So mechanical or routine: The not original in Feist

The United States Supreme Court case of 1991, Feist Publications, Inc. v. Rural Tel. Service Co., continues to be highly significant for property in data and databases, but remains poorly understood. The approach taken in this article contrasts with previous studies. It focuses upon the “not original” rather than the original. The delineation of the absence of a modicum of creativity in selection, coordination, and arrangement of data as a component of the not original forms a pivotal point in the Supreme Court decision. The author also aims at elucidation rather than critique, using close textual exegesis of the Supreme Court decision. The results of the exegesis are translated into a more formal logical form to enhance clarity and rigor.


The insufficiently creative is initially characterized as “so mechanical or routine.” Mechanical and routine are understood in their ordinary discourse senses, as a conjunction or as connected by AND, and as the central clause. Subsequent clauses amplify the senses of mechanical and routine without disturbing their conjunction.


The delineation of the absence of a modicum of creativity can be correlated with classic conceptions of computability. The insufficiently creative can then be understood as a routine selection, coordination, or arrangement produced by an automatic mechanical procedure or algorithm. An understanding of a modicum of creativity and of copyright law is also indicated.


The value of the exegesis and interpretation is identified as its final simplicity, clarity, comprehensiveness, and potential practical utility.

Millimeter-Wave Soldier-to-Soldier Communications for Covert Battlefield Operations

Mobile ad hoc networking of dismounted combat personnel is expected to play an important role in the future of network-centric operations. High-speed, short-range, soldier-to-soldier wireless communications will be required to relay information on situational awareness, tactical instructions, and covert surveillance related data during special operations reconnaissance and other missions. This article presents some of the work commissioned by the U. K. Ministry of Defence to assess the feasibility of using 60 GHz millimeter-wave smart antenna technology to provide covert communications capable of meeting these stringent networking needs. Recent advances in RF front-end technology, alongside physical layer transmission schemes that could be employed in millimeter-wave soldier-mounted radio, are discussed. The introduction of covert communications between soldiers will require the development of a bespoke directive medium access layer. A number of adjustments to the IEEE 802.11 distribution coordination function that will enable directional communications are suggested. The successful implementation of future smart antenna technologies and direction of arrival-based protocols will be highly dependent on thorough knowledge of transmission channel characteristics prior to deployment. A novel approach to simulating dynamic soldier-to-soldier signal propagation using state-of-the-art animation-based technology developed for computer game design is described, and important channel metrics such as root mean square angle and delay spread for a team of four networked infantry soldiers over a range of indoor and outdoor environments is reported.

Segmentation of optic disc in retinal images using an improved gradient vector flow algorithm

Image segmentation plays an important role in the analysis of retinal images as the extraction of the optic disk provides important cues for accurate diagnosis of various retinopathic diseases. In recent years, gradient vector flow (GVF) based algorithms have been used successfully to successfully segment a variety of medical imagery. However, due to the compromise of internal and external energy forces within the resulting partial differential equations, these methods can lead to less accurate segmentation results in certain cases. In this paper, we propose the use of a new mean shift-based GVF segmentation algorithm that drives the internal/external energies towards the correct direction. The proposed method incorporates a mean shift operation within the standard GVF cost function to arrive at a more accurate segmentation. Experimental results on a large dataset of retinal images demonstrate that the presented method optimally detects the border of the optic disc.

Performance Analysis of Novel Randomly Shifted Certification Authority Authentication Protocol for MANETs

The provision of security in mobile ad hoc networks is of paramount importance due to their wireless nature. However, when conducting research into security protocols for ad hoc networks it is necessary to consider these in the context of the overall system. For example, communicational delay associated with the underlying MAC layer needs to be taken into account. Nodes in mobile ad hoc networks must strictly obey the rules of the underlying MAC when transmitting security-related messages while still maintaining a certain quality of service. In this paper a novel authentication protocol, RASCAAL, is described and its performance is analysed by investigating both the communicational-related effects of the underlying IEEE 802.11 MAC and the computational-related effects of the cryptographic algorithms employed. To the best of the authors' knowledge, RASCAAL is the first authentication protocol which proposes the concept of dynamically formed short-lived random clusters with no prior knowledge of the cluster head. The performance analysis demonstrates that the communication losses outweigh the computation losses with respect to energy and delay. MAC-related communicational effects account for 99% of the total delay and total energy consumption incurred by the RASCAAL protocol. The results also show that a saving in communicational energy of up to 12.5% can be achieved by changing the status of the wireless nodes during the course of operation. Copyright (C) 2009 G. A. Safdar and M. P. O'Neill (nee McLoone).

Face Recognition in Global Harmonic Subspace

In this paper, a novel pattern recognition scheme, global harmonic subspace analysis (GHSA), is developed for face recognition. In the proposed scheme, global harmonic features are extracted at the semantic scale to capture the 2-D semantic spatial structures of a face image. Laplacian Eigenmap is applied to discriminate faces in their global harmonic subspace. Experimental results on the Yale and PIE face databases show that the proposed GHSA scheme achieves an improvement in face recognition accuracy when compared with conventional subspace approaches, and a further investigation shows that the proposed GHSA scheme has impressive robustness to noise.

A Low Complexity Video Watermarking in H.264 Compressed Domain

In this paper, a new blind and readable H.264 compressed domain watermarking scheme is proposed in which the embedding/extracting is performed using the syntactic elements of the compressed bit stream. As a result, it is not necessary to fully decode a compressed video stream both in the embedding and extracting processes. The method also presents an inexpensive spatiotemporal analysis that selects the appropriate submacroblocks for embedding, increasing watermark robustness while reducing its impact on visual quality. Meanwhile, the proposed method prevents bit-rate increase and restricts it within an acceptable limit by selecting appropriate quantized residuals for watermark insertion. Regarding watermarking demands such as imperceptibility, bit-rate control, and appropriate level of security, a priority matrix is defined which can be adjusted based on the application requirements. The resulted flexibility expands the usability of the proposed method.

SVD-Based Universal Spatial Domain Image Steganalysis

This paper is concerned with the universal (blind) image steganalysis problem and introduces a novel method to detect especially spatial domain steganographic methods. The proposed steganalyzer models linear dependencies of image rows/columns in local neighborhoods using singular value decomposition transform and employs content independency provided by a Wiener filtering process. Experimental results show that the novel method has superior performance when compared with its counterparts in terms of spatial domain steganography. Experiments also demonstrate the reasonable ability of the method to detect discrete cosine transform-based steganography as well as the perturbation quantization method.

A hybrid metaheuristic approach to the university course timetabling problem

This paper describes the development of a novel metaheuristic that combines an electromagnetic-like mechanism (EM) and the great deluge algorithm (GD) for the University course timetabling problem. This well-known timetabling problem assigns lectures to specific numbers of timeslots and rooms maximizing the overall quality of the timetable while taking various constraints into account. EM is a population-based stochastic global optimization algorithm that is based on the theory of physics, simulating attraction and repulsion of sample points in moving toward optimality. GD is a local search procedure that allows worse solutions to be accepted based on some given upper boundary or ‘level’. In this paper, the dynamic force calculated from the attraction-repulsion mechanism is used as a decreasing rate to update the ‘level’ within the search process. The proposed method has been applied to a range of benchmark university course timetabling test problems from the literature. Moreover, the viability of the method has been tested by comparing its results with other reported results from the literature, demonstrating that the method is able to produce improved solutions to those currently published. We believe this is due to the combination of both approaches and the ability of the resultant algorithm to converge all solutions at every search process.

Turbo-MIMO for wireless communications

This article reviews an important class of MIMO wireless communications, known collectively as turbo-MIMO systems. A distinctive property of turbo-MIMO wireless communication systems is that they can attain a channel capacity close to the Shannon limit and do so in a computationally manageable manner. The article focuses attention on a subclass of turbo-MIMO systems that use space-time coding based on bit-interleaved coded modulation. Different computationally manageable decoding (detection) strategies are briefly discussed. The article also includes computer experiments that are intended to improve the understanding of specific issues involved in the design of turbo-MIMO systems.

Turbo-BLAST: Performance evaluation in correlated Rayleigh-Fading environment

Recent theoretical investigations of spatially correlated multitransmit and multireceive (MTMR) links show that not only independently and identically distributed links, but also spatially correlated links can offer linear capacity growth with increasing number of transmit and receive antennas. In this paper, we explore the suitability of the turbo-BLAST architecture in correlated Rayleigh-fading MTMR environments. In particular, for an MTMR system with a large number of receive antennas, a near optimal performance can be achieved by the turbo-BLAST architecture in spatially and temporarily correlated Rayleigh-fading environments. The performance of turbo-BLAST, in terms of both bit-error rate and spectral efficiency, is analyzed empirically in indoors and correlated outdoor environments.

Analysis of information leakage from encrypted Skype conversations

Voice over IP (VoIP) has experienced a tremendous growth over the last few years and is now widely used among the population and for business purposes. The security of such VoIP systems is often assumed, creating a false sense of privacy. This paper investigates in detail the leakage of information from Skype, a widely used and protected VoIP application. Experiments have shown that isolated phonemes can be classified and given sentences identified. By using the dynamic time warping (DTW) algorithm, frequently used in speech processing, an accuracy of 60% can be reached. The results can be further improved by choosing specific training data and reach an accuracy of 83% under specific conditions. The initial results being speaker dependent, an approach involving the Kalman filter is proposed to extract the kernel of all training signals.

Adaptive Code Allocation for Interference Management on the Downlink of DS-CDMA Systems

A new technique based on adaptive code-to-user allocation for interference management on the downlink of BPSK based TDD DS-CDMA systems is presented. The principle of the proposed technique is to exploit the dependency of multiple access interference on the instantaneous symbol values of the active users. The objective is to adaptively allocate the available spreading sequences to users on a symbol-by-symbol basis to optimize the decision variables at the downlink receivers. The presented simulations show an overall system BER performance improvement of more than an order of a magnitude with the proposed technique while the adaptation overhead is kept less than 10% of the available bandwidth.

Incremental learning from stream data

Recent years have witnessed an incredibly increasing interest in the topic of incremental learning. Unlike conventional machine learning situations, data flow targeted by incremental learning becomes available continuously over time. Accordingly, it is desirable to be able to abandon the traditional assumption of the availability of representative training data during the training period to develop decision boundaries. Under scenarios of continuous data flow, the challenge is how to transform the vast amount of stream raw data into information and knowledge representation, and accumulate experience over time to support future decision-making process. In this paper, we propose a general adaptive incremental learning framework named ADAIN that is capable of learning from continuous raw data, accumulating experience over time, and using such knowledge to improve future learning and prediction performance. Detailed system level architecture and design strategies are presented in this paper. Simulation results over several real-world data sets are used to validate the effectiveness of this method.

A Capabilities-Aware Framework for Using Computational Accelerators in Data-Intensive Computing

Multicore computational accelerators such as GPUs are now commodity components for highperformance computing at scale. While such accelerators have been studied in some detail as stand-alone computational engines, their integration in large-scale distributed systems raises new challenges and trade-offs. In this paper, we present an exploration of resource management alternatives for building asymmetric accelerator-based distributed systems. We present these alternatives in the context of a capabilities-aware framework for data-intensive computing, which uses an enhanced implementation of the MapReduce programming model for accelerator-based clusters, compared to the state of the art. The framework can transparently utilize heterogeneous accelerators for deriving high performance with low programming effort. Our work is the first to compare heterogeneous types of accelerators, GPUs and a Cell processors, in the same environment and the first to explore the trade-offs between compute-efficient and control-efficient accelerators on data-intensive systems. Our investigation shows that our framework scales well with the number of different compute nodes. Furthermore, it runs simultaneously on two different types of accelerators, successfully adapts to the resource capabilities, and performs 26.9% better on average than a static execution approach.