Miracle-dz/UniFAR-FTUs huggingface dataset:
CameraDriver Vision System for Autonomous Driving

—The main purpose of this work is to develop a localization system based on computer vision to courses that are delimited by objects. This problem is based on the Formula Student Driverless competition on which a vehicle without driver must be able to locate on a cone delimited track. The developed system uses a 3D camera, which allows to calculate distances. The objects, in this case cones, are detected in the images through the YOLO system. The distances between cones are collected, and except for degenerated cases, the collection of every distances forms a unique map ”signature”. The possibility of a prior recognition of the course allows mapping using the ORB-SLAM2 system. Through its post-processing, the global map of the course is constructed, which includes the localization of the cones and the distances signatures. To estimate the course localization of the camera at every moment, the construction of the local map is based on the observed objects. The matching between the observed objects and the ones that are along the course is done using a search algorithm, which matches the local map with the global map, based on the distances signatures. Once they are matched, the localization and orientation of the camera is estimated. The system was tested using synthetic and real courses which led to very promising results.
Effectiveness of Computer-Based Testing Workshops in Dental Education

Hiroya Gotouda, Hitoshi Nishimura, Hiroko Matsumoto, Kaori Ishii, Makiko Kageyama, Takahiro Yazaki, Naotaka Kamiya, Junichi Yamane, Masanori Saito, Suguru Kimoto, Hiroyasu Endo, Masanobu Wakami, Ryo Tamamura, Hiroyuki Okada, Takehiko Shimizu, Koichi Hiratsuka, Takashi Kaneda, Satoshi Hirayama, Hirotaka Oomine, Kiyoshi Matsushima, Yasuhiko Kawai, Kazutaka Kasai, Koh Shibutani, Misao Kawara, and Ikuo Nasu Departments of Preventive and Public Oral Health, Oral Surgery, Pharmacology, Orthodontics, Maxillofacia Orthodontics, Removable Prosthodontics, Operative Dentistry, Microbiology and Immunology, Oral Diagnosis, Crown and Bridge Prosthodontics, Histology, Pedodontics, Biochemistry and Molecular Biology, Radiology,Endodontics, Anesthesiology, Oral Function and Rehabilitation, Nihon University School of Dentistry at Matsudo, Matsudo, Chiba 271-8587, Japan
Using Naïve Bayes and Genetic Algorithms to Find Influent Twitter Users to Forecast the S&P 500

Information available on Twitter about stock market is increasing. Through tweets, users express their predictions or opinions about companies or events. These tweets can influence the behavior of those who read them. With the cashtags creation, this information has become easier to identify and therefore to use. The main objective of this work is use this information to forecast stock market. For that purpose, it is suggested to characterize each user through several numerical features related to his Twitter account. A Genetic Algorithm is used to optimize and return values that best describe an influential user in the stock market. To check the impact of each user, the tweets sentiment is analyzed and compared to the change in the stock price of the mentioned company. In order to make this identification, a classifier was created using Naïve Bayes, with an accuracy of 60.65%. This classifier, when applied to a tweet already pre-processed and filtered by the system, returns whether the tweet is positive, negative or neutral. Based on these classifications an investment is made. The results indicate that the characteristics which best describe an influential user are the account time and the account activity, followed by the number of likes and retweets obtained by tweet and the low tendency of the user to talk to other users using tweets. The results were very interesting, since in the best case, a profit of 14.7% was obtained for a test period of 6 months.
Investigating the Performance of Generative Adversarial Networks for Prostate Tissue Detection and Segmentation

The manual delineation of region of interest (RoI) in 3D magnetic resonance imaging (MRI) of the prostate is time-consuming and subjective. Correct identification of prostate tissue is helpful to define a precise RoI to be used in CAD systems in clinical practice during diagnostic imaging, radiotherapy and monitoring the progress of disease. Conditional GAN (cGAN), cycleGAN and U-Net models and their performances were studied for the detection and segmentation of prostate tissue in 3D multi-parametric MRI scans. These models were trained and evaluated on MRI data from 40 patients with biopsy-proven prostate cancer. Due to the limited amount of available training data, three augmentation schemes were proposed to artificially increase the training samples. These models were tested on a clinical dataset annotated for this study and on a public dataset (PROMISE12). The cGAN model outperformed the U-Net and cycleGAN predictions owing to the inclusion of paired image supervision. Based on our quantitative results, cGAN gained a Dice score of 0.78 and 0.75 on the private and the PROMISE12 public datasets, respectively.
Synergetic Denial-of-Service Attacks and Defense in Underwater Named Data Networking

Due to the harsh environment and energy limitation, maintaining efficient communication is crucial to the lifetime of Underwater Sensor Networks (UWSN). Named Data Networking (NDN), one of future network architectures, begins to be applied to UWSN. Although Underwater Named Data Networking (UNDN) performs well in data transmission, it still faces some security threats, such as the Denial-of-Service (DoS) attacks caused by Interest Flooding Attacks (IFAs). In this paper, we present a new type of DoS attacks, named as Synergetic Denial-of-Service (SDoS). Attackers synergize with each other, taking turns to reply to malicious interests as late as possible. SDoS attacks will damage the Pending Interest Table, Content Store, and Forwarding Information Base in routers with high concealment. Simulation results demonstrate that the SDoS attacks quadruple the increased network traffic compared with normal IFAs and the existing IFA detection algorithm in UNDN is completely invalid to SDoS attacks. In addition, we analyze the infection problem in UNDN and propose a defense method Trident based on carefully designed adaptive threshold, burst traffic detection, and attacker identification. Experiment results illustrate that Trident can effectively detect and resist both SDoS attacks and normal IFAs. Meanwhile, Trident can robustly undertake burst traffic and congestion.
Capacity Increase in UMTS/HSPA+ Through the Use of MIMO Systems

The main purpose of this work was to study the implementation of MIMO in UMTS/HSPA+, in terms of gain dependence on scenarios and other MIMO parameters. For this purpose, a model that accounts for different system and MIMO parameters, in a single user scenario, was developed, and implemented in a simulator. The model accounts for several parameters, such as, modulation, layers’ overheads, MIMO configuration and receiver, antenna spacing, type of antenna power fed, etc. The results show that for HSPA+ in DL, the use of MIMO 4×4 jointly with 16QAM and 64QAM enables to achieve throughputs up to 29.5 and 43.8 Mbps, respectively, while in UL the achievable throughputs are up to 23 Mbps. MIMO 2×2 on top of HSPA+ 64QAM offers 19.4 Mbps. These throughputs are achieved within a pico-cell scenario, being lower for the micro-cell one. For UL, within the micro-cell scenario, MIMO 2×4 presents better performance than the 4×4 one, for distances higher than 250 m. Split power fed in DL does not have a major impact on the throughput; however, for UL, the throughput related to the use of the 4×4 configuration with split power is 22 % lower than the use of dedicated power.
Ontology of sands: design and implementation of an OWL ontology for sand samples

The web we know today is evolving. The semantic web and linked data principles aim to change the way we use computers on the web, by enabling them to not only display the data but also understand its meaning. By understanding the semantics of the data, computers can process it in much more meaningful ways and dynamically connect different data sources, creating a global web of data. In the sedimentology domain, various databases exist, containing data resulting from the analysis of sediment samples, that can be used mainly for research purposes. However, these databases are not related in any way, restricting researchers to smaller datasets and losing any expressiveness that could come from correlating samples from different data sets. In this work, we turn to the capabilities of the semantic web as a way of solving this problem. We describe the development of an ontology that intends to represent sand samples and their main characteristics. The purpose of this ontology is to provide a standard set of terms that enable applications in the area of sedimentology to expose their data in the semantic web, ultimately allowing them to relate data between each other seamlessly. This work starts with a general overview of the current web service standards and semantic web technologies. This overview also covered the related work in the area of sedimentology, as well as other relevant projects on the semantic web. Next, the development of the ontology itself is described, detailing the central concepts and properties that compose the final vocabulary. Finally, the ontology is validated by relating data from different data sources using the terms described in it.
Homophilic Self Organizing Feature Maps: Topic Detection on Socially Connected Data

With the evolution of social media platforms, the amount of unlabeled information has gone skyrocketing, the process of labeling this kind of information evermore complex. Typical approaches used on the WEB for Topic Detection and Tracking cannot be directly applied due to the small amount of text produced per tweet, orthographic errors, abbreviations and so on. We propose and analyze a new form of topic detection and tracking on social networks. By leveraging the social relations between authors of the gathered content, and apply them to the clustering process. In order to achieve this, we proposed some modifications to the artificial neural network and clustering algorithm — Self Organizing Maps.
Waseda_Meisei_SoftBank at TRECVID 2020: Ad-hoc Video Search

The Waseda Meisei SoftBank team participated in the TRECVID 2020 Ad-hoc Video Search (AVS) task [1]. As with last year’s AVS task, we submitted both manually assisted and fully automatic runs this year. Our approach consisted of concept-based video retrieval for manually assisted runs and visual-semantic embedding for fully automatic runs. Our best manually assisted run achieved a mean average precision (mAP) of 25.2%, which ranked the highest among all the manually assisted systems. Our fully automatic run achieved an mAP of 20.0%, which ranked ﬁfth among all participants.
Effects of ZnO NPs on phenolic compounds of rapeseed seeds under salinity stress

In this study, rapeseed Okapi cultivar was treated under three NaCl levels (0, 50, and 100 mM) and four Zinc oxide nanoparticle (ZnO NPs) concentrations (0, 20, 40, and 80 mgL -1 ) at flowering stage. The experiment was performed in a completely randomized design (CRD) in the greenhouse of Biology Department of Urmia University, in 2015. Results showed that NaCl (100 mM) significantly enhanced total phenolic, flavonoid anthocyanin and phosphorus contents and increased ability to scavenge the DPPH radical scavenging activity. Treatment with ZnO NPs (80 mgL -1 ) significantly elevated total phenolic and decreased anthocyanin, phosphorus and zinc contents and seed weight. Foliar application of ZnO NPs ameliorated the negative impacts of NaCl by reduction in phenolic, anthocyanin and phosphorus contents and elevation in seed oil content (20.82%) and seed weight (79.28%) at 50 mM salinity and 20 mgL -1 of ZnO NPs compared to NaCl (50 mM) alone. In conclusion, foliar application of ZnO NPs at 20 mgL -1 is recommended to farmers as replacement for fertilizers to alleviate the adverse effects of salinity stress.
Test Strategy for Digital Control/Power Switches

Fault modeling and simulation in analogue circuits is an issue which has been intensively studied and where it has been spent a high research effort by industrial and academic communities. Test strategies based on simulation provide a powerful support in the process of detection of physical defects. This thesis presents an original methodology for test preparation where a fault model is implemented by the introduction of two resistances on each transistor to allow simulate possible short or open circuits. The proposed analog test preparation methodology is implemented in a mixed-signal case study that consists on the power block and drivers of a DC-DC converter. The evaluation of the fault coverage of these analog blocks is carried out considering that controllability and observability is only possible through a digital interface. KeywordsTest preparation methodology, fault models, analog test, fault simulation.
Text-to-Speech Synthesis in European Portuguese using Deep Learning

This thesis has one main goal: to synthesise speech from text for European Portuguese, using recent deep learning techniques. The motivation was to use this work on one hand as a first step for building a much needed child’s voice, and on the other hand as a framework to synthesise expressive speech. These are two limitations that are faced by synthesizers in many areas of application, in particular, in the interaction with robots and serious games. This topic involves many areas of study such as machine learning, speech synthesis, linguistics and speech acquisition. This article reports the work done, the framework (Merlin) used to synthesise speech and the final conclusions of this project. The choice of Merlin was guided by the target application, the previous experience of the team, and the available information about each method, within the range of speech synthesis methods involving deep learning. The work done with Merlin will enable the synthesis of a child’s voice depending mainly on the recordings of a child.
What drives people to participate in online firestorms?

PurposeSocial media empower individuals to voice their opinions about issues that they perceive to be unacceptable. When many others add their opinions and large quantities of messages containing negative word-of-mouth suddenly spread online, an online firestorm occurs. By extending the situational theory of problem solving (Kim and Grunig, 2011) into the domain of online communication, this study aims to identify the drivers for participating in online firestorms.Design/methodology/approachWith reference to a fictitious online firestorm trigger (i.e. perceived moral misconduct) posted on Facebook, a qualitative pre-study and quantitative online survey were conducted. Based on the responses of 410 participants, an ordinary least squares regression was modeled to examine the factors of participating in the online firestorm. Later, structural equation modeling was applied to test the model and gauge its fit with the data.FindingsParticipants' involvement recognition, perception of being collective actors and approval of slacktivism behaviors positively predicted their participation in the online firestorm, whereas non-anonymity hampered it.Originality/valueThe study's findings not only contribute to the current understandings of online firestorms but are also valuable for developing theory and forms of professional crisis management. Moreover, they offer insights into the factors of online communication environments that encourage users to voice their opinions.
Map-Based Quadcopter Navigation

With the advances in technology the multirotor platform has become the preferred vehicle for aerial robotic research, especially the quadrotor due to this simplicity in design and maintenance, payload/size ratio and duration of ﬂight. These technologies advances also enable the reduction of both size and power consumption of computer boards, while maintaining or even increasing its computation power, making them a viable solution to be installed on the quadrotors. This paper describes the work done in the process of implementing map-based navigation on the Institute For Systems and Robotics quadrotor where all the required computation to achieve autonomous ﬂight is made on the computer installed onboard the quadrotor platform. The implementation is made ﬁrst in a gazebo simulated environment and second in the quadrotor platform itself. The ISR platform is ﬁtted with an autopilot, to facilitate the control process and a laser range ﬁnder sensor to both create a map and self localize in the environment during ﬂight.
How Context Affects Language Models' Factual Predictions

When pre-trained on large unsupervised textual corpora, language models are able to store and retrieve factual knowledge to some extent, making it possible to use them directly for zero-shot cloze-style question answering. However, storing factual knowledge in a fixed number of weights of a language model clearly has limitations. Previous approaches have successfully provided access to information outside the model weights using supervised architectures that combine an information retrieval system with a machine reading component. In this paper, we go a step further and integrate information from a retrieval system with a pre-trained language model in a purely unsupervised way. We report that augmenting pre-trained language models in this way dramatically improves performance and that the resulting system, despite being unsupervised, is competitive with a supervised machine reading baseline. Furthermore, processing query and context with different segment tokens allows BERT to utilize its Next Sentence Prediction pre-trained classifier to determine whether the context is relevant or not, substantially improving BERT's zero-shot cloze-style question-answering performance and making its predictions robust to noisy contexts.
Distributed Learning of Neural Networks using Independent Subnet Training

We alleviate costly communication/computation overhead in classical distributed learning by introducing independent subnet training : a novel, simple, jointly model-parallel and data-parallel approach to distributed neural network training.Our main idea is that, per iteration, the model’s neurons can be randomly divided into smaller surrogate models without replacement—dubbed as subnetworks or subnets —and each subwork is sent for training only to a single worker. This way, our algorithm broadcasts the whole model parameters only once into the distributed network per synchronization cycle. This not only reduces the overall communication overhead, but also the computation workload: each worker only receives the weights associated with the subwork it has been assigned to. Further, subwork generation and training reduces synchronization frequency: since workers train disjoint portions of the network as if they were independent models, training continues for longer periods of time before synchronization, similar to local SGD approaches. We test our approach on speech recognition and product recommendation applications, as well as image classiﬁcation tasks. Subnet training: i) leads to accelerated training, as compared to state of the art distributed models, and ii) often results into boosting the testing accuracy, as it implicitly leverages dropout regularization during training.
Performance results of a new DOI detector block for a high resolution PET-LSO research tomograph HRRT

To improve the spatial resolution and uniformity in modern high resolution brain PET systems over the entire field of view (FOV), it is necessary to archive the depth of interaction (DOI) information and correct for spatial resolution degradation. In this work the authors present the performance results of a high resolution LSO/GSO phoswich block detector with DOI capability. This detector design will be used in the new CTI High Resolution Research Tomograph, ECAT HRRT. The two crystal layer (19/spl times/19/spl times/7.5 mm/sup 3/) and a light guide are stacked on each other and mounted on a (2/spl times/2) PMT set, so that the corners of the phoswich are positioned over the PMT centers. The crystal phoswich is cut into a 8/spl times/8 matrix of discrete crystals. The separation of the LSO and the GSO layer by pulse shape discrimination allows discrete DOI information to be obtained. The high light output and the light guide design results in an accurate identification of the 128 single crystals per block. Flood source measurements document a very good homogeneity of events, energy centroid stability and energy resolution (14-20% FWHM) per single crystal. An intrinsic resolution of /spl sim/1.3 mm and the DOI feasibility is extracted by coincidence measurements with a single GSO crystal.
Douglas-Square Metrics with Vanishing Mean Stretch Curvature

In this paper, we consider the class of square metrics F = α+ 2β + β/α where α = √ aijyy is a Riemannian metric and β = bi(x)y is a one-form on a manifold M . Let (M,F ) be a Douglas-square manifold. We show that F is a Berwald metric if and only if it a weakly stretch metric. It results that, a Douglas-square metric is R-quadratic if and only if it is a Berwald metric.
DeepQB: Deep Learning with Player Tracking to Quantify Quarterback Decision-Making & Performance

DeepQB is a proposed application of deep neural networks to player tracking data from over two full seasons of American professional football. This novel approach demonstrates the ability to successfully understand complex aspects of the passing game, most notably quarterback decision-making. It can assess and compare individual quarterback pass target selection based on a snapshot presented to the passer by the receivers and defenders. Assessments of quarterback decision-making are made by comparing actual target selection to that predicted by our model. The model performs well, correctly identifying the targeted receiver in 60% of cross-validated cases. When passers target the predicted receiver, passes are completed 74% of the time, compared to 55% when the QB targets any other receiver. This performance is surprisingly strong, given that the offense often conceals its intent by design, while defenses try not to allow any single receiver to be open. Further, quarterback passing skills separate and apart from his receivers and defense are isolated and assessed by comparing metrics of actual play success to the metrics of success predicted by the situation presented to the passer. This approach represents a new way for teams, media, and fans to understand and quantitatively assess quarterback decision-making, an aspect of the sport which has previously been opaque and inaccessible.