title: Qualitative Grading of Severity of Lumbar Spinal Stenosis Based on the Morphology of the Dural Sac on Magnetic Resonance Images

abstract: STUDY DESIGN ::: Retrospective radiologic study on a prospective patient cohort. ::: ::: ::: OBJECTIVE ::: To devise a qualitative grading of lumbar spinal stenosis (LSS), study its reliability and clinical relevance. ::: ::: ::: SUMMARY OF BACKGROUND DATA ::: Radiologic stenosis is assessed commonly by measuring dural sac cross-sectional area (DSCA). Great variation is observed though in surfaces recorded between symptomatic and asymptomatic individuals. ::: ::: ::: METHODS ::: We describe a 7-grade classification based on the morphology of the dural sac as observed on T2 axial magnetic resonance images based on the rootlet/cerebrospinal fluid ratio. Grades A and B show cerebrospinal fluid presence while grades C and D show none at all. The grading was applied to magnetic resonance images of 95 subjects divided in 3 groups as follows: 37 symptomatic LSS surgically treated patients; 31 symptomatic LSS conservatively treated patients (average follow-up, 2.5 and 3.1 years); and 27 low back pain (LBP) sufferers. DSCA was also digitally measured. We studied intra- and interobserver reliability, distribution of grades, relation between morphologic grading and DSCA, as well relation between grades, DSCA, and Oswestry Disability Index. ::: ::: ::: RESULTS ::: Average intra- and interobserver agreement was substantial and moderate, respectively (k = 0.65 and 0.44), whereas they were substantial for physicians working in the study originating unit. Surgical patients had the smallest DSCA. A larger proportion of C and D grades was observed in the surgical group. Surface measurements resulted in overdiagnosis of stenosis in 35 patients and under diagnosis in 12. No relation could be found between stenosis grade or DSCA and baseline Oswestry Disability Index or surgical result. C and D grade patients were more likely to fail conservative treatment, whereas grades A and B were less likely to warrant surgery. ::: ::: ::: CONCLUSION ::: The grading defines stenosis in different subjects than surface measurements alone. Since it mainly considers impingement of neural tissue it might be a more appropriate clinical and research tool as well as carrying a prognostic value.


title: Tests of long-run neutrality using permanent monetary and real shocks

abstract: Abstract We present tests of long-run monetary neutrality using an empirical framework that allows for the effects of real shocks on the long-run behavior of both output and monetary aggregates. Our findings support long-run monetary neutrality as a feature of the post World War II U.S. economy. We further find that permanent innovations in labor supply, as measured by working-age population, are a principal factor in the long-run movements in U.S. output and monetary aggregates.


title: Side scan sonar segmentation using deep convolutional neural network

abstract: Side scan sonar (SSS) is a vital sensor for autonomous underwater vehicles (AUVs) to do ocean survey. Many methods have been proposed to carry out SSS image segmentation, among which machine learning algorithms provide outstanding performance. Machine learning algorithms like support vector machine (SVM) and convolutional neural networks (CNN) are the most used. When SVM is used to do pixel-level classification or segmentation, image features should be chosen based on researchers' experience. So the result may be not the optimal one. On the other hand, deep CNN can extract features of images automatically through its special architecture and can capture highly nonlinear mappings between inputs and outputs. Therefore, in this paper, we propose to use deep CNN to segment images of SSS into three parts: highlight areas with objects, regions of shadow and sea-bottom reverberation areas. The CNN used in this paper is derived from fully convolutional networks (FCN) which is trained by manually segmented sonar data, takes patches of the same size as inputs while training and outputs the segmentation results of images directly. It is an end-to-end architecture which can output segmentation results of side scan sonar images with any size after training. Then we post-process the results of CNN by using Markov random fields (MRF). Experimental results demonstrate that segmentation method based on CNN and MRF is applicable in SSS image segmentation.


title: Evaluation and treatment of oral candidiasis in HIV/AIDS patients in Enugu, Nigeria

abstract: INTRODUCTION ::: Oral candidiasis is one of the common diseases seen in HIV/AIDS patients. It is rare if CD4+ cell counts are above 500 microl. Outbreaks are more common as the count drops to 100 microl. It may be more difficult to treat when CD4+ cell counts fall below 50 microl. ::: ::: ::: MATERIALS AND METHODS ::: A retrospective review of 112 HIV/AIDS patients with lesions in the mouth, head, and neck seen at the oral and maxillofacial surgery units of two public hospitals in eastern Nigeria was carried out between 2000 and 2003. The focus was on oral candidiasis patients. Twenty-nine of these patients, made up of 11 males and 18 females, had oral candidiasis. To compare the action of two drugs, namely, nystatin (a topical antifungal drug) and ketoconazole (a systemic antifungal drug), we treated 15 of the patients with nystatin in the first 2 years and the remaining 14 with ketoconazole in the following 2 years. ::: ::: ::: RESULTS AND DISCUSSION ::: Amongst the 15 patients treated with topical drugs, 7 (46.7%) had complete remission, 2 (13.3%) had partial response, 4 (26.7%) remained stationary, and 2 (13.3%) died. Out of the 14 cases treated with systemic drugs, 11 (78.6%) had complete remission, 2 (14.3%) had partial response, and 1 (7.1%) died.


title: Toward the Next Generation of Recommender Systems: Applications and Research Challenges

abstract: Recommender systems are assisting users in the process of identifying items that fulfill their wishes and needs. These systems are successfully applied in different e-commerce settings, for example, to the recommendation of news, movies, music, books, and digital cameras. The major goal of this book chapter is to discuss new and upcoming applications of recommendation technologies and to provide an outlook on major characteristics of future technological developments. Based on a literature analysis, we discuss new and upcoming applications in domains such as software engineering, data and knowledge engineering, configurable items, and persuasive technologies. Thereafter we sketch major properties of the next generation of recommendation technologies.


title: 100 W GaN HEMT power amplifier module with > 60% efficiency over 100–1000 MHz bandwidth

abstract: We have demonstrated a decade bandwidth 100 W GaN HEMT power amplifier module with 15.5–18.6 dB gain, 104–121 W CW output power and 61.4–76.6 % drain efficiency over the 100–1000 MHz band. The 2 × 2 inch compact power amplifier module combines four 30 W lossy matched broadband GaN HEMT PAs packaged in a ceramic SO8 package. Each of the 4 devices is fully matched to 50 Ohms and obtains 30.8–35.7 W with 68.6–79.6 % drain efficiency over the band. The packaged amplifiers contain a GaN on SiC device operating at 48V drain voltage, alongside GaAs integrated passive matching circuitry. The four devices are combined using a broadband low loss coaxial balun. We believe this combination of output power, bandwidth and efficiency is the best reported to date. These amplifiers are targeted for use in multi-band public mobile radios and for instrumentation applications.


title: Cascaded Pyramid Network for 3D Human Pose Estimation Challenge

abstract: Over the past decade, there has been a growing interest in human pose estimation. Although much work has been done on 2D pose estimation, 3D pose estimation has still been relatively studied less. In this paper, we propose a top-bottom based two-stage 3D estimation framework. GloabalNet and RefineNet in our 2D pose estimation process enable us to find occluded or invisible 2D joints while 2D-to-3D pose estimator composed of residual blocks is used to lift 2D joints to 3D joints effectively. The proposed method achieves promising results with mean per joint position error at 42.39 on the validation dataset on `3D Human Pose Estimation within the ECCV 2018 PoseTrack Challenge.'


title: A deep learning pipeline for product recognition on store shelves

abstract: Recognition of grocery products in store shelves poses peculiar challenges. Firstly, the task mandates the recognition of an extremely high number of different items, in the order of several thousands for medium-small shops, with many of them featuring small inter and intra class variability. Then, available product databases usually include just one or a few studio-quality images per product (referred to herein as reference images), whilst at test time recognition is performed on pictures displaying a portion of a shelf containing several products and taken in the store by cheap cameras (referred to as query images). Moreover, as the items on sale in a store as well as their appearance change frequently over time, a practical recognition system should handle seamlessly new products/packages. Inspired by recent advances in object detection and image retrieval, we propose to leverage on state of the art object detectors based on deep learning to obtain an initial productagnostic item detection. Then, we pursue product recognition through a similarity search between global descriptors computed on reference and cropped query images. To maximize performance, we learn an ad-hoc global descriptor by a CNN trained on reference images based on an image embedding loss. Our system is computationally expensive at training time but can perform recognition rapidly and accurately at test time.


title: The anatomy of prototypes: Prototypes as filters, prototypes as manifestations of design ideas

abstract: The role of prototypes is well established in the field of HCI and Design. A lack of knowledge, however, about the fundamental nature of prototypes still exists. Researchers have attempted to identify different types of prototypes, such as low- vs. high-fidelity prototypes, but these attempts have centered on evaluation rather than support of design exploration. There have also been efforts to provide new ways of thinking about the activity of using prototypes, such as experience prototyping and paper prototyping, but these efforts do not provide a discourse for understanding fundamental characteristics of prototypes. In this article, we propose an anatomy of prototypes as a framework for prototype conceptualization. We view prototypes not only in their role in evaluation but also in their generative role in enabling designers to reflect on their design activities in exploring a design space. We base this framework on the findings of two case studies that reveal two key dimensions: prototypes as filters and prototypes as manifestations. We explain why these two dimensions are important and how this conceptual framework can benefit our field by establishing more solid and systematic knowledge about prototypes and prototyping.


title: Universal High-Frequency-Link Characterization and Practical Fundamental-Optimal Strategy for Dual-Active-Bridge DC-DC Converter Under PWM Plus Phase-Shift Control

abstract: This letter first develops a universal steady-state model to simply and accurately describe the analytical expressions for the high-frequency-link (HFL) electrical quantities of isolated dual-active-bridge (DAB) dc–dc converter under PWM plus phase-shift control. Second, a universal reactive power interaction among the HFL electrical quantities is present; using this interaction, the circulating current characteristic of DAB can be described accurately by HFL power factor. On this basis, a practical HFL fundamental-optimal strategy is proposed to decrease the circulating current and increase the efficiency. At last, experimental results verify the correctness of the universal model and the effectiveness of the fundamental-optimal strategy.


title: Comparative Analysis of Raw Images and Meta Feature based Urdu OCR using CNN and LSTM

abstract: Urdu language uses cursive script which results in connected characters constituting ligatures. For identifying characters within ligatures of different scales (font sizes), Convolution Neural Network (CNN) and Long Short Term Memory (LSTM) Network are used. Both network models are trained on formerly extracted ligature thickness graphs, from which models extract Meta features. These thickness graphs provide consistent information across different font sizes. LSTM and CNN are also trained on raw images to compare performance on both forms of inputs. For this research, two corpora, i.e. Urdu Printed Text Images (UPTI) and Centre for Language Engineering (CLE) Text Images are used. Overall performance of networks ranges between 90% and 99.8%. Average accuracy on Meta features is 98.08% while using raw images, 97.07% average accuracy is achieved.


Malicious URL Detection Using Convolutional Neural Network


title: Circuit Breaker Technologies for Advanced Ship Power Systems

abstract: The AC power systems on navy ships use circuit breakers based on traditional commercial technology. New naval power systems employ higher voltage DC distribution and use solid state power converters that actively (and instantaneously) limit the available fault current. When conventional circuit breakers are used in these systems, they have a relatively long clearing time, causing the voltage to collapse for a significant time. The application of fast-acting solid state circuit breakers, based on IGBTs and IGCTs will eliminate such voltage dips and result in a superior power system. Solid state circuit breakers also offer advantages when used in traditional ship power systems by limiting fault currents to levels far below those dictated by the supplying generators. This leads to lower magnetic and thermal stresses on distribution components, lower energy delivered to faults, faster fault isolation and minimal voltage disturbance.


title: Bone mass status in different degrees of chronic renal failure

abstract: Abstract Bone mineral density (BMD) was evaluated in 69 patients with chronic renal failure (CRF) and in healthy controls matched for age, sex, and, for women, menopausal status. Both appendicular (predominantly cortical) and axial (predominantly trabecular) bone mass were measured. Appendicular BMD was measured by single photon absorptiometry on the distal forearm and axial BMD was measured by dual photon absorptiometry on the lumbar spine. Patients were divided into three groups on the basis of creatinine clearance: group 1, 58 to 36 ml/min; group 2,30 to 18 ml/mm; group 3, 15 to 9 ml/min. Both appendicular and axial BMD decreased with the worsening of renal failure. Parathyroid hormone levels were inversely correlated with the values of appendicular BMD. BMD values were significantly lower in post-menopausal than in premenopausal women. Months of menopause were inversely correlated with axial BMD values. Appendicular BMD was lower in CRF patients than in controls, and both appendicular and axial BMD were significantly lower in postmenopausal uremic women than in post-menopausal healthy women.


title: Recognition of Offline Handwritten Chinese Characters Using the Tesseract Open Source OCR Engine

abstract: Due to the complex structure and handwritten deformation, the offline handwritten Chinese characters recognition has been one of the most challenging problems. In this paper, an offline handwritten Chinese character recognition tool has been developed based on the Tesseract open source OCR engine. The tool mainly contributes on the following two points: First, a handwritten Chinese character features library is generated, which is independent of a specific user's writing style, Second, by preprocessing the input image and adjusting the Tesseract engine, multiple candidate recognition results are output based on weight ranking. The recognition accuracy rate of this tool is above 88% for both known user test set and unknown user test set. It has shown that the Tesseract engine is feasible for offline handwritten Chinese character recognition to a certain degree.


title: Effect of preoperative chemotherapy on the outcome of women with operable breast cancer.

abstract: PURPOSETo determine, in women with primary operable breast cancer, if preoperative doxorubicin (Adriamycin) and cyclophosphamide (Cytoxan; AC) therapy yields a better outcome than postoperative AC therapy, if a relationship exists between outcome and tumor response to preoperative chemotherapy, and if such therapy results in the performance of more lumpectomies.PATIENTS AND METHODSWomen (1,523) enrolled onto National Surgical Adjuvant Breast and Bowel Project (NSABP) B-18 were randomly assigned to preoperative or postoperative AC therapy. Clinical tumor response to preoperative therapy was graded as complete (cCR), partial (cPR), or no response (cNR). Tumors with a cCR were further categorized as either pathologic complete response (pCR) or invasive cells (pINV). Disease-free survival (DFS), distant disease-free survival (DDFS), and survival were estimated through 5 years and compared between treatment groups. In the preoperative arm, proportional-hazards models were used to investigate the relationship b...


title: CAD/CAM ceramic fragments in anterior teeth: A clinical report

abstract: Objective ::: The treatment of fractured teeth has become a great opportunity to investigate new approaches in the field of restorative dentistry. Restoring teeth with ceramic fragments is a great challenge, however, the computer aided design/computer aided manufacturing (CAD/CAM) technology allows the fabrication of both laminate veneers and ceramic fragments. ::: ::: Materials and Methods ::: Thus, the aim of this article was to report a clinical case where anterior teeth, fractured due to a sport practice, were restored with CAD/CAM ceramic fragments. ::: ::: Conclusions ::: It was observed that with this kind of restoration optimum esthetic results might be achieved when treating anterior fractured teeth. ::: ::: Clinical significance ::: Ceramic fragments using CAD/CAM systems are a viable clinical alternative to composites for the reconstruction of fractured teeth. Function and esthetics can be restored with the use of this treatment quickly and efficiently.


title: Light-Weight Instruction Set Extensions for Bit-Sliced Cryptography

abstract: Bit-slicing is a non-conventional implementation technique for cryptographic software where an n-bit processor is considered as a collection of n1-bit execution units operating in SIMD mode. Particularly when implementing symmetric ciphers, the bit-slicing approach has several advantages over more conventional alternatives: it often allows one to reduce memory footprint by eliminating large look-up tables, and it permits more predictable performance characteristics that can foil time based side-channel attacks. Both features are attractive for mobile and embedded processors, but the performance overhead that results from bit-sliced implementation often represents a significant disadvantage. In this paper we describe a set of light-weight Instruction Set Extensions (ISEs) that can improve said performance while retaining all advantages of bit-sliced implementation. Contrary to other crypto-ISE, our design is generic and allows for a high degree of algorithm agility: we demonstrate applicability to several well-known cryptographic primitives including four block ciphers (DES, Serpent, AES, and PRESENT), a hash function (SHA-1), as well as multiplication of ternary polynomials.


title: DC 30-GHz DPDT Switch Matrix Design in High Resistivity Trap-Rich SOI

abstract: This paper presents low insertion loss, high isolation, ultra-wideband double-pole-double-throw (DPDT) switch matrix designed in a 0.13- $\mu \text{m}$ commercial high resistivity trap-rich silicon-on-insulator (SOI) CMOS process for the first time. The switches are designed using series–shunt–series configuration in a ring-type structure with input and output matching networks. Transistor width and transistor channel length effects on the wideband DPDT switch performance are thoroughly investigated. The designed switches achieve widest bandwidth from dc to 30 GHz with a low insertion loss of 2.5 dB and a high isolation of 32 dB up to 30 GHz. The measured input P1dB of designed switches is higher than 18 dBm. It was found both second and third harmonics can be improved by widening switch transistor channel width, and third harmonic can be improved by shortening channel length. The active chip area of designed $2 \times2$ switch matrix is very small size of only 0.28 mm $\times0.21$ mm.


( 07 ) STUDENTS ’ “ LANGUAGE BARRIER ” : PSYCHOLOGICAL FACTORS AND SOLUTIONS


title: Image based flow visualization for curved surfaces

abstract: A new method for the synthesis of dense, vector-field aligned textures on curved surfaces is presented, called IBFVS. The method is based on image based flow visualization (IBFV). In IBFV two-dimensional animated textures are produced by defining each frame of a flow animation as a blend between a warped version of the previous image and a number of filtered white noise images. We produce flow aligned texture on arbitrary three-dimensional triangular meshes in the same spirit as the original method: texture is generated directly in image space. We show that IBFVS is efficient and effective. High performance (typically fifty frames or more per second) is achieved by exploiting graphics hardware. Also, IBFVS can easily be implemented and a variety of effects can be achieved. Applications are flow visualization and surface rendering. Specifically, we show how to visualize the wind field on the earth and how to render a dirty bronze bunny.


Delayed healing: the rationale for zinc therapy.


title: From social interaction to ethical AI: a developmental roadmap

abstract: AI and robot ethics have recently gained a lot of attention because adaptive machines are increasingly involved in ethically sensitive scenarios and cause incidents of public outcry. Much of the debate has been focused on achieving highest moral standards in handling ethical dilemmas on which not even humans can agree, which indicates that the wrong questions are being asked. We suggest to address this ethics debate strictly through the lens of what behavior seems socially acceptable, rather than idealistically ethical. Learning such behavior puts the debate into the very heart of developmental robotics. This paper poses a roadmap of computational and experimental questions to address the development of socially acceptable machines. We emphasize the need for social reward mechanisms and learning architectures that integrate these while reaching beyond limitations of plain reinforcement-learning agents. We suggest to use the metaphor of “needs” to bridge rewards and higher level abstractions such as goals for both communication and action generation in a social context. We then suggest a series of experimental questions and possible platforms and paradigms to guide future research in the area.


Smart Avatars : using avatars to interact with objects


title: How reading acquisition changes children’s spoken language network

abstract: Abstract To examine the influence of age and reading proficiency on the development of the spoken language network, we tested 6- and 9-years-old children listening to native and foreign sentences in a slow event-related fMRI paradigm. We observed a stable organization of the peri-sylvian areas during this time period with a left dominance in the superior temporal sulcus and inferior frontal region. A year of reading instruction was nevertheless sufficient to increase activation in regions involved in phonological representations (posterior superior temporal region) and sentence integration (temporal pole and pars orbitalis). A top-down activation of the left inferior temporal cortex surrounding the visual word form area, was also observed but only in 9 year-olds (3 years of reading practice) listening to their native language. These results emphasize how a successful cultural practice, reading, slots in the biological constraints of the innate spoken language network.


title: Ontology-Driven Affective Chinese Text Analysis and Evaluation Method

abstract: This paper studies text affective analysis from three different levels: words, sentences and discourses. Firstly, affective lexicon ontology is constructed by employing the manual and automatic classification methods referred to the present emotional classification. Then affective analysis of sentences and discourses is completed by emotional information of affective lexicon ontology. For sentential recognition, the lexical emotion information and semantic features are appended into Condition Random Fields, and the emotional chain of a text document is generated. Finally affective structure of a text document is evaluated by two different methods as single sentence evaluation and joint sentence evaluation. In analyzing the discourses, affective tone is acquired through Emotional Largest Chain method and Support Vector Machine method.


title: Predictive Business Process Monitoring with LSTM Neural Networks

abstract: Predictive business process monitoring methods exploit logs of completed cases of a process in order to make predictions about running cases thereof. Existing methods in this space are tailor-made for specific prediction tasks. Moreover, their relative accuracy is highly sensitive to the dataset at hand, thus requiring users to engage in trial-and-error and tuning when applying them in a specific setting. This paper investigates Long Short-Term Memory (LSTM) neural networks as an approach to build consistently accurate models for a wide range of predictive process monitoring tasks. First, we show that LSTMs outperform existing techniques to predict the next event of a running case and its timestamp. Next, we show how to use models for predicting the next task in order to predict the full continuation of a running case. Finally, we apply the same approach to predict the remaining time, and show that this approach outperforms existing tailor-made methods.


title: A survey on bad data injection attack in smart grid

abstract: Bad data injection (BDI) is one of the most threatening attacks in smart grid, as it may cause energy theft of end users, false dispatch on the distribution process, and device breakdown during power generation. In this paper, the BDI attack is defined as a cyber-physical attack which is a combination of two aspects: 1) on the cyber side, modern attack techniques are exploited to intrude and inject bad data into the information system; 2) on the physical side, attackers construct the bad data to bypass the traditional error detection in power systems. Related work on BDI construction and implementation are reviewed. An attack simulation is constructed to illustrate how to launch a BDI attack. The countermeasures against the BDI are also summarized from the views of cyber-orientation, physical-orientation. Finally, our work on cyber-physical fusion detection is presented.


title: Embedding of Embedding (EOE): Joint Embedding for Coupled Heterogeneous Networks

abstract: Network embedding is increasingly employed to assist network analysis as it is effective to learn latent features that encode linkage information. Various network embedding methods have been proposed, but they are only designed for a single network scenario. In the era of big data, different types of related information can be fused together to form a coupled heterogeneous network, which consists of two different but related sub-networks connected by inter-network edges. In this scenario, the inter-network edges can act as comple- mentary information in the presence of intra-network ones. This complementary information is important because it can make latent features more comprehensive and accurate. And it is more important when the intra-network edges are ab- sent, which can be referred to as the cold-start problem. In this paper, we thus propose a method named embedding of embedding (EOE) for coupled heterogeneous networks. In the EOE, latent features encode not only intra-network edges, but also inter-network ones. To tackle the challenge of heterogeneities of two networks, the EOE incorporates a harmonious embedding matrix to further embed the em- beddings that only encode intra-network edges. Empirical experiments on a variety of real-world datasets demonstrate the EOE outperforms consistently single network embedding methods in applications including visualization, link prediction multi-class classification, and multi-label classification.


title: A Novel Authentication and Key Agreement Scheme for Implantable Medical Devices Deployment

abstract: Implantable medical devices (IMDs) are man-made devices, which can be implanted in the human body to improve the functioning of various organs. The IMDs monitor and treat physiological condition of the human being (for example, monitoring of blood glucose level by insulin pump). The advancement of information and communication technology enhances the communication capabilities of IMDs. In healthcare applications, after mutual authentication, a user (for example, doctor) can access the health data from the IMDs implanted in a patient's body. However, in this kind of communication environment, there are always security and privacy issues, such as leakage of health data and malfunctioning of IMDs by an unauthorized access. To mitigate these issues, in this paper, we propose a new secure remote user authentication scheme for IMDs communication environment to overcome security and privacy issues in existing schemes. We provide the formal security verification using the widely accepted Automated Validation of Internet Security Protocols and Applications tool. We also provide the informal security analysis of the proposed scheme. The formal security verification and informal security analysis prove that the proposed scheme is secure against known attacks. The practical demonstration of the proposed scheme is performed using the broadly accepted NS2 simulation tool. The computation and communication costs of the proposed scheme are also comparable with the existing schemes. Moreover, the scheme provides additional functionality features, such as anonymity, untraceability, and dynamic implantable medical device addition.


title: Collaborative Variational Autoencoder for Recommender Systems

abstract: Modern recommender systems usually employ collaborative filtering with rating information to recommend items to users due to its successful performance. However, because of the drawbacks of collaborative-based methods such as sparsity, cold start, etc., more attention has been drawn to hybrid methods that consider both the rating and content information. Most of the previous works in this area cannot learn a good representation from content for recommendation task or consider only text modality of the content, thus their methods are very limited in current multimedia scenario. This paper proposes a Bayesian generative model called collaborative variational autoencoder (CVAE) that considers both rating and content for recommendation in multimedia scenario. The model learns deep latent representations from content data in an unsupervised manner and also learns implicit relationships between items and users from both content and rating. Unlike previous works with denoising criteria, the proposed CVAE learns a latent distribution for content in latent space instead of observation space through an inference network and can be easily extended to other multimedia modalities other than text. Experiments show that CVAE is able to significantly outperform the state-of-the-art recommendation methods with more robust performance.


title: A Motion-Tolerant Adaptive Algorithm for Wearable Photoplethysmographic Biosensors

abstract: The performance of portable and wearable biosensors is highly influenced by motion artifact. In this paper, a novel real-time adaptive algorithm is proposed for accurate motion-tolerant extraction of heart rate (HR) and pulse oximeter oxygen saturation (SpO2) from wearable photoplethysmographic (PPG) biosensors. The proposed algorithm removes motion artifact due to various sources including tissue effect and venous blood changes during body movements and provides noise-free PPG waveforms for further feature extraction. A two-stage normalized least mean square adaptive noise canceler is designed and validated using a novel synthetic reference signal at each stage. Evaluation of the proposed algorithm is done by Bland-Altman agreement and correlation analyses against reference HR from commercial ECG and SpO2 sensors during standing, walking, and running at different conditions for a single- and multisubject scenarios. Experimental results indicate high agreement and high correlation (more than 0.98 for HR and 0.7 for SpO2 extraction) between measurements by reference sensors and our algorithm.


title: Combining regression and classification methods for improving automatic speaker age recognition

abstract: We present a novel approach to automatic speaker age classification, which combines regression and classification to achieve competitive classification accuracy on telephone speech. Support vector machine regression is used to generate finer age estimates, which are combined with the posterior probabilities of well-trained discriminative gender classifiers to predict both the age and gender of a speaker. We show that this combination performs better than direct 7-class classifiers. The regressors and classifiers are trained using longterm features such as pitch and formants, as well as shortterm (frame-based) features derived from MAP adaptation of GMMs that were trained on MFCCs.


title: A Computational Model of Plan-Based Narrative Conflict at the Fabula Level

abstract: Conflict is an essential element of interesting stories. In this paper, we operationalize a narratological definition of conflict and extend established narrative planning techniques to incorporate this definition. The conflict partial order causal link planning algorithm (CPOCL) allows narrative conflict to arise in a plan while maintaining causal soundness and character believability. We also define seven dimensions of conflict in terms of this algorithm's knowledge representation. The first three-participants, reason, and duration-are discrete values which answer the “who?” “why?” and “when?” questions, respectively. The last four-balance, directness, stakes, and resolution-are continuous values which describe important narrative properties that can be used to select conflicts based on the author's purpose. We also present the results of two empirical studies which validate our operationalizations of these narrative phenomena. Finally, we demonstrate the different kinds of stories which CPOCL can produce based on constraints on the seven dimensions.


title: Incremental analysis of real programming languages

abstract: A major research goal for compilers and environments is the automatic derivation of tools from formal specifications. However, the formal model of the language is often inadequate; in particular, LR(k) grammars are unable to describe the natural syntax of many languages, such as C++ and Fortran, which are inherently non-deterministic. Designers of batch compilers work around such limitations by combining generated components with ad hoc techniques (for instance, performing partial type and scope analysis in tandem with parsing). Unfortunately, the complexity of incremental systems precludes the use of batch solutions. The inability to generate incremental tools for important languages inhibits the widespread use of language-rich interactive environments.We address this problem by extending the language model itself, introducing a program representation based on parse dags that is suitable for both batch and incremental analysis. Ambiguities unresolved by one stage are retained in this representation until further stages can complete the analysis, even if the reaolution depends on further actions by the user. Representing ambiguity explicitly increases the number and variety of languages that can be analyzed incrementally using existing methods.To create this representation, we have developed an efficient incremental parser for general context-free grammars. Our algorithm combines Tomita's generalized LR parser with reuse of entire subtrees via state-matching. Disambiguation can occur statically, during or after parsing, or during semantic analysis (using existing incremental techniques); program errors that preclude disambiguation retsin multiple interpretations indefinitely. Our representation and analyses gain efficiency by exploiting the local nature of ambiguities: for the S PEC 95 C programs, the explicit representation of ambiguity requires only 0.5% additional space and less than 1% additional time during reconstruction.


title: Robust real-time object detection

abstract: This paper describes a visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features and yields extremely efficient classifiers [4]. The third contribution is a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. A set of experiments in the domain of face detection are presented. The system yields face detection performance comparable to the best previous systems [16, 11, 14, 10, 1]. Implemented on a conventional desktop, face detection proceeds at 15 frames per second. Author email: fPaul.Viola,Mike.J.Jonesg@compaq.com c Compaq Computer Corporation, 2001 This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of the Cambridge Research Laboratory of Compaq Computer Corporation in Cambridge, Massachusetts; an acknowledgment of the authors and individual contributors to the work; and all applicable portions of the copyright notice. Copying, reproducing, or republishing for any other purpose shall require a license with payment of fee to the Cambridge Research Laboratory. All rights reserved. CRL Technical reports are available on the CRL’s web page at http://crl.research.compaq.com. Compaq Computer Corporation Cambridge Research Laboratory One Cambridge Center Cambridge, Massachusetts 02142 USA


Secondary Indexing Techniques for Key-Value Stores: Two Rings To Rule Them All


title: Learning Semantic Correspondences with Less Supervision

abstract: A central problem in grounded language acquisition is learning the correspondences between a rich world state and a stream of text which references that world state. To deal with the high degree of ambiguity present in this setting, we present a generative model that simultaneously segments the text into utterances and maps each utterance to a meaning representation grounded in the world state. We show that our model generalizes across three domains of increasing difficulty---Robocup sportscasting, weather forecasts (a new domain), and NFL recaps.


title: Pediatric toxicologic concerns

abstract: Clinicians caring for poisoned pediatric patients should develop a level of clinical competence and factual comfort in this area to optimize assessment, stabilization, and ultimately patient outcome. This article focuses on clinically relevant considerations specific to poisoned pediatric patients. Special emphasis is given to unique physical examination findings and nuances, appropriateness of the various gastrointestinal decontamination techniques, and management of toxins that commonly result in severe, life-threatening toxicity in children. Last, case samples are provided for those toxins that are dangerous to children in small doses or those that act differently in children after overdose.


title: Experimental and early investigational drugs for androgenetic alopecia.

abstract: INTRODUCTION ::: Treatments for androgenetic alopecia constitute a multi-billion-dollar industry, however, currently available therapeutic options have variable efficacy. Consequently, in recent years small biotechnology companies and academic research laboratories have begun to investigate new or improved treatment methods. Research and development approaches include improved formulations and modes of application for current drugs, new drug development, development of cell-based treatments, and medical devices for modulation of hair growth. Areas covered: Here we review the essential pathways of androgenetic alopecia pathogenesis and collate the current and emerging therapeutic strategies using journal publications databases and clinical trials databases to gather information about active research on new treatments. Expert opinion: We propose that topically applied medications, or intra-dermal injected or implanted materials, are preferable treatment modalities, minimizing side effect risks as compared to systemically applied treatments. Evidence in support of new treatments is limited. However, we suggest therapeutics which reverse the androgen-driven inhibition of hair follicle signaling pathways, such as prostaglandin analogs and antagonists, platelet-rich plasma (PRP), promotion of skin angiogenesis and perfusion, introduction of progenitor cells for hair regeneration, and more effective ways of transplanting hair, are the likely near future direction of androgenetic alopecia treatment development.


title: Fuzzy-controlled Li-ion battery charge system with active state-of-charge controller

abstract: A fuzzy-controlled active state-of-charge controller (FC-ASCC) for improving the charging behavior of a lithium-ion (Li-ion) battery is proposed. The proposed FC-ASCC is designed to replace the general constant-voltage charging mode by two kinds of modes: sense and charge. A fuzzy-controlled algorithm is built with the predicted charger performance to program the charging trajectory faster and to keep the charge operation in a proposed safe-charge area (SCA). A modeling work is conducted for analyzing and describing the Li-ion battery in charging process. A three-dimensional Y-mesh diagram for describing the charging trajectories of the proposed FC charger is simulated. A prototype of a Li-ion battery charger with FC-ASCC is simulated and realized to assess the predicted charging performance. Experiment shows that the charging speed of the proposed FC charger compared with the general one increases about 23% and the charger can safely work in the SCA.


title: Predicting Age Range of Users over Microblog Dataset

abstract: In this paper, we present the idea and methodologies on predicting the age span of users over microblog dataset. Given a user’s personal information such as user tags, job, education, self-description, and gender, as well as the content of his/her microblogs, we automatically classify the user’s age into one of four predefined ranges. Particularly, we extract a set of features from the given information about the user, and employ a statistic-based framework to solve this problem. The measurement shows that our proposed method incorporating selected features has an accuracy of around 71% on average over the training dataset.


title: Fuzzy Image Processing: Potentials and State of the Art

abstract: In recent years, many researchers have a pplied the fuzzy logic to develop new image processing algorithms. Meanwhile, the fuzzy image processing is one of the important application areas of f uzzy logic. This paper gives a brief overview of potentials of f uzzy techniques. The state of the a rt i s described b y referring to some practical examples.


title: Sensor Cloud: A Cloud of Virtual Sensors

abstract: Newer models for interacting with wireless sensors such as Internet of Things and Sensor Cloud aim to overcome restricted resources and efficiency. The Missouri S&T (science and technology) sensor cloud enables different networks, spread in a huge geographical area, to connect together and be employed simultaneously by multiple users on demand. Virtual sensors, which are at the core of this sensor cloud architecture, assist in creating a multiuser environment on top of resource-constrained physical wireless sensors and can help in supporting multiple applications.


title: An interior-point stochastic approximation method and an L1-regularized delta rule

abstract: The stochastic approximation method is behind the solution to many important, actively-studied problems in machine learning. Despite its far-reaching application, there is almost no work on applying stochastic approximation to learning problems with general constraints. The reason for this, we hypothesize, is that no robust, widely-applicable stochastic approximation method exists for handling such problems. We propose that interior-point methods are a natural solution. We establish the stability of a stochastic interior-point approximation method both analytically and empirically, and demonstrate its utility by deriving an on-line learning algorithm that also performs feature selection via L1 regularization.


THE EFFECT OF SATELLITE FORMATION FLYING ON AUTONOMOUS NAVIGATION


title: Human-computer interaction: interdisciplinary roots and trends

abstract: Abstract Methodology, theory, and practice in the field of Human–Computer Interaction (HCI) all share the goal of producing interactive software that can be used efficiently, effectively, safely, and with satisfaction. HCI is cross-disciplinary in its conduct and multidisciplinary in its roots. The central concept of HCI is usability, ease of use plus usefulness. Achieving good usability requires attention to both product and development process, particularly for the user interaction design, which should serve as requirements for the user interface software component. This paper reviews some of the theory and modeling supporting the practice of HCI, development life cycles and activities, and much of the practice that constitutes “usability engineering”. Future application areas of interest in HCI include new interaction styles, virtual environments, the World Wide Web, information visualization, and wearable computing.


title: Adapting Crossover in Evolutionary Algorithms

abstract: One of the issues in evolutionary algorithms (EAs) is the relative importance of two search operators: mutation and crossover. Genetic algorithms (GAs) and genetic programming (GP) stress the role of crossover, while evolutionary programming (EP) and evolution strategies (ESs) stress the role of mutation. The existence of many different forms of crossover further complicates the issue. Despite theoretical analysis, it appears difficult to decide a priori which form of crossover to use, or even if crossover should be used at all. One possible solution to this difficulty is to have the EA be self-adaptive, i.e., to have the EA dynamically modify which forms of crossover to use and how often to use them, as it solves a problem. This paper describes an adaptive mechanism for controlling the use of crossover in an EA and explores the behavior of this mechanism in a number of different situations. An improvement to the adaptive mechanism is then presented. Surprisingly this improvement can also be used to enhance performance in a non-adaptive EA.


title: Intelligent, automated red team emulation

abstract: Red teams play a critical part in assessing the security of a network by actively probing it for weakness and vulnerabilities. Unlike penetration testing - which is typically focused on exploiting vulnerabilities - red teams assess the entire state of a network by emulating real adversaries, including their techniques, tactics, procedures, and goals. Unfortunately, deploying red teams is prohibitive: cost, repeatability, and expertise all make it difficult to consistently employ red team tests. We seek to solve this problem by creating a framework for automated red team emulation, focused on what the red team does post-compromise - i.e., after the perimeter has been breached. Here, our program acts as an automated and intelligent red team, actively moving through the target network to test for weaknesses and train defenders. At its core, our framework uses an automated planner designed to accurately reason about future plans in the face of the vast amount of uncertainty in red teaming scenarios. Our solution is custom-developed, built on a logical encoding of the cyber environment and adversary profiles, using techniques from classical planning, Markov decision processes, and Monte Carlo simulations. In this paper, we report on the development of our framework, focusing on our planning system. We have successfully validated our planner against other techniques via a custom simulation. Our tool itself has successfully been deployed to identify vulnerabilities and is currently used to train defending blue teams.


title: Wavelength-Tunable Laser for Signal Remodulation in WDM Access Networks Using DPSK Downlink and OOK Uplink

abstract: In this investigation, a simple wavelength-tunable laser based on a Fabry-Perot laser diode (FP-LD) and an erbium-doped fiber amplifier to serve as a downlink signal in a colorless wavelength-division-multiplexed passive optical network (PON) is proposed and experimentally demonstrated. The tuning range of the proposed laser is between 1529.48 and 1560.72 nm, and the output performance of proposed laser is discussed. Colorless operation is implemented by using an FP-LD and a reflective semiconductor optical amplifier in each optical network unit for uplink signal remodulation, respectively. In addition, error-free data signal remodulation using 10-Gb/s downlink differential phase-shift keying and 2.5-Gb/s uplink on-off keying is achieved in a 25-km reach PON.


title: Biological processes in prevention and intervention : The promotion of self-regulation as a means of preventing school failure

abstract: This paper examines interrelations between biological and social influences on the development of self-regulation in young children and considers implications of these interrelations for the promotion of self-regulation and positive adaptation to school. Emotional development and processes of emotion regulation are seen as influencing and being influenced by the development of executive cognitive functions, including working memory, inhibitory control, and mental flexibility important for the effortful regulation of attention and behavior. Developing self-regulation is further understood to reflect an emerging balance between processes of emotional arousal and cognitive regulation. Early childhood educational programs that effectively link emotional and motivational arousal with activities designed to exercise and promote executive functions can be effective in enhancing self-regulation, school readiness, and school success.


title: Language-model-based ranking for queries on RDF-graphs

abstract: The success of knowledge-sharing communities like Wikipedia and the advances in automatic information extraction from textual and Web sources have made it possible to build large "knowledge repositories" such as DBpedia, Freebase, and YAGO. These collections can be viewed as graphs of entities and relationships (ER graphs) and can be represented as a set of subject-property-object (SPO) triples in the Semantic-Web data model RDF. Queries can be expressed in the W3C-endorsed SPARQL language or by similarly designed graph-pattern search. However, exact-match query semantics often fall short of satisfying the users' needs by returning too many or too few results. Therefore, IR-style ranking models are crucially needed. In this paper, we propose a language-model-based approach to ranking the results of exact, relaxed and keyword-augmented graph pattern queries over RDF graphs such as ER graphs. Our method estimates a query model and a set of result-graph models and ranks results based on their Kullback-Leibler divergence with respect to the query model. We demonstrate the effectiveness of our ranking model by a comprehensive user study.


title: Enhanced Security Model For Mobile Banking Systems In Tanzania

abstract: In mobile banking schemes; financial services are availed and banking services are provided using mobile devices. GSM services are greatly utilized for data transmission by the technologies used in conducting mobile transactions. In their operations; these technologies send data in plaintext. Financial service providers tend to rely on the security services provided by the GSM which has been proved to be susceptible to cryptanalytic attacks. The used algorithms for crypto mechanisms are flawed leaving data carried through the network vulnerable upon interception. Operators need to take precaution by enforcing some protective measures on the information to be transmitted. This paper describes an SMS based model designed with security features to enhance data protection across mobile networks. Features for data encryption, integrity, secure entry of security details on the phone, and improved security policies in the application server are incorporated. We address issues of data confidentiality, user authentication and message integrity in order to provide end-to-end security of data carried on GSM networks.


title: Tumor necrosis factor-α, monocyte chemoattractant protein-1 and intercellular adhesion molecule-1 increase during the development of a 2,4-dinitrofluorobenzene-induced immediate-type dermatitis in rats

abstract: Background ::: Due to the steadily increasing incidence of atopic dermatitis, there is a great medical need for new therapies and improved animal models.


title: Modeling of wind direction signals in polarimetric sea surface brightness temperatures

abstract: There has been an increasing interest in the applications of polarimetric microwave radiometers for ocean wind remote sensing. Aircraft and spaceborne radiometers have found a few Kelvins wind direction signals in sea surface brightness temperatures, in addition to their sensitivities to wind speeds. However, it was not clear what physical scattering mechanisms produced the observed brightness dependence on wind direction. To this end, polarimetric microwave emissions from wind-generated sea surfaces are investigated with a polarimetric two-scale scattering model, which relates the directional wind-wave spectrum to passive microwave signatures of sea surfaces. Theoretical azimuthal modulations are found to agree well with experimental observations for all Stokes parameters from near nadir to 65/spl deg/ incidence angles. The upwind and downwind asymmetries of brightness temperatures were interpreted using the hydrodynamic modulation. The contributions of Bragg scattering by short waves, geometric optics scattering by long waves and sea foam are examined. The geometric optics scattering mechanism underestimates the directional signals in the first three Stokes parameters, and predicts no signals in the fourth Stokes parameter (V). In contrast, the Bragg scattering was found to dominate the wind direction signals from the two-scale model and correctly predicted the phase changes of the upwind and crosswind asymmetries in T/sub /spl upsi// and U from middle to high incidence angles. The phase changes predicted by the Bragg scattering theory for radiometric emission from water ripples is corroborated by the numerical Monte Carlo simulation of rough surface scattering. This theoretical interpretation indicates the potential use of polarimetric brightness temperatures for retrieving the directional wave spectrum of short gravity and capillary waves.


title: Online Convex Optimization for Sequential Decision Processes and Extensive-Form Games

abstract: Regret minimization is a powerful tool for solving large-scale extensive-form games. State-of-the-art methods rely on minimizing regret locally at each decision point. In this work we derive a new framework for regret minimization on sequential decision problems and extensive-form games with general compact convex sets at each decision point and general convex losses, as opposed to prior work which has been for simplex decision points and linear losses. We call our framework laminar regret decomposition. It generalizes the CFR algorithm to this more general setting. Furthermore, our framework enables a new proof of CFR even in the known setting, which is derived from a perspective of decomposing polytope regret, thereby leading to an arguably simpler interpretation of the algorithm. Our generalization to convex compact sets and convex losses allows us to develop new algorithms for several problems: regularized sequential decision making, regularized Nash equilibria in extensive-form games, and computing approximate extensive-form perfect equilibria. Our generalization also leads to the first regret-minimization algorithm for computing reduced-normal-form quantal response equilibria based on minimizing local regrets. Experiments show that our framework leads to algorithms that scale at a rate comparable to the fastest variants of counterfactual regret minimization for computing Nash equilibrium, and therefore our approach leads to the first algorithm for computing quantal response equilibria in extremely large games. Finally we show that our framework enables a new kind of scalable opponent exploitation approach.


title: The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure

abstract: Many modern machine learning classifiers are shown to be vulnerable to adversarial perturbations of the instances. Despite a massive amount of work focusing on making classifiers robust, the task seems quite challenging. In this work, through a theoretical study, we investigate the adversarial risk and robustness of classifiers and draw a connection to the well-known phenomenon of “concentration of measure” in metric measure spaces. We show that if the metric probability space of the test instance is concentrated, any classifier with some initial constant error is inherently vulnerable to adversarial perturbations.One class of concentrated metric probability spaces are the so-called Levy families that include many natural distributions. In this special case, our attacks only need to perturb the test instance by at most O(√n) to make it misclassified, where n is the data dimension. Using our general result about Levy instance spaces, we first recover as special case some of the previously proved results about the existence of adversarial examples. However, many more Levy families are known (e.g., product distribution under the Hamming distance) for which we immediately obtain new attacks that find adversarial examples of distance O(√n).Finally, we show that concentration of measure for product spaces implies the existence of forms of “poisoning” attacks in which the adversary tampers with the training data with the goal of degrading the classifier. In particular, we show that for any learning algorithm that uses m training examples, there is an adversary who can increase the probability of any “bad property” (e.g., failing on a particular test instance) that initially happens with non-negligible probability to ≈ 1 by substituting only Oe(√m) of the examples with other (still correctly labeled) examples.


title: S4D: Speaker Diarization Toolkit in Python

abstract: In this paper, we present S4D, a new open-source Python toolkit dedicated to speaker diarization. S4D provides various state-of-the-art components and the possibility to easily develop end-to-end diarization prototype systems. S4D offers a large panel of clustering, segmentation, scoring and visualization algorithms. S4D has been thought to be easily understood, installed, modified and used in order to allow fast transfers of diarization technologies to industry and facilitate development of new approaches. Examples, benchmarks on standard tasks and tutori-als are provided in this paper. S4D is an extension of the open-source toolkit for speaker recognition: SIDEKIT.


title: Relationship Between Visual-Motor Integration, Eye-Hand Coordination, and Quality of Handwriting

abstract: If the influence of visual-motor integration (copying forms) on the quality of handwriting has been widely investigated, the influence of eye-hand coordination (tracing item) has been less well analyzed. The Concise Assessment Scale for Children's Handwriting (BHK), the Developmental Test of Visual Perception (DTVP-2), and the section “Manual Dexterity” of the Movement Assessment Battery for Children (M-ABC) were administered to a group of second grade children (N = 75; 8.1-year-olds). The association of visual-motor integration and eye-hand coordination are predictive of the quality of handwriting (p < . 001). These two skills should be taken into consideration when children are referred to occupational therapy for difficulties in handwriting.


title: Their Application to Assessment of Psychopathology and Psychotherapy

abstract: Aristotle elaborated his theory of virtue in two texts, the Nicomachean and the Eudemian Ethics. Throughout the centuries, his theory of virtues has endured despite a number of attempts to eliminate it as a framework for how one should live and flourish. This essay revisits Aristotle’s theory of virtue for two purposes. The first is simply to note the remarkable depth of his understanding of human psychology and its development. The second focuses on his elaboration of the cardinal virtues and explores their application to modern psychopathology and intervention.


title: Learning macromanagement in starcraft from replays using deep learning

abstract: The real-time strategy game StarCraft has proven to be a challenging environment for artificial intelligence techniques, and as a result, current state-of-the-art solutions consist of numerous hand-crafted modules. In this paper, we show how macromanagement decisions in StarCraft can be learned directly from game replays using deep learning. Neural networks are trained on 789,571 state-action pairs extracted from 2,005 replays of highly skilled players, achieving top-1 and top-3 error rates of 54.6% and 22.9% in predicting the next build action. By integrating the trained network into UAlbertaBot, an open source StarCraft bot, the system can significantly outperform the game's built-in Terran bot, and play competitively against UAlbertaBot with a fixed rush strategy. To our knowledge, this is the first time macromanagement tasks are learned directly from replays in StarCraft. While the best hand-crafted strategies are still the state-of-the-art, the deep network approach is able to express a wide range of different strategies and thus improving the network's performance further with deep reinforcement learning is an immediately promising avenue for future research. Ultimately this approach could lead to strong StarCraft bots that are less reliant on hard-coded strategies.


title: VNF deployment and routing for NFV-enabled multicast: A steiner tree-based approach

abstract: This paper investigates the network function virtualization (NFV)-enabled multicast problem, and proposes a steiner tree-based algorithm to reduce the cost of virtual network function (VNF) deployment and routing. In the algorithm, to minimize the bandwidth consumption, the Steiner tree is constructed by taking all the destination nodes as the terminals. Then a interconnection node of the tree is selected to connect the source by the shortest path, in which the VNFs are deployed and shared by all the destination nodes. Simulation results show that the proposed algorithm is effective to reduce the number of deployed VNFs and the total cost of the networks.


The benefits for a teacher researcher: A case of teaching philosophy


title: RisQ: recognizing smoking gestures with inertial sensors on a wristband

abstract: Smoking-induced diseases are known to be the leading cause of death in the United States. In this work, we design RisQ, a mobile solution that leverages a wristband containing a 9-axis inertial measurement unit to capture changes in the orientation of a person's arm, and a machine learning pipeline that processes this data to accurately detect smoking gestures and sessions in real-time. Our key innovations are four-fold: a) an arm trajectory-based method that extracts candidate hand-to-mouth gestures, b) a set of trajectory-based features to distinguish smoking gestures from confounding gestures including eating and drinking, c) a probabilistic model that analyzes sequences of hand-to-mouth gestures and infers which gestures are part of individual smoking sessions, and d) a method that leverages multiple IMUs placed on a person's body together with 3D animation of a person's arm to reduce burden of self-reports for labeled data collection. Our experiments show that our gesture recognition algorithm can detect smoking gestures with high accuracy (95.7%), precision (91%) and recall (81%). We also report a user study that demonstrates that we can accurately detect the number of smoking sessions with very few false positives over the period of a day, and that we can reliably extract the beginning and end of smoking session periods.


title: Aesthetics of interaction: a literature synthesis

abstract: New technologies provide expanded opportunities for interaction design. The growing number of possible ways to interact, in turn, creates a new responsibility for designers: Besides the product's visual aesthetics, one has to make choices about the aesthetics of interaction. This issue recently gained interest in Human-Computer Interaction (HCI) research. Based on a review of 19 approaches, we provide an overview of today's state of the art. We focused on approaches that feature "qualities", "dimensions" or "parameters" to describe interaction. Those fell into two broad categories. One group of approaches dealt with detailed spatio-temporal attributes of interaction sequences (i.e., action-reaction) on a sensomotoric level (i.e., form). The other group addressed the feelings and meanings an interaction is enveloped in rather than the interaction itself (i.e., experience). Surprisingly, only two approaches addressed both levels simultaneously, making the explicit link between form and experience. We discuss these findings and its implications for future theory building.


title: Learning to interpret natural language commands through human-robot dialog

abstract: Intelligent robots frequently need to understand requests from naive users through natural language. Previous approaches either cannot account for language variation, e.g., keyword search, or require gathering large annotated corpora, which can be expensive and cannot adapt to new variation. We introduce a dialog agent for mobile robots that understands human instructions through semantic parsing, actively resolves ambiguities using a dialog manager, and incrementally learns from human-robot conversations by inducing training data from user paraphrases. Our dialog agent is implemented and tested both on a web interface with hundreds of users via Mechanical Turk and on a mobile robot over several days, tasked with understanding navigation and delivery requests through natural language in an office environment. In both contexts, We observe significant improvements in user satisfaction after learning from conversations.


title: SAP HANA Adoption of Non-Volatile Memory

abstract: Non-Volatile RAM (NVRAM) is a novel class of hardware technology which is an interesting blend of two storage paradigms: byte-addressable DRAM and block-addressable storage (e.g. HDD/SSD). Most of the existing enterprise relational data management systems such as SAP HANA have their internal architecture based on the inherent assumption that memory is volatile and base their persistence on explicit handling of block-oriented storage devices. In this paper, we present the early adoption of Non-Volatile Memory within the SAP HANA Database, from the architectural and technical angles. We discuss our architectural choices, dive deeper into a few challenges of the NVRAM integration and their solutions, and share our experimental results. As we present our solutions for the NVRAM integration, we also give, as a basis, a detailed description of the relevant HANA internals.


title: Effective Design Parameters on the End Effect in Single-Sided Linear Induction Motors

abstract: Linear induction motors are used in various industries but they have some specific phenomena which are the causes for some problems. The most important phenomenon is called end effect. End effect decreases efficiency, power factor and output force and unbalances the phase currents. This phenomenon is more important in medium and high speeds machines. In this paper a factor, EEF , is obtained by an accurate equivalent circuit model, to determine the end effect intensity. In this way, all of effective design parameters on end effect is described. Accuracy of this equivalent circuit model is evaluated by two dimensional finite-element analysis using ANSYS. The results show the accuracy of the equivalent circuit model. Keywords—Linear induction motor, end effect, equivalent circuit model, finite-element method.


title: A Survey of Data Mining Clustering Algorithms

abstract: is a technique used in data mining that groups similar objects into one cluster, while dissimilar objects are grouped into different clusters. The clustering techniques can be categorized into partitioning methods, hierarchical methods, density-based methods and grid-based methods. The different partitioning methods studied here are k-means and k- medoids. The different hierarchical techniques studied here are BIRCH and CHAMELEON. The different grid-based techniques which are described are DBSCAN and DENCLUE. Lastly, the different techniques which are used in grid-based technique, like STING and CLIQUE are described. This paper aims to provide a brief overview and comparison of these different clustering algorithms and methods.


title: The scaling laws of human travel

abstract: The website wheresgeorge.com invites its users to enter the serial numbers of their US dollar bills and track them across America and beyond. Why? “For fun and because it had not been done yet”, they say. But the dataset accumulated since December 1998 has provided the ideal raw material to test the mathematical laws underlying human travel, and that has important implications for the epidemiology of infectious diseases. Analysis of the trajectories of over half a million dollar bills shows that human dispersal is described by a ‘two-parameter continuous-time random walk’ model: our travel habits conform to a type of random proliferation known as ‘superdiffusion’. And with that much established, it should soon be possible to develop a new class of models to account for the spread of human disease. The dynamic spatial redistribution of individuals is a key driving force of various spatiotemporal phenomena on geographical scales. It can synchronize populations of interacting species, stabilize them, and diversify gene pools1,2,3. Human travel, for example, is responsible for the geographical spread of human infectious disease4,5,6,7,8,9. In the light of increasing international trade, intensified human mobility and the imminent threat of an influenza A epidemic10, the knowledge of dynamical and statistical properties of human travel is of fundamental importance. Despite its crucial role, a quantitative assessment of these properties on geographical scales remains elusive, and the assumption that humans disperse diffusively still prevails in models. Here we report on a solid and quantitative assessment of human travelling statistics by analysing the circulation of bank notes in the United States. Using a comprehensive data set of over a million individual displacements, we find that dispersal is anomalous in two ways. First, the distribution of travelling distances decays as a power law, indicating that trajectories of bank notes are reminiscent of scale-free random walks known as Levy flights. Second, the probability of remaining in a small, spatially confined region for a time T is dominated by algebraically long tails that attenuate the superdiffusive spread. We show that human travelling behaviour can be described mathematically on many spatiotemporal scales by a two-parameter continuous-time random walk model to a surprising accuracy, and conclude that human travel on geographical scales is an ambivalent and effectively superdiffusive process.


title: Tell me what i see: recognize RFID tagged objects in augmented reality systems

abstract: Nowadays, people usually depend on augmented reality (AR) systems to obtain an augmented view in a real-world environment. With the help of advanced AR technology (e.g. object recognition), users can effectively distinguish multiple objects of different types. However, these techniques can only offer limited degrees of distinctions among different objects and cannot provide more inherent information about these objects. In this paper, we leverage RFID technology to further label different objects with RFID tags. We deploy additional RFID antennas to the COTS depth camera and propose a continuous scanning-based scheme to scan the objects, i.e., the system continuously rotates and samples the depth of field and RF-signals from these tagged objects. In this way, by pairing the tags with the objects according to the correlations between the depth of field and RF-signals, we can accurately identify and distinguish multiple tagged objects to realize the vision of "tell me what I see" from the augmented reality system. For example, in front of multiple unknown people wearing RFID tagged badges in public events, our system can identify these people and further show their inherent information from the RFID tags, such as their names, jobs, titles, etc. We have implemented a prototype system to evaluate the actual performance. The experiment results show that our solution achieves an average match ratio of 91% in distinguishing up to dozens of tagged objects with a high deployment density.


title: The recognition of rice images by UAV based on capsule network

abstract: It is important to recognize the rice image captured by unmanned aerial vehicle (UAV) for monitoring the growth of rice and preventing the diseases and pests. Aiming at the image recognition, we use rice images captured by UAV as our data source, the structure of capsule network (CapsNet) is built to recognize rice images in this paper. The images are preprocessed through histogram equalization method into grayscale images and through superpixel algorithm into the superpixel segmentation results. The both results are output into the CapsNet. The function of CapsNet is to perform the reverse analysis of rice images. The CapsNet consists of five layers: an input layer, a convolution layer, a primary capsules layer, a digital capsules layer and an output layer. The CapsNet trains classification and predicts the output vector based on routing-by-agreement protocol. Therefore, the features of rice image by UAV can be precisely and efficiently extracted. The method is more convenient than the traditional artificial recognition. It provides the scientific support and reference for decision-making process of precision agriculture.


title: Learning Compact Appearance Representation for Video-Based Person Re-Identification

abstract: This paper presents a novel approach for video-based person re-identification using multiple Convolutional Neural Networks (CNNs). Unlike previous work, we intend to extract a compact yet discriminative appearance representation from several frames rather than the whole sequence. Specifically, given a video, the representative frames are selected based on the walking profile of consecutive frames. A multiple CNN architecture incorporated with feature pooling is proposed to learn and compile the features of the selected representative frames into a compact description about the pedestrian for identification. Experiments are conducted on benchmark datasets to demonstrate the superiority of the proposed method over existing person re-identification approaches.


title: Subspace clustering via new low-rank model with discrete group structure constraint

abstract: We propose a new subspace clustering model to segment data which is drawn from multiple linear or affine subspaces. Unlike the well-known sparse subspace clustering (SSC) and low-rank representation (LRR) which transfer the subspace clustering problem into two steps' algorithm including building the affinity matrix and spectral clustering, our proposed model directly learns the different subspaces' indicator so that low-rank based different groups are obtained clearly. To better approximate the low-rank constraint, we suggest to use Schatten p-norm to relax the rank constraint instead of using trace norm. We tactically avoid the integer programming problem imposed by group indicator constraint to let our algorithm more efficient and scalable. Furthermore, we extend our discussion to the general case in which subspaces don't pass the original point. The new algorithm's convergence is given, and both synthetic and real world datasets demonstrate our proposed model's effectiveness.


title: Modifications of Reward Expectation-Related Neuronal Activity During Learning in Primate Striatum

abstract: Tremblay, Leon, Jeffrey R. Hollerman, and Wolfram Schultz. Modifications of reward expectation-related neuronal activity during learning in primate striatum. J. Neurophysiol. 80: 964–977, 1998. Thi...


title: A Novel Carrier-Based PWM Method for Vienna Rectifier With a Variable Power Factor

abstract: The Vienna rectifier, which is a nongenerative-boost type rectifier, is used in many applications such as telecommunication systems and wind turbine systems. The Vienna rectifier has a special operating requirement. Many switching methods considering this requirement were proposed. These methods generally take into account the Vienna rectifier's operation with the unity power factor because the Vienna rectifier aims to provide the power to the load. However, as the applications for the Vienna rectifier are diversified, control requirements have been suggested, including the dc-link voltage balance control and the novel control under severely unbalanced grids. In this paper, we propose a carrier-based pulse-width modulation (CB-PWM) method for Vienna rectifier operation with a variable power factor. The proposed CB-PWM method adds the compensation voltage to the three-phase reference voltages, depending on the power factor, to maintain the sinusoidal input currents of the Vienna rectifier. The performance and effectiveness of the proposed CB-PWM method are verified by simulation and experiment.


title: Disentangling 3D Pose in a Dendritic CNN for Unconstrained 2D Face Alignment

abstract: Heatmap regression has been used for landmark localization for quite a while now. Most of the methods use a very deep stack of bottleneck modules for heatmap classification stage, followed by heatmap regression to extract the keypoints. In this paper, we present a single dendritic CNN, termed as Pose Conditioned Dendritic Convolution Neural Network (PCD-CNN), where a classification network is followed by a second and modular classification network, trained in an end to end fashion to obtain accurate landmark points. Following a Bayesian formulation, we disentangle the 3D pose of a face image explicitly by conditioning the landmark estimation on pose, making it different from multi-tasking approaches. Extensive experimentation shows that conditioning on pose reduces the localization error by making it agnostic to face pose. The proposed model can be extended to yield variable number of landmark points and hence broadening its applicability to other datasets. Instead of increasing depth or width of the network, we train the CNN efficiently with Mask-Softmax Loss and hard sample mining to achieve upto $15\%$ reduction in error compared to state-of-the-art methods for extreme and medium pose face images from challenging datasets including AFLW, AFW, COFW and IBUG.


Anaphora Resolution: The State Of The Art


title: Metrology and inspection required for next generation lithography

abstract: We summarize the metrology and inspection required for the development of nanoimprint lithography (NIL) and directed self-assembly (DSA), which are recognized as candidates for next generation lithography. For NIL, template inspection and residual layer thickness (RLT) metrology are discussed. An optical-based inspection tool for replica template inspection showed sensitivity for defects below 10 nm with sufficient throughput. Scatterometry was applied for RLT metrology. Feedback control with scatterometry improved RLT uniformity across an imprinting field. For DSA, metrology for image placement and cross-sectional profile are addressed. Design-based scanning electron microscope (SEM) metrology utilizing a die-to-database electron beam (EB) inspection tool was effective for image placement metrology. For the cross-sectional profile, a holistic approach combining scatterometry and critical dimension SEM was developed. The technologies discussed here will be important when NIL and DSA are applied for IC manufacturing, as well as in the development phases of those lithography technologies.


title: Attending to Distinctive Moments: Weakly-Supervised Attention Models for Action Localization in Video

abstract: We present a method for utilizing weakly supervised data for action localization in videos. We focus on sports video analysis, where videos contain scenes of multiple people. Weak supervision gathered from sports website is provided in the form of an action taking place in a video clip, without specification of the person performing the action. Since many frames of a clip can be ambiguous, a novel temporal attention approach is designed to select the most distinctive frames in which to apply the weak supervision. Empirical results demonstrate that leveraging weak supervision can build upon purely supervised localization methods, and utilizing temporal attention further improves localization accuracy.


title: Classification of loaded/unloaded micro-drones using multistatic radar

abstract: Preliminary results on the use of multistatic radar and micro-Doppler analysis to detect and discriminate between micro-drones hovering carrying different payloads are presented. Two suitable features related to the centroid of the micro-Doppler signature have been identified and used to perform classification, investigating also the added benefit of using information from a multistatic radar as opposed to a conventional monostatic system. Very good performance with accuracy above 90% has been demonstrated for the classification of hovering micro-drones.


title: Theory of energy transfer interactions near sphere and nanoshell based plasmonic nanostructures

abstract: Theory of energy transfer interactions between a pair of two level molecules in the molecular nanojunction including surface plasmon (SP) dressed interaction of plasmonic nanostructure, replicating metallic leads is presented. Results on the modification of bare dipolar interaction, known to be responsible for molecular energy transfer processes, in the proximity of metallic nanosystem are presented. Specifically, the manuscript includes theoretical investigation of nanosphere (NSP) monomer, nanoshell (NSH) monomer, and coupled nanosphere pair (dimer) based nanosystems. Closed form analytical expressions for NSP and NSH structures tailored for molecular nanojunction geometry are derived in the theoretical framework of multipole spectral expansion (MSE) method, which is straightforwardly extendible to dimers and multimers. The role of size and dielectric environment on energy transfer is investigated and interpreted. Theory predicts that the monomer and dimer both enhance the dipolar interaction, yet, dimer geometry is favorable due to its spectral tuning potential originated from plasmon hybridization and true resemblance with typical molecular nanojunctions.


title: Low-cost TSH (through-silicon hole) interposers for 3D IC integration

abstract: In this investigation, a SiP (system-in-package) which consists of a very low-cost interposer with through-silicon holes (TSHs) and with chips on its top- and bottom-side (a real 3D IC integration) is studied. Emphasis is placed on the fabrication of a test vehicle to demonstrate the feasibility of this SiP technology. The design, materials, and process of the top-chip, bottom-chip, TSH interposer, and final assembly will be presented. Shock and thermal cycling tests will be preformed to demonstrate the integrity of the SiP structure.


title: Self-calibration for a 3D laser

abstract: In this paper we describe a method for the automatic self-calibration of a 3D laser sensor. We wish to acquire crisp point clouds and so we adopt a measure of crispness to capture point cloud quality. We then pose the calibration problem as the task of maximizing point cloud quality. Concretely, we use Renyi Quadratic Entropy to measure the degree of organization of a point cloud. By expressing this quantity as a function of key unknown system parameters, we are able to deduce a full calibration of the sensor via an online optimization. Beyond details on the sensor design itself, we fully describe the end-to-end intrinsic parameter calibration process and the estimation of the clock skews between the constituent microprocessors. We analyse performance using real and simulated data and demonstrate robust performance over 30 test sites.


title: Integrating CYC and Wikipedia: Folksonomy meets rigorously defined common-sense

abstract: Integration of ontologies begins with establishing mappings between their concept entries. We map categories from the largest manually-built ontology, Cyc, onto Wikipedia articles describing corresponding concepts. Our method draws both on Wikipedia’s rich but chaotic hyperlink structure and Cyc’s carefully defined taxonomic and common-sense knowledge. On 9,333 manual alignments by one person, we achieve an F-measure of 90%; on 100 alignments by six human subjects the average agreement of the method with the subject is close to their agreement with each other. We cover 62.8% of Cyc categories relating to common-sense knowledge and discuss what further information might be added to Cyc given this substantial new alignment.


title: Credit card incidents and control systems

abstract: Abstract Credit and debit cards have spread and skyrocketed all around the world to become the most popular means of payments in many countries. Despite their enormous popularity, cards are not free of risk. Technology development and e-commerce have exponentially increased internal credit card incidents. This paper identifies and quantifies the different types of credit card fraud and puts into question the effectiveness of the role assigned to cardholders in its detection.


title: Algorithm for License Plate Localization and Recognition for Tanzania Car Plate Numbers

abstract: In this paper, License plate localization and recognition (LPLR) is presented. It uses image processing and character recognition technology in order to identify the license number plates of the vehicles automatically. This system is considerable interest because of its good application in traffic monitoring systems, surveillance devices and all kind of intelligent transport system. The objective of this work is to design algorithm for License Plate Localization and Recognition (LPLR) of Tanzanian License Plates. The plate numbers used are standard ones with black and yellow or black and white colors. Also, the letters and numbers are placed in the same row (identical vertical levels), resulting in frequent changes in the horizontal intensity. Due to that, the horizontal changes of the intensity have been easily detected, since the rows that contain the number plates are expected to exhibit many sharp variations. Hence, the edge finding method is exploited to find the location of the plate. To increase readability of the plate number, part of the image was enhanced, noise removal and smoothing median filter is used due to easy development. The algorithm described in this paper is implemented using MATLAB 7.11.0(R2010b).


title: Location-Aware Computing: A Neural Network Model For Determining Location In Wireless LANs

abstract: The strengths of the RF signals arriving from more access points in a wireless LANs are related to the position of the mobile terminal and can be used to derive the location of the user. In a heterogeneous environment, e.g. inside a building or in a variegated urban geometry, the received power is a very complex function of the distance, the geometry, the materials. The complexity of the inverse problem (to derive the position from the signals) and the lack of complete information, motivate to consider flexible models based on a network of functions (neural networks). Specifying the value of the free parameters of the model requires a supervised learning strategy that starts from a set of labeled examples to construct a model that will then generalize in an appropriate manner when confronted with new data, not present in the training set. The advantage of the method is that it does not require ad-hoc infrastructure in addition to the wireless LAN, while the flexible modeling and learning capabilities of neural networks achieve lower errors in determining the position, are amenable to incremental improvements, and do not require the detailed knowledge of the access point locations and of the building characteristics. A user needs only a map of the working space and a small number of identified locations to train a system, as evidenced by the experimental results presented.


Trusting tweets: The Fukushima disaster and information source credibility on Twitter


title: Potential of Combined Thromboxane A2 and Serotonin Antagonists to Prevent the Development of Unstable Angina and Acute Myocardial Infarction

abstract: In turn, platelet aggregation and dynamic vasoconstriction probably result from the local accumulation of thromboxane A 2 and serotonin at sites of coronary artery stenosis and endothelial injury; they may also result from relative decreases in the local concentrations of endothelium-derived vasodilators and inhibitors of platelet aggregation, such as endothelium-derived relaxing factors and prostacyclin. Because of severe reductions in coronary blood flow caused by these mechanisms platelet aggregation may increase, and an occlusive thrombus-composed of platelets, leukocytes, and red blood cells in a fibrin mesh-may develop. When coronary obstruction by these mechanisms is prolonged for several hours, however, Q-wave myocardial infarction results. Future clinical studies should provide further insights into these and additional mediators and mechanisms that may effect the abrupt transition from chronic to acute coronary disease syndromes in human beings


title: DRAW: A Recurrent Neural Network For Image Generation

abstract: This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel spatial attention mechanism that mimics the foveation of the human eye, with a sequential variational auto-encoding framework that allows for the iterative construction of complex images. The system substantially improves on the state of the art for generative models on MNIST, and, when trained on the Street View House Numbers dataset, it generates images that cannot be distinguished from real data with the naked eye.


title: Effects of the metacognitive computer-tool met.a.ware on the web search of laypersons

abstract: Today, laypersons often consult the Internet to inform themselves about health-related issues. However, the competent use of these often complex and heterogeneous information provisions cannot be taken for granted, because many Internet users are lacking the necessary metacognitive prerequisites. Therefore, we developed the metacognitive computer-tool met.a.ware, which supports laypersons' Internet research for medical information by the means of metacognitive prompting and ontological classification. In an experimental investigation of met.a.ware a total of 118 participants with little medical knowledge were asked to conduct an Internet research on a medical topic. Participants were randomly assigned to four experimental groups that worked with met.a.ware and either received evaluation prompts, monitoring prompts, both types of prompts, or no prompts. All experimental conditions were additionally provided with ontological classification. One control group took paper and pencil notes. A further control group took notes using a blank text window. Results showed that laypersons receiving evaluation prompts outperformed controls in terms of knowledge about sources and produced more arguments commenting on the source of information in an essay task. In addition, laypersons receiving monitoring prompts acquired significantly more knowledge about facts, but did not perform better on a comprehension test than the controls. The availability of ontological categories helped to structure the notes laypersons in the conditions working with ontological classification took during Internet research. Analyses of the notes further demonstrated that the availability of ontological categories guided information search in direction of the selected categories. It is concluded, that met.a.ware is an effective tool that supports laypersons' Internet research.


title: Object-Oriented Bayesian Networks

abstract: Bayesian networks provide a modeling language and associated inference algorithm for stochastic domains. They have been successfully applied in a variety of medium-scale applications. However, when faced with a large complex domain, the task of modeling using Bayesian networks begins to resemble the task of programming using logical circuits. In this paper, we describe an object-oriented Bayesian network (OOBN) language, which allows complex domains to be described in terms of inter-related objects. We use a Bayesian network fragment to describe the probabilistic relations between the attributes of an object. These attributes can themselves be objects, providing a natural framework for encoding part-of hierarchies, Classes are used to provide a reusable probabilistic model which can be applied to multiple similar objects. Classes also support inheritance of model fragments from a class to a subclass, allowing the common aspects of related classes to be defined only once. Our language has clear declarative semantics: an OOBN can be interpreted as a stochastic functional program, so that it uniquely specifies a probabilistic model. We provide an inference algorithm for OOBNs, and show that much of the structural information encoded by an OOBN--particularly the encapsulation of variables within an object and the reuse of model fragments in different contexts---can also be used to speed up the inference process.


Clitoral keloids after female genital mutilation/cutting


title: Correcting the Triplet Selection Bias for Triplet Loss

abstract: Triplet loss, popular for metric learning, has made a great success in many computer vision tasks, such as fine-grained image classification, image retrieval, and face recognition. Considering that the number of triplets grows cubically with the size of training data, triplet selection is thus indispensable for efficiently training with triplet loss. However, in practice, the training is usually very sensitive to the selection of triplets, e.g., it almost does not converge with randomly selected triplets and selecting the hardest triplets also leads to bad local minima. We argue that the bias in the selection of triplets degrades the performance of learning with triplet loss. In this paper, we propose a new variant of triplet loss, which tries to reduce the bias in triplet selection by adaptively correcting the distribution shift on the selected triplets. We refer to this new triplet loss as adapted triplet loss. We conduct a number of experiments on MNIST and Fashion-MNIST for image classification, and on CARS196, CUB200-2011, and Stanford Online Products for image retrieval. The experimental results demonstrate the effectiveness of the proposed method.


iPipe : A Framework for Building Datacenter Applications Using In-networking Processors


title: Object Detection in Very High-Resolution Aerial Images Using One-Stage Densely Connected Feature Pyramid Network

abstract: Object detection in very high-resolution (VHR) aerial images is an essential step for a wide range of applications such as military applications, urban planning, and environmental management. Still, it is a challenging task due to the different scales and appearances of the objects. On the other hand, object detection task in VHR aerial images has improved remarkably in recent years due to the achieved advances in convolution neural networks (CNN). Most of the proposed methods depend on a two-stage approach, namely: a region proposal stage and a classification stage such as Faster R-CNN. Even though two-stage approaches outperform the traditional methods, their optimization is not easy and they are not suitable for real-time applications. In this paper, a uniform one-stage model for object detection in VHR aerial images has been proposed. In order to tackle the challenge of different scales, a densely connected feature pyramid network has been proposed by which high-level multi-scale semantic feature maps with high-quality information are prepared for object detection. This work has been evaluated on two publicly available datasets and outperformed the current state-of-the-art results on both in terms of mean average precision (mAP) and computation time.