Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloudarmor: Supporting Reputation-Based Trust Management for Cloud Services

Cloud services have become predominant in the current technological era. For the rich set of features provided by cloud services, consumers want to access the services while protecting their privacy. In this kind of environment, protection of cloud services will become a significant problem. So, research has started for a system, which lets the users access cloud services without losing the privacy of their data. Trust management and identity model makes sense in this case. The identity model maintains the authentication and authorization of the components involved in the system and trust-based model provides us with a dynamic way of identifying issues and attacks with the system and take appropriate actions. Further, a trust management-based system provides us with a new set of challenges such as reputation-based attacks, availability of components, and misleading trust feedbacks. Collusion attacks and Sybil attacks form a significant part of these challenges. This paper aims to solve the above problems in a trust management-based model by introducing a credibility model on top of a new trust management model, which addresses these use-cases, and also provides reliability and availability.
Applications of logic constrained equilibria to traffic networks and to power systems with storage

Abstract We study equilibria in traffic networks and in power system networks with storage in the presence of logic constraints. These constraints consist of binary variables that are added to complementarity-based equilibrium models. Although these models have been thoroughly studied, the addition of logic constraints can provide additional benefits for practical applications. The main contribution of this work is to demonstrate, using two specific examples of applications, that logic constraints can render classical equilibrium models more realistic by allowing the inclusion of useful features such as equity in network flows or threshold events. Specifically, for the traffic equilibrium problem, we show how logic constraints can introduce some equity in the assignment of traffic when more than one equilibrium exists. For power system networks, we show that the presence of a storage operator acting as a service provider will not only support the operation of a power grid, but will also help stabilise the price of electricity and avoid the well-documented price-shifting effect. Unlike previous works, our model considers the storage operator as a service provider rather than a competitor to the producers. We also consider the minimum power output of production. We present results illustrating the expanded capabilities and insights provided by these new paradigms.
Improving the Model of Increasing the Quality of Auditing Services

It is proved that the quality of audit services has two aspects: firstly, the quality of the audit, in which the quality assurance object is an independent audit of the financial statements and the accounting system; secondly, the quality of audit activity in which the object of quality assurance is the work of the subject of audit activity in general. The Model of the internal quality control system of the audit activity as a system of two components: internal structure; external environment "exit", "entrance"; feedback and external environment. Some deficiencies were identified when creating and implementing quality control systems for audit services at the micro level.
Moving QA towards reading comprehension using context and default reasoning

For a question answering system to understand the underlying assumptions and contextual features of natural language, a contextually and semantically sensitive knowledge representation and reasoning module is essential. This paper proposes a three-layered knowledge representation coupled with reasoning mechanisms for defaults and contexts as a solution for pushing question answering towards a state capable of basic reading comprehension. A travel scenario is presented as a vehicle for demonstrating the complexities of this problem, and the ways in which semantic relations, contexts (for conditionality, planning, time, and space), and defaults address these complexities.
A Literature Survey on IoT Security Challenges

This survey paper discuss the security challenges in IoT network. IoT is hot research topic in between researchers. In the era of digital media IoT play an important role of information servers and data warehouses. IoT network are used in different cities of smart city network. That’s why the security of the IoT network is also a big task. There are many loop holes available in IoT based security network. In this survey paper discuss the security aspects in the IoT networks. Also discuss the different researches previous work and previous work in IoT security. This survey analyzes existing protocols and mechanisms to secure communications in the IoT, as well as open research issues. We analyze how existing approaches ensure fundamental security requirements and protect communications on the IoT, together with the open challenges and strategies for future research work in the area. This is, as far as our knowledge goes, the first survey with such goals.
Knowledge and attitude towards basics of communication skills amongst resident doctors

Background: The pillar of doctor patient relationship is the bond of mutual trust. This pillar is strengthened by effective communication between doctor and patient/relatives. It has been widely accepted that a doctor needs to have very good communication skills in addition to clinical knowledge and skills required for effective healthcare. Every doctor duly needs to demonstrate humanity, healthy behaviour, as well as sensible communication with patients which can build the sustainable ‘trust’ in the treatment offered by that doctor. However, the current curriculum of medical courses is enormously focused on providing competent medical knowledge but lacking the focus on inculcating communication skills.Methods: A cross sectional study was carried out amongst 377 resident doctors of first to third year, from randomly selected medical colleges of Maharashtra having postgraduate courses in clinical subjects and affiliated to Maharashtra University of Health Sciences, Nashik, Maharashtra, India during the period of February 2017 to January 2019. Data was entered in MS Excel sheet and analyzed using percentages and proportions wherever appropriate.Results: Highest study respondents were from General Medicine with 13% of total and having 17.4% males and 8% females among respective genders followed by General surgery and Pediatrics residents with 12.5% each specialty.Conclusions: The study has shown that there was varied knowledge about the communication skills among the resident doctors. The residents have shown fair attitudes and behaviours towards mindfulness and basic communication skills.
Near-Memory Address Translation

Virtual memory (VM) is a crucial abstraction in modern computer systems at any scale, from handheld devices to datacenters. VM provides programmers the illusion of an always sufficiently large and linear memory, making programming easier. Although the core components of VM have remained largely unchanged since early VM designs, the design constraints and usage patterns of VM have radically shifted from when it was invented. Today, computer systems integrate hundreds of gigabytes to a few terabytes of memory, while tightly integrated heterogeneous computing platforms (e.g., CPUs, GPUs, FPGAs) are becoming increasingly ubiquitous. As there is a clear trend towards extending the CPU's VM to all computing elements in the system for an efficient and easy to use programming model, the continuous demand for faster memory accesses calls for fast translations to terabytes of memory for any computing element in the system. Unfortunately, conventional translation mechanisms fall short of providing fast translations as contemporary memories exceed the reach of today's translation caches, such as TLBs. In this thesis, we provide fundamental insights into the reason why address translation sits on the critical path of accessing memory. We observe that the traditional fully associative flexibility to map any virtual page to any page frame precludes accessing memory before translating. We study the associativity in VM across a variety of scenarios by classifying page faults using the 3C model developed for caches. Our study demonstrates that the full associativity of VM is unnecessary, and only modest associativity is required. We conclude that capacity and compulsory misses---which are unaffected by associativity---dominate, while conflict misses rapidly disappear as the associativity of VM increases. Building on the modest associativity requirements, we propose a distributed memory management unit close to where the data resides to reduce or eliminate the TLB miss penalty.
Chapter 7 Sensors for Critical Path Monitoring

Modern processes are becoming more sensitive to noise [25]. In addition to technology parameters having larger variation with each new technology generation [1], [20], timing sensitivity to such environmental conditions as temperature [19], [31], aging [3], workload [19], [13], cross-talk noise in wires [18], NBTI [12], and many other effects is increasing. Noise processes that effect timing are described as random or systematic, and they are measured from die to die and within die [6], [32]. Random noise is less dependent on the integrated circuit’s design than systematic noise and it is characterized by a number of statistics such as its mean and standard deviation. Systematic noise results from characteristics of the manufacturing process or from the physical design and can be predicted once the underlying process causing the variation is understood. For example, the wire thickness in technologies that use copper metallization is dependent on the wire density and wire width [21]. Once the source of systematic variation is identified, designs can be adjusted or processing can be modified to reduce the variation [27], [1]. Die-to-die noise measurements measure the statistical difference between separate integrated circuit die and inter-die noise is measured within a single die [5], [4]. The increasing timing uncertainty due to noise processes as technology scales is creating a need for on-chip timing sensors that can be explained using Figure 7.1. During the early design stages of the integrated circuit, assuming it is a microprocessor, an iterative process is used to develop the architecture to meet the performance targets of the intended application. Once the architecture is defined, the microprocessor passes through logic,
M-Learning Applications as Language Teaching Tools: Study Cases and Comparisons

This work tries to comprehend and analyze the usability of m-learning and e-learning applications to provide Foreign Language (FL) content as a competitive advantage and support to the internationalization of companies. It presents these applications as a possible competitive advantage and support to facilitate access to communication abroad. The study departs from the general – the usability of m-learning and e-learning applications and the teaching-learning approach of FL, to the specific – the competitive advantage and support to the internationalization of companies.
Jigsaw: An infrastructure for cross-device mashups

The ubiquity of web applications and the increasing proliferation of computing devices in everyday life lead to situations and environments where collaboration and reuse of existing web-based resources become ever more important. While research usually focuses either on composing parts of different web sites to provide new applications (mashups) or distributing user interfaces across devices and users (DUIs), little work has been done in bringing the two research streams together. The existing solutions, furthermore, are complex and targeted at experienced developers. Sitting at the intersection of the two research areas of mashups and DUIs, as well as integrating aspects of collaborative browsing, this thesis presents Jigsaw—an infrastructure for cross-device mashups. The focus in Jigsaw is on direct manipulation techniques for existing web applications designed to enable developers as well as end-users to create DUIs by distributing and combining multiple web sites across multiple devices. Jigsaw is built upon a client-server remote control architecture that, by using a headless browser, provides synchronisation as well as continuity between devices, mashups, and web pages that are distributed as components. In a two-step evaluation involving both technical and conceptual considerations, we investigate Jigsaw’s breadth by testing a simple but representative use case scenario on a variety of top web sites selected according to popularity and traffic, and we discuss a number of dimensions defined by a logical framework for multi-device environments extended to cross-device mashups. Our results suggest that most web sites would work without major issues and could, therefore, be used in cross-device mashups based on Jigsaw.
Qualitätsmanagement in Online-Universitäten

In this study the aim is to introduce a system for managing quality in an online university. After exploring the literature regarding distance education (in general) and online universities, along with various aspects and features of managing quality in educational institutes, a model of essential components of a university with emphasis on online features, is illustrated, introduced, and discussed in detail. Then, a chain process model, is designed to indicate the main phases for providing a teaching-learning environment by designing and implementing a program in an online university. For each phase, various vital tasks and their indicators are defined, and based on these tasks and assigned indicators, a measurement table is designed with the aim to provide a method to estimate the quality in an online university and demonstrate the concept of quality via a model of quantitative measurement. For further studies regarding the managing quality in an online university based on this study, first, these designed models and the measurement system associated with them should be executed in an online university. Then, they could be modified and improved based on the outcome of a process of receiving feedback and evaluating the collected data and information.
Efficient 3D point clouds classification for face detection using linear programming and data mining

ABSTRACT Most of the applications related to security and biometric rely on skin region detection such as face detection, adult 3D objects filtering, and gesture recognition. In this paper, we propose a robust method for skin detection on 3D coloured point clouds. Then, we extend this method to solve the problem of 3D face detection. To do so, we construct a weighted graph from initial coloured 3D point clouds. Then, we present a linear programming algorithm using a predictive model based on a data mining approach to classify and label graph vertices as skin and non-skin regions. Moreover, we apply some refinement rules on skin regions to confirm the presence of a face. Furthermore, we demonstrate the robustness of our method by showing and analysing some experimental results. Finally, we show that our method deals with many data that can be represented by a weighted graph such as 2D images and 3D models.
MUDHARABAH AGRI-CROWDFUNDING IN THE EX-CLAY MINE UTILIZATION PROGRAM OF PT. SEMEN INDONESIA (PERSERO), TBK. CAMPUS OF C UNIVERSITAS INTERNASIONAL SEMEN INDONESIA

The objectives of research are to study market, technical, and financial feasibility of crowdfunding project using Syariah contract on specified agriculture project based on a higher education institute potency. Such process will be carried out to identify a feasibility of the land use in the project and the amount of funding needed to be collected with the concept of crowdfunding. The construction of a project requires analysis to determine the feasibility of the project. Method of research is quantitative by calculating financial feasibility. UISI has a land area of 3.8 hectares located on Campus of C UISI. This land is an ex-clay mining land of Semen Indonesia Company. Total investment and capital funding amount to Rp 100,000,000. The investment return period is 2 years. Based on the profit and loss calculation, it can be considered that capital and investment will return in the 6th month of the 1st year. In the 1st year the IRR obtained was 19%, whereas until the end of the second year the IRR was obtained by 42%. A significant difference occurred because in the second year Belimbing Tasikmadu, and Jambu Kristal fruit could already be harvested for commercial purpose.
Assessing Continuous Evaluation Approaches for Insider Threats: How Can the Security Posture of the U.S. Departments and Agencies Be Improved?

This exploratory project examines various continuous evaluation approaches to detecting insider threats that are available to the U.S. government and assesses the relevance of these approaches to the challenges posed by such insider threats. There are potential benefits in effectiveness and cost over the current method of granting security clearances to personnel based on periodic reinvestigation and readjudication.
Edf scheduling on heterogeneous multiprocessors

The goal of this dissertation is to expand the types of systems available for real-time applications. Specifically, this dissertation introduces tests that ensure jobs will meet their deadlines when scheduled on a uniform heterogeneous multiprocessor using the Earliest Dead line First (EDF) scheduling algorithm, in which jobs with earlier deadlines have higher priority. On uniform heterogeneous multiprocessors, each processor has a speed s, which is the amount of work that processor can complete in one unit of time. Multiprocessor scheduling algorithms can have several variations depending on whether jobs may migrate between processors—i.e., if a job that starts executing on one processor may move to another processor and continue executing. This dissertation considers three different migration strategies: full migration, partitioning, and restricted migration. The full migration strategy applies to all types of job sets. The partitioning and restricted migration strategies apply only to periodic tasks, which generate jobs at regular intervals. In the full migration strategy, jobs may migrate at any time provided a job never executes on two processors simultaneously. In the partitioning strategy, all jobs generated by a periodic task must execute on the same processor. In the restricted migration strategy, different jobs generated by a periodic task may execute on different processors, but each individual job can execute on only one processor. The thesis of this dissertation is: Schedulability tests exist for the Earliest Deadline First (EDF) scheduling algorithm on heterogeneous multiprocessors under different migration strategies including full migration, partitioning, and restricted migration. Furthermore, these tests have polynomial-time complexity as a function of the number of processors (m) and the number of periodic tasks (n). (1) The Schedulability test with full migration requires two phases: an O (m) one-time calculation, and an O (n) calculation for each periodic task set. (2) The Schedulability test with restricted migration requires an O (m + n) test for each multiprocessor/task set system. (3) The Schedulability test with partitioning requires two phases: a one-time exponential calculation, and an O (n) calculation for each periodic task set.
ICBSI International Conference on Business Sustainability and Innovation CORPORATE RISK DISCLOSURE AND BUSINESS PERFORMANCE: DOES BOARD GENDER DIVERSITY MATTERS?

The collapse of major corporates due to accounting scandals had drawn the public attention and created doubts on the traditional risk management and corporate governance mechanism. These scandals also destroyed a certain level of trustworthiness of an organization towards its stakeholders. Therefore, this study is aimed to examine the impact of corporate risk disclosure on business performance with board gender diversity as a moderator. The study uses secondary data gathered from year 2016 annual reports. Random sampling is chosen to collect data from 130 public listed companies on Main Board of Bursa Malaysia. Structural equation modelling is applied. The results demonstrate that corporate risk disclosure does influence business performance significantly and positively which is consistent with prior studies. However, the moderating effect is found to be insignificant although the relationship between corporate risk disclosure and business performance is slightly strengthen with the existence of female on board. This study is hoped to fill in the research gap on how gender proportions on corporate board playing a role in moderating the relationship between corporate risk disclosure and business performance.
Swarm-Based Approach to Path Planning Using Honey-Bees Mating Algorithm and ART Neural Network

In this paper, an integration of Honey bees mating algorithm (HBMA) and adaptive resonance theory neural network (ART1) for efficient path planning of a mobile robot in a static environment is presented. The robot must find shortest route from given origin to the target position. Moreover, it should be able to memorize the environment and, if it faces known world, execute already learned trajectory found by HBMA solver, or solve the world and memorize the trajectory for the given environment. This is done using Adaptive Resonance Theory based neural network. This way simulated robot is able to navigate through environment and to continuously increase its knowledge.
Laban movement analysis to classify emotions from motion

In this paper, we present the study of Laban Movement Analysis (LMA) to understand basic human emotions from nonverbal human behaviors. While there are a lot of studies on understanding behavioral patterns based on natural language processing and speech processing applications, understanding emotions or behavior from non-verbal human motion is still a very challenging and unexplored field. LMA provides a rich overview of the scope of movement possibilities. These basic elements can be used for generating movement or for describing movement. They provide an inroad to understanding movement and for developing movement efficiency and expressiveness. Each human being combines these movement factors in his/her own unique way and organizes them to create phrases and relationships which reveal personal, artistic, or cultural style. In this work, we build a motion descriptor based on a deep understanding of Laban theory. The proposed descriptor builds up on previous works and encodes experiential features by using temporal windows. We present a more conceptually elaborate formulation of Laban theory and test it in a relatively new domain of behavioral research with applications in human-machine interaction. The recognition of affective human communication may be used to provide developers with a rich source of information for creating systems that are capable of interacting well with humans. We test our algorithm on UCLIC dataset which consists of body motions of 13 non-professional actors portraying angry, fear, happy and sad emotions. We achieve an accuracy of 87.30% on this dataset.