ninh Province Based on the Statistical and Stable Random Function Methods

The Nui Beo is a famous coal mine in the Hon Gai coal zone, Quang Ninh province, where contains many coal resources in Vietnam. In this study, synthesizing and processing data of the mining geological parameters by using the stable random function and statistical methods are used to evaluate the coal reserve reliability in the Nui Beo mine. The results show that the coal reserve reliability depends on geological conditions, especially in the distances between exploration works and parameters of calculating coal reserves (i.e., thickness, density, and declination angle on coal seams). Most exploration efforts in the Nui Beo mine satisfy the suitable pattern exploration grid for reserve level 122, according to the stable random function used to calculate the affected range. Statistical models based on a confidence probability of 0.95 are used to evaluate coal reserve dependability in the research region, resulting in ± 10.12 ÷ ± 16.46% of coal reserve errors. Estimating coal resources during the exploratory process is sufficient for the exploitation design. The findings also give an overview of the parts of coal resource dependability that have been impacted and a foundation for assessing the coal resource error in the Nui Beo mine. These can be used on other coal resources with general comparable geological and mining conditions.
Parallel, Distributed Model Checking of Composite Web Services with Integrated Choreography and Orchestration

Business Process Execution Language (BPEL) specification is transformed into communicating finite state machines (CFSMs) specification for interacting, synchronizable web services. Computational distributed tree logic (CDTL) logic over a set of Kripke structures, called communicating minimal-prefix machines (CMPMs) which together constitute sum machine was proposed in previous work. Sum machine has a much better scalability of variety of peers as opposed to the more traditional product machine. This paper discusses about using sum machine and CDTL logic to model-check a web-service specification by integrating both choreography and orchestration in a modular fashion with bottom-up approach. We do a couple of case studies of Virtual Travel Agency (VTA) protocol and Fresh Market Update (FMU) service and illustrate the model-checking procedure on these protocols to verify the synchronizability and reachability properties of the protocol in an efficient manner, with parallel, distributed algorithm incurring polynomial time complexity.
Physicochemical Characteristics of the Middle Triassic Limestone in Ha Nam Province, Vietnam and the Ability of Adsorption of Heavy Metal Ions from Aqueous Environments

Calcium carbonate rocks of middle Triassic age formations account for a large amount, up to 25% of Vietnam’s limestone potential. Ha Nam is one of the areas with large reserves of middle Triassic limestone of the Dong Giao Formation, up to billions of m3. Dozens of limestone quarries have been licensed to exploit the area for different applications such as cement, lime, light powder, and common building materials. The research on limestone as a raw material in the treatment of contaminated water is still limited. In this study, the middle Triassic limestone sample (Dong Giao formation) from Ha Nam area is used to determine physicochemical characteristics and then to investigate the ability to remove Pb2+ ions from an aqueous solution. The analysis results show that the limestone in the study area is of good quality. The mineral composition is mainly calcite and the CaO content is up to 92–100%. Other properties of limestone such as physical and radioactive properties indicate that the limestone here can be used safely for different fields. For environmental treatment, different conditions such as contact time, solution pH, adsorbent weight and initial concentration of Pb2+ were tested. The results show that experimental conditions of pH0 = 6.2 and a temperature of 30 °C, with 0.1 g of limestone material and an initial concentration of Pb2+ of 50 mg/L, the Pb2+ adsorption can reach equilibrium after about 40 min and efficiency reached 98.88%. The adsorption process follows the Langmuir adsorption isotherm model with the maximum monolayer adsorption capacity of 76.22 mg/g and follows the pseudo-second-order kinetic equation. The result indicate that unmodified the Triassic limestone powder has a significant potential for the removal of heavy ions from an aqueous solution.
Licensing and Usage Rights of Language Data in Machine Translation

Machine translation (MT) is special in that it heavily relies on data. In rule-based MT, an engine performs the translation task by using language resources such as dictionaries and grammar rules, usually written by experts, but sometimes learned from monolingual or bilingual text. Corpus-based (statistical and, more recently, neural) MT leverages large amounts of monolingual and sentence-aligned bilingual text. Clearly, MT programs using these data are works of creation that may be copyright-protected, but this chapter focuses on data. Human labour, and therefore, creative authorship of works, is present in all forms of MT data: monolingual text has been authored, parallel text has been translated and aligned, and rules and dictionaries have been written by experts. Since its conception centuries ago, copyright protects the livelihoods of authors by regulating how copies of these data can be used and how works derived from them are used and published, using instruments such as licences. While the case of dictionaries and grammars as used in rule-based MT is reasonably clear, as they are purposely written for one or another language-processing application, monolingual and parallel text, as used in MT, were not created with MT in mind, and this has led some authors to ask whether authors and translators should get additional compensation for this unintended use of their work to generate new value downstream. This chapter gives an overview of the different sources of data used in MT, discussing authorship along the steps of creating, curating and transforming those data for use with MT, determining the kinds of implicit and explicit licensing schemes that apply to them and how they work. It also describes the controversy surrounding the use of published works to generate new, initially unintended, value through translation technologies and the various ways in which copyright issues are addressed.
The Ethics of Machine Translation Post-editing in the Translation Ecosystem

The metaphor of the translation ecosystem originates from situational models of translation that conceptualise the translation process as a complex system. This includes not only the translator, but also other people—cooperation partners such as clients, project managers, proof-readers or co-translators—their specific social and physical environments as well as their cultural artefacts (Risku, Translationsmanagement. Interkulturelle fachkommunikation im kommunikationszeitalter. Narr, Tübingen, p 19, 2004). These artefacts, understood as objects made or used by humans for a particular purpose, have a high relevance for the translation process and for the translator’s cognition. The artefact group of translation technology includes, among others, tools for terminology and project management, translation memory (TM) systems, alignment software and machine translation (MT) systems (Krüger, Lebende Sprachen 61(2):297–332, 2016a). From the perspective of ecosystemic theories of translation, we are able to include situational factors which are external and internal to the translator and provide a holistic means for the analysis of translation performance. In this respect, the ethics of machine translation post-editing (MTPE) poses a question of central importance, a question that can be addressed from the stance of the ecosystem metaphor. MTPE as an object of study is directly linked to the different developments in MT over time. During the first years of MT, it was largely empirical and focused on MT usability and comprehensibility, with a view to further developing the technology. Eventually, when MT reached a maturity, research interests concentrated on the practicalities of MTPE, with case studies and best practice examples (Garcia, Anglo Saxonica 3(3):291–308, 2012). With the latest developments in neural MT, MTPE is in a “state of terminological flux” (Vieira, The Routledge handbook of translation and technology. Routledge, London, p 320, 2019), comprising different, yet complementary, tasks and procedures: as a separate service with its own international standard, a dynamic activity that goes beyond the static cleaning of MT outputs, and a task associated by default with lower quality expectations. The instability of MTPE as a concept leads to the discussion of human agency in the MTPE process, and the exploration of the extent to which translators are able to intervene in the use of MT in MTPE. Furthermore, the analysis of the different degrees of human control triggers diverse issues in the ethics of MTPE. This chapter explores such issues in the light of the translation ecosystem, analysing three specific ethical dilemmas: (a) Dilemma #1: the post-editor’s status; (b) Dilemma #2: the post-editor’s commitment to quality; and (c) Dilemma #3: digital ethics and the post-editor’s responsibility. Rather than offering a set of closed conclusions, the chapter should be read as an invitation to the reader to think about key ethical elements and the way MTPE is affecting the translator’s work.
A Neural Network Based Approach for Estimation of Real Estate Prices

In this paper an automated approach is proposed for estimation of real estate prices based on historical examples. The approach is based on a neural network and various characteristics of the real estate, under consideration, are used in numerical values as input data. The output data is the prices, as there could be groups of examples according to some restriction as per the time period for selling.
Machine Learning-Based Classification Between Block Cipher and Stream Cipher

Rivest investigated the relationship between Machine Learning and cryptanalysis in 1991 by emphasizing how ideas from each field benefit the other. Encryption in block cipher is done block by block, whereas encryption in stream cipher is done digit by digit, preferably (bit by bit). The proposed research study has utilized a ML-based approach to distinguish between two types of ciphers. To validate the proposed system, block cipher AES and stream cipher Grain are used. The proposed experiment shows that these encryption algorithms are well randomized and do not provide any regularities in the cipher, which a ML method can use to differentiate between them.
None Shall Pass: A Blockchain-Based Federated Identity Management System

Authentication and authorization of a user’s identity are generally done by the service providers or identity providers. However, these centralized systems limit the user’s control of their own identity and are prone to massive data leaks due to their centralized nature. We propose a blockchain-based identity management system to authenticate and authorize users using attribute-based access control policies and privacy-preserving algorithms and finally returning the control of a user’s identity to the user. Our proposed system would use a private blockchain, which would store the re-certification events and data access and authorization requests for users’ identities in a secure, verifiable manner, thus ensuring the integrity of the data. This paper suggests a mechanism to digitize documents such as passports, driving licenses, and electricity bills, issued by any government authority or other authority in an immutable and secure manner. The data owners are responsible for authenticating and propagating the users’ identities as and when needed using the OpenID Connect protocol to enable single sign-on. We use advanced cryptographic algorithms to provide pseudonyms to the users, thus ensuring their privacy. These algorithms also ensure the auditability of transactions as and when required. Our proposed system helps in mitigating some of the issues in the recent privacy debates. The project finds its applications in citizen transfers, inter-country service providence, banks, ownership transfer, etc. The generic framework can also be extended to a consortium of banks, hospitals, etc.
Framework for Multi-factor Authentication with Dynamically Generated Passwords

One of the oldest authentication methods is through a password. The password represents the knowledge element – something you should know to be authenticated. It has been present in scientific publications for many years. Password authentication has been the only method in the early years in which the UNIX operating systems originates. Even nowadays, it can hardly be bypassed. It is still used as a single authentication method in many systems. If there are higher security requirements for a particular scenario, it is used in combination with some other authentication methods and forms two-factor or multi-factor authentications. Usually, the passwords are stored locally on a server, where the users will be authenticated in an encrypted format. Another authentication technique, Time-based One Time Password (TOTP), generates different passcodes, valid for a pre-set short period. The authentication method's cost is zero based on both techniques. Password authentication is one of the oldest and almost universal authentication methods. The paper suggests an approach for dynamically generated passwords without storing them anywhere and presents a framework for authentication based on combining OTP passcode and classical passwords. This framework for authentication can be used in both users’ and P2Ps’ (host-to-host) authentication.
The Ethics of Machine Translation

Language technologies are gradually turning into key modalities of our algorithmic present and future. Real world texts embed patterns and patterns hack natural meaning, following the language structure, word ordering, the underlying values and perceptions of a given semantic agent. Understanding the logic behind computational linguistics and meaning formulation provides the necessary theoretical substrate for ethical screening and reasoning. The present paper aims to map the components and functionalities of a random linguistic system, tracking all stages of semantic rendering throughout the semantic cycle and providing recommendations for ethical design optimisation. We attempt to articulate the information space as a transition process of natural and/or non-natural phenomena into subjective information models, driven by subjective human experience and worldview: To realise the information space as a dynamic space, full of arbitrary features, where the information maker and the information receiver both affect/influence and are affected/influenced by the system. This article is not a practical toolkit towards the application of ethics and further social considerations on Natural Language Processing at large and/or more specifically Machine Translation ones; it is rather a contribution in shaping the appropriate framework within which the Language Technology (LT) stakeholders may identify, analyse, understand and appropriately manage issues of ethical concern. It is a plea for multimodal computational linguistic management, via interdisciplinary groups of professionals, in order to optimise the research around the impact of computational linguistics on human rights, language diversity, self-identity and social behaviour. Language technologies need to articulate complex meanings and subtle semantic divergences. New external metrics need to be added, based on legal, ethical, social, anthropological grounds. This is a necessary puzzle to solve, in order not to discount future structural rewards.
Survey of Schema Languages: On a Software Complexity Metric

Length in Schema (LIS) is a numerical measurement of the schema documents (DS) of extensible markup language (XML) that contain schemas from xml schema language in a manuscript form. LIS likened to source line of codes (SLOC) in software complexity which is used to calculate the amount of effort that will be required to develop a schema document. Different LIS were considered such as Blank Length in Schema (BLIS), Total Length in Schema (TLIS), Commented Length in Schema (CLIS) and Effective Length in Schema (ELIS) for sixty (60) different schema documents acquired online through Web Services Description Language (WSDL) and implemented in two schema languages: Relax-NG (rng) and W3C XML schema (wxs) to estimate schema productivity and maintainability. It was discovered that overall understandability and flexibility of schemas become much easier with less maintenance efforts in rng than wxs.
Count the Number of Steel Bars Based on Deep Learning

Object detection is one of the most fundamental tasks in digital image processing and has been widely applied in industries. Advances in deep learning are accelerating object detection methods. This work presents the end-to-end two-stage object detection method and the steel bars counting application. In the first step, we collect data and labeling. Second, data is trained and fine-tuned by the Faster-RCNN FPN model. Finally, predict the test data from the trained model. Based on the mean Average Precision metric, the steel bars detection result is 67%. The experience shows that this approach is feasible for counting the steel bars.
AI in Cryptocurrency

This study investigates the predictability of six significant cryptocurrencies for the upcoming two days using AI techniques i.e., machine learning algorithms like random forest and gradient for predicting the price of these six cryptocurrencies. The study presents to us that machine learning can be seen as a medium to predict the prices of cryptocurrencies. A machine learning system learns from past data, constructs prediction models, and predicts the output for new data whenever it gets it. Predicted output’s accuracy is influenced by the quantity of data since the more data there is, the better the model can predict the output. The results show that with the accuracy score performance metric, which we employed for this study, we were able to calculate the accuracy of the algorithms and find that both algorithms random forest and gradient boosting respectively performed well for the cryptocurrencies such as Solana (98.07%,98.14%), Binance (96.56%, 96.85%), and Ethereum (96.61%, 96.60%)), with the exception of Tether (0.38%, 12.35%) and USD coin (–0.59%, 1.48%), the results demonstrate that both algorithms work effectively with the majority of cryptocurrencies which can be further increased by using deep learning algorithms like ANN, RNN or LSTM.
Authorship and Rights Ownership in the Machine Translation Era

A translation of a text of any kind is a derivative intellectual work. It involves a transformation of the original text, and therefore it is a right of the holder of that text to authorize (or not) its translation. This is covered in Article 8 of the Berne Convention for the Protection of Literary and Artistic Works of September 9, 1886, and the same Convention tells us in Article 2.3 that “Translations, (…) and other alterations of a literary or artistic work shall be protected as original works without prejudice to the copyright in the original work (…)”. The rights ownership of the translator implies that the translation cannot be used without authorization from the translation copyright owner. These premises are jeopardized with the use of AI systems and the introduction of machine translation. The key issues raised by the new technologies are the ownership of rights over machine translations, and the possibility of using the results associated with translation as data for the improvement of Machine Translation algorithms.
Ethics and Machine Translation: The End User Perspective

This chapter analyses existing research on the ethical implications of using MT in translation and communication, and it describes results from usability experiments that focus on the inclusion of raw and post-edited MT in multilingual products and creative texts with an emphasis on users’ feedback. It also offers suggestions on how MT content should be presented to users, readers, and consumers in general. It finally considers the ethical responsibility of all stakeholders in this new digital reality. If the ethical dimension is an ecosystem, users also have the responsibility to support products that protect language, translators, and future generations.
Factors Affecting the Persistence of Deleted Files on Digital Storage Devices

There is limited research addressing the factors that contribute to the persistence of deleted file contents on digital storage devices. This study presents a digital file persistence methodology applied to study the persistence of deleted files on magnetic disk drives, although the methodology could be applied to other media types as well. The experiments consisted of tracking approximately 1900 deleted files under combinations of different factors over 324 distinct experimental runs. The findings show that the persistence of deleted files depends on several factors, including the number and size of new files written to a disk by the operating system, disk-free space combined with disk fragmentation, and file type. Future work will apply this methodology to broader factors, including other devices and circumstances.