Reviewerly/exHarmony huggingface dataset:
Towards Standard Kubernetes Scheduling Interfaces for Converged Computing

High performance computing (HPC) and cloud technologies are increasingly coupled to accelerate the convergence of traditional HPC with new simulation, data analysis, machine-learning, and artificial intelligence approaches. While the HPC+cloud paradigm, or converged computing, is ushering in new scientific discoveries with unprecedented levels of workflow automation, several key mismatches between HPC and cloud technologies still preclude this paradigm from realizing its full potential. In this paper, we present a joint effort between IBM Research, Lawrence Livermore National Laboratory (LLNL), and Red Hat to address the mismatches and to bring full HPC scheduling awareness into Kubernetes, the de facto container orchestrator for cloud-native applications, which is being increasingly adopted as a key converged-computing enabler. We found Kubernetes lacking of interfaces to enable the full spectrum of converged-computing use cases in the following three areas: (A) an interface to enable HPC batch-job scheduling (e.g., locality-aware node selection), (B) an interface to enable HPC workloads or task-level scheduling, and (C) a resource co-management interface to allow HPC resource managers and Kubernetes to co-manage a resource set. We detail our methodology and present our results, whereby the advanced graph-based scheduler Fluxion – part of the open-source Flux scheduling framework – is integrated as a Kubernetes scheduler plug-in, KubeFlux. Our initial performance study shows that KubeFlux exhibits similar performance (up to measurement precision) to the default scheduler, despite KubeFlux’s considerably more sophisticated scheduling capabilities.
Quasi-Periodic Phase Coding for Long-Depth-Range 3-D Measurements of Moving Objects

Fringe projection profilometry (FPP) requires an auxiliary projection sequence or spatial landmark to remove phase ambiguity, thereby severely limiting the measurement speed or depth range for moving objects. In this article, we propose a novel quasi-periodic phase coding method for long-depth-range 3-D measurements of moving objects without temporal or spatial limitations. This article mainly focuses on the construction of the encoding schema for quasi-periodic fringe patterns and the execution of the phase decoding framework for long-depth-range 3-D measurements of moving surfaces. First, a set of stripes with similar periods is designed on the basis of a preset equivalent period, and the various combinations of these stripes constitute quasi-periodic fringe patterns. Second, we present a progressive phase recovery strategy for decoding quasi-periodic fringe images, in which the wrapped phase is converted first into a continuous phase distribution and then into the absolute phase. In this strategy, the absolute phase map is effectively screened via the piecewise linear transformation from the quasi-periodic phase to the equivalent phase. Compared with the existing FPP methods, the proposed method avoids the low-frame-rate constraint and the need for landmark configuration. Simulation and experimental results verify that the proposed method is applicable for dynamic 3-D measurements of moving objects over a large-depth range.
Multi-order nearest neighbor prediction for recommendation systems

Recommendation systems (RSs) can establish a relationship between users and items and recommend the seemingly unrelated but actually interesting items to target users by utilizing their behavior. However, the recommendation performance of RSs is seriously affected by the issue of data sparsity because the information amount that each user involves is very limited with the continuous growth of both users and items. To solve the issue of data sparsity in RSs, a novel collaborative filtering prediction method is proposed, called multi-order nearest neighbor prediction (MNNP). The concept of multi-order nearest neighbors is an extension of friends of a friend. By using multi-order nearest neighbors, MNNP can not only expand the range of neighbors but also implement the neighbor propagation efficiently. For a target user, MNNP needs to search its multi-order nearest neighbors and successively uses them to iteratively update the rating matrix. To show the procedure of MNNP, we illustrate it by an example. Extensive experiments on real-world datasets show that MNNP has a good performance.
A Comparative Study on Speaker-attributed Automatic Speech Recognition in Multi-party Meetings

In this paper, we conduct a comparative study on speaker-attributed automatic speech recognition (SA-ASR) in the multi-party meeting scenario, a topic with increasing attention in meeting rich transcription. Specifically, three approaches are evaluated in this study. The first approach, FD-SOT, consists of a frame-level diarization model to identify speakers and a multi-talker ASR to recognize utterances. The speaker-attributed transcriptions are obtained by aligning the diarization results and recognized hypotheses. However, such an alignment strategy may suffer from erroneous timestamps due to the modular independence, severely hindering the model performance. Therefore, we propose the second approach, WD-SOT, to address alignment errors by introducing a word-level diarization model, which can get rid of such timestamp alignment dependency. To further mitigate the alignment issues, we propose the third approach, TS-ASR, which trains a target-speaker separation module and an ASR module jointly. By comparing various strategies for each SA-ASR approach, experimental results on a real meeting scenario corpus, AliMeeting, reveal that the WD-SOT approach achieves 10.7% relative reduction on averaged speaker-dependent character error rate (SD-CER), compared with the FD-SOT approach. In addition, the TS-ASR approach also outperforms the FD-SOT approach and brings 16.5% relative average SD-CER reduction.
Enabling Zero-Shot Multilingual Spoken Language Translation with Language-Specific Encoders and Decoders

Current end-to-end approaches to Spoken Language Translation (SLT) rely on limited training resources, especially for multilingual settings. On the other hand, Multilingual Neural Machine Translation (MultiNMT) approaches rely on higher-quality and more massive data sets. Our proposed method extends a MultiNMT architecture based on language-specific encoders-decoders to the task of Multilingual SLT (Multi-SLT). Our method entirely eliminates the dependency from MultiSLT data and it is able to translate while training only on ASR and MultiNMT data. Our experiments on four different languages show that coupling the speech encoder to the MultiNMT architecture produces similar quality translations compared to a bilingual baseline (±0.2 BLEU) while effectively allowing for zero-shot MultiSLT. Additionally, we propose using an Adapter module for coupling the speech inputs. This Adapter module produces consistent improvements up to +6 BLEU points on the proposed architecture and +1 BLEU point on the end-to-end baseline.
Indian Sign Language Recognition Using 2-D Convolution Neural Network and Graphical User Interface

The emergence of the sign Language recollection method has a great effect on the day-to-day livings of human beings with hearing disabled individuals utilizing signs to speak with others.Much the same as verbally communicated in dialects, there is no general language as each nation has its communication in language, so every nation has its vernacular of gesture-based communication and in India, they utilize Indian Sign Language (ISL).Over the most recent couple of years, analysts have investigated the computerization of ISL.Here we developed the custom database for 26 English letters and each Letter narrates the 5 times by each person.Train the dataset using 2D CNN and create GUI for recognition.A few endeavors have been made in India and different nations.We attempt to investigate and dissect the ISL that has been made with the mechanization of communication through signing and motion acknowledgment.We attempted to investigate the difficulties that come in the ongoing sign acknowledgment framework.The testing accuracy of the proposed work is 95% and 95% for the validation accuracy.
Playful Probes for Design Interaction with Machine Learning: A Tool for Aircraft Condition-Based Maintenance Planning and Visualisation

Aircraft maintenance is a complex domain where designing new systems that include Machine Learning (ML) algorithms can become a challenge. In the context of designing a tool for Condition-Based Maintenance (CBM) in aircraft maintenance planning, this case study addresses (1) the use of Playful Probing approach to obtain insights that allow understanding of how to design for interaction with ML algorithms, (2) the integration of a Reinforcement Learning (RL) agent for Human–AI collaboration in maintenance planning and (3) the visualisation of CBM indicators. Using a design science research approach, we designed a Playful Probe protocol and materials, and evaluated results by running a participatory design workshop. Our main contribution is to show how to elicit ideas for integration of maintenance planning practices with ML estimation tools and the RL agent. Through a participatory design workshop with participants’ observation, in which they played with CBM artefacts, Playful Probes favour the elicitation of user interaction requirements with the RL planning agent to aid the planner to obtain a reliable maintenance plan and turn possible to understand how to represent CBM indicators and visualise them through a trajectory prediction.
Emp-RFT: Empathetic Response Generation via Recognizing Feature Transitions between Utterances

Each utterance in multi-turn empathetic dialogues has features such as emotion, keywords, and utterance-level meaning.Feature transitions between utterances occur naturally.However, existing approaches fail to perceive the transitions because they extract features for the context at the coarse-grained level.To solve the above issue, we propose a novel approach of recognizing feature transitions between utterances, which helps understand the dialogue flow and better grasp the features of utterance that needs attention.Also, we introduce a response generation strategy to help focus on emotion and keywords related to appropriate features when generating responses.Experimental results show that our approach outperforms baselines and especially, achieves significant improvements on multi-turn dialogues.
Implantation of Digital Technology in Engineering Education as an Example of Study at Bauman Moscow State Technical University Discipline “Technology of Construction Materials”

The article considers the possibility of using online courses in higher engineering education in order to increase its effectiveness. The organizational and methodological aspects of e-learning at the university are considered, the features of the development of an online technological course are analyzed. Based on a study of the best foreign and Russian practices, the authors suggest a possible option of combining traditional full-time education using an electronic lecture course with voice-overs by a teacher and an automated knowledge control system for students hosted on the Open BMSTU platform.
integrative model for the semantic space of music and a contemporary musical educational process

The advent of computer technology has led to the creation and development of new forms of teaching musical art. The emergence of music computer technologies (MCT) was the basis for creating both new forms in the educational process and new subjects, the appearance of new disciplines and new educational trends in the system of a contemporary musical education. These new subjects, such as “Musical Informatics,” “Computer Music Creative Work,” “Computer Arrangement and Composition, Sound and Timbre Programming,” “Electronic Musical Instruments” and many others, fully reflect the essence of the changes that have occurred in approaches to the study of music art. This article analyzes the possibility of including an integrative model for the semantic space of music, developed with the participation of the prominent cybernetics, scientist Mikhail Borisovich Ignatyev, in a contemporary musical educational process.
Evolutionary neural network classifiers for software effort estimation

The estimation of software development efforts has become a crucial activity in software project management. Due to this importance, many researchers focused their efforts on proposing models for relationship construction between efforts and software size and requirements. However, there are still gaps and problems in software effort's estimation process; due to the lack of enough data available in the initial stage of project life cycle. The need for an enhanced and an accurate method for software effort estimation is an urgent issue that challenged software project-management researchers around the world. This work proposes a model based on artificial neural network (ANN) and dragonfly algorithm (DA), in order to provide more accurate model for software effort estimation. The applicability of the model was evaluated using several experiments and the results were in favour of the enhancement with more accurate effort estimation.
MySQL

MySQL is a popular open source SQL database management system that is developed, distributed, and supported by Oracle Corporation. MySQL manages a structured collection of data. A MySQL database helps you to add, access, and process the data stored in the database. MySQL stores data in separate tables. The database structures are organized into physical files optimized for speed. The logical model, with objects such as databases, tables, views, rows, and columns, offers a flexible programming environment. The SQL part of “MySQL” stands for “Structured Query Language,” which is the most common standardized language used to access databases. MySQL software uses the GPL (GNU General Public) License and is open source software. You can get more details on MySQL at .
Two-level parallel CPU/GPU-based genetic algorithm for association rule mining

Genetic algorithms (GA) are widely used in the literature to extract interesting association rules. However, they are time consuming mainly due to the growing size of databases. To speed up this process, we propose two parallel GAs (ARMGPU and ARM-CPU/GPU). In ARM-GPU, parallelism is used to compute the fitness which is the most time consuming task; while, ARM-CPU/GPU proposes a two-level-based parallel GA. In the first level, the different cores of the CPU execute a GAARM on a sub-population. The second level of parallelism is used to compute the fitness, in parallel, on GPU. To validate the proposed two parallel GAs, several tests were conducted to solve well-known large ARM instances. Obtained results show that our parallel algorithms outperform state-of-the-art exact algorithms (APRIORI and FP-GROWTH) and approximate algorithms (SEGPU and ME-GPU) in terms of execution time.
Hybrid approach to enhance contrast of image for forensic investigation using segmented histogram

Digital images can be used in detection of various crimes, ranging from active to passive attack applications. To suit a particular attack application, an image needs to be enhanced and should have good quality in general for forensic investigation. For normal investigation use; vibrant, vivid and eye pleasing image is desired. In this paper, various existing methods and their drawbacks are examined. This information is then used to develop an approach for contained enhancement to retain natural look of image, enhance its quality to make it usable for evidence. Existence of a spike in histogram can result in Over-enhancement of image. Spike is created when a large no. of pixels are having small set of intensities. Ten most commonly used standard images are used for performance comparison. Proposed method outperforms compared methods in terms of PSNR and AMBE values, while keeping entropy and standard deviation almost similar to input image.
A Novel Observation on Cloud Computing in Education

Cloud computing is an emerging topic in today’s lifestyle in publications and among users. Cloud computing in educational sectors is going beyond classrooms as an essential service. The higher education, distance education, online education etc. uses the services of cloud computing for the flexibility available for the students. Cloud computing has new platforms for innovative teaching practice. In this paper, we have analyzed various educational sectors which use cloud computing as a service. We found that the above mentioned educational sectors are benefiting from the service of cloud computing. It needs some remedial actions to be done for its proper utilization as a service.
HYBCIM: Hypercube Based Cluster Initialization Method for k-means

Clustering is a data processing technique that is extensively used to find novel patterns in data in the field of data mining and also in classification techniques. The k-means algorithm is extensively used for clustering due to its ease and reliability. A major effect on the accuracy and performance of the k-means algorithm is by the initial choice of the cluster centroids. Minimizing Sum of Squares of the distance from the centroid of the cluster for cluster points within the cluster (SSW) and maximizing Sum of Square distance between the centroids of different clusters (SSB) are two generally used quality parameters of the clustering technique. To improve the accuracy, performance and quality parameters of the k-means algorithm, a new Hypercube Based Cluster Initialization Method, called HYBCIM, is proposed in this work. In the proposed method, collection of k equi-sized partitions of all dimensions is modeled as a hypercube. The motivation behind the proposed method is that the clusters may spread horizontally, vertically, diagonally or in arc shaped. The proposed method empirically evaluated on four popular data sets. The results show that the proposed method is superior to basic k-means. HYBCIM is applicable for clustering both discrete and continuous data. Though, HYBCIM is proposed for k-means but it can also be applied with other clustering algorithms which are based on initial cluster centroids.
Blockchain Technology and Cryptocurrency

As the online transactions aregrowing the security and new technological advancements are a recent concern. A decentralized architecture and distributed computing paradigm have given birth to cryptocurrencies that embed the blockchain technology. Bitcoin is one such currency which is revolutionizing the digital transaction field. The asymmetric cryptography is used to register and transfer the cryptocurrencies along with various algorithms like Proof-ofstake and Proof-of-work. The main advantage of the method is difficulty in counterfeiting, destroying and mining the currencies. In this paper, a detailed study on cryptocurrency and blockchain is carried out giving an oversight to the emerging technology. The recent trends have results showing the emergence of mobile cryptocurrency based digital transactions to be of higher rate due to the high popularity of Bitcoin and Ethereum. The use of Bitcoins and other cryptocurrencies have lured the transaction costs.
Blockchain Based Warehouse Supply Chain Management using Hyperledger Fabric and Hyperledger Composer

This research investigates the operation of smart transactions in the blockchain and demonstrates the process of transforming an existing system into the blockchain using the Hyperledger Blockchain Platform. The process includes determining business model requirements and redesigning the business model to be applied to the blockchain system using Hyperledger Composer. The supply management system is the selected case study due to its complexity that can be used to demonstrate blockchain implementation. This research explored the utilization of Hyperledger Fabric as well as Hyperledger Composer technology. The implemented system is then under test for performance evaluation. According to experimental results, read access has the shortest duration needed, which is 33% faster than Create, 69% compared to Delete, and 82% compared to Update. Update function has the longest average duration needed. Furthermore, reading using query would dramatically slow down the Read function with the increase of the average read duration over 10 commodities category by 12238.9%, over 100 commodities category by 1950%, and over 1000 commodities category by 308% than reading without using a query.
Recent Advances in the Design &amp; Development of A Drone Used for Bio-Medical Engineering Applications : Medi-Sky

The primary target or the motivation behind this paper is to build up a model of the automaton rescue vehicle to help the ambulances in sparing human lives by sending the drugs to the working environment where the influenced patients are available. A huge number of individuals bite the dust in light of rescue vehicle delays. The time taken by emergency vehicle to achieve a patient depends a great deal on the course and the traffic on the way. At the point when a medicinal crisis happens, the reaction time can have a significant effect between a real existence spared and an actual existence lost. Shockingly, ambulances can stall out in rush hour gridlock and arrive of late after the crisis call has been made, in which an unfortunate casualty may have endured a great deal of wounds and can even lose his/her life. The utilization of Unmanned Aerial Vehicles (UAV) or "Automatons" has been utilized for quite a while for a wide range of uses. The motivation behind this paper is to build up a model of automaton emergency vehicle to help the ambulances in sparing human lives. The emergency vehicle automaton enters the scene at the moment time and constant directions are given by the administrator. The automaton can gauge different constant wellbeing parameters of the patient, for example, temperature, pulse and heartbeat. The estimations of these basic parameters are then transmitted to the specialists present in a rescue vehicle. Well, the idea is to implement the same in the rural areas where penetration of healthcare is poor. The system is being designed to look after the infants and aged people in the fast-moving urban lives. The work developed in this paper along with the results shown depicts the effectivity of the methodology proposed.
Development of Smart Librarian with the Virtual Assistant (PRIMO)

In modern life, with the ability to perform tasks, the virtual assistant (VA) can make our lives easier and smart. The virtual assistant can perform as a librarian, very smartly, and effectively. We build our VA with Raspberry Pi and Alexa Voice Service. As a result, few discussions that occur in library environments such as find books, short review books, university notice are accurately performed. The common way of communication used by people in day to day life is through speech. If the assistant system can be heard to the customer for the handle of the day to day affairs, then grant the right reply, it will be much simple for customers to transmit with their assistant system, and the assistant will be much better “Smart” as a personal assistant. We heard a very old story “Ali Baba and the Forty Thieves”, where the mouth of a treasure cave secured by magic. It unrolls on the words "unroll sesame" and seals itself on the words "near sesame". The magic is a VA in the modern world. The VA system built on artificial intelligence (AI), machine learning, natural language processing, and voice recognition technology.