Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloud Networks as Platform-based Ecosystems : Detecting Management Implications for Actors in Cloud Networks

The rising adoption of cloud computing and increasing interconnections among its actors lead to the emergence of network-like structures and new associated risks. A major obstacle for addressing these risks is the lack of transparency concerning the underlying network structure and the dissemination of risks therein. Existing research does not consider the risk perspective in a cloud network’s context. We address this research gap with the construction of a reference model that can display such networks and therefore supports risk identification. We evaluate the reference model through real-world examples and interviews with industry experts and demonstrate its applicability. The model provides a better understanding of cloud networks and causalities between related risks. These insights can be used to develop appropriate risk management strategies in cloud networks. The reference model sets a basis for future risk quantification approaches as well as for the design of (IT) tools for risk analysis.
Entry-Exit event detection and learning

The notion of Entry-Exit Surveillance provides scope for monitoring subjects entering and exiting 'private areas' (places such as wash rooms and changing rooms where cameras are forbidden). The proposal here is to design a conceptual model that accurately detects the type of event such as entry/exit of subjects at the entrances and is robust to possible occlusions between subjects. A novel Entry-Exit event detection method that analyzes the three dimensional layout of the camera view as well as the transition of subjects through frames and determines the type of event as entry or exit or miscellaneous is presented in this paper. Extensive experiments on the benchmark EnEx,CAVIAR and PAMELA-UANDES datasets demonstrate the efficacy of the model.
The effects of syntactic pressures and pragmatic considerations on predictive dependency formation

ABSTRACT During sentence processing, comprehenders form predictions regarding the unfolding of the sentence. The current study is designed to tease apart the role of syntactic motivations, pragmatic considerations and contextual prediction in active dependency formation. Using both production and comprehension measures, we observe prediction for the resolution of a long-distance dependency, even in the absence of syntactic-licensing pressures. However, we find that predictive dependency formation is observed earlier when it is motivated by syntactic pressures. This suggests that the syntax plays a crucial role in this predictive process, even after controlling for pragmatic motivations. We propose that active dependency formation is faster or more binding in syntactically-motivated predictions (i.e. filler-gap dependencies) relative to pragmatically-motivated ones.
Algorithms for Coverage Improvement in a Sensor Network

Maddi, Sunil Reddy, M.S., Department of Computer Science, College of Science and Mathematics, North Dakota State University, March 2011. Algorithms for Coverage Improvement in a Sensor Network. Major Professor: Dr. Kendall E. Nygard. Sensors are devices which have the ability to receive and respond to a signal. These sensors, when used as a group, form a sensor network. Sensors in a sensor network can communicate and transmit data. In the early stages of research on sensor networks, only static sensors were used to form a sensor network. As research advanced, a combination of static and mobile sensors was used to form a wireless sensor network instead of just static sensors. The primary advantage of this type of sensor network over a sensor network with static sensors is the ability of mobile sensors to move to a new location in the network to increase the overall area covered by the sensors. Some concerns in a wireless sensor network are coverage area, energy consumption of the sensors, the ratio of static and mobile sensors to be used in sensor network, and the deployment of sensors in a network. Major research in sensor networks is focused on addressing the issue of coverage area. The objective of this paper is to design, implement and analyze Most Overlapped First and Highest Coverage Gain algorithms that address the issue of coverage area in a wireless sensor network. Local Spiral Search was used as the base to develop these two algorithms in combination with the Utility Function. Both algorithms were tested, and the results were analyzed with coverage area and change in overlap area as the metrics. Results showed a significant gain in coverage area using both the algorithms, and there was a consistent change in overlap area for a varying number of sensors.
Discovering the influence of sarcasm in social media responses

Sarcasm in verbal and nonverbal communication is known to attract higher attention and create deeper influence than other negative responses. Many people are adept at including sarcasm in written communication thus sarcastic comments have the potential to stimulate the virality of social media content. Although diverse computational approaches have been used to detect sarcasm in social media, the use of text mining to explore the influential role of sarcasm in spreading negative content is limited. Using tweets during a service disruption of a leading Australian organization as a case study, we explore this phenomenon using a text mining framework with a combination of statistical modeling and natural language processing (NLP) techniques. Our work targets two main outcomes: the quantification of the influence of sarcasm and the exploration of the change in topical relationships in the conversations over time. We found that sarcastic expressions during the service disruption are higher than on regular days and negative sarcastic tweets attract significantly higher social media responses when compared to literal negative expressions. The content analysis showed that consumers initially complaining sarcastically about the outage tended to eventually widen the negative sarcasm in a cascading effect towards the organization's internal issues and strategies. Organizations could utilize such insights to enable proactive decision‐making during crisis situations. Moreover, detailed exploration of these impacts would elevate the current text mining applications, to better understand the impact of sarcasm by stakeholders expressed in a social media environment, which can significantly affect the reputation and goodwill of an organization.
A Comprehensive Improved Salp Swarm Algorithm on Redundant Container Deployment Problem

As a representative of lightweight virtualization, container technology has been widely used in cloud services and edge computing applications. However, in the resource pool scenario composed by multiple intelligent terminal devices, considering the limited resources and poor stability of these devices, it is necessary to split the overall service into multiple microservices and deploy the backups of them in respective containers. Traditional container scheduling policies tend to be less effective in solving such problems. Therefore, the article used a meta-heuristic algorithm to solve this kind of problems. Based on a newly proposed salp swarm algorithm (SSA), the paper presented a comprehensive improved SSA (CISSA). CISSA improved the performance of the original SSA by 4 steps. To verify the performance of CISSA in different kinds of test functions, the algorithm was compared with 7 commonly-used meta-heuristic algorithms in 29 benchmark functions provided by the author of SSA. In addition, the article constructed three container cluster models of different sizes, all these algorithms were used to solve these redundant container deployment problems, the experimental results indicate that the CISSA is superior to other algorithms in such problems of different dimensions.
Gridded Emissions for CMIP6

Abstract. Spatially distributed anthropogenic and open burning emissions are fundamental data needed by Earth system models. We describe the methods used for generating gridded data sets produced for use by the modelling community, particularly for the Coupled Model Inter-comparison Project Phase 6. The development of three sets of gridded data for historical open burning, historical anthropogenic, and future scenarios were coordinated to produce consistent data over 1750–2100. Historical data up to 2014 were provided with annual resolution and future scenario data in 10-year intervals. Emissions are provided on a sectoral basis, along with additional files for speciated non-Methane Volatile Organic Compounds (NMVOCs). An automated framework was developed to produce these datasets to ensure that they are reproducible and facilitate future improvements. We discuss the methodologies used to produce these data along with limitations and potential for future work.
Web Scraping Using R

The ubiquitous use of the Internet in daily life means that there are now large reservoirs of data that can provide fresh insights into human behavior. One of the key barriers preventing more researchers from utilizing online data is that they do not have the skills to access the data. This Tutorial addresses this gap by providing a practical guide to scraping online data using the popular statistical language R. Web scraping is the process of automatically collecting information from websites. Such information can take the form of numbers, text, images, or videos. This Tutorial shows readers how to download web pages, extract information from those pages, store the extracted information, and do so across multiple pages of a website. A website has been created to assist readers in learning how to web-scrape. This website contains a series of examples that illustrate how to scrape a single web page and how to scrape multiple web pages. The examples are accompanied by videos describing the processes involved and by exercises to help readers increase their knowledge and practice their skills. Example R scripts have been made available at the Open Science Framework.
Learning Enhanced Environment Perception for Cooperative Power Control

— The vast proliferation of wireless networking devices, coupled with the trend for short-range communications in dense residential environments, imposes new challenges for the efficient addressing of problems resulting from co-existence of heterogeneous devices (e.g., interference) under capacity and energy constraints. This paper proposes and evaluates a cooperative distributed algorithm for power control and interference mitigation based on ad-hoc communication of heterogeneous yet peer networking devices, driven by enhanced situation awareness and learning capabilities; the learning capabilities evolve the way a network element perceives its environment. The gains of this approach are highlighted through its application in WiFi APs. The results reveal that the introduction of learning capabilities in cooperative power control leads to interference mitigation while introducing minimum overhead in the network nodes.
Effects of Local Latency on Games

Video games are a major type of entertainment for millions of people, and feature a wide variety of game genres. Many genres of video games require quick reactions, and in these games it is critical for player performance and player experience that the game is responsive. One of the major contributing factors that can make games less responsive is local latency — the total delay between input and a resulting change to the screen. Local latency is produced by a combination of delays from input devices, software processing, and displays. Due to latency, game companies spend considerable time and money play-testing their games to ensure the game is both responsive and that the in-game difficulty is reasonable. Past studies have made it clear that local latency negatively affects both player performance and experience, but there is still little knowledge about local latency’s exact effects on games. In this thesis, we address this problem by providing game designers with more knowledge about local latency’s effects. First, we performed a study to examine latency’s effects on performance and experience for popular pointing input devices used with games. Our results show significant differences between devices based on the task and the amount of latency. We then provide design guidelines based on our findings. Second, we performed a study to understand latency’s effects on ‘atoms’ of interaction in games. The study varied both latency and game speed, and found game speed to affect a task’s sensitivity to latency. Third, we used our findings to build a model to help designers quickly identify latency-sensitive game atoms, thus saving time during play-testing. We built and validated a model that predicts errors rates in a game atom based on latency and game speed. Our work helps game designers by providing new insight into latency’s varied effects and by modelling and predicting those effects.
Hybrid Adaptive Channel Access for LTE-U Systems

To alleviate the shortage of spectrum resource on licensed bands, long-term evolution in unlicensed bands (LTE-U) has been proposed. To facilitate the fair coexistence between LTE-U and the legacy WiFi networks on unlicensed bands, two well-known channel access mechanisms named as duty-cycle muting (DCM) and listen-before-talk (LBT) have been proposed and studied extensively. Accordingly, in this paper, a unified hybrid adaptive channel access scheme is proposed, which takes advantages of both the DCM and LBT mechanisms. Our proposal can realize the flexible handoff between the DCM and LBT mechanisms to meet the requirements of different markets. It can also adaptively adjust the important parameters, such as the back-off window size and the duty-cycle time fraction based on the WiFi traffic and the available licensed spectrum resource while guaranteeing the fair coexistence between the WiFi and LTE-U systems. Moreover, joint spectrum and transmission power allocation is also studied to improve the spectra efficiency (SE) on both licensed and unlicensed bands. Finally, analytical and numerical results are demonstrated to verify the effectiveness of the proposed scheme.
Transparent and Service-Agnostic Monitoring of Encrypted Web Traffic

Nowadays, most of Web services are accessed through HTTPS. While preserving user privacy is important, it is also mandatory to monitor and detect specific users’ actions, for instance, according to a security policy. This paper presents a solution to monitor HTTP/2 traffic over TLS. It highly differs from HTTP/1.1 over TLS traffic what makes existing monitoring techniques obsolete. Our solution, H2Classifier, aims at detecting if a user performs an action that has been previously defined over a monitored Web service, but without using any decryption. It is thus only based on passive traffic analysis and relies on random forest classifier. A challenge is to extract representative values of the loaded content associated to a Web page, which is actually customized based on the user action. Extensive evaluations with five top used Web services demonstrate the viability of our technique with an accuracy between 94% and 99%.
Strategi Penerapan Green Computing Pada Unit Pengelola Sistem Informasi Pembelajaran Berdasarkan Metode Pendekatan Analytic Network Process(ANP): Studi Kasus SMPN 109 Jakarta

Information technology is evolving so fast in the era of globalization influence in making a very important decision to be taken in dealing with existing problems. Decision-making can be influenced by several aspects, it greatly affects the speed in making decisions by the decision maker, where decisions must be taken quickly and accurately. This study was made by using the Analytical Network Process (ANP). Where the algorithm is doing the counting process to find the best decision, ie by calculating the value criteria and comparing it to the calculation of the probability of each decision to be taken. This research will be used to assist the decision maker in determining the application of the strategy alternatives Green Computing. The criteria used in the implementation of the Green Computing strategies according to the needs that exist in the SMP 109 Jakarta. From the research, Green Computing Implementation Strategies In Unit Information System Manager Learning Approach Method Based Analytic Network Process (ANP). Decision Support System will generate the right decision and flexible with the aspects that influence the decision, and to produce a report to the decision maker in order to assist the decision maker in making decisions.
Contract Theory Framework for Wireless Networking

With the rapid development of the modern communication networks, the problem we need to solve is no longer a pure engineering issue. In various heterogeneous network scenarios, there are service providers in need of performing economic analysis on how to ensure third parties’ cooperation or attract end-users. In the other way round, third parties or end-users need to evaluate the economic benefits of cooperating or using the services from different service providers. Overall, the current wireless networks are facing a problem in which there is a tight coupling of industry-specific technologies and non-technology related network externality. Contract theory, the 2014 Nobel Prize of economic science, has been widely used in industries, from banking to telecommunications. Particularly, contract theory is an efficient tool in dealing with asymmetric information between employer/seller(s) and employee/buyer(s) by introducing cooperation. In wireless networks, the employer/seller(s) and employee/buyer(s) can be of different roles depending on the scenario under consideration. Thus, there is a great potential to utilize the ideas, methods, and models of contract theory to design efficient wireless network mechanisms. Given this background, this dissertation provides a theoretical research between wireless communications, networking, and economics. Especially, different contract theory models have been applied in various wireless networks scenarios. The main contribution of this dissertation are as follows. • An overview of basic concepts, classifications, and models of contract theory is provided. Furthermore, comparisons with existing economics methods in wireless networks are conducted. • Applications of contract theory for wireless networks are studied. Specially, three contract theory problems: adverse selection, moral hazard, and a mixed of the two, are applied into device-to-device (D2D) communication, mobile crowdsourcing, cognitive radio network, respectively.
Deriving trace checkers for distributed systems

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Deriving trace checkers for distributed systems Claude Jard, Omar Drissi-Kaitouni
Patient-specific anatomical illustration via model-guided texture synthesis

ILKNUR KAYNAR KABUL: Patient-specific anatomical illustration via model-guided texture synthesis. (Under the direction of Stephen M. Pizer.) Medical illustrations can make powerful use of textures to attractively, effectively, and efficiently visualize the appearance of the surface or cut surface of anatomic structures. It can do this by implying the anatomic structure’s physical composition and clarifying its identity and 3-D shape. Current visualization methods are only capable of conveying detailed information about the orientation, internal structure, and other local properties of the anatomical objects for a typical individual, not for a particular patient. Although one can derive the shape of the individual patient‘s object from CT or MRI, it is important to apply these illustrative techniques to those particular shapes. In this research patient-specific anatomical illustrations are created by model-guided texture synthesis (MGTS). Given 2D exemplar textures and model-based guidance information as input, MGTS uses exemplar-based texture synthesis techniques to create patient-specific surface and solid textures. It consists of three main components. The first component includes a novel texture metamorphosis approach for creating interpolated exemplar textures given two exemplar textures. This component uses an energy optimization scheme derived from optimal control principles that utilizes intensity and structure information in obtaining the transformation. The second component consists of creating the model-based guidance information, such as directions and layers, for that specific model. This component uses coordinates implied by discrete medial 3D anatomical models (“m-reps”). The last component accomplishes exemplar-based texture synthesis by textures whose characteristics are spatially variant on and inside the 3D models. It considers the exemplar textures from the first component and guidance information from the second component in synthesizing high-quality, high-resolution solid and surface textures.
Neural network based non‐invasive method to detect anemia from images of eye conjunctiva

Detection of anemia can be done by examining the hemoglobin concentration level in the blood using complete blood count, which is an invasive, time‐consuming, and costly technique. Preliminary methods for detecting anemia include examining the color of the palpebral conjunctiva, which is a non‐invasive method, but color perception may vary from person to person. This study aims to develop a computerized non‐invasive technique for anemia detection. We propose a novel machine learning model using the artificial neural network to detect anemic patients from the images of eye conjunctiva. Since limited and small dataset has been used in the earlier approaches, this may cause over fitting of the model. We have improved the number of available training images using image augmentation techniques. To standardize a non‐invasive method, we have used computer vision algorithms for preprocessing and feature extraction. This article derives the backpropagation rules mathematically for adjusting the weights for the proposed neural network model. After hyper parameter tuning and using the mathematically derived backpropagation rules, the model was able to achieve the best accuracy of 97.00% with sensitivity 99.21% and specificity 95.42% on the created dataset.