Query: Point Pair Feature Based Object Detection for Random Bin Picking

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3D object recognition in range images using visibility context <=SEP=> Recognizing and localizing queried objects in range images plays an important role for robotic manipulation and navigation. Even though it has been steadily studied, it is still a challenging task for scenes with occlusion and clutter.

Rigid 3D geometry matching for grasping of known objects in cluttered scenes <=SEP=> In this paper, we present an efficient 3D object recognition and pose estimation approach for grasping procedures in cluttered and occluded environments. In contrast to common appearance-based approaches, we rely solely on 3D geometry information. Our method is based on a robust geometric descriptor, a hashing technique and an efficient, localized RANSAC-like sampling strategy. We assume that each object is represented by a model consisting of a set of points with corresponding surface normals. Our method simultaneously recognizes multiple model instances and estimates their pose in the scene. A variety of tests shows that the proposed method performs well on noisy, cluttered and unsegmented range scans in which only small parts of the objects are visible. The main procedure of the algorithm has a linear time complexity resulting in a high recognition speed which allows a direct integration of the method into a continuous manipulation task. The experimental validation with a 7-degrees-of-freedom Cartesian impedance controlled robot shows how the method can be used for grasping objects from a complex random stack. This application demonstrates how the integration of computer vision and softrobotics leads to a robotic system capable of acting in unstructured and occluded environments.

Discriminatively Trained Templates for 3D Object Detection: A Real Time Scalable Approach <=SEP=> In this paper we propose a new method for detecting multiple specific 3D objects in real time. We start from the template-based approach based on the LINE2D/LINEMOD representation introduced recently by Hinterstoisser et al., yet extend it in two ways. First, we propose to learn the templates in a discriminative fashion. We show that this can be done online during the collection of the example images, in just a few milliseconds, and has a big impact on the accuracy of the detector. Second, we propose a scheme based on cascades that speeds up detection. Since detection of an object is fast, new objects can be added with very low cost, making our approach scale well. In our experiments, we easily handle 10-30 3D objects at frame rates above 10fps using a single CPU core. We outperform the state-of-the-art both in terms of speed as well as in terms of accuracy, as validated on 3 different datasets. This holds both when using monocular color images (with LINE2D) and when using RGBD images (with LINEMOD). Moreover, we propose a challenging new dataset made of 12 objects, for future competing methods on monocular color images.

Object Recognition from Local Scale-Invariant Features <=SEP=> Proc. of the International Conference on Computer Vision, Corfu (Sept. 1999) An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearest-neighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a low-residual least-squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially-occluded images with a computation time of under 2 seconds.

Shape matching and object recognition using shape contexts <=SEP=> This paper presents my work on computing shape models that are computationally fast and invariant basic transformations like translation, scaling and rotation. In this paper, I propose shape detection using a feature called shape context. Shape context describes all boundary points of a shape with respect to any single boundary point. Thus it is descriptive of the shape of the object. Object recognition can be achieved by matching this feature with a priori knowledge of the shape context of the boundary points of the object. Experimental results are promising on handwritten digits, trademark images.

Modeling and simulation of a PV pumping system under real climatic conditions <=SEP=> This article presents the modeling and simulation of a PV pumping system operating over the sun which has been developed under PSIM environment. The main objective is to optimize the PV pumping system through the introduction of two stages. The first one concerns the insertion of a DC-DC converter working with an MPPT in order to get the maximum power from the PV array. The second concerns the optimization of the DC-AC inverter which is controlled through a PWM control in order to get good voltage and current outputs spectrum with reduced losses and harmonics rate. The improvements brought by the MPPT and the PWM on the PV pumping system are shown through the example treated under real climatic conditions.

A Novel Artificial Bee Colony Algorithm <=SEP=> Artificial bee colony algorithm is a new population-based evolutionary method based on the intelligent behavior of honey bee swarm. It has shown more effective than other biological-inspired algorithms. However, there are still insufficiencies in ABC algorithm, which is good at exploration but poor at exploitation and its convergence speed is also an issue in some cases. For these insufficiencies, we propose a novel artificial bee colony algorithm (NABC) for numerical optimization problems in this paper to improve the exploitation capability by incorporating the current best solution into the search procedure. Experiments are conducted on a set of unimodal/multimodal benchmark functions. The experiments results of NABC have been compared with Gbest-guided artificial bee colony algorithm (G-ABC), improved artificial bee colony algorithm (I-ABC), Elitist artificial bee colony algorithm (E-ABC). The results show that NABC is superior to those algorithms in most of the tested functions.

New York University 2016 System for KBP Event Nugget: A Deep Learning Approach <=SEP=> This is the first time New York University (NYU) participates in the event nugget (EN) evaluation of the Text Analysis Conference (TAC). We developed EN systems for both subtasks of event nugget, i.e, EN Task 1: Event Nugget Detection and EN Task 2: Event Nugget Detection and Coreference. The systems are mainly based on our recent research on deep learning for event detection (Nguyen and Grishman, 2015a; Nguyen and Grishman, 2016a). Due to the limited time we could devote to system development this year, we only ran the systems on the English evaluation data. However, we expect that the adaptation of the current systems to new languages can be done quickly. The development experiments show that although our current systems do not rely on complicated feature engineering, they significantly outperform the reported systems last year for the EN subtasks on the 2015 evaluation data.

A Survey on M2M Systems for mHealth: A Wireless Communications Perspective <=SEP=> In the new era of connectivity, marked by the explosive number of wireless electronic devices and the need for smart and pervasive applications, Machine-to-Machine (M2M) communications are an emerging technology that enables the seamless device interconnection without the need of human interaction. The use of M2M technology can bring to life a wide range of mHealth applications, with considerable benefits for both patients and healthcare providers. Many technological challenges have to be met, however, to ensure the widespread adoption of mHealth solutions in the future. In this context, we aim to provide a comprehensive survey on M2M systems for mHealth applications from a wireless communication perspective. An end-to-end holistic approach is adopted, focusing on different communication aspects of the M2M architecture. Hence, we first provide a systematic review ofWireless Body Area Networks (WBANs), which constitute the enabling technology at the patient's side, and then discuss end-to-end solutions that involve the design and implementation of practical mHealth applications. We close the survey by identifying challenges and open research issues, thus paving the way for future research opportunities.

Antimicrobial agents from plants: antibacterial activity of plant volatile oils. <=SEP=> The volatile oils of black pepper [Piper nigrum L. (Piperaceae)], clove [Syzygium aromaticum (L.) Merr. & Perry (Myrtaceae)], geranium [Pelargonium graveolens L'Herit (Geraniaceae)], nutmeg [Myristica fragrans Houtt. (Myristicaceae), oregano [Origanum vulgare ssp. hirtum (Link) Letsw. (Lamiaceae)] and thyme [Thymus vulgaris L. (Lamiaceae)] were assessed for antibacterial activity against 25 different genera of bacteria. These included animal and plant pathogens, food poisoning and spoilage bacteria. The volatile oils exhibited considerable inhibitory effects against all the organisms under test while their major components demonstrated various degrees of growth inhibition.

Generic decoding of seen and imagined objects using hierarchical visual features <=SEP=> Object recognition is a key function in both human and machine vision. While brain decoding of seen and imagined objects has been achieved, the prediction is limited to training examples. We present a decoding approach for arbitrary objects using the machine vision principle that an object category is represented by a set of features rendered invariant through hierarchical processing. We show that visual features, including those derived from a deep convolutional neural network, can be predicted from fMRI patterns, and that greater accuracy is achieved for low-/high-level features with lower-/higher-level visual areas, respectively. Predicted features are used to identify seen/imagined object categories (extending beyond decoder training) from a set of computed features for numerous object images. Furthermore, decoding of imagined objects reveals progressive recruitment of higher-to-lower visual representations. Our results demonstrate a homology between human and machine vision and its utility for brain-based information retrieval.

The Adaptive Markets Hypothesis Market efficiency from an evolutionary perspective <=SEP=> 30TH ANNIVERSARY ISSUE 2004 THE JOURNAL OF PORTFOLIO MANAGEMENT 15 The 30th anniversary of The Journal of Portfolio Management is a milestone in the rich intellectual history of modern finance, firmly establishing the relevance of quantitative models and scientific inquiry in the practice of financial management. One of the most enduring ideas from this intellectual history is the Efficient Markets Hypothesis (EMH), a deceptively simple notion that has become a lightning rod for its disciples and the proponents of behavioral economics and finance. In its purest form, the EMH obviates active portfolio management, calling into question the very motivation for portfolio research. It is only fitting that we revisit this groundbreaking idea after three very successful decades of this Journal. In this article, I review the current state of the controversy surrounding the EMH and propose a new perspective that reconciles the two opposing schools of thought. The proposed reconciliation, which I call the Adaptive Markets Hypothesis (AMH), is based on an evolutionary approach to economic interactions, as well as some recent research in the cognitive neurosciences that has been transforming and revitalizing the intersection of psychology and economics. Although some of these ideas have not yet been fully articulated within a rigorous quantitative framework, long time students of the EMH and seasoned practitioners will no doubt recognize immediately the possibilities generated by this new perspective. Only time will tell whether its potential will be fulfilled. I begin with a brief review of the classic version of the EMH, and then summarize the most significant criticisms leveled against it by psychologists and behavioral economists. I argue that the sources of this controversy can ANDREW W. LO is Harris & Harris Group Professor at the MIT Sloan School of Management, and chief scientific officer at AlphaSimplex Group, LLC, in Cambridge, MA. The Adaptive Markets Hypothesis

Heterogeneous Information Network Embedding for Recommendation <=SEP=> Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in recommender systems, called HIN based recommendation. It is challenging to develop effective methods for HIN based recommendation in both extraction and exploitation of the information from HINs. Most of HIN based recommendation methods rely on path based similarity, which cannot fully mine latent structure features of users and items. In this paper, we propose a novel heterogeneous network embedding based approach for HIN based recommendation, called HERec. To embed HINs, we design a meta-path based random walk strategy to generate meaningful node sequences for network embedding. The learned node embeddings are first transformed by a set of fusion functions, and subsequently integrated into an extended matrix factorization (MF) model. The extended MF model together with fusion functions are jointly optimized for the rating prediction task. Extensive experiments on three real-world datasets demonstrate the effectiveness of the HERec model. Moreover, we show the capability of the HERec model for the cold-start problem, and reveal that the transformed embedding information from HINs can improve the recommendation performance.

A Comprehensive Survey of Technologies for Building a Hybrid High Performance Intrusion Detection System <=SEP=> Intrusion detection plays a vital role in maintaining the stability of any network. The major requirements for any intrusion detection system are speed, accuracy and less memory. Though various intrusion detection methods are available, they excel at some points while lack in the others. This paper presents a comprehensive survey of the technologies that are used for detecting intrusions. It analyzes the pros and cons of each technology and the literature works that utilizes these technologies. Challenges faced by the current IDS and the requirements for IDS in the current network scenario are discussed in detail. A detailed study on the datasets that can be used for effective building of an IDS is discussed. The research framework is proposed and a discussion of the various technologies that can be used for improving the efficiency of the IDS is provided.

Mixed Reality: A Survey <=SEP=> This chapter presents an overview of the Mixed Reality (MR) paradigm, which proposes to overlay our real-world environment with digital, computer-generated objects. It presents example applications and outlines limitations and solutions for their technical implementation. In MR systems, users perceive both the physical environment around them and digital elements presented through, for example, the use of semitransparent displays. By its very nature, MR is a highly interdisciplinary field engaging signal processing, computer vision, computer graphics, user interfaces, human factors, wearable computing, mobile computing, information visualization, and the design of displays and sensors. This chapter presents potential MR applications, technical challenges in realizing MR systems, as well as issues related to usability and collaboration in MR. It separately presents a section offering a selection of MR projects which have either been partly or fully undertaken at Swiss universities and rounds off with a section on current challenges and trends.

Swish: a Self-Gated Activation Function <=SEP=> The choice of activation functions in deep networks has a significant effect on the training dynamics and task performance. Currently, the most successful and widely-used activation function is the Rectified Linear Unit (ReLU). Although various alternatives to ReLU have been proposed, none have managed to replace it due to inconsistent gains. In this work, we propose a new activation function, named Swish, which is simply f(x) = x · sigmoid(x). Our experiments show that Swish tends to work better than ReLU on deeper models across a number of challenging datasets. For example, simply replacing ReLUs with Swish units improves top-1 classification accuracy on ImageNet by 0.9% for Mobile NASNetA and 0.6% for Inception-ResNet-v2. The simplicity of Swish and its similarity to ReLU make it easy for practitioners to replace ReLUs with Swish units in any neural network.

Pharmacological profile of lurasidone, a novel antipsychotic agent with potent 5-hydroxytryptamine 7 (5-HT7) and 5-HT1A receptor activity. <=SEP=> Lurasidone [(3aR,4S,7R,7aS)-2-[(1R,2R)-2-[4-(1,2-benzisothiazol-3-yl)piperazin-1-ylmethyl]cyclohexylmethyl]hexahydro-4,7-methano-2H-isoindole-1,3-dione hydrochloride; SM-13496] is an azapirone derivative and a novel antipsychotic candidate. The objective of the current studies was to investigate the in vitro and in vivo pharmacological properties of lurasidone. Receptor binding affinities of lurasidone and several antipsychotic drugs were tested under comparable assay conditions using cloned human receptors or membrane fractions prepared from animal tissue. Lurasidone was found to have potent binding affinity for dopamine D(2), 5-hydroxytryptamine 2A (5-HT(2A)), 5-HT(7), 5-HT(1A), and noradrenaline alpha(2C) receptors. Affinity for noradrenaline alpha(1), alpha(2A), and 5-HT(2C) receptors was weak, whereas affinity for histamine H(1) and muscarinic acetylcholine receptors was negligible. In vitro functional assays demonstrated that lurasidone acts as an antagonist at D(2) and 5-HT(7) receptors and as a partial agonist at the 5-HT(1A) receptor subtype. Lurasidone showed potent effects predictive of antipsychotic activity, such as inhibition of methamphetamine-induced hyperactivity and apomorphine-induced stereotyped behavior in rats, similar to other antipsychotics. Furthermore, lurasidone had only weak extrapyramidal effects in rodent models. In animal models of anxiety disorders and depression, treatment with lurasidone was associated with significant improvement. Lurasidone showed a preferential effect on the frontal cortex (versus striatum) in increasing dopamine turnover. Anti-alpha(1)-noradrenergic, anticholinergic, and central nervous system (CNS) depressant actions of lurasidone were also very weak. These results demonstrate that lurasidone possesses antipsychotic activity and antidepressant- or anxiolytic-like effects with potentially reduced liability for extrapyramidal and CNS depressant side effects.

Emotion Recognition in Speech Using Neural Networks <=SEP=> Emotion recognition in speech is a topic on which little research has been done to-date. In this paper, we discuss why emotion recognition in speech is a significant and applicable research topic, and present a system for emotion recognition using oneclass-in-one neural networks. By using a large database of phoneme balanced words, our system is speakerand context-independent. We achieve a recognition rate of approximately 50% when testing eight emotions.

Building balanced scorecard with SWOT analysis , and implementing " Sun Tzu ' s The Art of Business Management Strategies " on QFD methodology <=SEP=> Article Summary) Conjoining the SWOT matrix with the balanced scorecard (BSC) makes a systematic and holistic strategic management system. The SWOT matrix clearly identifies the critical success factors that can be implemented into the identification of the different aspects toward the balanced scorecard. It is, therefore, a more structural approach in setting up the foundation of the balanced scorecard; instead of simply identifying the key performance indicators (KPI) via gut feeling or by brainstorming. The next step of the whole process is to make use of the quality function deployment (QFD) methodology with the balanced scorecard (BSC) attributes identified as the Whats on the vertical axis, and the major strategies of The Art of Business Management Sun Tzu's as the horizontal Hows axis. The relationships are then studied in the body of the QFD matrix. Consideration is then given as to how the model presented can be customised to allow companies using this approach to develop and implement their corporate business strategic plan. Full Text (5087 words) Copyright MCB UP Limited (MCB) 2000 S.F. Lee: Hong Kong Institute of Vocational Education, Vocational Training Council, HongKong, China Andrew Sai On Ko: International Management Centre, Oxford Brookes, UK 1. Overview Almost every successful manager understands the importance of offering quality service to gain a distinctive advantage over the competition. After all, gaining an advantage is the key to success and even for the sake of survival. However, many of the so-called organizational strategies that companies depend on are not endurable. Why? These strategies may not be well structured and cannot stand the test of time. Nevertheless, powerful organizational tactics cannot easily be imitated; things like a patented algorithm, exclusive right to a special resource, and brand name recognition. "If only I knew then what I know now, I would have done things differently". People always make this comment after they have implemented the wrong corporate strategies. Since we live in times of entwining complexity, acceleration, and change, making the right decision is extremely important for strategic planning. It is fair to say that every organization and individuals have their unique set of strengths, weaknesses, opportunities, and threats. It is very important that an organization determines its strengths, weaknesses, opportunities, and threats, as well as the competitions'. By linking the SWOT analysis with the balanced scorecard, an organization can balance its strengths against its competitions' weaknesses, and optimise its opportunities within the market. Sun Tzu's The Art of War is the most famous work on military operations in ancient China and it is also one of the most revered and well-known military texts outside China. Being the oldest military treatise in the world, it becomes one of the greatest cultural legacies of the Chinese nation. The significance of Sun Tzu's works and military statements in influencing military thoughts has seldom been questioned and were used extensively during the Warring States Period in Chinese history. Sun Tzu's philosophy has become part of the collective unconscious of most educated Chinese. These strategies encapsulated in mnemonic phrases; many of them passed into proverbs and are very widely known in the Chinese cultural sphere. In anticipation of the global economy competitions and business and trade wars among countries, the words of Sun Tzu are particularly useful and appropriate for evaluating business management strategies. Sun Tzu's main philosophy in military situations are identified and correlated to actual business environment. Apparently, relating modern management philosophies and organization behaviours to Sun Tzu's strategies by looking into the philosophies advocated by him and the situation of top management seems to be difficult and unrealistic. It is, however, essential to identify the differences between the ancient and modern times and also between military and business operations before proper management strategies were launched. His strategic planning in waging war in ancient China highlights the strategic management in today's corporations. The objective is to emulate the model set by Sun Tzu, presenting the essences in organising for strategy. Organizations should have their strategic plans constantly reviewed, continuously shaped and positioned in the struggle of continuous competition. QFD maintains customer ideas and requirements throughout the process that in turn leads to customer satisfaction. A further customer expectation is for the next purchase to be a better product. However, this is identified at no extra cost. When the satisfaction is not achieved, a competitor's product is likely to be chosen. QFD is a powerful technique that enables companies to anticipate and prioritise customer needs in their totality and to incorporate them effectively into the product and service provided for the end-user (Wassermann, 1993). 2. The SWOT analysis When implementing a SWOT analysis to devise a set of strategies, the following guidelines should be utilised. Strengths (Weihrich, 1982) Determine your organization's strong points. This should be from both your internal and external customers. Do not be humble; be as pragmatic as possible. Are there any unique or distinct advantages that makes your organization stand out in the crowd? What makes the customers choose your organization over the competition's? Are there any products or services in which your competition cannot imitate (now and in the future)? Weaknesses (Weihrich, 1982) Determine your organization's weaknesses, not only from your point of view, but also more importantly, from your customers. Although it may be difficult for an organization to acknowledge its weaknesses, it is best to handle the bitter reality without procrastination. Are there any operations or procedures that can be streamlined? What and why do your competition operate better than your organization? Is there any avoidance that your organization should be aware of? Does your competition have a certain market segment conquered? Opportunities (Weihrich, 1982) Another major factor is to determine how your organization can continue to grow within the marketplace. After all, opportunities are everywhere, such as changes in technology, government policy, social patterns, and so on. Where and what are the attractive opportunities within your marketplace? Are there any new emerging trends within the market? What does your organization predict in the future that may depict new opportunities? Threats (Weihrich, 1982) No one likes to think about threats, but we still have to face them, despite the fact that they are external factors that are out of our control. For example, the recent major economic slump in Asia. It is vital to be prepared and face threats even during turbulent situations. What is your competition doing that is suppressing your organizational development? Are there any changes in consumer demand, which call for new requirements of your products or services? Is the changing technology hurting your organization's position within the marketplace? 2.1 The wizardry of SWOT The wizardry of SWOT is the matching of specific internal and external factors, which creates a strategic matrix, which makes sense. (Note: The internal factors are within the control of your organization, such as operations, finance, marketing, and in other areas. The external factors are out of your organization's control, such as political and economic factors, technology, competition, and in other areas). The four combinations are called the Maxi-Maxi (Strengths/Opportunities), Maxi-Mini (Strengths/Threats), Mini-Maxi (Weakness/Opportunities), and Mini-Mini (Weaknesses/Threats). (See Figure 1.) 1 Maxi-Maxi (S/O): this combination shows the organization's strengths and opportunities. In essence, an organization should strive to maximise its strengths to capitalise on new opportunities (Weihrich, 1982). 2 Maxi-Mini (S/T): this combination shows the organization's strengths in consideration of threats, e.g. from competitors. In essence, an organization should strive to use its strengths to parry or minimise threats (Weihrich, 1982). 3 Mini-Maxi (W/O): this combination shows the organization's weaknesses in tandem with opportunities. It is an exertion to conquer the organization's weaknesses by making the most out of any new opportunities (Weihrich, 1982). 4 Mini-Mini (W/T): this combination shows the organization's weaknesses by comparison with the current external threats. This is most definitely defensive strategy, to minimise an organization's internal weaknesses and avoid external threats (Weihrich, 1982). 2.2 Collateral insight of SWOT As mentioned, the wizardry of SWOT is the matching of specific internal and external factors. However, what about the matching items within internal factors and items within external factors. The primary reason is that matching these factors will create strategies that will not make sense. For example, with a combination of strength and weakness (both are internal factors), lets say one of your organization's strengths is "plenty of cash" and one of your weaknesses is "lack of training". Therefore, mixing these two factors together, your management team might simply decide to plan more training for the staff members. The obvious remark for this purposeless strategy will be "so what!" Mainly because you should not train, just for the sake of training. A successful training program must have a specific target in response to external changes. You have to determine your organization's specific needs for training in line with the external and internal factors. In other words, the strategy must have an external factor as a trigger in order for it to be feasible. 3. The balanced scorecard Professor Robert Kaplan and David Norton developed the balanced scorecard (BS

Modelling and Simulation of Electric Vehicle Fast Charging Stations Driven by High Speed Railway Systems <=SEP=> The aim of this investigation is the analysis of the opportunity introduced by the use of railway infrastructures for the power supply of fast charging stations located in highways. Actually, long highways are often located far from urban areas and electrical infrastructure, therefore the installations of high power charging areas can be difficult. Specifically, the aim of this investigation is the analysis of the opportunity introduced by the use of railway infrastructures for the power supply of fast charging stations located in highways. Specifically, this work concentrates on fast-charging electric cars in motorway service areas by using high-speed lines for supplying the required power. Economic, security, safety and environmental pressures are motivating and pushing countries around the globe to electrify transportation, which currently accounts for a significant amount, above 70 percent of total oil demand. Electric cars require fast-charging station networks to allowing owners to rapidly charge their batteries when they drive relatively long routes. In other words, this means about the infrastructure towards building charging stations in motorway service areas and addressing the problem of finding solutions for suitable electric power sources. A possible and promising solution is proposed in the study that involves using the high-speed railway line, because it allows not only powering a high load but also it can be located relatively near the motorway itself. This paper presents a detailed investigation on the modelling and simulation of a 2 × 25 kV system to feed the railway. A model has been developed and implemented using the SimPower systems tool in MATLAB/Simulink to simulate the railway itself. Then, the model has been applied to simulate the battery charger and the system as a whole in two successive steps. The results showed that the concept could work in a real situation. Nonetheless if more than twenty 100 kW charging bays are required in each direction or if the line topology is changed for whatever reason, it cannot be guaranteed that the railway system will be able to deliver the additional power that is necessary.

A centralized detection and prevention technique against ARP poisoning <=SEP=> The Man-in-the-Middle (MITM) attack on ARP is presently a common attack and nuisance to the typical LAN environment. This type of MITM is brought to effect by ARP cache poisoning which is achieved using forged ARP packets. ARP poisoning is a mechanism in which a node poisons ARP cache table of another node forcing it to send packets to a destination other than the intended one. This paper presents a feasible solution to the ARP cache poisoning, removing inconsistencies from all ARP tables of all hosts in the network. This paper uses a centralized system and ARP Central Server (ACS) to manage ARP table entries in all hosts. All hosts in the network uses the ACS to validate their ARP table entries. The ACS validates and corrects the poisoned ARP entries of the attacked hosts and hence prevents ARP poisoning in the network.

Analysis of LCLC resonant converters for high-voltage high-frequency applications <=SEP=> This paper proposes a novel LCLC full-bridge resonant converter with ZCS and ZVS for high-voltage high-frequency applications. In this operating model, both of ZCS and ZVS can be achieved and the switching losses can be reduced to a minimum. The proposed converter adopts the parasitic parameters of the step-up voltage transformer and one other serial capacitor is needed to form the resonant tank. Compared with other resonant converters, where only ZCS or ZVS can be achieved, the proposed working model can increase conversion efficiency with soft-switching features. Finally, A 40 V input, 4800 V output and 300 W prototype with 500 kHz switching frequency was built to verify the benefits of the proposed converter. The achieved efficiency of the converter peaked at 96.7%.

Ontologies for Cultural Heritage <=SEP=> In the cultural heritage domain information systems are increasingly deployed, digital representations of physical objects are produced in immense numbers and there is a strong political pressure on memory institutions to make their holdings accessible to the public in digital form. The sector splits into a set of disciplines with highly specialized fields. Due to the resulting diversity, one can hardly speak about a “domain” in the sense of “domain ontologies” [33]. On the other side, study and research of the past is highly interdisciplinary. Characteristically, archaeology employs a series of “auxiliary” disciplines, such as archaeometry, archaeomedicine, archaeobotany, archaeometallurgy, archaeoastronomy, etc., but also historical sources and social theories. Interoperability between various highly specialized systems, integrated information access and information integration increasingly becomes a demand to support research, professional heritage administration, preservation, public curiosity and education. Therefore the sector is characterized by a complex schema integration problem of associating complementary information from various dedicated systems, which can be efficiently addressed by formal ontologies [14,32,33]. There is a proliferation of specialized terminology, but terminology is less used as a means of agreement between experts than as an intellectual tool for hypothesis building based on discriminating phenomena. Automated classification is a long established discipline of archaeology, but few terminological systems are widely accepted. The sector is, however, more focused on establishing knowledge about facts and context in the past than about classes of things and the laws of their behavior. Respectively, the concatenation of related facts by co-reference [56] to particulars, such as things, people, places, periods is a major open issue. Knowledge Organisation Systems (KOS, [62]) describing people and places are employed to a certain degree, and pose similar technical problems as ontologies, but the required scale is very large. In this chapter, we describe how ontologies are and could be employed to improve information management in the cultural heritage sector.

Segmentation Guided Attention Networks for Visual Question Answering <=SEP=> In this paper we propose to solve the problem of Visual Question Answering by using a novel segmentation guided attention based network which we call SegAttendNet. We use image segmentation maps, generated by a Fully Convolutional Deep Neural Network to refine our attention maps and use these refined attention maps to make the model focus on the relevant parts of the image to answer a question. The refined attention maps are used by the LSTM network to learn to produce the answer. We presently train our model on the visual7W dataset and do a category wise evaluation of the 7 question categories. We achieve state of the art results on this dataset and beat the previous benchmark on this dataset by a 1.5% margin improving the question answering accuracy from 54.1% to 55.6% and demonstrate improvements in each of the question categories. We also visualize our generated attention maps and note their improvement over the attention maps generated by the previous best approach.

Rollout Sampling Policy Iteration for Decentralized POMDPs <=SEP=> We present decentralized rollout sampling policy iteration (DecRSPI) — a new algorithm for multi-agent decision problems formalized as DEC-POMDPs. DecRSPI is designed to improve scalability and tackle problems that lack an explicit model. The algorithm uses MonteCarlo methods to generate a sample of reachable belief states. Then it computes a joint policy for each belief state based on the rollout estimations. A new policy representation allows us to represent solutions compactly. The key benefits of the algorithm are its linear time complexity over the number of agents, its bounded memory usage and good solution quality. It can solve larger problems that are intractable for existing planning algorithms. Experimental results confirm the effectiveness and scalability of the approach.

Internet of Robotic Things-Converging Sensing / Actuating , Hypoconnectivity , Artificial Intelligence and IoT Platforms <=SEP=> The Internet of Things (IoT) concept is evolving rapidly and influencing new developments in various application domains, such as the Internet of Mobile Things (IoMT), Autonomous Internet of Things (A-IoT), Autonomous System of Things (ASoT), Internet of Autonomous Things (IoAT), Internet of Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc. that are progressing/advancing by using IoT technology. The IoT influence represents new development and deployment challenges in different areas such as seamless platform integration, context based cognitive network integration, new mobile sensor/actuator network paradigms, things identification (addressing, naming in IoT) and dynamic things discoverability and many others. The IoRT represents new convergence challenges and their need

New folded configuration of rectangular waveguide filters with asymmetrical transmission zeros <=SEP=> This paper presents a novel filter topology for the implementation of asymmetric responses with transmission zeros (TZs) in rectangular waveguide technology. It is based on a compact folded E-plane arrangement where adjacent resonators are capacitively coupled through rectangular slots, and non-adjacent resonators are coupled through simple inductive windows. The new folded configuration allows the introduction of transmission zeros above the passband, which can be easily controlled. High order filters can be designed by cascading an arbitrary number of resonators in a folded layout. Components based on this novel configuration are amenable to simple manufacturing processes and can be used in high-power environments. A triplexer containing filters implemented with the proposed topology has been designed. Measurements of a manufactured prototype are included to validate the use of this topology in practical applications.

Microstrip series fed antenna array for millimeter wave automotive radar applications <=SEP=> A 2D tapered microstrip antenna array is analyzed and designed in the paper by adopting the method of Modified Transmission Line Model. The pattern of the E-plane is determined by 20 linear series fed rectangular microstrip patches with tapered width, and 16 linear arrays compose the 2D array fed by four-stage T-junction power divider network. The excitation coefficients in both E and H plane follow the Taylor distribution. The antenna array achieves a pencil beam of 29dB gain at W band, and its half power beamwidths(HPBW) for E plane and H plane are both about 5°. The Side Lobe Level (SLL) of the pattern at the center frequency is lower than -17dB and 19dB at E and H plane respectively.

Calibration of a dual-laser triangulation system for assembly line completeness inspection <=SEP=> In controlled industrial environments, laser triangulation is an effective technique for 3D reconstruction, which is increasingly being used for quality inspection and metrology. In this paper, we propose a method for calibrating a dual laser triangulation system - consisting of a camera, two line lasers, and a linear motion platform designed to perform completeness inspection tasks on an assembly line. Calibration of such a system involves the recovery of two sets of parameters - the plane parameters of the two line lasers, and the translational direction of the linear motion platform. First, we address these two calibration problems separately. While many solutions have been given for the former problem, the latter problem has been largely ignored. Next, we highlight an issue specific to the use of multiple lasers - that small errors in the calibration parameters can lead to misalignment between the reconstructed point clouds of the different lasers. We present two different procedures that can eliminate such misalignments by imposing constraints between the two sets of calibration parameters. Our calibration method requires only the use of planar checkerboard patterns, which can be produced easily and inexpensively.

Adiposity and cancer risk: new mechanistic insights from epidemiology <=SEP=> Excess body adiposity, commonly expressed as body mass index (BMI), is a risk factor for many common adult cancers. Over the past decade, epidemiological data have shown that adiposity–cancer risk associations are specific for gender, site, geographical population, histological subtype and molecular phenotype. The biological mechanisms underpinning these associations are incompletely understood but need to take account of the specificities observed in epidemiology to better inform future prevention strategies.