Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Network Alignment via Attack Signal Scaling and Adversarial Perturbation Elimination

Recent studies have shown that graph learning models are highly vulnerable to adversarial attacks, and network alignment methods are no exception. How to enhance the robustness of network alignment against adversarial attacks remains an open research problem. In this paper, we propose a robust network alignment solution, RNA, for offering preemptive protection of existing network alignment algorithms, enhanced with the guidance of effective adversarial attacks. First, we analyze how popular iterative gradient-based adversarial attack techniques suffer from gradient vanishing issues and show a fake sense of attack effectiveness. Based on dynamical isometry theory, an attack signal scaling (ASS) method with established upper bound of feasible signal scaling is introduced to alleviate the gradient vanishing issues for effective adversarial attacks while maintaining the decision boundary of network alignment. Second, we develop an adversarial perturbation elimination (APE) model to neutralize adversarial nodes in vulnerable space to adversarial-free nodes in safe area, by integrating Dirac delta approximation (DDA) techniques and the LSTM models. Our proposed APE method is able to provide proactive protection to existing network alignment algorithms against adversarial attacks. The theoretical analysis demonstrates the existence of an optimal distribution for the APE model to reach a lower bound. Last but not least, extensive evaluation on real datasets presents that RNA is able to offer the preemptive protection to trained network alignment methods against three popular adversarial attack models.
Intelligent Mobile Wireless Network for Toxic Gas Cloud Monitoring and Tracking

Intelligent wireless networks that comprise self-organizing autonomous vehicles equipped with punctual sensors and radio modules support many hostile and harsh environment monitoring systems. This work’s contribution shows the benefits of applying such networks to estimate clouds’ boundaries created by hazardous toxic substances heavier than air when accidentally released into the atmosphere. The paper addresses issues concerning sensing networks’ design, focussing on a computing scheme for online motion trajectory calculation and data exchange. A three-stage approach that incorporates three algorithms for sensing devices’ displacement calculation in a collaborative network according to the current task, namely exploration and gas cloud detection, boundary detection and estimation, and tracking the evolving cloud, is presented. A network connectivity-maintaining virtual force mobility model is used to calculate subsequent sensor positions, and multi-hop communication is used for data exchange. The main focus is on the efficient tracking of the cloud boundary. The proposed sensing scheme is sensitive to crucial mobility model parameters. The paper presents five procedures for calculating the optimal values of these parameters. In contrast to widely used techniques, the presented approach to gas cloud monitoring does not calculate sensors’ displacements based on exact values of gas concentration and concentration gradients. The sensor readings are reduced to two values: the gas concentration below or greater than the safe value. The utility and efficiency of the presented method were justified through extensive simulations, giving encouraging results. The test cases were carried out on several scenarios with regular and irregular shapes of clouds generated using a widely used box model that describes the heavy gas dispersion in the atmospheric air. The simulation results demonstrate that using only a rough measurement indicating that the threshold concentration value was exceeded can detect and efficiently track a gas cloud boundary. This makes the sensing system less sensitive to the quality of the gas concentration measurement. Thus, it can be easily used to detect real phenomena. Significant results are recommendations on selecting procedures for computing mobility model parameters while tracking clouds with different shapes and determining optimal values of these parameters in convex and nonconvex cloud boundaries.
Word Sense Disambiguation Using Graph-based Semi-supervised Learning

— Word Sense Disambiguation (WSD) is a well-known problem in the natural language processing. In recent years, there has been increasing interest in applying neural networks and machine learning techniques to solve WSD problems. However, these previous approaches often suffer from the lack of manually sense-tagged examples. Moreover, most supervised WSD methods suffer from small differences of examples within the overall training data or within each of the two sense labels. In this paper, to solve these problems, we propose a semi-supervised WSD method using graph convolutional neural network and investigate what kind of features are effective for this model. Experimental results show that the proposed method performs better than the previous supervised method and the morphological features obtained by the UniDic short-unit dictionary is effective for the semi-supervised WSD method. Moreover, the Jaccard coefficient is the most effective measure among three measures to construct a graph structure.
Cybersecurity in Machine Learning and Artiﬁcial Intelligence for Self Driving Vehicles A proposal for an uniﬁed and open source test framework

—This report summarizes the key methodologies applicable for attacks on Convolutional Neural Networks deployed in self driving vehicles. Furthemore, the need for an open source framework speciﬁcally designed to test such networks for robustness against these attacks is demonstrated. This need stems from the fact that solutions available so far only cover very generic scenarios of adversarial attacks. Hence these tools are not suited to speciﬁcally secure Convolutional Neural Networks deployed in in self driving vehicles. All in all the development of such a framework would beneﬁt all parties that are involved in the market of autonomously driving vehicles.
Inferring Resource Types in Knowledge Graphs using NLP analysis and human in-the-loop validation: The DBpedia Case

Deﬁning proper semantic types for resources in Knowledge Graphs is one of the key steps on building high quality data. Often, this information is either missing or incorrect. Thus it is crucial to deﬁne means to infer this information. Several approaches have been proposed, including reasoning, statistical analysis, and the usage of the textual information related to the resources. In this work we explore how textual information can be applied to existing semantic datasets for predicting the types for resources, relying exclusively on the textual features of their descriptions. We apply our approach to DBpedia entries, combining different standard NLP techniques and exploiting complementary information available to extract relevant features for different classiﬁers. Our results show that this approach is able to generate types with high precision and recall, above the state of the art, evaluated both on the DBpedia dataset (94%) as well as on the LDH gold standard dataset (80%). We also discuss the utility of the web tool we have created for this analysis, NLP4Types, which has been released as an online application to collect feedback from ﬁnal users aimed at enhancing the Knowledge graph.
PyPANDA: Taming the PANDAmonium of Whole System Dynamic Analysis

—When working with real world programs, dynamic analyses often must be run on a whole-system instead of just a single binary. Existing whole-system dynamic analysis platforms generally require analyses to be written in compiled languages, a suboptimal choice for many iterative analysis tasks. Furthermore, these platforms leave analysts with a split view between the behavior of the system under analysis and the analysis itself—in particular the system being analyzed must commonly be controlled manually while analysis scripts are run. To improve this process, we designed and implemented PyPANDA, a Python interface to the PANDA dynamic analysis platform. PyPANDA unifies the gap between guest virtual machines behavior and analysis tasks; enables painless integrations with other program analysis tools; and greatly lowers the barrier of entry to whole-system dynamic analysis. The capabilities of PyPANDA are demonstrated by using it to dynamically evaluate the accuracy of three binary analysis frameworks, track heap allocations across multiple processes, and synchronize state between PANDA and a binary analysis platform. Significant challenges were overcome to integrate a scripting language into PANDA with minimal performance impact.
Quantifying the adoption of Kotlin on Android stores: Insight from the bytecode

Android apps have been traditionally built using Java since the inception of Android. However, Google announced Kotlin as an official supported language for the Android platform in May 2017. Since then, the popularity of Kotlin for Android projects has steadily increased, to the point that Google announced in 2019 that "Android development will be Kotlin-first" with nearly 60% of the top 1,000 Android apps containing Kotlin code. Yet, the transition from Java to Kotlin seems gradual and most applications still partially use Java. Outside open-source apps, little is known about the real proportion of code written in Kotlin inside apps. This paper supports a better understanding of the adoption of Kotlin in the Android ecosystem. We propose an approach to identify the language, Java or Kotlin, in which a class bytecode of an Android Package Kit (APK) originate from. We applied our model on more than 200k closed-source APKs from app stores and found that (i) most of the apps classes are still written in Java, indicating a mitigated adoption of Kotlin in less popular apps, (ii) the penetration of Kotlin is steadily increasing since 2017. We believe our insights are valuable to assess the adoption of Kotlin at large.
Understanding Drivers’ Risk Behaviors from Dashcam Videos

Dangerous driving causes preventable injuries and deaths. To promote safe driving practices, a forward-facing dashboard camera or dashcam can provide visual context of the environment in which a driver exhibits dangerous driving behaviors. In this thesis, I designed and implemented a system that identifies and analyzes dangerous driving events using monocular videos from dashcams and odometry data from smartphone sensors. To extract useful information such as the position of the ego-vehicle in the lane, the following distance to the next vehicle, and the position and velocity of other vehicles in real-world coordinates, the system performsmultiple computer vision tasks, including 2D and 3D object detection and tracking, lane detection, and camera calibration. Then, the information from perception tasks are used to identify risky events, including tailgating and running stop signs, analyze the speed of traffic flow, and classify the cause for hard braking events, such as reacting to the next car in the lane, stopping for a pedestrian, or being inattentive. Using over 8 hours of dashcam videos in multiple driving scenarios, experiments were performed to demonstrate the system’s capabilities and limitations. Thesis Supervisor: Paresh Malalur Title: Head of AI and Vision, Cambridge Mobile Telematics Thesis Supervisor: Hari Balakrishnan Title: Professor of Computer Science
Critical Success Factors of Information Technology Outsourcing for Emerging Markets

Corresponding Author: Sushil Paudel Mewar University, Chittorgarh, Rajasthan, India Email: sushilpaudel@gmail.com Abstract: Outsourcing has gained popularity as several large U.S. companies in the 1980 s began delegating IT work to foreign firms. In outsourcing, one party (customer) asks another party (vendor) to do a particular job. Outsourcing has its own advantages, like reducing the cost and time and increasing performance and satisfaction. This study has focused on the critical success factors in information technology outsourcing for the emerging market and has considered the perspectives of the vendor. A snowball sampling technique was used to generate quantitative data among the respondents inside Kathmandu valley and variables were drawn from the available literature. Respondents included outsourcing vendors, freelancers, consultants and policymakers. Data were properly tested for reliability using Cronbach’s Alpha and results were validated using convergent and discriminant validity. The analysis included Structured Equation Modeling and Estimation was done using maximum likelihood and partial least squares. The study identified 21 critical success factors for the emerging market under seven categories: System quality, communication quality, service quality, system use, satisfaction, individual benefit and organizational benefit which is the main contribution of the paper. It is suggested that IT outsourcing vendors focus on these factors, policymakers implement new strategies to establish their presence in global outsourcing industries and researchers incorporate these variables in their future research.
Effect of aqueous extract of strawberry polyphenols on the growth of mesophillic mixed dahi culture (NCDC-167) and probiotic culture Lactobacillus rhamnosus GG

Polyphenols can selectively modulate the growth pattern of susceptible microorganisms. Present study was conducted to assess the viability of mesophillic mixed dahi culture (NCDC 167) and probiotic culture Lactobacillus rhamnosus GG in their growth medium viz. M17 and MRS with the added strawberry polyphenols aqueous extract. pH of growth medium was observed to be decreased with the addition of polyphenols extract at different concentration (0.25 to 1.0 mg/ml). Growth pattern of the cultures was estimated by measuring the optical density of pH adjusted samples and without pH adjusted samples. Growth of NCDC 167 and Lactobacillus rhamnosus GG was observed to be unaffected in the presence of polyphenols. Therefore incorporation of polyphenols in fermented dairy products in combination with probiotic bacteria can be a good option to deliver functional ingredients.
Artificial Intelligence: Challenges for the future

Artificial Intelligence (AI) is a term coined by Prof. J. McCarthy, one of the “founding fathers” of the area in 1955. AI may assist, augment or eventually automate processes. Colloquially AI can be used to describe machines (or computers) that mimic “cognitive” functions associated with the human mind, such as “learning” and “problem solving” [6] and can be used to tackle many of the problems usually solved by humans such as classification, summarisation, translation, diagnosis, grouping, finding outliers. AI algorithms have already found applications in most domains of human activity including finance, national security, health care, justice, transportation, smart cities, industrial processes education, etc.
Effects of Precise and Imprecise Value-Set Analysis (VSA) Information on Manual Code Analysis

—Binary reverse engineers combine automated and manual techniques to answer questions about software. However, when evaluating automated analysis results, they rarely have additional information to help them contextualize these results in the binary. We expect that humans could more readily understand the binary program and these analysis results if they had access to information usually kept internal to the analysis, like value-set analysis (VSA) information. However, these automated analyses often give up precision for scalability, and imprecise information might hinder human decision making. To assess how precision of VSA information affects human analysts, we designed a human study in which reverse engineers answered short information ﬂow problems, determining whether code snippets would print sensitive information. We hypothesized that precise VSA information would help our participants analyze code faster and more accurately, and that imprecise VSA information would lead to slower, less accurate performance than no VSA information. We presented hand-crafted code snippets with precise, imprecise, or no VSA information in a blocked design, recording participants’ eye movements, response times, and accuracy while they analyzed the snippets. Our data showed that precise VSA information changed participants’ problem-solving strategies and supported faster, more accurate analyses. However, surprisingly, imprecise VSA information also led to increased accuracy relative to no VSA information, likely due to the extra time participants spent working through the code.
“Healthy” Deceits with Mixed Reality

Because of its capacity to alter the perception of the surrounding environment, mixed reality technology (MR) has potential for in-the-wild management of anxiety — both chronic (e.g., anxiety disorders) and acute (e.g., situation-induced anxiety). Recently, several components of Cognitive Behavioural Therapy leverage these technologies as part of ongoing cognitive restructuring or as part of relaxation or exposure experiences. However, while most of these researchers focus on the promising benefits of these technologies, a deeper discussion on the potential harm through the misuse of these techniques is still missing. To ignite a conversation about the two sides of the same coin of MR for mental health, we present several scenarios where mixed reality approaches might be used to deceive users rather to support them.
2020 / 21 COMP 4801 Final Report Project title : Body Temperature Measuring System with Smart Patrol Robot

The report researches the use of smart patrol robots for health checking measures at the Hong Kong International Airport. With Covid-19 raging around the world in 2020, it not only sheds light on the critical role of airports to curb the spread of commutable diseases but also reveals the system’s loophole where travellers can cheat through the fever checking measures by taking a fever reducer. Therefore, the project explores the possibility of utilising the smart patrol robot to fill the existing loophole by incorporating another temperature measuring procedure before travellers come on board. The project incorporates a thermographic camera on the robot and performs different stages of data collection, data processing, and face recognition. A web portal has been developed for fever case management.
Inter-ethnic relations among black students at a South African higher education institution

This study explored the inter-ethnic relations and experiences of students at a higher education institution, using an exploratory case study approach. Purposive sampling was used to select students belonging to black South African ethnic groups. Semi-structured interviews were used to collect data from three participants; interpretative phenomenological analysis was used for analysis. The themes extracted were ethnic identity: identity confusion vs. certainty; personal experiences of inter-ethnic relations; and majority and minority ethnic groups’ experiences. Identity salience was found to not particularly be integral to the cultivation of inter-ethnic relations. Moreover, participants’ inter-ethnic experiences were varied; some were positive, and others, negative. Relations between majority and minority ethnic groups are seemingly supported by existing literature, as majority ethnic group members typically initiated negative interactions, more so than did minority ethnic groups. Future studies should interrogate inter-ethnic dynamics on a broader scale, and explore ethnic bullying and its impact on inter-ethnic relations.
The Economic Impact of On-Screen Tourism: The Case of The Lord of the Rings and the Hobbit

The economic impacts of on-screen tourism are particularly interesting, and research in this area can provide useful information to governments making decisions regarding subsidising film production and forming relevant marketing strategies. No reliable and systematic approach for measuring the economic impacts of on-screen tourism currently exists, and this study is the first to evaluate the overall economic impacts of on-screen tourism by comparing the impacts of two series of films, The Lord of the Rings and the Hobbit, both filmed in New Zealand. A new approach combining econometric and computable general equilibrium modelling techniques is used in the assessment. The results show that The Lord of the Rings did not significantly impact on the tourism and economy of New Zealand, while the Hobbit Trilogy had a significant positive impact, which may be due to effective marketing strategies and media convergence.
Paper Forager: Supporting the Rapid Exploration of Research Document Collections

We present Paper Forager, a web-based system which allows users to rapidly explore large collections of research documents. Our sample corpus uses 5,055 papers published at the ACM CHI and UIST conferences. Paper Forager provides a visually based browsing experience, allowing users to identify papers of interest based on their graphical appearance, in addition to providing traditional faceted search techniques. A cloud-based architecture stores the papers as multi-resolution images, giving users immediate access to reading individual pages of a paper, thus reducing the transaction cost between finding, scanning, and reading papers of interest. Initial user feedback sessions elicited positive subjective feedback, while a 24-month external deployment generated in-the-wild usage data which we analyze. Users of the system indicated that they would be enthusiastic to continue having access to the Paper Forager system in the future.
Measurement of the ! − " Correlation of 21 Na using Shakeoff Electrons

The ! − " correlation coef ﬁ cient, a ! " , is measured in 21 Na by detecting the time-of- ﬂ ight of the recoil nucleus detected in coincidence with the atomic electrons shaken off in beta decay. The sample of 21 Na is con ﬁ ned in a magneto-optic trap. High detection ef ﬁ ciency allows low trap density, which suppresses the photoassociation of molecular sodium, which can cause a large systematic error. Suppressing the fraction of trapped atoms in the excited state using a dark trap also reduces the photoassociation process, and data taken with this technique are consistent. The main remaining systematic uncertainties come from the measurement of the position and size of the atom trap, and the subtraction of background. We ﬁ nd a ! " = 0 . 5502 ( 60 ) , in agreement with the Standard Model prediction of a ! " = 0 . 553 ( 2 ) , and disagreeing with a previous measurement which was susceptible to an error introduced by the presence of molecular sodium.
Device for Hemodynamic Parameters Measurement in Veterinary Medicine of Small Animals

The paper describes the design and realization of a device for non-invasive sensing of hemodynamic parameters in veterinary medicine of small animals (dogs and cats). Two main hemodynamic parameters were selected for the measurement blood pressure and heart rate. An oscillometric method was chosen to measure blood pressure. The heart rate is determined from the electrocardiography (ECG) signal, i.e., by sensing the electrical activity of the heart from the body surface. The realized device saves the measured data on the SD card after measuring the signals. To evaluate them, a MATLAB script, which determines the values was implemented. The control of the measuring device and communication with the user is provided by the ESP32 microcontroller. The advantage of the realized device is the possibility of displaying oscillometric pulsations, which is not usually available in commercial devices. Thanks to the simultaneous measurement of the mentioned biological signals, the possibilities of the signal evaluation can be further extended, e.g., by measuring the pulse wave velocity measurement.