Reviewerly/exHarmony huggingface dataset:
Smart Contracts in the Cloud

Abstract The emergence of crypto currencies such as Bitcoin and Ethereum have shown the value in decentralized technologies. The idea of having 24/7 access to programmable money peaked the interest in the field, and as a by-product, came the realization that the same core technologies that enable programmable dog money, can enable highly-available DNS services, highly-available storage services, 24/7 asset exchanges, and peer-to-peer marketplaces to name a few. This paper explores the use of smart contracts in multi-cloud environments in order to facilitate business processes across multiple providers speaking different languages in terms of policies, best practices, APIs, and SLAs.
Contactless Interaction System Based on Facial Expression Recognition for Humanoid Piano Robot

With the wide application of service robots in people's daily life, people are not only satisfied with robots accomplishing tasks independently but also hope that robots can maintain sustainable interaction with human beings. Especially in the field of music, robots need to be equipped with intelligent cognitive skills in decision-making, so that the audience can enjoy immersive appreciation and emotional resonance in the robots performance. Visual perception and natural language understanding are essential for robots to establish human-robot friendly relationships. To enrich the interactive ability of music robots, we design a contactless interaction system based on facial expression recognition, which consists of four modules: voice wake-up, face detection, facial expression recognition, and music mapping. When people wake up the music robot with specific words, the robot will customize appropriate music to perform according to the recognition results of the FER module. As a final step, the interaction system has been successfully applied to the piano humanoid robot independently developed by our team.
Continuous Perception Garment Classification Based on Optical Flow Variation

A novel continuous perception garment classification mechanism is proposed in this paper, with the aim to identify the correct category of the garment from a set of known categories. It has been observed that due to the severe folding and overlapped texture of garments, treating a video of the continuous deformation of cloth as a set of disordered static figures would be ineffective which leads to low classification precision performed by an image-based garment classifier. In contrast, a high-level decision making module that leverages the classification results of each single image would significantly increase the algorithm performance. In this paper, we incorporate the optical flow variation of deformable cloth between consecutive configurations as a representative of how it is traversing within the confidence interval of the image-based classifier. We claim that it is not the number of video frames but the sum of optical flow variation of the garment configuration between consecutive frames having the same category label that constitutes the belief of garment classification. In other words, if two consecutive visual appearances of the garment could be identified as the same category by the image-based classifier, then the more diverged that two configurations are, the more confident that the garment is correctly identified. Experimental comparisons between the state-of-the-art algorithm and the proposed algorithm in a public dataset have been provided which prove the validity of the proposed algorithm.
Website Quality Assessment and Website Design Methods for Educational Organizations

To systemize existing methods for quality assessment and design of websites of educational organizations. To conduct as review of existing criteria for quality assessment of educational organization websites, their ratings. To consider existing restrictions in the design of educational organization web-sites, the means for their creation and their structure organization methods. Methods: Analysis of existing quality ratings of educational organization web-sites, requirements for their design from legislation view as well as of up-todate trends of web-design. Analysis of methods of web-site structure organization. Results: Conclusion was made on the quality importance of university website in terms of a content, structure and working quality for shaping educational institution image. Combining folksonomic and taxonomic approaches is proposed at structure design of website. The importance of using modern design tools to get a possibility for web-site functionality expansion is indicated. Practical significance: The proposed steps can allow to create a comprehensive ecosystem on web-site basis which providing for a big number of functions to satisfy educational process participants’ needs as well as better qualitive delivery of information to target audience.
Information Technologies: Physical and Mathematical Models of Detectors for Environment Monitoring Systems

Ecological monitoring is a real need in contemporary world with its growing tendency of environmental industrial pollution. Among such pollutants there are chemicals emanated in fires, explosions and other emergencies, in process of ruining of chemical enterprises, enterprises of oil and gas cycle, and etc. Some information systems for appropriate monitoring were described briefly in this article together with incorporated hybrid system of electronic chemical pollution detectors that were able to input information to such systems. In previous works of the authors the necessity of development of two types of such detectors with specified properties were substantiated: biosensors and detectors with elements – artificially elaborated selective membranes. This article describes the second type of detectors. The proposed detectors were able to detect harmful chemicals in the environment in the air and water phases; development of such devices was based on the theory of membrane systems. The detectors contained sensor elements covered with chemo-sensitive specific coatings – layers of substances: membranes, films, etc. When in contact with certain chemicals, these detectors demonstrate the properties of chemo-sensitivity and primary identification of such compounds. These studies were aimed on developing detectors of various environmental pollutants based on the theory that can be used as a basis for the detection of such substances. The works were based on a deep study of membrane systems with further development of physical and mathematical models of some substances detection taking into account basic chemical and physical properties of artificial and natural membranes, as well as their ionic selectivity. Some results of mathematical modeling of such detectors with artificial membranes – chemo-sensitive surfaces – were described in the article. Summarizing the previous experience, a brief review of authors’ publications in these items was done, as well as the works of some other authors. The purpose of the work was, basing on profound studying of artificial membrane systems, to suggest them as physical model and summarize the experience of development of their mathematical models.
Metrics for Software Process Simulation Modeling

Background: Software Process Simulation (SPS) has become an effective tool for software process management and improvement. However, its adoption in industry is less than what the research community expected due to the burden of measurement cost and the high demand for domain knowledge. The difficulty of extracting appropriate metrics with real data from process enactment is one of the great challenges. Objective: We aim to provide evidence-based support of the process metrics for software process (simulation) modeling. Method: A systematic literature review was performed by extending our previous review series to draw a comprehensive understanding of the metrics for process modeling following a meta-model of ontology of metrics in SPS. Results: We identified 145 process modeling studies that collectively involve 2130 metrics and classified them using the coding technique. Two diagrams which illustrate the high frequency causal relationships used between metrics are proposed in terms of two hierarchical levels of modeling purposes. We revisited the data issues encountered in SPS data preparing phases, as well as identified the corresponding strategies. Conclusion: The results of this study provide process modelers with an evidence-based reference of the identification and the use of metrics in SPS modeling, and further contribute to the development of the body of knowledge on software metrics in the context of process modeling. Furthermore, this study is not limited to process simulation but can be extended to software process modeling, in general. Taking simulation metrics as standards and references can further motivate and guide software developers to improve the collection, governance, and application of process data in practice.
Research on scheduling path planning of multi-objective unmanned tractor based on reinforcement learning method

In order to improve the efficiency of unmanned tractor ridge operation and save land costs, hence, a multi-objective optimization model is established in this paper, with the goal of minimizing the reserved turning distance and turning scheduling time at the headland. The model is solved by the improved reinforcement learning method according to the tractor’s turning action and motion state, and the optimal turning decision-making method that satisfies the multi-objective optimization conditions is obtained by using the TOPSIS method. On this basis, with the shortest global tractor turning time, the ant colony algorithm is used to plan the ridge operation path of the unmanned tractor. According to the experiment, the optimized unmanned tractor can save 17.8% of the turning time and 23.9% of the reserved length of the headland by operating in the shuttle operation mode; the total turning time of planning the global operation path combined with the ant colony algorithm can be saved by 48.22%.
Gaps in Generalization Theory of Neural Networks

In the last decade, neural networks have become exceptionally powerful. But there is a huge difference in practical achievement and theoretical understanding. One such underdeveloped theoretical concept is generalization of the deep nets. Many studies have been conducted to develop a better understanding of generalization in deep neural networks. In this paper a survey of studies has been done for exploring the generalization concept. The contradiction and overlapping with the statistical learning theory, conventional wisdom and unconventional results has also been highlighted. A sound understanding of generalization will enable researchers to model more cost effective and powerful networks. Demystifying generalization will also result in more informed architecture design decisions
Blockchain based Resource Governance for Decentralized Web Environments

Decentralization initiatives such as Solid and ActivityPub aim to give data owners more control over their data and to level the playing field by enabling small companies and individuals to gain access to data, thus stimulating innovation. However, these initiatives typically employ access control mechanisms that cannot verify compliance with usage conditions after access has been granted to others. In this paper, we extend the state of the art by proposing a resource governance conceptual framework, entitled ReGov, that facilitates usage control in decentralized web environments. We subsequently demonstrate how our framework can be instantiated by combining blockchain and trusted execution environments. Through blockchain technologies, we record policies expressing the usage conditions associated with resources and monitor their compliance. Our instantiation employs trusted execution environments to enforce said policies, inside data consumers' devices.} We evaluate the framework instantiation through a detailed analysis of requirements derived from a data market motivating scenario, as well as an assessment of the security, privacy, and affordability aspects of our proposal.
Research on optimization techniques of multi-frame blind deconvolution image restoration algorithm

Space object images obtained through ground-based telescopes tend to be heavily blurred and degraded by the atmospheric turbulence as well as detection noise and aberrations of optical systems. Multi-Frame Blind Deconvolution (MFBD) is currently the mainstream image restoration algorithm for images degraded by the atmospheric turbulence. MFBD jointly estimates the original image of object and the corresponding point spread functions (PSFs) from a sequence of short-exposure images. From our experience, there are still a lot of space for the improvement of the traditional MFBD algorithm. The mixed-Gaussian noise model that accounts for both the photonic and detector noise is used to replace the stationary Gaussian noise model. The L<sub>2</sub>-L<sub>1</sub> (quadratic-linear) regularization method is used to replace originally used TV regularization method or Tikhonov regularization method. The phase annealing method is used to improve the quality of initial phase estimation and the multi-round iterative MFBD algorithm is preliminarily implemented. The simulation results demonstrate that the restored images obtained by the multi-round iterative MFBD algorithm often have better quality than that restored by traditional MFBD.
Forecasting Quality of Service (QoS) Parameters of IoT based Web Services with an Artificial Electric Field Algorithm trained Artificial Neural Network

Development of an accurate forecasting model for effective prediction of Quality of Service (QoS) parameters of inter-net of things (IoT) based web services is highly desired, such that it improves service management and user experience.Mostly, QoS parameters are volatile in nature which make the IoT based service and recommendation process chal-lenging.Artificial neural network (ANN) based models are found to be worthy in modeling and forecasting nonlinear QoS parameter sequences.However, improper tuning of ANN parameters with conventional training algorithms may lead to a suboptimal model.Nature-inspired optimization methods are found suitable in fine tuning ANN parameters and have shown proficient results on real-world data mining problems.There is lack of such models for QoS parameters prediction that need to be explored.We develop an Artificial Electric Field Algorithm (AEFA) trained ANN (AEFANN) model for effective and accurate prediction of QoS parameters where AEFA is used to search an optimal ANN structure.The optimal ANN structure is achieved by AEFA through an evolutionary process.Two real-world IoT enabled web service datasets are used for evaluating effectiveness of AEFANN in terms of three performance metrics.Experimental procedures and comparative studies are conducted to establish the superiority of the proposed approach over four other similar forecasts.AEFANN obtained relative worth values of 4.13% ~ 69.12 % (5-min granularity) and 43.32% ~ 80.3 % (1-hr granularity) from SERVICE 1 dataset.Similarly, it obtained relative worth values of 7.25% ~ 65.57% (5-min granularity) and 43.38% ~ 72.43 % (1-hr granularity) from SERVICE 2 dataset when compared to oth-er models.This is a significant improvement over comparative existing similar model.
Trade Secret Protection as Part of Intellectual Property System: a Comparative Study of Indonesian and United States of America Trade Secret Law

Legal protection of intellectual property is an important element in supporting creativity and trade. Indonesia has ratified the Agreement and Trade Related Aspects of Intellectual Property Rights and has enacted Law number 30 of 2000 concerning Trade Secret. Legal protection of trade secrets aims to protect business actors in trade practices both regionally and internationally. In addition, protection of trade secrets can prevent unfair business competition. Thus, business people have wider opportunities to develop their creativity and business. This research method is a normative research with comparative research type. This study examines the comparison of intellectual property protection laws specifically regarding trade secrets with laws in United States. The purpose of this comparative normative study to examine the extent to which the Trade Secret Act can be effective in its application as can be applied in the Supreme Court Decision.
DynInt: Dynamic Interaction Modeling for Large-scale Click-Through Rate Prediction

Learning feature interactions is the key to success for the large-scale CTR prediction in Ads ranking and recommender systems. In industry, deep neural network-based models are widely adopted for modeling such problems. Researchers proposed various neural network architectures for searching and modeling the feature interactions in an end-to-end fashion. However, most methods only learn static feature interactions and have not fully leveraged deep CTR models' representation capacity. In this paper, we propose a new model: DynInt. By extending Polynomial-Interaction-Network (PIN), which learns higher-order interactions recursively to be dynamic and data-dependent, DynInt further derived two modes for modeling dynamic higher-order interactions: dynamic activation and dynamic parameter. In dynamic activation mode, we adaptively adjust the strength of learned interactions by instance-aware activation gating networks. In dynamic parameter mode, we re-parameterize the parameters by different formulations and dynamically generate the parameters by instance-aware parameter generation networks. Through instance-aware gating mechanism and dynamic parameter generation, we enable the PIN to model dynamic interaction for potential industry applications. We implement the proposed model and evaluate the model performance on real-world datasets. Extensive experiment results demonstrate the efficiency and effectiveness of DynInt over state-of-the-art models.
MedBust: Blockchain in Pharmaceutical Supply Chain

Counterfeit drugs are a major and growing concern globally, particularly during pandemics. One of the reasons for this is the inefficiency of the supply chain system in the pharmaceutical industry, which can make it difficult to trace the ownership of drugs from manufacturer to wholesaler, distributor, and pharmacist before it reaches the customer. In this study, we have evaluated different proposed blockchain-based supply chain management systems. The system that uses Hyperledger Fabric ensures data sharing, storage, transparency, and traceability in every stage of the supply chain. Meanwhile, the Ethereum architecture makes use of smart contracts to manage the communication between sender and receiver. The primary aim of the research is to improve the safety of pharmaceutical products and streamline supply chain operations with the most effective architecture. In addition to the benefits of transparency and traceability that blockchain technology can provide, it can also help to improve the overall efficiency and security of the supply chain. For example, by using smart contracts, automated processes can be put in place for the transfer of ownership and payment for drugs, reducing the need for manual intervention and reducing the risk of errors or fraud. Additionally, by creating a tamper-proof record of all transactions, blockchain technology can provide an auditable trail of the movement of drugs through the supply chain, which can be helpful for regulatory compliance and quality assurance. Furthermore, blockchain technology can also support the implementation of new business models in the pharmaceutical industry such as digitalized provenance records and tokenization of drugs to provide more transparency, security, and trust for the customer. It's worth noting that implementing blockchain technology in the pharmaceutical supply chain will require collaboration and cooperation between stakeholders in the industry, including manufacturers, wholesalers, distributors, and regulators. With the right approach and collaboration, blockchain technology has the potential to revolutionize the way that the pharmaceutical industry operates, making the supply chain more efficient, secure, and reliable, while also helping to reduce the risk of counterfeit drugs and protect public health.
Simulating Prosumer Data Trading: Testing a Blockchain Smart Contract Based Control

Online data trading has grown alongside the ever-increasing use of digital services. Industries are accruing the benefits of this data access to perform mission-critical tasks by analyzing available data for greater insight. Unsurprisingly, data trading has not focused on improved data seller protection with preferences and controls. The objective of this paper is to explore the enforcement of seller preferences within smart contracts using blockchain technology. Data trading is only possible when a buyer satisfies the conditions predefined by the seller. Geographic location, type or size of buyer's company are some examples of seller preferences. A preferences algorithm provides an automated contract between seller and buyer without the involvement of any broker or third party. Hybrid simulation (HS) methods are used to test and evaluate the viability of our novel data control approach.
Lexicon and attention based handwritten text recognition system

The handwritten text recognition problem is widely studied by the researchers of computer vision community due to its scope of improvement and applicability to daily lives. It is a sub-domain of pattern recognition. Due to advancement of computational power of computers since last few decades neural networks based systems heavily contributed towards providing the state-of-the-art handwritten text recognizers. In the same direction, we have taken two state-of-the art neural networks systems and merged the attention mechanism with it. The attention technique has been widely used in the domain of neural machine translations and automatic speech recognition and now is being implemented in text recognition domain. In this study, we are able to achieve 4.15% character error rate and 9.72% word error rate on IAM dataset, 7.07% character error rate and 16.14% word error rate on GW dataset after merging the attention and word beam search decoder with existing Flor et al. architecture. To analyse further, we have also used system similar to Shi et al. neural network system with greedy decoder and observed 23.27% improvement in character error rate from the base model.
Efficient Content-Based Image Retrieval System with Two-Tier Hybrid Frameworks

Abstract The Content Based Image Retrieval (CBIR) system is a framework for finding images from huge datasets that are similar to a given image. The main component of CBIR system is the strategy for retrieval of images. There are many strategies available and most of these rely on single feature extraction. The single feature-based strategy may not be efficient for all types of images. Similarly, due to a larger set of data, image retrieval may become inefficient. Hence, this article proposes a system that comprises of two-stage retrieval with different features at every stage where the first stage will be coarse retrieval and the second will be fine retrieval. The proposed framework is validated on standard benchmark images and compared with existing frameworks. The results are recorded in graphical and numerical form, thus supporting the efficiency of the proposed system.