Reviewerly/exHarmony huggingface dataset:
Development of Web Stock Opname Application With SAP Business One Using Scrum Method

SME Company (Small and Medium Enterprises) is a small-scale business unit, which require to have inventory function as an important asset in the company to ensure the availability of goods and stock within the warehouse process. Thus, an inventory management process calls Stock opname that is carried out to conduct calculation and computation have been established. Previously, majority activity process have been carried out manually in which the employees check and record the available items through reporting them to the managers without using any kind of software whatsoever, only a piece of document that have no integration with other business processes. Therefore, the data that is not processed in the real-time might be different at certain period and unmatched with the real situation can lead to the wrong decision making. Thus, the application should be developed, which in this case utilizing the web application to be integrated with SAP Business One to solve existing problems within the company to provide accurate and real-time inventory information management. It also has objective to maintain updated information with other department, unit or even business modules within the SME companies.
Modeling Local and Global Contexts for Image Captioning

Image captioning aims to first observe an image, most notably the involved objects that are highly context-dependent, and then depict it with a natural description. However, most of the current models solely use the isolated objects vectors as image representations, ignoring the contexts among them. In this paper, we introduce a Local-Global Context (LGC) network, endowing the independent object features with shortrange perception (local contexts) and long-range dependence (global contexts). LGC network can be viewed as feature refiner, much beneficial to reason the novel objects and verbal words for the caption decoder. The local contexts are modeled with 1-D group convolution on adjacent objects, strengthening the local connections. Still further, self-attention mechanism is utilized to model the global contexts by correlating all the local contexts. Extensive experiments on MSCOCO dataset demonstrate that LGC network can easily plug into almost any neural captioning models and significantly improve the model performance.
Understanding the Role of Values and Norms in Practical Reasoning

Mistrust poses a significant threat to the widespread adoption of intelligent agents. Guarantees about the behaviour of intelligent systems could help foster trust. In this paper, we investigate mechanisms to integrate value-based reasoning in practical reasoning as a way to ensure that agents actions align not only with society norms but also with user’s values. In particular, we expand a normative BDI agent architecture with an explicit representation of values.
Real Time Surrounding Identification for Visually Impaired using Deep Learning Technique

Navigating around unfamiliar places and performing other day to day physical tasks are some of the biggest challenges faced by visually impaired people. It is extremely difficult for visually impaired people to commute or perform daily tasks without physical assistance. The conventional methods to aid visually impaired people mostly uses sensors to estimate distances from objects which is very inefficient, expensive and difficult to use without assistance. The proposed work presents a way to provide sight to visually impaired in real time using deep learning by identifying some familiar places used in day to day life like Restrooms, Pharmacies and Metro Stations. This method uses convolutional neural networks to identify signs of public places which are similar around the globe. The proposed work was tested on large varying database and achieved a high accuracy of 90.992 percent. The experimental results show that this method for identifying Restrooms, Pharmacies and Metro Station signs is efficient, has low computational time and fulfils the needs of visually impaired people up to a large extent.
Construction and Application Evaluation of the Traffic Management Information Verification Service Platform

At present, many applications involving in traffic management information system query vehicles, violations, driving licenses from multiple data sources. There are many redundant codes in the query function and they are coupled with other services, which leads the code to be poor readable and difficult to maintain. In order to solve the above problems, this paper develops and provides a unified data verification service platform. The architecture of platform, among which connection protocol and distributed rate limiter are presented, is constructed and proposed. The most suitable signature algorithm and the best way of implementing distributed lock are adapted by comparison. Comprehensive computational and statistical tests show that the platform can query business data based on different data sources, which is suitable for different levels of security scenarios of the Internet and public security networks, and achieves unified prevention, control, monitoring, and tracking management goals.
Classification of High Dimensional Data Using Filtration Attribute Evaluation Feature Selection Method of Data mining

Feature selection is the process to reduce the dimension of the high dimensional data which reduces the time complexity of any classifier and to increase the predictive precisions from the classifier and also consider the effect from the suitable features on here preprocessing of the data is the crucial stage to form the correct and quality dimension for the classification. Therefore feature selection can be classified into two types one is forward selection and another is backward selection many research applications select any one of the method to classify the data here we are using the both backward and forward selection to improve the performance of the classifier after applying the feature selection methods. So in this work we proposed different feature selectors followed by different classification algorithms are applied and also with accuracy of the classifier in this the standard bench dataset UCI and Ken ridge biomedical data repository are used to estimate the classifier accuracy of different datasets. We applied the different classification algorithms like J48, Naïve bayes, k-star, Random forest for calculating the accuracy their comparison among with the feature selection averages.
Real Time Object Detection for Traffic Based on Knowledge Distillation: 3<sup>rd</sup> Place Solution to Pair Competition

In practical applications, the purpose of object detection is to determine the target space position based on the image. At the same time, better performance is obtained under the premise of reducing the computational overhead. The dataset of the PAIR competition has the characteristics of imbalanced categories, low quality of images and inconsistent annotations. To address this issue, firstly we adopt an improved cross entropy loss function and data augmentations to rebalance the data distribution. Then the extra datasets are involved to neutralize the low images quality and annotation inconsistency issues. Secondly, this competition focuses on object detection on embedded device. So we apply knowledge distillation to fine-tune a lightweight detection model. Our detection model uses MobileNetV3 Small as backbone and SSDLite as detector head. In order to improve detection performance on small targets, FPNLite is included so that low-level features can be utilized. And we also apply TensorRT library to accelerate the inference procedure further. Eventually, our method achieves the 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> place in the final score list of competition as the fastest, lightest and the most computation economically solution. Our code will soon be open source.
Improving Green IT Assessment Tool Using Case-Based Reasoning Concept

Abstract In order to know how to measure their level of green practices, green assessment tool is there to act as a guide or as some might called it as a bench marker. Although, there are existing green assessment tools but there is inadequate existing green assessment tool with a recommendation feature. Recommendation feature is meant to aid users especially new user with their green practices. In this paper, case-based reasoning concept is chosen to produce this recommendation features. Besides that, Rapid Application Development Model is used in this research. The expected result of this research is to improve an existing green assessment tool to provide recommendation to user. With this paper, it can help raise user’s level of green awareness and improve their practices.
Ranking based multi-label classification for sentiment analysis

This paper proposes a sentiment analysis framework based on ranking learning. The framework utilizes BERT model pre-trained on large-scale corpora to extract text features and has two sub-networks for different sentiment analysis tasks. The first sub-network of the framework consists of multiple fully connected layers and intermediate rectified linear units. The main purpose of this sub-network is to learn the presence or absence of various emotions using the extracted text information, and the supervision signal comes from the cross entropy loss function. The other sub-network is a ListNet. Its main purpose is to learn a distribution that approximates the real distribution of different emotions using the correlation between them. Afterwards the predicted distribution can be used to sort the importance of emotions. The two sub-networks of the framework are trained together and can contribute to each other to avoid the deviation from a single network. The framework proposed in this paper has been tested on multiple datasets and the results have shown the proposed framework’s potential.
The Use of National and International Standards in Developing CAD Models of Electrical Substations

To improve the quality of project documents, reduce the time and resources required for their preparation, it is necessary to use computer-aided design (CAD) systems. Many large electricity companies include the introduction of CAD in their innovative development programs. In the framework of this work, the authors studied issues related to the creation of a trial version of the training and research CAD system of electrical substations, gained experience in the application of national and international CAD regulatory documents.
Design Model for Energy Trading on Blockchain

The Power Grid is the major area where the rapid and drastic transformations are taking place. While the innovations in the recent grid enables in the higher rate of utilization of control methods which are in the advanced state, the demands for the distributed energy resources (DER) is increased by the next-level grid in order to make the integration process less complicated. The consumers who also involve themselves in seamlessly buying and selling energy are also benefitted by distributed energy resources. Blockchain (BC) is the technology which appears to be best for providing security and trust in the area of Smart Grid, Future Business and Economic Competition around the world. This paper presents the Blockchain based architecture using Hyperledger Composer Rest Server for demonstrating peer-to-peer energy trading and also explain briefly the authentications of both producers and consumers involved in the transactions. The implementations are divided into two phases: Phase I discusses about the authentication of both the parties and Phase II describes the transactions of energy trading. This paper also provides the parties to view the reports of their transactions along with the blockchain history.
Mobile Technologies in Blended Learning of Engineering Students in Digital Measurements on Geodetic Equipment

Vocational training of engineering students includes digital measurements using a lot of portable devices and instruments. The probability of erroneous actions also significantly increases with complications and number of devices. At the initial stages of training, students are encouraged to master the basic actions with virtual laboratory equipment before start and further in the process of working with devices. The article discusses the possible options for hybrid laboratory construction to support engineering courses such as enginery geodesy at the University of Architecture and Civil Engineering. Hybrid laboratories provide both physical and online forms of operation with the same equipment or devices of the same functionality. Since online tools should simulate correct and erroneous sampling with the possibility of user's automated control the choice of virtual tools rather than remote access to production laboratory equipment is more preferable for didactic tasks at initial stages. A simple configuration of hybrid learning environment based on interactive virtual learning tools and mobile devices is offered for training of inexperienced students to make digital measurements on geodetic equipment. The hybrid environment is equally suitable for use in stationary university laboratories and for terrain practice. In conclusion, the article discusses the use of distance and mobile technologies in blended learning for students using "flipped class" and professional business game methods in the engineering geodesy course at the University of Architecture and Civil Engineering.
Network Slice Management on top of ONAP

We develop and demonstrate a network slice management architecture on top of the so-called Open Network Automation Platform (ONAP). A slice manager is introduced to allow a client to describe the desired slice along with the associated Service Level Agreement (SLA), and to interface with ONAP in order to deploy and maintain the negotiated slice. This approach is illustrated by considering a private mobile network based on open source software components orchestrated by ONAP.
Generalized Column Distances

The notion of Generalized Hamming weights of block codes has been investigated since the nineties due to its significant role in coding theory and cryptography. In this paper we extend this concept to the context of convolutional codes. In particular, we focus on column distances and introduce the novel notion of generalized column distances (GCD). We first show that the hierarchy of GCD is strictly increasing. We then provide characterizations of such distances in terms of the truncated parity-check matrix of the code, that will allow us to determine their values. Finally, the case in which the parity-check matrix is in systematic form is treated.
TA-Student VQA: Multi-Agents Training by Self-Questioning

There are two main challenges in Visual Question Answering (VQA). The first one is that each model obtains its strengths and shortcomings when applied to several questions; what is more, the “ceiling effect” for specific questions is difficult to overcome with simple consecutive training. The second challenge is that even the state-of-the-art dataset is of large scale, questions targeted at a single image are off in format and lack diversity in content. We introduce our self-questioning model with multi-agent training: TA-student VQA. This framework differs from standard VQA algorithms by involving question-generating mechanisms and collaborative learning questions between question-answering agents. Thus, TA-student VQA overcomes the limitation of the content diversity and format variation of questions and improves the overall performance of multiple question-answering agents. We evaluate our model on VQA-v2, which outperforms algorithms without such mechanisms. In addition, TA-student VQA achieves a greater model capacity, allowing it to answer more generated questions in addition to those in the annotated datasets.
Relevance of PMBOK v6 Processes for Tailored Agile Project Categories

As a leading project management framework, PMBOK (Project Management Body Of Knowledge) defines 49 processes as relevant for project managers. This article is focused on IT projects and defines six project categories – three classical and three agile, more Scrum-oriented, project sizes as the basis for tailoring PMBOK processes in terms of their relevance for project success. Based on scientific literature research, initial suggestions of relevance factors show that not all processes are equally relevant for all project categories, enabling IT project managers of classical projects, as well as agile IT project managers, to focus their efforts on the most relevant processes. Processes of integration management, resource management and communication management in particular have been identified as being more relevant for project success than other processes. In addition to that result, processes regarding planning and communication seem to be more relevant in classical project categories than they are in agile project categories.
BOPS, A New Computation-Centric Metric for Datacenter Computing

For emerging datacenter (in short, DC) workloads, such as online Internet services or offline data analytics, how to evaluate the upper bound performance and provide apple-to-apple comparisons are fundamental problems. To this end, an unified computation-centric metric is an essential requirement. FLOPS (FLoating-point Operations Per Second) as the most important computation-centric performance metric, has guided computing systems evolutions for many years. However, our observations demonstrate that the average FLOPS efficiency of the DC workloads is only 0.1%, which implies that FLOPS is inappropriate for DC computing. To address the above issue, inspired by FLOPS, we propose BOPS (Basic Operations Per Second), which is the average number of BOPs (Basic OPerations) completed per second, as a new computation-centered metric. We conduct the comprehensive analysis on the characteristics of seventeen typical DC workloads and extract the minimum representative computation operations set, which is composed of integer and floating point computation operations of arithmetic, comparing and array addressing. Then, we propose the formalized BOPS definition and the BOPS based upper bound performance model. Finally, the BOPS measuring tool is also implemented. To validate the BOPS metric, we perform experiments with seventeen DC workloads on three typical Intel processors platforms. First, BOPS can reflect the performance gap of different computing systems, the bias between the peak BOPS performance (obtaining from micro-architecture) gap and the average DC workloads’ wall clock time gap is no more than 10%. Second, BOPS can not only perform the apple-to-apple comparison, but also reflect the upper bound performance of the system. For examples, we analyze the BOPS efficiency of the Redis (the online service) workload and the Sort (the offline analytics) workload. And using the BOPS measuring tool–Sort can achieve 32% BOPS efficiency on the experimental platform.
VISH: Does Your Smart Home Dialogue System Also Need Training Data?

The main objective of smart homes is to improve the quality of life and comfort of their inhabitants through automation systems and ambient intelligence. Voice-based interaction like dialogue systems is the current emerging trend in these systems. Natural Language Understanding (NLU) model can identify the end-users’ intentions in the utterances provided to spoken dialogue systems. The utility of dialogue systems is reliant on the quality of NLU models, which is in turn significantly dependent on the availability of a high-quality and sufficiently large corpus for training, containing diverse utterance structures. However, building such corpora is a complex task even for companies possessing significant human and infrastructure resources. On the other hand, the existing corpora for the smart home domain are either concerned with web services, focus on direct goals only, follow static command structure, or are not publicly available in English language which limits the development of goal-oriented dialogue systems for smart homes. In this paper, we propose a generic method to create training data for the NLU component using a generative grammar-based approach. Our method outputs, Voice Interaction in Smart Home (VISH) dataset consisting of five million unique utterances for the smart home. This dataset can greatly facilitate research in the area of voice-based dialogue systems for smart homes. We evaluate the approach by using VISH to train several state-of-the-art NLU models. Our experiment results demonstrate the capability of the corpus to support the development of goal-oriented voice-based dialogue systems in the context of smart homes.
Achieving a reversible lower dimensionality transformation for picture archiving and communication system in healthcare

With the progression of picture archiving and communication systems (PACSs) over the past decade, it has become imperative that such systems be optimised in security, storage, and transmission aspects. The work presented in this Letter shows a framework for medical image compression and secure image transmission for PACSs. The work aims to achieve a lower dimensionality of input medical image signified by a high-compression ratio, a secure image transmission that can withstand adversarial attacks and provide a reversible reconstruction with minimal error. The authors illustrate that sinusoid modulated Gaussian texture maps, multi-level chaotic maps, and high-frequency image maps can be efficiently fused and utilised in a deep learning architecture. The overall analysis depicts promising results with regard to the capability of image compression, security, and transmission. The proposed framework will be a potential candidate for use in PACSs, which effectively is the backbone of the current healthcare paradigm.
GPU Usage trends in Medical Image processing

Graphics processing units (GPUs), mainly named as general-purpose computing on graphics processing units (GPGPUs) became a widely used technology for many computer-aided procedures in different research or industry areas that require high algorithmic computation capabilities for parallel computing tasks. In the field of bioinformatics and especially in medical image processing, GPUs play an essential role to enable high performance and computation capabilities in order to facilitate the tasks related to image processing. This paper highlights the usage of the graphics processing units in medical image processing and three-dimensional medical image processing related applications (computer-aided image processing, volume rendering, and segmentation), such as image segmentation, noise reduction or denoising, and image registration. It gives a deep understanding of the influence of GPU cards on general purpose programming GPGPU in general and on medical image processing specifically. It presents a good starting point for further applications of GPU programming in the field of biomedical applications and mainly for medical image processing that requires high-performance capabilities for medical image treatment shortly after the medical imaging activity such as in the ICU or the emergency services.