Biblio

Hypertemporal visible imaging of an urban lightscape can reveal the phase of the electrical grid granular to individual housing units. In contrast to in-situ monitoring or metering, this method offers broad, persistent, real-time, and non-permissive coverage through a single camera sited at an urban vantage point. Rapid changes in the phase of individual housing units signal changes in load (e.g., appliances turning on and off), while slower building- or neighborhood-level changes can indicate the health of distribution transformers. We demonstrate the concept by observing the 120 Hz flicker of lights across a NYC skyline. A liquid crystal shutter driven at 119.75 Hz down-converts the flicker to 0.25 Hz, which is imaged at a 4 Hz cadence by an inexpensive CCD camera; the grid phase of each source is determined by analysis of its sinusoidal light curve over an imaging "burst" of some 25 seconds. Analysis of bursts taken at \textbackslashtextasciitilde 15 minute cadence over several hours demonstrates both the stability and variation of phases of halogen, incandescent, and some fluorescent lights. Correlation of such results with ground-truth data will validate a method that could be applied to better monitor electricity consumption and distribution in both developed and developing cities.

While both radio and visible light waves can serve as the transmission medium, the propagation channel plays a key role in the highly dynamic vehicular communication environment. We discuss salient properties of radio and visible light channels, including radiation pattern and path loss modeling. By comparing their similarities and highlighting the differences, we illustrate the unique capabilities and limitations of these two technologies with respect to the requirements of Cooperative Intelligent Transportation System applications.

Visible Light Communication (VLC) emerges as a new wireless communication technology with appealing benefits not present in radio communication. However, current VLC designs commonly require LED lights to emit shining light beams, which greatly limits the applicable scenarios of VLC (e.g., in a sunny day when indoor lighting is not needed). It also entails high energy overhead and unpleasant visual experiences for mobile devices to transmit data using VLC. We design and develop DarkLight, a new VLC primitive that allows light-based communication to be sustained even when LEDs emit extremely-low luminance. The key idea is to encode data into ultra-short, imperceptible light pulses. We tackle challenges in circuit designs, data encoding/decoding schemes, and DarkLight networking, to efficiently generate and reliably detect ultra-short light pulses using off-the-shelf, low-cost LEDs and photodiodes. Our DarkLight prototype supports 1.3-m distance with 1.6-Kbps data rate. By loosening up VLC's reliance on visible light beams, DarkLight presents an unconventional direction of VLC design and fundamentally broadens VLC's application scenarios.

Visible Light Communication (VLC) allows to reuse a lighting infrastructure for communication while its main purpose of illumination can be carried out at the same time. Light sources based on Light Emitting Diodes (LEDs) are attractive as they are inexpensive, ubiquitous, and allow rapid modulation. This paper describes how to integrate smartphones into such a communication system that supports networking for a wide range of devices, such as toys with single LEDs as transmitter and receivers as well as interconnected LED light bulbs. The main challenge is how to employ the smartphone without any (hardware) modification as a receiver, using the integrated camera as a (slow) light sampling device. This paper presents a simple software-based solution, exploiting the rolling shutter effect and slow motion video capturing capabilities of latest smartphones to enable continuous reception and real-time integration into an existing VLC system. Evaluation results demonstrate a working prototype and report communication distances up to 3m and a maximum data throughput of more than 1200b/s, improving upon previous work.

Pairing displays and cameras can open up convenient and "free" visible light communication channels. But in realistic settings, the synchronization between displays (transmitters) and cameras (receivers) can be far more involved than assumed in the literature. This study aims to analyze and model the temporal behaviors of displays and cameras to make the visible light communication channel between the two more robust, while maintaining perceptual transparency of the transmitted data.

A biometric technology is an emerging field of information technology which can be used to identifying identity of unknown individual based on some characteristics derived from specific physiological and/or behavioral characteristics that the individual possesses. Thus, among several biometric characteristics, which can be derived from the hand, palmprint has been effectively used to improve identification for last years. So far, majority of research works on this biometric trait are fundamentally based on a gray-scale image which acquired using a visible light. Recently, multispectral imaging technology has been used to make the biometric system more efficient. In this work, in order to increase the discriminating ability and the classification system accuracy, we propose a multimodal system which each spectral band of palmprint operates separately and their results are fused at matching score level. In our study, each spectral band is represented by features extracted by PCANet deep learning technique. The proposed scheme is validated using the available CASIA multispectral palmprint database of 100 users. The obtained results showed that the proposed method is very efficient, which can be improved the accuracy rate.

Current intrusion detection systems (IDS) for industrial control systems (ICS) mostly involve the retrofitting of conventional network IDSs, such as SNORT. Such an approach is prone to missing highly targeted and specific attacks against ICS. Where ICS-specific approaches exist, they often rely on passive network monitoring techniques, offering a low cost solution, and avoiding any computational overhead arising from actively polling ICS devices. However, the use of passive approaches alone could fail in the detection of attacks that alter the behaviour of ICS devices (as was the case in Stuxnet). Where active solutions exist, they can be resource-intensive, posing the risk of overloading legacy devices which are commonplace in ICSs. In this paper we aim to overcome these challenges through the combination of a passive network monitoring approach, and selective active monitoring based on attack vectors specific to an ICS context. We present the implementation of our IDS, SENAMI, for use with Siemens S7 devices. We evaluate the effectiveness of SENAMI in a comprehensive testbed environment, demonstrating validity of the proposed approach through the detection of purely passive attacks at a rate of 99%, and active value tampering attacks at a rate of 81-93%. Crucially, we reach recall values greater than 0.96, indicating few attack scenarios generating false negatives.

Supervisory control and data acquisition (SCADA) systems that run our critical infrastructure are increasingly run with Internet-based protocols and devices for remote monitoring. The embedded nature of the components involved, and the legacy aspects makes adding new security mechanisms in an efficient manner far from trivial. In this paper we study an anomaly detection based approach that enables detecting zero-day malicious threats and benign malconfigurations and mishaps. The approach builds on an existing platform (Bro) that lends itself to modular addition of new protocol parsers and event handling mechanisms. As an example we have shown an application of the technique to the IEC-60870-5-104 protocol and tested the anomaly detector with mixed results. The detection accuracy and false positive rate, as well as real-time response was adequate for 3 of our 4 created attacks. We also discovered some additional work that needs to be done to an existing protocol parser to extend its reach.

Honeypots are a common tool to set intrusion alarms and to study attacks against computer systems. In order to be convincing, honeypots attempt to resemble actual systems that are in active use. Recently, researchers have begun to develop honeypots for programmable logic controllers (PLCs). The tools of which we are aware have limited functionality compared to genuine devices. Particularly, they do not support running actual PLC programs. In order to improve upon the interactive capabilities of PLC honeypots we set out to develop a simulator for Siemens S7-300 series PLCs. Our current prototype XPOT supports PLC program compilation and interpretation, the proprietary S7comm protocol and SNMP. While the supported feature set is not yet comprehensive, it is possible to program it using standard IDEs such as Siemens' TIA portal. Additionally, we emulate the characteristics of the network stack of our reference PLC in order to resist OS fingerprinting attempts using tools such as Nmap. Initial experiments with students whom we trained in PLC programming indicate that XPOT may resist cursory inspection but still fails against knowledgeable and suspicious adversaries. We conclude that high-interactive PLC honeypots need to support a fairly complete feature set of the genuine, simulated PLC.

This paper describes a small experimental study into the use of avatars to remediate the lecturer's absence in voice-over-slide material. Four different avatar behaviours are tested. Avatar A performs all the upper-body gestures of the lecturer, which were recorded using a 3D depth sensor. Avatar B is animated using few random gestures in order to create a natural presence that is unrelated to the speech. Avatar C only performs the lecturer's pointing gestures, as these are known to indicate important parts of a lecture. Finally, Avatar D performs "lecturer-like" gestures, but these are desynchronised with the speech. Preliminary results indicate students' preference for Avatars A and C. Although the effect of avatar behaviour on learning did not prove statistically significant, students' comments indicate that an avatar that behaves quietly and only performs some of the lecturer's gestures (pointing) is effective. The paper also presents a simple empirical method for automatically detecting pointing gestures in Kinect recorded lecture data.

Never Alone (2016) is a generative large-scale urban screen video-sound installation, which presents the idea of generative choreographies amongst multiple video agents, or "digital performers". This generative installation questions how we navigate in urban spaces and the ubiquity and disruptive nature of encounters within the cities' landscapes. The video agents explore precarious movement paths along the façade inhabiting landscapes that are both architectural and emotional.

In this study, we used a humanoid robot as a telepresence robot and compared with the basic telepresence robot which can only rotate the display in order to reveal the effect of embodiment. We also investigated the effect caused by changing the body size of the humanoid robot by using two different size of robots. Our experimental results revealed that the embodiment increases the remote person's social telepresence, familiarity, and directivity. The comparison between small and big humanoid robots showed no difference and both of them were effective.

Clickjacking attacks are emerging threats to websites of different sizes and shapes. They are particularly used by threat agents to get more likes and/or followers in Online Social Networks (OSNs). This paper reviews the clickjacking attacks and the classic solutions to tackle various forms of those attacks. Different approaches of Cross-Site Scripting attacks are implemented in this study to study the attack tools and methods. Various iFrame attacks have been developed to tamper with the integrity of the website interactions at the application layer. By visually demonstrating the attacks such as Cross-Site scripting (XSS) and Cross-Site Request Forgery (CSRF), users will be able to have a better understanding of such attacks in their formulation and the risks associated with them.

In this paper, we present E-VOX, an emotionally enhanced semantic ECA designed to work as a virtual assistant to search information from Wikipedia. It includes a cognitive-affective architecture that integrates an emotion model based on ALMA and the Soar cognitive architecture. This allows the ECA to take into account features needed for social interaction such as learning and emotion management. The architecture makes it possible to influence and modify the behavior of the agent depending on the feedback received from the user and other information from the environment, allowing the ECA to achieve a more realistic and believable interaction with the user. A completely functional prototype has been developed showing the feasibility of our approach.

Social and emotional intelligence of computer systems is increasingly important in human-AI (Artificial Intelligence) interactions. This paper presents a tangible AI interface, T.A.I, that enhances physical engagement in digital communication between users and a conversational AI agent. We describe a compact, pneumatically shape-changing hardware design with a rich set of physical gestures that actuate on mobile devices during real-time conversations. Our user study suggests that the physical presence provided by T.A.I increased users' empathy for, and social connection with the virtual intelligent system, leading to an improved Human-AI communication experience.

This paper contributes a systematic research approach as well as findings of an empirical study conducted to investigate the effect of virtual agents on task performance and player experience in digital games. As virtual agents are supposed to evoke social effects similar to real humans under certain conditions, the basic social phenomenon social facilitation is examined in a testbed game that was specifically developed to enable systematical variation of single impact factors of social facilitation. Independent variables were the presence of a virtual agent (present vs. not present) and the output device (ordinary monitor vs. head-mounted display). Results indicate social inhibition effects, but only for players using a head-mounted display. Additional potential impact factors and future research directions are discussed.

The goal of this work is to model a virtual character able to converse with different interpersonal attitudes. To build our model, we rely on the analysis of multimodal corpora of non-verbal behaviors. The interpretation of these behaviors depends on how they are sequenced (order) and distributed over time. To encompass the dynamics of non-verbal signals across both modalities and time, we make use of temporal sequence mining. Specifically, we propose a new algorithm for temporal sequence extraction. We apply our algorithm to extract temporal patterns of non-verbal behaviors expressing interpersonal attitudes from a corpus of job interviews. We demonstrate the efficiency of our algorithm in terms of significant accuracy improvement over the state-of-the-art algorithms.

The design of systems with independent agency to act on the environment or which can act as persuasive agents requires consideration of not only the technical aspects of design, but of the psychological, sociological, and philosophical aspects as well. Creating usable, safe, and ethical systems will require research into human-computer communication, in order to design systems that can create and maintain a relationship with users, explain their workings, and act in the best interests of both users and of the larger society.

Personal agent software is now in daily use in personal devices and in some organizational settings. While many advocate an agent sociality design paradigm that incorporates human-like features and social dialogues, it is unclear whether this is a good match for professionals who seek productivity instead of leisurely use. We conducted a 17-day field study of a prototype of a personal AI agent that helps employees find work-related information. Using log data, surveys, and interviews, we found individual differences in the preference for humanized social interactions (social-agent orientation), which led to different user needs and requirements for agent design. We also explored the effect of agent proactive interactions and found that they carried the risk of interruption, especially for users who were generally averse to interruptions at work. Further, we found that user differences in social-agent orientation and aversion to agent proactive interactions can be inferred from behavioral signals. Our results inform research into social agent design, proactive agent interaction, and personalization of AI agents.

Given the advent of cyber-physical systems (CPS), event-based control paradigms such as complex event processing (CEP) are vital enablers for adaptive analytical control mechanisms. CPS are becoming a high-profile research topic as they are key to disruptive digital innovations such as autonomous driving, industrial internet, smart grid and ambient assisted living. However, organizational and technological scalability of today's CEP approaches is limited by their monolithic architectures. This leads to the research idea for atomic CEP entities and the hypothesis that a network of small event-based control services is better suited for CPS development and operation than current centralised approaches. In addition, the paper summarizes preliminary results of the presented doctoral work and outlines questions for future research as well as an evaluation plan.

This paper describes the hardware acceleration of various feature calculation functions used in activity recognition. In this work we have used a large scale sensing matrix which recognizes and counts gym exercises. Human activity is played on pressure matrix and the sensor data is sent to computer using a wired protocol for further processing. The recorded data from matrix is huge making it impractical to process on a smart phone. We propose a FPGA (Field Programmable Gate Array) based processing methodology which not only accelerates sensing data processing but also reduces the size of 2D sensor data matrix to 10 features. The resultant feature set can be transferred using wireless medium to a smart phone or other processing unit where the classification can be done. Our system takes a matrix of arbitrary size and output a 'features' set for each matrix frame. We used HLS (High Level Synthesis), an approach to write algorithm for FPGA using SystemC/C/C++ instead of traditional VHDL/Verilog. Results show promising improvement in processing time as compared to Matlab. Since the size of data is reduced, wireless medium can be use to transmit data. Additionally, the development time for FPGA designs is greatly reduced due to the usage of an abstracted high level synthesis approach. This system is currently developed for pressure sensing system but this strategy can be applied to other sensing application like temperature sensor grid.

Several duty-cycling and energy-efficient communication protocols have been presented to solve power constraints of sensor nodes. The battery power of sensor nodes can be also supplied by surrounding energy resources using energy harvesting techniques. However, communication protocols only offer limited power for sensor nodes and energy harvesting may encounter a challenge that sensor nodes are unable to draw power from surrounding energy resources in certain environments. Thus, an emerging technology, wireless rechargeable sensor networks (WRSNs), is proposed to enhance the proposed communication protocols and energy harvesting techniques [1]. With a WRSN, a mobile vehicle is used to supply power to sensor nodes by wireless energy transfer. One of the most significant issue in WRSNs is path planning of the mobile vehicle. The mobile vehicle based on its movement trajectory visits each sensor nodes to recharge them so that the sensor nodes can obtain sufficient energy to execute continuous missions. However, all of the existing mobile vehicles charging methods [2, 3] for WRSNs require the locations of the sensor nodes based on the assumption that the location of each sensor node is known in advance by one of the sensor network localization mechanisms. Therefore, the proposed system integrates both the localization and wireless charging mechanisms for WRSNs to decrease the system initialization time and cost.

Electric vehicles (EVs) are expected to be applicable to smart grids because they have large-capacity batteries. It is important that smart grid users be able to estimate surplus battery energy and/or surplus capacity in advance of deploying EVs. We constructed a database, the Energy COnsumption LOG (ECOLOG) Database System, to store vehicle daily logs acquired by smartphones placed in vehicles. The electrical energy consumption is estimated from GPS coordinate data using an EV energy-consumption model. This research specifically examines commuting with a vehicle used for same route every day. We corrected GPS coordinate data by map matching, and input the data to the EV energy consumption model. We regard the remaining battery capacity data acquired by the EV CAN as correct data. Then we evaluate the accuracy of driving energy consumption logs as estimated using the corrected GPS coordinate data.

Global Positioning System (GPS) is used ubiquitously in a wide variety of applications ranging from navigation and tracking to modern smart grids and communication networks. However, it has been demonstrated that modern GPS receivers are vulnerable to signal spoofing attacks. For example, today it is possible to change the course of a ship or force a drone to land in a hostile area by simply spoofing GPS signals. Several countermeasures have been proposed in the past to detect GPS spoofing attacks. These counter-measures offer protection only against naive attackers. They are incapable of detecting strong attackers such as those capable of seamlessly taking over a GPS receiver, which is currently receiving legitimate satellite signals, and spoofing them to an arbitrary location. Also, there is no hardware platform that can be used to compare and evaluate the effectiveness of existing countermeasures in real-world scenarios. In this work, we present SPREE, which is, to the best of our knowledge, the first GPS receiver capable of detecting all spoofing attacks described in the literature. Our novel spoofing detection technique called auxiliary peak tracking enables detection of even a strong attacker capable of executing the seamless takeover attack. We implement and evaluate our receiver against three different sets of GPS signal traces: (i) a public repository of spoofing traces, (ii) signals collected through our own wardriving effort and (iii) using commercial GPS signal generators. Our evaluations show that SPREE constraints even a strong attacker (capable of seamless takeover attack) from spoofing the receiver to a location not more than 1 km away from its true location. This is a significant improvement over modern GPS receivers that can be spoofed to any arbitrary location. Finally, we release our implementation and datasets to the community for further research and development.