Reviewerly/exHarmony huggingface dataset:
Hardware Acceleration of Transformer Networks using FPGAs

Natural Language Processing (NLP) allows program computers to process and analyze large amounts of natural language data. In the last few years, NLP has shown tremendous growth, with many organizations presenting models such as BERT (Bidirectional Encoder Representations from Transformers), GPT2 (Generative Pre-trained Transformer 2), GPT3 (Generative Pre-trained Transformer 3), etc. The cornerstone of these models and the reason for the growth of the NLP is mainly due to the Transformer networks. However, very few architectures have been presented, for the acceleration of the Transformer networks using FPGAs. In this paper, we propose a novel architecture for Transformer Networks optimized on FPGAs. The performance evaluation on a Xilinx Alveo U200 FPGA achieved up to 80.5x speed-up over a single-core CPU and up to 2.3x speedup over a 40-thread Xeon CPU running BERT model.
Experimental generation of coherent-state superpositions with a quantum memory

We implement an iterative scheme for the generation of coherent states superpositions, using a quantum memory to take advantage of the iterative nature of the protocol. The generation rate has thus been increased by one order of magnitude, reaching 100 (Hz). Furthermore, the generated states were stored in the quantum memory during 184 (ns) before their characterization, paving the way toward the implementation of more complex iterative protocols.
Liquid Light Computing: from logic to analogue simulation

Modern digital computers have changed our lives in a variety of ways, but the technology on which they are built is rapidly reaching a hard limit due to inherent quantum effects. Two of the main pillars of our modern digital computers are the electronic transistor and the von-Neumann computer architecture. While the von-Neumann architecture established the physical separation of computing tasks like storage and processing, transistors are the fundamental building blocks in digital computers. The drive for faster and more powerful computers can be realised by increasing the number of transistors in a processor and the clock frequency. However, Moore’s law will soon come to an end, whilst the breakdown of Dennard’s scaling law means that clock frequencies have remain unchanged since 2006. This leads to the pressing quest to develop new kinds of transistors and alternative computing architectures that could one day lead to more efficient computers.
T-GRU: conTextual Gated Recurrent Unit model for high quality Linguistic Steganography

Linguistic steganography based on text auto-generation technology has been developed greatly in recent years. Most of current researches utilized neural networks to achieve high quality text generation. However, generated text of current researches only depends on the initial word but considers no context imperceptibility, which weaken the steganography imperceptibility severely. In this paper, we propose a conTextual Gated Recurrent Unit (T-GRU) model for text steganography. T-GRU makes use of the good sequence modeling characteristics of Seq2Seq model, inserts GRU into it, and adds an attention mechanism at Decoder to generate steganographic text that conforms to the context. We trained our model with a Chinese lyric dataset which reflects different context information with different singers. We designed several experiments to prove the correctness of this method from the perspective of information hiding efficiency, information imperceptibility and information hiding ability. The experimental results show that the proposed method can generate steganographic texts with more contextual connections and achieve higher imperceptibility.
Optical Linear Operator with Optimal Fidelity

We introduce a novel coherent photonic crossbar (Xbar) architecture able to realize arbitrary linear transformations. Demarcating from the conventional Singular Value Decomposition based techniques, the proposed Xbar can yield unity fidelity, as well as high tolerance to phase mismatches.
Rethinking Adversarial Examples for Location Privacy Protection

We have investigated a new application of adversarial examples, namely location privacy protection against landmark recognition systems. We introduce mask-guided multimodal projected gradient descent (MM-PGD), in which adversarial examples are trained on different deep models. Image contents are protected by analyzing the properties of regions to identify the ones most suitable for blending in adversarial examples. We investigated two region identification strategies: class activation map-based MM-PGD, in which the internal behaviors of trained deep models are targeted; and human-vision-based MM-PGD, in which regions that attract less human attention are targeted. Experiments on the Places365 dataset demonstrated that these strategies are potentially effective in defending against black-box landmark recognition systems without the need for much image manipulation.
Development of VR and AR Learning Media for Enterprise Business Ethic Scenarios

Nowadays, information technology has impacted many sectors, such as education. In general, the way students learn has experienced a transformation. Most of the Y and Z generation prefer visual-based learning. Business ethics course is important in business practice. The course focuses on procedures and prevailing norms that workers should follow in their business activities. Immersive technology, such as Virtual Reality (VR) and Augmented Reality (AR) have an opportunity to bring a new experience to visual learners because they are visually interactive. It can be applied in business ethics course. The use of immersive technology-based media can support and enrich learning materials. In this research, we developed VR and AR learning media related to business ethic course. The performance is measured through System Usability, User Experience Questionnaire, and Black Box. The evaluation resulted VR value was below average and AR value was good. The improvement of VR application is required to meet the user's expectations. Meanwhile, AR application is working properly. Immersive technology is expected to be a promising media in the visual-based business ethics course.
Domain Analysis of Payment Gateway Product Line

Digitalization in the industry brings opportunities for financial technology (fintech) to improve and automate financial services and transactions. Payment gateway is a type of fintech that provides payment services by integrating the users' business process with various banks and financial entities. The payment gateway requires handling different users' needs, and the implementation is also related to various payment channels and environments. This kind of variation in the payment gateway could be managed using software product line engineering (SPLE). SPLE is an approach to developing various products by defining commonality and variability to achieve higher quality, productivity, and time-to-market. The first process in SPLE is a domain requirement engineering that analyzes the variability of the payment gateway. This paper presents the domain requirement engineering process of the payment gateway. We observe the existing payment gateway to analyze the feature variation. The output is a variability model of the payment gateway system in a feature model. Based on the feature model, the reusable components can be designed to implement the payment gateway product line.
Fuzzy Simple Additive Weighting for Polytechnic Major Selection Based on High school student Interest and Ability

Getting success in Profesional career for every worker is dependent on skill. A great motivation give energy in learning process, academic and career life. A school Counselling teacher only able to guide students based on Student's major at High school. It is not adequate to select College major based on ability and talent only. The selection in College major have not decided by Academic capability only but also influenced by talent. Nowadays, College student candidates difficult in selecting College majors in National Selection of State Polytechnic Entrance or Seleksi Nasional Masuk Politeknik Negeri (SNMPN) because there is not Interest and Ability testing in the beginning process of the Selection. This research using Fuzzy Simple Additive Weighting (SAW) methode to select some Polytechnic majors using talent and ability by using some criterias as follows: High school accreditation, Report score and Extra curricular achievement to select College Students more appropriate based on Interest and Ability so the percentage of college students to finish their study in College is high. Preference score for every alternative with Fuzzy Simple Additive Weighting Methode matched on 45 data from 70 data tested. Candidate students are able to select more appropriate major for them by using Fuzzy Simple Additive Weighting by getting the highest score on their most appropriate major by using following scores: School accreditation score, Subjects score on School Report and Student's Extra Curricular Achievement.
Hierarchical Feature Alignment for Transfer Learning on Neural Decoding Tasks

In this work, we propose two novel methods to align the feature representation of fMRI data to improve the transfer learning performance on brain decoding problem under a limited data regime. Transfer learning on brain decoding is most viable when the source and target datasets share the same feature space. In such a condition, the feature alignment method should prioritize dominant variance directions of data. Following this requirement, in our first method, we modify the standard formulation of maximum variance generalized canonical correlation analysis (maxvar-GCCA) to work on linear projectors, scaled by eigenvalues. Our method suppresses the low variance dimensions of the feature space, which, otherwise, can spoil the feature alignment process. We estimate a series of linear transformations, using principal component analysis to align hierarchically organized data at session, subject, and dataset levels. In the second method, we incorporate label information of fMRI recordings for better transferable features in a limited data regime. We extend our first method to utilize correlation between labels and data representations. Our solution imposes label dependence by maximizing the Hilbert Schmidt Independence Criterion as an intermediate step in our hierarchical feature alignment procedure. Both methods are applicable to heterogeneously labeled time samples, which do not impose the same label at each time instant of different sessions or require an equal number of time points across the sessions. We compare our methods with the state-of- the-art solution in transfer learning methods for neural decoding that follows the method of maxvar-GCCA. We evaluate our transfer learning methods using four non-intersecting stop-signal paradigm datasets. We observe that the suggested Hierarchical Feature Alignment method and its label-guided version improve the state-of-the-art transfer learning method for brain decoding consistently on all transfer learning cases.
Thread Programming for Blockchain Creation

The process of finding a block hash is a complex problem. One of the problems is making the hash from a generator algorithm that uses the existing string and the transaction data in the block. So far, the idea that has been used is to make it one step at a time using high-powered machines (supercomputing). Developing blockchain applications is hard because supercomputing is expensive. To solve this problem, developers need to use a different method, called parallel programming. This article talks about a parallel way to make block hashes, which are used to make new blocks. We use the SHA256 algorithm to process the hash. When creating a unique character string to be used as a block hash from a set of characters, thread division is done as part of the algorithmic skeleton process. Our experiments show that using threads to split work in parallel has speeded up the build process by 1.56 seconds compared to when blocks are hashed sequentially. In addition, it was also found that the threads that contributed the most to increasing speedup were 3 threads with an average processing time of 111,638 seconds. On average, the parallel hash generation process takes 707,853 seconds, while the sequential one takes 1756,56 seconds. We conclude the solution to building blockchain using the thread programming method with the number of threads is 3 for the computer specifications used to be the same or smaller than the computer specifications we use.
Algorithm 1033: Parallel Implementations for Computing the Minimum Distance of a Random Linear Code on Distributed-memory Architectures

The minimum distance of a linear code is a key concept in information theory. Therefore, the time required by its computation is very important to many problems in this area. In this article, we introduce a family of implementations of the Brouwer–Zimmermann algorithm for distributed-memory architectures for computing the minimum distance of a random linear code over 𝔽 2 . Both current commercial and public-domain software only work on either unicore architectures or shared-memory architectures, which are limited in the number of cores/processors employed in the computation. Our implementations focus on distributed-memory architectures, thus being able to employ hundreds or even thousands of cores in the computation of the minimum distance. Our experimental results show that our implementations are much faster, even up to several orders of magnitude, than current implementations widely used nowadays.
Multimodal attention-based deep learning for automatic modulation classification

Wireless Internet of Things (IoT) is widely accepted in data collection and transmission of power system, with the prerequisite that the base station of wireless IoT be compatible with a variety of digital modulation types to meet data transmission requirements of terminals with different modulation modes. As a key technology in wireless IoT communication, Automatic Modulation Classification (AMC) manages resource shortage and improves spectrum utilization efficiency. And for better accuracy and efficiency in the classification of wireless signal modulation, Deep learning (DL) is frequently exploited. It is found in real cases that the signal-to-noise ratio (SNR) of wireless signals received by base station remains low due to complex electromagnetic interference from power equipment, increasing difficulties for accurate AMC. Therefore, inspired by attention mechanism of multi-layer perceptron (MLP), AMC-MLP is introduced herein as a novel AMC method for low SNR signals. Firstly, the sampled I/Q data is converted to constellation diagram, smoothed pseudo Wigner-Ville distribution (SPWVD), and contour diagram of the spectral correlation function (SCF). Secondly, convolution auto-encoder (Conv-AE) is used to denoise and extract image feature vectors. Finally, MLP is employed to fuse multimodal features to classify signals. AMC-MLP model utilizes the characterization advantages of feature images in different modulation modes and boosts the classification accuracy of low SNR signals. Results of simulations on RadioML 2016.10A public dataset prove as well that AMC-MLP provides significantly better classification accuracy of signals in low SNR range than that of other latest deep-learning AMC methods.
COMMONKADS for Knowledge Based System Development: A Literature Study

COMMONKADS is a method for developing knowledge-based system. This method describes foundation, technique, modeling language and document structure for develop the knowledge-based system. COMMONKADS is people-oriented system development methodology, and this methodology is often used for developing organizational knowledge management system. COMMONKADS approach is divided based on context (organizational model, task model, agent model), concept (knowledge model) and artifact (design model). COMMONKADS have been used widely for knowledge-based system in several fields, such as COMMONKADS that integrated in tourism knowledge-based system, COMMONKADS for irrigation expert system, expertise model using COMMONKADS in manufactured company, COMMONKADS in energy management system and many more. Generally, there are eight strengths of COMMONKADS methodology for develop knowledge-based system. Its strength is flexible to use in any scope, represent knowledge (organizational, domain, task and inference knowledge), complete (representation, model, and form), powerful, accurate, comprehensive, represent KM process, systematic and effective. While the weakness of COMMONKADS methodology only three, there are don't have validation process and difficult to acquisition knowledge and use semi formal language, large data storage. Nevertheless, COMMONKADS is recommended methodology for develop knowledge-based system.
Emotionally Controllable Talking Face Generation from an Arbitrary Emotional Portrait

With the continuous development of cross-modality generation, audio-driven talking face generation has made substantial advances in terms of speech content and mouth shape, but existing research on talking face emotion generation is still relatively unsophisticated. In this work, we present Emotionally Controllable Talking Face Generation from an Arbitrary Emotional Portrait to synthesize lip-sync and an emotionally controllable high-quality talking face. Specifically, we take a facial reenactment perspective, using facial landmarks as an intermediate representation driving the expression generation of talking faces through the landmark features of an arbitrary emotional portrait. Meanwhile, decoupled design ideas are used to divide the model into three sub-networks to improve emotion control. They are the lip-sync landmark animation generation network, the emotional landmark animation generation network, and the landmark-to-animation translation network. The two landmark animation generation networks are responsible for generating content-related lip area landmarks and facial expression landmarks to correct the landmark sequences of the target portrait. Following this, the corrected landmark sequences and the target portrait are fed into the translation network to generate an emotionally controllable talking face. Our method controls the expressions of talking faces by driving the emotional portrait images while ensuring the generation of animated lip-sync, and can handle new audio and portraits not seen during training. A multi-perspective user study and extensive quantitative and qualitative evaluations demonstrate the superiority of the system in terms of visual emotion representation and video authenticity.
Presentation of Noise Elements of Chinese Plucked String Instruments in Electronic Music

Traditional Chinese music highlights the linear beauty, or the melody. In addition to the presentation of melody, traditional Chinese music tends to create rhythmic beauty with a broad sound language of different attributes, which is realised by controlling the sound variation with its rich playing fingerings and techniques. Instead of evading noises, traditional Chinese music even applies noises intentionally to meet certain demands, as is very commonly found in plucked string instruments. Many typical noise languages used in plucked string instruments, while in the context of electroacoustic music, demonstrate their features in multiple dimensions of sound, such as the basic form, colour, dynamics and longitudinal gradation structure, which are gained by changes in parameters such as sound pitch, frequency, time and space information. Therefore, they imply both the basic timbre characteristics of the musical instrument and the technical characteristics of the electroacoustic music.
DISCOvering Spanish Sonnets: A Circular Reading Experience

With DISCO, the DIachronic Spanish Sonnet COrpus, we collected 4,085 sonnets written from the fiftheenth to nineteenth centuries, including canonical and lesser-studied authors from both Spain and Latin-America, with detailed author metadata, metrics, rhyme scheme, and enjambment annotations.The dataset is available in public repositories, offered in plain text and TEI, and enriched with RDFa, a linked data format.The corpus was intended for research and teaching, as well as for non-specialist use.Some questions naturally emerge: How can we easily navigate this corpus and its rich metadata?How can we identify trends using its annotations?How can users not proficient in XML query languages and linked data benefit from such a dataset?To address these issues, we have created DISCOver, a user-friendly web interface that allows users to explore the DISCO corpus.It was conceived as a means to help students, researchers, and general readers, making the corpus accessible to a wider audience.The interface displays literary annotations in individual texts as well as quantitative data on user-defined subcorpora.In turn, from the aggregated data, we can go back to the texts on which the data are based.Thus, the interface helps us to assess the features of individual texts in the context of quantitative data about larger parts of the corpus and vice-versa.It also helps us to nuance hypotheses based on aggregated data by consulting the texts from which they are derived.
A Practical AR-based Surgical Navigation System Using Optical See-through Head Mounted Display

The work presents a practical Augmented Reality (AR) based surgical navigation system using optical see-through head-mounted display as a standalone solution, without the need of additional tracking hardware. Specifically, we propose a fiducial marker-based instrument tracking, which entirely relies on the built-in hardware of the Microsoft HoloLens 2. The tracking algorithm computes the pose of the tracked object from the real-time image obtained from the on-board front-facing RGB camera. The estimated transformation is then transmitted back to the HoloLens for visualization. Our experimental evaluation shows that the system can achieve 0.81 mm / 1.52 degree in tracking accuracy and sub-millimeter alignment accuracy.
Designing Service-oriented Platform for Remote Sensing Ground Station Data Management

The use of information technology is increasing rapidly becoming the main support in organizational activities, especially in data management activity. Remote Sensing Satellite Data Management, for example, is indispensable especially at ground stations. One of them is the Parepare Ground Station which has an important role in producing data to meet the sustainability of national data availability in Indonesia. Currently, data management for ground station uses an information system, but they are still in silos, so they cannot communicate with each other and are in sync. So that remote sensing ground station data management activities are important to be developed for the better. Therefore, design of service-oriented information technology in this activity is needed to determine the gaps that exist between the running system and the needs of users by using Service-oriented Platform. Therefore, Service-oriented Platform is designed using Services Computing Systems Engineering (SCSE) Framework for remote sensing ground station data management platform model. Remote sensing ground station data management platform model validated using Service-oriented Architecture (SOA) approach has coupling factor value of 0.0057 (loosely coupled), cohesion factor value of 0.8145 (high cohesion), complexity factor of 0.0070 (not complex), and the value of reusability factor of 2.5833 (reusable).