Reviewerly/exHarmony huggingface dataset:
A Framework for Trust Management in Cloud Computing Environment

Cloud Computing is considered as a business model for providing IT resources as services through the Internet based on pay-as-you-go principle. These IT resources are provided by Cloud Service Providers (CSPs) and requested by Cloud Service Consumers (CSCs). Selecting the proper CSP to deliver services is a critical and strategic process. According to the work in this paper, a framework for trust management in cloud computing has been introduced. The proposed framework consists of five stages; Filtrating, Trusting, Similarity, Ranking and Monitoring. In the Filtrating stage, the existing CSPs in the system will be filtered based on their parameters. The CSPs trust values are calculated in the Trusting stage. Then, the similarity between the CSC requirements and the CSPs data is calculated in the Similarity stage. The ranking of CSPs will be performed in Ranking stage. According to the Monitoring stage, after finishing the service, the CSC sends his feedbacks about the CSP who delivered the service to be used to monitor this CSP. To evaluate the performance of the proposed framework, a comparative study has been done for the Ranking and Monitoring stages using Armor dataset. According to the comparative results it is found that the proposed framework increases the reliability and performance of the cloud environment.
Expressive Power and Succinctness of the Positive Calculus of Relations

In this paper, we study the expressive power and succinctness of the positive calculus of relations. We show that (1) the calculus has the same expressive power as that of three-variable existential positive (first-order) logic in terms of binary relations, and (2) the calculus is exponentially less succinct than three-variable existential positive logic, namely, there is no polynomial-size translation from three-variable existential positive logic to the calculus, whereas there is a linear-size translation in the converse direction. Additionally, we give a more fine-grained expressive power equivalence between the (full) calculus of relations and three-variable first-order logic in terms of the quantifier alternation hierarchy. It remains open whether the calculus of relations is also exponentially less succinct than three-variable first-order logic.
Geostatistical simulation for filling misclassified pixels in hyperspectral images

Mine of the future or mining 4.0 is the subject of numerous current research projects. There are many research questions to answer. One important aspect is to investigate the possibility of an automated geological detection and face mapping, especially in underground mining. Optical sensors are used for automatic detection tasks in industrial sorting. Especially hyperspectral sensors are widely used to identify materials and are therefore able to identify the ore. This can be shown in various studies on the mapping of outcrops in open pits. To use this technique in an underground mine requires the development of a method that can cope with the challenges of this complex environment including inconsistent light conditions, high variability of the mineral distribution and associated reflection issues. Due to these environmental conditions, poor data quality must be expected at least in some image sections. As a result, many pixels cannot be classified correctly. To solve this problem, this contribution proposes in a first step to identify these not uniquely classified pixels. The image is classified using fuzzy C-means and the poorly classified pixels are identified. In a second step, an attempt is made to determine a possible class affiliation for these identified pixels by analyzing neighborhood conditions using geostatistics. In particular, possible realizations for these poorly classified pixels are simulated using the Single Normal Equation (SNESIM) algorithm. The combination of the classification action results with the geostatistical simulation using Bayes statistics leads to the final results. This paper presents an investigative study of this proposed approach and discusses results.
Bow Gesture Classification to Identify Three Different Expertise Levels: A Machine Learning Approach

To acquire new skills in a high-level music context, students need many years of conscious dedication and practice. It is understood that precise motor actions have to be incorporated into the musicians’ automatic executions, where a repertoire of technical actions must be learned and mastered. In this study, we develop a computer modelled assistant applying machine learning algorithms, for self-practice musicians with the violin as a test case. We recorded synchronized data from the performer’s forearms implementing an IMU device with ambient sound recordings. The musicians perform seven standard bow gesture. We tested the model with three different expertise levels to identify relevant dissimilitudes among students and teachers.
The impact of parental education levels on digital skills of students in urban sprawl impacted areas

Abstract The current research will describe developing cities in developing countries especially in Indonesia representing polycentric or urban sprawl growth patterns. The purpose of this study is to identify the influence of the level of education parents have on the digital expertise of students in supporting learning in urban sprawl impacted areas, this is intended to recommend handling the problem of low levels of knowledge of students about the use of technology to support learning and so that students have good digital expertise to support his work in the future. Data analysis used is descriptive qualitative to explore social factors of the community and students who develop in Colomadu District. Data were obtained through interview methods accompanied by questionnaires in the study area. The results of this study illustrate that the low level of education of parents will lead to low income and socio-economic conditions, thus becoming a barrier to information and digital abilities of students in urban sprawl affected areas. Students tend to have digital expertise when the level of education of parents is in a good level and has a good economic level, besides that it is found that regional development does not necessarily have a positive impact on increasing students’ knowledge about technology. Furthermore, the low level of digital ability of students will be predicted to become an obstacle in the future.
A Human-Object Interaction Detection Method Inspired by Human Body Part Information

In recent years, with the rapid development of big data and computer hardware, deep learning has regained vitality, and the field of computer vision has made great progress. Human Object Interaction (HOI) Detection is an important subject for image understanding in computer vision, which has not been effectively solved yet. Aiming at the problem that the human pose information is not effectively used in the current HOI detection methods, an HOI detection method that integrates human body part information (HBP) is proposed. By applying human body part information to HOI detection, it makes the best of the relationship between parts and objects that effectively improves detection performance. The experiments and results on Verbs in COCO (V-COCO) dataset show the effectiveness of our method.
A Reflection on “An Exploratory Study on Exception Handling Bugs in Java Programs”

Exception handling is a feature provided by most mainstream programming languages, and typically involves constructs to throw and handle error signals. On the one hand, early work has argued extensively about the benefits of exception handling, such as promoting modularity by defining how exception handlers can be implemented and maintained independently of the normal behavior of the system and easing but localization. On the other hand, some studies argue that exception handling can make the programming languages unnecessarily complex and promote the introduction of subtle bugs in programs. In 2015 we published a paper describing a study investigating the prevalence and nature of exception handling bugs in two large, widely adopted Java systems. This study also confronted its findings about real exception handling bugs with the perceptions of developers about those bugs, also accounting for bugs not related to exception handling. The goal of this reflection paper is to investigate the state of the art in exception handling research, with a particular emphasis on exception handling bugs, and how our paper has influenced other studies in the area. We found that our paper was cited by 33 articles, and all themes for future work we raised in our paper have been tackled by other studies in the short span of five years.
On the Effectiveness of Intrusion Response Systems against Persistent Threats

Persistent threats are the most sophisticated type of cyber attacks that involves multi-step attack sequences. There is a need to develop timely, efficient and appropriate intrusion response to mitigate the risk from persistent threats. In this paper, we model an intrusion response system by considering the diverse strategy an attacker could employ in his lateral movement within the target environment. Our model investigates attackers' opportunity corresponding to each strategy in their potential penetration path. We design a response mechanism by, first, analyzing prior steps of the attack sequence and their impacts, next, extracting attacker's characteristics from them, and finally, incorporating their influence on the next adversarial options, to efficiently restricts the follow up opportunities. The objective of our method is to deter attack occurrence by increasing the cost and uncertainty in attack planning and execution. Simultaneously, it is designed to ensure real-time situational awareness and operational efficiency. The proposed approach has been implemented with a case study on Industrial Control Systems. The empirical results show the significant improvement of impeding active threat propagation in the network.
Conceptual problems of contemporary additional education in the agro-industrial complex: employers’ personnel strategies

Abstract The paper critically reviews the conceptual approaches existing in contemporary additional education in the agro-industrial complex. In particular, employer’s personal strategies are discussed in current conditions.
Highly Reliable IoT Data Management Platform Using Blockchain and Transaction Data Analysis

In recent years, IoT (Internet of Things) systems that support human activities based on various types of real-world data (e.g., temperature, location, etc.) have been attracting attention in various fields. However, the behavior of the system is decided based on the real-world data, so the system may cause critical issues if the data is tampered by attackers. Therefore, in order to guarantee reliability of the real-world data, a data management platform utilizing blockchain technology has been studied by many researchers. The blockchain can guarantee integrity of the stored data for the IoT system, but cannot guarantee authenticity of the data because tampering of the data that has not been stored on the database yet cannot be prevented.Therefore, in this study, we propose a new data management platform using blockchain that contributes to provide reliable IoT services by guaranteeing both integrity and authenticity of the real-world data. The data for the IoT service is continuously generated on the real field, and there may exist not only a terminal (e.g., smartphone, sensor node) which is an origin of the data but also the other terminals on the field. In the proposed platform, the origin attempts to guarantee authenticity of the data by requesting the near terminals to generate the same types of data. By comparing the data and the update history of the data among the terminals, the proposed platform attempts to verify authenticity of the data because the tendency of the update history of the origin should be almost the same as that of the cooperators if the data tampering did not occur.In order to evaluate effectiveness of the proposed platform, we design and prototype a data management platform for a physical distribution system. In the prototyped system, the origin terminal which is deployed on a transport vehicle continuously measures the location information, and requests smartphones of pedestrians near the vehicle to obtain the data for verifying the authenticity.
Use of gradient and normal vectors for face recognition

The main objective of this study is to compare face recognition accuracies in the case when the grey levels in each pixel of the face images are replaced by the gradient and the surface normal vectors. Extensive information is provided to explain the differences between the gradient and the proposed features. Some well-known face recognition methods, such as common vector approach (CVA), discriminative CVA, and support vector machines are applied to the well-known databases of AR and Yale for comparison other than introducing a new method what the authors called as Sum of Pixel Slope Similarities Approach. The authors’ experimental results are compared with the state-of-the-art methods to the best of their knowledge. In conclusion, their results imply that using the surface normal vectors rather than the gradient vectors in each pixel with no additional work on their elements gives better recognition rates.
OIDPR: Optimized insulin dosage via privacy‐preserving reinforcement learning

Abstract The precision of insulin dosage is essential in the process of diabetes treatment. The fact is providing precise dosage is almost impossible for clinicians since blood sugar levels are dynamically affected by many factors. Even though some auxiliary dosing systems have been proposed, the required real‐time physical data about the health situation of diabetics is still hard to synchronize to the end‐devices instantly. The traditional personalized drug delivery frameworks for accurate dosing of insulin always collect and transmit medical data in cleartext, which raises privacy problems. In this article, we propose a framework for an optimized insulin dosage via privacy‐preserving reinforcement learning to diabetics (OIDPR). In OIDPR, both the additive secret sharing and edge computing are deployed to achieve data confidentiality and performance optimization. The medical data is divided into multiple secret shares uniformly at random for outsourcing and operating at the edge servers. During the computation task of reinforcement learning, data is encrypted and processed via our proposed additive secret sharing protocol, where the privacy is reserved by the efficient encryption mechanism and the secret sharing system only incurs little workload. We provide comprehensive theoretical analyses and experimental results that demonstrate the supervisor functionality and high performance of our framework.
Research on Automatic Decoding of Morse Code Based on Deep Learning

The current automatic decoding method of the Morse telegram has limited accuracy, and can't adapt to signal distortion and code length deviation of the manual telegram. This paper introduces the deep learning method and constructs an automatic decoding model, which integrates feature extraction, sequence modeling and transcription into an end-to-end training neural network. The time-frequency diagrams of signals are used for training and testing. Experimental results show that the decoding system has strong adaptability to manual deviation and frequency drift, and is robust in a noisy environment.
Formal verification of debates in argumentation theory

Humans engage in informal debates on a daily basis. By expressing their opinions and ideas in an argumentative fashion, they are able to gain a deeper understanding of a given problem and in some cases, find the best possible course of actions towards resolving it. In this paper, we develop a methodology to verify debates formalised as abstract argumentation frameworks. We first present a translation from debates to transition systems. Such transition systems can model debates and represent their evolution over time using a finite set of states. We then formalise relevant debate properties using temporal and strategy logics. These formalisations, along with a debate transition system, allow us to verify whether a given debate satisfies certain properties. The verification process can be automated using model checkers. Thus, we also measure their performance when verifying debates, and use the results to discuss the feasibility of model checking debates.
Partitioning Vulnerabilities: On the Paradoxes of Participatory Design in the City of Malmö

In this chapter, Björgvinsson and Keshavarz challenge the claims of Scandinavian participatory design in initiating bottom-up change, democratic engagement and overcoming the vulnerabilities of marginal groups. By following its trajectory from its engagement with trade unions in the 1970s to local communities in the 2000s and beyond, the chapter problematizes participatory design’s presuppositions in relation to vulnerabilities associated with different groups. Through analysing a participatory design project where Björgvinsson was involved, the authors show how the call to and the process of participation always happen in an already partitioned world. As such, they argue participation does not necessarily give equal voices to participating parts, as frequently claimed but, rather, produce new parts, ignore certain other parts and lift up particular parts depending on the power relations involved. The chapter concludes that participatory methods not only generate less recognized vulnerabilities, but also ignore the resistance made by vulnerable groups against uneven participation.
Consumer Cognizance about Forex Card in Digital Epoch

Forex cards are used for making payments while travelling abroad or when studying abroad. These are pre-loaded and enable to access money in the required provincialcash. The card allows withdrawing cash in foreign currency, check balance, shop and also topping it up depending on the requirement. This research has been based on the payment through the use of plastic card or money. Plastic card or money is another option to the cash or the standard money. In this digital era the use of plastic money is known to the Debit and Credit Cards that we use to make payment in our daily life & Forex Cards is also very beneficial for travelers who are going to foreign for visit or for corporate professionals who are going foreign for their work and also beneficial for the students who are taking oversea education. Forex Card money is very easy and convenient to carry all over the world and no need no to carry a huge amount of money. If anyone is in foreign country by using Forex Card, he/she can do payment according to the currency of that country and avoid for go to money exchanger in that country. Forex Card having many currencies in it for payment in different countries, this research is focusing to know about the awareness about the forex card service provide by the bank to the Tour & Travel Sector. The objective of this study is to find the level of awareness about the Forex Card in Tour & Travel Sector in India. A set of 26 research articles that studied about forex card and its awareness among the various sectors like tour & travel sector, education consultancy sector and many more sectors. The finding of this study by using SPSS tool confirm that cognizance about Forex Card in tour & travel sector is positively associated with income level of tour & travel organization, nature of business and target specific group. Which mean the tour & travel organization that have these positive associations are more aware about the forex card. Overall, and the line of business, registration of the organization with any foreign exchange association was found to be negatively associated with the level of awareness about the forex card.
The Impact of Comfortable Viewing Positions on Smart TV Gestures

Whereas gesture elicitation studies for TV interaction assume that participants adopt an upright, frontal viewingposition, we asked 21 participants to hold a natural, comfortableviewing position, the posture they adopt when watching TVat home. By involving a broad selection of users regardingage, profession, our study targets a higher ecological validitythan existing studies. Agreements rates were lower than existingstudies using an upright, frontal viewing position. Participantsexperienced problems due to (1) having to use their slave handinstead of their dominant hand, (2) being in a certain orientationwith their head making it more difficult to perform some physicalmovements, and (3) being hindered in their movement by thesofa there lay on. Since each person may have a differentposition inducing different gestures due to the aforementionedproblems, the effect of comfortable viewing position is analyzedby comparison to gestures for a frontal position.
Implementation of a parallel algorithm of image segmentation based on region growing

In computer vision and image processing, image segmentation remains a relevant research area that contains many partially answered research questions. One of the fields of most significant interest in Digital Image Processing corresponds to segmentation, a process that breaks down an image into its different components that make it up. A technique widely used in the literature is called Region Growing, this technique makes the identification of textures, through the use of characteristic and particular vectors. However, the level of its computational complexity is high. The traditional methods of Region growing are based on the comparison of grey levels of neighbouring pixels, and usually, fail when the region to be segmented contains intensities similar to adjacent regions. However, if a broad tolerance is indicated in its thresholds, the detected limits will exceed the region to identify; on the contrary, if the threshold tolerance decreases too much, the identified region will be less than the desired one. In the analysis of textures, multiple scenes can be seen as the composition of different textures. The visual texture refers to the impression of roughness or smoothness that some surfaces created by the variations of tones or repetition of visual patterns therein. The texture analysis techniques are based on the assignment of one or several parameters indicating the characteristics of the texture present to each region of the image. This paper shows how a parallel algorithm was implemented to solve open problems in the area of image segmentation research. Region growing is an advanced approach to image segmentation in which neighbouring pixels are examined one by one and added to an appropriate region class if no border is detected. This process is iterative for each pixel within the boundary of the region. If adjacent regions are found, a region fusion algorithm is used in which weak edges dissolve, and firm edges remain intact, this requires a lot of processing time on a computer to make parallel implementation possible
An investigation of the project management approaches of agile and plan-based companies

Background Agile Methods have been around for almost 20 years but still, there is an ongoing debate whether they are able to improve software development. In particular, project management is one of the most controversial aspect of Agile Methods.