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Contribution: This article features a systematic literature review with the objective of presenting a study that reflects the current scenario of research on the cognitive load theory (CLT) in the domain of teaching and learning computer programming. Background: Computer programming is a highly cognitive skill, requiring mastering multiple competencies, and recognized as being difficult to learn, for this reason, the cognitive load (CL) in the learner’s working memory emerged as an influential concept, making CLT one of the most common theories in computing education research. Research Questions: What are the approaches that relate CLT to teaching and learning computer programming? What CLT-related concepts are covered? What evidence is reported with respect to this context? Methodology: Following a formal protocol, a survey was performed for papers linking CLT to teaching and learning programming published between 2010 and 2020. The selection of papers was based on a set of criteria established to drive the selection process, including alignment with the research questions and paper quality assessment. Findings: The approaches applied in the papers are based on measuring the CL; through instructional design based on the development or use of resources or tools, a range of different pedagogy strategies and the CLT concepts. With respect to the concepts, the subjective measurement technique and worked example effect are the most commonly deployed. As far as the evidence reported, the better part is related to the worked example effect and measuring CLs. [ABSTRACT FROM AUTHOR]

The article presents the author's views on why he prefers paper to computers while programming. He views that paper is noiseless, does not consumer electricity, does not crash, and does not catch viruses. He views that the resolution with which he can draw on paper is lower than that of his computer's screen. He views that the same paper can be used for an arbitrary number of fonts and symbols.

The article offers information on the planning of the course titled "TRACES-Themes Still Relevant At Current Software Engineering Studies." It is implied that the course's structure resembles that of a seminar which ventures on computer science. One of the aims of the course is inculcating the historical aspects of the discipline into a computer science or informatics curriculum. The course's design and structure are presented in details in the article. Topics of papers chosen for the course are also included in the article.

The read channel of a Flash memory cell degrades after repetitive program and erase (P/E) operations. This degradation is often modeled as a function of the number of P/E cycles. In contrast, this paper models the degradation as a function of the cumulative effect of the charge written and erased from the cell. Based on this modeling approach, this paper dynamically allocates voltage using lower voltage write thresholds at the beginning of the device lifetime and increasing the thresholds as needed to maintain the mutual information of the read channel in the face of degradation. This paper introduces the technique in an idealized setting and then removes ideal assumptions about channel knowledge and available voltage resolution to conclude with a practical scheme with performance close to that of the idealized setting. [ABSTRACT FROM PUBLISHER]

In the last decade, machine-learning-based compilation has moved from an obscure research niche to a mainstream activity. In this paper, we describe the relationship between machine learning and compiler optimization and introduce the main concepts of features, models, training, and deployment. We then provide a comprehensive survey and provide a road map for the wide variety of different research areas. We conclude with a discussion on open issues in the area and potential research directions. This paper provides both an accessible introduction to the fast moving area of machine-learning-based compilation and a detailed bibliography of its main achievements. [ABSTRACT FROM AUTHOR]

Programming by demonstration (PBD) transforms the robot programming from the code level to automated interface between robot and human, promoting the flexibility of robotized automation. In this paper, we focus on programming the industrial robot for assembly tasks by parsing the human demonstration into a series of assembly skills and compiling the skill to the robot executables. To achieve this goal, an identification system using multimodal information to recognize the assembly skill, called MASD, is proposed including: 1) an initial learning stage using a hierarchical model to recognize the action by considering the features from action-object effect, gesture, and trajectory and 2) a retrospective thinking stage using a segmentation method to cut the continuous demonstrations into multiple assembly skills optimally. Using MASD, the demonstration of assembly tasks can be explained with high accuracy in real time, driving a hypothesis that a PBD system on the top of MASD can be extended to more realistic assembly tasks beyond pure positional moving and picking. In experiments, the skill identification module is used to recognize the five kinds of assembly skills in demonstrations of both single and multiple assembly skills, and outperforms the comparative action identification methods. Besides integrated with the MASD, the PBD system can generate the program based on the demonstration and successfully enable an ABB industrial robotic arm simulator to assemble a flashlight and a switch, verifying the initial hypothesis. Note to Practitioners—In the conventional robotized automation, the key role of the robot mainly owes to its capacity for repeating a wide variety of tasks with high speed and accuracy in long term, with a cost of days to months of programming for deployment. On the other hand, the new trend of customization brings the new characteristics: production in short cycle and small volume. This irreversible momentum urges the robot to switch from task to task efficiently. The biggest bottleneck here is the tedious programming, which also has high prerequisites for most practitioners in manufacturing. This situation motivates the development of a PBD system that can understand the assembly skills performed by the human experts in the demonstration and accordingly generate the program for robot’s execution of the taught task. In this paper, we present a skill decoding system to parse the observational raw demonstration into symbolic sequences, which is the crucial bridge to enable the automatic programming. The system achieves high performance in recognition and is tailored for the PBD in assembly tasks by considering both advantages and disadvantages in the background of assembly, such as controllable environment and limited computational resources. It is particularly useful for assembly tasks with modularized actions based on a set of standard parts. At the perspective of industrial application, the PBD upon the proposed system is a promising solution to improve the flexibility of manufacture, which is expected to be true in midterm but an important step toward this goal. [ABSTRACT FROM AUTHOR]

This paper presents a DC coefficient estimation algorithm for intra-predicted residual blocks in the High-Efficiency Video Coding (HEVC) standard. Discarding the DC coefficient directly in each transform block leads to substantial bit-saving, but at the same time produces strong discontinuities between neighboring blocks. To overcome this problem, we propose an estimation algorithm for the DC coefficient, which solves an optimal offset in a closed-form to recover the corresponding block edges. Then, we embed this algorithm into HEVC in its rate-distortion optimized quantization and sign bit hiding steps. Furthermore, a flag is signaled to decide whether the DC estimation strategy is used for each transform block. Test results under the common test condition show that our algorithm achieves 1.5% and 1.6% BD-rate reduction on average for luma and chroma, respectively, under all intra configuration. In the meantime, our simulation results show that both encoding time and decoding time increase only slightly (about 10%, without any special optimization on programming our proposed algorithm). When testing the proposed DC estimation algorithm on inter coding configurations, including low delay with P pictures, low delay with B pictures, and random access, we can also achieve 0.5%–1.1% bit-rate savings on average, while nearly no extra encoding and decoding time is needed. [ABSTRACT FROM AUTHOR]

Screen content coding plays an important role in many applications. Conventional reference picture management (RPM) strategies developed for natural content may not work well for screen content. This is because many regions in screen content remain static for a long time, causing a lot of repetitive contents to stay in the decoded picture buffer. The repetitive contents are not conducive to inter prediction, but still occupy valuable memory. This paper proposes a diversity-based RPM scheme for screen content coding. The concept of diversity is introduced for the reference picture set (RPS) to help formulate the RPM problem. By maximizing the diversity of RPS, more potentially better predictions are provided. Better compression performance can then be achieved. Meanwhile, the proposed scheme is nonnormative and compatible with existing video coding standards, such as High Efficiency Video Coding. The experimental results show that, for low delay screen content coding, the bit saving of the proposed scheme is 4.9% on average and up to 13.7%, without increasing encoding time. [ABSTRACT FROM AUTHOR]

The latest video coding standard, high efficiency video coding (HEVC), aims to achieve better coding efficiency than the H.264/AVC standard. To improve the coding performance of HEVC, tools and structures are included that also increase the computational complexity of encoding, especially for the mode decisions in the coding unit (CU), which is a structural element in the HEVC. For each CU size, the HEVC encoder performs mode decision and motion estimation using all prediction unit (PU) types and incurs massive computational burdens. In this paper, we investigate the depth correlation between the current and the collocated CU to avoid irrelevant CU procedure and truncate some PU predictions as a result. We also investigate the relationship of the rate-distortion (RD) costs after the Merge/SKIP prediction. By analyzing different CU sizes and quantization parameters, we build a mathematical model to represent the relationship for the RD costs after the Merge/SKIP prediction with an adaptive termination of the CU procedure. In addition, the relationship of the RD costs after the Merge/SKIP prediction and 2N \boldsymbol \times 2\textN mode is also explored. Search range reduction is used to further speed up our algorithm. Experimental results show that our algorithm can reduce the coding time by up to 80%, and provide average coding time savings of 46%, 45%, and 52%, for low delay B, low delay P, and random access configurations, respectively. Moreover, the proposed algorithm is able to maintain coding performance similar to HM 11.0. The proposed scheme outperforms previous work in terms of both the coding speed and the RD performance. [ABSTRACT FROM PUBLISHER]

The article presents information on the 20th anniversary of the IEEE International Conference on Software Maintenance (ICSM). Software maintenance and evolution continues to play a vital role in the development of software systems. For many years there has been an international effort among researchers and practitioners to control the costs associated with software maintenance and evolution and to improve the quality of the processes and products associated with this vital activity. Software maintenance encompasses human elements of software engineering, which require psychological, statistical, and empirical evaluation. It involves algorithms, methods, and techniques which require a deep understanding of all activities in the software development life cycle. Since its inception in 1983, ICSM has evolved into an international forum for researchers, practitioners, educators, technology transfer experts, project managers, tool developers, and users who explore current issues facing the computing community. In 2004, the ICSM program chairs, Mark Harman from King's College in London and Bogdan Korel from Illinois institute of Technology in Chicago, selected 38 papers for inclusion in the proceedings from 122 submissions.

Transform plays a crucial role in video coding, since it is in the transform domain that the bitrate reduction happens. Recently, coding with transform competition has emerged, especially inside the ITU-T and ISO/MPEG Joint Video Exploration Team, where the adaptive multiple transforms have been thoroughly tested. In this paper, it is proposed to investigate the design of transform competition systems for the purpose of inter-predicted residuals. Several systems are proposed and evaluated in terms of coding efficiency, storage requirement, and encoding complexity. A first approach selects the best transform for a residual block in a pre-established list of transforms. An extended version is also considered, where the number of transforms varies within an image area. Experimental results show that the proposed designs achieve significant bitrate savings over high efficiency video coding from −0.2% to −1.4%. [ABSTRACT FROM AUTHOR]

This special section presents expanded versions of three of the best papers from the 11th Annual ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA 2012), which was held in Lausanne, Switzerland, from 29-31 July 2012. SCA has established itself as the premier conference dedicated specifically to innovations in the software and technology of computer animation. SCA 2012 received 80 submissions and each submission was reviewed by at least three members of the international program committee. After a thorough online discussion, the 72-member international program committee decided on the 27 full papers and nine short presentation papers accepted for the final program. Out of 27 full papers, the symposium's Best Papers Award Committee selected one best paper, two runner-ups, and four honorable mentions. The selection was informed by the original reviews and the conference presentations. We are delighted to present three out of the six very best papers of SCA 2012 invited for this special section. Each of the invited papers contains a minimum of 30 percent new material and received at least three reviews, including one reviewer not among the original SCA reviewers. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM PUBLISHER]

Information and events of interest to Computer Society members. [ABSTRACT FROM PUBLISHER]

Recursion is one of the most difficult programming topics for students. In this paper, an instructional method is proposed to enhance students' understanding of recursion tracing. The proposal is based on the use of rules to translate linear recursion algorithms into equivalent, iterative ones. The paper has two main contributions: the instructional method itself, and its evaluation, which is based on previous works of other authors on mental models of recursion. As a result, an enhancement was measured in the viability of mental models exhibited by students (both for linear and multiple recursion), but no significant improvement was detected in their skills for designing recursive algorithms. Evidence was also obtained of the fact that many students with (relatively) viable mental models for linear recursion have unviable mental models for multiple recursion. Finally, it was noticed that many students adopt inaccurate mental models if those models are adequate to handle the given algorithm. [ABSTRACT FROM PUBLISHER]

This paper studies the problem of information symbol availability in codes: we refer to a systematic code as code with $(r, t)$ -availability if every information (systematic) symbol can be reconstructed from $t$ disjoint groups of other code symbols, each of the sizes at most $r$ . This paper shows that it is possible to construct codes that can support a scaling number of parallel reads while keeping the rate to be an arbitrarily high constant. It further shows that this is possible with the minimum Hamming distance arbitrarily close to the Singleton bound. This paper also presents a bound demonstrating a tradeoff between rate, minimum Hamming distance, and availability parameters. Our codes match the aforementioned bound, and their constructions rely on certain combinatorial structures. Resolvable designs provide one way to realize these required combinatorial structures. The two constructions presented in this paper require field sizes, which are linear and exponential in the code length, respectively. From a practical standpoint, our codes are relevant for distributed storage applications involving hot data, i.e., the information, which is frequently accessed by multiple processes in parallel. [ABSTRACT FROM AUTHOR]

This paper studies the problem of detecting the information source in a network in which the spread of information follows the popular Susceptible-Infected-Recovered (SIR) model. We assume all nodes in the network are in the susceptible state initially, except one single information source that is in the infected state. Susceptible nodes may then be infected by infected nodes, and infected nodes may recover and will not be infected again after recovery. Given a snapshot of the network, from which we know the graph topology and all infected nodes but cannot distinguish susceptible nodes and recovered nodes, the problem is to find the information source based on the snapshot and the network topology. We develop a sample-path-based approach where the estimator of the information source is chosen to be the root node associated with the sample path that most likely leads to the observed snapshot. We prove for infinite-trees, the estimator is a node that minimizes the maximum distance to the infected nodes. A reverse-infection algorithm is proposed to find such an estimator in general graphs. We prove that for g+1-regular trees such that gq>1, where g+1 is the node degree and q is the infection probability, the estimator is within a constant distance from the actual source with a high probability, independent of the number of infected nodes and the time the snapshot is taken. Our simulation results show that for tree networks, the estimator produced by the reverse-infection algorithm is closer to the actual source than the one identified by the closeness centrality heuristic. We then further evaluate the performance of the reverse infection algorithm on several real-world networks. [ABSTRACT FROM AUTHOR]

Quadtree-based partitioning (i.e., recursively dividing a picture into square blocks) is one of the most popular partitioning-based image coding schemes because of its computational simplicity and efficient representation of partitioning. However, the rate-distortion performance of quadtree-based partitioning reaches a limit because the dependence between child blocks of different parents is not exploited. In this paper, a new bottom-up-based block partitioning method called split-and-merge is proposed. This method splits an image into multiple square blocks and merges them into nonsquare blocks to exploit the dependence between split blocks. Moreover, a modification of the conventional intra prediction and transform is employed for nonsquare blocks. The experimental results indicate that the proposed method results in an average and maximum bit rate reduction of 3.1% and 8.3%, respectively, relative to High Efficiency Video Coding intra coding. [ABSTRACT FROM PUBLISHER]

Papers, books, and standards consider the problem of mutual interactions of ground systems (GSs) by experimental data, parametric curves, mathematical approaches, and computer programs. This paper aims to analyze the physical phenomenon and basics, exemplifying the interference between GSs, such as their comparability, common intersection, mutual transfer resistance, and coupling factors. These characteristics assist to suppose the systems behavior in interaction and to suggest practical criteria for verifying the adequacy of GSs also versus the external interferences among them or/and with buried extraneous conductive part. The presence of a “global grounding system” (GGS) in the influence zone of a GS is a relevant case of interference. For definition, the GGS is not interfering, but can be locally interfered with unexpected increases of touch and step voltages. The global performance of an interfered GS can be defined by the overlap of two opposite and simultaneous behaviors: the passive and active behaviors, useful to characterize a GGS. In fact, a GGS essentially presents a passive behavior. [ABSTRACT FROM AUTHOR]

The aim of this paper is to form the best collaborative networked organization of small manufacturers to fulfill a production plan. Manufacturing networks can be formed using two approaches. The conventional approach is the formation of networks by the grouping of the best manufacturers based on multiple criteria. The proposed approach is the formation of all feasible networks followed by the grouping of the best networks based on multiple criteria. The hypothesis of this paper is that the better group of manufacturing networks may not be the result of the selection of the best available manufacturers to form each network, based on the same criteria. The research methodology employed involves techniques such as interaction protocol development, ontology engineering, and principles from concepts such as the contract net protocol, the product-resource-order-staff-architecture, the auctioning process, agent-based modeling, and rule-based programming. Results from the proposed approach were compared to the conventional approach on multiple criteria namely lead time, quality, cost, and delivery reliability. On the basis of the results, the hypothesis was not rejected. [ABSTRACT FROM PUBLISHER]

The computationally demanding dynamic programming (DP) algorithm is frequently used in academic research to solve the energy management problem of a hybrid electric vehicle (HEV). This paper is exclusively focused on how the computational demand of such a computation can be reduced. The main idea is to use a local approximation of the gridded cost-to-go and derive an analytic solution for the optimal torque split decision at each point in the time and state grid. Thereby, it is not necessary to quantize the torque split and identify the optimal decision by interpolating in the cost-to-go. Two different approximations of the cost-to-go are considered in this paper: 1) a local linear approximation and 2) a quadratic spline approximation. The results indicate that computation time can be reduced by orders of magnitude with only a slight degradation in simulated fuel economy. Furthermore, with a spline approximated cost-to-go, it is also possible to significantly reduce the memory storage requirements. A parallel plug-in HEV is considered in this paper, but the method is also applicable to an HEV. [ABSTRACT FROM PUBLISHER]

When learning from high-dimensional data for symbolic regression (SR), genetic programming (GP) typically could not generalize well. Feature selection, as a data preprocessing method, can potentially contribute not only to improving the efficiency of learning algorithms but also to enhancing the generalization ability. However, in GP for high-dimensional SR, feature selection before learning is seldom considered. In this paper, we propose a new feature selection method based on permutation to select features for high-dimensional SR using GP. A set of experiments has been conducted to investigate the performance of the proposed method on the generalization of GP for high-dimensional SR. The regression results confirm the superior performance of the proposed method over the other examined feature selection methods. Further analysis indicates that the models evolved by the proposed method are more likely to contain only the truly relevant features and have better interpretability. [ABSTRACT FROM AUTHOR]

Any sequence of uniformly bounded N\times N Hermitian Toeplitz matrices \{H_{N}\} is asymptotically equivalent to a certain sequence of N\times N circulant matrices \CN\ derived from the Toeplitz matrices in the sense that \left \|{HN- CN }\right \|F = o(\sqrt {N}) as $N\rightarrow \infty $ . This implies that certain collective behaviors of the eigenvalues of each Toeplitz matrix are reflected in those of the corresponding circulant matrix and supports the utilization of the computationally efficient fast Fourier transform (instead of the Karhunen-Loève transform) in applications like coding and filtering. In this paper, we study the asymptotic performance of the individual eigenvalue estimates. We show that the asymptotic equivalence of the circulant and Toeplitz matrices implies the individual asymptotic convergence of the eigenvalues for certain types of Toeplitz matrices. We also show that these estimates asymptotically approximate the largest and smallest eigenvalues for more general classes of Toeplitz matrices. [ABSTRACT FROM AUTHOR]

Computer science (CS) is becoming an increasingly diverse domain. This paper reports on an initiative designed to introduce underrepresented populations to computing using an eclectic, multifaceted approach. As part of a yearlong computing course, students engage in Maker activities, participatory simulations, and computing projects that foreground the social and collaborative aspects of CS. Collectively, these activities are designed to introduce learners to the growing diversity of what CS looks like in the 21st century. This paper lays out the practical and theoretical motivations for the Computational Thinking for Girls (CT4G) project, specifically highlighting the use of Making through physical and social computing as ways to engage students in CS. A snapshot of one activity from the program is provided—Wearing the Web—in which students use open-hardware programmable badges to explore the underlying structure and technology that enables the Internet. Data from the first year of the CT4G program are presented to show the positive effects that this diverse introduction to CS is having on the students with respect to their attitudes toward CS. [ABSTRACT FROM PUBLISHER]

In cooperative cognitive radio networks (CRNs), multiple secondary base stations are managed by a central unit (CU) via finite-capacity backhaul links, which performs joint encoding and precoding of the messages intended for the secondary users (SUs). In this paper, the limitation of backhaul links is handled with the implementation of compression strategies, by which precoding is followed at the CU. In particular, focusing on underlay cooperative CRNs, we propose the joint optimal design of precoding and backhaul quantization with the aim of maximizing the sum throughput of the secondary network under limited backhaul capacity, transmit power, and interference power constraints. Furthermore, both perfect and imperfect channel state information (CSI) available at the CU are considered. In the imperfect-CSI case, the uncertainty of the channels is explicitly accounted for via a robust or worst-case optimization formulation. The proposed algorithmic solutions leverage rank relaxation and majorization-minimization programming, whose performances are evaluated via numerical results. [ABSTRACT FROM PUBLISHER]

The High Efficiency Video Coding (HEVC/H.265) standard achieves great improvement in compression efficiency over the widely used H.264/AVC standard at a cost of much higher complexity. When encoding videos using HEVC, the selection of the quantization parameter (QP) can significantly affect the coding efficiency. Typical algorithms for adaptive quantization employ fixed bitrate budgeting or fixed QP offsets for different frames and different blocks, without considering detailed input video characteristics, some of which might be captured using computer vision methods. The problem of determining adaptive settings of HEVC coding parameters has not been satisfactorily solved. In this paper, we proposed a Hadamard (“HAD”) energy-based optimized HEVC video encoder, in which HAD energy is used to measure the amount of residual information to be encoded in a block so as to determine the QP value for each block for a better coding efficiency. HAD energy is also used to expedite the time-consuming mode decision process and for a precise scene change detection to avoid flicker artifacts. Experiment using the widely used open source HEVC encoder $\times$ 265-v1.8 showed that the proposed algorithm was able to achieve an average of 14% saving in Bjøntegaard-delta-rate and an average of 20% saving in encoding time as compared the “medium” preset of $\times$ 265, while the proposed algorithm also produced an improvement of 3.3% in coding efficiency for the HEVC reference software HM-16.6. [ABSTRACT FROM AUTHOR]

Compute-and-forward (CF) harnesses interference in wireless communications by exploiting structured coding. The key idea of CF is to compute integer combinations of code words from multiple source nodes, rather than to decode individual code words by treating others as noise. Compute-compress-and-forward (CCF) can further enhance the network performance by introducing compression operations at receivers. In this paper, we develop a more general compression framework, termed generalized CCF (GCCF), where the compression function involves the selection of message segments over finite fields. We show that GCCF achieves a broader compression rate region than CCF. We also compare our compression rate region with the fundamental Slepian–Wolf (SW) region. We show that GCCF is optimal in the sense of achieving the minimum total compression rate. We also establish the criteria under which GCCF achieves the SW region. In addition, we consider a two-hop relay network employing the GCCF scheme. We formulate a sum-rate maximization problem and develop an approximate algorithm to solve the problem. Numerical results are presented to demonstrate the performance superiority of GCCF over CCF and other schemes. [ABSTRACT FROM AUTHOR]

In this paper, we consider a channel model that is often used to describe mobile wireless scenarios: multiple-antenna additive white Gaussian noise channels subject to random (fading) gains with full channel state information at the receiver. The dynamics of the fading process are approximated by a piecewise-constant process (frequency non-selective isotropic block fading). This paper addresses the finite blocklength fundamental limits of this channel model. Specifically, we give a formula for the channel dispersion—a quantity governing the delay required to achieve capacity. The multiplicative nature of the fading disturbance leads to a number of interesting technical difficulties that required us to enhance traditional methods for finding the channel dispersion. Alas, one difficulty remains: the converse (impossibility) part of our result holds under an extra constraint on the growth of the peak-power with blocklength. Our results demonstrate, for example, that while the capacities of $n_{t}\times n_{r}$ and $n_{r} \times n_{t}$ antenna configurations coincide (under fixed received power), the coding delay can be sensitive to this switch. For example, at the received SNR of 20 dB, the $16\times 100$ system achieves capacity with codes of length (delay) which is only 60% of the length required for the $100\times 16$ system. Another interesting implication is that for the MISO channel, the dispersion-optimal coding schemes require employing orthogonal designs such as Alamouti’s scheme—a surprising observation considering the fact that Alamouti’s scheme was designed for reducing demodulation errors, not improving coding rate. Finding these dispersion-optimal coding schemes naturally gives a criteria for producing orthogonal design-like inputs in dimensions where orthogonal designs do not exist. [ABSTRACT FROM AUTHOR]

To satisfy requirements such as scalability and availability, many of today's applications are distributed systems. But because designing and implementing such systems is inherently difficult, developers are increasingly turning to decades of distributed systems research for guidance. This article briefly explores important areas of distributed systems research along with some classic papers distributed systems developers will find useful. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Anytime transmission has been shown to be an effective means of stabilizing an unstable system over noisy channels. A crucial issue in implementing anytime transmission strategies is the design of anytime codes that offer good finite-length performance and asymptotic anytime properties. This paper introduces an efficient anytime code derived from protograph-based low-density parity-check convolutional codes known as extended S-LDPCC (XS-LDPCC) codes. The asymptotic anytime properties of XS-LDPCC codes over both the binary erasure channel (BEC) and the binary-input additive white Gaussian noise (BIAWGN) channel are demonstrated through density evolution. By means of certain observations verified by simulations, we provide accurate estimates of the delay exponents of XS-LDPCC codes over the BEC and the BIAWGN channel. Through simulations, the finite-length performance of XS-LDPCC codes and the effects of different code design parameters are quantified. [ABSTRACT FROM PUBLISHER]

MapReduce/Hadoop framework has been widely used to process large-scale datasets on computing clusters. Scheduling map tasks with data locality consideration is crucial to the performance of MapReduce. Many works have been devoted to increasing data locality for better efficiency. However, to the best of our knowledge, fundamental limits of MapReduce computing clusters with data locality, including the capacity region and theoretical bounds on the delay performance, have not been well studied. In this paper, we address these problems from a stochastic network perspective. Our focus is to strike the right balance between data locality and load balancing to simultaneously maximize throughput and minimize delay. We present a new queueing architecture and propose a map task scheduling algorithm constituted by the Join the Shortest Queue policy together with the MaxWeight policy. We identify an outer bound on the capacity region, and then prove that the proposed algorithm can stabilize any arrival rate vector strictly within this outer bound. It shows that the outer bound coincides with the actual capacity region, and the proposed algorithm is throughput-optimal. Furthermore, we study the number of backlogged tasks under the proposed algorithm, which is directly related to the delay performance based on Little's law. We prove that the proposed algorithm is heavy-traffic optimal, i.e., it asymptotically minimizes the number of backlogged tasks as the arrival rate vector approaches the boundary of the capacity region. Therefore, the proposed algorithm is also delay-optimal in the heavy-traffic regime. The proofs in this paper deal with random processing times with heterogeneous parameters and nonpreemptive task execution, which differentiate our work from many existing works on MaxWeight-type algorithms, so the proof techniques themselves for the stability analysis and the heavy-traffic analysis are also novel contributions. [ABSTRACT FROM AUTHOR]

Efficiently optimizing large-scale, complex analog systems requires to know the performance tradeoffs for various analog circuit blocks. In this paper, we propose a radically new approach for analog performance tradeoff modeling. Our key idea is to broadly search the rich design knowledge from the Internet, and then mathematically encode the knowledge as high-dimensional performance tradeoff curves that are referred to as Pareto fronts in the literature. Toward this goal, several novel numerical algorithms, such as sparse regression and semi-infinite programming, are developed in order to construct the high-dimensional Pareto front model while guaranteeing its monotonicity. Our numerical examples demonstrate that the proposed modeling technique can accurately capture the high-dimensional Pareto fronts for large-scale analog systems (e.g., analog-to-digital converter) while most traditional methods are limited to low-dimensional Pareto front modeling of small circuit blocks without considering layout parasitics and manufacturing nonidealities. [ABSTRACT FROM PUBLISHER]

In 1996, Zadeh proposed the paradigm of computing with words (CWW). A specific architecture for making subjective judgments using CWW was proposed by Mendel in 2001. It is called a Perceptual Computer (Per-C), and because words can mean different things to different people, it uses interval type-2 fuzzy set (IT2 FS) models for all words. The Per-C has three elements: the encoder, which transforms linguistic perceptions into IT2 FSs that activate a CWW engine; the decoder, which maps the output of a CWW engine back into a word; and the CWW engine. Although different kinds of CWW engines are possible, this paper only focuses on CWW engines that are rule-based and the computations that map its input IT2 FSs into its output IT2 FS. Five assumptions are made for a rule-based CWW engine, the most important of which is: The result of combining fired rules must lead to a footprint of uncertainty (FOU) that resembles the three kinds of FOU that have previously been shown to model words (interior, left-shoulder, and right-shoulder FOUs). Requiring this means that the output FOU from a rule-based CWW engine will look similar in shape to an FOU in a codebook (i.e., a vocabulary of words and their respective FOUs) for an application, so that the decoder can therefore sensibly establish the word most similar to the CWW engine output FOU. Because existing approximate reasoning methods do not satisfy this assumption, a new kind of rule-based CWW engine is proposed, one that is called Perceptual Reasoning, and is proved to always satisfy this assumption. Additionally, because all IT2 FSs in the rules as well as those that excite the rules are either an interior, left-shoulder, or right-shoulder FOU, it is possible to carry out the sup-min calculations that are required by the inference engine, and those calculations are also in this paper. The results in this paper let us implement a rule-based CWW engine for the Per-C. [ABSTRACT FROM AUTHOR]

Given a distributed computation and a global predicate, predicate detection involves determining whether there exists at least one consistent cut (or global state) of the computation that satisfies the predicate. On the other hand, computation slicing is concerned with computing the smallest subcomputation (with the least number of consistent cuts) that contains all consistent cuts of the computation satisfying the predicate. In this paper, we investigate the relationship between predicate detection and computation slicing and show that the two problems are actually equivalent. Specifically, given an algorithm to detect a predicate b in a computation C, we derive an algorithm to compute the slice of C with respect to b. The time complexity of the (derived) slicing algorithm is O(n∣E∣T), where n is the number of processes, E is the set of events, and O(T) is the time complexity of the detection algorithm. We discuss how the "equivalence" result of this paper can be utilized to derive a faster algorithm for solving the genera/predicate detection problem in many cases. Slicing algorithms described in our earlier papers are all offline in nature. In this paper, we also present two online algorithms for computing the slice. The first algorithm can be used to compute the slice for a general predicate. Its amortized time complexity is O(n(c+n)T) per event, where c is the average concurrency in the computation and O(T) is the time complexity of the detection algorithm. The second algorithm can be used to compute the slice for a regular predicate. Its amortized time complexity is only O(n2) per event. [ABSTRACT FROM AUTHOR]

Conventional humanoid robotic behaviors are directly programmed depending on the programmer's personal experience. With this method, the behaviors usually appear unnatural. It is believed that a humanoid robot can acquire new adaptive behaviors from a human, if the robot has the criteria underlying such behaviors. The aim of this paper is to establish a method of acquiring human behavioral criteria. The advantage of acquiring behavioral criteria is that the humanoid robots can then autonomously produce behaviors for similar tasks with the same behavioral criteria but without transforming data obtained from morphologically different humans every time for every task. In this paper, a manipulator robot learns a model behavior, and another robot is created to perform the model behavior instead of being performed by a person. The model robot is presented some behavioral criteria, but the learning manipulator robot does not know them and tries to infer them. In addition, because of the difference between human and robot bodies, the body sizes of the learning robot and the model robot are also made different. The method of obtaining behavioral criteria is realized by comparing the efficiencies with which the learning robot learns the model behaviors. Results from the simulation have demonstrated that the proposed method is effective for obtaining behavioral criteria. The proposed method, the details regarding the simulation, and the results are presented in this paper. [ABSTRACT FROM AUTHOR]

Short cycles in a nonbinary low-density parity-check (NB-LDPC) code may be even more harmful to its performance if they do not satisfy the so-called full rank condition (FRC). This is because they may induce low-weight codewords or absorbing sets in that case. Thus, it is important to count the number of short cycles not satisfying the FRC as well as the number of short cycles for analyzing the performance of an NB-LDPC code. In this paper, we first develop a novel message-passing algorithm and identify how it is related to the FRC. We then propose a low-complexity algorithm for counting the number of short cycles not satisfying the FRC in an NB-LDPC code, as well as the number of short cycles. Finally, we propose a low-complexity algorithm for designing an NB-LDPC code with low error floor. Depending on the modulation scheme, the codes constructed by the proposed design algorithm have similar or slightly worse performance, compared with those constructed via the method by Poulliatet al.However, the proposed design algorithm does not require a cycle enumeration algorithm with high complexity, and therefore is feasible even in the case of large code length, say ${\geq}5000$. [ABSTRACT FROM AUTHOR]

This paper studies the fundamental limits of storage for structured data, where statistics and structure are both critical to the application. Accordingly, a framework is proposed for optimal lossless and lossy compression of subsets of the possible realizations of a discrete memoryless source (DMS). For the lossless subset-compression problem, it turns out that the optimal source code may not index the conventional source-typical sequences, but rather index certain subset-typical sequences consistent with the subset of interest. Building upon an achievability and a strong converse, an analytic expression is given, based on the Shannon entropy, relative entropy, and subset entropy, which identifies such subset-typical sequences for a broad class of subsets of a DMS. Intuitively, subset-typical sequences belong to those typical sets which highly intersect the subset of interest but are still closest to the source distribution in the sense of relative entropy. For the lossy subset-compression problem, an upper bound is derived on the subset rate-distortion function in terms of the subset mutual information optimized over the set of conditional distributions that satisfy the expected distortion constraint with respect to the subset-typical distribution and over a set of certain auxiliary subsets. By proving a strong converse result, this upper bound is shown to be tight for a class of symmetric subsets. As shown in our numerical examples, more often than not, one achieves a gain in the fundamental limits, in that the optimal compression rate for the subset in both the lossless and lossy settings can be strictly smaller than the source entropy and the source rate-distortion function, respectively, although exceptions are also possible. [ABSTRACT FROM AUTHOR]

Locally repairable codes (LRCs) have been recently proposed and used in real-world distributed storage systems (DSSs) such as Microsoft Azure Storage and Facebook HDFS-RAID (Hadoop Distributed File System-Redundant Array of Independent Disks). Since information in DSSs is changed frequently, reducing update complexity (UC) of LRCs is of great interest. In this paper, we propose code design algorithms that can reduce UC of existing LRCs without sacrificing their important code parameters such as minimum distance, code rate, or locality. We establish bounds on UC, and use them to show that our algorithms can achieve optimal or near optimal UC for a large class of LRCs. [ABSTRACT FROM AUTHOR]

This paper discusses the mutual combination of the unconventional algorithm (in this case evolutionary algorithms), deterministic chaos and modeling on real data from astrophysics. Analytical programming with selected evolutionary algorithm is used to synthesize suitable models. The main attention in this paper is focused on various chaotic generators, which are used instead of classical pseudo-random number generators. Chaotic generators are used in conjunction with a special case - a generator based on a strange non-chaotic attractor. The performance of all chaotic and non-chaotic based generators is then mutually compared at the end. [ABSTRACT FROM AUTHOR]

Relay transmissions play an important role in many types of communication systems. In this paper, we consider packet-level coded transmissions over lossy relay links with the finite-buffer and coding coefficients delivery cost constraints. We propose a low-complexity coding scheme where packets are encoded from sequentially formed random subsets of source packets called batches. The relay recodes only from the buffered packets belonging to the same batch to maintain the code sparsity, lowering the packet header overhead and the decoding complexity compared to the random linear network coding (RLNC). To analyze the performance, we first propose an absorbing Markov chain model to analyze the RLNC transmission over finite-buffer relay links. The finite-length analysis not only provides a lower bound on the completion time using any sparser random codes but also characterizes each individual batch’s transmission of the proposed code. Based on the analysis, another Markov chain is proposed to determine the decoding failure probability and the expected completion time of the batch coding scheme. As shown through analysis and simulations, the proposed scheme achieves higher effective end-to-end rates than RLNC when the coding coefficients delivery cost is considered and is also with much lowered decoding complexity thanks to its sparseness. [ABSTRACT FROM AUTHOR]

In applications of distributed storage systems to distributed computing and implementation of key-value stores, the following property, usually referred to as consistency in distributed computing, is an important requirement: as the data stored changes, the latest version of the data must be accessible to a client that connects to the storage system. Motivated by technological trends where key-value stores are increasingly implemented in high-speed memory, an information theoretic formulation called multi-version coding is introduced in this paper in order to understand and minimize the memory overhead of consistent distributed storage. Multi-version coding is characterized by $\nu$ totally ordered versions of a message and a storage system with $n$ servers. At each server, values corresponding to an arbitrary subset of the $\nu$ versions are received and encoded. For any subset of $c$ servers in the storage system, the value corresponding to the latest common version or a later version, as per the total ordering, among the $c$ servers is required to be decodable. An achievable multi-version code construction via linear coding and a converse result that shows that the construction is asymptotically tight when $\nu |(c-1)$ are provided. An implication of the converse is that there is an inevitable price, in terms of storage cost, to ensure consistency in distributed storage systems. [ABSTRACT FROM AUTHOR]

Video surveillance systems have become extremely important recently. It has been observed that information extracted from a single spectrum video is often insufficient in adverse conditions like low illumination, shadowing, smoke, dust, unstable background, and camouflage. Real-time video processing systems also need to be very fast where future frames are often unavailable at the time of processing the current frame. In this paper, we propose a superpixel-based causal multisensor video fusion algorithm suitable for real-time surveillance tasks. We develop new superpixel level spatial and temporal saliency models. Novel superpixel level multiple fusion rules are also designed to obtain the fused output. Comprehensive comparisons with several existing works clearly indicate the benefit of our solution. [ABSTRACT FROM AUTHOR]

This paper proposes a new partial scan approach in which the problem of selecting flip-flops in partial scan designs is converted to the balanced graph transformation problem based on an integer linear programming formulation. The complexity of the problem is analyzed and a scalable approach is proposed to deal with complicated circuits. Experimental results on a set of benchmark circuits show that on average the proposed approach reduces the area overhead by 25% compared with the full scan approach and by 17.8% compared with the existing balanced structure-based partial scan approach. [ABSTRACT FROM PUBLISHER]

In pliable index coding, we consider a server with m messages and n clients, where each client has as side information a subset of the messages. We seek to minimize the number of broadcast transmissions, so that each client can recover any one unknown message she does not already have. Previous work has shown that the pliable index coding problem is NP-hard and requires at most \mathcal O(\log ^2(n)) broadcast transmissions, which indicates exponential savings over the conventional index coding that requires in the worst case \mathcal O(n) transmissions. In this paper, building on a decoding criterion that we propose, we first design a deterministic polynomial-time algorithm that can realize the exponential benefits, by achieving, in the worst case, a performance upper bounded by \mathcal O(\log ^2(n)) broadcast transmissions. We extend our algorithm to the t -requests case, where each client requires t unknown messages that she does not have, and show that our algorithm requires at most \mathcal O(t\log (n)+\log ^2(n)) broadcast transmissions. We construct lower bound instances that require at least \Omega (\log (n)) transmissions for linear pliable index coding and at least $\Omega (t+\log (n))$ transmissions for the t$ -requests case, indicating that both our upper and lower bounds are polynomials of \log (n)$ and differ within a factor of . We provide a probabilistic analysis over random instances and show that the required number of transmissions is almost surely $\Theta (\log (n))$ , as compared with the $\Theta (n/\log (n))$ for index coding. In addition, we show that these upper and lower bounds also hold for vector pliable index coding in the worst case instances and the random graph instances, implying that vector coding does not provide benefits in terms of these bounds. Our numerical experiments show that our algorithm outperforms existing algorithms for pliable index coding by up to 50% less transmissions. [ABSTRACT FROM AUTHOR]