Miracle-dz/UniFAR-FTUs huggingface dataset:
RepoBee: Developing Tool Support for Courses using Git/GitHub

The use of version control systems within computing education is growing in popularity. However, this is challenging because such systems are not particularly well designed to support educational situations, nor are they easy to use with confidence in teaching, as specialist knowledge and experience is required. This experience paper reports the development of the open source tool RepoBee, which assists in the use of Git/GitHub in an educational context. The tool provides a straightforward interface for managing batch tasks such as repository generation and cloning for setting and gathering assignments, opening and closing of issues to communicate with students, as well as facilitating peer reviews. Parts of RepoBee are open to integration with third party tools for additional tasks, such as running unit tests or static analysis on student repositories. We also include the perspectives of both teachers and teaching assistants who have been using the tool as part of a first year course for computer scientists.
Hybrid Position Forecasting Method for Mobile Robot Transportation in Smart Indoor Environment

Indoor mobile robot position forecasting can improve safety and robustness of the robot navigation systems in indoor environment. In this paper, a novel hybrid computing framework for indoor mobile robot position forecasting is proposed, namely EWT-MOFEPSO-MRMRMI-ORELM. The proposed model consists of three parts, decomposition, feature selection and forecasting. The EWT (Empirical Wavelet Transform) algorithm is utilized to decompose raw series into several more predictable sublayers. For each sublayer, the MRMRMI (Maximum Relevancy Minimum Redundancy Maximum Interaction) model optimized by MOFEPSO (Multi-objective Feasibility Enhanced Particle Swarm Optimization) is applied to generate Pareto set of candidate feature set. The best feature set is selected as the one with minimum forecasting error in validation data. The selected best feature set is used as input of the ORELM (Outlier Robust Extreme Learning Machine) model to generate forecasting value. The ORELM can prevent adverse effect of outlier. Two experiments are carried out to verify the effectiveness of the proposed computing framework. The results indicate that (a) the proposed hybrid computing framework has excellent forecasting performance, (b) The EWT, MOFEPSO-MRMRMI can enhance performance of the ORELM significantly; and (c) the proposed forecasting method can guarantee the safety transportation of mobile robots in smart indoor environment.
Combined Point and Line Features PL-SLAM Algorithm Research

ORB SLAM is the current state of art SLAM algorithm both on accuracy and efficiency, includes map reuse, loop closing and relocalization capabilities. Owing to ORB SLAM is constructed based on point features, it works well in most cases, but loses tracking, or reduces its tracking accuracy in low-texture scenes or when the camera moves too fast causing lacking of reliable point features. To solve this problem, in this paper, we proposed a PL-SLAM (Point and Line based SLAM) algorithm which handle both points and lines to improve the tracking accuracy and reduce tracking failures. The PL-SLAM is an extension of ORB SLAM, focusing on how to introduce the line features into SLAM, establishing the optimization model that depends on point and line features, and solving it. Finally, the PL-SLAM algorithm is verified on the TUM RGBD benchmark, the results show that PL-SLAM not only improves the robustness in challenging environments, but also systematically improves the tracking accuracy in sequence frames.
An empirical study of gamified learning application engagement to exceptional learners

Special education is a specialized area of education which uses unique instructional methods, materials, learning aids, and equipment to meet the educational needs of students with learning disabilities. Exceptional learner is an inclusive term that refers to students with learning and behavior problems, students with physical disabilities or sensory impairments, and students who are intellectually gifted or have a special talent. Computer games are increasingly part of the daily activities of students of all ages, and have been shown to support student motivation and learning however can be challenging to implement in the classroom. Gamification involves incorporating elements of computer games such as points, leaderboards, and badges into non-game contexts in order to take advantage of the motivation provided by a game environment. Based from literature analysis points, badges and achievements, leaderboards and levels are the most commonly implemented form of gamification. A study was conducted to determine to what extent gamification supports student achievement and motivation among exceptional learners. Incorporating gamification elements into exceptional learners' environments can motivate students and support student achievement. Increased class attendance and participation, which is positively correlated with improved student performance, was seen as a result of gamification.
Fully integrated cmos phase shifter/vco for mimo/ism application

Fully Integrated CMOS Phase Shifter/VCO for MIMO/ISM Application. (December 2007) Ahmad Reza Tavakoli Hosseinabadi, B.S., Sharif University of Technology Chair of Advisory Committee: Dr. Kamran Entesari A fully integrated CMOS 0 – 90 phase shifter in 0.18um TSMC technology is presented. With the increasing use of wireless systems in GHz range, there is high demand for integrated phase shifters in phased arrays and MIMO on chip systems. Integrated phase shifters have quite a high number of integrated inductors which consume a lot of area and introduce a huge amount of loss which make them impractical for on chip applications. Also tuning the phase shift is another concern which seems difficult with use of passive elements for integrated applications. This work is presents a new method for implementing phase shifters using only active CMOS elements which dramatically reduce the occupied area and make the tuning feasible. Also a fully integrated millimeter-wave VCO is implemented using the same technology. This VCO can be part of a 24 GHz frequency synthesizer for 24 GHz ISM band transceivers. The 24 GHz ISM band is the unlicensed band and available for commercial communication and automotive radar use, which is becoming attractive for high bandwidth data rate.
Revisiting Assumptions for Website Fingerprinting Attacks

Most privacy-conscious users utilize HTTPS and an anonymity network such as Tor to mask source and destination IP addresses. It has been shown that encrypted and anonymized network traffic traces can still leak information through a type of attack called a website fingerprinting (WF) attack. The adversary records the network traffic and is only able to observe the number of incoming and outgoing messages, the size of each message, and the time difference between messages. In previous work, the effectiveness of website fingerprinting has been shown to have an accuracy of over 90% when using Tor as the anonymity network. Thus, an Internet Service Provider can successfully identify the websites its users are visiting. One main concern about website fingerprinting is its practicality. The common assumption in most previous work is that a victim is visiting one website at a time and has access to the complete network trace of that website. However, this is not realistic. We propose two new algorithms to deal with situations when the victim visits one website after another (continuous visits) and visits another website in the middle of visiting one website (overlapping visits). We show that our algorithm gives an accuracy of 80 (compared to 63 in a previous work [24]) in finding the split point which is the start point for the second website in a trace. Using our proposed "splitting" algorithm, websites can be predicted with an accuracy of 70%. When two website visits are overlapping, the website fingerprinting accuracy falls dramatically. Using our proposed "sectioning'' algorithm, the accuracy for predicting the website in overlapping visits improves from 22.80% to 70%. When part of the network trace is missing (either the beginning or the end), the accuracy when using our sectioning algorithm increases from 20 to over 60%.
BTDP

Bilinear models are very powerful in multimodal fusion tasks like Visual Question Answering. The predominant bilinear methods can all be seen as a kind of tensor-based decomposition operation that contains a key kernel called “core tensor. ” Current approaches usually focus on reducing the computation complexity by applying low-rank constraint on the core tensor. In this article, we propose a novel bilinear architecture called Block Term Decomposition Pooling (BTDP), which not only maintains the advantages of previous bilinear methods but also conducts sparse bilinear interactions between modalities. Our method is based on Block Term Decompositions theory of tensor, which will result in a sparse and learnable block-diagonal core tensor for multimodal fusion. We prove that using such a block-diagonal core tensor is equivalent to conducting many “tiny” bilinear operations in different feature spaces. Thus, introducing sparsity into the bilinear operation can significantly increase the performance of feature fusion and improve VQA models. What is more, our BTDP is very flexible in design. We develop several variants of BTDP and discuss the effects of the diagonal blocks of core tensor. Extensive experiments on two challenging VQA-v1 and VQA-v2 datasets show that our BTDP method outperforms current bilinear models, achieving state-of-the-art performance.
A Pilot Study on Consumer IoT Device Vulnerability Disclosure and Patch Release in Japan and the United States

With our ever increasing dependence on computers, many governments have started to investigate regulations on vulnerabilities and their lifecycle management. Although many previous works have studied this problem space for mainstream software packages and web applications, few studies have targeted consumer IoT devices. As a first step towards filling this void, this paper presents a pilot study on the vulnerability disclosures and patch release behaviors related to 3 prominent consumer IoT vendors in Japan and 3 in the United States. Our goals include (i) characterizing trends and risks using accurate data that spans a long period, and (ii) identifying problems, challenges, and potential approaches for future studies of this problem space. To this end, we collected all published vulnerabilities and their patches for the consumer IoT products by the included vendors between 2006 and 2017; then, we analyzed our data from multiple perspectives such as the severity of the vulnerabilities and the timing of patch releases with respect to disclosures and exploits. Our work has uncovered several findings that may inform future studies, including (i) a stark contrast in the vulnerability disclosures between the two countries and (ii) three alarming vendor practices that may pose significant risks of 1-day exploits.
Development of Hybrid EEG-fEMG-based Stress Levels Classification and Biofeedback Training System

This paper proposes the stress levels classification system based on electroencephalography (EEG) and facial-electromyography (fEMG) signals. The proposed system consists of two main parts; the first is an observation of brain and facial muscular activities during stress state, which is activated by the mental arithmetic tests. The second is the development of stress level classification system via hybrid EEG-fEMG and the design of fEMG-based biofeedback training to reduce stress. The results revealed that EEG in frequency domain beta feature of frontal region achieved a high average accuracy. Hence, hybrid EEG-fEMG modality can be used to measure stress level. Moreover, fEMG-based biofeedback training system might be used to relieve stress. In the future, we will test this system on a variety of subjects and implement the proposed fEMG biofeedback training system for a stress relief.
Program Comprehension: Identifying Learning Trajectories for Novice Programmers

This working group asserts that Program Comprehension (PC) plays a critical part in the writing process. For example, this abstract is written from a basic draft that we have edited and revised until it clearly presents our idea. Similarly, a program is written in an incremental manner, with each step being tested, debugged and extended until the program achieves its goal. Novice programmers should develop their program comprehension as they learn to code, so that they are able to read and reason about code while they are writing it. To foster such competencies our group has identified two main goals: (1) to collect and define learning activities that explicitly cover key components of program comprehension and (2) to define possible learning trajectories that will guide teachers using those learning activities in their CS0/CS1 or K-12 courses. We plan to achieve these goals as follows: Step 1 Review the current state of research and development by analyzing literature on classroom activities that improve program comprehension. Step 2 Concurrently, survey lecturers at various institutions on their use of workshop activities to foster PC. Step 3 Use the outputs from both activities to define and conceptualize what is meant by PC in the context of novice programmers. Step 4 Catalog learning activities with regard to their prerequisites, intended learning outcomes and additional special characteristics. Step 5 Catalog learning activities with regard to their prerequisites, intended learning outcomes and additional special characteristics. Step 6 Develop a map of learning activities and thereby also models of probable learning trajectories.
Blockchain as a Value Enabler: Bridging Financial Assets and Intangibles

Blockchain technology was envisaged to bring a universal solution to strengthen digital democracy: ensure digital trust, distribute power between members, minimise e-monopoly, and it all through the innovative way of digitising and transacting 'value'. The latter however was deemed to represent financial assets only, neglecting the value created by the intangibles - the non-financial value. Nevertheless, intangibles are established to play a significant role in impacting financial value and therefore need to be incorporated into the existing value chain system. This paper explores how blockchain technology can capture and transact not just financial value, but non-financial one, conveying this way total value.
A group decision making technique for appraising the performance of organizations

The performance of an organization is usually appraised in terms of multiple criteria using a group of expert panels. However, when a decision is highly complex coupled with personal interests and emotional factors, a rational technique is necessary to extract the individual preferences objectively in order to optimize the decision outcomes. This article proposes a new evaluating technique for making group decisions efficiently and objectively. An application example appraising the performance of the industrial engineering department of all colleges in Taiwan is used to illustrate the applicability of the proposed method. Results indicate that the new approach is suitable for making decisions with multiple criteria.
Identity and Belonging for Graduate Apprenticeships in Computing: The experience of first cohort degree apprentices in Scotland

In September 2017, our university's first graduate apprentices began degrees in Software Development, Cybersecurity, and Information Technology Management for Business. This study explores how apprentices experience their association with the university and identities as students, but also employees. In Scotland, Graduate Apprenticeships (GAs) are undergraduate degrees in which the students are in full-time employment, while completing degree modules over four years, as for a traditional full-time degree. The curriculum follows a skills framework designed by employers so that graduates have the professional and technical attributes required by the industry. The degrees parallel Degree Apprenticeships in England, though there are national differences in implementation. Themes of identity and belonging are central to current investigations of the experience of STEM students, especially computing students, as fewer students choose STEM courses, and many transfer out of their subjects or do not complete their degrees. The research hypothesis is that the apprentices' employment will provide a strong IT professional identity supports their progress at university. Semi-structured interviews with apprentices in the first computing cohorts explored their situated perspectives. Responses were identified which concerned the apprentices' identity as students or employees, including themes around belonging. Thematic analysis of these responses revealed that apprentices defined themselves in opposition to traditional student identities and did draw strength from their identity as employees. They experienced belonging specifically within their GA cohort-the first of its kind in the university. A better understanding of identity and belonging can be used by universities to address the challenges of retention.
SoK: Using Dynamic Binary Instrumentation for Security (And How You May Get Caught Red Handed)

Dynamic binary instrumentation (DBI) techniques allow for monitoring and possibly altering the execution of a running program up to the instruction level granularity. The ease of use and flexibility of DBI primitives has made them popular in a large body of research in different domains, including software security. Lately, the suitability of DBI for security has been questioned in light of transparency concerns from artifacts that popular frameworks introduce in the execution: while they do not perturb benign programs, a dedicated adversary may detect their presence and defeat the analysis. The contributions we provide are two-fold. We first present the abstraction and inner workings of DBI frameworks, how DBI assisted prominent security research works, and alternative solutions. We then dive into the DBI evasion and escape problems, discussing attack surfaces, transparency concerns, and possible mitigations. We make available to the community a library of detection patterns and stopgap measures that could be of interest to DBI users.
Alexa Lied to Me: Skill-based Man-in-the-Middle Attacks on Virtual Assistants

Voice-based virtual personal assistants such as Amazon's Alexa or Google Assistant have become highly popular and are used for diverse daily tasks ranging from querying on-line information, shopping, smart home control and a variety of enterprise application scenarios. Capabilities of virtual assistants can be enhanced with so-called Skills , i.e., programmatic extensions that allow thirdparty providers to integrate their services with the respective voice assistant. In this paper, we show that specially crafted malicious Skills can use the seemingly limited Skill interaction model to cause harm. We present novel man-in-the-middle attacks against benign Skills and Virtual Assistant functionalities. Our attack uses loopholes in the Skill interface to redirect a victim's voice input to a malicious Skill, thereby hijacking the conversation between Alexa and the victim. To the best of our knowledge this is the first man-in-the-middle attack targeting the Skill ecosystem. We present the design of our attack and demonstrate its feasibility based on a proof-of-concept implementation attacking the Alexa Skills of a smart lock as well as a home security system.
Mayflies, Breeders, and Busy Bees in Ethereum: Smart Contracts Over Time

Smart contracts on a blockchain are programs running in a distributed, transparent, and trustless environment, being one of the major assets of this new technology. They give rise to innovative applications and business models, with their potential and lasting impact still open. In this situation, it is interesting to understand what smart contracts are actually doing. While public announcements, by their nature, make promises of what smart contracts might achieve, the openly available data of blockchains provides a more balanced view on what is actually going on. In this paper, we analyze the activities of smart contracts on the Ethereum blockchain, the most prominent platform for smart contracts with all blockchain data visible. However, contracts operate behind the scenes. Their activities are only accessible by looking beyond the mere blockchain data that records external transactions. We also use all internal messages caused by contracts interacting with other addresses. In particular, we investigate the activities of smart contracts in their quantitative and temporal aspects. Based on lifespan and activity patterns, we identify particular groups like mayflies, loners, breeders, busy bees, sleepers, self-destructed and bonkers contracts and visualize their temporal characteristics. To gain insights into the purpose of these smart contracts we perform a basic analysis of code and message content including deployment code. We consider data up to Ethereum block 6900000 (end of 2018).
Computing the Characteristic Polynomial of a Finite Rank Two Drinfeld Module

Motivated by finding analogues of elliptic curve point counting techniques, we introduce one deterministic and two new Monte Carlo randomized algorithms to compute the characteristic polynomial of a finite rank-two Drinfeld module. We compare their asymptotic complexity to that of previous algorithms given by Gekeler, Narayanan and Garai-Papikian and discuss their practical behavior. In particular, we find that all three approaches represent either an improvement in complexity or an expansion of the parameter space over which the algorithm may be applied. Some experimental results are also presented.
Predicting the Habitat for Alien Species based on GIS

Invasive organisms have broken the ecosystems around the world, and the economic losses are huge. It is vital to actively respond to the invasion of harmful organisms and protect the ecological environment in which we live. Predicting the habitats for alien species by using species distribution model is the most economical and effective preventive measure to prevent invasive species' occurrence and harm. By the prediction model of the biological climate similar model, namely, using the climatological data and the biological parameters of the origin of invasive species predicts global habitat so as to determine the scope of the invasion of alien species. In this study, the application Software for predicting the habitat for alien species were developed using C# (programming language) and GIS' secondary developing module---ArcGIS Engine. The experimental results have been verified, AUC value is 0.91. The case study of Pterygoplichthy spardalis, according to Pterygoplichthys pardalis'biological parameters and climatological data of the origin in South America, predicts the globally suitable areas. The research has shown that in addition to the origin of South America, North America countries such as Guatemala, Central African countries such as Gabon, Congo, the southern part of India, Thailand and Burma, Midwestern Indonesia, south Asian countries such as Malaysia and the Philippines are the high incidence area of the Pterygoplichthys pardalis' invasion. It provides the scientific basis for quarantine department of animals and plants in a timely manner to take corresponding measures to control the further spread of the Pterygoplichthys pardalis.
"I Don't Know Too Much About It": On the Security Mindsets of Future Software Creators

Anyone can learn to code and build software these days. But the intricate and convoluted nature of computer security means that not anyone can write proper secure code. The goal of this research is to help average software developers build secure software with little understanding of security by investigating the security and privacy perceptions, experiences, and practices of current computer science students.