Miracle-dz/UniFAR-FTUs huggingface dataset:
License Plate Detection Utilizing Synthetic Data from Superimposition

Machine learning projects are often constrained by data that is messy, scarce, sensitive or costly to produce. These are issues which could be mitigated by synthetic data. This thesis tries to improve Swedish license plate localization in images by synthesizing images through superimposition, a process that produces data cheaply and in abundance. A generative process algorithmically creates images of fake license plates which are glued on top of other images without any context awareness. The resulting images thus contain license plates where they normally don't exist, floating in the sky or across a person's face. To evaluate the usefulness of this method, a machine learning algorithm called YOLOv3 is trained on synthesized data and tested on real images containing Swedish license plates. Our findings show that training on synthesized data by itself is almost good enough to match the performance of training on publicly available real data containing international license plates. The best results are achieved by mixing real and synthetic data.
Vehicle Target Tracking Method for Crooked Roads Based on UKF and Sensor Fusion

Aiming at the problem that mm-wave radar tracking target is easy to lose when in crooked roads, a vehicle target tracking method based on Unscented Kalman Filter (UKF) and sensor fusion is proposed. This method uses mm-wave radar and camera to obtain vehicle targets’ information in crooked roads at the same time. Then, according to whether mm-wave radar tracking target is lost or not, different tracking methods based on UKF are adopted. When the mm-wave radar tracking target is not lost, the historical and current data information of mm-wave radar are taken as the state information and measurement information, directly calculating the tracking vehicle parameters such as position, distance and speed; When the mm-wave radar tracking target is lost, a sensor fusion strategy is adopted. The historical data information of mm-wave radar is still the state information, and the camera vision image information of the tracking target becomes the measurement information, integrating the two sensors’ information to get the tracking vehicle parameters. And the result will serve as next moment’s state information for continuous tracking to solve the mm-wave radar tracking target loss problem in crooked roads. Finally, the validity of this method is verified by real road experiments.
Universal Social Network Bus

Online Social Network Services (OSNSs) are changing the fabric of our society, impacting almost every aspect of it. Over the past few decades, an aggressive market rivalry has led to the emergence of multiple competing, “closed” OSNSs. As a result, users are trapped in the walled gardens of their OSNS, encountering restrictions about what they can do with their personal data, the people they can interact with, and the information they get access to. As an alternative to the platform lock-in, “open” OSNSs promote the adoption of open, standardized APIs. However, users still massively adopt closed OSNSs to benefit from the services’ advanced functionalities and/or follow their “friends,” although the users’ virtual social sphere is ultimately limited by the OSNSs they join. Our work aims at overcoming such a limitation by enabling users to meet and interact beyond the boundary of their OSNSs, including reaching out to “friends” of distinct closed OSNSs. We specifically introduce Universal Social Network Bus (USNB), which revisits the “service bus” paradigm that enables interoperability across computing systems to address the requirements of “social interoperability. ” USNB features synthetic profiles and personae for interaction across the boundaries of closed and open and profile- and non-profile-based OSNSs through a reference social interaction service. We ran a 1-day workshop with a panel of users who experimented with the USNB prototype to assess the potential benefits of social interoperability for social network users. Results show the positive evaluation of users for USNB, especially as an enabler of applications for civic participation. This further opens up new perspectives for future work, among which includes enforcing security and privacy guarantees.
Residual Carrier Frequency Offset Estimation and Compensation for Commodity WiFi

The various offsets existed in the commodity WiFi devices greatly limit the use of ubiquitous WiFi signals for indoor applications. In this paper, we focus on the estimation and compensation of the residual carrier frequency offset (CFO) for the commodity WiFi devices. Specifically, we consider a distorted channel state information (CSI) model by taking into consideration various CSI errors such as packet detection delay (PDD) and CFO. We propose a multiscale sparse recovery algorithm to get rid of the effect of PDD and extract the carrier frequency component out of CSI. Then, we formulate the residual CFO estimation as a spectrum estimation problem and utilize the MUSIC algorithm to estimate the residual CFO. Real experiments and numerical simulations are conducted to evaluate the performance of the proposed method. The experimental results and simulation results show that the residual CFO is time-varying, and compared with existing methods, the proposed method can better estimate and compensate the residual CFO, and thus achieve better results.
Physical performance characteristics of South African male and female Emergency Care students (ECS)

The physical capacity of emergency care practitioners (ECPs) plays a key role in their ability to carry out rescue and life-saving duties. Irrespective of the emergency scenario males and females are expected to be able to do the same tasks. This study assessed the differences in physical work capacity of male and female emergency care students (ECSs). Twenty males and 18 female students participated in the study. All mean anthropometric and physiological values between males and females were significantly different (p<0.05) with the exception of BMI, while in the performance assessments the females only outperformed males in sit-ups. In terms of cardio-respiratory measures the females demonstrated a congruency ratio of 76.21 compared to males, more specifically relative VO2 max (ml.kg.min-1) males = 47.91 (±7.76) versus females = 37.26 (±6.85). Males were significantly stronger than females (p<0.01) in all measured strength parameters and females achieved a congruency ratio of 64.03 compared to males. In conclusion these findings demonstrate that the mean performance characteristics of male and female ECS are significantly different. Exercise and conditioning intervention strategies need to be considered and implemented to ensure, where possible, that applicants are able to achieve the minimum physiological, fitness and strength profiles that enable them to competently execute their emergency care work.
Open Broadcast Media Audio from TV: A Dataset of TV Broadcast Audio with Relative Music Loudness Annotations

Open Broadcast Media Audio from TV (OpenBMAT) is an open, annotated dataset for the task of music detection that contains over 27 hours of TV broadcast audio from 4 countries distributed over 1647 one-minute long excerpts. It is designed to encompass several essential features for any music detection dataset and is the first one to include annotations about the loudness of music in relation to other simultaneous non-music sounds. OpenBMAT has been cross-annotated by 3 annotators obtaining high inter-annotator agreement percentages, which allows us to validate the annotation methodology and ensure the annotations reliability. In this work, we first review the current publicly available music detection datasets and state OpenBMAT’s contributions. After that, we detail its building process: the selection of the audio and the annotation methodology. Then, we analyze the produced annotations and validate their reliability. We continue with an experiment to highlight the value of these annotations and investigate the most challenging content in OpenBMAT. Finally, we describe the details about the format in which the dataset is presented and the platform where we have made it available. We believe OpenBMAT will contribute to major advancements of the research on music detection in real-life scenarios.
Naive Bayes classifier for infant weight prediction of hypertension mother

Classification is one method of data analysis in data mining that is used to form a model in order to describe the appropriate data class or model that predicts data trends. The Usage of classification has been applied in various areas, including in health areas. One of the classification methods used is Naive Bayes. This study aims to predict the weight of infants in maternal hypertensive and nonhypertensive conditions with Naive Bayes method. Data were taken as many as 219 data from pregnant women based on the medical record in Obstetrics and Gynecology of Muhammadiyah Palembang Hospital from January 1017 until September 2017. Data is divided into two groups, 188 for training data and 31 data for testing data. The performance data analysis was using WEKA and the results showed that the Naive Bayes’s accuracy is 80.372%. the accuracy score means Naive Bayes works well to predict the weight of infants in maternal hypertensive and nonhypertensive mothers. The result is expected to be a reference for others research by comparing it with other classification methods and incorporating other factors in pregnancy and multiple births or other factors.
Initial Results on Deep Learning-Based Pilot Contamination Mitigation in Massive MIMO Systems

In this brief letter we report our initial results on the application of deep-learning to the massive MIMO channel estimation challenge. We show that it is possible to estimate wireless channels and that the possibility of mitigating pilot-contamination with deep-learning is possible given that the leaning model underwent an extensive training-phase and that it has been presented with a large number of different channel conditions.
Robot Imitation Learning Based on Gaussian Processes

To acquire the control strategy in robot imitation learning,a Gaussian processes( GP)regression model based on GP is used to construct the mapping relationship between actions of teaching robot and perception by the sample data of the teaching behavior. Then,the mapping relationship is used as control the strategy to imitate the teaching action. The Braitenberg vehicles are used as simulation subject to study phototaxis imitation learning action. Simulation results show that robot imitation learning based on Gaussian processes is effective. Furthermore,the simulation results in various task environments indicate that the method is adaptive.
Passive Haptic Feedback for Object Manipulation in Virtual Reality

Thanks to technological advancement and lower priced head mounted displays, Virtual Reality is becoming popular in the consumer market. One of the main aspects on which researchers are focusing in these days is finding a way to interact in a way that is as similar as possible to the real world. In this document it is first analyzed some previous work done in this field. Then, it is presented an implementation of a passive feedback system which uses real objects to augment virtual reality experiences. In this research, the system is used on two different sets of real objects. The first is composed of the exact replica of the objects present in the virtual environment, the second is composed by objects with several levels of mismatched physical characteristics. A user study is conducted to compare the interaction in VR using passive haptics and two state-of-the-art interaction methods: VR controllers and Bare Hands Interaction. The goal of this user study is to observe which interaction method gives the best results in terms of realism, level of immersion, enjoyment and precision and quality of movement in VR. In addition, the execution of the task in VR is compared to the execution of the same task in the real world. Results show that enhancing a virtual environment with physical objects increases the level of perceived realism and immersion of the experience. The characteristics of the execution of a movement in a passive haptic environment are more similar to those done in the real world. The difference in the task execution using exact physical replica and different objects depends on the type of task (i.e. more focused on the object or on the action). Using VR controllers to interact with the environment yields to satisfactory results while the absence of a touch feedback in the Bare Hands Interaction brings to significantly worse results in the investigated categories.
Performance-aware test-time optimization schemes for analysis of logic level faults in channels of on-chip networks

Today manycore, multiprocessor systems-on-chip (MPSoCs) have been introduced to cope with the growing demand for high-speed communication requirements of intensive-computing applications. However, in spite of rapid advancements in deep-submicron (DSM) technology and the seamless integration of intellectual property (IP) modules in the SoCs, these busbased interconnection architectures have become unable to meet the performance requirements – bandwidth, throughput, latency, power, etc. in the applications where high-performance computation and communication is the dominant consideration. In other words, such SoCs often fail to sustain high-volume computation and high-speed communication among their components due to the use of global buses as the interconnects. The network-on-chip (NoC) as an alternate prevalent interconnection infrastructure has been continuously occupying the space of the SoC. An NoC comprises a large number of IP cores, routers, and high-speed channels (interconnects) that construct a structure (topology) spanning across the chip. However, aggressive CMOS scaling expedites interconnect and transistor wear-out, shortening the lifespan of these basic components which are often vulnerable to a number of manufacturing and transient faults due to aging, physical defects, or hostile attacks invoked by malicious third parties. For instance, basic classical logic level faults treated as the manufacturing faults, such as stuck-at, open, and shorts in NoC channels cause various system-level failures and subsequent degradation of reliability, yield, and performance of the computing platform. One approach to tackle channel-faults in NoCs is to replace the faulty channel-wires with spare wires. Such scheme is not cost-effective as the area overhead is substantially increased. Another approach is to exercise a fault-tolerant routing algorithm that directs traffic (application data packets) over channels by avoiding the faulty wires or an alternative fault-free path in order to connect the source and destination nodes, keeping the NoC functional. However, most of the fault-tolerant schemes do not consider any test mechanism for channels. One prerequisite of these approaches includes the knowledge about the health status of the channels which can only be inferred by an effective test method. Most of the commonly practiced approaches acknowledge numerous disadvantages in terms of test issues like high test volume, high silicon area overhead, high test time, low fault coverage, and lack of scalability.
Evaluating the Impacts of Detecting X.509 Covert Channels

This quasi-experimental before-and-after study examined the performance impacts of detecting X.509 covert channels in the Suricata intrusion detection system. Relevant literature and previous studies surrounding covert channels and covert channel detection, X.509 certificates, and intrusion detection system performance were evaluated. This study used Jason Reaves’ X.509 covert channel proof of concept code to generate malicious network traffic for detection (2018). Various detection rules for intrusion detection systems were created to aid in the detection of the X.509 covert channel. The central processing unit (CPU) and memory utilization impacts that each rule had on the intrusion detection system was studied and analyzed. Statistically significant figures found that the rules do have an impact on the performance of the system, some more than others. Finally, pathways towards future related research in creating efficient covert channel detection mechanisms were identified.
An Inferable Representation Learning for Fraud Review Detection with Cold-start Problem

Fraud review significantly damages the business reputation and also customers’ trust to certain products. It has become a serious problem existing on the current social media. Various efforts have been put in to tackle such problems. However, in the case of cold-start where a review is posted by a new user who just pops up on the social media, common fraud detection methods may fail because most of them are heavily depended on the information about the user’s historical behavior and its social relation to other users, yet such information is lacking in the cold-start case. This paper presents a novel Joint-bEhavior-and-Social-relaTion-infERable (JESTER) embedding method to leverage the user reviewing behavior and social relations for cold-start fraud review detection. JESTER embeds the deep characteristics of existing user behavior and social relations of users and items in an inferable user-item-review-rating representation space where the representation of a new user can be efficiently inferred by a closed-form solution and reflects the user’s most probable behavior and social relations. Thus, a cold-start fraud review can be effectively detected accordingly. Our experiments show JESTER (i) performs significantly better in detecting fraud reviews on four real-life social media data sets, and (ii) effectively infers new user representation in the cold-start problem, compared to three state-of-the-art and two baseline competitors.
Native language processing is influenced by L2-to-L1 translation ambiguity

ABSTRACT Words of one language often have multiple translations into another language. Does mapping of an L2 word onto multiple L1 words impact how these L1 words are represented in the bilingual lexicon? Russian-English bilinguals decided on the lexical status (Exp1) or the conceptual relatedness (Exp2) of pairs of Russian words that had the same or different translations in English. We obtained evidence for a facilitative effect of L2-to-L1 translation ambiguity. In Exp1, bilinguals were faster to respond to a Russian target if a prime had the same vs. different English translation as the target. Further, the magnitude of the N400 ERP component was reduced and the P200 was enhanced in the translation ambiguous compared to non-ambiguous condition. In Exp2, translation alternatives were rated as being more conceptually similar than words with different translations. Thus, the presence of a shared L2 translation leads to some convergence of corresponding L1 lexico-conceptual representations.
Protecting Web Single Sign-on against Relying Party Impersonation Attacks through a Bi-directional Secure Channel with Authentication

Author(s): Cao, Yinzhi; Shoshitaishvili, Yan; Borgolte, Kevin; Kruegel, Christopher; Vigna, Giovanni; Chen, Yan | Abstract: Web-based single sign-on describes a class of protocols where a user signs into a web site with the authentication provided as a service by a third party. In exchange for the increased complexity of the authentication procedure, SSO makes it convenient for users to authenticate themselves to many different web sites (relying parties), using just a single account at an identity provider such as Facebook or Google.Single sign-on (SSO) protocols, however, are not immune to vulnerabilities. Recent research introduced several attacks against existing SSO protocols, and further work showed that these problems are prevalent: 6.5% of the investigated relying parties were vulnerable to impersonation attacks, which can lead to account compromises and privacy breaches. Prior work used formal verification methods to identify vulnerabilities in SSO protocols or leveraged invariances of SSO interaction traces to identify logic flaws. No prior work, however, systematically studied the actual root cause of impersonation attacks against the relying party.In this paper, we systematically examine existing SSO protocols and determine the root cause of the aforementioned vulnerabilities: the design of the communication channel between the relying party and the identity provider, which, depending on the protocol and implementation, suffers from being a one-way communication protocol, or from a lack of authentication. We (a) systematically study the weakness responsible for the vulnerabilities in existing protocols that allow impersonation attacks against the relying party, (b) introduce a dedicated, authenticated, bi-directional, secure channel that does not suffer from those shortcomings, (c) formally verify the authentication property of this channel using a well-known cryptographic protocol verifier (ProVerif), and (d) evaluate the practicality of a prototype implementation of our protocol.Ultimately, to support a smooth and painless transition from existing SSO protocols, we introduce a proxy setup in which our channel can be used to secure existing SSO protocols from impersonation attacks. Furthermore, to demonstrate the flexibility of our approach, we design two different SSO protocols: an OAuth-like and an OpenID-like protocol.
FireTracker: Using beacons and indoor positioning to enhance firefighter’s training exercise scenarios

This master thesis presents the FireTracker Indoor Positioning System (FireTracker) for tracking movement of firefighters in cold smoke diving exercises. The research presented in this thesis is a part of the iComPAss project (Inquire Competence for better Practice and Assessment), a collaboration between NORCE (Norwegian Research Center) and the University of Bergen. The research went through design and development that included testing and feedback from Sotra Fire and Rescue Service (SFRS). Much of the development in this thesis was done with a co-student. This Design Science research project engaged four iterations of design, development and testing. The result, FireTracker, comprised of a mobile application, a web application, and a back-end server. With the use of Bluetooth beacons, the system was able to create visualizations based on firefighter movements in an exercise building. The two first iterations ended with a test of the system, followed by an interview. The prototype was tested with the SFRS in these iterations. The third iteration ended with a heuristic evaluation with four IT-students. The feedback given from these tests was used to further improve the prototype. In the final evaluation, the prototype was field trialled with the SFRS in a live exercise. The system was tested multiple times in several sessions. After the test, the firefighters involved in the testing were interviewed and given questionnaires to assess their opinions of the system. The results presented in this research are based on the analysis of the data collected in the evaluation of the final prototype.
Urban Perspectives: A Raster-Based Approach to 3D Generalization of Groups of Buildings

The suggested method for 3D generalization of groups of buildings is based on rasterization of 2D footprints of the 3D buildings. The rasterization is processed within quarters, which are automatically defined by using Digital Elevation Model (DEM), water objects and roads. The 3D urban perspective is computed based on separate levels of generalization of each quarter as a function of its distance from a pre-defined view point. Keywords-Generalization; 3D urban model; Groups of