title: A New Resource Scheduling Strategy Based on Genetic Algorithm in Cloud Computing Environment

abstract: In view of the load balancing problem in VM resources scheduling, this paper presents a scheduling strategy on load balancing of VM resources based on genetic algorithm. According to historical data and current state of the system and through genetic algorithm, this strategy computes ahead the influence it will have on the system after the deployment of the needed VM resources and then chooses the least-affective solution, through which it achieves the best load balancing and reduces or avoids dynamic migration. At the same time, this paper brings in variation rate to describe the load variation of system virtual machines, and it also introduces average load distance to measure the overall load balancing effect of the algorithm. The experiment shows that this strategy has fairly good global astringency and efficiency, and the algorithm of this paper is, to a great extent, able to solve the problems of load imbalance and high migration cost after system VM being scheduled. What is more, the average load distance does not grow with the increase of VM load variation rate, and the system scheduling algorithm has quite good resource utility.


Neural Monkey: The Current State and Beyond.


title: An MTD-Based Self-Adaptive Resilience Approach for Cloud Systems

abstract: Advances in cloud computing have made it a feasible and cost-effective solution to improve the resiliency of enterprise systems. However, the replication approach taken by cloud computing to provide resiliency leads to an increase in the number of ways an attacker can exploit or penetrate the systems. This calls for designing cloud systems that can accurately detect anomalies and dynamically adapt themselves to keep performing mission-critical functions even under attacks and failures. In this paper, we propose a self-adaptive resiliency approach for cloud enterprise systems that employs a live monitoring and moving target defense based approach to automatically detect deviations from normal behavior and reconfigure critical cloud processes through software-defined networking to mitigate attacks and reduce system downtime. The proposed solution is promising to present a unified framework for resilient cloud systems.


title: Monte Carlo estimates of specific absorbed fractions for an I-125 point source in water.

abstract: Monte Carlo estimates of eight specific absorbed fractions associated with an I‐125 point source in water are presented for distances ranging from 1–10 cm.


title: Getting Formal with Dopamine and Reward

abstract: Recent neurophysiological studies reveal that neurons in certain brain structures carry specific signals about past and future rewards. Dopamine neurons display a short-latency, phasic reward signal indicating the difference between actual and predicted rewards. The signal is useful for enhancing neuronal processing and learning behavioral reactions. It is distinctly different from dopamine's tonic enabling of numerous behavioral processes. Neurons in the striatum, frontal cortex, and amygdala also process reward information but provide more differentiated information for identifying and anticipating rewards and organizing goal-directed behavior. The different reward signals have complementary functions, and the optimal use of rewards in voluntary behavior would benefit from interactions between the signals. Addictive psychostimulant drugs may exert their action by amplifying the dopamine reward signal.


title: Constrained Arc-Eager Dependency Parsing

abstract: Arc-eager dependency parsers process sentences in a single left-to-right pass over the input and have linear time complexity with greedy decoding or beam search. We show how such parsers can be constrained to respect two different types of conditions on the output dependency graph: span constraints, which require certain spans to correspond to subtrees of the graph, and arc constraints, which require certain arcs to be present in the graph. The constraints are incorporated into the arc-eager transition system as a set of preconditions for each transition and preserve the linear time complexity of the parser.


title: A robust image fingerprinting system using the Radon transform

abstract: With the ever-increasing use of multimedia contents through electronic commerce and on-line services, the problems associated with the protection of intellectual property, management of large database and indexation of content are becoming more prominent. Watermarking has been considered as efficient means to these problems. Although watermarking is a powerful tool, there are some issues with the use of it, such as the modification of the content and its security. With respect to this, identifying content itself based on its own features rather than watermarking can be an alternative solution to these problems. The aim of fingerprinting is to provide fast and reliable methods for content identification. In this paper, we present a new approach for image fingerprinting using the Radon transform to make the fingerprint robust against affine transformations. Since it is quite easy with modern computers to apply affine transformations to audio, image and video, there is an obvious necessity for affine transformation resilient fingerprinting. Experimental results show that the proposed fingerprints are highly robust against most signal processing transformations. Besides robustness, we also address other issues such as pairwise independence, database search efficiency and key dependence of the proposed method.


title: Social Collaborative Viewpoint Regression with Explainable Recommendations

abstract: A recommendation is called explainable if it not only predicts a numerical rating for an item, but also generates explanations for users' preferences. Most existing methods for explainable recommendation apply topic models to analyze user reviews to provide descriptions along with the recommendations they produce. So far, such methods have neglected user opinions and influences from social relations as a source of information for recommendations, even though these are known to improve the rating prediction. In this paper, we propose a latent variable model, called social collaborative viewpoint regression (sCVR), for predicting item ratings based on user opinions and social relations. To this end, we use so-called viewpoints, represented as tuples of a concept, topic, and a sentiment label from both user reviews and trusted social relations. In addition, such viewpoints can be used as explanations. We apply a Gibbs EM sampler to infer posterior distributions of sCVR. Experiments conducted on three large benchmark datasets show the effectiveness of our proposed method for predicting item ratings and for generating explanations.


title: H.: Churn reduction in the wireless industry

abstract: Competition in the wireless telecommunications industry is rampant. To maintain profitability, wireless carriers must control churn, the loss of subscribers who switch from one carrier to another. We explore statistical techniques for churn prediction and, based on these predictions. an optimal policy for identifying customers to whom incentives should be offered to increase retention. Our experiments are based on a data base of nearly 47,000 U.S. domestic subscribers, and includes information about their usage, billing, credit, application, and complaint history. We show that under a wide variety of assumptions concerning the cost of intervention and the retention rate resulting from intervention, churn prediction and remediation can yield significant savings to a carrier. We also show the importance of a data representation crafted by domain experts.


title: Symmetric searchable encryption with efficient range query using multi-layered linked chains

abstract: Searchable encryption is an encryption system which provides confidentiality of stored documents and usability of document search at the same time. Remote cloud storage is the most typical application for searchable encryption. By applying searchable encryption, inside attacks can be prevented fundamentally. Designing searchable encryption which supports various queries--for example, range query, conjunctive keyword query, etc.--is also important for convenience. In this paper, we focus on range query problem. Up to now, only a few results have been proposed, in which the main functions are constructed using public key cryptosystems. Therefore, the amount of computations for encryption and search is large. Furthermore, search time depends on the size of the entire database. We propose a new searchable encryption protocol which is based on a symmetric key encryption and linked chain structure instead of public key-based techniques. The main contribution of the protocol is reducing the computation cost for search remarkably. In reality, the amount of computations depends on the number of documents that are matched to a query instead of the size of the entire database.


title: An Introduction to Quantum Computing, Without the Physics

abstract: This paper is a gentle but rigorous introduction to quantum computing intended for discrete mathematicians. Starting from a small set of assumptions on the behavior of quantum computing devices, we analyze their main characteristics, stressing the differences with classical computers, and finally describe two well-known algorithms (Simon's algorithm and Grover's algorithm) using the formalism developed in previous sections. This paper does not touch on the physics of the devices, and therefore does not require any notion of quantum mechanics. Numerical examples on an implementation of Grover's algorithm using open-source software are provided.


title: Exploring the Syntactic Abilities of RNNs with Multi-task Learning

abstract: Recent work has explored the syntactic abilities of RNNs using the subject-verb agreement task, which diagnoses sensitivity to sentence structure. RNNs performed this task well in common cases, but faltered in complex sentences (Linzen et al., 2016). We test whether these errors are due to inherent limitations of the architecture or to the relatively indirect supervision provided by most agreement dependencies in a corpus. We trained a single RNN to perform both the agreement task and an additional task, either CCG supertagging or language modeling. Multi-task training led to significantly lower error rates, in particular on complex sentences, suggesting that RNNs have the ability to evolve more sophisticated syntactic representations than shown before. We also show that easily available agreement training data can improve performance on other syntactic tasks, in particular when only a limited amount of training data is available for those tasks. The multi-task paradigm can also be leveraged to inject grammatical knowledge into language models.


title: Testicular adrenal rest tissue in congenital adrenal hyperplasia: comparison of MR imaging and sonographic findings.

abstract: The purpose of our study was to describe the MR features of testicular adrenal rest tissue in patients with congenital adrenal hyperplasia and to compare the usefulness of MR imaging with that of sonography for the detection of testicular adrenal rest tissue.Nineteen patients with congenital adrenal hyperplasia underwent MR imaging and gray-scale and color Doppler sonography of the testicles during the same visit to our institution. Findings were compared.MR features of testicular adrenal rest tissue included the following: isointensity (71% of the masses) and slight hyperintensity (29% of the masses) relative to normal testicular tissue on T1-weighted images; hypointensity relative to normal testicular tissue on T2-weighted images (100% of the masses); and diffuse enhancement on contrast-enhanced T1-weighted images (85% of the masses). MR imaging and sonography revealed the testicular lesions equally well. Eleven (58%) of 19 patients had normal findings on MR imaging and sonography. Eight (42%) of 19 pat...


title: A risk constrained control approach for adaptive cruise control

abstract: This work investigates the incorporation of a risk metric based on time to collision for Adaptive Cruise Control applications on multi-lane motorways. To improve the fuel-efficiency and safety, a stochastic model predictive control approach is suggested that limits the violation probability of the imposed risk metric. For this reason, a Bayesian network is used to predict the probability distributions of the surrounding vehicles' future motion based on actual measurements. Subsequently, these distribution functions are incorporated into a stochastic model predictive control algorithm. The novel strategy is evaluated in simulation using an artificial evaluation cycle and shows significant improvements in terms of fuel efficiency and safety in comparison to conventional Adaptive Cruise Control when sharp cut-off maneuvers occur.


title: Growth and Feed utilization of Catfish Hybrid (Heterobranchus longifilis x Clarias gariepinus ) Fingerlings fed practical diets

abstract: The effects of four practical diets (3 commercial and one locally prepared) on growth, feed utilization and survival of catfish hybrid ( Heterobranchus longifilis x Clarias gariepinus ) fingerlings were compared in plastic aquaria in triplicates for 56days. Diets differed in crude protein (CP) and digestible energy as follows: Commercial Diet 1 (39.73% CP, 3911.8Kcal/kg), Diet 2 (34.50% CP, 3926.8Kcal/kg), Diet 3 (45.37%, 3883.6Kcal/kg) and local Diet 4 (40.34% CP, 3861.8Kcal/kg). Observations made indicated higher performance in weight and percent weight gain (2.299g, 411%), specific growth rate (0.78% day -1 ), condition factor (0.96), feed conversion ratio (2.67) and protein efficiency ratio (10.73) for fingerlings fed Diet 3 than other diets. Commercial Diet 2 appeared to perform least among the test diets. Fish survival was high and varied between 76.7% (Diet 4) and 96.7% (Diet 1). These results demonstrated that catfish hybrid fingerlings can be successfully reared in aquaria with good feed conversion, growth and high survival on commercially or locally prepared diets with 45.37% CP and adequate energy.


Learning to Attack: Adversarial Transformation Networks


title: VQA: Visual Question Answering

abstract: We propose the task of free-form and open-ended Visual Question Answering (VQA). Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. Visual questions selectively target different areas of an image, including background details and underlying context. As a result, a system that succeeds at VQA typically needs a more detailed understanding of the image and complex reasoning than a system producing generic image captions. Moreover, VQA is amenable to automatic evaluation, since many open-ended answers contain only a few words or a closed set of answers that can be provided in a multiple-choice format. We provide a dataset containing ~0.25M images, ~0.76M questions, and ~10M answers (www.visualqa.org), and discuss the information it provides. Numerous baselines for VQA are provided and compared with human performance.


title: Integrating network function virtualization with SDR and SDN for 4G/5G networks

abstract: The rapidly diversified market demands have presented a huge challenge to the conventional mobile broadband network architecture. On one hand, the limited machine room space and insufficient power supply make it impossible to accommodate exponentially growing amount of network equipment of operators. On the other hand, net heterogeneity caused by different specifications of wireless access equipment causes costly trouble related to management and optimization. This article, correspondingly, proposes a holistic solution involving different technologies, i.e. network function virtualization (NFV), software defined radio (SDR), and software defined network (SDN). In particular, we investigate both existing standards and possible extensions for 4G/5G mobile networks, followed by a few open issues for future research.


title: White Blood Cells Identication and Classication from Leukemic Blood Image

abstract: The counting and classification of blood cells allows the evaluation and diagnosis of a vast number of diseases. Through the analysis of white blood cells (WBCs) the ALL Acute Lymphocytic Leukemia, a blood cancer that can be fatal if left untreated, can be detected. Nowadays the morphological analysis of blood cells is performed manually by skilled operators. This involves numerous drawbacks, such as slowness of the analysis and a non-standard accuracy, dependent on the operator skills. In literature there are only few examples of automated systems in order to analyze and classify the blood cells, most of which only partial. This paper presents a complete and fully automatic method for WBCs identification and classification from microscopic images. The proposed method firstly individuates WBCs from which, subsequently, are extracted morphological features necessary for the final stage of classification. The whole work has been developed using MATLAB environment.


title: Comparison of two protocols for maxillary protraction: bone anchors versus face mask with rapid maxillary expansion

abstract: OBJECTIVE ::: To test the hypothesis that there is no difference in the active treatment effects for maxillary advancement induced by bone-anchored maxillary protraction (BAMP) and the active treatment effects for face mask in association with rapid maxillary expansion (RME/FM). ::: ::: ::: MATERIALS AND METHODS ::: This is a study on consecutively treated patients. The changes in dentoskeletal cephalometric variables from start of treatment (T1) to end of active treatment (T2) with an average T1-T2 interval of about 1 year were contrasted in a BAMP sample of 21 subjects with a RME/FM sample of 34 patients. All subjects were prepubertal at T1. Statistical comparison was performed with t-tests for independent samples. ::: ::: ::: RESULTS ::: The BAMP protocol produced significantly larger maxillary advancement than the RME/FM therapy (with a difference of 2 mm to 3 mm). Mandibular sagittal changes were similar, while vertical changes were better controlled with BAMP. The sagittal intermaxillary relationships improved 2.5 mm more in the BAMP patients. Additional favorable outcomes of BAMP treatment were the lack of clockwise rotation of the mandible as well as a lack of retroclination of the lower incisors. ::: ::: ::: CONCLUSIONS ::: The hypothesis is rejected. The BAMP protocol produced significantly larger maxillary advancement than the RME/FM therapy.


title: Semantic Scoring Based on Small-World Phenomenon for Feature Selection in Text Mining

abstract: This paper proposes an effective scoring scheme for feature selection in Text Mining, using characteristics of Small-World Phenomenon on the semantic networks of documents. Our focus is on the reservation of both syntactic and statistical information of words, rather than solely simple frequency summarization in prevailing scoring schemes, such as TFIDF. Experimental results on TREC dataset show that our scoring scheme outperforms the prevailing schemes.


title: An algorithm for fast edit distance computation on GPUs

abstract: The problem of finding the edit distance between two sequences (and its closely related problem of longest common subsequence) are important problems with applications in many domains like virus scanners, security kernels, natural language translation and genome sequence alignment. The traditional dynamic-programming based algorithm is hard to parallelize on SIMD processors as the algorithm is memory intensive and has many divergent control paths. In this paper we introduce a new algorithm which modifies the dynamic programming method to reduce its amount of data storage and eliminate control flow divergences. Our algorithm divides the problem into independent ‘quadrants’ and makes efficient use of shared memory and registers available in GPUs to store data between different phases of the algorithm. Further, we eliminate any control flow divergences by embedding condition variables in the program logic to ensure all the threads execute the same instructions even though they work on different data items. We present an implementation of this algorithm on an NVIDIA GeForce GTX 275 GPU and compare against an optimized multi-threaded implementation on an Intel Core i7-920 quad core CPU with hyper-threading support. Our results show that our GPU implementation is up to 8x faster when operating on a large number of sequences.


Cystatin C as a novel predictor of preterm labor in severe preeclampsia


title: Energy-Efficient Resource Allocation in Downlink OFDM Wireless Systems With Proportional Rate Constraints

abstract: Orthogonal frequency-division multiplexing (OFDM), with its own advantages of spectral efficiency (SE) enhancement and flexible resource allocation, is a promising basic technique for fulfilling the ever increasing demand for high-data-rate transmission and various service type support for mobile multimedia communications. In the meantime, energy efficiency (EE) has now become a critical metric for green system design. In this paper, energy-efficient and fair resource allocation is investigated in a downlink OFDM-based mobile communication system. Given a subcarrier assignment, the bisection-based optimal power allocation (BOPA) is proposed at first, which achieves the maximum EE and guarantees proportional data rates for users. Then, a two-step subcarrier assignment is designed to avoid unaffordable computational complexity of exhaustive search. In the first step, the estimated energy-efficient transmit power is found via the assumptions on flat fading and subcarrier sharing. In the second step, the traditional spectral-efficient subcarrier assignment (SESA) is introduced to complete the bandwidth resource allocation among users. Although the two-step subcarrier assignment is suboptimal due to the fact that the optimization is done independently in two separate steps, numerical results demonstrate that its performance is very close to the optimum. This paper also studies the difference between the energy-efficient solution and the traditional spectral-efficient policy and observes that they are similar with each other in the low channel-gain-to-noise ratio (CNR) regime. This observation is helpful and could enable that the energy-efficient design can be turned into the relatively simpler spectral-efficient policy when the CNR is low.


XtratuM: a Hypervisor for Safety Critical Embedded Systems


title: Non-small cell lung cancer staging: proposed revisions to the TNM system

abstract: Patients with non-small cell lung cancer (NSCLC) require careful staging at the time of diagnosis to determine prognosis and guide treatment recommendations. The seventh edition of the TNM Classification of Malignant Tumors is scheduled to be published in 2009 and the International Association for the Study of Lung Cancer (IASLC) created the Lung Cancer Staging Project (LCSP) to guide revisions to the current lung cancer staging system. These recommendations will be submitted to the American Joint Committee on Cancer (AJCC) and to the Union Internationale Contre le Cancer (UICC) for consideration in the upcoming edition of the staging manual. Data from over 100,000 patients with lung cancer were submitted for analysis and several modifications were suggested for the T descriptors and the M descriptors although the current N descriptors remain unchanged. These recommendations will further define homogeneous patient subsets with similar survival rates. More importantly, these revisions will help guide clinicians in making optimal, stage-specific, treatment recommendations.


title: The Rise of Expanded Hemodialysis

abstract: The low water permeability feature of original cellulosic membranes was considered an advantage in the absence of dialysis equipment that are capable of controlling water removal. The advent of ultrafiltration control systems led to the development and use of high-flux (HF) membranes that allowed improved middle molecule removal including β-2 microglobulin. Further advances in technology allowed better control over the structure and permeability of membranes. Different polymers and improved spinning modalities led to significant advances in solute removal and hemocompatibility. Inner surface modification produced a reduction in membrane thrombogenicity and protein-membrane interaction with a less tendency to fouling and permeability decay. Further evolution in technology led to the development of a new class of membranes referred to as protein-leaking membranes or super-flux or high cutoff (HCO). These membranes are more permeable than conventional HF membranes and allow some passage of proteins, including albumin. The rationale for these membranes is the need for increased clearance of low molecular weight proteins and protein-bound solutes. However, albumin loss in protein-leaking HCO membranes represents a limitation whose effect in patients is still controversial. The last evolution in the field of membranes is the development of a new class defined as "high retention onset" (HRO) due to the peculiar high sieving value in the middle to high molecular weight range. The introduction of HRO membranes in the clinical routine has enabled the development of a new concept therapy called "expanded hemodialysis." Its simple set up and application offer the possibility to use it even in patients with suboptimal vascular access or even with an indwelling catheter. The system does not require particular hardware or unusual nursing skill. The quality of dialysis fluid is, however, mandatory to ensure a safe conduction of the dialysis session. This new therapy is likely to modify the outcome of end-stage kidney disease patients, thanks to the enhanced removal of molecules traditionally retained by current dialysis techniques.


title: Design of a Compact UWB Out-of-Phase Power Divider

abstract: The design of a compact out-of-phase uniplanar power divider operating over an ultra wide frequency band is presented. To achieve an out-of-phase signal division over a large frequency range, a T-junction formed by a slotline and a microstrip line accompanied by wideband microstrip to slotline transitions is employed. The simulated and experimental results of the developed divider show a low insertion loss and good return loss performance of the three ports across the band 3.1-10.6 GHz


title: On the differential benchmarking of promotional efficiency with machine learning modeling (I): Principles and statistical comparison

abstract: Sales promotions have become in recent years a paramount issue in the marketing strategies of many companies, and they have even more relevance in the present economic situation. Currently, the empirical models, aimed at assessing consumers behavior in response to certain sales promotions activities such as temporary price reductions, are receiving growing attention in this relevant research field, due to two reasons mainly: (1) the complexity of the interactions among the different elements incorporated inside promotions campaigns attracts growing attention; and (2) the increased availability of electronic records on sales history. Hence, it will become important that the performance description and comparison among all available machine learning promotion models, as well as their design parameters selection, will be performed using a robust and statistically rigorous procedure, while keeping functionality and usefulness. In this paper, we first propose a simple nonparametric statistical tool, based on the paired bootstrap resampling, to allow an operative result comparison among different learning-from-samples promotional models. Secondly, we use the bootstrap statistical description to evaluate the models in terms of average and scatter measurements, for a more complete efficiency characterization of the promotional sales models. These statistical characterizations allow us to readily work with the distribution of the actual risk, in order to avoid overoptimistic performance evaluation in the machine learning based models. We also present the analysis performed to determinate whether the figure of merit has a significant impact on final result, together with an in depth design parameter selection to optimize final results during the promotion evaluation using statistical learning techniques. No significant difference was obtained in terms of figure of merit choice, and Mean Absolute Error was selected for performance measurement. As a summary, the applied technique allows clarifying the design of the promotional sales models for a real database (milk category), according to the influence of the figure of merit used for design parameters selection, showing the robustness of the machine learning techniques in this setting. Results obtained in this paper will be subsequently applied, and presented in the companion paper, devoted to a more detailed quality analysis, to evaluate four well-known machine learning algorithms in real databases for two categories with different promotional behavior.


title: Supply Chain Management (SCM) and Organizational Key Factors for Successful Implementation of Enterprise Resource Planning (ERP) Systems

abstract: This paper reports some findings of an on-going research into ERP implementation issues. A part of the research, which this paper reports, consists of reviewing several cases of successful implementations of ERP systems. The analysis of all these cases revealed that the critical factors for successful implementation of ERP systems fall into two main categories: technological and organizational factors. However, the organizational factors seem to be more important than the technological ones as far as the successful implementation of ERP systems under SCM is concerned.


title: End-to-End Optimization for Geo-Distributed MapReduce

abstract: MapReduce has proven remarkably effective for a wide variety of data-intensive applications, but it was designed to run on large single-site homogeneous clusters. Researchers have begun to explore the extent to which the original MapReduce assumptions can be relaxed, including skewed workloads, iterative applications, and heterogeneous computing environments. This paper continues this exploration by applying MapReduce across geo-distributed data over geo-distributed computation resources. Using Hadoop, we show that network and node heterogeneity and the lack of data locality lead to poor performance, because the interaction of MapReduce phases becomes pronounced in the presence of heterogeneous network behavior. To address these problems, we take a two-pronged approach: We first develop a model-driven optimization that serves as an oracle, providing high-level insights. We then apply these insights to design cross-phase optimization techniques that we implement and demonstrate in a real-world MapReduce implementation. Experimental results in both Amazon EC2 and PlanetLab show the potential of these techniques as performance is improved by 7-18 percent depending on the execution environment and application.


title: Smart Gas Level Monitoring, Booking & Gas Leakage Detector over IoT

abstract: This journal explains about the most common problem experienced in our day-to-day lives that is regarding GAS container going empty. We bring this paper to create awareness about the reducing weight of the gas in the container, and to place a gas order using IOT. The gas booking/order is being done with the help IOT and that the continuous weight measurement is done using a load cell which is interfaced with a Microcontroller (to compare with an ideal value). For ease it is even has a been added with an RF TX & Rx modules which will give the same information. When it comes it to security of the kit as well as gas container we have an MQ-2(gas sensor), LM 35(temperature sensor), which will detect the surrounding environment for any chance of error. When ever any change is subjected in any of the sensors (load cell, LM35, Mq-2) a siren (60db) is triggered.


title: Communication-optimal parallel algorithm for strassen's matrix multiplication

abstract: Parallel matrix multiplication is one of the most studied fundamental problems in distributed and high performance computing. We obtain a new parallel algorithm that is based on Strassen’s fast matrix multiplication and minimizes communication. The algorithm outperforms all known parallel matrix multiplication algorithms, classical and Strassen-based, both asymptotically and in practice. A critical bottleneck in parallelizing Strassen’s algorithm is the communication between the processors. Ballard, Demmel, Holtz, and Schwartz (SPAA’11) prove lower bounds on these communication costs, using expansion properties of the underlying computation graph. Our algorithm matches these lower bounds, and so is communication-optimal. It exhibits perfect strong scaling within the maximum possible range. Research supported by Microsoft (Award #024263) and Intel (Award #024894) funding and by matching funding by U.C. Discovery (Award #DIG07-10227). Additional support comes from Par Lab aliates


title: Corporate Real Estate Sale and Leaseback Effect: Empirical Evidence from Europe

abstract: Corporate real estate disposals have increased in Europe during the past few years. In this research paper, we study market reactions of publicly traded European companies' real estate sale and leaseback announcements. The results are in line with the results of Slovin et al. (1990), Rutherford (1990) and Fisher (2004). We find that the sale and leaseback announcements have on average positive impact to firm's value. The results support Lewis and Schallheim's (1992) arguments that leasing and debt are rather complements that substitutes. Leasing can also be seen as a mechanism for selling tax deductions. We also find that the average increase in firms' value is clearly higher in sale and leaseback deals, which size is large in comparison with the market value of the seller-firm. Sale and leaseback arrangements seem to offer potential tax and efficiency gains. These and the release of hidden reserves can lead to the positive valuation effects found in the study.


title: A method for deciding node densities in non-uniform deployment of wireless sensors

abstract: In a wireless sensor network, if the sensors are deployed uniformly across the network, they experience different traffic intensities, thereby, different energy depletion rates depending on their locations. Usually, the sensors near the sink tend to deplete their energy sooner; when enough of them exhaust their energy, they leave holes in the network, causing the remaining nodes to be disconnected from the sink. One of the solutions to this energy-hole problem is to deploy the sensors non-uniformly. This paper describes a method for deciding the sensor deployment densities so as to equalize the energy consumption rates of all nodes. The method is general and can be applied to other objectives and constraints.


title: An ASIC Implementation of the AES SBoxes

abstract: This article presents a hardware implementation of the S-Boxes from the Advanced Encryption Standard (AES). The SBoxes substitute an 8-bit input for an 8-bit output and are based on arithmetic operations in the finite field GF(28). We show that a calculation of this function and its inverse can be done efficiently with combinational logic. This approach has advantages over a straight-forward implementation using read-only memories for table lookups. Most of the functionality is used for both encryption and decryption. The resulting circuit offers low transistor count, has low die-size, is convenient for pipelining, and can be realized easily within a semi-custom design methodology like a standard-cell design. Our standard cell implementation on a 0.6 ?m CMOS process requires an area of only 0.108 mm2 and has delay below 15 ns which equals a maximum clock frequency of 70 MHz. These results were achieved without applying any speed optimization techniques like pipelining.


title: Receiver-Initiated Spectrum Management for Underwater Cognitive Acoustic Network

abstract: Cognitive acoustic (CA) is emerging as a promising technique for environment-friendly and spectrum-efficient underwater communications. Due to the unique features of underwater acoustic networks (UANs), traditional spectrum management systems designed for cognitive radio (CR) need an overhaul to work efficiently in underwater environments. In this paper, we propose a receiver-initiated spectrum management (RISM) system for underwater cognitive acoustic networks (UCANs). RISM seeks to improve the performance of UCANs through a collaboration of physical layer and medium access control (MAC) layer. It aims to provide efficient spectrum utilization and data transmissions with a small collision probability for CA nodes, while avoiding harmful interference with both “natural acoustic systems”, such as marine mammals, and “artificial acoustic systems”, like sonars and other UCANs. In addition, to solve the unique challenge of deciding when receivers start to retrieve data from their neighbors, we propose to use a traffic predictor on each receiver to forecast the traffic loads on surrounding nodes. This allows each receiver to dynamically adjust its polling frequency according to the variation of a network traffic. Simulation results show that the performance of RISM with smart polling scheme outperforms the conventional sender-initiated approach in terms of throughput, hop-by-hop delay, and energy efficiency.


title: Low-Profile Three-Arm Folded Dipole Antenna for UHF Band RFID Tags Mountable on Metallic Objects

abstract: A low-profile radio frequency identification (RFID) tag mountable on metallic objects is presented, which employs a multiple-arm folded dipole antenna. The proposed antenna is printed on a single-layer circuit board with an overall thickness of 1.5 mm not requiring any via or shorting plate. The presented configuration allows to match an RFID chip impedance with an imaginary part around - 400 Ω . A slightly modified version of the tag that allows an easy tuning of the frequency response is illustrated as well as a more compact tag obtained by allowing the use of vias. Prototypes for the RFID UHF band allocated in Europe have been fabricated, and measurements of the impedance and read range are provided.


title: SVM or deep learning? A comparative study on remote sensing image classification

abstract: With constant advancements in remote sensing technologies resulting in higher image resolution, there is a corresponding need to be able to mine useful data and information from remote sensing images. In this paper, we study auto-encoder (SAE) and support vector machine (SVM), and to examine their sensitivity, we include additional umber of training samples using the active learning frame. We then conduct a comparative evaluation. When classifying remote sensing images, SVM can also perform better than SAE in some circumstances, and active learning schemes can be used to achieve high classification accuracy in both methods.


title: From Computational Thinking to Systems Thinking: A conceptual toolkit for sustainability computing

abstract: If information and communication technologies (ICT) are to bring about a transformational change to a sustainable society, then we need to transform our thinking. Computer professionals already have a conceptual toolkit for problem solving, sometimes known as computational thinking. However, computational thinking tends to see the world in terms a series of problems (or problem types) that have computational solutions (or solution types). Sustainability, on the other hand, demands a more systemic approach, to avoid technological solutionism, and to acknowledge that technology, human behaviour and environmental impacts are tightly inter-related. In this paper, I argue that systems thinking provides the necessary bridge from computational thinking to sustainability practice, as it provides a domain ontology for reasoning about sustainability, a conceptual basis for reasoning about transformational change, and a set of methods for critical thinking about the social and environmental impacts of technology. I end the paper with a set of suggestions for how to build these ideas into the undergraduate curriculum for computer and information sciences.


title: Cache-Aided Heterogeneous Networks: Coverage and Delay Analysis

abstract: This paper characterizes the performance of a generic $K$-tier cache-aided heterogeneous network (CHN), in which the base stations (BSs) across tiers differ in terms of their spatial densities, transmission powers, pathloss exponents, activity probabilities conditioned on the serving link and placement caching strategies. We consider that each user connects to the BS which maximizes its average received power and at the same time caches its file of interest. Modeling the locations of the BSs across different tiers as independent homogeneous Poisson Point processes (HPPPs), we derive closed-form expressions for the coverage probability and local delay experienced by a typical user in receiving each requested file. We show that our results for coverage probability and delay are consistent with those previously obtained in the literature for a single tier system.


title: Personalized expertise search at LinkedIn

abstract: Linkedln is the largest professional network with more than 350 million members. As the member base increases, searching for experts becomes more and more challenging. In this paper, we propose an approach to address the problem of personalized expertise search on LinkedIn, particularly for exploratory search queries containing skills. In the offline phase, we introduce a collaborative filtering approach based on matrix factorization. Our approach estimates expertise scores for both the skills that members list on their profiles as well as the skills they are likely to have but do not explicitly list. In the online phase (at query time) we use expertise scores on these skills as a feature in combination with other features to rank the results. To learn the personalized ranking function, we propose a heuristic to extract training data from search logs while handling position and sample selection biases. We tested our models on two products — LinkedIn homepage and LinkedIn recruiter. A/B tests showed significant improvements in click through rates — 31% for CTR@1 for recruiter (18% for homepage) as well as downstream messages sent from search — 37% for recruiter (20% for homepage). As of writing this paper, these models serve nearly all live traffic for skills search on LinkedIn homepage as well as LinkedIn recruiter.


title: Age-fitness pareto optimization

abstract: We propose a multi-objective method for avoiding premature convergence in evolutionary algorithms, and demonstrate a three-fold performance improvement over comparable methods. Previous research has shown that partitioning an evolving population into age groups can greatly improve the ability to identify global optima and avoid converging to local optima. Here, we propose that treating age as an explicit optimization criterion can increase performance even further, with fewer algorithm implementation parameters. The proposed method evolves a population on the two-dimensional Pareto front comprising (a) how long the genotype has been in the population (age); and (b) its performance (fitness). We compare this approach with previous approaches on the Symbolic Regression problem, sweeping the problem difficulty over a range of solution complexities and number of variables. Our results indicate that the multi-objective approach identifies the exact target solution more often that the age-layered population and standard population methods. The multi-objective method also performs better on higher complexity problems and higher dimensional datasets -- finding global optima with less computational effort.


title: The Art of Getting Deep Neural Networks in Shape

abstract: Training a deep neural network (DNN) involves selecting a set of hyperparameters that define the network topology and influence the accuracy of the resulting network. Often, the goal is to maximize prediction accuracy on a given dataset. However, non-functional requirements of the trained network -- such as inference speed, size, and energy consumption -- can be very important as well. In this article, we aim to automate the process of selecting an appropriate DNN topology that fulfills both functional and non-functional requirements of the application. Specifically, we focus on tuning two important hyperparameters, depth and width, which together define the shape of the resulting network and directly affect its accuracy, speed, size, and energy consumption. To reduce the time needed to search the design space, we train a fraction of DNNs and build a model to predict the performances of the remaining ones. We are able to produce tuned ResNets, which are up to 4.22 times faster than original depth-scaled ResNets on a batch of 128 images while matching their accuracy.


title: Malaysian Internet Surfing Addiction (MISA): Factors affecting the Internet use and its consequences

abstract: Internet addiction provoked as a serious mental health issue in the recent decade. In the recent times, Internet addiction had become a global concern to the human society. As the electronic world has become more and more common to the young, younger generations are alleged to be susceptible to the risk of Internet addiction. This research outlines a method to examine the addictive level to the Malaysians Internet surfing. The purpose of our study is to identify the Internet use among Malaysians and the relation of excessive use to the addictive level. We propose and devise an online survey to study the Internet use among the younger generations. The questionnaire consists of five subsections; such as the relevant demographic questions, the diagnosis of the Internet addiction level, related questions on the Internet surfing behavior, the reasons of excessive Internet usage, and the consequences of the addictive behavior. Participants of the online survey were selected randomly from Malaysia, ranging from the age of 7-year old to the age of 30-year old. We found that the Internet use of the younger generations was susceptible to the Internet addiction. The findings reflect that younger generations are vulnerable to the fantasized world.


title: Using Semantics in Predictive Big Data Analytics

abstract: Predictive analytics in the big data era is taking on an ever increasingly important role. Issues related to choice on modeling technique, estimation procedure/algorithm and efficient execution can present significant challenges. For example, selection of appropriate/optimal models for big data analytics often requires careful investigation and considerable expertise which might not always be readily available. In this paper, we propose to use semantic technology to assist data analysts/data scientists in selecting appropriate modeling techniques and building specific models as well as the rationale for the techniques and models selected. To formally describe the modeling techniques, models and results, we developed the Analytics Ontology that supports inferencing for semi-automated model selection. The ScalaTion framework, which currently supports over thirty modeling techniques for predictive big data analytics is used as a test bed for evaluating the use of semantic technology.


title: Dietary Seaweed Modifies Estrogen and Phytoestrogen Metabolism in Healthy Postmenopausal Women

abstract: Seaweed and soy foods are consumed daily in Japan, where breast cancer rates for postmenopausal women are significantly lower than in the West. Likely mechanisms include differences in diet, especially soy consumption, and estrogen metabolism. Fifteen healthy postmenopausal women participated in this double-blind trial of seaweed supplementation with soy challenge. Participants were randomized to 7 wk of either 5 g/d seaweed (Alaria) or placebo (maltodextrin). During wk 7, participants also consumed a daily soy protein isolate (2 mg isoflavones/kg body weight). After a 3-wk washout period, participants were crossed over to the alternate supplement schedule. There was an inverse correlation between seaweed dose (mg/kg body weight) and serum estradiol (E2) (seaweed-placebo = y = -2.29 x dose + 172.3; r = -0.70; P = 0.003), [corrected] which was linear across the range of weights. Soy supplementation increased urinary daidzein, glycitein, genistein, and O-desmethylangolensin (P = 0.0001) and decreased matairesinol and enterolactone (P < 0.05). Soy and seaweed plus soy (SeaSoy) increased urinary excretion of 2-hydroxyestrogen (2-OHE) (P = 0.0001) and the ratio of 2-OHE:16alpha-hydroxyestrone (16alphaOHE(1)) (P = 0.01). For the 5 equol excretors, soy increased urinary equol excretion (P = 0.0001); the combination of SeaSoy further increased equol excretion by 58% (P = 0.0001). Equol producers also had a 315% increase in 2:16 ratio (P = 0.001) with SeaSoy. Seaweed favorably alters estrogen and phytoestrogen metabolism and these changes likely include modulation of colonic bacteria.


title: Interrelations between Service Quality Attributes, Customer Satisfaction and Customer Loyalty in the Retail Banking Sector in Bangladesh

abstract: The main objective of this study is to find the interrelationships between service quality attributes, customer satisfaction and customer loyalty in the retail banking sector in Bangladesh. The study sought to identify the most important attributes in bank settings, which may be used to review characteristics of the banks as experienced by customers. In Bangladesh, no study has yet investigated the above mentioned interrelationship. The purpose of this study is to fill this gap. A review of literature was conducted to find out the relationship among service quality, customer satisfaction and customer loyalty. The literature review confirms this relationship. A survey was conducted to collect data. The sample size of 100 retail banking customers was drawn from different banks in Bangladesh. The result shows that all the service quality attributes are positively related to customer satisfaction and customer satisfaction is positively related to customer loyalty in the retail banking settings in Bangladesh. Empathy demonstrates the highest positive correlation with customer satisfaction and tangibility shows the least positive correlation with customer satisfaction. This study suggests that SERVQUAL [service quality model] is a suitable instrument for measuring the bank service quality in the Bangladeshi context. Therefore, bank managers can use this instrument to assess the bank service quality in Bangladesh.


title: Situation awareness and safety in offshore drill crews

abstract: In many industrial settings, the situation awareness (SA) of workers needs to be maintained at a high level to ensure the safety of their operation. This is particularly relevant to offshore oil-drilling and gas-drilling crews, given the interactive and hazardous nature of their work. This paper presents a review of SA in drilling incidents and results from interviews with oil and gas industry drilling personnel regarding SA in this environment. Accident analysis showed that most errors in SA (67%) occurred at the perceptual level, 20% occurred at comprehension, and 13% arose during projection. Interview findings concluded that isolation from events at home was perceived to be the largest contributory factor for reducing awareness, followed by fatigue and stress. Character change was the most cited indicator of reduced awareness, and communication was thought to be the best method of increasing awareness. Consistency, cohesion, adaptability, and trust were identified as requirements for good team SA.


title: Behavior-Rule Based Intrusion Detection Systems for Safety Critical Smart Grid Applications

abstract: In this paper, a behavior-rule based intrusion detection system (BRIDS) is proposed for securing head-ends (HEs), distribution access points/data aggregation points (DAPs) and subscriber energy meters (SEMs) of a modern electrical grid in which continuity of operation is of the utmost importance. The impact of attacker behaviors on the effectiveness of a behavior-rule intrusion detection design is investigated. Using HEs, DAPs and SEMs as examples, it is demonstrated that a behavior-rule based intrusion detection technique can effectively trade false positives for a high detection probability to cope with sophisticated and hidden attackers to support ultra safe and secure applications. It is shown that BRIDS outperforms contemporary anomaly-based IDSs via comparative analysis.


title: Purposeful Sampling for Qualitative Data Collection and Analysis in Mixed Method Implementation Research

abstract: Purposeful sampling is widely used in qualitative research for the identification and selection of information-rich cases related to the phenomenon of interest. Although there are several different purposeful sampling strategies, criterion sampling appears to be used most commonly in implementation research. However, combining sampling strategies may be more appropriate to the aims of implementation research and more consistent with recent developments in quantitative methods. This paper reviews the principles and practice of purposeful sampling in implementation research, summarizes types and categories of purposeful sampling strategies and provides a set of recommendations for use of single strategy or multistage strategy designs, particularly for state implementation research.


title: Challenges of Adding Causation to Richer Event Descriptions

abstract: The goal of this study is to create guidelines for annotating cause-effect relations as part of the Richer Event Description schema. We present the challenges faced using the definition of causation in terms of counterfactual dependence and propose new guidelines for cause-effect annotation using an alternative definition which treats causation as an intrinsic relation between events. To support the use of such an intrinsic definition, we examine the theoretical problems that the counterfactual definition faces, show how the intrinsic definition solves those problems, and explain how the intrinsic definition adheres to psychological reality, at least for our annotation purposes, better than the counterfactual definition. We then evaluate the new guidelines by presenting results obtained from pilot annotations of ten documents, showing that an inter-annotator agreement (F1-score) of 0.5753 was achieved. The results provide a benchmark for future studies concerning cause-effect annotation in the RED schema.


title: Stochastic Training of Graph Convolutional Networks with Variance Reduction

abstract: Graph convolutional networks (GCNs) are powerful deep neural networks for graph-structured data. However, GCN computes the representation of a node recursively from its neighbors, making the receptive field size grow exponentially with the number of layers. Previous attempts on reducing the receptive field size by subsampling neighbors do not have a convergence guarantee, and their receptive field size per node is still in the order of hundreds. In this paper, we develop control variate based algorithms which allow sampling an arbitrarily small neighbor size. Furthermore, we prove new theoretical guarantee for our algorithms to converge to a local optimum of GCN. Empirical results show that our algorithms enjoy a similar convergence with the exact algorithm using only two neighbors per node. The runtime of our algorithms on a large Reddit dataset is only one seventh of previous neighbor sampling algorithms.


title: Learning complex sequential tasks from demonstration: A pizza dough rolling case study

abstract: This paper introduces a hierarchical framework that is capable of learning complex sequential tasks from human demonstrations through kinesthetic teaching, with minimal human intervention. Via an automatic task segmentation and action primitive discovery algorithm, we are able to learn both the high-level task decomposition (into action primitives), as well as low-level motion parameterizations for each action, in a fully integrated framework. In order to reach the desired task goal, we encode a task metric based on the evolution of the manipulated object during demonstration, and use it to sequence and parametrize each action primitive. We illustrate this framework with a pizza dough rolling task and show how the learned hierarchical knowledge is directly used for autonomous robot execution.


title: Randomized crossover study of 40% vs. 55% carbohydrate weight loss strategies in women with previous gestational diabetes mellitus and non-diabetic women of 130-200% ideal body weight.

abstract: OBJECTIVE ::: The optimum diet composition for weight loss in obese women with or without previous gestational diabetes mellitus remains to be determined. Weight loss may be especially important for the latter group in terms of preventing future gestational as well as non-insulin dependent diabetes mellitus. We studied 40% vs. 55% carbohydrate calorically restricted diets to compare weight loss and metabolic response. ::: ::: ::: METHODS ::: We performed a prospective, 12-week, blinded, randomized crossover study of 25 obese women; 13 of whom had previous gestational diabetes. Each woman was allocated to a treatment regimen for 6 weeks and then "crossed over" to the alternative regimen for an additional 6 weeks. Calories were provided in the form of nutritional supplement bars except for the evening meal that comprised 1/3 of the caloric needs. All subjects were seen and weighed weekly. Metabolic variables including glucose tolerance, glycated proteins, lipids, and percent body fat were measured at the beginning, crossover, and end of the study. ::: ::: ::: RESULTS ::: Women with previous gestational diabetes mellitus were comparable to obese women without a history of previous gestational diabetes except that the former had higher maximum levels of glucose on a glucose tolerance test and higher fasting insulin levels consistent with greater insulin resistance. Weight loss was comparable for all groups during the first 6 weeks but attenuated in all groups during the second 6 weeks of the trial regardless of diabetes history or treatment group allocation. Women with or without a previous history of gestational diabetes had higher triglycerides while on a 55% carbohydrate diet than while on a 40% carbohydrate diet. ::: ::: ::: CONCLUSIONS ::: A weight loss regimen consisting of 40% carbohydrate results in lower triglyceride levels than those achieved with a 55% carbohydrate content diet in obese women. Thus, the hypocaloric diet with the higher fat content produced the more favorable lipid profile in all obese women.


title: Shielded passive devices for silicon-based monolithic microwave and millimeter-wave integrated circuits

abstract: This paper introduces floating shields for on-chip transmission lines, inductors, and transformers implemented in production silicon CMOS or BiCMOS technologies. The shield minimizes losses without requiring an explicit on-chip ground connection. Experimental measurements demonstrate Q-factor ranging from 25 to 35 between 15 and 40 GHz for shielded coplanar waveguide fabricated on 10 /spl Omega//spl middot/cm silicon. This is more than a factor of 2 improvement over conventional on-chip transmission lines (e.g., microstrip, CPW). A floating-shielded, differentially driven 7.4-nH inductor demonstrates a peak Q of 32, which is 35% higher than an unshielded example. Similar results are realizable for on-chip transformers. Floating-shielded bond-pads with 15% less parasitic capacitance and over 60% higher shunt equivalent resistance compared to conventional shielded bondpads are also described. Implementation of floating shields is compatible with current and projected design constraints for production deep-submicron silicon technologies without process modifications. Application examples of floating-shielded passives implemented in a 0.18-/spl mu/m SiGe-BiCMOS are presented, including a 21-26-GHz power amplifier with 23-dBm output at 20% PAE (at 22 GHz), and a 17-GHz WLAN image-reject receiver MMIC which dissipates less than 65 mW from a 2-V supply.


Big Data Cognitive Storage: An Optimum Way of handling the Educational Data in Indian Scenario


title: B4. Analysis and design of single section and three-section ultra-wideband quadrature hybrid couplers

abstract: This paper presents analysis and design of two different slot coupled ultra-wideband quadrature couplers, employing a single section and a three-section configuration. A design procedure based on analysis of different design parameters of both circuit configurations is proposed. The simulation results of the single section coupler shows an amplitude imbalance ± 1dB form 4.8–10.6-GHz. The return loss and the isolation are better than 19dB and 25dB, respectively. The manufactured single section can exhibit 6° phase imbalance from 3–8 GHz. An improved three-section slot coupled hybrid coupler is designed by adding strips and slots of elliptical shape on the both sides of the tapered hexagon. The final circuit of three section coupler can exhibit a coupling of 3dB ±0.75 dB from 3.1–10.6-GHz. The corresponding return loss and isolation is the order of 22 dB and 26 dB, respectively. A phase deviation of ±1.8° between the ports is confirmed.


title: Involuntary covert orienting is contingent on attentional control settings

abstract: Four experiments tested a new hypothesis that involuntary attention shifts are contingent on the relationship between the properties of the eliciting event and the properties required for task performance. In a variant of the spatial cuing paradigm, the relation between cue property and the property useful in locating the target was systematically manipulated. In Experiment 1, invalid abrupt-onset precues produced costs for targets characterized by an abrupt onset but not for targets characterized by a discontinuity in color. In Experiment 2, invalid color precues produced greater costs for color targets than for abrupt-onset targets. Experiment 3 provided converging evidence for this pattern. Experiment 4 investigated the boundary conditions and time course for attention shifts elicited by color discontinuities. The results of these experiments suggest that attention capture is contingent on attentional control settings induced by task demands. Language: en


title: Learning Recurrent Span Representations for Extractive Question Answering

abstract: The reading comprehension task, that asks questions about a given evidence document, is a central problem in natural language understanding. Recent formulations of this task have typically focused on answer selection from a set of candidates pre-defined manually or through the use of an external NLP pipeline. However, Rajpurkar et al. (2016) recently released the SQUAD dataset in which the answers can be arbitrary strings from the supplied text. In this paper, we focus on this answer extraction task, presenting a novel model architecture that efficiently builds fixed length representations of all spans in the evidence document with a recurrent network. We show that scoring explicit span representations significantly improves performance over other approaches that factor the prediction into separate predictions about words or start and end markers. Our approach improves upon the best published results of Wang & Jiang (2016) by 5% and decreases the error of Rajpurkar et al.’s baseline by > 50%.


title: Evolution of Smart Homes for the Elderly

abstract: Smart home technology provides benefits for the elderly in six primary categories: safety, health and nutrition, physical activity, personal hygiene and care, social engagement, and leisure. Safety is about detecting and mitigating, if not removing, hazards from the user's environment. Social engagement relates to the smart home functions that allow the elderly to combat social isolation, such as by connecting the elderly with friends and family. Leisure activities are about how a smart home can allow users to spend their free time. Physical Activity relates to the concept of movement from the user, such as having them engage in non-sedentary activities. Nutrition and Health is related to the monitoring of a user's state of health. Personal hygiene and care encompasses the ways that a smart home can improve the user's well-being and assist in his/her daily activities. This workshop paper will present existing technologies in the aforementioned fields and highlight areas where development is lacking. In addition, an evaluation on past smart home designs is conducted to determine whether they fulfill the six proposed primary categories.


title: Permission-based Malware Detection Mechanisms on Android: Analysis and Perspectives

abstract: Android security has been built upon a permission-based mechanism which restricts accesses of third-party Android applications to critical resources on an Android device. The user must accept the set of permissions an application requires, before proceeding the installation. This process aims to inform the users of the risk of installing and using an application on their device; but most often, even when the permission system is well understood, users are not aware enough of the threat endangered, and trust either the application store or the popularity of the application, and accept the installation without trying to analyse the intentions of the developer. Increasingly, one develops approaches aiming to characterise malware with the permissions, either individually or associatively, with machine learning classifiers. The objective in this paper is to investigate in the literature mechanisms for the characterisation and detection of malware based on the previous aspects. For that, we illustrate and describe limitations of existing works and promising considerations for future research.


title: MedChatBot: An UMLS based Chatbot for Medical Students

abstract: use of natural dialog has great significance in the design of interactive tutoring systems. The nature of student queries can be confined to a small set of templates based on the task domain. This paper describes the development of a chatbot for medical students, that is based on the open source AIML based Chatterbean. We deploy the widely available Unified Medical Language System (UMLS) as the domain knowledge source for generating responses to queries. The AIML based chatbot is customized to convert natural language queries into relevant SQL queries. The SQL queries are run against the knowledge base and results returned to the user in natural dialog. Student survey was carried out to identify various queries posed by students. The chatbot was designed to address common template queries. Knowledge inference techniques were applied to generate responses for queries for which knowledge was not explicitly encoded. Query responses were rated by three experts on a 1-5 point likert scale, who agreed among themselves with Pearson Correlation Coefficient of 0.54 and p < 0.05. The overall average rating assigned by experts was 3.4.


title: Barriers Hindering Ecommerce Adoption: A Case Study of Kurdistan Region of Iraq

abstract: Electronic commerce EC has the potential to improve efficiency and productivity in many areas and has received significant attention in many countries. However, there has been some doubt about the relevance of ecommerce for developing countries. The absence of adequate basic infrastructural, socio-economic, socio-cultural, and government ICT strategies have created a significant barrier in the adoption and growth of ecommerce in the Kurdistan region of Iraq. In this paper, the author shows that to understand the adoption and diffusion of ecommerce in Kurdistan, socio-cultural issues like transactional trust and social effect of shopping must be considered. The paper presents and discusses these issues hindering ecommerce adoption in Kurdistan.


title: 0.5 V output digital low dropout (DLDO) voltage regulator with VCO-based digital feedback loop

abstract: The emergence of wireless sensor networks and its increase in applications call for increasing the number of sensors to extend its functionality. These networks sense and send data periodically hence the need for power management. Two techniques are usually used to extend a node's life namely low power operation and energy harvesting. The power management block would maximize both techniques. A digital low dropout regulator is chosen for its ability to operate in low voltage supplies as well as its ability to regulate and produce an output that is stable with minimal ripple. A VCO was used as a A/D Converter to improve transition response time by allowing multiple PMOS switches to be turned on/off. The proposed regulator was able to supply the necessary voltage of 500mV from an input supply of 1V with a 0.275% regulation error. It achieved 84.78% current efficiency and a settling time of 160.7 us. The digital low dropout regulator with VCO-based digital feedback loop is capable of delivering challenging requirements to a wireless sensor node and enables integration with dynamic voltage and frequency scaling to further improve the lifetime of a single wireless sensor node.


title: Enhanced algorithm for QRS detection using discrete wavelet transform (DWT)

abstract: This paper aims at the detection of QRS position from the ECG signal using wavelet coefficients. An enhanced algorithm has been proposed in this work in order to approve the performance of QRS detection. This algorithm is based on three level, namely detection of suspect QRS regions, the discarding of false detected regions and the extraction of the highest peaks of the true QRS regions detected. This method has been tested on some of the MIT-BIH Arrhythmia database signals. The results of this method are promising in comparison with alternative methods recently published.


title: Assessment of intracranial translucency (IT) in the detection of spina bifida at the 11-13-week scan

abstract: Objective ::: Prenatal diagnosis of open spina bifida is carried out by ultrasound examination in the second trimester of pregnancy. The diagnosis is suspected by the presence of a ‘lemon-shaped’ head and a ‘banana-shaped’ cerebellum, thought to be consequences of caudal displacement of the hindbrain. The aim of the study was to determine whether in fetuses with spina bifida this displacement of the brain is evident from the first trimester of pregnancy. ::: ::: Methods ::: In women undergoing routine ultrasound examination at 11–13 weeks' gestation as part of screening for chromosomal abnormalities, a mid-sagittal view of the fetal face was obtained to measure nuchal translucency thickness and assess the nasal bone. In this view the fourth ventricle, which presents as an intracranial translucency (IT) between the brain stem and choroid plexus, is easily visible. We measured the anteroposterior diameter of the fourth ventricle in 200 normal fetuses and in four fetuses with spina bifida. ::: ::: Results ::: In the normal fetuses the fourth ventricle was always visible and the median anteroposterior diameter increased from 1.5 mm at a crown–rump length (CRL) of 45 mm to 2.5 mm at a CRL of 84 mm. In the four fetuses with spina bifida the ventricle was compressed by the caudally displaced hindbrain and no IT could be seen. ::: ::: Conclusion ::: The mid-sagittal view of the face as routinely used in screening for chromosomal defects can also be used for early detection of open spina bifida. Copyright © 2009 ISUOG. Published by John Wiley & Sons, Ltd.


title: Ginja: one-dollar cloud-based disaster recovery for databases

abstract: Disaster Recovery (DR) is a crucial feature to ensure availability and data protection in modern information systems. A common DR approach requires the replication of services in a set of virtual machines running in the cloud as backups. This leads to considerable monetary costs and managing efforts to keep such cloud VMs. We present G inja , a DR solution for transactional database management systems (DBMS) that uses only cloud storage services such as Amazon S3. G inja works at file-system level to efficiently capture and replicate data updates to a remote cloud storage service, achieving three important goals: (1) reduces the costs for maintaining a cloud-based DR to less than one dollar per month for relevant databases' sizes and workloads (up to 222 x less than the traditional approach of having a DBMS replica in a cloud VM); (2) allows a precise control of the operational costs, durability and performance trade-offs; and (3) introduces a small performance overhead to the DBMS (e.g., less than 5% overhead for the TPC-C workload with ≈ 10 seconds of data loss in case of disasters).


title: a love that Doesn't last: PornoGraPhy ConsumPtIon anD weakeneD CommItment to one's romantIC Partner

abstract: We examined whether the consumption of pornography affects romantic relationships, with the expectation that higher levels of pornography consumption would correspond to weakened commitment in young adult romantic relationships. Study 1 (n = 367) found that higher pornography consumption was related to lower commitment, and Study 2 (n = 34) replicated this finding using observational data. Study 3 (n = 20) participants were randomly assigned to either refrain from viewing pornography or to a self-control task. Those who continued using pornography reported lower levels of commitment than control participants. In Study 4 (n = 67), participants consuming higher levels of pornography flirted more with an extradyadic partner during an online chat. Study 5 (n = 240) found that pornography consumption was positively related to infidelity and this association was mediated by commitment. Overall, a consistent pattern of results was found using a variety of approaches including cross-sectional (Study 1), observati...


title: Gas Sensors Based on Conducting Polymers

abstract: The gas sensors fabricated by using conducting polymers such as polyaniline (PAni), polypyrrole (PPy) and poly (3,4-ethylenedioxythiophene) (PEDOT) as the active layers have been reviewed. This review discusses the sensing mechanism and configurations of the sensors. The factors that affect the performances of the gas sensors are also addressed. The disadvantages of the sensors and a brief prospect in this research field are discussed at the end of the review.


title: Semantic Perceptual Image Compression Using Deep Convolution Networks

abstract: It has long been considered a significant problem to improve the visual quality of lossy imageand video compression. Recent advances in computing power together with the availabilityof large training data sets has increased interest in the application of deep learning cnnsto address image recognition and image processing tasks. Here, we present a powerful cnntailored to the specific task of semantic image understanding to achieve higher visual qualityin lossy compression. A modest increase in complexity is incorporated to the encoder whichallows a standard, off-the-shelf jpeg decoder to be used. While jpeg encoding may beoptimized for generic images, the process is ultimately unaware of the specific content ofthe image to be compressed. Our technique makes jpeg content-aware by designing andtraining a model to identify multiple semantic regions in a given image. Unlike objectdetection techniques, our model does not require labeling of object positions and is able toidentify objects in a single pass. We present a new cnn architecture directed specifically toimage compression, which generates a map that highlights semantically-salient regions sothat they can be encoded at higher quality as compared to background regions. By addinga complete set of features for every class, and then taking a threshold over the sum of allfeature activations, we generate a map that highlights semantically-salient regions so thatthey can be encoded at a better quality compared to background regions. Experimentsare presented on the Kodak PhotoCD dataset and the MIT Saliency Benchmark dataset, in which our algorithm achieves higher visual quality for the same compressed size whilepreserving PSNR1.


title: Leveraging Weak Semantic Relevance for Complex Video Event Classification

abstract: Existing video event classification approaches suffer from limited human-labeled semantic annotations. Weak semantic annotations can be harvested from Web-knowledge without involving any human interaction. However such weak annotations are noisy, thus can not be effectively utilized without distinguishing its reliability. In this paper, we propose a novel approach to automatically maximize the utility of weak semantic annotations (formalized as the semantic relevance of video shots to the target event) to facilitate video event classification. A novel attention model is designed to determine the attention scores of video shots, where the weak semantic relevance is considered as atten-tional guidance. Specifically, our model jointly optimizes two objectives at different levels. The first one is the classification loss corresponding to video-level groundtruth labels, and the second is the shot-level relevance loss corresponding to weak semantic relevance. We use a long short-term memory (LSTM) layer to capture the temporal information carried by the shots of a video. In each timestep, the LSTM employs the attention model to weight the current shot under the guidance of its weak semantic relevance to the event of interest. Thus, we can automatically exploit weak semantic relevance to assist video event classification. Extensive experiments have been conducted on three complex large-scale video event datasets i.e., MEDTest14, ActivityNet and FCVID. Our approach achieves the state-of-the-art classification performance on all three datasets. The significant performance improvement upon the conventional attention model also demonstrates the effectiveness of our model.


title: Automatic Ransomware Detection and Analysis Based on Dynamic API Calls Flow Graph

abstract: In recent cyber incidents, Ransom software (ransomware) causes a major threat to the security of computer systems. Consequently, ransomware detection has become a hot topic in computer security. Unfortunately, current signature-based and static detection model is often easily evadable by obfuscation, polymorphism, compress, and encryption. For overcoming the lack of signature-based and static ransomware detection approach, we have proposed the dynamic ransomware detection system using data mining techniques such as Random Forest (RF), Support Vector Machine (SVM), Simple Logistic (SL) and Naive Bayes (NB) algorithms for detecting known and unknown ransomware. We monitor the actual (dynamic) behaviors of software to generate API calls flow graphs (CFG) and transfer it in a feature space. Thereafter, data normalization and feature selection were applied to select informative features which are the best for discriminating between various categories of software and benign software. Finally, the data mining algorithms were used for building the detection model for judging whether the software is benign software or ransomware. Our experimental results show that our proposed system can be more effective to improve the performance for ransomware detection. Especially, the accuracy and detection rate of our proposed system with Simple Logistic (SL) algorithm can achieve to 98.2% and 97.6%, respectively. Meanwhile, the false positive rate also can be reduced to 1.2%.


title: Data Augmentation for Low-Resource Neural Machine Translation

abstract: The quality of a Neural Machine Translation system depends substantially on the availability of sizable parallel corpora. For low-resource language pairs this is not the case, resulting in poor translation quality. Inspired by work in computer vision, we propose a novel data augmentation approach that targets low-frequency words by generating new sentence pairs containing rare words in new, synthetically created contexts. Experimental results on simulated low-resource settings show that our method improves translation quality by up to 2.9 BLEU points over the baseline and up to 3.2 BLEU over back-translation.


title: Data Encoding Visualization Based Cognitive Emotion Recognition with AC-GAN Applied for Denoising

abstract: Emotion is a subjective, conscious experience when people facing internal or external stimuli. This paper addresses the problem that affective computing is difficult to be put into real-world practical fields intuitively, such as emotion disease diagnosis and so on, due to the non-intuitive data representation. In view of the fact that people's ability to understand two-dimensional images is much higher than that of one-dimensional data, we use Markov Transition Fields to visualize time series signals. MTF images represent the first order Markov transition probability along one dimension and temporal dependency along the other. Besides, with the limitation of experimental equipment and individual differences among volunteers, noise is inevitable. We apply AC-GAN to remove noisy pixels within high dimension and acquire high resolution images before making classification. Then we use Tiled Convolutional Neural Networks on 2 real world datasets to learn high-level features from MTF images. The classification results of our approach are competitive with the state-of-the-art approach. This method makes the visualization based emotion recognition become possible, which is beneficial for the application of cognitive robots or in the medical fields, such as depression and other psychological problems diagnosis, and can help doctors and patients understand the condition more intuitively.


title: Mining for Causal Relationships: A Data-Driven Study of the Islamic State

abstract: The Islamic State of Iraq and al-Sham (ISIS) is a dominant insurgent group operating in Iraq and Syria that rose to prominence when it took over Mosul in June, 2014. In this paper, we present a data-driven approach to analyzing this group using a dataset consisting of 2200 incidents of military activity surrounding ISIS and the forces that oppose it (including Iraqi, Syrian, and the American-led coalition). We combine ideas from logic programming and causal reasoning to mine for association rules for which we present evidence of causality. We present relationships that link ISIS vehicle-bourne improvised explosive device (VBIED) activity in Syria with military operations in Iraq, coalition air strikes, and ISIS IED activity, as well as rules that may serve as indicators of spikes in indirect fire, suicide attacks, and arrests.


title: R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization

abstract: Principal component analysis (PCA) minimizes the sum of squared errors (L 2 -norm) and is sensitive to the presence of outliers. We propose a rotational invariant L 1 -norm PCA (R 1 -PCA). R 1 -PCA is similar to PCA in that (1) it has a unique global solution, (2) the solution are principal eigenvectors of a robust covariance matrix (re-weighted to soften the effects of outliers), (3) the solution is rotational invariant. These properties are not shared by the L 1 -norm PCA. A new subspace iteration algorithm is given to compute R 1 -PCA efficiently. Experiments on several real-life datasets show R 1 -PCA can effectively handle outliers. We extend R 1 -norm to K-means clustering and show that L 1 -norm K-means leads to poor results while R 1 -K-means outperforms standard K-means.


title: Competition-based networks for expert finding

abstract: Finding experts in question answering platforms has important applications, such as question routing or identification of best answers. Addressing the problem of ranking users with respect to their expertise, we propose Competition-Based Expertise Networks (CBEN), a novel community expertise network structure based on the principle of competition among the answerers of a question. We evaluate our approach on a very large dataset from Yahoo! Answers using a variety of centrality measures. We show that it outperforms state-of-the-art network structures and, unlike previous methods, is able to consistly outperform simple metrics like best answer count. We also analyse question answering forums in Yahoo! Answers, and show that they can be characterised by factual or subjective information seeking behavior, social discussions and the conducting of polls or surveys. We find that the ability to identify experts greatly depends on the type of forum, which is directly reflected in the structural properties of the expertise networks.


title: Storm Pub-Sub: High Performance, Scalable Content Based Event Matching System Using Storm

abstract: Storm pub-sub is a novel high performance publish subscribe system designed to efficiently match events and the subscriptions with high throughput. Moving a content based pub-sub system first to a local cluster and then to a distributed cluster framework is for high performance and scalability. We depart from the use of broker overlays, where each server must support the whole range of operations of a pub-sub service, as well as overlay management and routing functionality. In this system different operations involved in pub-sub are separated to leverage their natural potential for parallelization using bolts. The storm pub-sub is compared with the traditional pub-sub system Siena, a broker based architecture. Through experimentation on local cluster as well as on distributed cluster we show that our approach of designing publish subscribe system on storm scales well for high volume of data. Storm pub-sub system approximately produces 2200 event/s on distributed cluster. In this paper we describe design and implementation of storm pub-sub and evaluate it in terms of scalability and throughput.


title: Mal-Netminer: Malware Classification Approach based on Social Network Analysis of System Call Graph

abstract: As the security landscape evolves over time, where thousands of species of malicious codes are seen every day, antivirus vendors strive to detect and classify malware families for efficient and effective responses against malware campaigns. To enrich this effort and by capitalizing on ideas from the social network analysis domain, we build a tool that can help classify malware families using features driven from the graph structure of their system calls. To achieve that, we first construct a system call graph that consists of system calls found in the execution of the individual malware families. To explore distinguishing features of various malware species, we study social network properties as applied to the call graph, including the degree distribution, degree centrality, average distance, clustering coefficient, network density, and component ratio. We utilize features driven from those properties to build a classifier for malware families. Our experimental results show that “influence-based” graph metrics such as the degree centrality are effective for classifying malware, whereas the general structural metrics of malware are less effective for classifying malware. Our experiments demonstrate that the proposed system performs well in detecting and classifying malware families within each malware class with accuracy greater than 96%.


title: WFST Enabled Solutions to ASR Problems: Beyond HMM Decoding

abstract: During the last decade, weighted finite-state transducers (WFSTs) have become popular in speech recognition. While their main field of application remains hidden Markov model (HMM) decoding, the WFST framework is now also seen as a brick in solutions to many other central problems in automatic speech recognition (ASR). These solutions are less known, and this work aims at giving an overview of the applications of WFSTs in large-vocabulary continuous speech recognition (LVCSR) besides HMM decoding: discriminative acoustic model training, Bayes risk decoding, and system combination. The application of the WFST framework has a big practical impact: we show how the framework helps to structure problems, to develop generic solutions, and to delegate complex computations to WFST toolkits. In this paper, we review the literature, discuss existing approaches, and provide new insights into WFST enabled solutions. We also present a novel, purely WFST-based algorithm for computing the exact Bayes risk hypothesis from a lattice with the Levenshtein distance as loss function. We present the problems and their solutions in a unified framework and discuss the advantages and limits of using WFSTs. We do not provide new experimental results, but refer to the existing literature. Our work helps to identify where and how the transducer framework can contribute to a compact and generic solution to LVCSR problems.


title: Modeling the Dynamic Characteristics of Pneumatic Muscle

abstract: A pneumatic muscle (PM) system was studied to determine whether a three-element model could describe its dynamics. As far as the authors are aware, this model has not been used to describe the dynamics of PM. A new phenomenological model consists of a contractile (force-generating) element, spring element, and damping element in parallel. The PM system was investigated using an apparatus that allowed precise and accurate actuation pressure (P) control by a linear servo-valve. Length change of the PM was measured by a linear potentiometer. Spring and damping element functions of P were determined by a static perturbation method at several constant P values. These results indicate that at constant P, PM behaves as a spring and damper in parallel. The contractile element function of P was determined by the response to a step input in P, using values of spring and damping elements from the perturbation study. The study showed that the resulting coefficient functions of the three-element model describe the dynamic response to the step input of P accurately, indicating that the static perturbation results can be applied to the dynamic case. This model is further validated by accurately predicting the contraction response to a triangular P waveform. All three elements have pressure-dependent coefficients for pressure P in the range 207 < or = P < or = 621 kPa (30 < or = P < or = 90 psi). Studies with a step decrease in P (relaxation of the PM) indicate that the damping element coefficient is smaller during relaxation than contraction.


title: A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories

abstract: Representation and learning of commonsense knowledge is one of the foundational problems in the quest to enable deep language understanding. This issue is particularly challenging for understanding casual and correlational relationships between events. While this topic has received a lot of interest in the NLP community, research has been hindered by the lack of a proper evaluation framework. This paper attempts to address this problem with a new framework for evaluating story understanding and script learning: the 'Story Cloze Test'. This test requires a system to choose the correct ending to a four-sentence story. We created a new corpus of ~50k five-sentence commonsense stories, ROCStories, to enable this evaluation. This corpus is unique in two ways: (1) it captures a rich set of causal and temporal commonsense relations between daily events, and (2) it is a high quality collection of everyday life stories that can also be used for story generation. Experimental evaluation shows that a host of baselines and state-of-the-art models based on shallow language understanding struggle to achieve a high score on the Story Cloze Test. We discuss these implications for script and story learning, and offer suggestions for deeper language understanding.


title: Playing for real: video games and stories for health-related behavior change.

abstract: BACKGROUND ::: Video games provide extensive player involvement for large numbers of children and adults, and thereby provide a channel for delivering health behavior change experiences and messages in an engaging and entertaining format. ::: ::: ::: METHOD ::: Twenty-seven articles were identified on 25 video games that promoted health-related behavior change through December 2006. ::: ::: ::: RESULTS ::: Most of the articles demonstrated positive health-related changes from playing the video games. Variability in what was reported about the games and measures employed precluded systematically relating characteristics of the games to outcomes. Many of these games merged the immersive, attention-maintaining properties of stories and fantasy, the engaging properties of interactivity, and behavior-change technology (e.g., tailored messages, goal setting). Stories in video games allow for modeling, vicarious identifying experiences, and learning a story's "moral," among other change possibilities. ::: ::: ::: CONCLUSIONS ::: Research is needed on the optimal use of game-based stories, fantasy, interactivity, and behavior change technology in promoting health-related behavior change.


title: Psychiatry & the psychedelic drugs. Past, present & future

abstract: &NA; The classical psychedelic drugs, including psilocybin, lysergic acid diethylamide and mescaline, were used extensively in psychiatry before they were placed in Schedule I of the UN Convention on Drugs in 1967. Experimentation and clinical trials undertaken prior to legal sanction suggest that they are not helpful for those with established psychotic disorders and should be avoided in those liable to develop them. However, those with so‐called ‘psychoneurotic’ disorders sometimes benefited considerably from their tendency to ‘loosen’ otherwise fixed, maladaptive patterns of cognition and behaviour, particularly when given in a supportive, therapeutic setting. Pre‐prohibition studies in this area were sub‐optimal, although a recent systematic review in unipolar mood disorder and a meta‐analysis in alcoholism have both suggested efficacy. The incidence of serious adverse events appears to be low. Since 2006, there have been several pilot trials and randomised controlled trials using psychedelics (mostly psilocybin) in various non‐psychotic psychiatric disorders. These have provided encouraging results that provide initial evidence of safety and efficacy, however the regulatory and legal hurdles to licensing psychedelics as medicines are formidable. This paper summarises clinical trials using psychedelics pre and post prohibition, discusses the methodological challenges of performing good quality trials in this area and considers a strategic approach to the legal and regulatory barriers to licensing psychedelics as a treatment in mainstream psychiatry. This article is part of the Special Issue entitled ‘Psychedelics: New Doors, Altered Perceptions’. HighlightsPsychedelics may be useful for treating resistant depression, anxiety & addictions.Pilot trials with psilocybin in depression show early evidence of safety & efficacy.The legal & regulatory hurdles to approval are formidable, but surmountable.


title: Agile development: overcoming a short-term focus in implementing best practices

abstract: Agile development deemphasizes long-term planning in favor of short-term adaptiveness. This is a strength in a rapidly changing development environment. However, this short-term focus creates a temptation to neglect best practices that are essential to long-term success. This report outlines my experience as a software developer in a leading internet portal that thrives on agile development using SCRUM. It describes the problems that arose when best practices were ignored and how our team overcame them.


title: Benchmark synthesis using the LRU cache hit function

abstract: The LRU cache hit function is used as a general characterization of locality of reference to address the synthesis question of whether benchmarks can be created that have a required locality of reference. Several results are given that show circumstances under which this synthesis can or cannot be achieved. An additional characterization called the warm-start cache hit function is introduced and shown to be efficiently computable. The operations of repetition and replication are used to form new programs, and their characteristics are derived. Using these operations, a general benchmark synthesis technique is obtained and demonstrated with an example. >


title: doi:10.1111/j.1420-9101.2006.01258.x MINI REVIEW Social semantics: altruism, cooperation, mutualism, strong reciprocity and group selection

abstract: From an evolutionary perspective, social behaviours are those which have fitness consequences for both the individual that performs the behaviour, and another individual. Over the last 43 years, a huge theoretical and empirical literature has developed on this topic. However, progress is often hindered by poor communication between scientists, with different people using the same term to mean different things, or different terms to mean the same thing. This can obscure what is biologically important, and what is not. The potential for such semantic confusion is greatest with interdisciplinary research. Our aim here is to address issues of semantic confusion that have arisen with research on the problem of cooperation. In particular, we: (i) discuss confusion over the terms kin selection, mutualism, mutual benefit, cooperation, altruism, reciprocal altruism, weak altruism, altruistic punishment, strong reciprocity, group selection and direct fitness; (ii) emphasize the need to distinguish between proximate (mechanism) and ultimate (survival value) explanations of behaviours. We draw examples from all areas, but especially recent work on humans and microbes.