Reviewerly/exHarmony huggingface dataset:
Business Service Management using Blockchain

Blockchain is one of emerging research area and gaining attention due to its wide range of applications. Smart contracts are one of the latest application areas of blockchain, which enables secure, transparent and tamperproof transactions. Smart contracts create and verify the data with the help of hash functions. Hash functions are mathematical algorithms that take input and produces hashed output of specified length. It is very difficult to regenerate the original input from the hashed output. Ethereum is one of the leading blockchain platforms to provide functionality of smart contracts. This paper carries out study of blockchain on Ethereum platform. We have developed authentication-based business application with the help of smart contract for marketing services on Ethereum platform. Solidity is used to generate and manage smart contract.
Ensemble of the nature-inspired algorithms with success-history based position adaptation

Abstract Previously, a meta-heuristic approach called Co-Operation of Biology Related Algorithms, or COBRA for short, based on a fuzzy logic controller for solving real-valued optimization problems was introduced and described. The basic idea of the originally proposed approach consists in a cooperative work of six well-known biology-inspired algorithms (components) with similar schemes. Furthermore, the fuzzy logic controller determines which biology-inspired algorithms should be included in the co-operative work and their population sizes at a given moment for solving optimization problems using the COBRA approach. In this study a new modification of the COBRA approach based on an alternative way of generating potential solutions is proposed. The stated technique uses a historical memory of successful positions found by individuals to guide them in different directions and thus to improve their exploration and exploitation abilities. The proposed method was applied to the components of the COBRA approach and to its basic procedures. The modified meta-heuristic as well as other variants of the COBRA algorithm and components (with and without the proposed modification) were evaluated on three sets of low- and high-dimensional benchmark problems. The experimental results obtained by all algorithms are presented and compared. It was concluded that the fuzzy-controlled COBRA with success-history based position adaptation allows better solutions to be found than the other mentioned biology-inspired algorithms with the same computational effort. Thus, the usefulness of the proposed position adaptation technique was demonstrated.
Scalability Evaluation of Aspen Tree and Fat Tree Using NS3

When discussing data center networks (DCN), topology has a significant influence on the availability of data to the host. The performance of DCN is relative to the scale of the network. On a particular network scale, it can even cause a connection to the host to be disconnected due to the overhead of routing information. It takes a long time to get connected again so that the data packet that has been sent is lost. The length of time for updating routing information to all parts of the topology so that it can be reconnected or referred to as the time of convergence is the cause. Scalability of a network is proportional to the time of convergence. This article discusses Aspen Tree and Fat Tree, which is about the modification of multi-root trees that have been modified. In Fat Tree, a final set of hosts from a network can be disconnected from a network topology until there is an update of routing information that is disseminated to each switch on the network, due to a link failure. Aspen Tree is a reference topology because it is considered to reduce convergence time and control the overhead of network failure recovery. The DCN topology performance models are implemented using the open source NS-3 platform to support validation of performance evaluations.
Model-based vibration control for optical lenses

Abstract This work presents a contribution to the active image stabilization of optical systems, involving model development, control design, and the hardware setup. A laboratory experiment is built, which demonstrates the vibration sensitivity of a mechanical-optical system. In order to stabilize the undesired image motion actively, a model-based compensation of the image vibration is developed, realized and tested. Beside a linear actuator motion system, a force sensor system and a position sensor system are introduced and analyzed. In particular, various low-cost hardware components of the Arduino platform are used, which support the deployment of the controller software based on Matlab-Simulink. The remaining image motion is measured with a high-speed vision sensor system and the performance of the overall system is assessed.
Forensic Detection Based on Color Label and Oriented Texture Feature

Copy-move forgery is one of the most tampered means. In this paper, we propose a blind method based on the color label and oriented color texture feature for copy-move detection. Firstly, we compute local color entropy of every pixel, which is grouped into several categories as color labels. Then an image is divided into overlapping blocks, oriented color texture feature of which is extracted. Similar blocks are searched in these blocks with the same color label, and then we fuse these similar block pairs into several regions. According to the linkage relation of these regions, the tampered regions are located. Experiment results have demonstrated that the proposed algorithm has good performance in terms of improved detection accuracy and reduced execution time, at the same time, it also can detect these tampered images inpainted by exemplar-based inpainting technique.
Automatic Clustering by Detecting Significant Density Dips in Multiple Dimensions

Clustering algorithms are used to find groups of similar items in a dataset. Automatic clustering algorithms achieve this task without requiring users to input critical parameters. A recent automatic clustering methodology uses Hartigan's dip test to detect significant peaks in the distribution of a dataset. This test can detect peaks in the distribution of a one-dimensional variable. To perform clustering in multiple dimensions, algorithms of this methodology rely on one-dimensional transformations of the dataset, which limits their effectiveness. In this paper, we present M-Dip, an automatic clustering algorithm that works directly on multi-dimensional space. M-Dip also assumes that clusters correspond to different peaks in the distribution of the dataset. It separates clusters at the dips that form between neighboring peaks. Dips are detected directly in multi-dimensional space, using a graph-based method. Their statistical significance is evaluated through appropriate simulations. Our experimental evaluation indicates that M-Dip achieves significantly better results than existing algorithms based on Hartigan's dip, as well as other state-of-the-art automatic clustering algorithms.
Semi-supervised Domain Adaptation with Representation Learning for Semantic Segmentation Across Time

Deep learning generates state-of-the-art semantic segmentation provided that a large number of images together with pixel-wise annotations are available. To alleviate the expensive data collection process, we propose a semi-supervised domain adaptation method for the specific case of images with similar semantic content but different pixel distributions. A network trained with supervision on a past dataset is finetuned on the new dataset to conserve its features maps. The domain adaptation becomes a simple regression between feature maps and does not require annotations on the new dataset. This method reaches performances similar to classic transfer learning on the PASCAL VOC dataset with synthetic transformations.
The logic of modal changes LMC

The logic of change LC formulated by K. Świętorzecka, has its motivation coming from the Aristotelian theory of substantial change which is undrstood as a transformation consisting in the disappearing and becoming of individual substances. The transition: becoming/disapearing (and conversely) is expressed in LC by the primitive operator C, to be read: it changes that …, and it is mapped by the progressively expanding LC language. We are interested in attributive changes of individual substances. We consider a formalism with two non-normal and not mutually definable operators of possible and necessary change, inspired by Aristotelian distinction of accidental and essential attributes. From LC we adopt the idea that temporal concepts are defined via change operators, and the idea of an expanding language. In what follows, We axiomatise our new logic LMC and describe its semantics, giving the proof of its completeness. We compare our formalism with selected modal logics.
Systematic benchmarking for reproducibility of computer vision algorithms for real-time systems: The example of optic flow estimation

Until now there have been few formalized methods for conducting systematic benchmarking aiming at reproducible results when it comes to computer vision algorithms. This is evident from lists of algorithms submitted to prominent datasets, authors of a novel method in many cases primarily state the performance of their algorithms in relation to a shallow description of the hardware system where it was evaluated. There are significant problems linked to this non-systematic approach of reporting performance, especially when comparing different approaches and when it comes to the reproducibility of claimed results. Furthermore how to conduct retrospective performance analysis such as an algorithm's suitability for embedded real-time systems over time with underlying hardware and software changes in place. This paper proposes and demonstrates a systematic way of addressing such challenges by adopting containerization of software aiming at formalization and reproducibility of benchmarks. Our results show maintainers of broadly accepted datasets in the computer vision community to strive for systematic comparison and reproducibility of submissions to increase the value and adoption of computer vision algorithms in the future.
Rapid Estimation of Optical Properties for Simulation-Based Evaluation of Pose Estimation Performance

A growing trend in computer vision is the use of synthetic images for the evaluation of computer vision algorithms such as 3D pose estimation. This is partly due to the availability of high-quality render engines, which provide highly realistic synthetic images. However, the realism of the rendered images, and thus the reliability of the evaluations, strongly depends on how accurately the scenes are modeled and it requires considerable time and knowledge to do the modeling manually. Automating the modeling process is therefore crucial for making the rendering of photo-realistic synthetic images accessible to the wider robotics community. We present a method for automatically modeling object and light properties for rigid, opaque plastic objects commonly found in industry. Our method relies on recordings of the environment captured with a consumer $360^{\mathrm{o}}$ camera to model the light, and on analysis-by-synthesis to estimate the optical properties of the objects. We show that the synthetic images rendered based on our automatic modeling method can be used to predict the overall performance of a monocular 3D pose estimation algorithm.
Automatic Generation of a Simulated Robot from an Ontology-Based Semantic Description

Humans are capable of generating simulated mental worlds based on their past experiences and use such an environment for prospecting, planning, and learning. Such capabilities could enhance current robotic systems, allowing them to plan ahead based on predicted outputs, and even compare their performance with a different agent. In this work, we propose a semantic robot modeling framework, which is able to express intrinsic semantic knowledge in order to better represent the robot and its surrounding environment. We also show that such data can be used to automatically generate a simulated model, allowing robots to simulate themselves and other modeled agents.
Optimizing agricultural land area to increase productivity for anticipating decreasing irrigation water supply

Abstract In order to maintain food security in Indonesia it is very necessary to organize the provision of irrigation water as well as possible. Current surface water discharge conditions have decreased compared to 10 years ago so that there really must be efficiency and wise efforts in the provision of irrigation water in paddy fields. Many efforts to increase the productivity of paddy fields and one of them is by optimizing the area of paddy fields and regulating cropping patterns in the paddy fields. In the initial stage of this study an analysis of the availability of surface water discharge was then analyzed for irrigation water requirements that could be given to each paddy field in accordance with a predetermined cropping pattern. Then a land area simulation is carried out to obtain the most optimal land area. This simulation is carried out in the conditions of the wet season, the normal season and the dry season. Simulations are carried out with various cropping patterns including rice, secondary crops and sugar cane. Simulation results show that the most optimal land area is in the wet season, then the normal season and the dry season.
Information Retrieval in Conjunction With Deep Learning

Information is second level of abstraction after data and before knowledge. Information retrieval helps fill the gap between information and knowledge by storing, organizing, representing, maintaining, and disseminating information. Manual information retrieval leads to underutilization of resources, and it takes a long time to process, while machine learning techniques are implications of statistical models, which are flexible, adaptable, and fast to learn. Deep learning is the extension of machine learning with hierarchical levels of learning that make it suitable for complex tasks. Deep learning can be the best choice for information retrieval as it has numerous resources of information and large datasets for computation. In this chapter, the authors discuss applications of information retrieval with deep learning (e.g., web search by reducing the noise and collecting precise results, trend detection in social media analytics, anomaly detection in music datasets, and image retrieval).
Transfer Learning for Low Resource Spoken Language Understanding without Speech-to-Text

Spoken Language Understanding (SLU) without speech-to-text conversion is more promising in low resource scenarios. These could be applications where there is not enough labeled data to train reliable speech recognition and language understanding systems, or where running SLU on edge is preferred over cloud based services. In this paper, we present an approach for building SLU without speech-to-text conversion in low resource scenarios using a transfer learning approach. We show that the intermediate layer representations from a pre-trained model outperforms the typically used Mel filter bank features. Moreover, the representations extracted from a model pre-trained on one language perform well even for SLU tasks on a different language.
PopEval: A Character-Level Approach to End-to-End Evaluation Compatible with Word-Level Benchmark Dataset

The most prevalent scope of interest for OCR applications used to be scanned documents, but it has now shifted towards the natural scene. Despite the change of times, the existing evaluation methods are still based on the old criteria suited better for the past interests. In this paper, we propose PopEval, a novel evaluation approach for the recent OCR interests. The new and past evaluation algorithms were compared through the results on various datasets and OCR models. Compared to the other evaluation methods, the proposed evaluation algorithm was closer to the human's qualitative evaluation than other existing methods. Although the evaluation algorithm was devised as a character-level approach, the comparative experiment revealed that PopEval is also compatible on existing benchmark datasets annotated at word-level. The proposed evaluation algorithm is not only applicable to current end-to-end tasks, but also suggests a new direction to redesign the evaluation concept for further OCR researches.
Tight upper and lower bounds on suffix tree breadth

The suffix tree — the compacted trie of all the suffixes of a string — is the most important and widely-used data structure in string processing. We consider a natural combinatorial question about suffix trees: for a string S of length n, how many nodes νS(d) can there be at (string) depth d in its suffix tree? We prove ν(n,d)=maxS∈Σn⁡νS(d) is O((n/d)log⁡(n/d)), and show that this bound is asymptotically tight, describing strings for which νS(d) is Ω((n/d)log⁡(n/d)).
Cost-Based Meta-Task Scheduling Algorithm for MultiCloud Computing (CBMTSA)

MultiCloud plays vital role in providing the heterogeneous types of resources to the user on-demand with minimum cost and time. Resource management and scheduling act as an influential aspect in the improvement of the performance of the MultiCloud environment. Scheduling deeds a considerable challenge in the distributed heterogeneous multiple cloud systems. The existing min-min algorithm is suitable for smaller number of tasks. The tasks are allocated to the VMs with high-processing speed. The proposed cost-based meta-task scheduling algorithm (CBMTSA) is feasible for the passive autonomous task-based scheduling for MultiCloud systems. Scheduling and rescheduling are two stages involved in this algorithm. Scheduling stage is used to allocate the tasks to the high-speed VMs. Makespan value is computed in the scheduling stage. The computed makespan is an outset value for rescheduling the tasks. The rescheduling stage is used to reschedule the tasks from high-speed VMs to low-speed VMs. The CBMTSA surpasses the existing min-min algorithm. Makespan, execution cost, and cloud server utilization ratio are the metrics considered in this algorithm.
Architectural Risk Analysis in Agile Development of Cloud Software

Software in the cloud is predominantly developed using agile methodologies, where practices such as continuous deployment and DevOps contribute to increased speed and quick turnarounds. This increased speed does however require additional focus on software security in order to avoid security bugs and architectural flaws from crippling a cloud business. Architectural Risk Analysis has been touted as one of the most powerful software security activities, but in some agile development projects there is no distinct architecture activity, and often no dedicated architects. In this paper we will examine the challenges of performing Architectural Risk Analysis in agile development projects.
Recommendation Algorithm Based on Explicit and Implicit Feedback Matrix Factorization

Matrix factorization is an effective solution to sparse data problem in the recommendation algorithm, but it relies too much on the user's direct behavior, preventing the algorithm from representing the user's true preference for an item. In order to address this challenge, this paper establishes an implicit feedback model through the movie view duration based on explicit feedback. The alternating least-squares method and its optimization method ALS-WR are introduced to factorize the explicit feedback model and the implicit feedback model respectively into two user-latent factor matrices and two item-latent factor matrices. Then the concept of weight-value is introduced to integrate each pair of user-latent factor matrices and item-latent factor matrices to finally produce recommendations. The simulation experiment on the MovieLens dataset is compared with the ALS-based, ALS-WR-based, and SVD-based recommendation algorithms. The experiment results show that the algorithm proposed in the paper is superior to the above algorithms in terms of mean absolute error (MAE) and root mean square error (RMSE), and is effective in improving the quality of recommendation.
Design Space Exploration of Stochastic Computing Architectures Implemented using Integrated Optics

Approximate computing allows to trade-off design energy efficiency with computing accuracy. Stochastic computing is an approximate computing technique, where numbers are represented as bit streams corresponding to probabilities. The serial computation of the bit streams leads to reduced hardware complexity but involves high latency, which is the main limitation of the technique. Integrated optics technology relies on high propagation speed of signals, which has the potential to reduce the processing latency in stochastic computing. However, the design of stochastic computing architectures implemented using integrated optics involves the exploration of numerous parameters at system and technological levels. In this work, we propose a design space exploration framework that allows to optimize energy efficiency, computing accuracy, and latency of such architectures. The efficiency of the framework is evaluated using a Gamma correction image processing application. Results show that, for processing 160 x 160 pixels images, an acceptable <inline-formula><tex-math notation="LaTeX">$ \times 4.5$</tex-math></inline-formula> increase in the errors leads to <inline-formula><tex-math notation="LaTeX">$ \times 47$</tex-math></inline-formula> energy efficiency and <inline-formula><tex-math notation="LaTeX">$ \times 16$</tex-math></inline-formula> processing speed. We also show that the same computing accuracy can be obtained for different energy efficiency and computing latency, thus, validating the ability of the framework to explore the design space.
Artificial Intelligence Theory in Service Management

Artificial intelligence (AI) is expected to be more promising in the coming years, with, for example, notable gains in productivity, although there may be a significant impact on job reduction, which may jeopardize labor sustainability. Accordingly, there is a need to better understand this phenomenon and to analyze it in the light of a particular theory. However, there is a scarcity of AI theories in the service management literature. In order to obtain a better understanding of the subject, we have conducted a systematic review of the literature to provide a comprehensive analysis of the theories developed regarding AI in service management. The results have showed a wide range of theories, but not all directly related with AI; the latter are smaller in number making it difficult to draw a clear pattern. At current days, researchers are slowly advancing with new AI theories and moving away from those already in use, such as in computer science, ethics, philosophical theories, and so on.