Miracle-dz/UniFAR-FTUs huggingface dataset:
This paper is included in the Proceedings of the 29th USENIX Security Symposium.

Smartphone loss affects millions of users each year and causes signiﬁcant monetary and data losses. Device tracking services (e.g., Google’s “Find My Device”) enable the device owner to secure or recover a lost device, but they can be easily circum-vented with physical access (e.g., turn on airplane mode). An effective loss prevention solution should immediately lock the phone and alert the owner before they leave without the phone. We present such an opensource, real-time system called Chaperone that does not require additional hardware. Chaperone adopts active acoustic sensing to detect a phone’s unattended status by tracking the owner’s departure via the built-in speaker and microphone. It is designed to robustly operate in real-world scenarios characterized by bursting high-frequency noise, bustling crowds, and diverse environmental layouts. We evaluate Chaperone by conducting over 1,300 experiments at a variety of locations including coffee shops, restaurants, transit stations, and cars, under different testing conditions. Chaperone provides an overall precision rate of 93% and an overall recall rate of 96% for smartphone loss events. Chaperone detects these events in under 0.5 seconds for 95% of the successful detection cases. We conduct a user study (n = 17) to investigate participants’ smartphone loss experiences, collect feedback on using Chaperone, and study different alert methods. Most participants were satisﬁed with Chaperone’s performance for its detection ability, detection accuracy, and power consumption. Finally, we provide an implementation of Chaperone as a standalone Android app.
The Impact of Anthropisation on the Floristic Composition, the Structure and Ecological Characterization of the Ngaoundéré Cliff, Cameroon

A study was carried out on the impact of the anthropisation on woody vegetation of the Ngaoundere cliff. Its main objective was to assess the floristic composition, the structure and the ecological parameters of that vegetation. The research work was carried out on four formations of vegetation (fallows, fields, shrub savannahs, and arborescent savannahs). The floristic composition and structure vegetation were conducted on some plots of land covering 20m x 50m, giving a total of 24 ha on aggregate. The study of the data were done with the help of soft ware’s like Excel, STATGRAPHICS plus 5,0 and XLSTAT all showed that the Ngaoundere woody vegetation shows an ‘‘L’’ structure under the influence of anthropic activities revealing a great number of waste and a small amount of mature stems. The specific richness is the most elaborate in savannahs planted with trees and the smallest in the fields and fallows lands. The Isoberlinia doka specie offers a comfortable ecological spectrum on the cliff even if it is running high risk of zooanthropic factors. The Cameroon Government should create and authenticate the cliff vegetation for a durable management.
TREES FOR INTER-WORD CONTEXT DEPENDENCIES IN SPANISH CONTINUOUS SPEECH RECOGNITION TASKS

Context Dependent Units are broadly used in Continuous Speech Recognition (CSR) system, being decision trees a suitable clustering technique to obtain this kind of units. This work was aimed to extend the decision tree based clustering to model inter-word context dependencies in Spanish CSR tasks. We first used a set of previously defined context dependent units to model word boundaries. A decision tree derived pair grammar was then used at decoding time to prune each network connecting pairs of words. Then, specific sets of decision tree based inner context dependent units were obtained to model word boundaries. Both approaches were experimentally evaluated and compared to classical approaches over a Spanish CSR task. Experimental results showed the potential contribution of modelling between-word contexts to CSR systems. These units were selected by decision trees and provided full coverage while keeping a suitable computational cost.
The Effect of AH Plus and GuttaFlow Bioseal Sealers on the Fracture Resistance of Endodontically Treated Roots Instrumented withReciprocal Rotary Systems

Objective: The objective of this study was to assess and compare the fracture resistance of roots treated endodontically using WaveOne gold, Reciproc blue, AH plus sealer and GuttaFlow Bioseal sealer. Materials and methods: Total 48 human mandibular premolars with single root were decoronated to a length of 13 mm. The roots were arbitrarily divided into 3 main groups (n=16 each group). Group I control group (roots were instrumented but not obturated), Group II obturated with gutta-percha and GuttaFlow Bioseal sealer and Group III obturated with gutta-percha and AH plus sealer. Each main group was additionally divided into 2 subgroups (n=8 each subgroup) conforming to the system used in root canal preparations, Subgroup A: Reciproc blue system and Subgroup B: WaveOne gold system. All root samples were kept for 1 week at 100% humidity to permit the sealers to set completely. Each sample was then exposed to fracture test utilizing a universal test machine, the data were analyzed statistically with one-way ANOVA test, two-way ANOVA test and Dunnett test (2-sided) at a significant level of 0.05. Results: The results revealed that the highest mean value of fracture resistance was noted in Group IIIB (611.125) followed by Group IIIA (543.250), Group IIB (519.875), Group IIA (511.875), Group IB (270.125) and Group IA (267.875) respectively with statistically highly significant differences among the different groups when compared to the control group. Conclusion: From the results of this study, it seems that the use of AH plus and GuttaFlow Bioseal sealers improved the fracture strength of instrumented roots whereas preparation of the root canals with WaveOne gold and Reciproc blue systems without obturation weakened the tooth structures.
Realizing and Refining Architectural Tactics: Availability

Abstract : Architectural tactics are fundamental design decisions. They are the building blocks for both architectural design and analysis. A catalog of architectural tactics has now been in use for several years in academia and industry. This report illustrates the use of this catalog in industrial applications, describing how tactics can be used in both design and analysis. The report further shows how the needs of practice have caused the catalog of availability tactics to be updated, but demonstrates that the underlying structure of the tactics categorization has re-mained stable. Finally, a real-world example is provided of the application of the updated set of availability tactics, showing how it illuminates design decisions, as guided by associated heuristics and analytic models.
Reactive Statistical Mapping: Towards the Sketching of Performative Control with Data

MAGE is an open-source performative speech synthesis system built upon the HTS HMM-based speech synthesizer. The aim of this project is to broaden the scope of its potential applications, by generalizing the concepts of performative speech synthesis to other use cases and making MAGE the frst tool for the performative HMM-based synthesis of potentially any phenomenon. HMM-based performative synthesis is an approach suited for many real-world phenomena which can be seen as time series, such as speech, singing, laughter or motion. However the complexity of its implementation can restrain most potential users from testing it, or prevent them to beneft from the whole control and fexibility that such an approach can give. In addition to generalizing MAGE to other domains than speech, we aim at making it a userfriendly system, lowering the difculty to access HMM-based performative synthesis. Furthermore, we will extend the real-time properties of the system, and validate their consistency across the diferent aspects of the workfow: mapping, HMM solving, rendering and user interaction.
The precision of large radio continuum source catalogues An application of the SPECFIND tool

The accuracy in position and flux density of 19 large radio continuum source catalogues has been determined using SPECFIND, a new tool recently made available through the CDS. The ∼67 000 radio continuum spectra with three or more frequencies produced by SPECFIND were used to cross-correlate sources from different catalogues and to calculate offsets in right ascension and declination in the various catalogues with respect to the positions given in the NRAO VLA Sky Survey (NVSS) catalogue, which was adopted as a reference. The flux densities reported in the catalogues were compared to those predicted by the composite spectra, enabling us to assess the quality of the flux density calibration of the different catalogues.
AUGMENTED REALITY

: Many learning media models of Augmented Reality were developed in South Korea. Because such learning media are able to support students' understanding more than just reading texts. In conditions of low literacy levels in Indonesia while the trend of technological and information advancements in Indonesia is so rapid, especially those that lead to the industrial revolution 4.0, it is necessary to propose the application of Augmented Reality to reading books with the aim of becoming an effort to increase students' reading interest in Indonesia and especially in the school where the author teaches. The stages in the development of this AR-based book use the Multimedia Development Methodology sourced from Luther, with six stages namely: Concept (concept), Design (design), Collecting material (material collecting), Making (assembly), Testing (testing), and Distribution (distribution). To create developments in the world of education and increase the attractiveness and effectiveness of learning, Augmented Reality technology can be utilized in the learning process both inside and outside of school. In order to achieve this, collaboration between digital creative actors or the IT profession and collaborators or teachers in creating AR media is needed in order to match the material and information to be conveyed.
A research in sustainable competitive positional advantage of english dailies in Tamilnadu (India)

The aim of this study is to identify and to describe the sustainable competitive positional advantage of various English Dailies in Tamilnadu (India) in general. Cost leaderships (price) and the differentiations based on demographics, psychographics, reading activity, opinion and interest are particularly considered. In order to reach this scope a theory of sustainable competitive positional advantage is proposed, in the context of print media in India. This study draws perceptual maps of English Dailies top of mind brands based on cost leadership and differentiation dimensions. Besides, it has been based on a new marketing scale (Selvarasu and Kathiravan, 2007). There are nine variables that are adopted from McCauley and Nesbitt (2004): Motivators, Inhibitors, Personality, News Content, Advertisement Content, Innovation, Quality and Service Excellence. They are used to describe competitive advantage of English Dailies. All these variables of English Dailies have been redefined using four variables of sustainability, as follows: r are, valuable, inimitable and non-substitute (Barney, 1991 and Coyne, 1986), The overall scores for SCPA have been computed at three level as low score, medium score and high score of English Dailies in accordance with approach of Competitive advantage as cost leadership, differentiation and focus (Porter, 1985). response to responses of bad. maximum of response of minimum score of one for response and the score of three neutral
Efficient and Portable Virtual NVMe Storage on ARM SoCs

Today’s cloud storage stack is extremely resource hungry, burning 10-20% of datacenter x86 cores, a major “storage tax” that cloud providers must pay. Yet complex cloud storage stack is not completely offload ready to today’s IO accelerators. We present LEAP, a new cloud storage stack that leverages ARM-based co-processors to offload complex storage services. LEAP addresses many deployment challenges such as hardware fungibility, software portability, virtualizability, composability, and efficiency, with a set of OS/software techniques and new hardware properties that provide a uniform address space across the x86 and ARM cores and expose a virtual NVMe storage to unmodified guest VMs, with a competitive speed to bare-metal performance.
Statistical Relational Artificial Intelligence with Relative Frequencies: A Contribution to Modelling and Transfer Learning across Domain Sizes

Dependencies on the relative frequency of a state in the domain are common when modelling probabilistic dependencies on relational data. For instance, the likelihood of a school closure during an epidemic might depend on the proportion of infected pupils exceeding a threshold. Continuous dependencies are also common: for instance; the likelihood of any one mosquito bite transmitting an illness depends on the proportion of carrier mosquitoes. Current approaches usually only consider probabilities over possible worlds rather than over domain elements themselves. We introduce two formalisms that explicitly incorporate relative frequencies into statistical relational artificial intelligence. The first formalism, Lifted Bayesian Networks for Conditional Probability Logic, expresses discrete dependencies on probabilistic data. The second formalism, Functional Lifted Bayesian Networks, expresses continuous dependencies. Incorporating relative frequencies is not only beneficial to modelling; it also provides a more rigorous approach to learning problems where training and test or application domains have different sizes. To this end, we provide a representation of the asymptotic probability distributions induced by the two formalisms on domains of increasing sizes. Since that representation has well-understood scaling behaviour across domain sizes, it can be used to estimate parameters for a large domain consistently from randomly sampled subpopulations.
Aspect Sentiment and Cultural Bias Mining from Reviews with User Profiling for Mobile Games on App Store

To our knowledge, our novelty appear in three dimensions: first, we incorporate approaches concerning aspect level sentiment analysis and emotion dynamics with the state of the art techniques to understand the cognitive process of users; second, we conduct crosscultural study and discover hidden biases in the data; third we profile users to facilitate more accurate personalization. We focus the related work discussion in four areas: mining user reviews, aspect level sentiment extraction, cross-cultural study and cultural bias mining, and user profiling.
Application of novel chaotic neural network on Mandarin digital speech recognition

-Modeling olfactory neural systems, the KIII model proposed by Freeman exhibits chaotic dynamic characteristics and has potential for pattern recognition. Fuzzy c-means clustering can classify an object to several classes at the same time but with different degrees based on fuzzy sets theory. Based on the KIII model, mandarin digital speech is recognized utilizing the features extracted by the fuzzy c-means clustering. Experimental results show that the KIII model can perform digital speech recognition efficiently and the fuzzy c-means clustering has better performance than the hard k-means clustering.
Machine Learning Predicts Treatment Response in Bipolar & Major Depressive Disorders

Diagnosis of bipolar disorder (BD) patients with complex symptoms presents a challenge to clinicians. Patients tend to spend more time in a depressive state than a manic state. In such complex cases, the current Diagnostic and Statistical Manual (DSM), which is not based on pathophysiology, can lead to misdiagnosis as major depressive disorder (MDD) and an imperfect or even harmful medication response. A biologically-based classification algorithm is needed to improve the accuracy of diagnosis. Osuch et al. (2018) presented a kernel support vector machine (SVM) algorithm to predict the medication-class of response from new patient samples whose diagnoses were unclear. Here we also utilize the kernel support vector machine (SVM) algorithm but with a few novel contributions. We applied the robust, fully automated neuromark independent component analysis (ICA) framework to extract comparable features in a multi-dataset setting and learn a kernel function for support vector machine (SVM) on multiple feature subspaces. The neuromark framework successfully replicates the prior result with 95.45% accuracy (sensitivity 90.24%, specificity 92.3%). To further evaluate the generalizability of our approach, we incorporated two additional datasets comprising bipolar disorder (BD) and major depressive disorder (MDD) patients. We validated the trained algorithm on these datasets, resulting in a testing accuracy of up to 89% (sensitivity 0.88, specificity 0.89) without using site or scanner harmonization techniques. We also translated the model to predict improvement scores of major depressive disorder (MDD) with up to 70% accuracy. This approach reveals some salient biological markers of medication-class of response within mood disorders. Highlights We demonstrate a DSM-free approach for predicting treatment response from resting-state functional magnetic resonance imaging (fMRI) data. We identify several replicable biomarkers using the approach. Our work has potential for clinical application by replacing trial-and-error in treating complex psychiatric disorders.
Visualization for Large‐scale Gaussian Updates

In geostatistics and also in other applications in science and engineering, it is now common to perform updates on Gaussian process models with many thousands or even millions of components. These large‐scale inferences involve modelling, representational and computational challenges. We describe a visualization tool for large‐scale Gaussian updates, the ‘medal plot’. The medal plot shows the updated uncertainty at each observation location and also summarizes the sharing of information across observations, as a proxy for the sharing of information across the state vector (or latent process). As such, it reflects characteristics of both the observations and the statistical model. We illustrate with an application to assess mass trends in the Antarctic Ice Sheet, for which there are strong constraints from the observations and the physics.
Prototype Development in General Purpose Representation and Association Machine Using Communication Theory

Biological system study has been an intense research area in neuroscience and cognitive science for decades of years. Biological human brain is created as an intelligent system that integrates various types of sensor information and processes them intelligently. Neurons, as activated brain cells help the brain to make instant and rough decisions. From the 1950s, researchers start attempting to understand the strategies the biological system employs, then eventually translate them into machine-based algorithms. Modern computers have been developed to meet our need to handle computational tasks which our brains are not capable of performing with precision and speed. While in these existing man-made intelligent systems, most of them are designed for specific purposes. The modern computers solve sophistic problems based on fixed representation and association formats, instead of employing versatile approaches to explore the unsolved problems. Because of the above limitations of the conventional machines, General Purpose Representation and Association Machine (GPRAM) System is proposed to focus on using a versatile approach with hierarchical representation and association structures to do a quick and rough assessment on multitasks. Through lessons learned from neuroscience, error control coding and digital communications, a prototype of GPRAM system by employing (7,4) Hamming codes and short LowDensity Parity Check (LDPC) codes is implemented. Types of learning processes are presented, which prove the capability of GPRAM for handling multitasks. Furthermore, a study of low resolution simple patterns and face images recognition using an Image Processing Unit (IPU) structure for GPRAM system is presented. IPU structure consists of a randomly constructed LDPC code, an iterative decoder, a switch and scaling, and decision devices. All the input images have been severely degraded to mimic human Visual Information Variability (VIV) experienced in human visual system. The numerical results show that 1) IPU can reliably
A Scalable and Robust Multi-Agent Approach to Distributed Optimization

Modularizing a large optimization problem so that the solutions to the subproblems provide a good overall solution is a challenging problem. In this paper we present a multi-agent approach to this problem based on aligning the agent objectives with the system objectives, obviating the need to impose external mechanisms to achieve collaboration among the agents. This approach naturally addresses scaling and robustness issues by ensuring that the agents do not rely on the reliable operation of other agents. We test this approach in the difficult distributed optimization problem of imperfect device subset seletion [Challet and Johnson, 2002]. In this problem, there are n devices, each of which has a “distortion”, and the task is to find the subset of those n devices that minimizes the average distortion. Our results show that in large systems (1000 agents) the proposed approach provides improvements of over an order of magnitude over both traditional optimization methods and traditional multi-agent methods. Furthermore, the results show that even in extreme cases of agent failures (i.e., half the agents fail midway through the simulation) the system remains coordinated and still outperforms a failurefree and centralized optimization algorithm.
Traffic risk mining from heterogeneous road statistics

Lately, a large amount of traffic-related data, such as traffic statistics, accident statistics, road information, and drivers' and pedestrians' comments, has been collected through sensors and social media networks. In this paper, we propose a novel framework for mining traffic risk from such heterogeneous data. Traffic risk refers to the possibility of traffic accidents occurring. We specifically focus on two issues: 1) predicting the number of accidents for any road and intersection and 2) clustering roads to identify the risk factors that are common to risky road clusters. We followed a unifying approach to these issues by using feature-based non-negative matrix factorization (FNMF). More specifically, we developed a new multiplicative updating FNMF algorithm capable of processing large traffic data. Using real traffic data from Tokyo, we demonstrate that our proposed algorithm is able to predict traffic risk at any location more accurately and efficiently than existing methods. A number of clusters containing high-risk roads were identified and their risk factors were characterized. Through this study we have opened a new research area of traffic risk mining.
A systematic market approach to distributed constraint problems

MarCon (Market-based Constraints) applies market-based control to distributed constraint problems. It offers a new approach to distributing constraint problems that avoids challenges to current approaches in some domains, and it provides a systematic way to apply markets to many problems. Constraint agents interact with one another via the variable agents in which they share an interest, expressing their preferences over sets of assignments. Each variable integrates this information from the constraints interested in it and provides feedback that enables the constraints to shrink their sets of assignments until they converge on a solution. MarCon has been tested in the domain of mechanical design, in which its set-narrowing process is particularly useful.