Nicolas Dejon1, 2, Chrystel Gaber1 and Gilles Grimaud2, 1Orange Labs, Châtillon, France, 2Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL - Centre de Recherche en Informatique Signal et Automatique de Lille, F-59000 Lille, France
This article presents a hardware-based memory isolation solution for constrained devices. Existing solutions target high-end embedded systems (typically ARM Cortex-A with a Memory Management Unit, MMU) such as seL4 or Pip (formally verified kernels) or target low-end devices such as ACES, MINION, TrustLite, EwoK but with limited flexibility by proposing a single level of isolation. Our approach consists in adapting Pip to inherit its flexibility (multiple levels of isolation) but using the Memory Protection Unit (MPU) instead of the MMU since the MPU is commonly available on constrained embedded systems (typically ARMv7 Cortex-M4 or ARMv8 Cortex-M33 and similar devices). This paper describes our design of Pip-MPU (Pip’s variant based on the MPU) and the rationale behind our choices. We validate our proposal with an implementation on an nRF52840 development kit and we perform various evaluations such as memory footprint, CPU cycles and energy consumption. We demonstrate that although our prototyped Pip-MPU causes a 16% overhead on both performance and energy consumption, it can reduce the attack surface of the accessible application memory from 100% down to 2% and the privileged operations by 99%. Pip-MPU takes less than 10 kB of Flash (6 kB for its core components) and 550 B of RAM.
constrained devices, MPU, memory isolation, Pip, OS kernel, secure systems.
Jean-Marie Kuate Fotso*1, ElieFute Tagne2, Bénite Isaoura1, Guy Phares Fotso Fotso1, PélagieFloreTemgoua Nanfack1, Patrice Abiama Ele3, 1National Committee for Development of Technologies, Ministry of Scientific Research and Innovation, Yaoundé, Cameroon, 2Department of Mathematics and Computer Science, University of Dschang, Department of Computer Engineering, University of Buea, Cameroon, 3Energy Research Laboratory, Institute of Geological and Mining Research, Yaoundé, Cameroon
Cloud computing has made it easier to access various forms of data and services through the Internet. Its coupling to sensor networks makes it possible to significantly overcome the storage and computing performance limits of heterogeneous objects in the Internet of Things (IoT). In Cameroon, IoT is not yet very widespread. We use it here to monitor and prevent urban fires in real time, using a set of temperature, humidity, gas, flame and electrical power sensors that are integrated on NodeMCU, in view to detect fire starts and generate an alert. The data collected is stored and processed on ThingSpeak; a second analysis is carried out locally. After calibrating the sensors, we analyzed the data and carried out a correlation test to identify the most sensitive data for the alert system.
Cloud computing, Fire, NodeMCU, IoT, electric current, Urbanization .
Mubeena Nazar1 and Minu R Nath2, 1Department of Computer Applications, College of Engineering Trivandrum, Kerala, India, 2Associate Professor, Department of Computer Applications, College of Engineering Trivandrum, Kerala, India
Navigating from place to place is one of the biggest problems for the visually impaired people The traditional white cane they use only detects obstacles once they touch it. The goal of this project is to solve this issue.The proposed solution employs the IoT paradigm to provide a medium between the blind and the environment. Here we, intent to develop a “Smart Blind Stick”, which increases the accessibility of blind person to move around by providing alerts about the staircase, potholes, water, fire and other obstacles that might occur on his/her path.With the system, an emergency alert can be sent to the concerned persons. Furthermore, the proposed system includes an app that allows users to locate the stick using a buzzer sound and configure its settings. The smart blind stick is user friendly, gives quick response, has very low power consumption and lighter weight.
IoT based smart blind stick, detection of various obstacles,sending alert message,configuration setting using app, buzzer to locate stick.
Ada Alevizaki and Niki Trigoni, Department of Computer Science, University of Oxford
The increasing at-home way of living yields significant interest in analysing human behaviour at home. Estimating a person’s room-level position inside their house can provide essential information to improve situation awareness, but such information is constrained by the cost of required infrastructure, as well as privacy concerns for the monitored household. In this paper, we contribute a comprehensive dataset that combines real-world BLE RSSI data and smartwatch IMU data from houses. We propose a probabilistic framework that leverages the two sensor modalities to effectively track the user around rooms of the house without any additional infrastructure. Over time, through transition-events and stay-events, the model can learn to infer the user’s room position, as well as a semantic map of the rooms of the house. Performance has been evaluated on the collected dataset. Our proposed approach achieves a 19.73% improvement on standard BLE RSSI localisation.
indoor localisation, semantic mapping, smartwatches, BLE, IMU.
Sangeetha S and Aishwarya Lakshmi, PSG College of Technology, Peelamedu,, Coimbatore, 641004, Tamil Nadu, India
This is the age of instant gratification. Browsing the entire Publication is a dawdle , hence we have propose an application that summarizes all the reading’s in a snap of time using AI technologies. The system is composed of Optical Character Recognition(OCR) engine to convert the image to text and transformer to summarize the text, dispensing recurrent networks followed by feature prediction network that maps character embeddings to mel-spectrogram and a GAN based vocoder to convert spectrogram to time based waveforms. Through extensive experiments we demonstrate digest podcast ability to recognize, summarize, speech synthesis for summarized audio generation.
segmentation, summarisation, mel-spectrogram, GAN, speech synthesis.
Xiaoyan Dai and Yisan Hsieh, Advanced Technology Research Institute, Minatomirai Research Center, Kyocera Corporation, Japan
Current Point-of-Sale processing is complex and time consuming. In this paper, we propose an image-based discount sticker and barcode “scan” system for automations. Recognition of discount stickers and barcodes is quite a big challenge, as different shooting conditions can result in different appearances. We design a deep learning classifier of various discount rates and barcode basing on YOLACT detection network. We also propose a data augmentation to generate various data that are close to real scene to improve the classification performance of deep learning model. Evaluation with our original data set shows that the proposed approach achieves high performance and applicable in the real-world scenario.
Classification, data augmentation, discount sticker, barcode, image-based, deep learning.
Assia Kamal-idrissi1 and Abdelouadoud Kerarmi2, 1Ai movement, Center of Artificial Intelligence, Mohammed VI Polytechnic University, Rabat, Morocco, 2Lip6, Sorbonne University, Paris, France
Failure Mode and Effect Critical Analysis method attempts to identify potential modes and treat failures before they occur based on experts evaluation. However, this method is extremely cost-intensive in terms of failure modes since it evaluates each one of them. Moreover, this method is not able to properly treat uncertainty during logical reasoning as it is based on subjective expert judgments and requires a lot of information. Previous studies proposed several versions of Fuzzy Logic but have not explicitly focused on the combinatorial complexity nor justified the choice of membership functions in Fuzzy Logic modeling. In this paper, we develop an optimization-based approach referred to Integrated Truth Table and Fuzzy Logic Model (ITTFLM) that generates smartly fuzzy logic rules using Truth Tables. This approach allows generating fuzzy rules quickly and smartly by assuring consistency and non-redundancy through logical evaluation. We propose to implement ITTFLM for three types of membership functions (Triangular, Trapezoidal, and Gaussian) to choose the best function that fits our real data. The ITTFLM was tested on fan data collected in real time from a plant machine. The experimental evaluation demonstrates that our model identifies the failure states with high accuracy and can deal with large numbers of rules and thus meets the real-time constraints that usually impact user experience.
FMECA, Fuzzy Logic, Truth Table, Combinatorial Complexity, Real-time, Industrial fan motor, Proactive maintenance, Knowledge, Big Data, Artificial Intelligence.
Hend Faisal1,2, Hanan Hindy1, Samir Gaber3, Abdel-Badeeh Salem1, 1Faculty of Computer and Information Sciences, Ain Shams University, Egypt, 2Egyptian Computer Emergency and Readiness Team (EG-CERT), National Telecom Regulatory Authority (NTRA), 3Faculty of Engineering, Helwan University, Egypt
During the rapid evolution of technology and being in the era of digital transformation, attackers take the advantage to spread malicious software (malware). Nowadays, malware is increasing at a terrifying rate, and it comes with different generations and forms, making it difficult for researchers to develop efficient tools for malware detection. Over the years, attacks became, not only limited to computer-based operating systems, but also to that of mobile-based, which makes it even harder for analysts. Furthermore, this increases the need for more research in this direction. The technological evolution also gives researchers the chance to utilize Artificial Intelligence widely and leverage its capabilities in many fields in general and in the field of malware detection in particular. This paper provides a literature review on malware detection using Artificial Intelligence techniques and specifically, Machine Learning and Deep Learning techniques. The paper helps researchers to have a broad idea of the latest malware detection techniques, available datasets, challenges, and limitations.
Malware Detection, Artificial Intelligence, Machine Learning, Deep Learning, Android Malware.
Honoré Hounwanou and Mohamed Mejri, Université Laval, Québec QC G1V 0A6, Canada
The security of information systems is one of the most important concerns of today’s computer science field. It’s almost impossible nowadays to find a business, authority or organization that doesn’t make use of computer systems for its proper functioning. As a result, it becomes vital to ensure that the various programs we write work as expected and are not strewn with security vulnerabilities. Even the slightest security vulnerability can cause enormous damage and huge financial losses. Given a program P and a security policy Φ, this paper gives an approach allowing to generate another program P0 that respects the policy Φ and behaves (with respect to trace equivalence) like P except that it stops any execution path whenever the enforced security policy is about to be violated. The proposed approach transforms the problem of finding P 0 to solving a linear system under a given algebra and for which we know how to get the solution.
Program Rewriting, Formal Methods, Computer Security.
Sonam Pankaj and Amit Gautam, Saama Technologies
NLP Augmentation is recently gaining attention, Unlike computer vision, where image data augmentation is standard, text data augmentation in NLP is uncommon. And we have seen advantages of augmentation where there are fewer data available, and it can play a huge role. We have implemented Augmentation in Pairwise sentence scoring in the biomedical domain. We have looked into the solution to improve Bi-encoders’ sentence transformer performance using silver data-set generated by cross-encoders by experimenting with our approach on biomedical domain data-sets like Biosses and MedNli, where it has significantly improved the results.
Augmentation, datasets, sentence-transformers.
Dimpal Janu, Dept. of ECE, Malaviya National Institute Technology, Jaipur, India, Kuldeep Singh, Dept. of ECE, Malaviya National Institute of Technology, Jaipur, India, Sandeep Kumar, Central Research Lab, Bharat Electronics Ltd., Ghaziabad, India
In this paper, we have analysed the detection performance of various Machine learning (ML) and Deep Learning (DL) algorithms based cooperative spectrum sensing (CSS) models such as K-means clustering algorithm, Gaussian mixture model (GMM), support vector machine (SVM), Decision Tree (DT) and the DL architectures such as artificial neural network (ANN), convolutional neural network (CNN). We have evaluate the performance of CSS models by considering the multi-antenna multiple secondary users (SUs) Cognitive radio scenario and also catered the scenario of hidden node. The system models adopted by theexisting DL based CSS models have not considered such kind of scenarios for detecting the presence of PU. The fusion centre collects the SU data and computes the statistical features, sensing data fusion method is adopted. The FC divides sensing data collected from all SU into two clusters and extracts one-dimensional feature vector, and these features are used to train the ML classifiers. In case of DL based models, the FC computes covariance matrices from the sensing data collected from each SU. These covariance matrices are fed as input to DL based CSS models. The results are showing that CNN based models outperform the ANN, and other ML based models in terms of detection probability and classification accuracy.
Cognitive Radio, Cooperative spectrum sensing, Support Vector Machine, K-means clustering, Gaussian Mixture Model.
Ananya Chakraborty, Mampi Devi, Alak Roy, Department of Information Technology, Tripura University, India
Gesture recognition means recognizing the different expressions by which physically challenged people or hearing-impaired people can communicate with the outer world. In gesture recognition, hand gestures are one of the most common forms of communication and they can communicate with a wide range of meanings. Dance gestures recognition is one of the challenging tasks in pattern recognition where hand gestures are used. It is a linguistic treatment of human motion by which we can depict the dance drama and will be able to communicate with people culturally. The concept of dance gestures recognition can be used to classify Manipuri classical dance of India where 25 single-hand gestures and 12 dual-hand gestures are available. Unlike other Indian classical dance forms (eg: Bharatnatyam, Odissi, Kathak) there are no dataset available for Manipuri classical dance. In this thesis, a dataset for 25 single-hand gestures of Manipuri classical dance with 1500 mudras collected from 6 volunteers in different angels are presented. An unbiased dataset is targetted to enhance the gesture recognition. This thesis also presents a study on various methods for gesture recognition with their applications. Moreover, this thesis presents four features for recognizing 25 single-hand gestures of manipuri dance which are used to identify hand gestures using skeletization technique.
Gestures recognition, Single-hand gestures, Manipuri classical dance of India, Dataset, Skeletization technique.
Rina Su1*, Yumeng Li2, Xin Yin32*, Tao Chen4, Dr. Chen Tao4, 1Sun Yat-sen University Library, Guangzhou 510205, 2School of Journalism and Communication, Jiangxi Normal University, Jiangxi 330022, 3School of Information Resource Management, Renming University of China, Beijing 100872, 4School of Information Management, Sun Yat-sen University, Guangzhou 510205
The digitization of displaced archives is of great historical and cultural significance. Through the construction ofdigital humanistic platforms represented by MISS Platform, and the comprehensive application of IIIF technology, knowledge graph technology, ontology technology, and other popular information technologies. We can find that the digital framework of displaced archives built through the MISS platform can promote the establishment of a standardized cooperation and dialogue mechanism between the archives’ authorities and other government departments. At the same time, it can embed the works of archives in the construction of digital government and the economy, promote the exploration of the integration of archives management, data management, and information resource management, and ultimately promote the construction of a digital society. By fostering a new partnership between archives departments and enterprises, think tanks, research institutes, and industry associations, the role of multiple social subjects in the modernization process of the archives governance system and governance capacity will be brought into play. The National Archives Administration has launched a special operation to recover scattered archives overseas, drawing up a list and a recovery action plan for archives lost to overseas institutions and individuals due to war and other reasons. Through the National Archives Administration, the State Administration of Cultural Heritage, the Ministry of Foreign Affairs, the Supreme Peoples Court, the Supreme Peoples Procuratorate, and the Ministry of Justice, specific recovery work is carried out by studying and working on international laws.
Digital Humanity, Displaced Archive, MISS Platform, International Image Interoperability Framework (IIIF), Linked Data.
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