Miracle-dz/UniFAR-FTUs huggingface dataset:
Acoustics, psychoacoustics and spectral music

The aim of this article is to examine the points at which acoustics, psychoacoustics and what has been called 'spectral music" meet. The motivations behind this bringing together of three more or less barbaric words are to be found in the principle of the spectral approach itself. The tonal system is governed by a set of harmonic rules that embody a compromise between the will to modulate among keys, the system of symbolically notating music, the available set of instruments, certain laws of acoustics, as well as many other concerns. This imposing edifice was patiently constructed by an accumulation of experience and benefited from a slow, cultural maturation. But the bases of this edifice were shaken in the evolution of contemporary music by recent developments in our relation to sound: previously of a fleeting and evanescent, ungraspable nature, sound has been captured and manipulated by way of recording technology. The theory of signals, associated with the computational power of m o d e m computers, has made it possible to analyze sound, to understand its fine structure, and to fashion it at will. The potential musical universe has thus 'exploded' in a certain sense. Sound synthesis opens truly unheard perspectives, extending the act of composition to the sound material itself. The distinctions between note, frequency, timbre, and harmony become fuzzy, or even irrelevant, and accumulated traditional experience finds itself impotent to organize the emerging sound world. After such a shock, new means of formalizing and structuring needed to be defined. Rather than establish a series of arbitrary rules, the spectral
Butterfly

RFID tag authentication is challenging because most commodity tags cannot run cryptographic algorithms. Prior research demonstrates that physical layer information based authentication is a promising solution, which uses special features from the physical backscatter signals from tags as their fingerprints. However, our recent studies show that existing physical-layer authentication may fail if feature collection and authentication are conducted in different locations, due to location-dependent noises, environmental factors, or reader hardware differences. This paper presents a new physical layer authentication scheme, called Butterfly, which is resilient to environment and location changes. Butterfly utilizes a pair of adjacent tags as an identifier of each object. By using the difference between the RF signals of the two tags as their fingerprint, the environmental factors can be effectively canceled. Butterfly is fully compatible with commodity RFID systems and standards. We set up a prototype Butterfly using commodity readers, tags, and RF devices. Extensive experiments show that Butterfly achieves high authentication accuracy for substantially different environments and device changes.
Spatial Data Locality in Scalable and Fault-tolerant Distributed Spatial Computing Systems

In the last decade, spatial datasets started to grow from small collections of high quality geospatial information into huge collections of data covering the whole planet with varying formats and qualities. Large-scale spatial datasets are about to create significant value in varying application fields including navigation, autonomous driving, urban geography, agriculture, and climate research. Therefore, large datasets are actively acquired. In addition, social networks such as Facebook, Twitter, and Flickr provide text, video, and images with associated geospatial information from the crowd. These sources are highly interesting as they provide near-realtime insights into aspects of human behavior and dynamics. Finally, global and long-running satellite missions such as Landsat, Sentinel, World-View, or TerraSAR add large amounts of geospatial information. It is a matter of fact that these data collections are putting challenges to the computational infrastructure used for spatial computing. Not only do we need a lot of computation, we also need to think about how to organize and design distributed systems that can help tackle the volume, velocity, and variety of current and future geospatial datasets. Modern big data systems employ data replication for two main reasons: first, for increased fault tolerance, and, second, for higher flexibility in scheduling tasks across a large cluster of machines. This paper proposes and compares novel data replication schemata for scalable spatial computing and analyzes the impact on the communication complexity of global spatial joins of a large collection of tweets collected from the Twitter API and building polygons extracted from OpenStreetMap.
Combining Low and Mid-Level Gaze Features for Desktop Activity Recognition

Human activity recognition (HAR) is an important research area due to its potential for building context-aware interactive systems. Though movement-based activity recognition is an established area of research, recognising sedentary activities remains an open research question. Previous works have explored eye-based activity recognition as a potential approach for this challenge, focusing on statistical measures derived from eye movement properties---low-level gaze features---or some knowledge of the Areas-of-Interest (AOI) of the stimulus---high-level gaze features. In this paper, we extend this body of work by employing the addition of mid-level gaze features; features that add a level of abstraction over low-level features with some knowledge of the activity, but not of the stimulus. We evaluated our approach on a dataset collected from 24 participants performing eight desktop computing activities. We trained a classifier extending 26 low-level features derived from existing literature with the addition of 24 novel candidate mid-level gaze features. Our results show an overall classification performance of 0.72 (F1-Score), with up to 4% increase in accuracy when adding our mid-level gaze features. Finally, we discuss the implications of combining low- and mid-level gaze features, as well as the future directions for eye-based activity recognition.
What Will You Do for the Rest of the Day?

Understanding and predicting human mobility is vital to a large number of applications, ranging from recommendations to safety and urban service planning. In some travel applications, the ability to accurately predict the user's future trajectory is vital for delivering high quality of service. The accurate prediction of detailed trajectories would empower location-based service providers with the ability to deliver more precise recommendations to users. Existing work on human mobility prediction has mainly focused on the prediction of the next location (or the set of locations) visited by the user, rather than on the prediction of the continuous trajectory (sequences of further locations and the corresponding arrival and departure times). Furthermore, existing approaches often return predicted locations as regions with coarse granularity rather than geographical coordinates, which limits the practicality of the prediction. In this paper, we introduce a novel trajectory prediction problem: given historical data and a user's initial trajectory in the morning, can we predict the user's full trajectory later in the day (e.g. the afternoon trajectory)? The predicted continuous trajectory includes the sequence of future locations, the stay times, and the departure times. We first conduct a comprehensive analysis about the relationship between morning trajectories and the corresponding afternoon trajectories, and found there is a positive correlation between them. Our proposed method combines similarity metrics over the extracted temporal sequences of locations to estimate similar informative segments across user trajectories. Our evaluation shows results on both labeled and geographical trajectories with a prediction error reduced by 10-35% in comparison to the baselines. This improvement has the potential to enable precise location services, raising usefulness to users to unprecedented levels. We also present empirical evaluations with Markov model and Long Short Term Memory (LSTM), a state-of-the-art Recurrent Neural Network model. Our proposed method is shown to be more effective when smaller number of samples are used and is exponentially more efficient than LSTM.
Modeling and Forecasting the Popularity Evolution of Mobile Apps

In recent years, with the rapid development of mobile app ecosystem, the number and categories of mobile apps have grown tremendously. However, the global prevalence of mobile apps also leads to fierce competition. As a result, many apps will disappear. To thrive in this competitive app market, it is vital for app developers to understand the popularity evolution of their mobile apps, and inform strategic decision-making for better mobile app development. Therefore, it is significant and necessary to model and forecast the future popularity evolution of mobile apps. The popularity evolution of mobile apps is usually a long-term process, affected by various complex factors. However, existing works lack the capabilities to model such complex factors. To better understand the popularity evolution, in this paper, we aim to forecast the popularity evolution of mobile apps by incorporating complex factors, i.e., exogenous stimulis and endogenous excitations. Specifically, we propose a model based on the Multivariate Hawkes Process (MHP), which is an exogenous stimulis-driven self-exciting point process, to model the exogenous stimulis and endogenous excitations simultaneously. Extensive experimental studies on a real-world dataset from app store demonstrate that MHP outperforms the state-of-the-art methods regarding popularity evolution forecasting.
The Application Research of Digital Technology in the Education for Major of Machinery in New Engineering Background

The modernization of education relies on the use of digital technology to change traditional teaching methods. Under the background of Emerging Engineering Education, the application of digital technology will be an important way for teaching reform of mechanical specialty. Therefore, this paper firstly analyzes the shortcomings of teaching methods, the defects of experimental training equipments and the lack of practical projects in the teaching process of mechanical course, then provides some measures to solving these problems with the help of digital technology. These measures cover introducing digital technology into teaching content, create a digital training environment and cultivate students' innovative ability by digital technology, some examples of practical applications are given. These examples show the feasibility of these measures and play a positive role in the teaching of mechanical engineering. Under the background of Emerging Engineering Education, this paper has reference significance for perfected the teaching of the traditional mechanical courses through digital technology.
Amateur

This paper presents Amateur, an augmented reality based vehicle navigation system using commodity smart phones. Amateur reads the navigation information from a digital map, matches it into live road condition video captured by smart phone, and directly annotates the navigation instructions on the video stream. The Amateur design entails two major challenges, including the lane identification and the intersection inference so as to correctly annotate navigation instructions for lane-changing and intersection-turning. In this paper, we propose a particle filter based design, assisted by inertial motion sensors and lane markers, to tolerate incomplete and even erroneous detection of road conditions. We further leverage traffic lights as land markers to estimate the position of each intersection to accurately annotate the navigation instructions. We develop a prototype system on Android mobile phones and test our system in a total number of more than 300km travel distance on different taxi cabs in a city. The evaluation results suggest that our system can timely provide correct instructions to navigate drivers. Our system can identify lanes in 2s with 92.7% accuracy and detect traffic lights with 95.29% accuracy. Overall, the accuracy of the navigation signs placement is less than 105 pixels on the screen throughout the experiments. The feedback from 50 taxi drivers indicates that Amateur provides an improved experience compared to traditional navigation systems.
Large-scale Automatic Depression Screening Using Meta-data from WiFi Infrastructure

Depression is a serious public health problem. Current diagnosis techniques rely on physician-administered or patient self-administered interview tools, which are burdensome and suffer from recall bias. Recent studies have proposed new approaches that use sensing data collected on smartphones to serve as "human sensors" for automatic depression screening. These approaches, however, require running an app on the phones for continuous data collection. We explore a novel approach that uses data collected from WiFi infrastructure for large-scale automatic depression screening. Specifically, when smartphones connect to a WiFi network, their locations (and hence the locations of the users) can be determined by the access points that they associate with; the location information over time provides important insights into the behavior of the users, which can be used for depression screening. To investigate the feasibility of this approach, we have analyzed two datasets, each collected over several months, involving tens of participants recruited from a university. Our results demonstrate that WiFi meta-data is effective for passive depression screening: the F1 scores are as high as 0.85 for predicting depression, comparable to those obtained by using sensing data collected directly from smartphones.
Crowd-enabled Processing of Trustworthy, Privacy-Enhanced and Personalised Location Based Services with Quality Guarantee

We propose a novel approach for enabling trustworthy, privacy-enhanced and personalised location based services (LBSs) that find nearby points of interests (POIs) such as restaurants, ATM booths, and hospitals in a crowdsourced manner. In our crowdsourced approach, a user forms a group from the crowd and processes the LBS using the POI knowledge of the group members without involving an external service provider. We use personalised rating in addition to the distance of a POI for finding the answers of the location based queries. The personalised rating of a POI is computed using individual POI ratings given by the group members and the query requestor's trust and similarity scores for the group members. The major challenges for the crowdsourced data are incompleteness and inaccuracy, which may result in lower quality answer for the LBS. In this paper, we first present techniques to select knowledgeable group members for processing LBSs and thereby increase the accuracy and the confidence level of the query answers. We then develop efficient algorithms to process LBSs in real time and enhance privacy by reducing the number of the group members' POIs shared with the query requestor. Finally, we run extensive experiments using real datasets to show the efficiency and effectiveness of our approach.
Driving with the Fishes

We present the use of in-car virtual reality (VR) as a way to create calm, mindful experiences for passengers and, someday, autonomous vehicle occupants. Specifically, we describe a series of studies aimed at exploring appropriate VR content, understanding the influence of car movement, and determining the length and other parameters of the simulation to avoid physical discomfort. Overall, our quantitative and qualitative insights suggest calm VR applications are well suited to an automotive context. Testing combinations of VR content designed to provide the participant with a static or dynamic experience versus stationary and moving vehicle modes, we find that a simulated experience of diving in the ocean while in a moving car elicited significantly lower levels of autonomic arousal as compared with a static VR plus stationary car condition. No significant motion sickness effects were subjectively reported by participants nor observable in the data, though a crossover interaction effect reveals how incongruence between the movement of the car and movement in VR could affect nausea. We conclude with recommendations for the design of calming and mindful VR experiences in moving vehicles.
Evolving Needs in IoT Control and Accountability

A key issue for smart home systems is supporting non-expert users in their management. Whereas feedback design on use cases (such as energy feedback) have gained attention, current approaches to providing awareness on the system state typically provide a rather technical view. Long-term investigations of the practices and resources needed for maintaining Do-It-Yourself smart home systems, are particularly scarce. We report on a design case study in which we equipped 12 households with DIY smart home systems for two years and studied participants' strategies for maintaining system awareness, from learning about its workings to monitoring its behavior. We find that people's needs regarding system accountability changed over time. Their privacy needs were also affected over the same period. We found that participants initially looked for in-depth awareness information from the dedicated web-based dashboard. In the later phases of appropriation, however, their interaction and information needs shifted towards management by exception on mobile or ambient displays -- only focusing on the system when things were 'going wrong'. In terms of system accountability, we find that a system's self-declaration should focus on being socially meaningful rather than technically complete, for instance by relating itself to people's activities and the home routines.
CrowdX

The lack of floorplan limits the spread of pervasive indoor location-based services. Existing crowdsourcing based approaches mostly rely on identifying, locating landmarks in the environment and utilizing the spatial relationship between the landmarks and traces for efficiently constructing fine-grained floorplan. However, these methods are always restricted by the sparse landmark distribution or may cause privacy leakage. In this paper, we propose CrowdX, a crowdsourcing system for accurate, low-cost indoor floorplan construction enhanced with opportunistic encounters among mobile users. The key insight is that the spatial relation (i.e., the displacement of each user and the distance between each other during the encounter) will be extracted from the audio and inertia data, which are aligned by the proposed vibration event-based method. Such information can be used to calibrate the drift of encounter position. The calibrated encounter position is beneficial to most of the floorplan generation steps, such as trace drift elimination, landmark positioning, hallway assembling, and room area estimation. Our experiments in three shopping malls show that CrowdX achieves an average F-measure around 89.4%. In addition, the average estimated room area error within about 20%. The evaluation results demonstrate a significant improvement of accuracy enhanced with opportunistic encounters.
Information-Theoretic Private Information Retrieval: A Unied Construction (Extended Abstract)

A Private Information Retrieval (PIR) protocol enables a user to retrieve a data item from a database while hiding the identity of the item being retrieved. In a t-private, k-server PIR protocol the database is replicated among k servers, and the user's privacy is protected from any collusion of up to t servers. The main cost-measure of such protocols is the communication complexity of retrieving a single bit of data. This work addresses the information-theoretic setting for PIR, in which the user's privacy should be unconditionally protected from collusions of servers. We present a unied general construction, whose abstract components can be instantiated to yield both old and new families of PIR protocols. A main ingredient in the new protocols is a generalization of a solution by Babai, Kimmel, and Lokam to a communication complexity problem in the so-called simultaneous messages model. Our construction strictly improves upon previous constructions and re- solves some previous anomalies. In particular, we obtain: (1) t-private k-server PIR protocols with O(n 1=b(2k 1)=tc ) communication bits, where n is the database size. For t> 1, this is a substantial asymptotic im- provement over the previous state of the art; (2) a constant-factor im- provement in the communication complexity of 1-private PIR, providing the rst improvement to the 2-server case since PIR protocols were in- troduced; (3) ecient PIR protocols with logarithmic query length. The latter protocols have applications to the construction of ecient families of locally decodable codes over large alphabets and to PIR protocols with reduced work by the servers.
Topical pimecrolimus in the treatment of genital lichen sclerosus

Pimecrolimus is a drug belonging to the class of macrolactamic immunosuppressants, similar to tacrolimus, and has been employed mainly in the treatment of atopic dermatitis, but is also active in other skin diseases, including genital lichen sclerosus (LS). The safety profile of pimecrolimus was the subject of special attention by the US FDA, which in 2005 issued a Public Health Advisory on the potential risk of cancer associated with the use of topical calcineurin inhibitors. However, many scientific societies have criticized the FDA note, referring to the vast literature on the subject in which there is no evidence available that topical use of the drug is dangerous. Patients with LS, of both sexes, may develop genital squamous cell carcinoma. This event, however, is not a contraindication to the use of pimecrolimus.
Antenne active multi-facettes à balayage électronique en technologie « tuile »

REE N°5/2015 75 RADARS A ANTENNES ELECTRONIQUES RADAR 2014 Antenne active multi-facettes a balayage electronique en technologie « tuile » Par G Byrne, A M Kinghorn, R W Lyon et G D Morrison Selex ES Ltd This paper describes the development and deployment of “tile” array technology, which is the foundation of lighter and cheaper AESA antennas. Close agreement is demonstrated between the modelled and measured performances of an X-band array, which provided azimuth and elevation beam-steering over a 20 % bandwidth. Furthermore the authors show how the thinness of the tile can be exploited in a multi-facet antenna, and present the measured beam patterns of an experimental dihedral antenna. ABSTRACT Introduction Les antennes actives aeroportees a balayage electronique (Airborne Elec- tronically Scanned Antennas, AESAs) ont generalement ete concues sous forme de “racks”, configuration pour laquelle les circuits comportant les modules emis- sion-reception sont places perpendiculai- rement a la face rayonnante de l’antenne. Typiquement, les modules d’emission- reception sont contraints a un espacement d’une demi-longueur d’onde, mais l’utili- sation