Miracle-dz/UniFAR-FTUs huggingface dataset:
Introduction of a Tool-Based Continuous Information Security Management System: An Exploratory Case Study

Tighter regulatory demands and higher customer expectations regarding the protection of information force enterprises to systematically ensure confidentiality, integrity and availability of stored information and processing facilities. Information Security Management Systems (ISMSs) are used to address these challenges. Recent studies show that the majority of companies plans to establish at least basic information security management to prepare for future developments. Larger enterprises have already embraced ISMSs, whereas small and medium-sized enterprises (SMEs) are catching up and require support in defining, introducing and operating them. We developed ADAMANT, an SME-friendly tool that supports continuous information security management incorporating stakeholders of different domains. In this paper, we evaluated our approach to introduce an ISMS in SMEs using an introductory information security training. The evaluation shows that our tool improves critical information security management tasks. Furthermore, integrating ADAMANT in customized security trainings allows companies to directly use training results to implement an ISMS.
A Method of Optimizing Multi-Locators Based on Machine Learning

Due to the rapid iteration of Web applications, there are some broken test cases in regression tests. The main reason for the appearance of broken test cases is the failure of element location in the new web page. The element locators in the test cases come from various Web element locating tools, which are used to identify the elements to be convenient for testers to operate them and eventually to test the Web application. Therefore, the Web element locating tools play an essential role in web testing. At present, there are some Web element locating tools, which are supported by a single locating algorithm or multiple locating algorithms. Moreover, the Multi-Locators supported by multiple algorithms are obviously more robust than the one supported by a single algorithm. However, when synthesizing all locating algorithm to generate Multi-Locators, a better method can be selected in assigning weights to each algorithm. Based on this observation, we propose a method to optimize Multi-Locators. In assigning weight to each algorithm, it chooses a weight distribution method based on machine learning, named Learned Weights. Through experimental comparison, it is shown that the locating tool supported by algorithm based on machine learning is more robust than these existing locating tools.
Sparse Ordinal Logistic Regression and Its Application to Brain Decoding

Brain decoding with multivariate classification and regression has provided a powerful framework for characterizing information encoded in population neural activity. Classification and regression models are respectively used to predict discrete and continuous variables of interest. However, cognitive and behavioral parameters that we wish to decode are often ordinal variables whose values are discrete but ordered, such as subjective ratings. To date, there is no established method of predicting ordinal variables in brain decoding. In this study, we present a new algorithm, sparse ordinal logistic regression (SOLR), that combines ordinal logistic regression with Bayesian sparse weight estimation. We found that, in both simulation and analyses using real functional magnetic resonance imaging data, SOLR outperformed ordinal logistic regression with non-sparse regularization, indicating that sparseness leads to better decoding performance. SOLR also outperformed classification and linear regression models with the same type of sparseness, indicating the advantage of the modeling tailored to ordinal outputs. Our results suggest that SOLR provides a principled and effective method of decoding ordinal variables.
Combination of Oil Film Bearing and Bearingless Motor for High Load Capacity and Stable Rotation

This paper proposes a rotor system, in which the rotor load is supported by oil film bearings, and radially controlled and driven by a bearingless motor (BELM). This system can take advantage of the high load capacity of the oil film bearing and the superior controllability of the BELM. However, the oil film bearing induces the self-excited vibration at a high rotational speed. Therefore, the purpose of this study is the realization of the hybrid rotor system, which has the oil film bearings and the BELM, and the suppression of the self-excited vibration. The proposed rotor system is fabricated and the basic characteristics are measured. Finally, a high-speed test is carried out.
Detecting Data Integrity Attacks on Correlated Solar Farms Using Multi-layer Data Driven Algorithm

This paper proposes a novel server-centric anomaly detection strategy for large-scale multi-modal time-series data from NSERC solar farm. The proposed framework utilizes spatial and temporal correlation between multiple solar farms to defend against data integrity attacks and learns the inter-farm/intra-farm relationship between measurements to perform anomaly detection. We demonstrated the performance of five machine learning algorithms—vector autoregressive models (VAR), deep neural networks (DNN), long short-term memory networks (LSTM), inverse PCA reconstruction (iPCA), and deep auto-encoders (DAE)—and evaluated the models on five varieties of data integrity attacks including replay, correlated, random, delay, and scaling attacks. Results show that the proposed framework is capable of detecting data anomalies over these large variety of attacks.
Stable Throughput Regions of Opportunistic NOMA and Cooperative NOMA With Full-Duplex Relaying

In this paper, we consider downlink non-orthogonal multiple access (NOMA) transmission with dynamic traffic arrival for spatially random users of different priorities. By exploiting limited channel state information, we propose an opportunistic NOMA scheme to enable NOMA for high- and low-priority users when high-priority users experience good channel conditions. Opportunistic NOMA improves the transmission opportunities of low-priority users while reducing the adverse effect of NOMA on high-priority users. Moreover, we propose a cooperative NOMA scheme with full-duplex relaying, where low-priority users act as full-duplex relays to assist the high-priority users. The high-priority user constructively combines the signal and its delayed version transmitted by the base station and a selected relay, respectively. The adopted relay selection scheme takes into account the users’ spatial distribution, queue status, and channel conditions. By using tools from queueing theory and stochastic geometry, we derive the stable throughput regions of both proposed schemes. Furthermore, we derive the conditions under which the proposed NOMA schemes achieve larger stable throughput regions than orthogonal multiple access (OMA). At the expense of a higher implementation complexity and with appropriate parameter setting, cooperative NOMA with full-duplex relaying achieves a larger stable throughput region than opportunistic NOMA, which in turn outperforms OMA.
NOMA Aided Interference Management for Full-Duplex Self-Backhauling HetNets

The presence of mutual-coupled interference in full-duplex self-backhauling heterogeneous networks raises challenges and difficulties in practical scenarios. In this letter, we address this issue by developing a two-tier non-orthogonal multiple access (NOMA) scheme together with efficient power control to enable aggressive frequency reuse and alleviate co-channel interference. For the considered multi-tier and multi-cell NOMA scenario, we formulate a power minimization problem, and develop an efficient algorithm with guaranteed convergence to enable optimal power control, such that users’ data demand is satisfied and backhauling bottleneck is avoided. Numerical results show the fast convergence of the proposed algorithm, and demonstrate that NOMA is in particular favorable for the high-demand cases in power savings.
A Novel Wideband Filtering Power Divider With Embedding Three-Line Coupled Structures

This paper presents a novel wideband filtering power divider. Instead of the T-junction in the traditional power divider design, a three coupled-line structure is utilized to provide the equal power division to every outputs. Together with the quarter-wavelength coupled line in each way, a wideband filtering response can be obtained. In order to enhance the frequency selectivity, a pair of half-wavelength open stubs are loaded between the three-line coupled structure and parallel coupled lines to produce a pair of transmission zeros on both sides of the passband. To enhance the upper stopband rejection, two additional open stubs are installed at output ports. A new three-port equivalent network for the three-line coupled structure is proposed, and the circuit model for the filtering power divider can be developed for illustrating the working mechanism and the design process. Furthermore, the generalized Chebyshev function is applied to synthesize and analyze the whole structure systematically. For demonstration, a prototype of the wideband filtering power divider operating at 3 GHz is designed and fabricated. The measured results show a 16-dB bandwidth of 62%, an isolation better than 16.5 dB, and a 29-dB wide stopband from 4.39 to 7.73 GHz ( $1.1f_{0}$ ).
Approach for moving object detection using visible spectrum and thermal infrared imaging

Abstract. Moving object detection is one of the most promising research areas, which is required in different applications, such as video monitoring and surveillance systems, human activity recognition systems, vehicle counting, and anomaly detection. Various methods for object detection using single sensor and a few using multimodal techniques have been reported in the literature. However, such systems fail to handle adverse or challenging atmospheric conditions such as illumination variations, scale and appearance change of objects or targets, occlusions, and camouflaged conditions. We have presented an approach for the detection of moving objects using structural similarity metric (SSIM) and Gaussian mixture model (GMM). SSIM is used to compute similarity between reference mean background frame and foreground frame of visible spectrum (VIS) and thermal infrared (IR) independently. The computation of similarity measure is performed in an image spatial domain. The threshold results of SSIM are fused together using different pixel-level fusion methods such logical “OR,” discrete wavelet transform, and principal components analysis. Temporal analysis is performed to eliminate noise and false positives (unwanted background regions) using GMM on fused results. We have compared the results with recent methods for different complex scenarios and found out that approximately F-measure increases up to 80%. Hence, the proposed method proves to be a robust moving object detection technique in multimodality domain.
The Impact of Head of Line Blocking in Highly Dynamic WLANs

Wireless LAN (WLAN) based networks can rely on the concept of automatic repeat request to overcome temporary channel outages, such as those due to interference. However, the used acknowledgement scheme for unicast transmissions blocks other transmissions until a message has either been successfully transmitted or until the retry limit has been reached. We investigate this head of line blocking problem, its impact, and first steps toward a final solution. As a research methodology, we build upon an analytical model, computer simulations, and also experimentation in a lab environment. We consider the effects for a single node in a small and static network as well as studying the application layer performance in a highly dynamic network—we investigate Vehicular Ad Hoc Networks (VANETs) as a prime example—spanning several kilometers. Aside from considering the case of malicious users conducting easy active and passive attacks, we show that in highly dynamic networks this blocking behavior can (massively) be observed as local topologies change. Here, the discussed effects can lead to massive message loss and increased latencies in the order of seconds.
Classification of Bipolar Disorder and Schizophrenia Using Steady-State Visual Evoked Potential Based Features

The accurate discrimination between bipolar disorder (BD) and schizophrenic patients is crucial because of the considerable overlap between their clinical signs and symptoms (e.g., hallucination and delusion). Recently, electroencephalograms (EEGs) measured in the resting state have been vastly analyzed as a means for classifying the BD and schizophrenic patients, but EEGs evoked by external audio/visual stimuli have been rarely investigated, despite their high signal-to-noise ratio (SNR). In this study, in order to investigate whether EEGs evoked by external stimuli can be used for classifying the BD and schizophrenic patients, we used a visual stimulus modulated at a specific frequency to induce steady-state visual evoked potential (SSVEP). In the experiment, a photic stimulation modulated at 16 Hz was presented to two groups of the schizophrenic and BD patients for 95 s, during which the EEG data were recorded. Statistical measures of SSVEPs (mean, skewness, and kurtosis) described in SNR units were extracted as features to characterize and classify the variations of brain activity patterns in the two groups. Two brain areas, O1 and Fz, showed a statistically significant difference between the two groups for SNR mean and kurtosis, respectively. Among five applied classifiers, k-nearest neighbor provided the highest classification accuracy of 91.30% with the best feature set selected by Fisher score. An acceptable accuracy for binary classification (>70%) was retained until analysis time was reduced up to 10 s using a support vector machine classifier, and 20 s for other classifiers. Our results demonstrate the potential applicability of the proposed SSVEP-based classification approach with relatively short single-trial EEG signals.
Impact of Teaching Approaches to Computational Thinking on High School Students: A Systematic Mapping

The role of computing in society is fundamental for the political, economic and cultural development of countries, especially in a globalized context. Computational thinking is an extremely important skill in the education of young students, since the ability of intuitively abstracting information and knowledge promotes an expansion of the learning dimension. This study aims to gather approaches used for teaching computational thinking exclusively to high school students, highlighting their performance and motivation resulting from the applications of such approaches. In order to answer our research question, a systematic mapping of the analysis of 15 studies was conducted. The use of computer programming proved to be the most used way to disseminate computational thinking. The students who participated in approaches regarding the insertion of computational thinking in the classroom showed positive impact on the development of classroom activities, as well as motivation to learn.
A Review of Multimodal Learning Analytics Architectures

There is an increasing interest in Multimodal Learning Analytics (MMLA), which involves complex technical issues in gathering, merging and analyzing different types of learning data from heterogeneous data sources. However, there is still no common reference architecture to face these technical challenges of MMLA. This paper summarizes the state of the art of MMLA software architectures through a systematic literature review. Our analysis of nine architecture proposals highlights the uneven support provided by existing architectures to the different activities of the analytics data value chain (DVC). We find out in those infrastructures that data organization and decision-making support have been under-explored so far. Based on the lessons learnt from the review, we also identify that design tensions like architecture distribution, flexibility and extensibility (and an increased focus on data organization and decision making) are some of the most promising issues to be addressed by the MMLA community in the near future.
A Sensory Substitution Device Inspired by the Human Visual System

The purpose of this paper is to introduce and initially evaluate an experimental sensory substitution device (SSD), which converts color to audible sound. Our system, called Colorophone provides continuous information about the color, light intensity and distance. The color sonification method is inspired by the human visual system. While designing sonification method we took into consideration mismatch in sensory bandwidth, possible sensory overload, nature of sensory spatiotemporal continuity and cross-modal correspondences between colors and sounds. We conducted a preliminary experimental evaluation of the color and object recognition abilities as well as orientation ability. We show that blindfolded participants can easily acquire the color information coded in the auditory stimuli generated by the Colorophone system after a short learning session. Our preliminary experiments indicate that sonified color information helps in object identification and enhances orientation ability. The Colorophone proved to be an easy to understand and intuitive visual-to-auditory coding system, which shows promising results in future visual rehabilitation of the blind.
A Flow-Level Performance Evaluation of Elastic Traffic Under Low Latency Queuing System

Internet tends progressively to be more interactive by supporting real-time communications into this packet-based environment. It is anticipated that online applications will rapidly develop and contribute by a significant amount of traffic in the near future. These applications have strict requirements in terms of delay and throughput which can't be met only if they are prior. However, at high demand of real-time traffic, elastic traffic (which is transported generally by TCP) may not have sufficient resources to be transported with reasonable quality of service (QoS). Therefore, it is primordial to manage properly the resource-network in order to provide the QoS required by both each type of services. In this paper, we present a new fluid model to evaluate the performance of elastic traffic under Low Latency Queuing (LLQ) system combining a priority queue for delay-sensitive applications with a number of Class Based Weighed Fair Queues (CBWFQ) for elastic traffic. The originality of our contribution consists on focusing on the average total number of flows passing through the whole system by approximating it as a lossless best effort system with total load equal to the total carried load of the original system (no blocked load). The core of our analysis is based on some approximations proven for balanced fairness allocation, which provides a reasonable framework for estimating bandwidth sharing among elastic traffic for best effort allocations. Results issued from this allocation are then exploited to deduce the performance of CBWFQ system. Detailed packet level simulations are used to verify the effectiveness and the accuracy of our analysis. The approach presented in this paper allows a rapid performance evaluation of elastic or rate-adaptive traffic circulating in the actual networks.
Cognitive Radio Overlay Paradigm Towards Satellite Communications

In this paper, we investigate the application of cognitive radio techniques within the overlay paradigm toward satellite communications, where the primary and cognitive users transmit concurrently at the same frequency, time and space. By means of dirty paper coding (DPC) and superposition techniques, a low complexity scheme is proposed when compared to classical precoding for interference pre-subtraction. The system performance is evaluated in different realistic scenarios, resulting in no degradation for primary user and a significant recovery of the shaping loss for the cognitive user.
Model Driven Deployment of Auto-Scaling Services on Multiple Clouds

Hybrid cloud platforms have been adopted to facilitate different parts of services to deliver functionalities to service consumers. Each cloud platform offers elastic resource allocation, which accommodates fluctuating demands on services by automating the provision/deprovision of resources, referred as auto-scaling. In term of service deployment, auto-scaling is usually not interoperable between multiple cloud platforms. As a result, the service level auto-scaling strategy needs to be configured separately on disparate cloud platforms, which incurs difficulties in tracing the configuration and maintaining consistent deployment. This paper presents a model-driven method to connect a cloud platform independent model of services with cloud specific operations. Through the automated transformation from model to the configuration, we use cloud management tools to deliver auto-scaling deployment across clouds. We demonstrate our method with scaling configuration and deployment of an open source benchmark application - Dell DVD store on two cloud platforms, AWS and Rackspace. The experiment demonstrates our proposed method resolves the vendor lock issues by a model-to-configuration-to-deployment automation. The empirical measurement shows our method reduces the effort of deploying auto-scaling services on cloud platforms.
Perceived video quality evaluation on different mobile devices combining video and screen characteristics

Abstract. Recent years have witnessed the rapid development of mobile video services. The diversity of mobile devices and video services can result in a difference in a user’s perceptual video quality, which presents a significant challenge for video quality evaluation. An objective assessment model is proposed that considers both video and screen characteristics, where the video characteristics include the video resolution and video coding quality (VCQ), and the screen characteristics include the screen size and screen resolution. By analyzing the relationships between the perceived video quality (PVQ) and the video and screen characteristics, an influential parameter, effective video pixels per inch (EV-PPI), is proposed that combines the two characteristics. Then, the PVQ is estimated with the VCQ and EV-PPI. Experiment results confirm that the proposed method can effectively estimate the user’s PVQ. It can be used as a guideline for both video service providers and mobile device manufacturers to improve the performance of their services and devices.
A Data-Driven Process Monitoring Approach for Dynamic Processes with Deterministic Disturbance

This paper presents the study on the data-driven process monitoring system design for the dynamic processes with deterministic disturbance. The basic idea of the proposed method is to identify the stable kernel representation (SKR) of the dynamic process by decomposing the process data into different subspaces. By extracting the maximum influence of the disturbance from fault-free data, a process monitoring system is developed based on the identified data-driven SKR. The performance and effectiveness of the proposed scheme are verified and demonstrated through the numerical study on randomly generated systems.