Miracle-dz/UniFAR-FTUs huggingface dataset:
Notes on the megalithic sites of southern Ethiopia, with highlights on late Paleolithic occupation sites

L'etude systematique de sites megalithiques a permis de mettre en evidence leur role comme site funeraire. C'est le cas de Tiya, mais aussi celui d'autres sites du sud de l'Ethiopie. Ces sites sont caracterises par des rites funeraires mis en evidence par de la ceramique et des objets de parure
Merging the fields of swarm robotics and new media: Perceiving swarm robotics as new media

The aim of this paper is to provide evidence that swarm robotic systems can be perceived as new media objects. A thorough description of the five principles of new media proposed by Lev Manovich in "The Language of New Media" is presented. This is complemented by a state of the art on swarm robotics with an in-depth comparison of the characteristics of both fields. Also presented are examples of swarm robotics used in new media installations in order to illustrate the cutting-edge applications of robotics and artificial intelligence achieved through the unity of both fields. The hypothesis of this research is that a novel point of view would be introduced by examining the field of swarm robotics through the scope of new media, which would benefit the work of both new media and swarm robotic researchers.
Solving Integer Quadratic Programming via Explicit and Structural Restrictions

We study the parameterized complexity of Integer Quadratic Programming under two kinds of restrictions: explicit restrictions on the domain or coefficients, and structural restrictions on variable interactions. We argue that both kinds of restrictions are necessary to achieve tractability for Integer Quadratic Programming, and obtain four new algorithms for the problem that are tuned to possible explicit restrictions of instances that we may wish to solve. The presented algorithms are exact, deterministic, and complemented by appropriate lower bounds.
PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO GRANDE DO SUL FACULDADE DE INFORMÁTICA PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIA DA COMPUTAÇÃO

Physiological measures, such as heart rate (HR) and skin conductance (SC), offer objective data about the user stress during the interaction process in virtual environments. This could be useful, for instance, to evaluate qualitative aspects of 3D interaction techniques without relying solely on subjective data and considering the different user responses. With this in mind, the goal of this project is to present a new methodology for quality assessment of the interaction techniques in immersive virtual environments, based on the study of the relationship between usability metrics and physiological measures using multivariate data analysis. Our methodology defines a testing protocol, a normalization procedure and statistical methods to correlate multiple measures. Applying this methodology, a case study comparison between two interaction techniques (ray-casting and HOMER) shows highly significant results, pointing to heart rate variability, as measured by the number of interval differences of successive inter-beat intervals greater than 50 ms (NN50), as a potential index to measure the time spent to perform a task. The study also shows that HR and SC measures reflect the user's performance during an interaction task. In spite of these results, this work reveals that physiological measures still cannot be considered as substitutes of usability metrics, but may be useful in the interpretation and understanding process of them. Discussions also indicate the need of further studies about the use of physiological measures in predicting outcomes, as a way to establish future guidelines based on a well-defined association between human body behaviors and the actions performed in 3D user interfaces.
Image registration for defects detection of high-speed train bogies

Defects detection of high-speed train bogies by image processing has the advantages of non-contact, fast and high precision. Small defects can be identified and located quickly by comparing the current captured image with the previously saved defects-free image. Actually, the viewing angles and positions of images taken on the same location at different time are usually different. So image registration is needed before the image comparison. This paper proposes an image registration based on binocular stereo matching. The two images taken under different viewing angles or positions can be considered as a stereo image pair. The proposed method utilizes the results of image feature matching and the corresponding essential matrix to obtain the 3D coordinates of feature points. And the 3D coordinates of non-feature points can be estimated by those of adjacent feature points’. By this way, each single pixel on the current image can be re-projected into the normal image, and image registration is accomplished. Compared with the traditional methods, the result image of the proposed approach is more accurate.
Applications of the BMS Algorithm to decoding of algebraic codes

I will discuss several applications of the BMS algorithm [1][2][3][4][5][6] to decoding of algebraic codes: (1) syndrome decoding of codes from curves up to the FengRao bound by using the BMS algorithm with majority logic; (2) list decoding of RS codes and codes and Hermitian codes. The computational complexities of our methods are much less than the other decoding methods including Feng-Rao algorithm simply based on Gaussian elimination. These reductions in computational complexity are based on the special structures or properties of the given input data (syndrome arrays, etc.) which are used cleverly by the BMS algorithm. Furthermore, we will show that multi-variable interpolation problem can be solved by the BMS algorithm.
Simplifying HW/SW integration to deploy multiple accelerators for CPU-FPGA heterogeneous platforms

FPGAS became an interesting option for developing hardware accelerators due to their energy efficiency and recent improvements in CPU-FPGA communication speeds. In order to accelerate the development cycle, FPGA high-level synthesis tools have been developed such as Intel HLS, OpenCL, and OpenSPL. These tools aim to free the designer from having to know all the FPGA low-level details. However, in order to achieve high performance processing, the developer should still understand the details of the deeper system layers. Moreover, OpenCL usually consumes more resources/compile time than a design developed directly in RTL. In this work we propose a novel framework to automatically integrate hardware accelerator cores in a final architecture by generating a HW/SW interface for an Intel CPU-FPGA modern platform. Our goal is to simplify the Intel Open Programmable Acceleration Engine (OPAE) by introducing a novel abstraction layer with a simple stream protocol channel. The experimental results for a set of dataflow benchmarks show a performance of up to 131.7 Gops/s, and a power efficiency of up to 353.7 Gops/W even when we bound the memory bandwidth to 12 GB/s.
Topic Modelling for Identification of Vaccine Reactions in Twitter

Background: Detection of vaccine safety signals depends on various established reporting systems, where there is inevitably a lag between an adverse reaction to a vaccine and the reporting of it, and subsequent processing of reports. Therefore, it is desirable to try and detect safety signals earlier, ideally close to real-time. Extensive use of social media has provided a platform for sharing and seeking health-related information, and the immediacy of social media conversations mean that they are an ideal candidate for early detection of vaccine safety signals. The objective of this study is to evaluate topic models for identifying user posts on Twitter that most likely contain vaccine safety signals. This is an initial step in the overall research to determine if reliable vaccine safety signals can be detected in social media streams. The techniques used were focused on identifying the model design and number of topics that best revealed documents that contained vaccine safety signals, to assist with dimension reduction and subsequent labelling of the text data. The study compared Gensim LDA, MALLET, and jLDADMM DMM models to determine the most effective model for detecting vaccine safety signals, assisted by an evaluation process that used an adjusted F-Scoring technique over a labelled subset of the documents.
Learning to program: from problems to code

This paper introduces the approach to teaching problem-solving and text-based programming that has been adopted in a large, post-18, undergraduate, key introductory module (L4 FHEQ) on Computing and Information Technology at the Open University (UK). We describe how students are equipped with programming, but foremost problem-solving skills. Key ingredients of the approach are interleaving of skills, explicit worked examples of decomposition, formulation of algorithms (with the help of patterns for recurring problems) and translation to code. Preliminary results are encouraging: students' average course work scores increase as they progress through the course.
Design and Implementation of a Framework for a Health Information Survey System

There are many health and wellbeing self-assessment tools, but using paper-based versions of these tools limits the wider applicability of them in the current digital world. Hence many self-assessment tools are implemented as web/mobile applications, allowing efficient mass data collection. The current implementation approaches of computerising health and wellbeing self-assessment tools exclude these tools being re-used in a different setting, such as in a different research project or by another healthcare practitioner; where the collected data needs to be independent of the original implementation. This paper presents a framework that facilitates the implementation of health and wellbeing self-assessment tools as templates, much like an MS PowerPoint or Word template, allowing new instances of the tool to be easily created and data to be independent of the original implementation. This framework provides both a research and a clinical component. The research component gives the ability to collect data, view graphs to enable some basic analysis and the possibility to download the data for further analysis. The clinical component enables the delivery of relevant information and resources to clients/patients depending on the outcomes of the questionnaires. This can be used strategically to tailor information and interventions for the particular stage or category a client is at, and thereby avoiding irrelevant information and information overload for the client.
Betweenness Centrality Based k-Anonymity for Privacy Preserving in Social Networks

In order to reduce information loss rate of the k-anonymous network, we propose an anonymous network reconstruction algorithm based on nodes' betweenness centrality. Betweenness centrality measures the nodes' significance in graph based on the shortest paths. In our algorithm, nodes are sorted according to betweenness centrality and the candidate nodes with great value are reconnected preferentially. Then the backbone structure of the network could be retained which guarantee the important features of the reconstruct network. We evaluate our method on real data sets by different metrics. The experiment results justify the efficiency and practical utility of our proposed method.
Mobile Sign Language Recognition for Bahasa Indonesia using Convolutional Neural Network

Hand gestures for speech impaired community have their usage for specific language. In Indonesia, hand gesture has their natural two hands sign and widely accepted usage, BISINDO (Bahasa Indonesia Sign Language). In this paper, we propose to use a mobile application to support people who want to communicate with speech impaired people based on BISINDO. We make use the Convolutional Neural Network method to identify the hand gesture in a real time Android mobile application. For training the image dataset, we make use of MobileNet algorithm that have satisfied us with good result, on top of a Machine Learning Framework, TensorFlow. The percentage of success has been influenced by the image reference size and the optimizer algorithm. The highest performance of implemented model reached 95.13% on its accuracy rate from 23 hand gestures from 13.802 images as dataset, and achieved 100% success on some hand gestures.
Security and Cryptographic Challenges for Authentication Based on Biometrics Data

Authentication systems based on biometrics characteristics and data represents one of the most important trend in the evolution of the society, e.g., Smart City, Internet-of-Things (IoT), Cloud Computing, Big Data. In the near future, biometrics systems will be everywhere in the society, such as government, education, smart cities, banks etc. Due to its uniqueness, characteristic, biometrics systems will become more and more vulnerable, privacy being one of the most important challenges. The classic cryptographic primitives are not sufficient to assure a strong level of secureness for privacy. The current paper has several objectives. The main objective consists in creating a framework based on cryptographic modules which can be applied in systems with biometric authentication methods. The technologies used in creating the framework are: C#, Java, C++, Python, and Haskell. The wide range of technologies for developing the algorithms give the readers the possibility and not only, to choose the proper modules for their own research or business direction. The cryptographic modules contain algorithms based on machine learning and modern cryptographic algorithms: AES (Advanced Encryption System), SHA-256, RC4, RC5, RC6, MARS, BLOWFISH, TWOFISH, THREEFISH, RSA (Rivest-Shamir-Adleman), Elliptic Curve, and Diffie Hellman. As methods for implementing with success the cryptographic modules, we will propose a methodology which can be used as a how-to guide. The article will focus only on the first category, machine learning, and data clustering, algorithms with applicability in the cloud computing environment. For tests we have used a virtual machine (Virtual Box) with Apache Hadoop and a Biometric Analysis Tool. The weakness of the algorithms and methods implemented within the framework will be evaluated and presented in order for the reader to acknowledge the latest status of the security analysis and the vulnerabilities founded in the mentioned algorithms. Another important result of the authors consists in creating a scheme for biometric enrollment (in Results). The purpose of the scheme is to give a big overview on how to use it, step by step, in real life, and how to use the algorithms. In the end, as a conclusion, the current work paper gives a comprehensive background on the most important and challenging aspects on how to design and implement an authentication system based on biometrics characteristics.
Hidden-Markov-model-based voice activity detector with high speech detection rate for speech enhancement

A new voice activity detection (VAD) algorithm with soft decision output in Mel-frequency domain is developed based on hidden Markov model (HMM) and is incorporated in an HMM-based speech enhancement system. The proposed VAD uses a two-state ergodic HMM representing speech presence and speech absence. The states are constructed from noisy speech and noise HMMs used in the speech enhancement system. This composite model provides a robust detection of speech segments in the presence of noise and obviates the need for extra modeling in HMM-based speech enhancement applications. As the main purpose of the proposed VAD is to detect speech segments accurately, a hang-over mechanism is proposed and is applied on the output of the VAD to improve the speech detection rate. The VAD is integrated in the HMM-based speech enhancement system in Mel-frequency spectral (MFS) and cepstral (MFC) domains. The performance of the proposed VAD, the effectiveness of the hang-over mechanism and the performance of the VAD-integrated speech enhancement system are evaluated on four noise types at different SNR levels. The experimental results confirm the superiority of the proposed VAD compared to the reference methods particularly for speech detection rate at the dominant noisy conditions.
BOSMI: a framework for non-intrusive monitoring and testing of embedded multithreaded software on the logical level

Traditional software testing methods are inefficient in cases where data inputs alone do not determine the outcome of a program's execution. In order to verify such software, testing is often complemented by analysis of the execution trace. For monitoring the execution trace, most approaches today insert additional instructions at the binary level, making the monitoring intrusive. Binary instrumentation operate on a low level, making it difficult to properly modify a program's states and to quantify its code coverage. In this paper, we present a framework for testing complex embedded multithreaded software on the logical level. Testing software on this level avoids dependency on concrete compilers and relates the execution to the source code, thus enabling coverage. Our non-intrusive execution monitoring and control is implemented using the LLVM interpreter compiler infrastructure. Instead of forcing thread interleaving, we suggest simulating interleaving effects through non-intrusive changes of shared variables. This makes it possible to test a single thread without executing the full software stack, which is especially useful in situations where the full software stack is not available (e.g., pre-integration testing). We complement existing approaches with new features such as dynamic configuration of monitoring and execution rollback to the checkpoints. Our approach introduces acceptable overhead without any complex setup.
Ambiguity function based on the linear canonical transform

The ambiguity function (AF) as an important tool for time-frequency analysis, has widely been used in radar signal processing, sonar technology and so on. It does well in analysing chirp signals. However, it fails in estimating the cubic phase (CP) signal, which is required in many applications. As the generalisation of the Fourier transform (FT), the linear canonical transform (LCT) has received much attention, and has found many applications in filter design, pattern recognition, optics and so on. In this study, the authors define a new kind of AF - the AF based on the LCT (LCTAF), which is proposed to estimate the CP signal. Some important properties of LCTAF are discussed, such as symmetry and conjugation property, shifting property and Moyal formula. The relationships between the LCTAF and other time-frequency analysis distributions are derived, including the classical AF, the Wigner distribution function (WDF) based on LCT, the short-FT and the wavelet transform. The linear canonical AF (LCAF) is another kind of AF. The authors also discuss its relation with AF and WDF and give some new properties of the LCAF. At last, the LCTAF is applied for estimating the CP signal. The simulation indicates that the LCTAF is useful and effective.
Visual attention toward Socially Rich context information for Autism Spectrum Disorder (ASD) and Normal Developing Children: An Eye Tracking Study

The paper presents an eye tracking analysis study to help us understand the visual behavior and pattern of Normal Developing children and Autistic children while viewing a socially-rich stimulus consists of human and social interactions. An eye tracking experiment consists of displaying different images with social stimuli (containing human faces) to the child and the eye tracker captured and tracked the child's eye gaze movements, then analyzed to identify where specifically in the stimulus is the child looking at. The finding indicates a significant difference was found between the two groups viewing patterns and behavior when were presented with a scene included material with human and social interaction content. The study also reveals that large percentage of Autistic participants expressed minimum interest and time looking at face area, evident by significant time spent fixations on non-face regions, linking to lack of interest in socially relevant information, especially the two small Areas of Interests (AOIs) eyes and mouth regions comparing to the normal developing children. The results can be used to improve the life style of other children who have a risk to develop Autism as well as discover earlier signs of ASD.
Nurturing the Companion ChatBot

Although recent technical progress of artificial intelligence is impressive, the affective effect of intelligent agents has not been investigated sufficiently. This research focuses on the affective effect of ChatBot. We observe an interesting phenomenon that not only the affective response from ChatBot influences user's experience, but also the manner that user interacts with ChatBot affects the development of the ChatBot's intelligence. So this work proposes an ethical issue that it is necessary to regularize the user's behavior. We validate this argument by designing a novel paradigm, which enables the users to nurture companion ChatBots via developmental artificial intelligence techniques. With only twenty days nurturing, the users build affective bonding with the ChatBots and the ChatBots show significant progress in communication skills.
AivoTTA: an energy efficient programmable accelerator for CNN-based object recognition

Battery driven intelligent cameras used, e.g., in police operations or pico drone based surveillance require good object detection accuracy and low energy consumption at the same time. Object recognition algorithms based on Convolutional Neural Networks (CNN) currently produce the best accuracy, but require relatively high computational power. General purpose CPU and GPU implementations of CNN-based object recognition provide flexibility and performance, but this flexibility comes at a high energy cost. Fixed function hardware acceleration of CNNs provides the best energy efficiency, with a trade-off in reduced flexibility. This paper presents AivoTTA, a flexible and energy efficient CNN accelerator with a SIMD Transport-Triggered Architecture that is programmable in C and OpenCL C. The proposed accelerator makes use of smart memory access patterns and fusion of layers to greatly reduce the number of memory transfers and improve energy efficiency. The accelerator was synthesized using 28 nm ASIC technology for different supply voltages and clock frequencies. The most power efficient design points consume 11.3 mW for an object recognition network running 16 GOPS at 400 MHz. The maximum clock frequency is 1.4 GHz. With the maximum clock, the accelerator consumes 116 mW for an effective 57 GOPS. To the best of our knowledge, it is the most energy efficient compiler programmable CNN accelerator published.
A Team Based Player Versus Player Recommender Systems Framework For Player Improvement

Modern Massively Multi-player Online Games (MMOGs) have grown to become extremely complex in terms of the usable resources in the games, resulting in an increase in the amount of data collected by tracking the in-game activities of players. This has opened the door for researchers to come up with novel methods to utilize this data to improve and personalize the user experience. In this paper, a novel but flexible framework towards building a team based recommender system for player-versus-player (PvP) content in such MMOGs is presented, and applied to a case study in the context of the major commercial title Destiny 2. The framework combines behavioral profiling via cluster analysis with recommendation systems to look at teams of players as a unit, as well as the individual players, to make recommendations to the players, with the purpose of providing information to them towards improving their performance.