OpenRaiser/Intern-Atlas huggingface dataset:
Link Quality Aware Channel Allocation for Multichannel Body Sensor Networks
Venue: arXiv cs.NI 2017
Year: 2017

Body Sensor Network (BSN) is a typical Internet-of-Things (IoT) application for personalized health care. It consists of economically powered, wireless and implanted medical monitoring sensor nodes, which are designed to continually collect the medical information of the target patients. Multichannel is often used in BSNs to reduce the spectrum competition of the tremendous sensor nodes and the problem of channel assignment has attracted much research attention. The health sensing data in BSNs is often required to be delivered to a sink node (or server) before a certain deadline for real time monitoring or health emergency alarm. Therefore, deadline is of significant importance for multichannel allocation and scheduling. The existing works, though designed to meet the deadline, often overlook the impact of the unreliable wireless links. As a result, the health sensing data can still be overdue because of the scheduled lossy links. Besides, potential collisions in the schedules also incur considerable delay in delivering the sensing data. In this paper, we propose a novel deadline- driven Link quality Aware Channel Assignment scheme (LACA), where link quality, deadlines and collisions are jointly considered. LACA prioritizes links with urgent deadlines and heavy collisions. Besides, LACA allows the exploition of the spare slots for retransmissions on lossy links, which can further reduce the retransmission delay. Extensive simulation experiments show that compared to the existing approaches, LACA can better utilize the wireless spectrum and achieve higher packet delivery ratio before the deadline.
Unity 2.0: Secure and Durable Personal Cloud Storage
Venue: arXiv cs.CR 2017
Year: 2017

While personal cloud storage services such as Dropbox, OneDrive, Google Drive and iCloud have become very popular in recent years, these services offer few security guarantees to users. These cloud services are aimed at end users, whose applications often assume a local file system storage, and thus require strongly consistent data. In addition, users usually access these services using personal computers and portable devices such as phones and tablets, which are upload bandwidth constrained and in many cases battery powered. Unity is a system that provides confidentiality, integrity, durability and strong consistency while minimizing the upload bandwidth of its clients. We find that Unity consumes minimal upload bandwidth for compute-heavy workload compared to NFS and Dropbox, while uses similar amount of upload bandwidth for write-heavy workload relative to NBD. Although read-heavy workload tends to consume more upload bandwidth with Unity, it is no more than an eighth of the size of blocks replicated and there is much room for optimization. Moreover, Unity provides flexibility to maintain multiple DEs to provide scalability for multiple devices to concurrently access the data with the minimal lease switch cost.
Fine-Grained Prediction of Syntactic Typology: Discovering Latent Structure with Supervised Learning
Venue: arXiv cs.CL 2017
Year: 2017

We show how to predict the basic word-order facts of a novel language given only a corpus of part-of-speech (POS) sequences. We predict how often direct objects follow their verbs, how often adjectives follow their nouns, and in general the directionalities of all dependency relations. Such typological properties could be helpful in grammar induction. While such a problem is usually regarded as unsupervised learning, our innovation is to treat it as supervised learning, using a large collection of realistic synthetic languages as training data. The supervised learner must identify surface features of a language's POS sequence (hand-engineered or neural features) that correlate with the language's deeper structure (latent trees). In the experiment, we show: 1) Given a small set of real languages, it helps to add many synthetic languages to the training data. 2) Our system is robust even when the POS sequences include noise. 3) Our system on this task outperforms a grammar induction baseline by a large margin.
Error Rates Analysis of MIMO Space-Time Block Codes in Generalized Shadowed Fading Channels
Venue: arXiv cs.IT 2017
Year: 2017

This paper introduces a new and unified bit error rates performance analysis of space-time block codes (STBC) deployed in wireless systems with spatial diversity in generalized shadowed fading and noise scenarios. Specifically, we derive a simple and a very accurate approximate expressions for the average error rates of coherent modulation schemes in generalized $η-μ$ and $κ-μ$ shadowed fading channels with multiple input multiple output (MIMO) systems. The noise in the network is assumed to be modeled using the additive white generalized Gaussian noise (AWGGN), which encompasses the classical Laplacian and the Gaussian noise environments as special cases. The derived results obviate the need to re-derive the error rates for MIMO STBC systems under many multipath fading and noise conditions while avoiding any special functions with high computational complexity. Published results from the literature, as well as numerical evaluations, corroborate the accuracy of our derived generalized expressions.
Understanding Organizational Approach towards End User Privacy
Venue: arXiv cs.CR 2017
Year: 2017

End user privacy is a critical concern for all organizations that collect, process and store user data as a part of their business. Privacy concerned users, regulatory bodies and privacy experts continuously demand organizations provide users with privacy protection. Current research lacks an understanding of organizational characteristics that affect an organization's motivation towards user privacy. This has resulted in a "one solution fits all" approach, which is incapable of providing sustainable solutions for organizational issues related to user privacy. In this work, we have empirically investigated 40 diverse organizations on their motivations and approaches towards user privacy. Resources such as newspaper articles, privacy policies and internal privacy reports that display information about organizational motivations and approaches towards user privacy were used in the study. We could observe organizations to have two primary motivations to provide end users with privacy as voluntary driven inherent motivation, and risk driven compliance motivation. Building up on these findings we developed a taxonomy of organizational privacy approaches and further explored the taxonomy through limited exclusive interviews. With his work, we encourage authorities and scholars to understand organizational characteristics that define an organization's approach towards privacy, in order to effectively communicate regulations that enforce and encourage organizations to consider privacy within their business practices.
Temporal and Physical Reasoning for Perception-Based Robotic Manipulation
Venue: arXiv cs.RO 2017
Year: 2017

Accurate knowledge of object poses is crucial to successful robotic manipulation tasks, and yet most current approaches only work in laboratory settings. Noisy sensors and cluttered scenes interfere with accurate pose recognition, which is problematic especially when performing complex tasks involving object interactions. This is because most pose estimation algorithms focus only on estimating objects from a single frame, which means they lack continuity between frames. Further, they often do not consider resulting physical properties of the predicted scene such as intersecting objects or objects in unstable positions. In this work, we enhance the accuracy and stability of estimated poses for a whole scene by enforcing these physical constraints over time through the integration of a physics simulation. This allows us to accurately determine relationships between objects for a construction task. Scene parsing performance was evaluated on both simulated and real- world data. We apply our method to a real-world block stacking task, where the robot must build a tall tower of colored blocks.
Exploring Cross-Domain Data Dependencies for Smart Homes to Improve Energy Efficiency
Venue: arXiv cs.CY 2017
Year: 2017

Over the past decade, the idea of smart homes has been conceived as a potential solution to counter energy crises or to at least mitigate its intensive destructive consequences in the residential building sector.
Science and its significant other: Representing the humanities in bibliometric scholarship
Venue: arXiv cs.DL 2017
Year: 2017

Bibliometrics offers a particular representation of science. Through bibliometric methods a bibliometrician will always highlight particular elements of publications, and through these elements operationalize particular representations of science, while obscuring other possible representations from view. Understanding bibliometrics as representation implies that a bibliometric analysis is always performative: a bibliometric analysis brings a particular representation of science into being that potentially influences the science system itself. In this review we analyze the ways the humanities have been represented throughout the history of bibliometrics, often in comparison to other scientific domains or to a general notion of the sciences. Our review discusses bibliometric scholarship between 1965 and 2016 that studies the humanities empirically. We distinguish between two periods of bibliometric scholarship. The first period, between 1965 and 1989, is characterized by a sociological theoretical framework, the development and use of the Price index, and small samples of journal publications as data sources. The second period, from the mid-1980s up until the present day, is characterized by a new hinterland, that of science policy and research evaluation, in which bibliometric methods become embedded.
Marine Wireless Big Data: Efficient Transmission, Related Applications, and Challenges
Venue: arXiv cs.IT 2017
Year: 2017

The vast volume of marine wireless sampling data and its continuously explosive growth herald the coming of the era of marine wireless big data. Two challenges imposed by these data are how to fast, reliably, and sustainably deliver them in extremely hostile marine environments and how to apply them after collection. In this article, we first propose an architecture of heterogeneous marine networks that flexibly exploits the existing underwater wireless techniques as a potential solution for fast data transmission. We then investigate the possibilities of and develop the schemes for energy-efficient and reliable undersea transmission without or slightly with data rate reduction. After discussing the data transmission, we summarize the possible applications of the collected big data and particularly focus on the problems of applying these data in sea-surface object detection and marine object recognition. Open issues and challenges that need to be further explored regarding transmission and detection/recognition are also discussed in the article.
Porcellio scaber algorithm (PSA) for solving constrained optimization problems
Venue: arXiv cs.NE 2017
Year: 2017

In this paper, we extend a bio-inspired algorithm called the porcellio scaber algorithm (PSA) to solve constrained optimization problems, including a constrained mixed discrete-continuous nonlinear optimization problem. Our extensive experiment results based on benchmark optimization problems show that the PSA has a better performance than many existing methods or algorithms. The results indicate that the PSA is a promising algorithm for constrained optimization.
Finding Euler Tours in One Pass in the W-Streaming Model with O(n log(n)) RAM
Venue: arXiv cs.DS 2017
Year: 2017

We study the problem of finding an Euler tour in an undirected graph G in the W-Streaming model with O(n polylog(n)) RAM, where n resp. m is the number of nodes resp. edges of G. Our main result is the first one pass W-Streaming algorithm computing an Euler tour of G in the form of an edge successor function with only O(n log(n)) RAM which is optimal for this setting (e.g., Sun and Woodruff (2015)). The previously best-known result in this model is implicitly given by Demetrescu et al. (2010) with the parallel algorithm of Atallah and Vishkin (1984) using O(m/n) passes under the same RAM limitation. For graphs with ω(n) edges this is non-constant. Our overall approach is to partition the edges into edge-disjoint cycles and to merge the cycles until a single Euler tour is achieved. Note that in the W-Streaming model such a merging is far from being obvious as the limited RAM allows the processing of only a constant number of cycles at once. This enforces us to merge cycles that partially are no longer present in RAM. Furthermore, the successor of an edge cannot be changed after the edge has left RAM. So, we steadily have to output edges and their designated successors, not knowing the appearance of edges and cycles yet to come. We solve this problem with a special edge swapping technique, for which two certain edges per node are sufficient to merge tours without having all of their edges in RAM. Mathematically, this is controlled by structural results on the space of certain equivalence classes corresponding to cycles and the characterization of associated successor functions. For example, we give conditions under which the swapping of edge successors leads to a merging of equivalence classes.
Proposal of a Carsharing System to Improve Urban Mobility
Venue: arXiv cs.CY 2017
Year: 2017

Carsharing is a model of renting vehicles for short periods of time, where the payment is made according to the time and distance effectively traveled. Carsharing offers a simple, economical and smart alternative to urban mobility, that is already being adopted in the major cities in the world. The proposed methodology consisted in the development of a decision support system that simplifies the process of choosing carsharing services. Adopting the AHP method, the user can indicate their preferences in the choice of vehicles, and the system returns an ordered list of the most suitable available vehicles based on their geographic location. The findings of the project indicate that the use of this system encourage and simplify the use of carsharing services, which will allow to enhance the financial, mobility and environment advantages inherent to their use.
Towards In-Transit Analytics for Industry 4.0
Venue: arXiv cs.CY 2017
Year: 2017

Industry 4.0, or Digital Manufacturing, is a vision of inter-connected services to facilitate innovation in the manufacturing sector. A fundamental requirement of innovation is the ability to be able to visualise manufacturing data, in order to discover new insight for increased competitive advantage. This article describes the enabling technologies that facilitate In-Transit Analytics, which is a necessary precursor for Industrial Internet of Things (IIoT) visualisation.
Identifying Student Communities in Blended Courses
Venue: arXiv cs.CY 2017
Year: 2017

Blended courses have become the norm in post-secondary education. Universities use large-scale learning management systems to manage class content. Instructors deliver readings, lectures, and office hours online; students use intelligent tutors, web forums, and online submission systems; and classes communicate via web forums. These online tools allow students to form new social networks or bring social relationships online. They also allow us to collect data on students' social relationships. In this paper we report on our research on community formation in blended courses based on online forum interactions. We found that it was possible to group students into communities using standard community detection algorithms via their posts and reply structure and that the students' grades are significantly correlated with their closest peers.
A Simple Nonlinearity-Tailored Probabilistic Shaping Distribution for Square QAM
Venue: arXiv cs.IT 2017
Year: 2017

A new probabilistic shaping distribution that outperforms Maxwell-Boltzmann is studied for the nonlinear fiber channel. Additional gains of 0.1 bit/symbol MI or 0.2 dB SNR for both DP-256QAM and DP-1024QAM are reported after 200 km nonlinear fiber transmission.
GUIDES - Geospatial Urban Infrastructure Data Engineering Solutions
Venue: arXiv cs.CY 2017
Year: 2017

As the underground infrastructure systems of cities age, maintenance and repair become an increasing concern. Cities face difficulties in planning maintenance, predicting and responding to infrastructure related issues, and in realizing their vision to be a smart city due to their incomplete understanding of the existing state of the infrastructure. Only few cities have accurate and complete digital information on their underground infrastructure (e.g., electricity, water, natural gas) systems, which poses problems to those planning and performing construction projects. To address these issues, we introduce GUIDES as a new data conversion and management framework for urban underground infrastructure systems that enable city administrators, workers, and contractors along with the general public and other users to query digitized and integrated data to make smarter decisions. This demo paper presents the GUIDES architecture and describes two of its central components: (i) mapping of underground infrastructure systems, and (ii) integration of heterogeneous geospatial data.
Approaches to Modeling the Impact of Cyber Attacks on a Mission
Venue: arXiv cs.CR 2017
Year: 2017

The success of a business mission is highly dependent on the Communications and Information Systems (CIS) that support the mission. Mission Impact Assessment (MIA) seeks to assist the integration of business or military operations with cyber defense, particularly in bridging the cognitive gap between operational decision-makers and cyber defenders. Recent years have seen a growing interest in model-driven approaches to MIA. Such approaches involve construction and simulation of models of the mission, systems, and attack scenarios in order to understand an attack's impact, including its nature, dependencies involved, and the extent of consequences. This paper discusses representative examples of recent research on model-driven approach to MIA, highlights its potential value and cautions about serious remaining challenges.
On Data-Driven Saak Transform
Venue: arXiv cs.CV 2017
Year: 2017

Being motivated by the multilayer RECOS (REctified-COrrelations on a Sphere) transform, we develop a data-driven Saak (Subspace approximation with augmented kernels) transform in this work. The Saak transform consists of three steps: 1) building the optimal linear subspace approximation with orthonormal bases using the second-order statistics of input vectors, 2) augmenting each transform kernel with its negative, 3) applying the rectified linear unit (ReLU) to the transform output. The Karhunen-Loéve transform (KLT) is used in the first step. The integration of Steps 2 and 3 is powerful since they resolve the sign confusion problem, remove the rectification loss and allow a straightforward implementation of the inverse Saak transform at the same time. Multiple Saak transforms are cascaded to transform images of a larger size. All Saak transform kernels are derived from the second-order statistics of input random vectors in a one-pass feedforward manner. Neither data labels nor backpropagation is used in kernel determination. Multi-stage Saak transforms offer a family of joint spatial-spectral representations between two extremes; namely, the full spatial-domain representation and the full spectral-domain representation. We select Saak coefficients of higher discriminant power to form a feature vector for pattern recognition, and use the MNIST dataset classification problem as an illustrative example.
Physical-Layer Network Coding with Multiple Antennas: An Enabling Technology for Smart Cities
Venue: arXiv cs.IT 2017
Year: 2017

Efficient heterogeneous communication technologies are critical components to provide flawless connectivity in smart cities. The proliferation of wireless technologies, services and communication devices has created the need for green and spectrally efficient communication technologies. Physical- layer network coding (PNC) is now well-known as a potential candidate for delay-sensitive and spectrally efficient communication applications, especially in bidirectional relaying, and is therefore well-suited for smart city applications. In this paper, we provide a brief introduction to PNC and the associated distance shortening phenomenon which occurs at the relay. We discuss the issues with existing schemes that mitigate the deleterious effect of distance shortening, and we propose simple and effective solutions based on the use of multiple antenna systems. Simulation results confirm that full diversity order can be achieved in a PNC system by using antenna selection schemes based on the Euclidean distance metric.
NeuroTrainer: An Intelligent Memory Module for Deep Learning Training
Venue: arXiv cs.AR 2017
Year: 2017

This paper presents, NeuroTrainer, an intelligent memory module with in-memory accelerators that forms the building block of a scalable architecture for energy efficient training for deep neural networks. The proposed architecture is based on integration of a homogeneous computing substrate composed of multiple processing engines in the logic layer of a 3D memory module. NeuroTrainer utilizes a programmable data flow based execution model to optimize memory mapping and data re-use during different phases of training operation. A programming model and supporting architecture utilizes the flexible data flow to efficiently accelerate training of various types of DNNs. The cycle level simulation and synthesized design in 15nm FinFET showspower efficiency of 500 GFLOPS/W, and almost similar throughput for a wide range of DNNs including convolutional, recurrent, multi-layer-perceptron, and mixed (CNN+RNN) networks
Characterization of Model-Based Detectors for CPS Sensor Faults/Attacks
Venue: arXiv cs.SY 2017
Year: 2017

A vector-valued model-based cumulative sum (CUSUM) procedure is proposed for identifying faulty/falsified sensor measurements. First, given the system dynamics, we derive tools for tuning the CUSUM procedure in the fault/attack free case to fulfill a desired detection performance (in terms of false alarm rate). We use the widely-used chi-squared fault/attack detection procedure as a benchmark to compare the performance of the CUSUM. In particular, we characterize the state degradation that a class of attacks can induce to the system while enforcing that the detectors (CUSUM and chi-squared) do not raise alarms. In doing so, we find the upper bound of state degradation that is possible by an undetected attacker. We quantify the advantage of using a dynamic detector (CUSUM), which leverages the history of the state, over a static detector (chi-squared) which uses a single measurement at a time. Simulations of a chemical reactor with heat exchanger are presented to illustrate the performance of our tools.