Reviewerly/exHarmony huggingface dataset:
Heisenberg uncertainty of spatially gated electromagnetic fields

A Heisenberg uncertainty relation is derived for spatially-gated electric and magnetic field fluctuations. The uncertainty increases for small gating sizes which implies that in confined spaces the quantum nature of the electromagnetic field must be taken into account. Optimizing the state of light to minimize the electric at the expense of the magnetic field, and vice versa should be possible. Spatial confinements and quantum fields may alternatively be realized without gating by interaction of the field with a nanostructure. Possible applications include nonlinear spectroscopy of nanostructures and optical cavities and chiral signals.
Development of Interactive Dashboards and Intelligent Data Analytics for Visual Decision-Making in The Underground Mining Environment: The Sterkfontein Cave Case Study

In the 21st century, the advancements of computing, Internet of Things and wireless communications are enabling mining companies to collect a vast amount of datasets during risk management. Multipurpose sensors installed at many locations in underground mining environment provide data about the safety, security and mine conditions in near realtime. However, it is more important to convert this data into strategic intelligence, which means the communication of the right information to the right people at the right time to enable them to make optimal and informed decisions on risk control and process optimization. The Sterkfontein caves is a world heritage site, and over the past few years, some deformation was recorded inside the caves. Possible reasons include urbanization in the surrounding area, illegal dolomite mining in the past, weathering, man-made archaeological activities and continuous pouring of polluted and acidic water into the caves system. Acid water has high potential to dissolve the sensitive dolomite, create new and enlarge existing rock cavities, which can destabilize rock formations. The Wits Mining Institute (WMI) has installed a geotechnical and geo-environmental wireless sensor network inside the caves for monitoring and to gain a better understanding of deformation in a cave system. In this research, data analytics and visualization concepts have been applied to the data collected from the sensors in order to identify the trends or patterns inside the caves. Several dashboards have been developed to analyze and visualize the ground movement, wind velocity, temperature, humidity and toxic gases (carbon monoxide) using Tableau code. The data from ground movement monitoring sensors placed at different locations can be analyzed and visualized in relation to each other and in different time steps (minutes to hours, daily, weekly, monthly and/or yearly basis). The analytics can also be applied to the aggregated data based on seasonal, time and/or location variation. The results of this study showed that interactive intelligent dashboards are key to interpret underground data insights and to provide the deep understanding required for smart, near real-time decision-making.
Optimization Technology of Human Resources Management (HRM) Education Resources Based on Hadoop Cloud Storage Architecture

In order to solve the problems of poor throughput and low storage efficiency of existing HRM education resource management technology, Hadoop cloud storage architecture is designed to optimize HRM education resource management technology. This paper uses Hadoop cloud storage architecture to manage HRM education resources, and obtains the cloud structure of HRM education resources. On this basis, the HRM education resource data center is constructed by using cloud computing technology. Based on the HRM education data center, and based on the Hadoop cloud storage architecture, the HRM education resources are uploaded and downloaded, realizing the management and optimization of HRM education resources. The simulation results show that compared with the existing HRM educational resource management technology, the proposed HRM educational resource management optimization technology greatly improves the throughput and storage efficiency of educational resources, which fully demonstrates that the proposed HRM educational resource management optimization technology has better performance.
Building the Turkish FrameNet.

FrameNet (Lowe, 1997; Baker et al., 1998; Fillmore and Atkins, 1998; Johnson et al., 2001) is a computational lexicography project that aims to offer insight into the semantic relationships between predicate and arguments. Having uses in many NLP applications, FrameNet has proven itself as a valuable resource. The main goal of this study is laying the foundation for building a comprehensive and cohesive Turkish FrameNet that is compatible with other resources like PropBank (Kara et al., 2020) or WordNet (Bakay et al., 2019; Ehsani, 2018; Ehsani et al., 2018; Parlar et al., 2019; Bakay et al., 2020) in the Turkish language.
Efficient Implementation of the CKKS Scheme Using a Quadratic Residue Number System

the use of homomorphic encryption to ensure computational security imposes additional overhead associated with encryption and decryption of data. One of the mechanisms that can be used to increase the data coding rate is the quadratic residue number system. The quadratic residue number system reduces the number of decimal multiplications by half when multiplying two complex numbers. However, the use of a quadratic residue number system imposes a number of restrictions related to the representation of a number. In this article, we propose a mechanism for choosing the parameters of a quadratic residue number system and develop an efficient data encoding algorithm.
URLCam: Toolkit for malicious URL analysis and modeling

The World-Wide Web technology has become an indispensable part in human’s life for almost all activities. On the other hand, the trend of cyberattacks is on the rise in today’s modern Web-driven world. Therefore, effective countermeasures for the analysis and detection of malicious websites is crucial to combat the rising threats to the cyber world security. In this paper, we systematically reviewed the state-of-the-art techniques and identified a total of about 230 features of malicious websites, which are classified as internal and external features. Moreover, we developed a toolkit for the analysis and modeling of malicious websites. The toolkit has implemented several types of feature extraction methods and machine learning algorithms, which can be used to analyze and compare different approaches to detect malicious URLs. Moreover, the toolkit incorporates several other options such as feature selection and imbalanced learning with flexibility to be extended to include more functionality and generalization capabilities. Moreover, some use cases are demonstrated for different datasets.
LibSWIFFT - A fast C/C++ Library for the SWIFFT Secure Homomorphic Hash Function

LibSWIFFT is an open-source, production-ready C/C++ library providing SWIFFT, one of the fastest available secure hash functions that is also collision-resistant. SWIFFT also facilitates post-quantum digital signature schemes and zero-knowledge proofs of knowledge of a preimage (ZKPoKP).LibSWIFFT is optimized for short blocks of input and runs at a rate of less than 5 cycles/byte single-threaded on a modern commodity computer with AVX2.Other software providing SWIFFT, which are not claiming production-readiness as LibSWIFFT is, are the original implementation by the authors of SWIFFT (Micciancio, 2016) and the SWIFFT 8-bit (Karati & Safavi-Naini, 2018b) and 16-bit (Karati & Safavi-Naini, 2018a) AVX2 implementations for the multi-signature scheme K2SN-MSS (Karati & Safavi-Naini, 2019).LibSWIFFT is currently intended to be used by cryptography researchers and developers.It provides clean, easy-to-use C/C++ APIs with high-performance implementations and is welltested and well-documented.Other available implementations of the SWIFFT function do not provide all these benefits.For further details, the reader is referred to the official LibSWIFFT repository (Gvili Tech Ltd, 2021).
Research on the Application of Children’s Reading Analysis Based on Artificial Intelligence——Take Small “raccoon Reading” and “Jiao Jiao Reading” as examples

Abstract Artificial intelligence technology is widely used in the field of education, and the development of children’s Chinese reading field is increasingly mature. Using the boundary awareness of AI algorithm for reference, the Chinese reading class should also awaken the boundary awareness of Chinese subject. In the AI algorithm, only the annotated data are valid data. The reading text can become effective learning materials through natural language processing only when it is “annotated” out of ability points. Through machine learning, AI constantly generates self-learning data sets based on trial-and-error feedback. The feedback mechanism, trial-and-error opportunity and Chinese teaching design are transformed into each other to build student portrait and specifically improve students’ independent Chinese learning ability. This article analyzes the application of artificial intelligence technology in the development of children’s Chinese reading services by taking the examples of [[CHECK_DOUBLEQUOT_ENT]] Small raccoon Reading” and “Jiao Jiao Reading.
Communicating evidence in icons and summary formats for policymakers: what works?

Abstract Policy decisions have vast consequences, but there is little empirical research on how best to communicate underlying evidence to decision-makers. Groups in diverse fields (e.g., education, medicine, crime) use brief, graphical displays to list policy options, expected outcomes and evidence quality in order to make such evidence easy to assess. However, the understanding of these representations is rarely studied. We surveyed experts and non-experts on what information they wanted and tested their objective comprehension of commonly used graphics. A total of 252 UK residents from Prolific and 452 UK What Works Centre users interpreted the meaning of graphics shown without labels. Comprehension was low (often below 50%). The best-performing graphics combined unambiguous metaphorical shapes with color cues and indications of quantity. The participants also reported what types of evidence they wanted and in what detail (e.g., subgroups, different outcomes). Users particularly wanted to see intervention effectiveness and quality, and policymakers also wanted to know the financial costs and negative consequences. Comprehension and preferences were remarkably consistent between the two samples. Groups communicating evidence about policy options can use these results to design summaries, toolkits and reports for expert and non-expert audiences.
Emotional Voice Reconstruction Based on Intonation Alteration

Emotion recognition and voice emotional alteration are major topics in the field of artificial intelligence. Their possible applications include driver's physical state detection, human-machine interaction, monitoring of students, voice training and robotics. There are many models to express a wide variety of emotions felt or expressed by humans. While there is much work regarding emotion detection, our work will focus on creating emotional voice by altering intonation and rhythm by methods such as overlap-add. We succeeded in detecting an emotional intonation pattern and reconstructing it on a same phrase said by a different voice.
BabelFish: Fusing Address Translations for Containers

Cloud computing has begun a transformation from using virtual machines to using containers. Containers are attractive because of their “build once, run anywhere” computing model and their minimal performance overhead. Cloud providers leverage the lean nature of containers to run hundreds of them or more on a few cores. Furthermore, containers enable the serverless paradigm, which involves the creation of short-lived processes. In this work, we identify that containerized environments create page translations that are extensively replicated across containers in the TLB and in page tables. The result is high TLB pressure and redundant kernel work during page table management. To remedy this situation, this article proposes BabelFish, a novel architecture to share page translations across containers in the TLB and in page tables. BabelFish reduces the mean and tail latency of containerized workloads, cold-start effects of function execution, and container bring-up time. This work also advocates for the need to provide more hardware support for containerized and serverless environments.
Digital Image Transmission Using Combination of DWT-DCT Watermarking and AES Technique

The Internet technology brings big revolution in twenty-first century. It facilitates communication between man-to-man, man-to-machine, machine-to-machine, and vice versa. There are many applications, viz. (i) vehicle-to-vehicle, (ii) vehicle-to-infrastructure, (iii) drone communication, etc., which transmit and receive data in various formats like image data, audio data, video data, text data, etc. Bank system uses data communication technique for transferring money through debit card, net banking, credit card, demand draft, cheques, RTGS, NEFT, etc. Bank cheques are cleared through CTS system. For clearing the cheque, it is scanned, and image is transferred to cheque clearing house. During cheque image transmission, there is a need of security, confidentiality, integrity, authorization, copyright protection, and indexing services. There are many techniques and algorithm which provide this facility to overcome this issue. To solve this issue, combination of DWT-DCT watermarking along with AES technique is used. Its performance and analysis against various attacks are also explained in this research paper.
Methods for Handling Rob Floods in the Banger River Basin in Semarang City

Abstract In the area of North Semarang often occurs tidal floods. The current government has dealt with the flooding of Rob by making dykes on the north coast of semarang. There is a method of handling tidal flooding by making a retention pool, Longstorage equipped with a pump if there is a flood the water is stored in a retention pond then the water is pumped into the river. The purpose of this study is to formulate a correlation between pumps and retention ponds. Alternative flood control using the count of 50 years. The method used is using HEC-HMS. There are three alternatives used in the simulation: 1 st alternative pond with an area of 3.8 m 3 and a storage volume of 114,000 m 3 , with inflow of 31.4 m 3 /s in this alternative the capacity of pump is 30 m 3 /s, second alternative pool with an area of 4.7 m 2 and reservoir volume of 142,000 m 3 , with inflow of 31.4 m 3 /s, with pump capacity of 27 m 3 /s. third alternative ponds with an area of 5.0 m 2 and a reservoir volume of 150,000 m 3 , with inflow of 31.4 m 3 /s. In this alternative a pump with a capacity of 25 m 3 /s used.
Research on Behavior Recognition of Wearable Devices Based on Inertial Sensors

With the increasing demand for intelligent interaction and health monitoring, human behavior recognition based on acceleration sensors has attracted extensive attention in the fields of medical care, motion detection, energy consumption assessment and so on. MPU9250 was used to extract acceleration signals and angular velocity signals in human behavior. The recognition results are obtained through the steps of signal preprocessing, feature selection, feature extraction and recognition algorithm. The behavior recognition method based on decision tree is used to identify the stillness, walking, running and jumping.
Content Based Image Retrieval using Multi-level 3D Color Texture and Low Level Color Features with Neural Network Based Classification System

Content based image retrieval (CBIR), is an application of real-world computer vision domain where from a query image, similar images are searched from the database. The research presented in this paper aims to find out best features and classification model for optimum results for CBIR system.Five different set of feature combinations in two different color domains (i.e., RGB &amp; HSV) are compared and evaluated using Neural Network Classifier, where best results obtained are 88.2% in terms of classifier accuracy. Color moments feature used comprises of: Mean, Standard Deviation,Kurtosis and Skewness. Histogram features is calculated via 10 probability bins. Wang-1k dataset is used to evaluate the CBIR system performance for image retrieval.Research concludes that integrated multi-level 3D color-texture feature yields most accurate results and also performs better in comparison to individually computed color and texture features.
Geometrical Consistency Modeling on B-Spline Parameter Domain for 3D Face Reconstruction From Limited Number of Wild Images

A number of methods have been proposed for face reconstruction from single/multiple image(s). However, it is still a challenge to do reconstruction for limited number of wild images, in which there exists complex different imaging conditions, various face appearance, and limited number of high-quality images. And most current mesh model based methods cannot generate high-quality face model because of the local mapping deviation in geometric optics and distortion error brought by discrete differential operation. In this paper, accurate geometrical consistency modeling on B-spline parameter domain is proposed to reconstruct high-quality face surface from the various images. The modeling is completely consistent with the law of geometric optics, and B-spline reduces the distortion during surface deformation. In our method, 0th- and 1st-order consistency of stereo are formulated based on low-rank texture structures and local normals, respectively, to approach the pinpoint geometric modeling for face reconstruction. A practical solution combining the two consistency as well as an iterative algorithm is proposed to optimize high-detailed B-spline face effectively. Extensive empirical evaluations on synthetic data and unconstrained data are conducted, and the experimental results demonstrate the effectiveness of our method on challenging scenario, e.g., limited number of images with different head poses, illuminations, and expressions.
PRNG Quality Assessment Methods Based on the Analysis of Generated Sequence Properties Using Visualisation

The paper considers various methods for assessing the quality of pseudorandom number generators. We present visual methods for detecting anomalies, patterns, and deviations in pseudorandom sequences and define quality criteria for the presented methods. Also, we presented results of PRNG research by the proposed methods and proofs of the described methods correctness. Finally, we formulated recommendations to increase the effectiveness of the presented in research methods.
Enhancing Governance for Environmental Sustainability in Sub-Saharan Africa

This study assesses whether improving governance standards affects environmental quality in 44 countries in sub-Saharan Africa for the period 2000-2012. The empirical evidence is based on Generalised Method of Moments. Bundled and unbundled governance dynamics are used notably: (i) political governance (consisting of political stability and “voice & accountability”); (ii) economic governance (entailing government effectiveness and regulation quality), (iii) institutional governance (represented by the rule of law and corruption-control) and (iv) general governance (encompassing political, economic and institutional governance dynamics). The following hypotheses are tested: (i) Hypothesis 1 (Improving political governance is negatively related to CO2 emissions); (ii) Hypothesis 2 (Increasing economic governance is negatively related to CO2 emissions) and (iii) Hypothesis 3 (Enhancing institutional governance is negatively related to CO2 emissions. Results of the tested hypotheses show that: the validity of Hypothesis 3 cannot be determined based on the results; Hypothesis 2 is not valid while Hypothesis 1 is partially not valid. The main policy implication is that governance standards need to be further improved in order for government quality to generate the expected unfavorable effects on CO2 emissions.
Blockchain for Automotive Security and Privacy with Related Use Cases

Nowadays, smart vehicles are getting manufactured in such a way that it can communicate with other smart devices or smart vehicles through wired or wireless technologies. Such high range of communication expressed by the smart vehicles makes many existing tasks easier to the vehicle owners and also introduces new features to increase the ease of the vehicle usage for the owners. Further, this high degree of communications also paves road for security threats which makes the smart vehicles highly vulnerable to privacy attacks like location tracking, remote hijacking of the vehicle. Such security attacks on smart vehicles not only questions the security of the vehicle, it also has greater effects in the safety of the passengers. Also, it is obvious that smart vehicles do have lot of sensors, cameras, devices like GPS and others which produce huge volume of data to be sent to the drivers or to the other sensor or devices connected to it. Such data carries sensitive information that needs to be protected to avoid privacy breach. So security is very important in dealing with smart vehicles. Traditional security and privacy strategies used in smart vehicles are not efficient to meet the threats that the vehicles are fronting mainly because they follow centralized mechanism to extend the security. Blockchain technology provides a decentralized, distributed ledger technology that maintains a chain of blocks to provide security and privacy. Blockchain provides security from all dimensions, and hence, it is popular in crypto-currencies, smart contracts. Each communication is referred as transaction in blockchain technologies and the transaction will be broadcasted to all participating nodes of the transaction for the verification and once verified the transaction will be added as new block to already existing blocks. To overcome the drawbacks in the traditional automotive security, blockchain technologies can be implemented to meet the requirement for the automotive security and privacy. The blockchain is formed by chaining the time-stamped blocks one after the other and a block includes the hash of the previous block in the blockchain. Hence, any changes made in a block will be notified easily by propagating changes in the hash of the following blocks. This feature makes the blockchain a better technology for automotive security. Additionally, blockchain enables the user to have flexible public key system, which makes the tracking ability of the user a tough task and hence protects the privacy of the user. Moreover, blockchain has its own reasons, why it cannot be implemented readily in automotive industry. The reasons be like, delay in getting verifications from participating nodes, where vehicles cannot wait so long to make decisions if there is delay. Other reasons may include scalability issues when there are more nodes involved in a transaction and what if there is increase in the number of nodes. This chapter reveals about the utilization of blockchain in automotive security in automobile sectors.