title: Circumcision of male infants and children as a public health measure in developed countries: A critical assessment of recent evidence

abstract: ABSTRACTIn December of 2014, an anonymous working group under the United States’ Centers for Disease Control and Prevention (CDC) issued a draft of the first-ever federal recommendations regarding male circumcision. In accordance with the American Academy of Pediatrics’ circumcision policy from 2012 – but in contrast to the more recent 2015 policy from the Canadian Paediatric Society as well as prior policies (still in force) from medical associations in Europe and Australasia – the CDC suggested that the benefits of the surgery outweigh the risks. In this article, we provide a brief scientific and conceptual analysis of the CDC’s assessment of benefit versus risk, and argue that it deserves a closer look. Although we set aside the burgeoning bioethical debate surrounding the moral permissibility of performing non-therapeutic circumcisions on healthy minors, we argue that, from a scientific and medical perspective, current evidence suggests that such circumcision is not an appropriate public health measur...


title: 60-GHz 64- and 256-Elements Wafer-Scale Phased-Array Transmitters Using Full-Reticle and Subreticle Stitching Techniques

abstract: This paper presents 60-GHz wafer-scale transmit phased arrays with 64- and 256-elements spaced $\lambda $ /2 apart in the $x$ - and $y$ -directions, and occupying an area of 21.4 $\times $ 22 mm2 (471 mm2) and 41.4 $\times $ 42 mm2 (1740 mm2), respectively. The 64-element phased array is built as a complete reticle and includes 64 independent transmit channels with 5-b phase control, 3-b (9 dB) amplitude control, a saturated output power of 3 dBm at the antenna port, a 1–64 distribution network with redundant line amplifiers, and a high-efficiency on-chip antenna at each element. In addition, redundant serial digital interface and power strips, dual series metal–insulator–metal capacitors, and multiple RF inputs are employed for improved yield. The 256-element array uses the same phased-array blocks as the 64-element design, but is built using a subreticle stitching technique so as to result in a chip which is larger than the standard reticle size (22 $\times $ 22 mm2). The 64- and 256-element arrays result in a half-power beamwidth of 12° and 6° in the $E$ - and $H$ -planes, a directivity of 23 and 29 dB, respectively, and scan to ±55° in the $E$ - and $H$ -planes with near-ideal patterns and a cross-polarization level of lesser than −30 dB. The measured equivalent isotropically radiated power (EIRP) of the 64-element array is 38 dBm at 62 GHz with a 3-dB bandwidth of 61–63 GHz, while that of the 256-element array is 45 dBm at 61 GHz with a 3-dB beamwidth of 58–64 GHz. A 1–4-Gb/s communication system is also demonstrated using the 64-element phased array up to ±45° scan angles, and at 4-, 30-, and 100-m ranges. To the best of our knowledge, this paper represents the first demonstration of large size (64- and 256-element) phased-array transmitters on a single wafer.


Keynote comment: nitrosatable drugs, cancer, and guidelines for genotoxicity


title: A Comparison of Competitive Neural Network with Other AI Techniques in Manufacturing Cell Formation

abstract: The cell formation (CF) problem aims to transform the incidence matrix into block diagonal form. Numerous techniques are developed for this purpose ranging from mathematical programming to heuristic and AI techniques. In this study a simple but effective competitive neural network algorithm (CNN) is applied and compared with genetic algorithms, tabu search, simulated annealing and ant systems by making use of some well known data sets from literature. As a result at 14 out of 15 cases, better results are obtained by CNN.


title: Solving odd even sudoku puzzles by binary integer linear programming

abstract: Solving sudoku is a very challenging problem. It is not only a logic problem, but also can be seen as a mathematic problem. In this paper, we develop a binary integer linear programming (BILP) model for solving Odd/Even sudoku puzzles. Consequently, we solve the BILP model by using the optimization toolbox of MATLAB software. The performance of the proposed model was tested under different difficulty levels of Odd/Even sudoku puzzles. Numerical results show that it can successfully solve the Odd/Even sudoku puzzles in a quick time.


Large-Step Markov Chains for the Traveling Salesman Problem


title: Empathy deficits and trait emotional intelligence in psychopathy and Machiavellianism

abstract: This study investigated the relationships between psychopathy (primary and secondary), Machiavellianism, trait emotional intelligence and empathy, using an image task that required an appropriate empathic response to the emotional displays of others (happy, sad and neutral). Results indicated that primary psychopathy and Machiavellianism were positively associated with the experience of positive affect from sad stimuli, while secondary psychopathy and Machiavellianism were positively associated with the experience of negative affect in response to neutral stimuli, and the opposite pattern was found for trait emotional intelligence. Regressional analyses demonstrated that secondary psychopathy, Machiavellianism, trait emotional intelligence and state anxiety are important predictors when stimuli are ambiguous.


title: Concept indexing

abstract: Marking text in a document is a convenient way of identifying bits of knowledge that are relevant for the reader, a colleague or a larger group. Based on such markings, networks of concepts with hyperlinks to their occurrences in a collection of documents can be developed. On the Internet, marked documents can easily be shared, concepts can be constructed collaboratively and the concept-document network can be used for navigation and direct access. Text marking, grounded concepts and the Internet as base technology are characteristics of our tool for managing so called “concept indexes”. We describe the current and the new design and outline some application scenarios: electronic help desks, information digests on the Web, teaching design in virtual classes and planning under quality control in distributed teams.


title: Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: Application to epilepsy lesion screening.

abstract: Abstract In this study, we propose a novel anomaly detection model targeting subtle brain lesions in multiparametric MRI. To compensate for the lack of annotated data adequately sampling the heterogeneity of such pathologies, we cast this problem as an outlier detection problem and introduce a novel configuration of unsupervised deep siamese networks to learn normal brain representations using a series of non-pathological brain scans. The proposed siamese network, composed of stacked convolutional autoencoders as subnetworks is designed to map patches extracted from healthy control scans only and centered at the same spatial localization to ‘close’ representations with respect to the chosen metric in a latent space. It is based on a novel loss function combining a similarity term and a regularization term compensating for the lack of dissimilar pairs. These latent representations are then fed into oc-SVM models at voxel-level to produce anomaly score maps. We evaluate the performance of our brain anomaly detection model to detect subtle epilepsy lesions in multiparametric (T1-weighted, FLAIR) MRI exams considered as normal (MRI-negative). Our detection model trained on 75 healthy subjects and validated on 21 epilepsy patients (with 18 MRI-negatives) achieves a maximum sensitivity of 61% on the MRI-negative lesions, identified among the 5 most suspicious detections on average. It is shown to outperform detection models based on the same architecture but with stacked convolutional or Wasserstein autoencoders as unsupervised feature extraction mechanisms.


title: High-throughput determination of structural phase diagram and constituent phases using GRENDEL

abstract: Advances in high-throughput materials fabrication and characterization techniques have resulted in faster rates of data collection and rapidly growing volumes of experimental data. To convert this mass of information into actionable knowledge of material process–structure–property relationships requires high-throughput data analysis techniques. This work explores the use of the Graph-based endmember extraction and labeling (GRENDEL) algorithm as a high-throughput method for analyzing structural data from combinatorial libraries, specifically, to determine phase diagrams and constituent phases from both x-ray diffraction and Raman spectral data. The GRENDEL algorithm utilizes a set of physical constraints to optimize results and provides a framework by which additional physics-based constraints can be easily incorporated. GRENDEL also permits the integration of database data as shown by the use of critically evaluated data from the Inorganic Crystal Structure Database in the x-ray diffraction data analysis. Also the Sunburst radial tree map is demonstrated as a tool to visualize material structure–property relationships found through graph based analysis.


title: Effects of first aid training in the kindergarten - a pilot study

abstract: ObjectiveChildren can be the only persons present in an emergency situation. Aim of the study was to evaluate the effects of a first aid course for 4-5-year-old kindergarten children given by a first aid instructor and kindergarten teachers.MethodsA mixed methods approach using both quantitative and qualitative methods was used to investigate the effects of teaching first aid in the kindergarten in the present study. 10 kindergarten children at the age of 4-5 years were included in a pilot-study, 5 girls and 5 boys. Three of them were four years and seven were five years old. Two months after completion of the first aid course children were tested in a scenario where the children had to provide first aid to an unconscious victim after a cycle accident. The next seven months the children were followed by participant observation.ResultsThe findings suggest that 4-5-year-old children are able to learn and apply basic first aid. Tested two months after course completion 70% of the children assessed consciousness correctly and knew the correct emergency telephone number; 60% showed correct assessment of breathing and 40% of the participants accomplished the other tasks (giving correct emergency call information, knowledge of correct recovery position, correct airway management) correctly. Many of the children showed their capabilities to do so in a first aid scenario although some participants showed fear of failure in the test scenario. In an informal group testing most of these children could perform first aid measures, too. Teaching first aid also lead to more active helping behaviour and increased empathy in the children.ConclusionKindergarten children aged 4-5 years can learn basic fist aid. First aid training should start in the kindergarten.


title: One-shot and few-shot learning of word embeddings

abstract: Standard deep learning systems require thousands or millions of examples to learn a concept, and cannot integrate new concepts easily. By contrast, humans have an incredible ability to do one-shot or few-shot learning. For instance, from just hearing a word used in a sentence, humans can infer a great deal about it, by leveraging what the syntax and semantics of the surrounding words tells us. Here, we draw inspiration from this to highlight a simple technique by which deep recurrent networks can similarly exploit their prior knowledge to learn a useful representation for a new word from little data. This could make natural language processing systems much more flexible, by allowing them to learn continually from the new words they encounter.


title: VR is on the Edge: How to Deliver 360° Videos in Mobile Networks

abstract: VR/AR is rapidly progressing towards enterprise and end customers with the promise of bringing immersive experience to numerous applications. Soon it will target smartphones from the cloud and 360° video delivery will need unprecedented requirements for ultra-low latency and ultra-high throughput to mobile networks. Latest developments in NFV and Mobile Edge Computing reveal already the potential to enable VR streaming in cellular networks and to pave the way towards 5G and next stages in VR technology. In this paper we present a Field Of View (FOV) rendering solution at the edge of a mobile network, designed to optimize the bandwidth and latency required by VR 360° video streaming. Preliminary test results show the immediate benefits in bandwidth saving this approach can provide and generate new directions for VR/AR network research.


title: Semantic object segmentation via detection in weakly labeled video

abstract: Semantic object segmentation in video is an important step for large-scale multimedia analysis. In many cases, however, semantic objects are only tagged at video-level, making them difficult to be located and segmented. To address this problem, this paper proposes an approach to segment semantic objects in weakly labeled video via object detection. In our approach, a novel video segmentation-by-detection framework is proposed, which first incorporates object and region detectors pre-trained on still images to generate a set of detection and segmentation proposals. Based on the noisy proposals, several object tracks are then initialized by solving a joint binary optimization problem with min-cost flow. As such tracks actually provide rough configurations of semantic objects, we thus refine the object segmentation while preserving the spatiotemporal consistency by inferring the shape likelihoods of pixels from the statistical information of tracks. Experimental results on Youtube-Objects dataset and SegTrack v2 dataset demonstrate that our method outperforms state-of-the-arts and shows impressive results.


title: THE SHRINKING RATE OF UTILISED AGRICULTURAL LAND AND ITS COMPONENTS IN THE BALTIC SEA REGION COUNTRIES

abstract: The temporal and spatial changes of land use are important while studying the rural landscapes on both the regional-geographical and environmentalscales. In the Baltic Sea region with nine countries (including Russian Federation, represented by Kaliningrad oblast, the utilisedagricultural land (UAL) makes about 34 % of total region area on the average. The current research revealed the trends of different shrinkingrate of UAL in nine studied countries from 1993 to 2008: it varied within the range between 3 % in Denmark and 43 % in Estonia. Researchfocus was given to the changes in ratio between arable land and area under meadows and pastures: it increased from 6.5 to 10.9 during 15years period (1993–2008) on the average, that shows the increase in land use and farming intensity even the UAL area was shrunk.KEY WORDS: utilised agricultural land, arable land, meadows and pastures, shrinking rate.


title: Robust random cut forest based anomaly detection on streams

abstract: In this paper we focus on the anomaly detection problem for dynamic data streams through the lens of random cut forests. We investigate a robust random cut data structure that can be used as a sketch or synopsis of the input stream. We provide a plausible definition of non-parametric anomalies based on the influence of an unseen point on the remainder of the data, i.e., the externality imposed by that point. We show how the sketch can be efficiently updated in a dynamic data stream. We demonstrate the viability of the algorithm on publicly available real data.


title: Robust Reinforcement Learning

abstract: This letter proposes a new reinforcement learning (RL) paradigm that explicitly takes into account input disturbance as well as modeling errors. The use of environmental models in RL is quite popular for both offline learning using simulations and for online action planning. However, the difference between the model and the real environment can lead to unpredictable, and often unwanted, results. Based on the theory of H∞ control, we consider a differential game in which a "disturbing" agent tries to make the worst possible disturbance while a "control" agent tries to make the best control input. The problem is formulated as finding a min-max solution of a value function that takes into account the amount of the reward and the norm of the disturbance. We derive online learning algorithms for estimating the value function and for calculating the worst disturbance and the best control in reference to the value function. We tested the paradigm, which we call robust reinforcement learning (RRL), on the control task of an inverted pendulum. In the linear domain, the policy and the value function learned by online algorithms coincided with those derived analytically by the linear H∞ control theory. For a fully nonlinear swing-up task, RRL achieved robust performance with changes in the pendulum weight and friction, while a standard reinforcement learning algorithm could not deal with these changes. We also applied RRL to the cart-pole swing-up task, and a robust swing-up policy was acquired.


title: Recollection and Familiarity in Recognition Memory: An EventRelated

abstract: The question of whether recognition memory judgments with and without recollection reflect dissociable patterns of brain activity is unresolved. We used event-related, functional magnetic resonance imaging (fMRI) of 12 healthy volunteers to measure hemodynamic responses associated with both studying and recognizing words. Volunteers made one of three judgments to each word during recognition: whether they recollected seeing it during study (R judgments), whether they experienced a feeling of familiarity in the absence of recollection (K judgments), or whether they did not remember seeing it during study (N judgments). Both R and K judgments for studied words were associated with enhanced responses in left prefrontal and left parietal cortices relative to N judgments for unstudied words. The opposite pattern was observed in bilateral temporoccipital regions and amygdalae. R judgments for studied words were associated with enhanced responses in anterior left prefrontal, left parietal, and posterior cingulate regions relative to K judgments. At study, a posterior left prefrontal region exhibited an enhanced response to words subsequently given R versus K judgments, but the response of this region during recognition did not differentiate R and K judgments. K judgments for studied words were associated with enhanced responses in right lateral and medial prefrontal cortex relative to both R judgments for studied words and N judgments for unstudied words, a difference we attribute to greater monitoring demands when memory judgments are less certain. These results suggest that the responses of different brain regions do dissociate according to the phenomenology associated with memory retrieval.


title: LVDS driver design for high speed serial link in 0.13um CMOS technology

abstract: This paper presents a LVDS (low voltage differential signal) driver, which works at 2Gbps, with a pre-emphasis circuit compensating the attenuation of limited bandwidth of channel. To make the output common-mode (CM) voltage stable over process, temperature, and supply voltage variations, a closed-loop negative feedback circuit is added in this work. The LVDS driver is designed in 0.13um CMOS technology using both thick (3.3V) and thin (1.2V) gate oxide device, simulated with transmission line model and package parasitic model. The simulated results show that this driver can operate up to 2Gbps with random data patterns.


title: Emergence of simple-cell receptive field properties by learning a sparse code for natural images

abstract: THE receptive fields of simple cells in mammalian primary visual cortex can be characterized as being spatially localized, oriented1–4 and bandpass (selective to structure at different spatial scales), comparable to the basis functions of wavelet transforms5,6. One approach to understanding such response properties of visual neurons has been to consider their relationship to the statistical structure of natural images in terms of efficient coding7–12. Along these lines, a number of studies have attempted to train unsupervised learning algorithms on natural images in the hope of developing receptive fields with similar properties13–18, but none has succeeded in producing a full set that spans the image space and contains all three of the above properties. Here we investigate the proposal8,12 that a coding strategy that maximizes sparseness is sufficient to account for these properties. We show that a learning algorithm that attempts to find sparse linear codes for natural scenes will develop a complete family of localized, oriented, bandpass receptive fields, similar to those found in the primary visual cortex. The resulting sparse image code provides a more efficient representation for later stages of processing because it possesses a higher degree of statistical independence among its outputs.


title: Dog's life: wearable activity recognition for dogs

abstract: Health and well-being of dogs, either domesticated pets or service animals, are major concerns that are taken seriously for ethical, emotional, and financial reasons. Welfare assessments in dogs rely on objective observations of both frequency and variability of individual behaviour traits, which is often difficult to obtain in a dog's everyday life. In this paper we have identified a set of activities, which are linked to behaviour traits that are relevant for a dog's wellbeing. We developed a collar-worn accelerometry platform that records dog behaviours in naturalistic environments. A statistical classification framework is used for recognising dog activities. In an experimental evaluation we analysed the naturalistic behaviour of 18 dogs and were able to recognise a total of 17 different activities with approximately 70% classification accuracy. The presented system is the first of its kind that allows for robust and detailed analysis of dog activities in naturalistic environments.


Of Special Lys Steel Plate Shear Walls


title: Translation Microscopy (TRAM) for super-resolution imaging

abstract: Super-resolution microscopy is transforming our understanding of biology but accessibility is limited by its technical complexity, high costs and the requirement for bespoke sample preparation. We present a novel, simple and multi-color super-resolution microscopy technique, called translation microscopy (TRAM), in which a super-resolution image is restored from multiple diffraction-limited resolution observations using a conventional microscope whilst translating the sample in the image plane. TRAM can be implemented using any microscope, delivering up to 7-fold resolution improvement. We compare TRAM with other super-resolution imaging modalities, including gated stimulated emission deletion (gSTED) microscopy and atomic force microscopy (AFM). We further developed novel ‘ground-truth’ DNA origami nano-structures to characterize TRAM, as well as applying it to a multi-color dye-stained cellular sample to demonstrate its fidelity, ease of use and utility for cell biology.


title: Force Generation by Parallel Combinations of Fiber-Reinforced Fluid-Driven Actuators

abstract: The compliant structure of soft robotic systems enables a variety of novel capabilities in comparison to traditional rigid-bodied robots. A subclass of soft fluid-driven actuators known as fiber reinforced elastomeric enclosures (FREEs) is particularly well suited as actuators for these types of systems. FREEs are inherently soft and can impart spatial forces without imposing a rigid structure. Furthermore, they can be configured to produce a large variety of force and moment combinations. In this paper we explore the potential of combining multiple differently configured FREEs in parallel to achieve fully controllable multi-dimensional soft actuation. To this end, we propose a novel methodology to represent and calculate the generalized forces generated by soft actuators as a function of their internal pressure. This methodology relies on the notion of a state dependent fluid Jacobian that yields a linear expression for force. We employ this concept to construct the set of all possible forces that can be generated by a soft system in a given state. This force zonotope can be used to inform the design and control of parallel combinations of soft actuators. The approach is verified experimentally with the parallel combination of three carefully designed actuators constrained to a 2DOF testing rig. The force predictions matched measured values with a root-mean-square error of less than 1.5 N force and 8 x 10^(-3)Nm moment, demonstrating the utility of the presented methodology.


title: Visual abstraction and stylisation of maps

abstract: We explore visual map abstraction for the generation of stylized renderings of 2D map data. We employ techniques that are centred around the concept of shape simplification and graph layout and that allow iterative abstraction of 2D maps. We use data from publicly available sources and show how we can iteratively generate aesthetic renditions of these maps. These renditions do not have the goal to allow for navigation tasks, but instead show the map data in a distorted manner. The techniques used to create these images apply simplification, abstraction/generalisation, and displacement operations to the map elements in varying orders and add stylistic shading to produce aesthetic renditions for print or electronic displays. The degree of abstraction/generalisation can be individually chosen and determines the characteristics of the distorted map: whether components retain their shape, degenerate, or are processed in a manner that the abstraction becomes the focus of the image rather than the underlying map data. The renditions can be further personalized by choosing shading and colours for this shading. Together, the presented techniques allow for playful and creative exploration of aesthetic renditions of 2D map data.


title: A Turing Test for Computer Game Bots

abstract: In this paper, a version of the Turing Test is proposed, to test the ability of computer game playing agents (ldquobotsrdquo) to imitate human game players. The proposed test has been implemented as a bot design and programming competition, the 2K BotPrize Contest. The results of the 2008 competition are presented and analyzed. We find that the Test is challenging, but that current techniques show promise. We also suggest probable future directions for developing improved bots.


title: Extended XML Tree Pattern Matching: Theories and Algorithms

abstract: As business and enterprises generate and exchange XML data more often, there is an increasing need for efficient processing of queries on XML data. Searching for the occurrences of a tree pattern query in an XML database is a core operation in XML query processing. Prior works demonstrate that holistic twig pattern matching algorithm is an efficient technique to answer an XML tree pattern with parent-child (P-C) and ancestor-descendant (A-D) relationships, as it can effectively control the size of intermediate results during query processing. However, XML query languages (e.g., XPath and XQuery) define more axes and functions such as negation function, order-based axis, and wildcards. In this paper, we research a large set of XML tree pattern, called extended XML tree pattern, which may include P-C, A-D relationships, negation functions, wildcards, and order restriction. We establish a theoretical framework about “matching cross” which demonstrates the intrinsic reason in the proof of optimality on holistic algorithms. Based on our theorems, we propose a set of novel algorithms to efficiently process three categories of extended XML tree patterns. A set of experimental results on both real-life and synthetic data sets demonstrate the effectiveness and efficiency of our proposed theories and algorithms.


title: Fibroblast Growth Factor Receptor 2 (FGFR2) Mutation Related Syndromic Craniosynostosis

abstract: Craniosynostosis results from the premature fusion of cranial sutures, with an incidence of 1 in 2,100-2,500 live births. The majority of cases are non-syndromic and involve single suture fusion, whereas syndromic cases often involve complex multiple suture fusion. The fibroblast growth factor receptor 2 (FGFR2) gene is perhaps the most extensively studied gene that is mutated in various craniosynostotic syndromes including Crouzon, Apert, Pfeiffer, Antley-Bixler, Beare-Stevenson cutis gyrata, Jackson-Weiss, Bent Bone Dysplasia, and Seathre-Chotzen-like syndromes. The majority of these mutations are missense mutations that result in constitutive activation of the receptor and downstream molecular pathways. Treatment involves a multidisciplinary approach with ultimate surgical fixation of the cranial deformity to prevent further sequelae. Understanding the molecular mechanisms has allowed for the investigation of different therapeutic agents that can potentially be used to prevent the disorders. Further research efforts are need to better understand screening and effective methods of early intervention and prevention. Herein, the authors provide a comprehensive update on FGFR2-related syndromic craniosynostosis.


title: A Top-Down Approach for Video Summarization

abstract: While most existing video summarization approaches aim to identify important frames of a video from either a global or local perspective, we propose a top-down approach consisting of scene identification and scene summarization. For scene identification, we represent each frame with global features and utilize a scalable clustering method. We then formulate scene summarization as choosing those frames that best cover a set of local descriptors with minimal redundancy. In addition, we develop a visual word-based approach to make our approach more computationally scalable. Experimental results on two benchmark datasets demonstrate that our proposed approach clearly outperforms the state-of-the-art.


title: Revisiting Stress Majorization as a Unified Framework for Interactive Constrained Graph Visualization

abstract: We present an improved stress majorization method that incorporates various constraints, including directional constraints without the necessity of solving a constraint optimization problem. This is achieved by reformulating the stress function to impose constraints on both the edge vectors and lengths instead of just on the edge lengths (node distances). This is a unified framework for both constrained and unconstrained graph visualizations, where we can model most existing layout constraints, as well as develop new ones such as the star shapes and cluster separation constraints within stress majorization. This improvement also allows us to parallelize computation with an efficient GPU conjugant gradient solver, which yields fast and stable solutions, even for large graphs. As a result, we allow the constraint-based exploration of large graphs with 10K nodes — an approach which previous methods cannot support.


title: Hybrid HMM/BLSTM-RNN for robust speech recognition

abstract: The question how to integrate information from different sources in speech decoding is still only partially solved (layered architecture versus integrated search). We investigate the optimal integration of information from Artificial Neural Nets in a speech decoding scheme based on a Dynamic Bayesian Network for noise robust ASR. A HMM implemented by the DBN cooperates with a novel Recurrent Neural Network (BLSTM-RNN), which exploits long-range context information to predict a phoneme for each MFCC frame. When using the identity of the most likely phoneme as a direct observation, such a hybrid system has proved to improve noise robustness. In this paper, we use the complete BLSTM-RNN output which is presented to the DBN as Virtual Evidence. This allows the hybrid system to use information about all phoneme candidates, which was not possible in previous experiments. Our approach improved word accuracy on the Aurora 2 Corpus by 8%.


title: Automatic Evaluation Of Machine Translation Quality Using Longest Common Subsequence And Skip-Bigram Statistics

abstract: In this paper we describe two new objective automatic evaluation methods for machine translation. The first method is based on longest common subsequence between a candidate translation and a set of reference translations. Longest common subsequence takes into account sentence level structure similarity naturally and identifies longest co-occurring in-sequence n-grams automatically. The second method relaxes strict n-gram matching to skip-bigram matching. Skip-bigram is any pair of words in their sentence order. Skip-bigram cooccurrence statistics measure the overlap of skip-bigrams between a candidate translation and a set of reference translations. The empirical results show that both methods correlate with human judgments very well in both adequacy and fluency.


title: Dot-product engine for neuromorphic computing: programming 1T1M crossbar to accelerate matrix-vector multiplication

abstract: Vector-matrix multiplication dominates the computation time and energy for many workloads, particularly neural network algorithms and linear transforms (e.g, the Discrete Fourier Transform). Utilizing the natural current accumulation feature of memristor crossbar, we developed the Dot-Product Engine (DPE) as a high density, high power efficiency accelerator for approximate matrix-vector multiplication. We firstly invented a conversion algorithm to map arbitrary matrix values appropriately to memristor conductances in a realistic crossbar array, accounting for device physics and circuit issues to reduce computational errors. The accurate device resistance programming in large arrays is enabled by close-loop pulse tuning and access transistors. To validate our approach, we simulated and benchmarked one of the state-of-the-art neural networks for pattern recognition on the DPEs. The result shows no accuracy degradation compared to software approach (99 % pattern recognition accuracy for MNIST data set) with only 4 Bit DAC/ADC requirement, while the DPE can achieve a speed-efficiency product of 1,000× to 10,000× compared to a custom digital ASIC.


title: Inference in Text Understanding

abstract: The problem of deciding what was implied by a written text, of "reading between the lines" is the problem of inference. To extract proper inferences from a text requires a great deal of general knowledge on the part of the reader. Past approaches have often postulated an algorithm tuned to process a particular kind of knowledge structure (such as a script, or a plan). An alternative, unified approach is proposed. The algorithm recognizes six very general classes of inference, classes that are not dependent on individual knowledge structures, but instead rely on patterns of connectivity between concepts. The complexity has been effectively shifted from the algorithm to the knowledge base; new kinds of knowledge structures can be added without modifying the algorithm.


title: Security and Privacy Approaches in Mixed Reality: A Literature Survey

abstract: Mixed reality (MR) technology development is now gaining momentum due to advances in computer vision, sensor fusion, and realistic display technologies. With most of the research and development focused on delivering the promise of MR, the privacy and security implications of this technology are yet to be thoroughly investigated. This survey article aims to put in to light these risks and to look into the latest security and privacy work on MR. Specifically, we list and review the different protection approaches that have been proposed to ensure user and data security and privacy in MR. We extend the scope to include work on related technologies such as augmented reality, virtual reality, and human-computer interaction as crucial components, if not the origins, of MR, as well as numerous related work from the larger area of mobile devices, wearables, and Internet-of-Things. We highlight the lack of investigation, implementation, and evaluation of data protection approaches in MR. Further challenges and directions on MR security and privacy are also discussed.


title: High Efficiency Compression for Object Detection

abstract: Image and video compression has traditionally been tailored to human vision. However, modern applications such as visual analytics and surveillance rely on computers seeing and analyzing the images before (or instead of) humans. For these applications, it is important to adjust compression to computer vision. In this paper we present a bit allocation and rate control strategy that is tailored to object detection. Using the initial convolutional layers of a state-of-the-art object detector, we create an importance map that can guide bit allocation to areas that are important for object detection. The proposed method enables bit rate savings of 7% or more compared to default HEVC, at the equivalent object detection rate.


title: A low-power dynamic comparator with time-domain bulk-driven offset cancellation

abstract: This paper presents a low-power dynamic comparator utilizing a novel time-domain bulk-driven offset cancellation scheme with minimal additional power consumption and delay. It uses an open loop dynamic phase detector to achieve very high precision. The circuit is designed in a 0.6-µm process. The simulation results show that the circuit consumes 540 nA current at 100 kHz clock frequency and the proposed offset cancellation scheme is able to reduce the input referred offset to within 25 µV from an initial value of 20 mV.


title: Animating pictures with stochastic motion textures

abstract: In this paper, we explore the problem of enhancing still pictures with subtly animated motions. We limit our domain to scenes containing passive elements that respond to natural forces in some fashion. We use a semi-automatic approach, in which a human user segments the scene into a series of layers to be individually animated. Then, a "stochastic motion texture" is automatically synthesized using a spectral method, i.e., the inverse Fourier transform of a filtered noise spectrum. The motion texture is a time-varying 2D displacement map, which is applied to each layer. The resulting warped layers are then recomposited to form the animated frames. The result is a looping video texture created from a single still image, which has the advantages of being more controllable and of generally higher image quality and resolution than a video texture created from a video source. We demonstrate the technique on a variety of photographs and paintings.


title: Counting Apples and Oranges With Deep Learning: A Data-Driven Approach

abstract: This paper describes a fruit counting pipeline based on deep learning that accurately counts fruit in unstructured environments. Obtaining reliable fruit counts is challenging because of variations in appearance due to illumination changes and occlusions from foliage and neighboring fruits. We propose a novel approach that uses deep learning to map from input images to total fruit counts. The pipeline utilizes a custom crowdsourcing platform to quickly label large data sets. A blob detector based on a fully convolutional network extracts candidate regions in the images. A counting algorithm based on a second convolutional network then estimates the number of fruits in each region. Finally, a linear regression model maps that fruit count estimate to a final fruit count. We analyze the performance of the pipeline on two distinct data sets of oranges in daylight, and green apples at night, utilizing human generated labels as ground truth. We also show that the pipeline has a short training time and performs well with a limited data set size. Our method generalizes across both data sets and is able to perform well even on highly occluded fruits that are challenging for human labelers to annotate.


title: Unsupervised method to ensemble results of multiple clustering solutions for bibliographic data

abstract: Multiobjective optimization refers to optimization of multiple conflicting objective functions simultaneously. Clustering problem is often formulated as a multiobjective optimization problem where multiple cluster quality measures are simultaneously optimized and Pareto based approaches are popular in solving that Pareto based approaches yield a set of solutions known as Pareto front where all the solutions are non-dominated with respect to each other. A single solution is selected by the decision maker according to his/her preference. But when the number of non-dominated solutions is large in number, then it is difficult for the decision maker to choose the one solution. The selection of a solution from the given Pareto front is known as Post-Pareto optimality analysis. In the past many approaches were proposed for solving the aforementioned problem, but most of these involve the decision maker. In this paper, we have proposed an approach to obtain a single solution from a set of non-dominated solutions by combining these solutions without the intervention of the decision maker. We have evaluated our approach on the set of solutions obtained after application of a newly developed multiobjective based clustering technique on bibliographic databases like DBLR.


title: A Survey on Hair Modeling: Styling, Simulation, and Rendering

abstract: Systems and methods for facilitating treatment are disclosed that can include the use of imaging techniques in conjunction with one or more non-invasive techniques for modifying tissue, e.g., tissue in or around the spine. Depending on the type of treatment to be performed, various imaging modalities may be suitable for visualizing the tissue. Energy can be applied to tissue from outside of the body such that the tissue is modified in a non-invasive manner. For example, focused ultrasound can be used to dissect body tissues or can be applied to specific regions of tissue to change a characteristic of the tissue, e.g., increase its elasticity. The methods disclosed herein can be stand-alone treatments or can be performed prior to one or more surgical procedures. For example, non-invasive imaging and modification of body tissues can facilitate accessing the spine, preparing an interbody space, and/or surgically correcting the spine.


title: A Life Study of Ausforged, Standard Forged, and Standard Machined AISI M-50 Spur Gears

abstract: Tests were conducted at 350 K (170° F) with three groups of 8.9 cm (3.5 in.) pitch diameter spur gears made of vacuum-induction melted (VIM), vacuum-arc remelted (VAR), AISI M-50 steel and one group of vacuum-arc remelted (VAR) AISI 9310 steel. The pitting fatigue life of the standard forged and ausforged gears was approximately five times that of the VAR AISI 9310 gears and ten times that of the bending fatigue life of the standard machined VIM-VAR AISI M-50 gears run under identical conditions. There was a slight decrease in the 10-percent life of the ausforged gears from that for the standard forged gears. However, the difference is not statistically significant. The standard machined gears failed primarily by gear tooth fracture while the forged and ausforged VIM-VAR AISI M-50 and the VAR AISI 9310 gears failed primarily by surface pitting fatigue. The ausforged gears had a slightly greater tendency to fail by tooth fracture than the standard forged gears.


title: 3D Printing for the Rapid Prototyping of Structural Electronics

abstract: In new product development, time to market (TTM) is critical for the success and profitability of next generation products. When these products include sophisticated electronics encased in 3D packaging with complex geometries and intricate detail, TTM can be compromised - resulting in lost opportunity. The use of advanced 3D printing technology enhanced with component placement and electrical interconnect deposition can provide electronic prototypes that now can be rapidly fabricated in comparable time frames as traditional 2D bread-boarded prototypes; however, these 3D prototypes include the advantage of being embedded within more appropriate shapes in order to authentically prototype products earlier in the development cycle. The fabrication freedom offered by 3D printing techniques, such as stereolithography and fused deposition modeling have recently been explored in the context of 3D electronics integration - referred to as 3D structural electronics or 3D printed electronics. Enhanced 3D printing may eventually be employed to manufacture end-use parts and thus offer unit-level customization with local manufacturing; however, until the materials and dimensional accuracies improve (an eventuality), 3D printing technologies can be employed to reduce development times by providing advanced geometrically appropriate electronic prototypes. This paper describes the development process used to design a novelty six-sided gaming die. The die includes a microprocessor and accelerometer, which together detect motion and upon halting, identify the top surface through gravity and illuminate light-emitting diodes for a striking effect. By applying 3D printing of structural electronics to expedite prototyping, the development cycle was reduced from weeks to hours.


CLASSIFICATION OF DATABASES AND METHODS FOR SEISMIC DATA ANALYSIS AND EARTHQUAKE PREDICTION


Querying and mining data streams: you only get one look a tutorial


title: Reengineering Aircraft Structural Life Prediction Using a Digital Twin

abstract: Reengineering of the aircraft structural life prediction process to fully exploit advances in very high performance digital computing is proposed. The proposed process utilizes an ultrahigh fidelity model of individual aircraft by tail number, a Digital Twin, to integrate computation of structural deflections and temperatures in response to flight conditions, with resulting local damage and material state evolution. A conceptual model of how the Digital Twin can be used for predicting the life of aircraft structure and assuring its structural integrity is presented. The technical challenges to developing and deploying a Digital Twin are discussed in detail.


Neurobiological Sequelae of Childhood Trauma: Post-traumatic Stress Disorders in Children


title: A Novel Power-Divider Design With Unequal Power-Dividing Ratio and Simple Layout

abstract: This paper presents the design of a novel power-divider circuit with an unequal power-dividing ratio. Unlike the conventional approaches, the characteristic impedance values of all the branch lines involved are independent of the dividing ratio. The electrical lengths of the line sections are the only circuit parameters to be adjusted. Moreover, the proposed structure does not require impedance transformers at the two output ports. By the introduction of a transmission line between one of the output ports and the isolation resistor, a flexible layout design with reduced parasitic coupling is achieved. For verification, the measured results of a 2 : 1 and a 4 : 1 power-divider circuits operating at 1 GHz are given. A relative bandwidth of over 20% is obtained based on a return loss and port isolation requirement of -20 dB.


title: Learning to Predict Chemical Reactions

abstract: Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Approaches to the reaction prediction problems can be organized around three poles corresponding to: (1) physical laws; (2) rule-based expert systems; and (3) inductive machine learning. Previous approaches at these poles, respectively, are not high throughput, are not generalizable or scalable, and lack sufficient data and structure to be implemented. We propose a new approach to reaction prediction utilizing elements from each pole. Using a physically inspired conceptualization, we describe single mechanistic reactions as interactions between coarse approximations of molecular orbitals (MOs) and use topological and physicochemical attributes as descriptors. Using an existing rule-based system (Reaction Explorer), we derive a restricted chemistry data set consisting of 1630 full multistep reactions with 2358 distinct starting materials and intermediates, associated with 2989 ...


title: Coverage metrics for requirements-based testing

abstract: In black-box testing, one is interested in creating a suite of tests from requirements that adequately exercise the behavior of a software system without regard to the internal structure of the implementation. In current practice, the adequacy of black box test suites is inferred by examining coverage on an executable artifact, either source code or a software model.In this paper, we define structural coverage metrics directly on high-level formal software requirements. These metrics provide objective, implementation-independent measures of how well a black-box test suite exercises a set of requirements. We focus on structural coverage criteria on requirements formalized as LTL properties and discuss how they can be adapted to measure finite test cases. These criteria can also be used to automatically generate a requirements-based test suite. Unlike model or code-derived test cases, these tests are immediately traceable to high-level requirements. To assess the practicality of our approach, we apply it on a realistic example from the avionics domain.


title: Anti-ProGuard: Towards Automated Deobfuscation of Android Apps

abstract: A wide adoption of obfuscation techniques by Android application developers, and especially malware authors, introduces a high degree of complication into the process of reverse engineering, analysis, and security evaluation of third-party and potentially harmful apps. In this paper we present the early results of our research aiming to provide reliable means for automated deobfuscation of Android apps. According to the underlying approach, deobfuscation of a given app is performed by matching its code parts to the unobfuscated code stored in a database. For this purpose we apply well-known software similarity algorithms, such as SimHash and n-gram based ones. As a source of unobfuscated code can serve open source apps and libraries, as well as previously analyzed and manually deobfuscated code. Although the presented techniques are generic in their nature, our current prototype mainly targets Proguard, as one of the most widely used protection tools for Android performing primarily renaming obfuscation. The evaluation of the presented Anti-ProGuard tool witnesses its effectiveness for the considered task and supports the feasibility of the proposed approach.


title: Adding the Next Nine: An Investigation of Mobile Broadband Networks Availability

abstract: The near ubiquitous availability and success of mobile broadband networks has motivated verticals that range from public safety communication to intelligent transportation systems and beyond to consider choosing them as the communication mean of choice. Several of these verticals, however, expect high availability of multiple nines. This paper leverages end-to-end measurements to investigate the potential of current mobile broadband networks to support these expectations. We conduct a large-scale measurement study of network availability in four networks in Norway. This study is based on three years of measurements from hundreds of stationary measurement nodes and several months of measurements from four mobile nodes. We find that the mobile network centralized architecture and infrastructure sharing between operators are responsible for a non-trivial fraction of network failures. Most episodes of degraded availability, however, are uncorrelated. We also find that using two networks simultaneously can result in more than five nines of availability for stationary nodes and three nines of availability for mobile nodes. Our findings point to potential avenues for enhancing the availability of future mobile networks.


Digital Signal Processing 24 . Digital Signal Processing


title: Biowatch: Estimation of heart and breathing rates from wrist motions

abstract: Continued developments of sensor technology including hardware miniaturization and increased sensitivity have enabled the development of less intrusive methods to monitor physiological parameters during daily life. In this work, we present methods to recover cardiac and respiratory parameters using accelerometer and gyroscope sensors on the wrist. We demonstrate accurate measurements in a controlled laboratory study where participants (n = 12) held three different positions (standing up, sitting down and lying down) under relaxed and aroused conditions. In particular, we show it is possible to achieve a mean absolute error of 1.27 beats per minute (STD: 3.37) for heart rate and 0.38 breaths per minute (STD: 1.19) for breathing rate when comparing performance with FDA-cleared sensors. Furthermore, we show comparable performance with a state-of-the-art wrist-worn heart rate monitor, and when monitoring heart rate of three individuals during two consecutive nights of in-situ sleep measurements.


title: A methodology for energy-quality tradeoff using imprecise hardware

abstract: Recent studies have demonstrated the potential for reducing energy consumption in integrated circuits by allowing errors during computation. While most proposed techniques for achieving this rely on voltage overscaling (VOS), this paper shows that Imprecise Hardware (IHW) with design-time structural parameters can achieve orthogonal energy-quality tradeoffs. Two IHW adders are improved and two IHW multipliers are introduced in this paper. In addition, a simulation-free error estimation technique is proposed to rapidly and accurately estimate the impact of IHW on output quality. Finally, a quality-aware energy minimization methodology is presented. To validate this methodology, experiments are conducted on two computational kernels: DOT-PRODUCT and L2-NORM -- used in three applications -- Leukocyte Tracker, SVM classification and K-means clustering. Results show that the Hellinger distance between estimated and simulated error distribution is within 0.05 and that the methodology enables designers to explore energy-quality tradeoffs with significant reduction in simulation complexity.


title: Six-axis nanopositioning device with precision magnetic levitation technology

abstract: In this paper, we present the design, control, and testing of a 6 degrees-of-freedom magnetically-levitated system with nanometer-precision positioning capability and several-hundred-micrometer travel range. This system levitates a triangular single-moving-part platen, and produces the six-axis motion with six single-axis linear actuators. One of the prominent advantages of this magnetic levitation (maglev) system is that there is no physical contact between the moving part and the stator, which eliminates friction, wear, backlash, and hysteresis. As compared to other traditional devices, the present system is very compact with the minimum number of actuators for six-axis motion generation. The maglev device presented herein shows the position resolution better than 5 nm with 2-nm rms position noise, and is capable of a velocity of 0.5 m/s and an acceleration of 30 m/s/sup 2/. The nominal power consumption is only 15 mW by each horizontal actuator, and 320 mW by each vertical actuator. The actuators are sized to be able to orient and position a maximum payload of 1 kg. The key application of this maglev device is the manipulation at nanoscale for microassemblies and manufacture of their parts. Other potential applications are stereolithography, vibration-free delicate instrumentation, and microscale rapid prototyping.


title: The Influence of Social Network Sites (SNS) upon Academic Performance of Malaysian Students

abstract: Social network sites (SNS) have attracted considerable attention among teens and young adults who tend to connect and share common interest. Despite this popularity, the issue of students’ adoption of social network sites is still being unexplored fully in Malaysia. Driven by this factor, this study was designed to analyze the impact of social network sites on students’ academic performance in Malaysia. Using a conceptual approach, the study gathered that more students prefer the use of Facebook and Twitter in academic related discussions in complementing conventional classroom teaching and learning process. Thus, it is imperative that lecturers and academic institutions should implement the use of these applications in promoting academic excellence. As for profit oriented organizations such as bookshops, computer and smartphone one vendors, they can promote their products through these applications and engage students to make purchases via them having understood that many students prefer and use Facebook, Twitter and Google+. The discussion from this study however does not represent the general sampling of Malaysian university students.


title: An FPGA-Based Real-Time System for 3D Stereo Matching, Combining Absolute Differences and Census with Aggregation and Belief Propagation

abstract: The implementation of 3D stereo matching in real time is an important problem for many vision applications and algorithms. The current work, extending previous results by the same authors, presents in detail an architecture which combines the methods of Absolute Differences, Census, and Belief Propagation in an integrated architecture suitable for implementation with Field Programmable Gate Array (FPGA) logic. Emphasis on the present work is placed on the justification of dimensioning the system, as well as detailed design and testing information for a fully placed and routed design to process 87 frames per sec (fps) in \(1920 \times 1200\) resolution, and a fully implemented design for \(400 \times 320\) which runs up to 1570 fps.


title: Monolithic 3-Axis MEMS Multi-Loop Magnetometer: A Performance Analysis

abstract: The work introduces the first MEMS Lorentz-force magnetometer that both includes multiple current recirculation loops and combines in a single, low-footprint ( $\mu \text{A}_{\text {rms}}$ sinewave current delivered from an MEMS-based oscillator, the sensor reaches on all axes noise levels of 100 nT/ $\surd $ Hz over a 50-Hz 3-dB bandwidth, with a full-scale range of ±7 mT and 0.1% nonlinearity. Cross-axis contributions, inherently higher than in separate-axis sensors, are linearly compensated to less than 1.5%. [2018-0056]


title: A Multimodal Emotion Recognition System Using Facial Landmark Analysis

abstract: This paper introduces a multimodal emotion recognition system based on two different modalities, i.e., affective speech and facial expression. For affective speech, the common low-level descriptors including prosodic and spectral audio features (i.e., energy, zero crossing rate, MFCC, LPC, PLP and temporal derivatives) are extracted, whereas a novel visual feature extraction method is proposed in the case of facial expression. This method exploits the displacement of specific landmarks across consecutive frames of an utterance for feature extraction. To this end, the time series of temporal variations for each landmark is analyzed individually for extracting primary visual features, and then, the extracted features of all landmarks are concatenated for constructing the final feature vector. The analysis of displacement signal of landmarks is performed by the discrete wavelet transform which is a widely used mathematical transform in signal processing applications. In order to reduce the complexity of derived models and improve the efficiency, a variety of dimensionality-reduction schemes are applied. Furthermore, to exploit the advantages of multimodal emotion recognition systems, the feature-level fusion of the audio and the proposed visual features is examined. Results of experiments conducted on three SAVEE, RML and eNTERFACE05 databases show the efficiency of proposed visual feature extraction method in terms of performance criteria.


An Ensemble Approach for Event Detection and Characterization in Dynamic Graphs


title: The first facial expression recognition and analysis challenge

abstract: Automatic Facial Expression Recognition and Analysis, in particular FACS Action Unit (AU) detection and discrete emotion detection, has been an active topic in computer science for over two decades. Standardisation and comparability has come some way; for instance, there exist a number of commonly used facial expression databases. However, lack of a common evaluation protocol and lack of sufficient details to reproduce the reported individual results make it difficult to compare systems to each other. This in turn hinders the progress of the field. A periodical challenge in Facial Expression Recognition and Analysis would allow this comparison in a fair manner. It would clarify how far the field has come, and would allow us to identify new goals, challenges and targets. In this paper we present the first challenge in automatic recognition of facial expressions to be held during the IEEE conference on Face and Gesture Recognition 2011, in Santa Barbara, California. Two sub-challenges are defined: one on AU detection and another on discrete emotion detection. It outlines the evaluation protocol, the data used, and the results of a baseline method for the two sub-challenges.


title: Microservices Validation: Methodology and Implementation

abstract: Due to the wide spread of cloud computing, arises actual question about architecture, design and implementation of cloud applications. The microservice model describes the design and development of loosely coupled cloud applications when computing resources are provided on the basis of automated IaaS and PaaS cloud platforms. Such applications consist of hundreds and thousands of service instances, so automated validation and testing of cloud applications developed on the basis of microservice model is a pressing issue. There are constantly developing new methods of testing both individual microservices and cloud applications at a whole. This article presents our vision of a framework for the validation of the microservice cloud applications, providing an integrated approach for the implementation of various testing methods of such applications, from basic unit tests to continuous stability testing.


title: The DNA sequence and comparative analysis of human chromosome 5

abstract: Chromosome 5 is one of the largest human chromosomes and contains numerous intrachromosomal duplications, yet it has one of the lowest gene densities. This is partially explained by numerous gene-poor regions that display a remarkable degree of noncoding conservation with non-mammalian vertebrates, suggesting that they are functionally constrained. In total, we compiled 177.7 million base pairs of highly accurate finished sequence containing 923 manually curated protein-coding genes including the protocadherin and interleukin gene families. We also completely sequenced versions of the large chromosome-5-specific internal duplications. These duplications are very recent evolutionary events and probably have a mechanistic role in human physiological variation, as deletions in these regions are the cause of debilitating disorders including spinal muscular atrophy.


title: Agraph convolution-based classification model for identifying anticancer metabolites from traditional vietnamese herbal medicine database

abstract: Vietnam has been well known as a source of abundantly diverse herbal medicines for thousands of years, which serves a variety of purposes in drug development in attempts to address health issues, such as cancer. As claimed by a chemoinformatics-related principle that structurally similar chemical compounds will very likely have similar biological activity, this study employs molecular graph convolution, a machine learning architecture for extracting features from small molecules as undirected graphs, to predict anticancer ability of Vietnamese herbal medicines based on their metabolites' structures. In addition to molecular graph convolution, extended connectivity fingerprint (ECFP), a traditional featurizer for exploiting details of molecules, is also performed in order to make performance comparison. Finally, we successfully constructed a graph convolution-based neural network with high predictive accuracy on both training and validation set, suggesting that the model is reliable in detecting anticancer activity.


title: Oncology staff: burnout, job satisfaction and coping with stress

abstract: AbstractObjective:The oncology staff is at high risk for developing psychological disorders and burnout. In this study, we aimed to evaluate their burnout levels, job satisfaction, psychological statement and ways of coping with stress and the relationship between these variables and their sociodemo


title: Adiabatic 4-2 compressors for low-power multiplier

abstract: This paper presents two adiabatic 4-2 compressors with complementary pass-transistor logic (CPAL). One is based on basic CPAL gates, while the other is realized using CPAL full adders. The architecture of an 8/spl times/8-bit adiabatic multiplier using adiabatic 4-2 compressors is described. The proposed compressors are verified using 0.25/spl mu/m TSMC CMOS process. The power consumption is significantly reduced, because the non-adiabatic energy loss of output loads has been eliminated using complementary pass-transistor logic for evaluation and transmission gates for energy-recovery. The simulation results show that the compressor using CPAL full adders consumes about 60% of the dissipated energy of the compressor using CPAL basic gates at 100MHz. Compared to the conventional CMOS implementation, the CPAL compressor attains energy savings of 85% to 95% for clock rates ranging from 25 to 150 MHz.


title: Comparison of Processing Algorithms for a Delay/Doppler Altimeter

abstract: The use of a radar altimeter is to measure the height of the reflecting surface when the instrument passes overhead. A delay/Doppler altimeter (DDA) reduces the along-track resolution cell using correlation of pulses within burst, as in a synthetic aperture radar, with the net effect of synthesizing an array with a narrower lobe. The processing of DDA data therefore implies the correction of the phase delay of pulses, which is said to be Doppler beam sharpening since it can be reduced to a DFT in the along-track direction. The noise term on Doppler echoes is decreased by incoherent summing of contributions of different bursts. Summation of these contributions requires that pointing of each beam is directed toward a given output grid of samples on ground, and this can be a nontrivial task, depending on the topography profile. In this letter, a comparison between different beam sharpening algorithms is presented: Two of them are similar in their basic idea even if with different implementations; the third one can be considered as reference since no approximations are needed in principle, but is computationally inefficient. What results is that the first couple can be considered basically equivalent in terms of precision and computational efficiency, with little differences at the change of the environmental conditions. Results based on simulated data of simple and complex scenarios are presented as support of the reasoning.


title: Android power management: Current and future trends

abstract: Saving power of Android enabled devices have become a significant issue with 400,000 such devices being activated daily. Android smartphones and tablets offer several power hungry hardware components and the app developers are exploiting these components at disposal to provide revolutionary user experience. But the battery life has not increased at the same pace to support the power demand. Thus many researches have been carried out to investigate how to minimize the power consumption in smartphones. This paper reports four different research themes towards the reduction of smartphone power consumption. Efforts have been made to survey Android power saving apps available in Google play Apps store as the basis to find out different power saving approaches, operations and limitations. Then we present four different avenues to prolong the batter life of Android devices. The first two approaches include usage pattern analysis to generate power saving profiles. The advantage being that the power saving profiles are customized to actual user behavior. Integrating a photovoltaic film on top of the smartphone and tablet touch screen to generate electricity is also mentioned. The usage pattern based power saving profile generation has several privacy concerns which are discussed and countermeasures are proposed. Some emerging security attacks are also briefed.


title: Computer-Mediated Communication Effects on Disclosure, Impressions, and Interpersonal Evaluations: Getting to Know One Another a Bit at a Time

abstract: This investigation examined how computer-mediated communication (CMC) partners exchange personal information in initial interactions, focusing on the effects of communication channels on self-disclosure, question-asking, and uncertainty reduction. Unacquainted individuals (N = 158) met either face-to-face or via CMC. Computer-mediated interactants exhibited a greater proportion of more direct and intimate uncertainty reduction behaviors than unmediated participants did, and demonstrated significantly greater gains in attributional confidence over the course of the conversations. The use of direct strategies by mediated interactants resulted in judgments of greater conversational effectiveness by partners. Results illuminate some microstructures previously asserted but unverified within social information processing theory (Walther, 1992), and extend uncertainty reduction theory (Berger & Calabrese, 1975) to CMC interaction.


Ultra-Low latency Use Cases Market considerations and opportunities Driving /


title: You've Got Vulnerability: Exploring Effective Vulnerability Notifications.

abstract: Security researchers can send vulnerability notifications to take proactive measures in securing systems at scale. However, the factors affecting a notification’s efficacy have not been deeply explored. In this paper, we report on an extensive study of notifying thousands of parties of security issues present within their networks, with an aim of illuminating which fundamental aspects of notifications have the greatest impact on efficacy. The vulnerabilities used to drive our study span a range of protocols and considerations: exposure of industrial control systems; apparent firewall omissions for IPv6-based services; and exploitation of local systems in DDoS amplification attacks. We monitored vulnerable systems for several weeks to determine their rate of remediation. By comparing with experimental controls, we analyze the impact of a number of variables: choice of party to contact (WHOIS abuse contacts versus national CERTs versus US-CERT), message verbosity, hosting an information website linked to in the message, and translating the message into the notified party’s local language. We also assess the outcome of the emailing process itself (bounces, automated replies, human replies, silence) and characterize the sentiments and perspectives expressed in both the human replies and an optional anonymous survey that accompanied our notifications. We find that various notification regimens do result in different outcomes. The best observed process was directly notifying WHOIS contacts with detailed information in the message itself. These notifications had a statistically significant impact on improving remediation, and human replies were largely positive. However, the majority of notified contacts did not take action, and even when they did, remediation was often only partial. Repeat notifications did not further patching. These results are promising but ultimately modest, behooving the security community to more deeply investigate ways to improve the effectiveness of vulnerability notifications.


title: A New Discrete Particle Swarm Optimization Algorithm

abstract: Particle Swarm Optimization (PSO) has been shown to perform very well on a wide range of optimization problems. One of the drawbacks to PSO is that the base algorithm assumes continuous variables. In this paper, we present a version of PSO that is able to optimize over discrete variables. This new PSO algorithm, which we call Integer and Categorical PSO (ICPSO), incorporates ideas from Estimation of Distribution Algorithms (EDAs) in that particles represent probability distributions rather than solution values, and the PSO update modifies the probability distributions. In this paper, we describe our new algorithm and compare its performance against other discrete PSO algorithms. In our experiments, we demonstrate that our algorithm outperforms comparable methods on both discrete benchmark functions and NK landscapes, a mathematical framework that generates tunable fitness landscapes for evaluating EAs.


title: A Comparative Analysis of Structural Brain MRI in the Diagnosis of Alzheimer’s Disease

abstract: Dementia is a debilitating and life-altering disease which leads to both memory impairment and decline of normal executive functioning. While causes of dementia are numerous and varied, the leading cause among patients 60 years and older is Alzheimer’s disease. The gold standard for Alzheimer’s diagnosis remains histological identification of amyloid plaques and neurofibrillary tangles within the medial temporal lobe, more specifically the entorhinal cortex and hippocampus. Although no definitive cure for Alzheimer's disease currently exists, there are treatments targeted at preserving cognition and memory while delaying continued loss of function. Alzheimer's disease exists along a spectrum of cognitive decline and is often preceded by Mild Cognitive Impairment (MCI). Patients with MCI demonstrate memory loss and cognitive impairment while still continuing normal activities of daily living, and are considered to be at increased risk for developing Alzheimer's Dementia. Identifying patients with prodromal states of Alzheimer's dementia such as MCI may allow initiation of appropriate treatment planning and delay of cognitive decline. Therefore, the need for a non-invasive early biomarker for the detection of Alzheimer's disease has never been greater. Multiple neuroimaging methods utilizing visual rating scales, volumetric measurements, and automated methods have been developed to identify, quantify, and track anatomic sequelae of Alzheimer’s Disease.


title: Dynamic Modeling and Control of Nonholonomic Mobile Robot with Lateral Slip

abstract: Nonholonomic mobile robots are characterized by no-slip constraints. However, in many practical situations, slips are inevitable. In this work, we develop a theoretical and systematic framework to include slip dynamics into the overall dynamics of the wheeled mobile robot (WMR). Such a dynamic model is useful to understand the slip characteristics during navigation of the WMR. We further design a planner and a controller that allow efficient navigation of the WMR by controlling the slip. Preliminary simulation results are presented to demonstrate the usefulness of the proposed modeling and control techniques.


title: Faster reinforcement learning after pretraining deep networks to predict state dynamics

abstract: Deep learning algorithms have recently appeared that pretrain hidden layers of neural networks in unsupervised ways, leading to state-of-the-art performance on large classification problems. These methods can also pretrain networks used for reinforcement learning. However, this ignores the additional information that exists in a reinforcement learning paradigm via the ongoing sequence of state, action, new state tuples. This paper demonstrates that learning a predictive model of state dynamics can result in a pretrained hidden layer structure that reduces the time needed to solve reinforcement learning problems.


title: Price Sensitivity Measurement and New Product Pricing: A Cognitive Response Approach

abstract: Understanding the price sensitivity of potential buyers is a requirement for successfully determining the price for a new product. Marketers desire to set a price that will maximize demand and ensure a profitable business result. The price sensitivity measurement approach to price setting provides a methodology for mapping the upper and lower bounds of acceptable prices from the customer's perspective. Price sensitivity measurement is based on value perceptions and is a useful tool for pricing new products. This paper explores the role of cognitive response theory as a primary construct underlying the determination of the price customers are willing to pay. Potential customers for a new software service generated significantly more support arguments at the optimum price point than they did at price points above or below the acceptable pricing range. Concomitantly, counterarguments were significantly higher at prices above the upper price threshold and below the lower price threshold. The implications of these findings are discussed. The complementary use of price sensitivity measurement and cognitive response methodologies provides a convenient and easily implemented set of customer- value based tools for pricing new products.


title: Concepts of cognition and consciousness: four voices

abstract: This paper considers theories of cognition and consciousness in four traditions: neuroscience, cognitive science, activity theory and the distributed cognition approach. It is most concerned with social theories of consciousness---activity theory and distributed cognition---but briefly considers biological and computational models as a foil or backdrop against which the social theories stand out more clearly.


title: Saturn: a Distributed Metadata Service for Causal Consistency

abstract: This paper presents the design, implementation, and evaluation of Saturn, a metadata service for geo-replicated systems. Saturn can be used in combination with several distributed and replicated data services to ensure that remote operations are made visible in an order that respects causality, a requirement central to many consistency criteria. Saturn addresses two key unsolved problems inherent to previous approaches. First, it eliminates the tradeoff between throughput and data freshness, when deciding what metadata to use for tracking causality. Second, it enables genuine partial replication, a key property to ensure scalability when the number of geo-locations increases. Saturn addresses these challenges while keeping metadata size constant, independently of the number of clients, servers, data partitions, and locations. By decoupling metadata management from data dissemination, and by using clever metadata propagation techniques, it ensures that the throughput and visibility latency of updates on a given item are (mostly) shielded from operations on other items or locations. We evaluate Saturn in Amazon EC2 using realistic benchmarks under both full and partial geo-replication. Results show that weakly consistent datastores can lean on Saturn to upgrade their consistency guarantees to causal consistency with a negligible penalty on performance.


title: Human Interaction with Levels of Automation and Decision-Aid Fidelity in the Supervisory Control of Multiple Simulated Unmanned Air Vehicles

abstract: Remotely operated vehicles (ROVs) are vehicular robotic systems that are teleoperated by a geographically separated user. Advances in computing technology have enabled ROV operators to manage multiple ROVs by means of supervisory control techniques. The challenge of incorporating telepresence in any one vehicle is replaced by the need to keep the human "in the loop" of the activities of all vehicles. An evaluation was conducted to compare the effects of automation level and decision-aid fidelity on the number of simulated remotely operated vehicles that could be successfully controlled by a single operator during a target acquisition task. The specific ROVs instantiated for the study were unmanned air vehicles (UAVs). Levels of automation (LOAs) included manual control management-by-consent, and management-by-exception. Levels of decision-aid fidelity (100% correct and 95% correct) were achieved by intentionally injecting error into the decision-aiding capabilities of the simulation. Additionally, the number of UAVs to be controlled varied (one, two, and four vehicles). Twelve participants acted as UAV operators. A mixed-subject design was utilized (with decision-aid fidelity as the between-subjects factor), and participants were not informed of decision-aid fidelity prior to data collection. Dependent variables included mission efficiency, percentage correct detection of incorrect decision aids. workload and situation awareness ratings, and trust in automation ratings. Results indicate that an automation level incorporating management-by-consent had some clear performance advantages over the more autonomous (management-by-exception) and less autonomous (manual control) levels of automation. However, automation level interacted with the other factors for subjective measures of workload, situation awareness, and trust. Additionally, although a 3D perspective view of the mission scene was always available, it was used only during low-workload periods and did not appear to improve the operator's sense of presence. The implications for ROV interface design are discussed, and future research directions are proposed.


title: Gut/brain axis and the microbiota

abstract: Tremendous progress has been made in characterizing the bidirectional interactions between the central nervous system, the enteric nervous system, and the gastrointestinal tract. A series of provocative preclinical studies have suggested a prominent role for the gut microbiota in these gut-brain interactions. Based on studies using rodents raised in a germ-free environment, the gut microbiota appears to influence the development of emotional behavior, stress- and pain-modulation systems, and brain neurotransmitter systems. Additionally, microbiota perturbations by probiotics and antibiotics exert modulatory effects on some of these measures in adult animals. Current evidence suggests that multiple mechanisms, including endocrine and neurocrine pathways, may be involved in gut microbiota-to-brain signaling and that the brain can in turn alter microbial composition and behavior via the autonomic nervous system. Limited information is available on how these findings may translate to healthy humans or to disease states involving the brain or the gut/brain axis. Future research needs to focus on confirming that the rodent findings are translatable to human physiology and to diseases such as irritable bowel syndrome, autism, anxiety, depression, and Parkinson's disease.


title: A Smart Automated Signature Extraction Scheme for Mobile Phone Number in Human-Centered Smart Home Systems

abstract: Human-centered smart devices profiling with Wi-Fi networks has received much attention from both research and industry, especially those network operators and security agencies who aim to enhance user experience and security of home network as well as free Wi-Fi services. One type of such profiling is the extraction of mobile phone numbers. In traditional cellular networks, such as 3G and 4G, mobile phone number extraction can be achieved from the analysis of the authentication signaling. However, this method cannot be used in the broadband network environment, e.g., Wi-Fi. Operators and security agencies of Wi-Fi networks often apply manual statistics, telephone inquiries or user input for information. Unfortunately, those traditional methods are inefficient in practice. Moreover, authenticity cannot be guaranteed with the traditional methods. In this paper, we propose a smart method for mobile phone number extraction in smart home networks and systems. In particular, the proposed method is based on deep packet inspection of home broadband traffic. To improve the efficiency and accuracy of detection, we further propose a smart automated signature extraction method of mobile phone numbers from home network traffic. Our proposed method can achieve 86.2% accuracy in the real-life human-centered smart home network test.


title: Escaping from the Filter Bubble? The Effects of Novelty and Serendipity on Users' Evaluations of Online Recommendations

abstract: Recommender systems aim to support consumers in identifying the most ::: relevant items. However, there are concerns that recommenders may ::: imprison users in a “filter bubble” by recommending items predominantly ::: known to users. On the other hand, providing unconventional items ::: increases risks of not meeting users’ taste. Given this trade-off, we analyze ::: the effects of consumers’ perceived levels of recommendation novelty and ::: serendipity on perceived preference fit and enjoyment. We find that merely ::: increasing the level of novel recommendations is insufficient. Instead, ::: recommenders should provide more serendipitous recommendations as ::: this leads to higher perceived preference fit and enjoyment. In addition, ::: market and recommender technology characteristics need to be taken into ::: account as they partially determine the level of novel and serendipitous ::: recommendations. Our findings have significant implications for research ::: as they add additional insights on users’ evaluations of recommender ::: systems. For practice, our results enable online retailers to develop better ::: recommenders.


title: Nopol: Automatic Repair of Conditional Statement Bugs in Java Programs

abstract: We propose Nopol , an approach to automatic repair of buggy conditional statements (i.e., if-then-else statements). This approach takes a buggy program as well as a test suite as input and generates a patch with a conditional expression as output. The test suite is required to contain passing test cases to model the expected behavior of the program and at least one failing test case that reveals the bug to be repaired. The process of Nopol consists of three major phases. First, Nopol employs angelic fix localization to identify expected values of a condition during the test execution. Second, runtime trace collection is used to collect variables and their actual values, including primitive data types and objected-oriented features (e.g., nullness checks), to serve as building blocks for patch generation. Third, Nopol encodes these collected data into an instance of a Satisfiability Modulo Theory (SMT) problem; then a feasible solution to the SMT instance is translated back into a code patch. We evaluate Nopol on 22 real-world bugs (16 bugs with buggy if conditions and six bugs with missing preconditions) on two large open-source projects, namely Apache Commons Math and Apache Commons Lang. Empirical analysis on these bugs shows that our approach can effectively fix bugs with buggy if conditions and missing preconditions. We illustrate the capabilities and limitations of Nopol using case studies of real bug fixes.


title: Design of wind farm layout for maximum wind energy capture

abstract: Wind is one of the most promising sources of alternative energy. The construction of wind farms is destined to grow in the U.S., possibly twenty-fold by the year 2030. To maximize the wind energy capture, this paper presents a model for wind turbine placement based on the wind distribution. The model considers wake loss, which can be calculated based on wind turbine locations, and wind direction. Since the turbine layout design is a constrained optimization problem, for ease of solving it, the constraints are transformed into a second objective function. Then a multi-objective evolutionary strategy algorithm is developed to solve the transformed bi-criteria optimization problem, which maximizes the expected energy output, as well as minimizes the constraint violations. The presented model is illustrated with examples as well as an industrial application.


title: Regular property guided dynamic symbolic execution

abstract: A challenging problem in software engineering is to check if a program has an execution path satisfying a regular property. We propose a novel method of dynamic symbolic execution (DSE) to automatically find a path of a program satisfying a regular property. What makes our method distinct is when exploring the path space, DSE is guided by the synergy of static analysis and dynamic analysis to find a target path as soon as possible. We have implemented our guided DSE method for Java programs based on JPF and WALA, and applied it to 13 real-world open source Java programs, a total of 225K lines of code, for extensive experiments. The results show the effectiveness, efficiency, feasibility and scalability of the method. Compared with the pure DSE on the time to find the first target path, the average speedup of the guided DSE is more than 258X when analyzing the programs that have more than 100 paths.


title: Data mining in sport : A neural network approach.

abstract: Data Mining techniques have been applied successfully in many scientific, industrial and business domains. The area of professional sport is well known for the vast amounts of data collected for each player, training session, team, game and season, however the effective use of this data continues to be limited. Many sporting organisations have begun to realise that there is a wealth of untapped knowledge contained in their data and there is an increasing interest in techniques to utilize the data. The aim of this study is to investigate the potential of neural networks (NNs) to assist in the data mining process for the talent identification problem. Neural networks use a supervised learning approach, learning from training examples, adjusting weights to reduce the error between the correct result and the result produced by the network. They endeavour to determine a general relationship between the inputs and outputs provided. Once trained, neural networks can be used to predict outputs based on input data alone. The neural network approach will be applied to the selection of players in the annual Australian Football League (AFL) National Draft. Results from this study suggest that neural networks have the potential to assist recruiting managers in the talent identification process.


title: Classifying Relations by Ranking with Convolutional Neural Networks

abstract: Relation classification is an important semantic processing task for which state-ofthe-art systems still rely on costly handcrafted features. In this work we tackle the relation classification task using a convolutional neural network that performs classification by ranking (CR-CNN). We propose a new pairwise ranking loss function that makes it easy to reduce the impact of artificial classes. We perform experiments using the the SemEval-2010 Task 8 dataset, which is designed for the task of classifying the relationship between two nominals marked in a sentence. Using CRCNN, we outperform the state-of-the-art for this dataset and achieve a F1 of 84.1 without using any costly handcrafted features. Additionally, our experimental results show that: (1) our approach is more effective than CNN followed by a softmax classifier; (2) omitting the representation of the artificial class Other improves both precision and recall; and (3) using only word embeddings as input features is enough to achieve state-of-the-art results if we consider only the text between the two target nominals.


title: A proof-carrying based framework for trusted microprocessor IP

abstract: We introduce a proof-carrying based framework for assessing the trustworthiness of third-party hardware Intellectual Property (IP), particularly geared toward microprocessor cores. This framework enables definition of and formal reasoning on security properties, which, in turn, are used to certify the genuineness and trustworthiness of the instruction set and, by extension, are used to prevent insertion of malicious functionality in the Hardware Description Language (HDL) code of an acquired microprocessor core. Security properties and trustworthiness proofs are derived based on a new formal hardware description language (formal-HDL), which is developed as part of the framework along with conversion rules to/from other HDLs to enable general applicability to IP cores independent of coding language. The proposed framework, along with the ability of a sample set of pertinent security properties to detect malicious IP modifications, is demonstrated on an 8051 microprocessor core.


title: Medical image registration using machine learning-based interest point detector

abstract: This paper presents a feature-based image registration framework which exploits a novel machine learning ::: (ML)-based interest point detection (IPD) algorithm for feature selection and correspondence detection. We use a feed-forward ::: neural network (NN) with back-propagation as our base ML detector. Literature on ML-based IPD is scarce and ::: to our best knowledge no previous research has addressed feature selection strategy for IPD purpose with cross-validation ::: (CV) detectability measure. Our target application is the registration of clinical abdominal CT scans with ::: abnormal anatomies. We evaluated the correspondence detection performance of the proposed ML-based detector ::: against two well-known IPD algorithms: SIFT and SURF. The proposed method is capable of performing affine rigid ::: registrations of 2D and 3D CT images, demonstrating more than two times better accuracy in correspondence detection ::: than SIFT and SURF. The registration accuracy has been validated manually using identified landmark points. Our ::: experimental results shows an improvement in 3D image registration quality of 18.92% compared with affine ::: transformation image registration method from standard ITK affine registration toolkit.


title: An Asian Origin of Virulent Aeromonas hydrophila Responsible for Disease Epidemics in United States-Farmed Catfish

abstract: UNLABELLED ::: Since 2009, catfish farming in the southeastern United States has been severely impacted by a highly virulent and clonal population of Aeromonas hydrophila causing motile Aeromonas septicemia (MAS) in catfish. The possible origin of this newly emerged highly virulent A. hydrophila strain is unknown. In this study, we show using whole-genome sequencing and comparative genomics that A. hydrophila isolates from diseased grass carp in China and catfish in the United States have highly similar genomes. Our phylogenomic analyses suggest that U.S. catfish isolates emerged from A. hydrophila populations of Asian origin. Furthermore, we identified an A. hydrophila strain isolated in 2004 from a diseased catfish in Mississippi, prior to the onset of the major epidemic outbreaks in Alabama starting in 2009, with genomic characteristics that are intermediate between those of the Asian and Alabama fish isolates. Investigation of A. hydrophila strain virulence demonstrated that the isolate from the U.S. catfish epidemic is significantly more virulent to both channel catfish and grass carp than is the Chinese carp isolate. This study implicates the importation of fish or fishery products into the United States as the source of highly virulent A. hydrophila that has caused severe epidemic outbreaks in United States-farmed catfish and further demonstrates the potential for invasive animal species to disseminate bacterial pathogens worldwide. ::: ::: ::: IMPORTANCE ::: Catfish aquaculture farming in the southeastern United States has been severely affected by the emergence of virulent Aeromonas hydrophila responsible for epidemic disease outbreaks, resulting in the death of over 10 million pounds of catfish. Because the origin of this newly emerged A. hydrophila strain is unknown, this study used a comparative genomics approach to conduct a phylogenomic analysis of A. hydrophila isolates obtained from the United States and Asia. Our results suggest that the virulent isolates from United States-farmed catfish have a recent common ancestor with A. hydrophila isolates from diseased Asian carp. We have also observed that an Asian carp isolate, like recent U.S. catfish isolates, is virulent in catfish. The results from this study suggest that the highly virulent U.S. epidemic isolates emerged from an Asian source and provide another example of the threat that invasive species pose in the dissemination of bacterial pathogens.


title: Survey of Software Inspection Research

abstract: There is a great need to assure and improve the reliability and quality of software. Software inspections were introduced over 30 years ago as an answer for this need and they have inspired a lot of research covering many different kinds of aspects. There is a need for an up-to-date survey revealing the current state and the overall evolution of the most prominent research on the area. This paper presents a comprehensive survey focusing on the most relevant 16 interna- tional high-impact scientific publication series. There are 153 articles included in the survey covering both technical and management aspects. The main results include a description of the research trends during 1980-2008 and a description of the main results of the included studies. The description is organized based on a taxonomy of the inspection research as having emerged based on the survey. At general level the surveyed research provides clear evidence that inspections gen- erally benefit software development and quality assurance. There are several proposed theoretical variations for the in- spection process but also many empirical studies. Although the conducted research is relatively scattered, proper science- based understanding about some of the most studied issues has been achieved. Our main conclusion is that conducting empirical research needs to be continued in order to validate the effects of the different kinds of proposed theoretical con- structs in practice. Empirical studies are needed especially in order to better understand the proper implementation and the actual impacts of applying inspections in different kinds of industrial and organizational settings.