Reviewerly/exHarmony huggingface dataset:
‘We can’t read it all’: Theorizing a hermeneutics for large-scale data in the humanities with a case study in stylometry

Abstract Computational methods often produce large amounts of data about texts, which create theoretical and practical challenges for textual interpretation. How can we make claims about texts, when we cannot read every text or analyze every piece of data produced? This article draws on rhetorical and literary theories of textual interpretation to develop a hermeneutical theory for gaining insight about texts with large amounts of computational data. It proposes that computational data about texts can be thought of as analytical lenses that make certain textual features salient. Analysts can read texts with these lenses, and argue for interpretations by arguing for how the analyses of many pieces of data support a particular understanding of text(s). By focusing on validating an understanding of the corpus rather than explaining every piece of data, we allow space for close reading by the human reader, focus our contributions on the humanistic insight we can gain from our corpora, and make it possible to glean insight in a way that is feasible for the limited human reader while still having strategies to argue for (or against) certain interpretations. This theory is demonstrated with an analysis of academic writing using stylometry methods, by offering a view of knowledge-making processes in the disciplines through a close analysis of function words.
Identifying Voice Individuality Unaffected by Age-Related Voice Changes during Adolescence

Identifying voice individuality is a key issue in the biometrics field. Previous studies have demonstrated that voice individuality is caused by differences in the shape and size of the vocal organs; however, these studies did not discuss voice individuality over a long term that includes periods of voice change. Therefore, we focus on adolescence (early teens to early twenties), which includes voice changes due to growth of vocal organs, and we reveal invariant voice individuality over a long period. In this study, the immature and mature periods during vocal organ development were defined as unstable and stable periods, respectively. We performed speaker verification tests across these two periods and evaluated voice features that are common to these periods using Fisher's F-ratio. The results of the speaker verification test demonstrated a verification accuracy of 60% or more in most cases, and the results of the evaluation using Fisher's F-ratio demonstrated that robust voice individuality existed in the frequency regions of 1-2 kHz and 4-6 kHz regardless of the period. These results suggest that voice individuality is unaffected by age-related changes over the long term, including adolescence.
On the analysis of sequences generated by S-boxes cascaded with σ-LFSRs

A σ-LFSR is a word-oriented LFSR which exploits the parallelism offered by the modern processors. σ-LFSRs having primitive characteristic polynomials are known as primitive σ-LFSRs. Vector sequences generated from a primitive σ-LFSR have desirable randomness properties. However, such sequences are marred by their low linear complexity. To overcome this shortcoming, nonlinear S-boxes can be used in conjunction with primitive σ-LFSRs. In this work, properties of the sequences generated by such schemes have been analyzed. We also have shown that such sequences retain some of the above mentioned desirable randomness properties. Further, we show that such an arrangement can bring about a significant increase in linear complexity and find an upper bound for the linear complexity of these sequences.
Ob.EMMA: A pioneer observatory of electronic music and media art in Portugal

The Observatory of Electronic Music and Media Art (Ob.EMMA) is a pioneer observatory created in 2019 for monitoring, mapping, diagnosing and promoting the investigation of electronic music and media art in Portugal. Ob.EMMA is part of the ID research center CITCEM_Center for Transdisciplinary Research on Culture, Space and Memory. Given its hybrid nature, this observatory includes artists and academics from various institutions and areas, namely, communication sciences, social sciences, technologies, humanities and arts. Ob.EMMA integrates two main lines of research: 1_Electronic Music and 2_Media Art. explored both autonomously and also together.
Pengaruh Model Pembelajaran Think Pair Share (TPS) Terhadap Hasil Belajar Ekonomi Pada Siswa Kelas X Di SMA Kutabumi I Tangerang Banten

Abstract&#x0D; Problem: From observations and preliminary research conducted, information is obtained that the daily test for the field of Economics study is still below the KKM, in this case SMA Kutabumi I Tangerang, West Java set the KKM for the field of Economics study is 70. However, the average student learning outcomes X for the first daily test reaches a value of 60.22 so 40% of students must take remedial. &#x0D; Purpose: To determine the effect of the Think Pair Share Model on Economics Learning Outcomes in Class X Students at SMA Kotabumi Tangerang, Banten. &#x0D; Methodology: Methodology This research uses quantitative methods which are carried out through the experimental method, namely by giving different treatments to the control class and the experimental class, then from the treatment, the learning outcomes that may be achieved by the two groups will be compared. &#x0D; Results/Findings: learning through Think Pair Share model has an average learning outcome of 85.26, while the lowest score is 73.00 and the highest score is 97.00. This value is higher than the achievement of student learning outcomes who are given learning through Problem Based Instruction (PBI) where the average value of learning outcomes is 63.68 and the lowest score is 57.00 and the highest score is 73.00. &#x0D; Paper type : Eksperiment. &#x0D; Keywords: Think Pair Share Model, Economic Learning Outcomes,
A Hybrid TLBO-TS Algorithm Based Mobile Service Selection for Composite Services

Service selection for composite service has been a hot research issue in service computing field. With the proliferation of mobile devices, service selection confronts new challenges in the mobile environment due to the mobility, unpredictability, and variation of signal strength of mobile networks, since quality of service (QoS) is closely related to these factors. In this work, we aim to address the problem of mobile service selection for composite service in terms of QoS. Specifically, based on the mobility model and mobility-aware QoS computation rule, we propose a hybrid service composition optimization algorithm, named TLBO-TS, by integrating Teaching-Learning-Based Optimization (TLBO) algorithm and Tabu Search (TS) algorithm. Through the optimization of service selection with TLBO-TS algorithm, the global QoS of the generated mobile service composition is approximately optimal. Extensive experiments are conducted and the experimental results show that the proposed approach can derive more optimized mobile service composition with acceptable scalability compared with the traditional approach and other baselines.
Drilling a Large Corpus of Document Images of Geological Information Extraction

Geo-energy is a resource widely available on Earth that consists in using the first 10–100 meters below the surface where the temperature is low and constant during the year. This 12 to 15 \(^\circ \)C is ideal for heat pumps to provide heat and cold with an excellent coefficient of performance. Despite a very high potential, this resource is not often integrated in France. One of the main reasons is the lack of knowledge of rocks capacity to deliver sufficient power on surface. More than 2 millions of scanned documents are available on the french geological survey. We propose a way to classify and analyze them in order to quantify the underground resource.
Similarity ranking using handcrafted stylometric traits in a swedish context

In this paper we introduce a new type of handcrafted textual features called stylometric traits, used to create a stylistic writeprint of an author's writing style. These can be divided into four categories: (i) word variations, (ii) abbreviations, (iii) internet jargon, and (iv) numbers.
Wavelet Threshold Image Filtering Based on Improved Fruit Fly Optimization Algorithm

Wavelet threshold de-noising is an effective method for eliminating noise component from original image. However, traditional wavelet threshold methods have low adaptability to mixture-noise images because of the difficulty in selecting threshold. To overcome the drawback, a novel wavelet threshold de-noising method for mixture-noise image based on the improved fruit fly optimization algorithm (IGFOA) is proposed in this paper. The adaptive step distance range following Gaussian distribution, instead of the uniform distribution in basic fruit fly optimization algorithm (FOA), is applied to enhance convergence rate and the ability to jump out from local optimum throughout iterations. Then the IGFOA is used to select the optimal threshold for wavelet threshold de-noising. Finally, some simulation examples and application are shown in this paper to verify the effectiveness and the adaptability of the proposed method.
Invention of a computer "Kyiv" architecture using a concept of Addressed Programming Language

The article is devoted to the history of the origin of high-level programming in Ukraine. The transition from calculations by arithmometers and logarithmic rulers to solving problems on the computer "Kyiv" using pointers and tree-like formats (abstract data types are analogous) is described. The factors that contributed to this transition include: the experience of providing instructions for calculations by arithmometers, and the experience of programming on MESM. As a result a computer "Kyiv" has been developed with a hardware-implemented possibility of high-level programming, invention of the Addressed Programming Language with indirect addressing (pointers), tree formats and declarative capabilities. Hardware-realized demining of pointers in the computer "Kyiv" is one of the outstanding inventions of Ukrainian engineers and mathematicians at the initial stage of the information technologies development. At that time it was significantly ahead of the world technologies. Programming in computer "Kyiv", unlike Plankalkül, could identify and process complex structures. The paper describes the individual applications of the Address Programming Language, which was implemented on many Soviet-made computers and has been used by programmers for more than 20 years. Due to the so-called "Iron Curtain", scientists in the field of programming outside the post-socialist world still do not know about the invention of pointers by Kiev scientists. A textbook describing the Addressed Programming Language was translated into many languages. A monograph with a description of the computer architecture "Kyiv" and of the Addressed Programming Language was translated into English and published in the United States in 1966.
Implemetnasi Metode Fuzzy Associative Memory Dalam Menentukan Tingkat Kepuasan Pelayanan Di Perpustakaan

Creating satisfaction with the library is certainly not easy, librarians must be able to provide quality services, both directly and indirectly to students, good service can certainly be of more value than librarians to students. less friendly in serving users who need information. For this reason, a Decision Support System that applies Fuzzy Associate Memory was developed to determine the level of satisfaction of library services at STMIK Triguna Dharma. In general, Decision Support Systems are computer-based information systems, including knowledge-based systems that can be used in making decisions in organizations or companies. The level of user satisfaction makes it easy for STMIK Triguna Dharma in evaluating the extent to which the quality they provide in the library is related to data on library facilities, services, and convenience. is quite satisfying.
Night Vision Bot using Dynamic IR and Object Detection

Robotics as an extension to the field of Engineering has been of great support to the evolution of mankind. Through robotics, even complex tasks can be simplified through simple automation and decreased human intervention. Robots even reduce the workload on humans and increase the level of efficiency and accuracy in doing any task. Robotics has branched out and helped in the development and evolution of many fields such as agriculture, defense, parenting, healthcare, industry, aerospace and, so on. The robots which are used for survey or exploration can be used for many purposes in the fields of Wildlife, Industry and, Defense. These robots are capable of going to the areas which are vulnerable for humans, and they can be controlled from a reasonable distance. This robot due to its small size and in-built night vision camera makes it a very useful tool for surveillance and exploration. This robot has the ability to be controlled from a distance and it will enable the users to view objects clearly at night (or in darkness). These types of robots have been in existence for a couple of years, only for new developments to be added to them. The very recent development involves the integration of IoT (Internet of Things) to control the robot remotely and obtain information from it. The latest addition however is the addition of a new concept, known as Dynamic IR, which involves the control of the intensity of the IR light to produce better images. Moreover, with the addition of Object Detection using Machine Learning Algorithms, it can predict what type of obstacle it may face.
Post-hoc Counterfactual Generation with Supervised Autoencoder

Nowadays, AI is increasingly being used in many fields to automate decisions that largely affect the daily lives of humans. The inherent complexity of these systems makes them so-called black-box models. Explainable Artificial Intelligence (XAI) aims at solving this issue by providing methods to overcome this lack of transparency. Counterfactual explanation is a common and well-known class of explanations that produces actionable and understandable explanations for end-users. However, generating realistic and useful counterfactuals remains a challenge. In this work, we investigate the problem of generating counterfactuals that are both close to the data distribution, and to the distribution of the target class. Our objective is to obtain counterfactuals with likely values (i.e. realistic). We propose a model agnostic method for generating realistic counterfactuals by using class prototypes. The novelty of this approach is that these class prototypes are obtained using a supervised auto-encoder. Then, we performed an empirical evaluation across several interpretability metrics, that shows competitive results with the state-of-the-art method.
An Intelligent Allocation Mechanism Based on Ethereum Blockchain in Microgrid

The data security and the efficiency of energy scheduling are two main challenges for the practical application of microgrid. The existing researches have disadvantages of a single system component, unreasonable scheduling, and the lack of analysis on the main grid. In light of this, we formalize an efficient microgrid system based on Ethereum blockchain. We first use the blockchain network to upload the IoT data and predict the total load in some region and the energy generation of renewable energy, and then complete the deployment of energy based on the predicted results under the restrictions of relevant resources. In particular, we introduce a credit bidding mechanism that is based on Etheruem smart contracts to optimize energy allocation. It maximizes the proportional-fairness participation of all parties and avoids the waste of energy. In addition, we adopt group signature to preserve the privacy of user data. Simulation results show that the proposed scheme can significantly reduce users’ cost, increase the profit rate, enforce proportional fairness, and improve the operation stability of the main grid.
Using Fuzzy Cognitive Maps to Explore Interactions between Factors Impeding Rural Green Housing Development in China

Under the strategy of rural revitalization, China has a considerable demand for building rural green housing, and the self-built mode of rural housing is in favor of green building (GB) development. However, the GB development in rural housing still remains in demonstration phase within recent decades. Current studies of GB development in rural housing has been overlooked. This research fills this gap by exploring the interactions among the factors that hinder the GB development in rural housing via the method of fuzzy cognitive maps (FCM). 15 factors were identified and the interrelationships between factors has been visualized. Six central factors were found as well as a vicious cycle consisting by economic factors, social factors and technological factors. Facilitating the GB development in rural self-built housing needs to break the vicious cycle, which should start with the transmitters, namely policy and regulations, and self-management of housing construction. The effects of related inventions were examined by FCM computation. The utilization of FCM helps to deeply understand how these factors interact with each other, and the results provides a valuable reference to both policy makers and industry practitioners to facilitate the development of rural green housing.
Ensuring Security of User Data Using Federated Learning for the Sentiment Classification

Federated Learning, which we can name as Secured Learning, is the latest entrant into the domain of Machine Learning. It is a secure way to train machine learning models on user’s private data without compromising their privacy. This article demonstrates federated training of sentiment140 dataset with applications in sentiment classification. In this proposed approach, the model that is to be trained on a particular user’s data is sent into that user’s mobile device. This is different from the conventional approach because training takes place on the user’s device rather than the cloud. Moreover, the data of the user is completely encrypted before getting used for training. After the training is completed, essential statistics are uploaded back to the cloud, and the model is deleted from the user’s device. The model trains on different devices under feasible conditions, i.e., the device is plugged in and connected to the internet and gets updated each time. Hence, this approach is novel because it preserves data privacy which has become a burning issue nowadays. To implement the proposed approach, we used different Neural Network architectures and compared their performances, including variants such as Feed Forward, Convolutional, Recurrent, etc. We used Tensor flow’s Federated Core package to design these architectures, train models, and simulate Secured Learning. For visualizing the simulation of the entire backend, we developed a dashboard-like web interface. This dashboard displays how training on various devices occurs and how the model will constantly learn and get updated by user data.
ArEnAlg: A Pedagogical Mobile Application for Algorithms Linguistics Based on a Semantic Bilingual Ontology

Abstract Ontologies are knowledge structures that serve as the foundation for semantic applications. These semantic artifacts organize and classify knowledge to define the semantic relations among the concepts. Some ontologies are constructed for a single natural language, whereas others are built for dual or multi-natural languages. The goal of this study is to design and implement a mobile educational application to help students learn more effectively and to increase the learner’s acquisition of computational linguistics in the Algorithm domain. The methodology is to develop an application with unique features that can display the terms and the concepts in both Arabic and English languages in two different layouts: a list form and a flashcard form. The created app “ArEnAlg” can generate the terms in the algorithm domain. The major objective of the exercise modules is to examine the user’s knowledge of the concepts in the domain of algorithms. The evaluation results tested three factors: the learner’s enjoyment, the acquisition of the computational linguistics, and the application presentation, which showed that behavioral responses support the purpose of the developed application with a max-min range of 99% -81%.
Reusable Group Fuzzy Extractor and Group-Shared Bitcoin Wallet

Abstract In this paper, we propose a novel cryptographic primitive named reusable group fuzzy extractor (RGFE) allowing any member of a group to extract and reproduce random strings from a fuzzy and non-uniform source of high entropy (called fingerprint). Any group member can anonymously generate a random string for the group using his fingerprint and can be traced when needed, whereas other members can reproduce the string using their own fingerprints. Moreover, a fingerprint can be repeatedly used to generate multiple random strings. Basing on RGFE, we present group-shared Bitcoin wallet, which can be used by a group of users to receive or spend coins via biometrics in a traceable way.
Non-staff Detection for End-Intrusion in Railway Station Platform Based on YOLO and DCF Techniques

With more and more people choose to travel by EMU train, several passengers in railway station with some purposes, for example curiosity or free-tickets, invade the railway line at the end of platform occasionally. This behavior threatens the normal railway operation directly. Therefore, to deal with the difficulties caused by illegal end-intrusion occurring at platform, this paper introduced a method based deep learning for detecting end-intrusion for non-staff using surveillance video. The proposed framework utilizes YOLO v4 to segregate non-staff from the background and Discriminative Correlation Filter (DCF) approach to track the identified non-staff related to end-intrusion with the help of bounding boxes and assigned IDs. Firstly, this paper contributes a high-quality Staff classification dataset from railway stations, named Railway Station Staff Dataset (RSSD, a total of 35262 images including 8 categories). It raises a brand-new practical challenge of image data of railway station. Then, the RSSD were used to finetune pre-trained model. Finally, From the experimental result, it is observed that the YOLO v4 with DCF techniques demonstrated good performance to monitor the end-intrusion at platform. Furtherly, the economic and feasibility assessment are finished. It indicates that the proposed method can be utilized in practical economically and real-time.
Need, Role, and Impact of Blockchain in the Manufacturing and Logistics Industries

Blockchain technologies have lately risen to the top of the academic and industry agendas, owing to their potential advantages across a wide range of sectors. This is due to their practical skills in resolving many problems that are presently impeding progress in different industrial sectors. These problems include securely capturing and exchanging transactional data, creating automated and efficient supply chain procedures, and improving transparency throughout the whole value chain. Blockchain provides an effective method to address these problems using distributed, shared, secure, and permission transactional ledgers. The uses of blockchain technology in the manufacturing and logistics sectors have been examined in this chapter. The study shows many possibilities for using blockchain in different industrial sectors; nevertheless, certain obstacles must be solved before this technology can be fully used. This chapter also covers case studies and difficulties encountered in the industrial and logistics sectors while using blockchain.