Reviewerly/exHarmony huggingface dataset:
Bitcoin Systematic Trading Algorithms in the Cloud: Challenges and Opportunities

The growth of Big Data is concurrent with the expansion and democratization of cloud computing. The ability to access a super computer is now available to any user with a credit card. Wide adoption has fueled the rise of many new industries and disruptive technologies. Building competitive, highly performing systematic algorithms is within reach for even small, bootstrapped organizations, but the availability of cheap computing power alone is insufficient to be highly successful. What matters is the amount of data an organization can access and analyze in a time sensitive fashion. We analyze the intersection of Big Data analytics and cloud computing with a focus on creating investment algorithmic strategies to trade different cryptocurrencies. It is now possible to access high quality, inexpensive time series data sets. However, the exponential increase in the size of data sets, as well as the number of data sets required makes analysis infeasible for even very powerful desktop computers. Specifically, this paper will delve into the application of Machine Learning, its unique demands, and its application to modeling cryptocurrencies through the use of Big Data and cloud computing leveraging AWS and PaperSpace. We will explore the unique approach of using a circuit of ML algorithms to improve predictive power and its dependence on cloud computing structure.
Data Hiding in Computer-Generated Stained Glass Images and Its Applications to Information Protection

The two issues of art image creation and data hiding are integrated into one and solved by a single approach in this study. An automatic method for generating a new type of computer art, called stained glass image, which imitates the stained-glass window picture, is proposed. The method is based on the use of a tree structure for region growing to construct the art image. Also proposed is a data hiding method which utilizes a general feature of the tree structure, namely, number of tree nodes, to encode the data to be embedded. The method can be modified for uses in three information protection applications, namely, covert communication, watermarking, and image authentication. Besides the artistic stego-image content which may distract the hacker's attention to the hidden data, data security is also considered by randomizing both the input data and the seed locations for region growing, yielding a stego-image which is robust against the hacker's attacks. Good experimental results proving the feasibility of the proposed methods are also included.
Assisted Learning and Imitation Privacy

Motivated by the emerging needs of decentralized learners with personalizedlearning objectives, we present an Assisted Learning framework where a serviceprovider Bob assists a learner Alice with supervised learning tasks withouttransmitting Bob's private algorithm or data. Bob assists Alice either bybuilding a predictive model using Alice's labels, or by improving Alice'sprivate learning through iterative communications where only relevantstatistics are transmitted. The proposed learning framework is naturallysuitable for distributed, personalized, and privacy-aware scenarios. Forexample, it is shown in some scenarios that two suboptimal learners couldachieve much better performance through Assisted Learning. Moreover, motivatedby privacy concerns in Assisted Learning, we present a new notion of privacy toquantify the privacy leakage at learning level instead of data level. This newprivacy, named imitation privacy, is particularly suitable for a market ofstatistical learners each holding private learning algorithms as well as data.
Improved RawNet with Feature Map Scaling for Text-independent Speaker Verification using Raw Waveforms

Recent advances in deep learning have facilitated the design of speaker verification systems that directly input raw waveforms. For example, RawNet extracts speaker embeddings from raw waveforms, which simplifies the process pipeline and demonstrates competitive performance. In this study, we improve RawNet by scaling feature maps using various methods. The proposed mechanism utilizes a scale vector that adopts a sigmoid non-linear function. It refers to a vector with dimensionality equal to the number of filters in a given feature map. Using a scale vector, we propose to scale the feature map multiplicatively, additively, or both. In addition, we investigate replacing the first convolution layer with the sinc-convolution layer of SincNet. Experiments performed on the VoxCeleb1 evaluation dataset demonstrate the effectiveness of the proposed methods, and the best performing system reduces the equal error rate by half compared to the original RawNet. Expanded evaluation results obtained using the VoxCeleb1-E and VoxCeleb-H protocols marginally outperform existing state-of-the-art systems.
A True Random Number Generator Method Embedded in Wireless Communication Systems

To increase the number of wireless devices such as mobile or IoT terminals, cryptosystems are essential for secure communications. In this regard, random number generation is crucial because the appropriate function of cryptosystems relies on it to work properly. This paper proposes a true random number generator (TRNG) method capable of working in wireless communication systems. By embedding a TRNG in such systems, no additional analog circuits are required and working conditions can be limited as long as wireless communication systems are functioning properly, making TRNG method cost-effective. We also present some theoretical background and considerations. We next conduct experimental verification, which strongly supports the viability of the proposed method.
Genetically Optimized Deep Neural Learning for Breast Cancer Prediction

Breast cancer is one of the most commonly occurring cancers among women. Early detection of the disease is quite tricky as it involves multiple numbers of tests to evaluate factors such as location, type and size of tumor which influence the valid identification of cancer that can be time-consuming for the doctor. To increase the efficiency of disease detection, a deep neural network with an evolutionary optimization technique is implemented which will target at searching optimal weights simultaneously for multiple neurons of the net. It involves the optimization of weights and the number of iterations required to train the machine which will help in better prediction of cancer. This is experimented to classify the breast cancer (Wisconsin Breast Cancer Dataset) into benign and malignant classes. This technique will help in reducing the time required to diagnose breast cancer at an early stage making treatment effective. The experimentation result is comparable with other state-of-the-art methods in terms of the classification accuracy.
JAVANESE LANGUAGE INTERFERENCE IN THE PRONUNCIATION OF ENGLISH PHONEMES

The objective of this research was to investigate the difficulties encountered by Javanese learners inpronouncing the English phonemes and identify factors affecting their pronunciation of the phonemesin the Department of English Language Teaching of Universitas Muhammadiyah Purwokerto (UMP). The researcher used survey as the research method. The population in this research was 6th and 8th semester students who had already passed pronunciation and phonetics subject. While the samples are those who had Javanese background. The data was collected through pronunciation test in a form of audio recording and interview. The major findings of this research are 1) the researcher found that the Javanese learners of English encountered difficulties in pronouncing 13 consonant sounds /ʒ, v, θ, ð, z, ʃ, f, g, k, d, ʧ, ɳ, j/ and 17 vowel sounds /æ, ɛə, i:, eɪ, aʊ, ə:, ɔ:, u:, ɒ, ɪə, əʊ, ʊ, ɑ:, ɪ, ʊə, ɔɪ, aɪ/. While the rest, 14 English sounds /p, b, t, s, h, ʤ, m, n, l, r, w, ʌ, ə, ɛ/ were clearly pronounced by the Javanese learners of English. 2) There are four factors influencing Javanese in learning English pronunciation, among others are; age, first language, exposure, and motivation.
Signed-distance function based non-rigid registration of image series with varying image intensity

<p style='text-indent:20px;'>In this paper we propose a method for locally adjusted optical flow-based registration of multimodal images, which uses the segmentation of object of interest and its representation by the signed-distance function (OF<inline-formula><tex-math id="M1">\begin{document}$ ^{dist} $\end{document}</tex-math></inline-formula> method). We deal with non-rigid registration of the image series acquired by the Modiffied Look-Locker Inversion Recovery (MOLLI) magnetic resonance imaging sequence, which is used for a pixel-wise estimation of <inline-formula><tex-math id="M2">\begin{document}$ T_1 $\end{document}</tex-math></inline-formula> relaxation time. The spatial registration of the images within the series is necessary to compensate the patient's imperfect breath-holding. The evolution of intensities and a large variation of image contrast within the MOLLI image series, together with the myocardium of left ventricle (the object of interest) typically not being the most distinct object in the scene, makes the registration challenging. The paper describes all components of the proposed OF<inline-formula><tex-math id="M3">\begin{document}$ ^{dist} $\end{document}</tex-math></inline-formula> method and their implementation. The method is then compared to the performance of a standard mutual information maximization-based registration method, applied either to the original image (MIM) or to the signed-distance function (MIM<inline-formula><tex-math id="M4">\begin{document}$ ^{dist} $\end{document}</tex-math></inline-formula>). Several experiments with synthetic and real MOLLI images are carried out. On synthetic image with a single object, MIM performed the best, while OF<inline-formula><tex-math id="M5">\begin{document}$ ^{dist} $\end{document}</tex-math></inline-formula> and MIM<inline-formula><tex-math id="M6">\begin{document}$ ^{dist} $\end{document}</tex-math></inline-formula> provided better results on synthetic images with more than one object and on real images. When applied to signed-distance function of two objects of interest, MIM<inline-formula><tex-math id="M7">\begin{document}$ ^{dist} $\end{document}</tex-math></inline-formula> provided a larger registration error, but more homogeneously distributed, compared to OF<inline-formula><tex-math id="M8">\begin{document}$ ^{dist} $\end{document}</tex-math></inline-formula>. For the real MOLLI image series with left ventricle pre-segmented using a level-set method, the proposed OF<inline-formula><tex-math id="M9">\begin{document}$ ^{dist} $\end{document}</tex-math></inline-formula> registration performed the best, as is demonstrated visually and by measuring the increase of mutual information in the object of interest and its neighborhood.
Optimizing the Operational Time of Ambient Assisting Living Robots

In the context of H2020 RADIO project, a novel approach is proposed exploiting low power mobile robotic embedded systems toward enhancing quality of life of the elderly users in their domestic environments. Consequently, unobtrusiveness is of paramount importance. However, the utilization of state-of-the-art robotic platforms in real-life scenarios is undermined by limited autonomy and scarce battery capacity. This article presents a profile driver approach to minimize the usage of the excessively power consuming components, thus drastically extend the time duration the platform can operate without requiring recharging during the day. The proposed methodology takes as inputs: first, the daily activity patterns of the patient, second, information about the domestic environment, and third, the power profile of the platform and outputs the robot operational time.
Model of Tools for Requirements Elicitation Process for Children’s Learning Applications

Requirements Elicitation are the initial stages in the application development process, where a set of needs from the system will be built and obtained by communicating with stakeholders who have a direct and indirect influence on those needs.Failure in the requirements elicitation process was caused by weak communication.Communication is an essential thing in carrying out the requirements elicitation process.The selection of the right elicitation technique is not only a solution.Informants as sources of information on requirements also need to be considered.The choice of the correct technique often fails because of the tools not useful.The availability of the right form of equipment needs to be considered so that the communication between the elicitation team and the informant goes well.Children have characteristics not the same as adults.Limitations in terms of psychomotor, cognitive, and emotional children are considered in choosing elicitation techniques and tools.These limitations are also influenced by the age range of child development.The use of digital elicitation devices is recommended to be used in the requirements elicitation process.The presentation of interactive tools makes it easier for children to convey their desires.In learning applications for children, aspects of pedagogy that need to be explored are learning styles and children's thinking abilities.Every child in every age range has a different preference for learning style.That is because children do not have learning experiences.That also applies to the level of thinking ability of children.Therefore, these two things need to be appropriately explored when the learning application development process.The proposed elicitation tool model was made by taking into account both components of that pedagogical aspects.The test results of the built model show that the application has satisfaction.That means that children can communicate well in conveying the needed as requirements to the learning application.
Improve Speed Real-Time Rendering in Mixed Reality HOLOLENS during Training

Augmented reality (AR), virtual reality (VR), and mixed reality (MR) are advanced applications of computer visualization Which a hybrid structure allows users to explore novel used with other technologies in healthcare among other sectors give a promising future owing to the capabilities that come along with the technology that enables the medical personnel can carry out their surgical operations precisely.HOLOLENS 1, an MR product by Microsoft, is one of the first AR devices that have been widely applied in medicine for the treatment of complex diseases.It is also applied in operations that require a lot of care, for example, surgery of the liver.It is the main objective in the research to use HOLOLENS in performing surgeries while increasing Time Interval and controlling Semantic Segmentation while maintaining the truth of patient liver data during surgery for the segmentation liver 3d model.Next, we describe a new technology that increases the points of light, and the more the 3D intensity, the brighter the images the easier to interact with them.Holographic intensity is also to avoid blurring images to the point where the user sees through transparent HOLOLENS lenses.This improves Time Interval lens sensitivity and user detection in the environment.Finally, we describe a new framework for improving speed realtime render and model segmentation used hybrid Visualization between VR & AR called MR in which we decree render time speed through increase point light throw color calculations and energy function to be fast in sending and receiving data via WIFI unit.
Vector properties of entanglement in a three-qubit system

We suggest a dynamical vector model of entanglement in a three qubit system based on isomorphism between $su(4)$ and $so(6)$ Lie algebras. Generalizing Pl\"ucker-type description of three-qubit local invariants we introduce three pairs of real-valued $3D$ vector (denoted here as $A_{R,I}$ , $B_{R,I}$ and $C_{R,I}$). Magnitudes of these vectors determine two- and three-qubit entanglement parameters of the system. We show that evolution of vectors $A$, $B$ , $C$ under local $SU(2)$ operations is identical to $SO(3)$ evolution of single-qubit Bloch vectors of qubits $a$, $b$ and $c$ correspondingly. At the same time, general two-qubit $su(4)$ Hamiltonians incorporating $a-b$, $a-c$ and $b-c$ two-qubit coupling terms generate $SO(6)$ coupling between vectors $A$ and $B$, $A$ and $C$, and $B$ and $C$, correspondingly. It turns out that dynamics of entanglement induced by different two-qubit coupling terms is entirely determined by mutual orientation of vectors $A$, $B$, $C$ which can be controlled by single-qubit transformations. We illustrate the power of this vector description of entanglement by solving quantum control problems involving transformations between $W$, Greenberg-Horne-Zeilinger ($GHZ$ ) and biseparable states.
Wavelet based machine learning approach for spectral seismic signal analysis: A case study North Tapanuli earthquake

Machine Learning of seismic signals is considered to automatize the analysis of years of the recorded signal. In this research, we considered using the wavelet transform based machine learning approach to analysis the spectrum signal which recorded from Broadband Seismic Network in North Tapanuli area. We use the signal seismic which recorded from GSI, MNSI, PBSI, PSI, SBSI, and TDNI which has been deployed in North Tapanuli, North Sumatera, Indonesia. The main aim of this paper to extract the different the value and spectrum of the seismic signal and identify the energy of signal of seismic which recorded from the sensor. The result shows that for all signal, which recorded form broadband seismic network information for detection and characterization is found in the instantaneous spectrum, and the makes seismic signal most useful is that this spectrum changes over time. The wavelet-based machine learning approach utilizing a complex Morlet analyzing wavelet is not only particularly suitable to clearly and simultaneously of seismic signals but also powerful for processing frequency-energy with variant signal seismic with a short period.
TADOC : Tool for Automated Detection of Oral Cancer

Cancer is a group of related diseases and it is necessary to classify the type and its impact.In this paper an automated learning-based system for detection of oral cancer from Whole Slide Images (WSI) has been designed.The main challenges of the system were to handle the huge dataset and to train the machine learning model as it consumed more time for each iteration involved.This further increased the time consumed to get a proper model and decrease of freedom for experimentation.Other important key features of the system were to implement a futuristic deep learning architecture to classify small patches from the large whole slide images and use of carefully designed post-processing methods for the slide-based classification.
Extended validity of weak measurement*

We introduce a modified weak value that is related to the mean value of input meter variable. With the help of the modified weak value, the validity conditions for various modified versions of weak value formalism are investigated, in terms of the dependence of the pointer shift on the mean value of the input meter. The weak value formalism, often used to represent the pointer shift, with the modified weak value is of great use in simplifying calculations and giving guidance of practical experiments whenever the mean value of the input meter variable is nonzero. The simulation in a qubit system is presented and coincident well with our theoretical result.
What Do We Know About Data Breaches? Empirical Evidence from the United States

The aim of the chapter is to assess data breach risk. In particular, severity of the risk is quantified and factors determining its severity are identified. We take a number of records compromised in one data breach incident as a proxy for severity of the data breach risk. This chapter helps to learn from the experience of almost 15 years of data protection and data breach notification regulations in the United States. It offers an interesting insight into the state of cybersecurity that can be indicated by a number of data breaches. Based on the Privacy Rights Clearinghouse database, we examine the statistical properties of data on data breaches disclosed in the United States from 2005 to 2016. The size of our dataset is 5102. The Kruskal–Wallis test is applied to verify our hypotheses. The severity of data breach is modeled by the Pareto distribution. The chapter concludes with several interesting results. Negligent data breaches appear twice more frequently than malicious ones. The dominant causes of data breaches vary by organization type. It suggests that cyber risk management strategies should be tailored to the individual profile of an entity. Surprisingly, implementation of the data breach notification state laws in the United States has not affected the number of breach incidents reported in particular states. Cause of data breach, type of organization, and geographical region are statistically significant factors that diversify the population of affected organizations in terms of severity of the loss.
A Semantic Model Toward Smart IoT Device Management

The Device Management domain concerns remote administration of customer devices for an operator, such as a telecommunication operator or device manufacturer. Benefits of Device Management become all the more crucial with the advent of the Internet of Things. However, current traditional solutions, based on a centralized architecture, are not suited anymore. The tremendous explosion of heterogeneous connected objects implies a need for handling heterogeneity of distributed Device Management solutions. In order to tackle this challenge, an ontology modeling the set of common concepts of the domain is proposed, as an extension of the SAREF ontology. An enrichment of the model, alongside a set of rules, is presented to validate the contribution for the integration of a non-managed device by an operator, through a proxy.
Sign Language Translation with Transformers.

Sign Language Translation (SLT) first uses a Sign Language Recognition (SLR) system to extract sign language glosses from videos. Then, a translation system generates spoken language translations from the sign language glosses. Though SLT has gathered interest recently, little study has been performed on the translation system. This paper focuses on the translation system and improves performance by utilizing Transformer networks. We report a wide range of experimental results for various Transformer setups and introduce the use of Spatial-Temporal Multi-Cue (STMC) networks in an end-to-end SLT system with Transformer. We perform experiments on RWTH-PHOENIX-Weather 2014T, a challenging SLT benchmark dataset of German sign language, and ASLG-PC12, a dataset involving American Sign Language (ASL) recently used in gloss-to-text translation. Our methodology improves on the current state-of-the-art by over 5 and 7 points respectively in BLEU-4 score on ground truth glosses and by using an STMC network to predict glosses of the RWTH-PHOENIX-Weather 2014T dataset. On the ASLG-PC12 corpus, we report an improvement of over 16 points in BLEU-4. Our findings also demonstrate that end-to-end translation on predicted glosses provides even better performance than translation on ground truth glosses. This shows potential for further improvement in SLT by either jointly training the SLR and translation systems or by revising the gloss annotation system.