Miracle-dz/UniFAR-FTUs huggingface dataset:
From Communication to Sensing : Recognizing and Counting Repetitive Motions with Wireless Backscattering

Recently several ground-breaking RF-based motion recognition systems were proposed to detect and/or recognize macro/micro human movements. These systems often suffer from various interferences caused by multiple-users moving simultaneously, resulting in extremely low recognition accuracy. To tackle this challenge, we propose a novel system, called Motion-Fi, which marries battery free wireless backscattering and device-free sensing. Motion-Fi is an accurate, interference tolerable motion-recognition system, which counts repetitive motions without using scenario-dependent templates or profiles and enables multi-users performing certain motions simultaneously because of the relatively short transmission range of backscattered signals. Although the repetitive motions are fairly well detectable through the backscattering signals in theory, in reality they get blended into various other system noises during the motion. Moreover, irregular motion patterns among users will lead to expensive computation cost for motion recognition. We build a backscattering wireless platform to validate our design in various scenarios for over 6 months when different persons, distances and orientations are incorporated. In our experiments, the periodicity in motions could be recognized without any learning or training process, and the accuracy of counting such motions can be achieved within 5% count error. With little efforts in learning the patterns, our method could achieve 93.1% motion-recognition accuracy for a variety of motions. Moreover, by leveraging the periodicity of motions, the recognition accuracy could be further improved to nearly 100% with only 3 repetitions. Our experiments also show that the motions of multiple persons separating by around 2 meters cause little accuracy reduction in the counting process.
Automatic emotion recognition in English and Arabic text

This study investigated the automatic recognition of emotion in English and Arabic text. We perform experiments with a new method of classification for recognising emotions using the Prediction by Partial Matching (PPM) character-based text compression scheme. These experiments involve both document level classification (whether a text of document is emotional or not) and also fine-grained classification such as recognising Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three English datasets (the LiveJournal’s blogs dataset, Aman’s blogs dataset, and Alm’s fairy tales dataset) show that the new method significantly outperforms the traditional word-based text classification methods. The results show that the PPM compression-based classification method is able to distinguish between emotional and non-emotional text with high accuracy, between texts involving Happiness and Sadness emotions (with 79.1% accuracy for Aman’s dataset and 76.9% for Alm’s datasets) and texts involving Ekman’s six basic emotions for the LiveJournal dataset (87.4% accuracy). Results also show that the method outperforms traditional feature-based classifiers such as Näıve Bayes and SMO in most cases in terms of accuracy, precision, recall and F-measure. In order to see how well the classifier performs on another language not related to English and also in order to create another Arabic benchmark corpus for future emotion classification experiments, we created a new Iraqi Arabic Emotion Corpus (IAEC) dataset annotated according to Ekman’s basic emotions. This dataset is composed of Facebook posts written in the Iraqi dialect. We evaluated the quality of this dataset using four external judges which resulted in an average inter-annotation agreement of 0.751. We then explored six different supervised machine learning methods to test the new dataset. We used standard Weka classifiers ZeroR, J48, Naive Bayes, Multinomial Naive Bayes for Text and SMO. We compared these results with our compression-based classifier PPM. Our study reveals that the PPM classifier significantly outperforms the other classifiers for the new dataset achieving the highest results in terms of accuracy, precision, recall, and Fmeasure. We also designed and investigated another new classification technique motivated by information divergence to recognize Ekman’s emotions in text. We used the three datasets written in the English Language and the one in the Arabic Language to evaluate the new method. The new method was able to achieve a better result for Alm’s dataset in terms of accuracy, precision, recall and F-measure than PPM and standard Weka classifiers. The new method also outperforms all standard Weka classifiers for all four datasets. Finally, these results show that our proposed technique is promising as an alternative technique for English and Arabic text categorization in general.
Theorising Sensory Cultures in Asia: Sociohistorical Perspectives

ABSTRACT Sociocultural meanings of the senses in society have recently received scholarly attention in disciplines such as sociology, anthropology, philosophy, history and geography. With some exceptions, extant works have mainly focused on Euro–American contexts, or non-industrial societies. This article ethnographically documents and analyses the social life of the senses in everyday life historical contexts within Asia. I propose three theoretical themes: sensory models and modalities; sensory moral economies; and sensory transnationalism. These themes exemplify how social actors and institutions employ and accord meanings to the senses that can be located in the fabric of everyday cultural experiences, spanning different social arrangements and encounters. I examine more closely indigenous sensoria where sensory practices stemming from different parts of Asia are marshalled as sources of sociocultural theorising. This is a response to and continuation of extant works on sensory cultures in other contexts, raising, concurrently, cross-cultural comparisons. These comparisons will also initiate a rethinking of “Asia” through sensory intercrossings and encounters.
Effect of Acute Vigorous Intensity Physical Activity on Cognitive Control in College-Aged Students

ABSTRACT To determine the effect of physical activity (PA) on three differing types of cognitive processes, within an under-researched segment of the population, 77 college-aged students were recruited from a university in the southwestern United States. These participants completed three computer-driven tests from a battery of measurements that assess various components of cognitive control (CC)—One Card Learning Test (attention), Two Back Card Test (working memory), and Revised Groton Learning Maze Test (inhibition/ executive function). Testing took place after a bout of sedentary activity, and two weeks later, after a bout of individualized PA (based on VO2 max). Due to the high intellect/high fit nature of the participants, there was no statistical significance with the One Card learning or Two Card Back Tests (i.e., many reached the test ‘ceiling’ on the initial attempt). After controlling for sex, age, BMI, estimated aerobic capacity, and GPA, there were significant differences between sedentary and physical activity conditions in both moves-per-second (adjusted b-coefficient = 0.132, p < 0.001) and duration (adjusted b-coefficient = −22,653 ms, p < 0.001) within the Revised Groton Maze Learning Test. The differences between conditions represented a medium-to-large effect. There were no moderating influences on the condition differences. This study adds to the existent literature that suggests PA may facilitate increases in CC, dependent on the nature of the cognitive task.
Forensic Analysis Utilizing Virtualization Onthe-Fly Forensische Analyse mittels dynamischer Virtualisierung

To approach the ever growing complexity of modern malware, security applications increasingly leverage virtualization technology to perform Virtual Machine Introspection (VMI). VMI constitutes techniques that allow the observation, analysis, and control of guest Virtual Machines (VMs) from the outside. This lends VMI-based applications an omniscient character gaining a complete and untainted view over the VM state. Apart from that, modern hardware-assisted virtualization technology allows transparent onthe-fly virtualization. This technique allows to migrate a live Operating System (OS) into a virtual environment on demand. This thesis consolidates VMI with on-the-fly virtualization. We elaborate the design and architecture of the WhiteRabbit VMI framework in form of a microkernel-based Virtual Machine Monitor (VMM). WhiteRabbit is designed to transparently virtualize a running OS on-the-fly for the purpose of forensic analysis. Towards this direction, our prototype employs Intel VT-x hardware virtualization extensions and is able to virtualize running Linux OSs. To harden potential exposure by malware, the design of WhiteRabbit considers anti-virtualization techniques and incorporates the hardware-assisted nested paging mechanism. After deployment, WhiteRabbit exposes VMI services towards remote applications. More precisely, WhiteRabbit extends the popular LibVMI interface and thus facilitates the use of custom and already existing LibVMI-based security applications from remote. In this way, WhiteRabbit represents an effective means for the purpose of forensic analysis that can be employed on demand.
Optimal Path Planning using Hybrid Bat Algorithm and Cuckoo Search

Path planning is key research topic in the field of robotics research, transportation, bioinformatics, virtual prototype designing, gaming, computer aided designs, and virtual reality estimation. In optimal path planning, it is important to determine the collision free optimal and shortest path. There may be various aspects to determine the optimal path based on workspace environment and obstacle types. In this research work, optimal path is determined based on the workspace environment having static obstacles and unknown environment area. A hybrid approach of meta-heuristic algorithm of Bat Algorithm (BA) and Cuckoo Search (CS) is used to determine the optimal path from defined source to destination. For experimentation, case study area of Alwar region, Rajasthan is considered which consist of urban and vegetation area. The reason for the selection of BA and CS for the path planning is the wide application and success of implementation of these concepts in the field of robotics and path planning. The consideration of individual BA for path planning can lead to problem of trapping between local optima. This obligates us to hybridize the concept of BA with some other efficient problem solving concept like CS. The hybridized concept of BA and CS is initially tested with standard benchmarks functions, after that considered for the application of path planning. Results of hybrid path planning concept are compared with individual CS and BA concepts in terms of simulation time and minimum number of iteration required to achieve the optimal path from defined source to destination. The evaluated results comparison of hybrid approach with individual concepts indicates the dominance of proposed hybrid concept in terms of standard benchmarks functions and other parameters as well.
Case Study of Data Mining Mutual Engagement

This paper presents a case study of using conventional Data Mining techniques to identify clusters of creative contributions made by users in a collaborative music making system. Results of the clustering suggest that when people mutually engage with each other they tend to converge on similar creative contributions, whereas smaller clusters may indicate higher quality contributions. We also show how we used Data Mining to discriminate between kinds of creative contributions including: complex melodic structures, simple melodic structures, musical motifs, and rhythmical contributions. Our use of Data Mining does not require direct interaction with users and so it may be useful in real-world study contexts.
PRIVACY LEAKAGE THROUGH SENSORY DATA ON SMART DEVICES

Mobile devices are becoming more and more indispensable in people’s daily life. They bring variety of conveniences. However, many privacy issues also arise along with the ubiquitous usage of smart devices. Nowadays, people rely on smart devices for business and work, thus much sensitive information is released. Although smart device manufactures spend much effort to provide system level strategies for privacy preservation, lots of studies have shown that these strategies are far from perfect. In this dissertation, many privacy risks are explored. Smart devices are becoming more and more powerful as more and more sensors are embedded into smart devices. In this thesis, the relationship between sensory data and a user’s location information is analyzed first. A novel inference model and a corresponding algorithm are proposed to infer a user’s location information solely based on sensory data. The proposed approach is validated towards real-world sensory data. Another privacy issue investigated in this thesis is the inference of user behaviors based on sensory data. From extensive experiment results, it is observed that there is a strong correlation between sensory data and the tap position on a smart device’s screen. A sensory data collection app is developed to collect sensory data from more than 100 volunteers. A conventional neural network based method is proposed to infer a user’s input on a smart phone. The proposed inference model and algorithm are compared with several previous methods through extensive experiments. The results show that our method has much better accuracy. Furthermore, based on this inference model, several possible ways to steal private information are illustrated. INDEX WORDS: Privacy preserving, Localization, Smart devices, Sensors, Data mining, Deep learning PRIVACY LEAKAGE THROUGH SENSORY DATA ON SMART DEVICES
Statistics of Risk Aversion

Information about risk preferences from investors is essential for modelling a wide range of quantitative finance applications. Valuable information related to preferences can be extracted from option prices through pricing kernels. In this paper, pricing kernels and their term structure are estimated in a time varying approach from DAX and ODAX data using dynamic semiparametric factor model (DSFM). DSFM smooths in time and space simultaneously, approximating complex dynamic structures by basis functions and a time series of loading coefficients. Contradicting standard risk aversion assumptions, the estimated pricing kernels indicate risk proclivity in certain levels of return. The analysis of the time series of loading coefficients allows a better understanding of the dynamic behaviour from investors preferences towards risk.
Design of Impedance Measurement Module for an EEG and EIT Integrated System

Electrical impedance tomography (EIT) has been performed with the electroencephalogram (EEG) reduce errors associated with the inverse problem due to unknown conductivity distribution in the brain. EIT is known to have the potential to produce images for detecting brain activities e. g. epileptic seizures, as a means complementary to EEG. Since EEG signal is generated from internal sources in the brain and EIT voltage signal is induced from externally injected current, the measurement system requires high temporal resolution to measure fast EIT signals and high resolution ADC for small EEG signals in order to measure EEG signals concurrent with EIT signals. We proposed the new design of EEG and EIT integrated system based on multiple channels of upgraded impedance measurement module (IMM3). IMM3 has adopted a direct digital synthesizer (DDS) to generate injection current. Using internal PLL, we could generate fast acquisition timing signal for EIT measurement. We used a 24 bit ADC with 2.5 MSPS measurement speed. Real and imaginary components of EIT signal could be calculated by the phase sensitive demodulation or FFT method. EEG data was decimated to improve the signal quality and to reduce the amount of data to be recorded. Other features were inherited from previous impedance measurement module (IMM2.5) including operation mode for external triggering and cascaded connection with EIT systems, the selfcalibration to maintain the performance, multiplexing, and phase sensitive demodulation. This document describes the design of the IMM3 for EEG and EIT integrated system in detail and presents the basic performance indices of tested module. It can be assembled to configure a multi-channel EEG and EIT integrated system.
A survey of probabilistic logic programming

The combination of logic programming and probability has proven useful for modeling domains with complex and uncertain relationships among elements. Many probabilistic logic programming (PLP) semantics have been proposed; among these, the distribution semantics has recently gained increased attention and has been adopted by many languages such as the Independent Choice Logic, PRISM, Logic Programs with Annotated Disjunctions, ProbLog, and P-log. This chapter reviews the distribution semantics, beginning with the simplest case with stratified Datalog programs, and showing how the definition is extended to programs that include function symbols and non-stratified negation. The languages that adopt the distribution semantics are also discussed and compared both to one another and to Bayesian networks.We then survey existing approaches for inference in PLP languages that follow the distribution semantics. We concentrate on the PRISM, ProbLog, and PITA systems. The PRISM system was one of the first and can be applied when certain restrictions on the program hold. ProbLog introduced the use of Binary Decision Diagrams that provide a computational basis for removing these restrictions and so performing inference over more general classes of logic programs. PITA speeds up inference by using tabling and answer subsumption. It supports general probabilistic programs, but can easily be optimized for simpler settings and even possibilistic uncertain reasoning. The chapter also discusses the computational complexity of the various approaches together with techniques for limiting it by resorting to approximation.
How do participatory constitution building processes effect the quality of democracy

Public participation in constitution building processes has been advanced as a standard operational measure, in post-conflict contexts, when states transition from non-democratic to democratic rule and during other periods of institutional crisis or reform. This is the case regardless of the scope of the constitution making activity be it a complete overhaul of the pre-existing constitution and the creation of an entirely new document or a more incremental approach to the endeavour. Ordinary citizens, civil society organizations and local nongovernmental organizations are now sitting in the front seat doing the steering, whereas domestic as well as international legal experts and national political elites serve to properly incorporate, endorse and, in the case of domestic authorities, finally approve by making it to legal provisions, the opinions of the people in the final constitution. Top-down constitution making has been replaced by bottom-up constitution building. This is at least the idealistic idea. Indeed, public participation in constitution building processes is gaining ground for a number of reasons, which will be presented on the following pages. The question of how public participation effects the subsequent quality of democracy in states that have undergone such processes however remains unanswered. As there is not one uniform, predefined “toolkit” that guides the procedures of a participatory process, there are as many methods to engage in the endeavour as there are cases. “Public participation” is by no means a neutral term, and how a given constitution building process is shaped in terms of public involvement will have consequences for the state of democracy subsequent the venture. In this paper I will present my dissertation project and discuss the need for research in the specific area. Systematic comparisons of participatory constitution building processes are needed both for the purpose of being able to detect patterns as to what it is in the process that leads to various levels of democracy as well as for the purpose of untangling the concept of public participation in the various forms that this may actually be exercised. The theoretical field of participatory constitution building will be presented as will the quality of democracy concept. As the dissertation will take on a different methodological approach than previous research within this specific area, the method and the rational for applying this procedure will also be presented.
Enhanced Phase-Driven $Q$ -Learning-Based DRM for Multicore Processors

In this paper, we propose a new dynamic reliability management technique for multicore processors using phase-driven <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-learning-based method. Our technique considers a wide range of long-term reliability issues and maximizes the throughput of the processor subject to the reliability constraint. We employ ON/OFF switching actions and dynamic voltage and frequency scaling as control knobs (i.e., working modes) to tune the state of cores of the processor. In order to achieve this, our technique detects program phases and adaptively determines the optimal working modes for each phase using the <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-learning-based method. By integrating the phase detection into the <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-learning-based management, our technique can provide efficient management for the programs with highly diverse phases. We also propose three additional modules to improve the management efficiency of our technique. In order to evaluate our technique, we use it to manage a 3-D CPU with high-diver programs. Several failure mechanisms are considered in this case study. Our proposed technique is compared with two existing <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-learning-based techniques. The experimental results demonstrate that when the number of phases is smaller than the number of working modes, our technique can achieve more than <inline-formula> <tex-math notation="LaTeX">$1.36{ \times }$ </tex-math></inline-formula> improvement in performance with 60% memory space savings.
Digital holography in hydrobiological research

In paper the device – set of hardware and software for non-contact investigation of marine particles is presented. The device is designed on digital holography principles. Hardware features of submersible holocamera (or DHC sensor) are considered. Methods are described for the processing of holograms and retrieve information. Results of approbation of the device are presented.
How Do Information and Experience Play a Role in the Discovery of Entrepreneurial Opportunities? The Case of Latin-American Immigrants in Barcelona

ABSTRACT This study contributes to expand the knowledge available on immigrant entrepreneurship. Consistent with the notion of entrepreneurship as a process, a qualitative exploration of information and experiences acquired by immigrants in the catering sector was performed to ascertain their role in discovering entrepreneurial opportunities. Our sample consisted of four cases of Latin American immigrants. The findings showed that the discovery process is based on the interrelation established between information per se and the experiences acquired throughout the migration periods. The knowledge acquired in their home country constituted the bases for our study, while that acquired in the host country is supplementary as it fine-tunes, expands, and influences the discovery process. Furthermore, findings revealed that immigrants trust information coming from their social environment and consider it sufficient to take a decision to set up a business. Finally, this study suggests further lines of inquiry.