Miracle-dz/UniFAR-FTUs huggingface dataset:
Challenges and Experiences with Applying Microsoft Threat Modeling in Agile Development Projects

The goal of secure software engineering is to create software that keeps performing as intended even when exposed to attacks. Threat modeling is considered to be a key activity, but can be challenging to perform for developers, and even more so in agile software development. Hence, threat modeling has not seen widespread use in agile software projects. The goal of this paper is to investigate the challenges facing adoption of threat modeling using the Microsoft approach with STRIDE. We performed a case study in a company comprising five agile development projects. We identified 21 challenges to threat modeling that emerged from our observations. We then mapped these challenges to challenges found in the literature. Some challenges overlap the findings from the literature; the extra challenges we have found in our exploratory study came mostly from the activities of asset identification and also from our observations on what happened after the threat modeling meetings. This study shows that we still have to address many challenges in order to get a proper adoption of threat modeling in agile development projects.
REVAM: A Virtual Reality Application for Inducing Body Size Perception Modifications

In this paper, we present a 3D virtual environment for inducing body ownership illusion. The key idea is to take advantage of virtual reality potential regarding body size perception. The application, called REVAM, links two main aspects of body size perception: the first one focuses on its modification and the second one concerns its assessment. Based on some previous evidence that it is possible to modify body-size perception through an illusion of ownership over a virtual body, the application proposes to couple a tactile stimulation when viewing an avatar from a third person perspective (a condition known to produce this kind of illusion). In addition, the application offers the possibility to choose between avatars of different builds, and to perform morphing to reduce the avatars body. Moreover, the application allows to implicitly measure how people perceive their body size from an affordance estimation task in which people have to appreciate if they can pass through doors of different sizes without twisting their shoulders. To test the application we carried out an experiment on 16 female participants who performed the affordance estimation task five times: the first time before being exposed to their chosen avatar to get a baseline measure, and the four other times after being exposed to their avatar in different situations. These different situations are defined by the crossing of two experimental factors: morphing (presence or absence) and simultaneous visuotactile stimulation (presence or absence). A two-way repeated-measures Anova showed a main effect of the morphing: mean door width through which the participants estimate they can fit was significantly reduced (p <:05) when morphing was present. However, this effect did not interact with the presence of a simultaneous tactile stimulation. This indicates that exposing people to a virtual body reduced in size, as proposed in the present application, could be an effective way to modify body size perception, at least temporarily. The final goal would be to help patients with body image disorders, such as anorexia nervosa.
Low Cost Microcontroller-Based Dataglove

Professionals of different areas are expected to have programming skills specially considering engineers that must learn the subject during their graduation. The problem in this case is that engineering students are not enthusiastic about learning programming without using it in more practical situations. Teaching basic programming using devices became an interesting alternative since hardware as Arduino became available. In this work we present a case study of undergraduate mechanical engineering students that assembled a dataglove using low cost materials. The results were presented as the final project of the introduction to programming course and showed that is possible to achieve interesting results in mapping movements form the dataglove to other devices. Also the project-based learning experience achieved good results considering students programming skills to use Arduino boards and also their final grades.
Designing a Digital Fitness Game System for Older Adults in Community Settings

Exergames is one of the new innovative approaches used in primary healthcare programmes. The current study introduces a fitness game system, HOCAMOSE-VETS, which includes digital rehabilitation exercises and exergames designed with a particular focus on older adults. Besides the new exergames, the system also allows care staff to actively schedule, monitor and assess the progress of older adults' exercise activities. A survey-based study was conducted to investigate the overall user acceptance of the digital fitness game system. We found that users' perceived ease of use and usefulness have a significant impact on their actual intention of using the game system. However, the output quality of the system is not significantly associated with users' perception of the usefulness and ease of use of the system. The findings from this research have provided new insights into designing elderly fitness games.
Building City-Scale Walking Itineraries Using Large Geospatial Datasets

Nowadays, social networks play an important role in many aspects of people’s life and in traveling in particular. People share their experience and opinions not only on specialized sites, like TripAdvisor, but also in social networks, e.g. Instagram. Combining information from different sources we can get a manifold dataset, which covers main sights, famous buildings as well as places popular with city residents. In this paper, we propose method for generation of walking tours based on large multi-source dataset. In order to create this dataset, we developed data crawling framework, which is able to collect data from Instagram at high speed. We provide several use cases for the developed itinerary generation method and demonstrate that it can significantly enrich standard touristic paths provided by official site.
An Energy-Aware Real-Time Search Approach for Cooperative Patrolling Missions with Multi-UAVs

This paper proposes a novel energy-aware approach to solve the patrolling problem using multi-UAVs (Unmanned Aerial Vehicles), called NC-Drone, which extends the Node Counting method aiming to reduce the number of turns in order to save energy. We present two versions of NC-Drone: a Centralized version and a Decentralized one. The Centralized version collects/spreads the information, reading/writing pheromones in the scenario and it is compared to the real-time search methods. Our results point out that the Centralized NC-Drone overcomes all real-time search methods in most of the performance metrics, presenting better results in Quadratic Mean of the Intervals and drastically reducing the Number of 90° Turning Maneuvers, and thus impacting positively on energy consumption. The Decentralized NC-Drone version uses individual pheromone matrices to guide the vehicles, replacing the centralized scheme of pheromones disposed in the scenario. When a vehicle is close to another, they send its matrices to each other, simulating the use of a short-range communication technology. Additional experiments compare variations of the Decentralized NC-Drone to analyze the impact of the adopted information merge scheme on coverage. The Decentralized version emerges as a promising solution to support real-world applications using low-cost aerial vehicles.
Trust Building in Dynamic Process of Internet Entrepreneurial Social Network

Nowadays, the emerging Internet technology plays an essential role in pulling entrepreneurs from financial dilemmas and promoting their social capital development. Using the crawling technology, we obtained 2214 entrepreneurial social networks and entrepreneurial project data from http://Indiegogo.com, which helps to investigate the influence of entrepreneurs’ information sharing behavior on entrepreneurial financial outcomes. We discovered that entrepreneurs’ trust-building behavior comes with different stages; in personal social network stage, personal-related trust building can boost better financial outcomes, while project-related information sharing can bring about superior financial results in the socialized social network stage. The two relationships reflected above are also affected by the entrepreneurs’ online entrepreneurial experience. Taking a dynamic view instead of a static one in entrepreneurial social capital development, this research sets up a paradigm for entrepreneurial events by proposing entrepreneurial information sharing and Internet venture financing model. It also has a strong practical implication for Internet entrepreneurship by offering the two-stage trust-building tactics, which can be further conducive to understanding and applying the information sharing mechanism in the context of e-commerce and sharing economy.
A Contour-Based Algorithm for Automated Detection of Overshooting Tops Using Satellite Infrared Imagery

Overshooting top (OT) is a proxy for deep convection with intense updrafts. Using Himawari-8 infrared window (IRW) and water vapor (WV) imagery, this paper presents a contour-based OT detection algorithm based on the premise that the OT center is in a cold region enclosed by the local coldest (innermost) brightness temperature (BT) contour. Relative to existing IRW-texture algorithms, the contour-based algorithm offers improvement in two aspects. First, the new algorithm utilizes the WV minus IRW BT difference to capture the deep-convection pixels, and then removes the warmer pixels far from the cold center. Thus, compared with directly using a simple 215-K BT threshold to determine the possible OT pixels, many false alarms are avoided. Second, the new algorithm utilizes the BT contours to roughly depict and separate the anvil clouds and OT regions, and the anvil cloud pixels used to estimate the mean anvil cloud BT are outside the OT regions. Therefore, the computed BT gradient is more credible compared with the simple 225-K BT threshold. The accuracies of the OT detection algorithms are evaluated with 159 Global Precipitation Measurement-observed OT events collected from July 2015 to July 2017. The results indicate that the contour-based algorithm performs better than the original IRW-texture algorithm. The contour-based algorithm achieves a probability of detection (POD) of 67% and a false-alarm ratio (FAR) of 46%. Compared to the original IRW-texture algorithm, the contour-based algorithm increases the POD by 9% and decreases the FAR by 42%.
ASIC Coprocessor for 254-bit Prime-Field Pairing based on General Purpose Arithmetic Unit on Quadratic Extension Field

A hardware accelerator for the 254-bit prime-field optimal-ate pairing over Barreto-Naehrig (BN) curve is proposed. The data path of the proposed crypto processor is designed to compute five ${\mathbb{F}_{{p^2}}}$ operations, a multiplication, three addition/-subtractions, and an inversion in parallel. We further propose a design methodology to automate the instruction scheduling by using a combinatorial optimization solver, with which the total cycle count is reduced to 1/2 compared with ever reported. The proposed crypto processor is designed by using a 65 nm silicon-on-thin-box (SOTB) CMOS process. The post-layout simulation revealed that the proposed accelerator can compute a pairing in 62.3 µ seconds when a typical operating voltage of 1.2 V is applied, which corresponds to 8.4× improvement from the current state-of-the-art ASIC-based accelerator.
Security-Reliability Analysis of Cognitive Radio Network using Harvest-to-Jam Protocol with Primary Co-Channel Interference

This paper investigates the trade-off between reliability and security for a multi-user cognitive radio (CR) network under joint impact of interference constraint and co-channel interference from primary network. In the proposed method, a joint user and jammer selection method is proposed to enhance the system performance. Moreover, the jammer which is selected from one of the available users can harvest energy from the primary co-channel interference to use for generating artificial noises on the secondary eavesdropper. For performance analysis, we derive exact expressions of outage probability (OP) and intercept probability (IP), which are validated by computer simulations.
Decomposing TripAdvisor: Detecting Potentially Fraudulent Hotel Reviews in the Era of Big Data

The impact of customer reviews on user purchase decisions has been well documented. For example, a one-star increase in a restaurant's Yelp rating can lead to a 5 to 9 percent increase in revenue. Unfortunately, this has motivated some businesses in review-dependent industries to falsify reviews. In the era of big data, analytical methods have made detection of these false reviews easier. We perform a longitudinal study of 2.65 million hotel reviews made by nearly 320,000 reviewers on TripAdvisor (which does not verify customers stayed at the property they reviewed) and compare them to 2.93 million reviews on other two other booking platforms, Agoda and Booking.com (which verify its reviewers stayed at least one night at the property they reviewed). We analyze the language used, the patterns of reviewer activity, and the change in hotel's reputation score over time across more than 5.5 million reviews. We find the word frequency between the two types of websites and the patterns of reviewer activity differ considerably, even though the relative ranking of hotel reputation scores across review platforms are similar.
Short-Text Lexical Normalisation on Industrial Log Data

Lexical normalisation aims to computationally correct errors in text so that the data may be more successfully analysed. Noisy, unstructured short-text data presents unique challenges as it contains multiple types of Out Of Vocabulary (OOV) words. Some are spelling mistakes, which should be normalised to in-dictionary words; some are acronyms or abbreviations, which should be expanded to the corresponding phrases; and some are domain specific terms which should remain in their original form not to be mis-corrected to conform with the dictionary used. Despite its critical significance in assuring data quality, text normalisation is an area with a less cohesive and focused research effort, evidenced by the diverse set of keywords used and scattered publication venues. Integrated approaches that address all three types of OOV terms are scarce. Here we introduce a two-stage, modular classification-based framework that specifically targets the various types of Out Of Vocabulary terms prevalent in short-text data. To avoid laborious feature engineering, our system utilises a Bi-Directional Long Short-Term Memory + CRF model to classify each erroneous token into a particular class. The system then selects an appropriate normalisation technique based on the predicted class of each token. For spell-checking, we introduce two learning models that predict the correct spelling of a word given its context: one that utilises word embeddings, and another that uses a quasi-recurrent neural network. We compare our system to two existing state of the art lexical normalisation systems and find that our system achieves greater performance on the log data domain.
GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection

Change detection (CD) is an important application of remote sensing, which provides timely change information about large-scale Earth surface. With the emergence of hyperspectral imagery, CD technology has been greatly promoted, as hyperspectral data with high spectral resolution are capable of detecting finer changes than using the traditional multispectral imagery. Nevertheless, the high dimension of the hyperspectral data makes it difficult to implement traditional CD algorithms. Besides, endmember abundance information at subpixel level is often not fully utilized. In order to better handle high-dimension problem and explore abundance information, this paper presents a general end-to-end 2-D convolutional neural network (CNN) framework for hyperspectral image CD (HSI-CD). The main contributions of this paper are threefold: 1) mixed-affinity matrix that integrates subpixel representation is introduced to mine more cross-channel gradient features and fuse multisource information; 2) 2-D CNN is designed to learn the discriminative features effectively from the multisource data at a higher level and enhance the generalization ability of the proposed CD algorithm; and 3) the new HSI-CD data set is designed for objective comparison of different methods. Experimental results on real hyperspectral data sets demonstrate that the proposed method outperforms most of the state of the arts.
Asymptotic Effective Capacity of Multichannel ALOHA with Queues

In this paper, we study multichannel ALOHA with queues in conjunction with fast retrial to re-transmit collided packets. Fast retrial is considered as it can shorten the access delay due to no backlogging. We propose to use an erasure channel model based on the widely used collision model, which allows us to find the effective capacity and the quality of service (QoS) exponent of multichannel ALOHA with queues. Under certain conditions, the asymptotic performances are obtained. In particular, it is shown that the QoS exponent grows logarithmically with the system dimension (i.e., the number of subchannels), which implies that the average access delay can be inversely proportional to the system dimension. However, the growth rate of the effective capacity is shown to be slower than linear when the system dimension increases.
Evaluation of the Channel Spacing and Transceiver Bandwidth for PAM-4 Modulated WDM-PON

Considering the exponential traffic growth in access networks, this paper focuses on the four-level pulse amplitude (PAM-4) modulated wavelength-division multiplexing passive optical network (WDM-PON), that is promising solution for the increase of per-wavelength data rate of the PON. The paper evaluates the utilization of different bandwidths of the transmitter (12, 20, 30 GHz) and receiver (10, 20, 30 GHz) as well as channel spacing (50 GHz and 100 GHz) in eight-channel WDM-PON transmission system with per-wavelength data rates of 25 and 40 Gbit/s. The paper focuses on the adjustment of the electrical filter of the receiver for ensuring smaller transmitter and receiver bandwidth, higher receiver performance and longer transmission distance for given data rate and channel spacing. Based on the obtained results paper determines the most appropriate PAM-4 modulated WDM-PON transmission system configuration for the investigated channel spacing up to 100 GHz and bitrates up to 40 Gbit/s.
Residential Short-Term Load Forecasting Using Convolutional Neural Networks

Low aggregations of electric load profiles are more fluctuating, relative forecast errors are comparatively high, and it has been shown that different forecast models and feature configurations may be best suitable for specific households or buildings. However, at low aggregations, the monetary incentive for manual feature engineering and model selection is low, as benefits from forecast improvements are small. Convolutional Neural Networks (CNN) have proven to achieve high accuracy in an end-to-end fashion with minimal effort for manual feature selection. WaveNet, a CNN-based approach, has been developed to handle noisy time-series data for speech recognition and synthesis. In this work we explore if WaveNet is suitable for short-term forecasts of lowly aggregated electric loads. We find that WaveNet performs similarly to, and slightly better than, typical benchmark models for individual households, at the cost of higher model complexity. Preliminary experiments show that transfer learning can further improve results and decrease training times for individual households, as a pattern such as the correlation between outside temperature and load can be learned as general features. For aggregations of 10–200 households WaveNet improves most over the benchmarks, e.g., 13% compared to vanilla Artificial Neural Networks at 200 households, making it possibly suitable for aggregated load forecasting.
Towards Understanding Programs by Counting Objects

A question familiar to any developer facing legacy code for the first time is, where to start? Understanding the entire code base of any large project is impractical, so techniques for identifying the most relevant code are needed. We discuss one potential technique, which we call "object counts:"' a report of the number of objects created during the execution of an object-oriented program. We show how object counts can help when comparing small designs, and comprehending a larger design—specifically JUnit. Our results indicate that the small amount of data provided by object counts can provide significant insight into a system's design.
Towards High-Level Software Approaches to Reduce Virtualization Overhead for Parallel Applications

Due to its numerous advantages, the IT industry is moving more and more towards the cloud for hosting applications. Reducing the virtualization overhead inherent to cloud computing has therefore been a topic of much research and innovation, resulting in a drastic reduction of this overhead for most workloads. For specific tasks however, challenges remain. One example is parallel, CPU-intensive workloads. Studies concerning this specific workload exist, but focus on low-level properties of the virtualization process, which are often out of reach of the application programmer in commercial cloud environments. Therefore, we aim to approach virtualization overhead from an application architecture and implementation perspective, and provide guidelines for application programmers to develop their software in such a way that they avoid virtualization overhead without the need for access to the hypervisor or specific hardware. As a first step towards this goal, this paper offers a proof of concept applied to the dedup benchmark from the PARSEC benchmark suite, which is notorious for its high virtualization overhead. We provide an alternative implementation of this benchmark, which suffers negligible virtualization overhead compared to the original, thus increasing performance by up to 45%.
Cross Dataset Workload Classification Using Encoded Wavelet Decomposition Features

For practical applications, it is desirable for a trained classification system to be independent of task and/or subject. In this study, we show one-way transfer between two independent EEG workload datasets: from a large multitasking dataset with 48 subjects to a second Stroop test dataset with 18 subjects. This was achieved with a classification system trained using sparse encoded representations of the decomposed wavelets in the alpha, beta and theta power bands, which learnt a feature representation that outperformed benchmark power spectral density features by 3.5%. We also explore the possibility of enhancing performance with the utilization of domain adaptation techniques using transfer component analysis (TCA), obtaining 30.0% classification accuracy for a 4-class cross dataset problem.