Miracle-dz/UniFAR-FTUs huggingface dataset:
Studying practicing kuwaiti kindergarten teachers' attitudes, knowledge and reported practices regarding computer integration into the curriculum

This study aimed to investigate the attitudes, knowledge and reported practices of Kuwaiti kindergarten teachers regarding computer use in the classroom. The study was completed using a mixed methods research design whereby both quantitative and qualitative data was collected and analyzed in two phases. In the first phase of the study, a sample of 174 practicing kindergarten teachers in Kuwait, obtained from 16 kindergarten schools, completed a questionnaire. In particular, these participants volunteered to complete a questionnaire that measured their attitudes, knowledge and practices regarding the use of computers in their classroom. In the second phase, in-depth semistructured interviews with six kindergarten teachers were conducted to investigate those teachers' in depth perceptions and personal experiences with computers in their classrooms. Results from the questionnaires in the first phase of this study indicated that the majority of the participating teachers reported relatively positive attitudes and were knowledgeable regarding computer use in the curriculum. Contrary to what was proposed, however, the following independent variables were not statistically significant predictors of either teachers' attitudes or knowledge regarding the use of computers in their classroom: age, number of years of teaching experience, level of education, type of computer training, and ways of using computers. A significant relationship was found between computer attitudes and having access to a home computer. No significant relationship was found between teacher's knowledge and having access to a home computer. Although the majority had a positive attitude toward and were knowledgeable about the use of computers, interview data analysis in the second phase of this study showed that kindergarten teachers struggled to integrate computers into the curriculum and that they rarely used them in their lessons even though they perceived computer implementation to be beneficial to both teachers and children. The analysis revealed that teachers need more computer training, with other factors, to best integrate computers into the curriculum. The findings are discussed in terms of recommendations for practical applications and future research.
Design and Implementation of Offloading and Resource Management Techniques in a Mobile Cloud Environment

Mobile Cloud Computing (MCC) is used to extend the computing capability of mobile devices in terms of calculation, storage and energy. Offloading technologies are extensively utilized as the enabler for resource provisioning to achieve energy reservation. In this paper, we discuss an present the design and implementation of energy-aware offloading and resource management schemes for mobile cloud systems while identifying the challenging design at the system-level. We also present a guideline towards their design and present a classification of the proposed techniques based upon the offloading process and resource management technologies.
Hybrid Underwater Robot System Based on ROS

Underwater Robots play an important role in a number of shallow and deep-water missions. In recent years, Underwater Robots system design has been an active field of engineering researches. This paper proposes new system design for underwater vehicles combining the best features of both ROV (remotely operated vehicle) and AUV (autonomous underwater vehicle) technologies. The system based on ROS platform and Ardupilot software is equipped with two types of navigation system setup (autonomous navigation and teleoperation navigation) capable of realizing precise motion control, navigating while performing SLAM (Simultaneous Localization and Mapping) path-planning and obstacles avoidance.
User acceptance of cognition-aware e-learning: an online survey

The idea to enhance the learning experience of e-learning platforms by incorporating measures about the user's cognitive state, e.g. the cognitive load, boredom, or attention, has been proposed several times and shows promising results. However, the works have mostly dealt with conceptual implications and technical possibilities to detect the state, without considering the user acceptance. This paper therefore investigates whether users would actually be willing to provide access to sensor data such as heart or skin measurements for the sake of making e-learning systems cognition-aware. The results of an online survey with 50 participants show that people would provide access to behavioral data like keyboard input without major concerns; however, other sensors are considered more sensitive and would require strong learning experience improvements to make disclosure worthwhile. Participants also appear less concerned about sensors that are integrated into consumer devices than about less widespread ones. Furthermore, we report the general opinions regarding cognition-aware e-learning and discuss ideas on how best to adapt to the cognitive state.
EAST-DNN: Expediting architectural SimulaTions using deep neural networks: work-in-progress

A rapid and accurate architectural simulator is a cornerstone for an efficient design-space exploration of computing systems. In this paper, we introduce EAST-DNN, a feed-forward deep neural network, to accelerate architectural simulations. EAST-DNN achieves > 106X speedup with an average prediction error of 4.3% over the baseline simulator. It also achieves an average of 2X better accuracy with at least 2.3X speedup compared to state-of-the-art.
Soft tactile sensor and curvature sensor for caterpillar-like soft robot's adaptive motion

Soft robots are designed to interact safely with their environment and move through narrow place in a way a rigid robot cannot. In order for the soft robot to be completely soft, it should consist of as more as soft component. In this paper, a PneuNet soft robot imitating bionic caterpillar are fabricated. In addition, the soft tactile sensor and curvature sensor consisted of silicon rubber and liquid metal are made and characterized. The soft robot combine with the sensors to realize the external environment perception and motion detection. The soft robot can adaptively crawl across different height channel without complex control and can measure the height of the channel.
Fault Diagnosis Method of Mechanical Equipment Based on Convolutional Neural Network

Mechanical equipment is becoming much larger, more precise and more autonomous in current industrial society. The mechanical equipment fault detection is entering the age of 'big data' for much more monitoring points and sampling rate. Traditional diagnosis methods based on "signal processing feature extraction + machine learning classification" require a large amount of signal processing technology and diagnostic experience and can no longer meet the requirements of mechanical 'big data'. To solve this problem, an important part bearing in mechanical equipment is taken as the research object, and a diagnosis method based on convolutional neural network is proposed. This method uses the vibration signal as the monitoring signal and uses the Fourier transform to generate the vibration signal spectrum picture as the input of the whole system. Using the powerful feature extraction capability of convolutional neural network can automatically complete fault feature extraction and fault identification. The results show that the proposed method is able to not only adaptively mine available fault characteristics from the data, but also obtain higher identification accuracy than the existing methods.
Toward Accurate Clock Drift Modeling in Wireless Sensor Networks Simulation

The protocols used in Wireless Sensor Networks are subject to very strict temporal synchronization constraints. Radio Duty Cycle (RDC) protocols in particular are characterized by their very high synchronization accuracy requirements. In these protocols, synchronization errors are primarily caused by the natural clock drift observed in real nodes. The impact of the clock drift on the desynchronization issues can be investigated by the use of low-level node simulators. In this paper, we show the limitation of the COOJA simulator to evaluate the impact of the clock drift. We show that its current mote execution model does not faithfully reproduce the requested clock drift. We identify the root cause of these inaccuracies and present a new algorithm that is able to precisely reproduce any requested clock drifts in simulation. To demonstrate the new algorithm, we consider a well-documented RDC protocol issue where the clock drift would cause periodic communication blackouts. This phenomenon was observed on real nodes, but was previously impossible to reproduce by simulation. Our new algorithm not only allows to reproduce the blackouts but also provides additional insights that help to confirm the initial analysis of the phenomenon.
A Distance-based Data Fusion Technique for Minimizing GPS Positioning Error in Vehicular Ad Hoc Networks

A precise localization system is a key factor for several critical safety applications in Vehicular Ad Hoc Networks (VANets). Even though the Global Positioning System (GPS) can be used to provide the position estimation of vehicles, it still has an undesired error that can increase even more in some areas, making it unreliable and unfeasible for most critical safety applications. In this paper, we propose a new position estimation technique, named BOuND (acronym for Based ON Distance) localization, which can improve the GPS positions of nearby vehicles and minimize their errors based on the use of trigonometry concepts. Our solution uses an extended Kalman Filter to perform Data Fusion of both GPS and distance information among vehicles to provide an improved and precise position estimation. We evaluated the performance of our method using OMnet++ and SUMO. The obtained results clearly show that our proposal is capable of reducing the localization error in 53% that of GPS, and 37% when compared to the state-of-the-art VLOCI algorithm.
A concept of a hardware design environment with the functional language elixir: work-in-progress

The functional language Elixir is designed to be effective for the application. One of the most considerable feature of Elixir is that it is easy to realize the parallel processing with the standard library, such as Flow. In this paper, we study a design environment for hardware circuits using Elixir as a design language. We propose a synthesize flow for data flow hardware on the FPGA from the native Elixir code. Our method synthesizes functional equivalence circuits from the description of Enum and Flow in Elixir, which are libraries for direct manipulation and parallel processing of data collection in Elixir. Data flow is implemented base on the pipeline operator |> which connects the processing relation of the function by the data processing order. To realize a hardware design environment by Elixir, this paper shares current status of our implementation.
MalRank: a measure of maliciousness in SIEM-based knowledge graphs

In this paper, we formulate threat detection in SIEM environments as a large-scale graph inference problem. We introduce a SIEM-based knowledge graph which models global associations among entities observed in proxy and DNS logs, enriched with related open source intelligence (OSINT) and cyber threat intelligence (CTI). Next, we propose MalRank, a graph-based inference algorithm designed to infer a node maliciousness score based on its associations to other entities presented in the knowledge graph, e.g., shared IP ranges or name servers. After a series of experiments on real-world data captured from a global enterprise's SIEM (spanning over 3TB of disk space), we show that MalRank maintains a high detection rate (AUC = 96%) outperforming its predecessor, Belief Propagation, both in terms of accuracy and efficiency. Furthermore, we show that this approach is effective in identifying previously unknown malicious entities such as malicious domain names and IP addresses. The system proposed in this research can be implemented in conjunction with an organization's SIEM, providing a maliciousness score for all observed entities, hence aiding SOC investigations.
Quality Improvement of Lamination Process Results in the Packaging Industry by Using the Six Sigma Method

The production of quality plastic packaging is very important for companies to gain benefits that can be obtained if the quality of the product complies with the prescribed standards. This study aims to reduce losses that occur due to the presence of defective products in Jelly packaging products. Six Sigma methods through DMAIC stages (Define, Measure, Analyze, Improve, and Control) are used to find solutions and improve product quality. Based on the calculation of defective product data, it is known that Uneven surface defects, wrinkled defects, and Pitch Unstd defects are the highest number of defects. In the measure phase, the DPMO (Defects Per Million Opportunities) value were 32707.271 defect units with a value of 3.34 sigma. Failure Mode and Effect Analysis (FMEA) is used to determine the potential risk of failure from a factor that gets a high value. Improvements use the 5S approach in the form of a maintenance check sheet on the Tandem Extrusion Lamination Machine and work standards of Workstation in the lamination process. After repairs, the DPMO value drops to 7272.111 defect units and the sigma value rises to 3.94 sigma. Changes in sigma values that occur amounted to 0.60. Control can be done with the inspection and checks in each process so that the number of defects can continue to decrease. After improvement, the results show a decrease in the number and percentage of defects, which previously reached a number of 5.89% towards 1.31%.
Optimization of Computer Resources Using OpenStack Private Cloud

The rapid development of computer technology and applications has caused the increasing need for computer use in an organization. The performance of personal computers has increased rapidly, but they cannot be utilized properly. Therefore, personal computers can be used together to perform parallel processing, in order to shorten data processing time. This paper discusses Private cloud, which could be utilized as an alternative solution to provide cloud services to organizations that are in the same network. The service provided was Infrastructure as a Service (IaaS), where users can request certain types of services and use certain operating systems. Most universities have many computers in the laboratory that have considerable resources for a superb processing potential if they optimized properly. Supported by high specification on each computer, the laboratory is suitable for private cloud implementation. The use of the OpenStack framework can produce sufficient performance to perform parallel data processing. The test results showed that the parallel processing performance worked as expected.
Electronic Health Readiness (EHR) in Indonesia: Managerial Perspectives

This paper presents the readiness factor of e-health systems in Indonesia based on hospital managers' perspectives. The need of information systems in healthcare activities in particularly to improve hospital service quality and performance is required. The objective of this study is identified the readiness factors including the core, structural, societal and engagement to measure e-health adoption. The descriptive analysis of the data obtained using a survey method on a-104 hospital managers in Indonesia is presented. Again, research respondent selected using convenience sampling. Finally, result of this study shown various indicators of readiness factors descriptively, in core, structural, societal and engagement have been identified. The core readiness was identified as highest factor in adopting e-health and the societal readiness was the lowest factors in adopting e-health in Indonesia. The further study is would be completed with empirical study toward e-health adoption in Indonesia's hospitals.
Scanning-based 3D reconstruction of large-scale objects

This paper designs a novel and practical 3D reconstruction system for large scale objects, where we make use of one 3D scanner, two guide rails, and one high precise motion control system based on PLC (Programmable Logic Controller) module. Two guide rails are parallel. The 3D scanner, used to reconstruct partial sections, is installed on the guide rails and uniformly moved by motion control system. The partial reconstructions are aligned according to uniform motion. The performance and effectiveness are evaluated by experiments. Compared with similar methods, our method has its advantages.
eSPICE: Probabilistic Load Shedding from Input Event Streams in Complex Event Processing

Complex event processing systems process the input event streams on-the-fly. Since input event rate could overshoot the system's capabilities and results in violating a defined latency bound, load shedding is used to drop a portion of the input event streams. The crucial question here is how many and which events to drop so the defined latency bound is maintained and the degradation in the quality of results is minimized. In stream processing domain, different load shedding strategies have been proposed but they mainly depend on the importance of individual tuples (events). However, as complex event processing systems perform pattern detection, the importance of events is also influenced by other events in the same pattern. In this paper, we propose a load shedding framework called eSPICE for complex event processing systems. eSPICE depends on building a probabilistic model that learns about the importance of events in a window. The position of an event in a window and its type are used as features to build the model. Further, we provide algorithms to decide when to start dropping events and how many events to drop. Moreover, we extensively evaluate the performance of eSPICE on two real-world datasets.
PrivaTube: Privacy-Preserving Edge-Assisted Video Streaming

Video on Demand (VoD) streaming is the largest source of Internet traffic. Efficient and scalable VoD requires Content Delivery Networks (CDNs) whose cost are prohibitive for many providers. An alternative is to cache and serve video content using end-users devices. Direct connections between these devices complement the resources of core VoD servers with an edge-assisted collaborative CDN. VoD access histories can reveal critical personal information, and centralized VoD solutions are notorious for exploiting personal data. Hiding the interests of users from servers and edge-assisting devices is necessary for a new generation of privacy-preserving VoD services. We introduce PrivaTube, a scalable and cost-effective VoD solution. PrivaTube aggregates video content from multiple servers and edge peers to offer a high Quality of Experience (QoE) for its users. It enables privacy preservation at all levels of the content distribution process. It leverages Trusted Execution Environments (TEEs) at servers and clients, and obfuscates access patterns using fake requests that reduce the risk of personal information leaks. Fake requests are further leveraged to implement proactive provisioning and improve QoE. Our evaluation of a complete prototype shows that PrivaTube reduces the load on servers and increases QoE while providing strong privacy guarantees.
TLS certificate as a sign of establishing a connection with the network Tor

Ensuring the confidentiality, integrity and availability of electronic resources in data networks is one of the important tasks of information security. The purpose of this article is to present the basic points of X.509 certificates of SSL/TLS encryption protocol analysis using the freely distributed software tools Wireshark and NetworkMiner of the Swedish company Netresec in data networks that are based on identifying key features inherent in software certificates of Tor Bundle. In the course of this study, the main features that allow determining the belonging of the transmitted data to the Tor network without using the list of its IP addresses are highlighted. To determine the signs, a number of certificate parameters are used that are transmitted during the implementation of the so-called SSL/TLS handshake between the client equipment and the Tor node. At the same time, identification of certificates is based not on a statistical, but on a discrete value of features, which simplifies the identification process and ensures greater accuracy. This approach to identifying the use of anonymizer allows to apply ready-made server solutions to block access to users already at the stage of establishing a connection to the Tor network. The principle of determining belonging to this anonymous network can be used in the development of a set of measures to legally block the use of the Tor Bundle at the stage of connecting it to a provider in the segment between the user equipment and the Internet service provider.
Monitorless: Predicting Performance Degradation in Cloud Applications with Machine Learning

Today, software operation engineers rely on application key performance indicators (KPIs) for sizing and orchestrating cloud resources dynamically. KPIs are monitored to assess the achievable performance and to configure various cloud-specific parameters such as flavors of instances and autoscaling rules, among others. Usually, keeping KPIs within acceptable levels requires application expertise which is expensive and can slow down the continuous delivery of software. Expertise is required because KPIs are normally based on application-specific quality-of-service metrics, like service response time and processing rate, instead of generic platform metrics, like those typical across various environments (e.g., CPU and memory utilization, I/O rate, etc.) In this paper, we investigate the feasibility of outsourcing the management of application performance from developers to cloud operators. In the same way that the serverless paradigm allows the execution environment to be fully managed by a third party, we discuss a monitorless model to streamline application deployment by delegating performance management. We show that training a machine learning model with platform-level data, collected from the execution of representative containerized services, allows inferring application KPI degradation. This is an opportunity to simplify operations as engineers can rely solely on platform metrics -- while still fulfilling application KPIs -- to configure portable and application agnostic rules and other cloud-specific parameters to automatically trigger actions such as autoscaling, instance migration, network slicing, etc. Results show that monitorless infers KPI degradation with an accuracy of 97% and, notably, it performs similarly to typical autoscaling solutions, even when autoscaling rules are optimally tuned with knowledge of the expected workload.
Annotif: A System for Annotating Mobile Notifcations in User Studies

Notifications are an essential feature of smartphones. While they support users in staying up-to-date, they are also a prominent source of interruptions. A deeper understanding of mobile notifications is required to avoid adverse effects. However, assessing mobile notifications is challenging as user studies on mobile notifications are typically conducted in-situ. Surveying users may lead to additional interruptions, and the content of notifications is inherently private. In this paper, we introduce a privacy-aware system for annotating mobile notifications in user studies. In an in-situ case study, participants annotated their notifications for one week. Participants perceived 38.91% of their notifications as not important and over half (51.75%) as non-urgent. Only 6.33% of the notifications were rated as both very important and very urgent. We discuss influencing factors, including a breakdown of messaging notifications, and implications for future smart notification systems that continue to fulfill users' information need while respecting their digital well-being.