Biblio

Each year, large ground motions from earthquakes cause infrastructure damage and loss of life worldwide. Here we present a novel concept that redirects and attenuates hazardous seismic waves using an engineered seismic-muffler acting as a cloaking device. The device employs vertically-oriented, sloping-opposing boreholes or trenches to form muffler walls and is designed to: 1) reflect and divert large amplitude surface waves as a barrier, while 2) dissipating body and converted waves traveling from depth upward into the muffler duct. Seismic wave propagation models suggest that a seismic-muffler can effectively reduce broadband ground motion directly above the muffler. 3D simulations are also compared for validation with experimental data obtained from bench-scale blocks containing machined borehole arrays and trenches. Computer models are then scaled to an earth-sized model. Results suggest a devastating seismic energy magnitude 7.0-\$\textbackslashtextbackslashmathrm M\_\textbackslashtextbackslashmathrm E\$ earthquake can be reduced to less damaging magnitudes experienced in the muffler vicinity, 4.5- \$\textbackslashtextbackslashmathrm M\_\textbackslashtextbackslashmathrm E\$ (surface wave) and 5.7- \$\textbackslashtextbackslashmathrm M\_\textbackslashtextbackslashmathrm E\$ (upgoing coupling into the muffler). Our findings imply that seismic-muffler structures significantly reduce the impact of the peak ground velocity of dangerous surface waves, while, seismic transmission upward through the muffler base at depth has marginal effects.

Stereophonic acoustic echo cancellation is widely used in the high quality audio/video teleconference systems to reduce the echoes coupling between microphones and loudspeakers. In the specific application scenarios, adaptive filters require a very high filter length to deal with the same long echo path. When time domain algorithm is used to estimate the echo path, the cost of complexity is very high, which can be optimized by frequency domain adaptive filters. In this paper, an efficient two-channel frequency domain algorithm is used to achieve this goal. Meanwhile, double-talk often occurs in the teleconference system, so the robustness of the algorithm is equally important. We also propose a robust two-path updating control transfer logic for stereophonic echo cancellation to solve the double-talk problems.

Magnetic induction (MI) has shown a great potential for underwater communications due to its immunity to acoustic noise and low latency. However, the transmission distance of MI is limited since the magnetic field attenuates very fast in the near field. In this work, a magneto inductive antenna design is studied to achieve two modes of operation: 1) a static quasi omni-directional magnetic coupling; 2) a dynamic rotation of magnetic coupling. A design procedure is described to define the strength of the magnetic field, bandwidth (BW) and the path loss (PL) of the underwater communication link. Both modes are simulated and the corresponding antenna configurations are described. The proposed antenna has three coils separated between each other by 120 degrees. The coils have a radius of 5 cm and a length of 8 cm. The simulation results illustrate how this design can provide an omni-directional magnetic coupling and a more directional performance in the rotation mode. In the rotation mode, simulations also confirmed that the magnetic field can be controllable by changing the phases of input currents.

In this work we analyze the importance of lexical and acoustic modalities in behavioral expression and perception. We demonstrate that this importance relates to the amount of therapy, and hence communication training, that a person received. It also exhibits some relationship to gender. We proceed to provide an analysis on couple therapy data by splitting the data into clusters based on gender or stage in therapy. Our analysis demonstrates the significant difference between optimal modality weights per cluster and relationship to therapy stage. Given this finding we propose the use of communication-skill aware fusion models to account for these differences in modality importance. The fusion models operate on partitions of the data according to the gender of the speaker or the therapy stage of the couple. We show that while most multimodal fusion methods can improve mean absolute error of behavioral estimates, the best results are given by a model that considers the degree of communication training among the interlocutors.

Acoustic speaker recognition systems are very vulnerable to spoofing attacks via replayed or synthesized utterances. One possible countermeasure is audio-visual speaker recognition. Nevertheless, the addition of the visual stream alone does not prevent spoofing attacks completely and only provides further information to assess the authenticity of the utterance. Many systems consider audio and video modalities independently and can easily be spoofed by imitating only a single modality or by a bimodal replay attack with a victim's photograph or video. Therefore, we propose the simultaneous verification of the data synchronicity and the transcription in a challenge-response setup. We use coupled hidden Markov models (CHMMs) for a text-dependent spoofing detection and introduce new features that provide information about the transcriptions of the utterance and the synchronicity of both streams. We evaluate the features for various spoofing scenarios and show that the combination of the features leads to a more robust recognition, also in comparison to the baseline method. Additionally, by evaluating the data on unseen speakers, we show the spoofing detection to be applicable in speaker-independent use-cases.

In this work, a measurement system is developed based on acoustic resonance which can be used for classification of materials. Basically, the inspection methods based on acoustic, utilized for containers screening in the field, identification of defective pills hold high significance in the fields of health, security and protection. However, such techniques are constrained by costly instrumentation, offline analysis and complexities identified with transducer holder physical coupling. So a simple, non-destructive and amazingly cost effective technique in view of acoustic resonance has been formulated here for quick data acquisition and analysis of acoustic signature of liquids for their constituent identification and classification. In this system, there are two ceramic coated piezoelectric transducers attached at both ends of V-shaped glass, one is act as transmitter and another as receiver. The transmitter generates sound with the help of white noise generator. The pick up transducer on another end of the V-shaped glass rod detects the transmitted signal. The recording is being done with arduino interfaced to computer. The FFTs of recorded signals are being analyzed and the resulted resonant frequency observed for water, water+salt and water+sugar are 4.8 KHz, 6.8 KHz and 3.2 KHz respectively. The different resonant frequency in case different sample is being observed which shows that the developed prototype model effectively classifying the materials.

The inherent diversity of human behavior limits the capabilities of general large-scale machine learning systems, that usually require ample amounts of data to provide robust descriptors of the outcomes of interest. Motivated by this challenge, personalized and population-specific models comprise a promising line of work for representing human behavior, since they can make decisions for clusters of people with common characteristics, reducing the amount of data needed for training. We propose a multi-task learning (MTL) framework for developing population-specific models of interpersonal conflict between couples using ambulatory sensor and mobile data from real-life interactions. The criteria for population clustering include global indices related to couples' relationship quality and attachment style, person-specific factors of partners' positivity, negativity, and stress levels, as well as fluctuating factors of daily emotional arousal obtained from acoustic and physiological indices. Population-specific information is incorporated through a MTL feed-forward neural network (FF-NN), whose first layers capture the common information across all data samples, while its last layers are specific to the unique characteristics of each population. Our results indicate that the proposed MTL FF-NN trained solely on the sensor-based acoustic, linguistic, and physiological modalities provides unweighted and weighted F1-scores of 0.51 and 0.75, respectively, outperforming the corresponding baselines of a single general FF-NN trained on the entire dataset and separate FF-NNs trained on each population cluster individually. These demonstrate the feasibility of such ambulatory systems for detecting real-life behaviors and possibly intervening upon them, and highlights the importance of taking into account the inherent diversity of different populations from the general pool of data.

This demo shows how two objects that each live within their own world, i.e., the are not in each others transitive closure of object references, can get to know each other in a well-defined manner using a new language construct. The basic problem is that if two object are in different worlds, there is no way they can communicate. Our proposed language construct, added to the Emerald programming language, allows objects in close proximity to get to know each other in a well-defined, language based manner.

In this paper, we present a novel acoustic sensing technique that recognizes two convenient input actions: hand gestures and on-body touch. We achieved them by observing the frequency spectrum of the wave propagated in the body, around the periphery of the wrist. Our approach can recognize hand gestures and on-body touch concurrently in real-time and is expected to obtain rich input variations by combining them. We conducted a user study that showed classification accuracy of 97%, 96%, and 97% for hand gestures, touches on the forearm, and touches on the back of the hand.

Over the last decade, differential privacy has achieved widespread adoption within the privacy community. Moreover, it has attracted significant attention from the verification community, resulting in several successful tools for formally proving differential privacy. Although their technical approaches vary greatly, all existing tools rely on reasoning principles derived from the composition theorem of differential privacy. While this suffices to verify most common private algorithms, there are several important algorithms whose privacy analysis does not rely solely on the composition theorem. Their proofs are significantly more complex, and are currently beyond the reach of verification tools. In this paper, we develop compositional methods for formally verifying differential privacy for algorithms whose analysis goes beyond the composition theorem. Our methods are based on deep connections between differential privacy and probabilistic couplings, an established mathematical tool for reasoning about stochastic processes. Even when the composition theorem is not helpful, we can often prove privacy by a coupling argument. We demonstrate our methods on two algorithms: the Exponential mechanism and the Above Threshold algorithm, the critical component of the famous Sparse Vector algorithm. We verify these examples in a relational program logic apRHL+, which can construct approximate couplings. This logic extends the existing apRHL logic with more general rules for the Laplace mechanism and the one-sided Laplace mechanism, and new structural rules enabling pointwise reasoning about privacy; all the rules are inspired by the connection with coupling. While our paper is presented from a formal verification perspective, we believe that its main insight is of independent interest for the differential privacy community.

Existing anti-debugging protections are relatively weak. In existing self-debugger approaches, a custom debugger is attached to the main application, of which the control flow is obfuscated by redirecting it through the debugger. The coupling between the debugger and the main application is then quite loose, and not that hard to break by an attacker. In the tightly-coupled self-debugging technique proposed in this paper, full code fragments are migrated from the application to the debugger, making it harder for the attacker to reverse-engineer the program and to deconstruct it into the original unprotected program to attach a debugger or to collect traces. We evaluate a prototype implementation on three complex, real-world Android use cases and present the results of tests conducted by professional penetration testers.

This paper presents DopEnc, an acoustic-based encounter profiling system on smartphones. DopEnc can automatically identify the persons that users interact with in the context of encountering. DopEnc performs encounter profiling in two major steps: (1) Doppler profiling to detect that two persons approach and stop in front of each other via an effective trajectory, and (2) voice profiling to confirm that they are thereafter engaged in an interactive conversation. DopEnc is further extended to support parallel acoustic exploration of many users by incorporating a unique multiple access scheme within the limited inaudible acoustic frequency band. All implementation of DopEnc is based on commodity sensors like speakers, microphones and accelerometers integrated on commercial-off-the-shelf smartphones. We evaluate DopEnc with detailed experiments and a real use-case study of 11 participants. Overall DopEnc achieves an accuracy of 6.9% false positive and 9.7% false negative in real usage.

This paper presents a simulator for swarm operations designed to verify algorithms for a swarm of autonomous underwater robots (AUVs), specifically for constructing an underwater communication network with AUVs carrying acoustic communication devices. This simulator consists of three nodes: a virtual vehicle node (VV), a virtual environment node (VE), and a visual showing node (VS). The modular design treats AUV models as a combination of virtual equipment. An expert acoustic communication simulator is embedded in this simulator, to simulate scenarios with dynamic acoustic communication nodes. The several simulations we have performed demonstrate that this simulator is easy to use and can be further improved.

Collaborative information seeking frequently occurs in an opportunistic and loosely-coupled fashion that is supported by awareness of others' activities on the web. Automatically sharing traces of information about web activity could substantially improve these collaborative information tasks, but conventional wisdom suggests that people are very reluctant to share information about web usage. Because work settings have different rules and practices about privacy, we carried out the first systematic study of people's privacy concerns about sharing web activity within workgroups. To provide a better understanding of privacy concerns about sharing web activity at work, we conducted a two-week diary study with 18 participants. Our study system asked participants to report on their search tasks and privacy concerns. Surprisingly, our results showed that people have little concern about sharing the majority of their activities with their work colleagues, and had even fewer concerns with sharing work-related activities. Our results provide new insights into the possibilities of sharing web activities within workgroups, and provide evidence that tools based on automatic sharing of awareness information can be feasible.

This paper aims at building a sparse deep autoencoder network with joint regularizations for image super-resolution. A map is learned from the low-resolution feature space to high-resolution feature space. In the training stage, two autoencoder networks are built for image representation for low resolution images and their high resolution counterparts, respectively. A neural network is constructed to learn a map between the features of low resolution images and high resolution images. Furthermore, due to the local smoothness and the redundancy of an image, the joint variation regularizations are unified with the coupled autoencoder network (CAN). For the local smoothness, steerable kernel variation regularization is designed. For redundancy, non-local variation regularization is designed. The joint regularizations improve the quality of the super resolution image. Experimental results on Set5 demonstrate the effectiveness of our proposed method.

In this paper we explore how to embed status information of IoT-enabled devices in the acoustic atmosphere using melodic ambient sounds while limiting obtrusiveness for the user. The user can use arbitrary sound samples to represent the devices he wants to monitor. Our system combines these sound samples into a melodic ambient rhythm that contains information on all the processes or variables that user is monitoring. We focus on continuous rather than binary information (e.g. "monitoring progress status" rather then "new message received"). We evaluate our system in a machine monitoring scenario focusing on 5 distinct machines/processes to monitor with 6 priority levels for each. 9 participants use our system to monitor these processes with an up to 92.44% detection rate, if several levels are combined. Participants had no previous experience with this or similar systems and had only 5-10 minute training session before the tests.

We consider 3-dimensional(3D) underwater random network (UAN) where the nodes are uniformly distributed in a cuboid region. Then we derive the closed-form probability of finding a receiver in an ellipsoid neighborhood of an arbitrary sender. Computer simulation shows that the analytical result is generally consistent with the simulated result.

Differential privacy is a promising formal approach to data privacy, which provides a quantitative bound on the privacy cost of an algorithm that operates on sensitive information. Several tools have been developed for the formal verification of differentially private algorithms, including program logics and type systems. However, these tools do not capture fundamental techniques that have emerged in recent years, and cannot be used for reasoning about cutting-edge differentially private algorithms. Existing techniques fail to handle three broad classes of algorithms: 1) algorithms where privacy depends on accuracy guarantees, 2) algorithms that are analyzed with the advanced composition theorem, which shows slower growth in the privacy cost, 3) algorithms that interactively accept adaptive inputs. We address these limitations with a new formalism extending apRHL, a relational program logic that has been used for proving differential privacy of non-interactive algorithms, and incorporating aHL, a (non-relational) program logic for accuracy properties. We illustrate our approach through a single running example, which exemplifies the three classes of algorithms and explores new variants of the Sparse Vector technique, a well-studied algorithm from the privacy literature. We implement our logic in EasyCrypt, and formally verify privacy. We also introduce a novel coupling technique called optimal subset coupling that may be of independent interest.

Software security is an important concern in the world moving towards Information Technology. Detecting software vulnerabilities is a difficult and resource consuming task. Therefore, automatic vulnerability prediction would help development teams to predict vulnerability-prone components and prioritize security inspection efforts. Software source code metrics and data mining techniques have been recently used to predict vulnerability-prone components. Some of previous studies used a set of unit complexity and coupling metrics to predict vulnerabilities. In this study, first, we compare the predictability power of these two groups of metrics in cross-project vulnerability prediction. In cross-project vulnerability prediction we create the prediction model based on datasets of completely different projects and try to detect vulnerabilities in another project. The experimental results show that unit complexity metrics are stronger vulnerability predictors than coupling metrics. Then, we propose a new set of coupling metrics which are called Included Vulnerable Header (IVH) metrics. These new coupling metrics, which consider interaction of application modules with outside of the application, predict vulnerabilities highly better than regular coupling metrics. Furthermore, adding IVH metrics to the set of complexity metrics improves Recall of the best predictor from 60.9% to 87.4% and shows the best set of metrics for cross-project vulnerability prediction.

Conventional photoacoustic microscopy (PAM) involves detection of optically induced thermo-elastic waves using ultrasound transducers. This approach requires acoustic coupling and the spatial resolution is limited by the focusing properties of the transducer. We present an all-optical PAM approach that involved detection of the photoacoustically induced surface displacements using an adaptive, two-wave mixing interferometer. The interferometer consisted of a 532-nm, CW laser and a Bismuth Silicon Oxide photorefractive crystal (PRC) that was 5×5×5 mm3. The laser beam was expanded to 3 mm and split into two paths, a reference beam that passed directly through the PRC and a signal beam that was focused at the surface through a 100-X, infinity-corrected objective and returned to the PRC. The PRC matched the wave front of the reference beam to that of the signal beam for optimal interference. The interference of the two beams produced optical-intensity modulations that were correlated with surface displacements. A GHz-bandwidth photoreceiver, a low-noise 20-dB amplifier, and a 12-bit digitizer were employed for time-resolved detection of the surface-displacement signals. In combination with a 5-ns, 532-nm pump laser, the interferometric probe was employed for imaging ink patterns, such as a fingerprint, on a glass slide. The signal beam was focused at a reflective cover slip that was separated from the fingerprint by 5 mm of acoustic-coupling gel. A 3×5 mm2 area of the coverslip was raster scanned with 100-μm steps and surface-displacement signals at each location were averaged 20 times. Image reconstruction based on time reversal of the PA-induced displacement signals produced the photoacoustic image of the ink patterns. The reconstructed image of the fingerprint was consistent with its photograph, which demonstrated the ability of our system to resolve micron-scaled features at a depth of 5 mm.