Miracle-dz/UniFAR-FTUs huggingface dataset:
Attacks Against BLE Devices by Co-located Mobile Applications

Bluetooth Low Energy (BLE) is a fast-growing wireless technology with a large number of potential use cases, particularly in the IoT domain. With many of these use cases, the BLE device stores sensitive user data or critical device controls, which may be accessed by an augmentative Android or iOS application. Uncontrolled access to such data could violate a user's privacy, cause a device to malfunction, or even endanger lives. The BLE specification aims to solve this with network layer security mechanisms such as pairing and bonding. Unfortunately, this doesn't take into account the fact that many applications may be co-located on the same mobile device, which introduces the possibility of unauthorised applications being able to access and modify sensitive data stored on a BLE device. In this paper, we present an attack in which an unauthorised Android application can access pairing-protected data from a BLE device by exploiting the bonding relationship previously triggered by an authorised application. We discuss possible mitigation strategies, and perform an analysis over 13,500+ BLE-enabled Android applications to identify how many of them implement such strategies to avoid this attack. Our results indicate that over 60% of these applications do not have mitigation strategies in place in the form of application-layer security, and that cryptography is sometimes implemented incorrectly in those that do. This implies that the corresponding BLE devices are potentially vulnerable to unauthorised data access by malicious applications.
A Gauss-Newton Approach to Real-Time Monocular Multiple Object Tracking

We propose an algorithm for real-time 6DOF pose tracking of rigid 3D objects using a monocular RGB camera. The key idea is to derive a region-based cost function using temporally consistent local color histograms. While such region-based cost functions are commonly optimized using first-order gradient descent techniques, we systematically derive a Gauss-Newton optimization scheme which gives rise to drastically faster convergence and highly accurate and robust tracking performance. We furthermore propose a novel complex dataset dedicated for the task of monocular object pose tracking and make it publicly available to the community. To our knowledge, It is the first to address the common and important scenario in which both the camera as well as the objects are moving simultaneously in cluttered scenes. In numerous experiments - including our own proposed data set - we demonstrate that the proposed Gauss-Newton approach outperforms existing approaches, in particular in the presence of cluttered backgrounds, heterogeneous objects and partial occlusions.
Implementation of pixel level digital TDI for scanning type LWIR FPAs

Implementation of a CMOS digital readout integrated circuit (DROIC) based on pixel level digital time delay integration (TDI) for scanning type LWIR focal plane arrays (FPAs) is presented. TDI is implemented on 8 pixels with over sampling rate of 3. Analog signal integrated on integration capacitor is converted to digital domain in pixel, and digital data is transferred to TDI summation counters, where contributions of 8 pixels are added. Output data is 16 bit, where 8 bits are allocated for most significant bits and 8 bits for least significant bits. Control block of the ROIC, which is responsible of generating timing diagram for switches controlling the pixels and summation counters, is realized with VerilogHDL. Summation counters and parallel-to-serial converter to convert 16 bit parallel output data to single bit output are also realized with Verilog HDL. Synthesized verilog netlists are placed&routed and combined with analog under-pixel part of the design. Quantization noise of analog-to-digital conversion is less than 500e-. Since analog signal is converted to digital domain in-pixel, inaccuracies due to analog signal routing over large chip area is eliminated. ROIC is fabricated with 0. 18μm CMOS process and chip area is 10mm2. Post-layout simulation results of the implemented design are presented. ROIC is programmable through serial or parallel interface. Input referred noise of ROIC is less than 750 rms electron, while power consumption is less than 30mW. ROIC is designed to perform in cryogenic temperatures.
Learning Cross-Domain Disentangled Deep Representation with Supervision from A Single Domain

The recent progress and development of deep generative models have led to remark- able improvements in research topics in computer vision and machine learning. In this paper, we address the task of cross-domain feature disentanglement. We advance the idea of unsupervised domain adaptation and propose to perform joint feature disentanglement and adaptation. Based on generative adversarial networks, we present a novel deep learning architecture with disentanglement ability, which observes cross-domain image data and derives latent features with the underly- ing factors (e.g., attributes). As a result, our generative model is able to address cross-domain feature disentanglement with only the (attribute) supervision from the source-domain data (not the target-domain ones). In the experiments, we apply our model for generating and classifying images with particular attributes, and show that satisfactory results can be produced.
Teaching Instructional Design: Reframing the Relationships between Teachers and Designers.

This paper presents the results of the implementation of an instructional approach that supports teachers' learning of instructional design (ID) and honors their beliefs and concerns within the ID process; participants included two instructors and 23 practicing K-12 teachers enrolled in an instructional design course as part of a masters program. The theoretical framework that supports this inquiry is presented, including how ID is viewed and consequently taught (the designer's view), how ID is used by teachers (the teacher's perspective), collaboration between teacher and designer, and teacher inquiry into their teaching. The research method is described, including the co-participatory, reflexive approach to instruction, in which all participants were viewed as learners who continually reflected and appraised joint efforts at teaching and learning ID. Findings are summarized and related to ways that teachers used to describe and represent their practice in ID projects, personal ID models, and course evaluation. How the co-participatory approach helped teachers and designers to learn from each other is discussed. (Contains 50 references.) (MES) Reproductions supplied by EDRS are the best that can be made from the original document. Teaching Instructional Design: Reframing the Relationships Between Teachers and Designers PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY
A Process Algebra for Wireless Mesh Networks used for Modelling, Verifying and Analysing AODV

We propose AWN (Algebra for Wireless Networks), a process algebra tailored to the modelling of Mobile Ad hoc Network (MANET) and Wireless Mesh Network (WMN) protocols. It combines novel treatments of local broadcast, conditional unicast and data structures. In this framework we present a rigorous analysis of the Ad hoc On-Demand Distance Vector (AODV) protocol, a popular routing protocol designed for MANETs and WMNs, and one of the four protocols currently standardised by the IETF MANET working group. We give a complete and unambiguous specification of this protocol, thereby formalising the RFC of AODV, the de facto standard specification, given in English prose. In doing so, we had to make non-evident assumptions to resolve ambiguities occurring in that specification. Our formalisation models the exact details of the core functionality of AODV, such as route maintenance and error handling, and only omits timing aspects. The process algebra allows us to formalise and (dis)prove crucial properties of mesh network routing protocols such as loop freedom and packet delivery. We are the first to provide a detailed proof of loop freedom of AODV. In contrast to evaluations using simulation or model checking, our proof is generic and holds for any possible network scenario in terms of network topology, node mobility, etc. Due to ambiguities and contradictions the RFC specification allows several interpretations; we show for more than 5000 of them whether they are loop free or not, thereby demonstrating how the reasoning and proofs can relatively easily be adapted to protocol variants. Using our formal and unambiguous specification, we find shortcomings of AODV that affect performance, e.g. the establishment of non-optimal routes, and some routes not being found at all. We formalise improvements in the same process algebra; carrying over the proofs is again easy.
Trust and Cluster Based Attribute Based Double Encryption Authentication Mechanism in MANET

Mobile adhoc network (MANET) is a wireless communication network with collection of nodes and have no specific structure and are able to determine the nearby node through lack of centralized control . due to this protecting data from multiple nodes becomes major important, existing work focus on key distribution and management scheme to make safety. In existing system propose a secure public key and public key based encryption and decryption algorithm such as RSA, AES to make more security in the MANET based system, it also uses an authentication service based trust model and a network model to prevent nodes from obtaining false public keys of the others while they present a malicious node in the networks. In all of the RSA ,DES based algorithm based on the public key when the user easily identified the private key by identification of the key values ,so still it becomes major issues in the protection of data from malicious nodes in the trust and cluster based model . To overcome these problems we proposed a double encryption and decryption based schema first the original user sends data was encrypted using algorithm like AES,DES and triple DES ,it generated public key are send to user personal mailed and then encrypted data again encrypted using system generated key after the encryption process completed then perform the decryption. Proposed system we realize a double attribute based encryption schema, what time only user oneto-many encryption method, and then user again encrypted the data after that double decryption where only users having the proper attributes and original system generated password only can decrypt their original message that are received from the network . For performing the decryption system generated based key are given and then the again it was decrypted using the key are generated from AES,DES ,triple DES . Similar to this proposed Double encryption based scheme performs and each and every time user selects someone of the algorithm AES ,DES and Tripe DES based on that performs the second encryption with system generated key only allowed to saw the login user or authentication user. This protection system uses some algorithms to charge data into unreadable text into two times which can be only being decoded or decrypted by party individuals possesses the associated key and their own system generated key also. Investigational results estimate the whole performance of the scheme with time; it indicates the proposed double encryption and decryption system providing more safety than the obtainable distributed authentication schema.
People Tracking with Anonymous and ID-Sensors Using Rao-Blackwellised Particle Filters

Estimating the location of people using a network of sensors placed throughout an environment is a fundamental challenge in smart environments and ubiquitous computing. Id-sensors such as infrared badges provide explicit object identity information but coarse location information while anonymous sensors such as laser range-finders provide accurate location information only. Tracking using both sensor types simultaneously is an open research challenge. We present a novel approach to tracking multiple objects that combines the accuracy benefits of anonymous sensors and the identification certainty of id-sensors. Rao-Blackwellised particle filters are used to estimate object locations. Each particle represents the association history between Kalman filtered object tracks and observations. After using only anonymous sensors until id estimates are certain enough, id assignments are sampled as well resulting in a fully Rao-Blackwellised particle filter over both object tracks and id assignments. Our approach was implemented and tested successfully using data collected in an indoor environment.
Deterministic Memory Abstraction and Supporting Cache Architecture for Real-Time Systems

Poor timing predictability of multicore processors has been a long-standing challenge in the real-time systems community. In this paper, we make a case that a fundamental problem that prevents efficient and predictable real-time com- puting on multicore is the lack of a proper memory abstraction to express memory criticality, which cuts across various layers of the system: the application, OS, and hardware. We therefore propose a new holistic resource management approach driven by a new memory abstraction, which we call Deterministic Memory. The key characteristic of deterministic memory is that the platform - the OS and hardware - guarantees small and tightly bounded worst-case memory access timing. In contrast, we call the conventional memory abstraction as best-effort memory in which only highly pessimistic worst-case bounds can be achieved. We present how the two memory abstractions can be realized with small extensions to existing OS and hardware architecture. In particular, we show the potential benefits of our approach in the context of shared cache management, by presenting a deterministic memory-aware cache architecture and its manage- ment scheme. We evaluate the effectiveness of the deterministic memory-aware cache management approach compared with a conventional way-based cache partitioning approach, using a set of synthetic and real benchmarks. The results show that our approach achieves (i) the same degree of temporal determinism of traditional way-based cache partitioning for deterministic memory, (ii) while freeing up to 49% of additional cache space, on average, for best-effort memory, and consequently improving the cache hit rate by 39%, on average, for non-real-time workloads. We also discuss how the deterministic memory abstraction can be leveraged in other parts of the memory hierarchy, particularly in the memory controller.
Hybrid parallel computing architecture for multiview phase shifting

Abstract. The multiview phase-shifting method shows its powerful capability in achieving high resolution three-dimensional (3-D) shape measurement. Unfortunately, this ability results in very high computation costs and 3-D computations have to be processed offline. To realize real-time 3-D shape measurement, a hybrid parallel computing architecture is proposed for multiview phase shifting. In this architecture, the central processing unit can co-operate with the graphic processing unit (GPU) to achieve hybrid parallel computing. The high computation cost procedures, including lens distortion rectification, phase computation, correspondence, and 3-D reconstruction, are implemented in GPU, and a three-layer kernel function model is designed to simultaneously realize coarse-grained and fine-grained paralleling computing. Experimental results verify that the developed system can perform 50 fps (frame per second) real-time 3-D measurement with 260 K 3-D points per frame. A speedup of up to 180 times is obtained for the performance of the proposed technique using a NVIDIA GT560Ti graphics card rather than a sequential C in a 3.4 GHZ Inter Core i7 3770.
Automatically detecting authors' native language

Abstract : When non-native speakers learn English, their first language influences how they learn. This is known as L1-L2 language transfer, and linguistic studies have shown that these language transfers can affect writing as well. If there were a model that exploits L1-L2 language transfer to identify the authors' native language, it would be an invaluable tool for the intelligence community as well as in the field of education. Therefore, the objective of this research is to find out if it is possible to automatically detect the author's native language based on his/her writing in English using traditional machine learning techniques. For this research, we used eight different collections of writings by speakers of eight different nationalities: native English speakers as well as speakers of Bulgarian, Chinese, Czech, French, Japanese, Russian, and Spanish. Among the various feature sets used in this research, character trigrams and bag of words alone achieved higher than 80% accuracy, and the empirical analysis of character trigrams revealed that the character trigrams just model lexical usage. When content words were extracted, the performance dropped and the results revealed that the topic words were doing all the work.
Using probabilistic techniques to aid in password cracking attacks

At its heart, a password cracking attack is just a guessing attack. An attacker makes guesses about a user's password until they guess correctly or they give up. While the defender may limit the number of guesses an attacker is allowed, a password's strength often depends on how hard it is for an attacker to model and reproduce the way a user created their password. If humans were effective at practicing unique habits, or generating and remembering random values, cracking passwords would be a near impossible task. In reality, that isn't true. A vast majority of people still follow common patterns, from capitalizing the first letter of their password to putting numbers at the end. What is changing though are the protective techniques being employed that are independent of user behavior. Practices such as salting password hashes negate the ability to pre-compute attacks. Likewise, password hashes are becoming more computationally complex, raising the costs for each guess an attacker makes. While before an attacker could rely on simple brute force methods and ad-hoc models, there is a growing demand for more effective ways to predict what a user's password will be. The need for this is especially strong in the law enforcement community, where tough encryption is encountered regularly. It is also important for the defender to be able to accurately model the security that user generated passwords provide. This paper details several new ways that probability information can be applied to maximize the success of password cracking attacks. From evaluating the effectiveness of known probabilistic techniques such as Markov models, to designing novel techniques such as using probabilistic context free grammars to create password guesses, there are many different ways probability information can be incorporated into modeling user behavior. Furthermore, the techniques described in this paper have been developed using real life passwords and have been tested in actual controlled password cracking attacks. This focus on training and testing against large sets of real life passwords is fairly unique, and only possible due to the increasing availability of disclosed password lists. In addition to allowing the development of more effective attacks, knowledge of how people select passwords can then be applied to evaluating the effectiveness of password creation policies. For example, how much stronger is an eight character password compared to a seven character password? In short, a better understanding of how users create passwords can benefit both the attacker and the defender.
Speech Reconstitution using Multi-view Silent Videos

Speechreading broadly involves looking, perceiving, and interpreting spoken symbols. It has a wide range of multimedia applications such as in surveillance, Internet telephony, and as an aid to a person with hearing impairments. However, most of the work in speechreading has been limited to text generation from silent videos. Recently, research has ventured into generating (audio) speech from silent video sequences but there have been no developments in using multiple cameras for speech generation. To this end, this paper presents the world's first ever multi-view speech reading and reconstruction system. This work encompasses the boundaries of multimedia research by putting forth a model which leverages silent video feeds from multiple cameras recording the same subject to generate intelligent speech for a speaker. Initial results confirm the usefulness of exploiting multiple views in building an efficient speech reading and reconstruction system. It further shows the optimal placement of cameras which would lead to the maximum intelligibility of speech. Next, it lays out various innovative applications for the proposed system focusing on its potential prodigious impact in not just security arena but in many other multimedia analytics problems.
Deeply Self-Supervising Edge-to-Contour Neural Network Applied to Liver Segmentation

Accurate segmentation of liver is still a challenging problem due to its large shape variability and unclear boundaries. The purpose of this paper is to propose a neural network based liver segmentation algorithm and evaluate its performance on abdominal CT images. First, we develop a fully convolutional network (FCN) for volumetric image segmentation problem. To guide a neural network to accurately delineate the target liver object, we apply self-supervising scheme with respect to edge and contour responses. The deeply supervising method is also applied to our low-level features for further combining discriminative features in the higher feature dimensions. We used 160 abdominal CT images for training and validation. Quantitative evaluation of our proposed network is presented with 8-fold cross-validation. The result showed that our method successfully segmented liver more accurately than any other state-of-the-art methods without expanding or deepening the neural network. The proposed approach can be easily extended to other imaging protocols (e.g., MRI) or other target organ segmentation problems without any modifications of the framework.
How Digital Library Services Contribute to Undergraduate Learning: An Evaluation of the “Understanding Library Impacts” Protocol

“Understanding Library Impacts” is a qualitative protocol designed to investigate the ways library services and resources contribute to undergraduate learning using interviews with librarians, faculty, and students. The Critical Incident Technique (CIT) is used in the student interviews to identify aspects of library services and resources that are critical to student success. The Revised Taxonomy of Educational Objectives is used to classify faculty goals for student learning and student interpretations of these goals. An alignment process using these data is proposed to connect library contributions to institutional goals for student learning. This paper reports on a pilot study conducted in 2005 at a liberal arts college to evaluate the protocol. Results of the pilot study suggest the protocol can detect library contributions to institutional goals for student learning. Future studies should be carried out to evaluate the protocol in other post-secondary settings. The protocol should be of benefit to libraries seeking to improve library services and to communicate library contributions to institutional goals to stakeholders.