Miracle-dz/UniFAR-FTUs huggingface dataset:
Re-inventing the journey experience - A multifaceted framework to comfort in autonomous vehicles

Future vehicles provide scope to completely re-invent the journey experience. Technological advances have enabled fast progression of driving automation which has the potential to deliver efficient, accessible, sustainable and clean transport systems. Level 4 autonomous vehicles provide an exciting opportunity for drivers and passengers to engage in many activities unrelated to the driving task (e.g. reading, work communication/social networking on mobile technologies, relaxing, watching films etc.) leading to benefits in terms of comfort, pleasure and productivity. There has already been a lot of work looking at the active safety systems autonomous vehicles will need to use as well as the accompanying Human Machine Interface (HMI). For example, studies that look at the time it takes to hand over control from the vehicle to the occupant, and from the occupant to the vehicle. However, little is known regarding the nature of the secondary activities that drivers will want to undertake, and how this will impact occupant comfort, the vehicle architecture, its features and functional safety systems. To understand the ergonomic and engineering impact, first we must capture and fully understand user needs and their preferences in terms of the type of activities that could be undertaken in-vehicle. Re-inventing the journey experience is a research program addressing the lack of research around the user experience of autonomous vehicles. The main aims of the program are to: (1) understand potential for improving the travelling experience; (2) understand what the ergonomic, legislative, safety and comfort constraints are in order to identify design constraints; (3) understand how design innovations can support new occupant requirements. This paper presents a multifaceted framework which aims to guide researchers and industry professionals to more pragmatic vehicle concepts.
Indoor swarm exploration with Pocket Drones

Pocket drones, weighing less than 50 grams, are small, agile and inherently safe. This makes them suitable for several surveillance tasks such as search and rescue, green-house monitoring and pipe-line inspection. In order for a more efficient search, a swarm of pocket drones would be ideal to explore these types of areas faster. Current methods and navigation techniques will not be suitable due to their extensive requirements for the platform's computational capabilities and memory storage. This dissertation will therefore focus on designing a new strategy for a swarm of pocket drones for both low-level and high-level navigation in an indoor environment. The first part of this dissertation focuses on the low-level-navigation capabilities of the swarm of pocket drones, by first looking at the individual. We developod Edge-FS (Flow \& Stereo), which enabled the stereo-camera to detect both obstacles and the drone's velocity at the same time. A further necessity for swarm operations is for multiple pocket drones to avoid each other. An on-board relative localization scheme based on the Received Signal Strength Intensity (RSSI) of the inter-drone communication was developed to make this possible. Two pocket drones with a forward-facing stereo-camera were communicating with each other by means of Bluetooth and by fusing the RSSI with their velocity (estimated by Edge-FS). With this, two pocket drones were able to fly together in a room while avoiding the walls and each other. The second part of this dissertation focuses on high-level-navigation. Since conventional navigation strategies cannot fit on-board the pocket drones, we investigated an alternative method: bug algorithms. We present a literature survey and comparison of the existing techniques and evaluation on their suitability for deployment for real-world scenarios. We found that with increasing sensor errors and estimation drift, all existing bug algorithms’ performances decreased. This provided us valuable insights for the design of a novel bug algorithm for high-level-navigation. Finally, we developed and demonstrated a bug-algorithm-based navigation strategy for multiple pocket drones for indoor exploration and homing. We named this technique the swarm gradient bug algorithm (SGBA) and it enabled the pocket drones to explore a floor of an inside building and return to its original position by the RSSI-gradient of a radio beacon. Once two pocket drones come into each other's proximity, one will avoid the other and coordinate its own preferred search direction based on the information it has received (from the other).
Dynamic Behavior Extraction from Social Interactions Using Machine Learning and Study of Over Fitting Problem

People in this world interact with each other directly (face-to-face) or indirectly (by sending text messages). As humans are linked with emotions themselves it is easy for them to detect emotions but same is not true with computers. But for a computer detecting an emotion will be a difficult job to perform. Emotion is cognitive process where machine learning techniques are used to detect it. The main goal of the machine learning algorithms is to build computer system that can adopt and learn from their experience or by examples. This paper presents a systematic performance analysis of four classification algorithms for the extraction of human emotions and does also over fitting problem analysis. Our main attempt in this paper is to detect emotions from social interactions on twitter social network. Four machine learning algorithm are used for text classification under seven emotional classes. The work is carried on two data bases one data base is build by collecting live tweets from twitter social network site and second ISEAR (International Survey on Emotion Antecedents and Reactions) database. WEKA interface is used for the implementation of our work which shows above 85% accuracy in identifying the emotion classes. The ranking and standard deviation functionalities provided by the WEKA experimenter helps to determine the effectiveness of a classifier model.
Comparing the performance of different quantum error correcting codes

1 ACKNOWLEDGMENTS . 2 NOMENCLATURE . 3 LIST OF FIGURES . 4
GRAPHIC MODEL OF PROFESSIONAL ACTIVITY. GRAPHICAL ANALYSIS METHOD

Натепер різними науковими школами використовуються декілька методів аналізу робіт, які, як правило, ґрунтуються на одному з чотирьох методів, розроблених на початку ХХ ст. Також відсутні наукові роботи, які б розглядали професійну діяльність з погляду системного аналізу, як складну відкриту соціо-техніяну систему взаємопов’язаних операцій (задач). В звязку з цим, питання розроблення графічної моделі професійної діяльності та оцінювання характеристик її елементів є актуальним науковим завданням. Аналіз останніх досліджень та публікацій, постановка проблеми . Як показали дослідження існуючих теоретичних основ побудови систем аналітичного оцінювання професійної діяльності не існує затвердженого та стандартизованого підходу до цього питання. В статті розглянуто основні недоліки, притаманні існуючим методам і системам. Вирішення зазначених недоліків можливе через вирішення суперечності, яка полягає в необхідності одночасного забезпечення універсальності моделі з одного боку та її адекватності з другого. Вирішення зазначеної суперечності можливе шляхом розроблення абсолютно нових підходів щодо подання професійної діяльності, в тому числі і у вигляді графічної моделі. Мета та задачі дослідження . Мета дослідження – отримання необхідної та достатної для аналізу професійної діяльності кількості характеристик, які описують її складові – операції (завдання). Основним завданням для досягнення поставленої мети є побудова графічної моделі та дослідження основних характеристик її вузлів. Матеріали та методи дослідження . Для вирішення завдання дослідження використовувалися: метод експертного оцінювання (у процесі оцінювання характеристик роботи); параметричні та непараметричні методи математичної статистики; теорія графів для побудови графічної моделі та її дослідження. Результати досліджень . Розроблено метод графічного аналізу професійної діяльності, який передбачає аналіз даних щодо операцій та зв’язків між ними, автоматичне формування матриць суміжності, інцидентності та розрахунок коефіцієнтів операцій і діяльності в цілому. Обговорення результатів . Всі розглянуті коефіцієнти вичерпно характеризують операції з погляду їх важливості та місця в професійної діяльності. Розроблений метод графічного аналізу професійної діяльності направлений на вирішення задачі оцінювання характеристик кожної операції як структурного елементу системи «професійна діяльність».
Language identification using text, audio and video feature mapping

Unlike text language identification techniques, which are now quite mature, audio and video language identification techniques still face many challenges. One of the main challenges, due to a variety of reasons, is that there are not enough audio and video datasets. However, text data are sufficient for experiments and many text databases are free for research which leads to an interesting question: can we identify an unknown video or audio language based on the relationship between the known text languages? To answer this question, it requires us to examine two issues: language identification and language mapping. In language identification, we compare two methods which are zipping classification and N-gram modelling. An advantage of zipping classification is that it tolerates the lack of long training data and can be applied to a large variety of problems without modification. However, the N-gram model provides a high classification accuracy and efficiency which makes it worthy of consideration. Also, we evaluate another audio classification method based on the MPEG compression to compare with the general zipping tools and the N-gram model. For the language mapping section, we firstly use the Robinson-Foulds tree distance to measure the distances between the language trees and also use Sammon mapping and Shepard’s interpolation to map the language distance results from the higher dimensions to the lower dimensions and try to find the optimal language relationships in the specific dimension.
Measurement of Entrance Skin Doses to Patients in Four Common Diagnostic Examinations by Thermoluminescence Dosimetry in Nigeria

Entrance surface doses (ESD) to patients were monitored in four common diagnostic X ray examinations (chest, hand and wrist, lumbar spine and skull) at the outpatient X ray department of the University College Hospital (UCH), Ibadan. Measurement was based on thermoluminescence dosimetry (TLD) techniques using lithium fluoride discs and a Vinten Solaro TLD reader. Some spread was observed in the values of doses received by patients for each of the examinations. It is greatest for the examination of the skull PA and least for the examination of the skull LAT and lumbar spine AP; due mainly to variations in the sizes of the patients examined. For all examinations the values of the mean ESD obtained range from a minimum of 0.310 ± 0.071 mGy to a maximum of 5.66 ± 0.782 mGy for the examination of the hand and wrist PA and lumbar spine LAT, respectively.
Analysis of Pressure Drop Data in Channel Flows Over Foul-Control Coatings

The two-dimensional channel flow is of great interest for experimental as well as numerical studies. From the experimental perspective test in channel equipment is preferred, because it is simple, practical and offers a favorable economic running cost. Especially with the growing interest in marine coating research, it is critical that coating testing equipment delivers realistic flow conditions, is simple to cut the experimental time and effort and yet accurate. In this sense, the flow channel facility offers more advantages than classical cases for experimental investigations. Whereas from the numerical investigations point of view, channel flow exhibits favorable boundary conditions to save computational eﬀort, while providing a deep insight into details of the flow structures. An initiative is taken at Chalmers university to develop the channel flow (or a flowcell) experimental facility to cater for the need of studies on coatings. Therefore, the current paper, in the first step, describes the design and manufacture of the flowcell. Secondly, it presents the thoroughly conducted verification study of the smooth reference test section to demonstrate that experimental facility holds the measurement expectations. Subsequently, skin friction data for selected foul control coatings obtained from the pressure drop measurements in the flowcell are presented.
Ambient OFDM Pilot-Aided Delay-Shift Keying and Its Efficient Detection for Ultra Low-Power Communications

This paper proposes delay-shift keying (DSK) based on ambient pilot orthogonal frequency-division multiplexing (OFDM) signals transmitted by existing systems such as IEEE 802.11 and its optimal maximum likelihood (ML) detection for ultra-low-power ambient backscatter communications (AmBCs). To realize the proposed ML detection, we further propose a feasible channel estimation method. Numerical results confirm that our proposed system achieves the lower bit error rate (BER) than conventional AmBC based on well-known energy detection over additive white Gaussian noise (AWGN) and fading channels.
AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue

The problem of building a coherent and non-monotonous conversational agent with proper discourse and coverage is still an area of open research. Current architectures only take care of semantic and contextual information for a given query and fail to completely account for syntactic and external knowledge which are crucial for generating responses in a chit-chat system. To overcome this problem, we propose an end to end multi-stream deep learning architecture that learns unified embeddings for query-response pairs by leveraging contextual information from memory networks and syntactic information by incorporating Graph Convolution Networks (GCN) over their dependency parse. A stream of this network also utilizes transfer learning by pre-training a bidirectional transformer to extract semantic representation for each input sentence and incorporates external knowledge through the neighborhood of the entities from a Knowledge Base (KB). We benchmark these embeddings on the next sentence prediction task and significantly improve upon the existing techniques. Furthermore, we use AMUSED to represent query and responses along with its context to develop a retrieval based conversational agent which has been validated by expert linguists to have comprehensive engagement with humans.
Improving Classification by Using MASI Algorithm for Resampling Imbalanced Dataset

— At present, financial fraud detection is interested by many machine learning researchers. This is because of existing a big ratio between normal transactions and abnormal ones in data set. Therefore, a good result of prediction rate does not mean that there is a good detection result. This is explained that the experimental result might be effected by the imbalance in the dataset. Resampling a dataset before putting to classification process can be seen as the required task for researching in financial fraud detection area. An algorithm, so-called as MASI, is proposed in this paper in order to improve the classification results. This algorithm breaks the imbalance in the data set by re-labelling the major class samples (normal transactions) to the minor class ones basing the nearest neighbor’s samples. This algorithm has been validated with UCI machine learning repository data domain. Then, the algorithm is also used with data domain, which is taken from a Vietnamese financial company. The results show
Performance Analysis of PostgreSQL and MongoDB Databases for Unstructured Data

— The storage of unstructured data plays an important role in the implementation of big data environment, thus, choosing an efficient database can provide an excellent solution for data mining. In this paper, two database technologies, MongoDB and PostgreSQL, are used as performance tests for storing unstructured data. Remote sensing data in GeoTIFF format is the most representative unstructured data. Large amounts of data are stored in MongoDB and PostgreSQL databases by designing metadata table for GeoTIFF data to test the performance of both. The results show that MongoDB storage is six times faster than PostgreSQL, however PostgreSQL compresses data up to 95%. Therefore, MongoDB is suitable for rapid storage of remote sensing data, while PostgreSQL is more suitable for operations with small data volumes. In a word, this research work has completed the database performance test of unstructured remote sensing data. (Abstract)
BNU-LCSAD: a video database for classroom student action recognition

With the development and application of digital cameras, especially in education, a great number of digital video recordings are produced in classrooms. Taking Beijing Normal University as an example, 3.4 TB of videos are recorded every day in more than 200 classrooms. Such huge data is beneficial for us, computer vision researchers, to automatically recognize students' classroom actions and even evaluate the quality of classroom teaching. To focus action recognition on students, we propose Beijing Normal University Large-scale Classroom Student Action Database version 1.0(BNU-LCSAD) which is the first large-scale classroom student action database for student action recognition and consists of 10 classroom student action classes from digital camera recordings at BNU. We introduce the construct and label Processing of this database in detail. In Addition , we provide baseline of student action recognition results based our new database using C3D network.
Assessing and Mitigating Impact of Time Delay Attack: Case Studies for Power Grid Controls

Due to recent cyber attacks on various cyber-physical systems (CPSes), traditional isolation based security schemes in the critical systems are insufficient to deal with the smart adversaries in CPSes with advanced information and communication technologies (ICTs). In this paper, we develop real-time assessment and mitigation of an attack’s impact as a system’s built-in mechanisms. We study a general class of attacks, which we call time delay attack, that delays the transmissions of control data packets in the CPS control loops. Based on a joint stability-safety criterion, we propose the attack impact assessment consisting of (i) a machine learning (ML) based safety classification, and (ii) a tandem stability-safety classification that exploits a basic relationship between stability and safety, namely that an unstable system must be unsafe whereas a stable system may not be safe. In this assessment approach, the ML addresses a state explosion problem in the safety classification, whereas the tandem structure reduces false negatives in detecting unsafety arising from imperfect ML. We apply our approach to assess the impact of the attack on power grid automatic generation control, and accordingly develop a two-tiered mitigation that tunes the control gain automatically to restore safety where necessary and shed load only if the tuning is insufficient. We also apply our attack impact assessment approach to a thermal power plant control system consisting of two PID control loops. A mitigation approach by tuning the PID controller is also proposed. Extensive simulations based on a 37-bus system model and a thermal power plant control system are conducted to evaluate the effectiveness of our assessment and mitigation approaches.
Modified fingerprinting localization technique of indoor positioning system based on coordinates

The fingerprinting localization technique is the most commonly used localization technique of the indoor positioning system. It is used by several technologies for short and long range position estimation like wireless fidelity and radio frequency. There are several schemes used to estimate a location for the indoor environment but the drawbacks resulted in complexity issues. These drawbacks have negative effects on location estimation. In order to address these drawbacks, this work attempted to explore the fingerprinting localization technique for location estimation of the indoor environment that focuses on position estimation. Results showed that the simplicity of the design of position estimation without compromising the functionality of the operations was observed with 100% accuracy on position estimation.
A Novel Multi-Stage Forest-Based Key-Value Store for Holistic Performance Improvement

Key-value (KV) stores based on multi-stage structures are widely deployed to organize massive amounts of easily searchable user data. However, current KV storage systems inevitably sacrifice at least one of the performance objectives, such as write, read, space efficiency etc., for the optimization of others. To understand the root cause of and ultimately remove such performance disparities among the representative existing KV stores, we analyze their enabling mechanisms and classify them into two fundamental models of data structures facilitating KV operations, namely, the multi-stage tree (MS-tree), and the multi-stage forest (MS-forest). We build SifrDB, a KV store on a novel split forest structure, that achieves the lowest write amplification across all workload patterns and minimizes space reservation for the compaction. To mitigate the read amplification inherent in MS-forest, we introduce a bloom filer mechanism based on Sorted String Tables (SSTs). Furthermore, we also present a highly efficient parallel search approach that fully exploits the access parallelism of modern flash-based storage devices to substantially boost the read performance. Evaluation results show that under both micro and YCSB benchmarks, SifrDB outperforms its closest competitors, i.e., the popular MS-forest implementations, making it a highly desirable choice for the modern KV stores.
A One-and-Half Stage Pedestrian Detector

Pedestrian detection is a specific instance of the more general problem of object detection in computer vision. A balance between detection accuracy and speed is a desirable trait for pedestrian detection systems in many applications such as self-driving cars. In this paper, we follow the wisdom of " and less is often more" to achieve this balance. We propose a lightweight mechanism based on semantic segmentation to reduce the number of anchors to be processed. We furthermore unify this selection with the intra-anchor feature pooling strategy adopted in high performance two-stage detectors such as Faster-RCNN. Such a strategy is avoided in one-stage detectors like SSD in favour of faster inference but at the cost of reducing the accuracy vis-à-vis two-stage detectors. However our anchor selection renders it practical to use feature pooling without giving up the inference speed.Our proposed approach succeeds in detecting pedestrians with state-of-art performance on caltech-reasonable and ciypersons datasets with inference speeds of ∼ 32 fps.
The Terminodes Project: Towards Mobile Ad-Hoc WAN

The Terminode project follows a system approach to investigate wide area, large, totally wireless networks that we call mobile ad-hoc wide area networks. A netw ork of terminodes is an autonomous, self-operated, wireless multimedia network, independent of any infrastructure. In this paper we present the main challenges and certain initial technical directions.