title: Evaluating the Visualization of What a Deep Neural Network Has Learned

abstract: Deep neural networks (DNNs) have demonstrated impressive performance in complex machine learning tasks such as image classification or speech recognition. However, due to their multilayer nonlinear structure, they are not transparent, i.e., it is hard to grasp what makes them arrive at a particular classification or recognition decision, given a new unseen data sample. Recently, several approaches have been proposed enabling one to understand and interpret the reasoning embodied in a DNN for a single test image. These methods quantify the “importance” of individual pixels with respect to the classification decision and allow a visualization in terms of a heatmap in pixel/input space. While the usefulness of heatmaps can be judged subjectively by a human, an objective quality measure is missing. In this paper, we present a general methodology based on region perturbation for evaluating ordered collections of pixels such as heatmaps. We compare heatmaps computed by three different methods on the SUN397, ILSVRC2012, and MIT Places data sets. Our main result is that the recently proposed layer-wise relevance propagation algorithm qualitatively and quantitatively provides a better explanation of what made a DNN arrive at a particular classification decision than the sensitivity-based approach or the deconvolution method. We provide theoretical arguments to explain this result and discuss its practical implications. Finally, we investigate the use of heatmaps for unsupervised assessment of the neural network performance.


title: US and CT Evaluation of Acute Pelvic Pain of Gynecologic Origin in Nonpregnant Premenopausal Patients

abstract: The interpretation of imaging findings in the premenopausal patient with acute pelvic pain is influenced by knowledge of the physiologic changes that occur in the pelvis as well as by the patient’s clinical history. Although ultrasonography (US) is the modality of choice for initial imaging, gynecologic disease is detected or suspected with increasing frequency at computed tomography (CT) because of the increasing availability and use of this modality. As a result, the recognition of common features of gynecologic entities on both US and CT images is essential for prompt diagnosis and expeditious management. Categorizing lesions according to their anatomic location, physiologic or pathologic origin, and internal characteristics (cystic, solid, or mixed) allows efficient and accurate diagnosis. © RSNA, 2008


title: A handheld mirror simulation

abstract: We present the design and construction of a handheld mirror simulation device. The perception of the world reflected through a mirror depends on the viewer's position with respect to the mirror and the 3-D geometry of the world. In order to simulate a real mirror on a computer screen, images of the observed world, consistent with the viewer's position, must be synthesized and displayed in real- time. Our system is build around a LCD screen manipulated by the user, a single camera fixed on the screen, and a tracking device. The continuous input video stream and tracker data is used to synthesize, in real-time, a continuous video stream displayed on the LCD screen. The synthesized video stream is a close approximation of what the user would see on the screen surface if it were a real mirror. Our system provides a generic interface for applications involving rich, first-person interaction, such as the virtual daguerreotype.


title: Computer analysis of face beauty: A survey☆

abstract: Abstract The human face conveys to other human beings, and potentially to computer systems, information such as identity, intentions, emotional and health states, attractiveness, age, gender and ethnicity. In most cases analyzing this information involves the computer science as well as the human and medical sciences. The most studied multidisciplinary problems are analyzing emotions, estimating age and modeling aging effects. An emerging area is the analysis of human attractiveness. The purpose of this paper is to survey recent research on the computer analysis of human beauty. First we present results in human sciences and medicine pointing to a largely shared and data-driven perception of attractiveness, which is a rationale of computer beauty analysis. After discussing practical application areas, we survey current studies on the automatic analysis of facial attractiveness aimed at: (i) relating attractiveness to particular facial features; (ii) assessing attractiveness automatically; (iii) improving the attractiveness of 2D or 3D face images. Finally we discuss open problems and possible lines of research.


title: Phase noise in inverter-based & differential CMOS ring oscillators

abstract: Simple but precise expressions are derived for jitter, and thus for phase noise, in inverter-based and differential ring oscillators. White and flicker noise are included. The expressions are validated by measurement and simulation.


title: Big data analytics enhanced healthcare systems: a review

abstract: There is increased interest in deploying big data technology in the healthcare industry to manage massive collections of heterogeneous health datasets such as electronic health records and sensor data, which are increasing in volume and variety due to the commoditization of digital devices such as mobile phones and wireless sensors. The modern healthcare system requires an overhaul of traditional healthcare software/hardware paradigms, which are ill-equipped to cope with the volume and diversity of the modern health data and must be augmented with new “big data” computing and analysis capabilities. For researchers, there is an opportunity in healthcare data analytics to study this vast amount of data, find patterns and trends within data and provide a solution for improving healthcare, thereby reducing costs, democratizing health access, and saving valuable human lives. In this paper, we present a comprehensive survey of different big data analytics integrated healthcare systems and describe the various applicable healthcare data analytics algorithms, techniques, and tools that may be deployed in wireless, cloud, Internet of Things settings. Finally, the contribution is given in formation of a convergence point of all these platforms in form of SmartHealth that could result in contributing to unified standard learning healthcare system for future.


title: Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering

abstract: In this paper, we analyze several neural network designs (and their variations) for sentence pair modeling and compare their performance extensively across eight datasets, including paraphrase identification, semantic textual similarity, natural language inference, and question answering tasks. Although most of these models have claimed state-of-the-art performance, the original papers often reported on only one or two selected datasets. We provide a systematic study and show that (i) encoding contextual information by LSTM and inter-sentence interactions are critical, (ii) Tree-LSTM does not help as much as previously claimed but surprisingly improves performance on Twitter datasets, (iii) the Enhanced Sequential Inference Model is the best so far for larger datasets, while the Pairwise Word Interaction Model achieves the best performance when less data is available. We release our implementations as an open-source toolkit.


Multilingual Neural Network Acoustic Modelling for ASR of Under-Resourced English-isiZulu Code-Switched Speech


title: CloudPoS: A Proof-of-Stake Consensus Design for Blockchain Integrated Cloud

abstract: Maintaining data provenance in cloud in a tamper-resistant manner that cannot be breached by malicious parties is a necessity from the current security standpoint. Blockchain technology has emerged as a secure solution to store and share information by offering an immutable distributed ledger service. Its effectiveness hinges on the infrastructure supporting the distributed ledger and consensus protocol that governs the validity of entries in the Blockchain. Hence, Blockchain can be a potential candidate to implement data provenance; however, traditional cryptocurrency-based consensus models become a bottleneck in the cloud environment. Therefore, in this paper, we propose a Blockchain based data provenance architecture (BlockCloud) that incorporates a proof-of-stake (PoS)-based consensus protocol (CloudPoS) for securely recording the data operations occurring in cloud environment. The critical operational phases of the protocol are discussed in depth, which leverages the cloud users' cyber infrastructure resources. A cloud-based testbed environment is created using a local cluster of physical machines managed by Xen hypervisor. Resource elasticity is enabled using Kubernetes setup that interacts with the dockerized containers, which emulate as peers in the Blockchain network. We then evaluate the effectiveness of the protocol in a simulated environment and conduct performance tests of the proposed consensus.


title: Thermal evolution of defects produced by implantation of H, D and He in Silicon

abstract: Abstract Despite decades of study, voids in silicon produced by implantation of H or He followed by annealing continue to be a topic of interest. There are two key applications: gettering of heavy metal impurities, and “ion cutting” used in silicon-on-insulator fabrication. Positron annihilation is one of the few techniques that can probe the vacancies and vacancy clusters that are the precursors to void formation. Data from recent studies will be discussed, including (I) isotopic substitution, in which comparisons of H vs. D implantation permit examination of the impact of primary point defects vs. chemical effects. Remarkable differences exist between H and D in blistering of silicon - ion doses 2–3 times higher are required for blistering with D than with H, despite a higher rate of primary defect production for D; (II) the effect of annealing temperature ramp-rate, in which we show that ramp-rate has a significant impact on residual defects, despite which it is so disregarded as to often be omitted from published reports; and (III) comparisons with electron microscopy which suggest that positron annihilation can be insensitive to large voids. In these studies, positron annihilation augments data from techniques including ion channelling, Raman scattering and electron microscopy; the suite of techniques allows elucidation of the interplay between implanted impurities and the vacancies and interstitials created by implantation.


title: Compiling standard ML to Java bytecodes

abstract: MLJ compiles SML'97 into verifier-compliant Java byte-codes. Its features include type-checked interlanguage working extensions which allow ML and Java code to call each other, automatic recompilation management, compact compiled code and runtime performance which, using a 'just in time' compiling Java virtual machine, usually exceeds that of existing specialised bytecode interpreters for ML. Notable features of the compiler itself include whole-program optimisation based on rewriting, compilation of polymorphism by specialisation, a novel monadic intermediate language which expresses effect information in the type system and some interesting data representation choices.


title: Papilledema: clinical clues and differential diagnosis.

abstract: The term "papilledema" describes optic disc swelling resulting from increased intracranial pressure. A complete history and direct funduscopic examination of the optic nerve head and adjacent vessels are necessary to differentiate papilledema from optic disc swelling due to other conditions. Signs of optic disc swelling include elevation and blurring of the disc and its margins, venous congestion, and retinal hard exudates, splinter hemorrhages and infarcts. Patients with papilledema usually present with signs or symptoms of elevated intracranial pressure, such as headache, nausea, vomiting, diplopia, ataxia or altered consciousness. Causes of papilledema include intracranial tumors, idiopathic intracranial hypertension (pseudotumor cerebri), subarachnoid hemorrhage, subdural hematoma and intracranial inflammation. Optic disc edema may also occur from many conditions other than papilledema, including central retinal artery or vein occlusion, congenital structural anomalies and optic neuritis.


title: Minimizing symmetric submodular functions

abstract: We describe a purely combinatorial algorithm which, given a submodular set functionf on a finite setV, finds a nontrivial subsetA ofV minimizingf[A] + f[V ∖ A]. This algorithm, an extension of the Nagamochi—Ibaraki minimum cut algorithm as simplified by Stoer and Wagner [M. Stoer, F. Wagner, A simple min cut algorithm, Proceedings of the European Symposium on Algorithms ESA '94, LNCS 855, Springer, Berlin, 1994, pp. 141–147] and by Frank [A. Frank, On the edge-connectivity algorithm of Nagamochi and Ibaraki, Laboratoire Artemis, IMAG, Universite J. Fourier, Grenbole, 1994], minimizes any symmetric submodular function using O(|V|3) calls to a function value oracle. © 1998 The Mathematical Programming Society, Inc. Published by Elsevier Science B.V.


title: An IoT-based E-parking system for smart cities

abstract: The increasing number of vehicles on the road along with the mismanagement of available parking space leads to the parking related problems as well as increased traffic congestion in urban areas. Thus it is highly required to develop an automated smart parking management system that would help the driver to find out some suitable parking space for his/her vehicle very quickly. Although ample amount of research works on the development of smart parking system exist in literature, but most of them have not addressed the problem of real-time detection of improper parking and automatic collection of parking charges. In this paper, a prototype of internet-of-thing based E-parking system is proposed. The proposed E-parking system uses an integrated component called parking meter to address the above-mentioned issues as well as to provide smart parking management throughout the city.


Flap necrosis after palatoplasty in irradiated patient and its reconstruction with tunnelized-facial artery myomucosal island flap


title: Gentrification and Urban Public School Reforms: The Interest Divergence Dilemma

abstract: Cities across the country cities are experiencing rapid increases in gentrification: the influx of middle-class, usually white, residents into cities with large minority populations. Reversing a decades long trend of white flight out of urban city schools, a significant number of white middle-class gentrifiers are now also enrolling their children in urban city public schools. Local officials in many gentrifying cities value the renewed interest of middle-class white in urban public schools because it represents an opportunity to finally racially integrate urban public schools. Further, it also represents an opportunity to keep middle-class gentrifiers,and their tax dollars, from fleeing to the suburbs and suburban school districts once they have school aged children. In order to attract white middle-class gentrifiers, this Article suggests that some local officials in gentrifying cities are implementing certain public school reforms for the specific purpose of making their school systems more palatable to gentrifiers. Such reforms, the article argues, harm poor and minority students by disproportionally displacing them from their neighborhood public schools while simultaneously limiting the number of quality public and charter schools available to them. While advocates for poor and minority students are mounting legal challenges to the reforms, to date the legal challenges have not been successful because courts lack the doctrinal support to find that that the reforms constitute an actionable form of intentional discrimination. This Article applies Derek Bell’s Interest Convergence Theory and argues for the implementation of legislative solutions that can benefit both poor and minority students and gentrifiers. It suggests that such an approach is a more effective way than litigation to capitalize on the renewed interest of white middle class residents in urban public schools and to improve educational opportunities for poor and minority students in urban schools.


title: How and Why is Autism Spectrum Disorder Misdiagnosed in Adult Patients? - From Diagnostic Problem to Management for Adjustment -

abstract: Autism spectrum disorder (ASD) is often overlooked or misdiagnosed in adult patients especially in those with other psychiatric comorbidities. Several reasons possibly account for this fact, for example, difficulty in obtaining accurate developmental history from a patient, insufficient experience among psychiatrists detecting ASD in adult patients, and mild/atypical autistic traits in contrast to prominent symptoms of psychiatric comorbidities. We conducted a search of databases (PubMed, PsycINFO, and ERIC) for relevant articles published from January 2000 to May 2015 and summarized unrecognized or misdiagnosed cases as well as major psychiatric comorbidities among adults with ASD from previous reports. Five disorders, (i.e., schizophrenia, psychotic disorder, bipolar disorder, major depressive disorder, and personality disorder) were specifically highlighted as misdiagnosed psychiatric diseases or comorbidities responsible for unrecognized ASD. This review also proposes plausible pathways for patterns of maladjustment and processes leading to misdiagnosis among adults with ASD, together with necessary approaches to support their goals of a requisite social life. In conclusion, clinicians should be more concerned with correct diagnosis of ASD as well as treatments of psychiatric comorbidities and need perspective of management for better mental health and social adjustment in ASD patients.


title: Over-The-Top Bypass: Study of a Recent Telephony Fraud

abstract: In this paper, we study the Over-The-Top (OTT) bypass fraud, a recent form of interconnect telecom fraud. In OTT bypass, a normal phone call is diverted over IP to a voice chat application on a smartphone, instead of being terminated over the normal telecom infrastructure. This rerouting (or hijack) is performed by an international transit operator in coordination with the OTT service provider, but without explicit authorization from the caller, callee and their operators. By doing so, they collect a large share of the call charge and induce a significant loss of revenue to the bypassed operators. Moreover, this practice degrades the quality of service without providing any benefits for the users. In this paper, we study the possible techniques to detect and measure this fraud and evaluate the real impact of OTT bypass on a small European country. For this, we performed more than 15,000 test calls during 8 months and conducted a user study with more than 8,000 users. In our measurements, we observed up to 83% of calls being subject to OTT bypass. Additionally, we show that OTT bypass degrades the quality of service, and sometimes collide with other fraud schemes, exacerbating the quality issues. Our user study shows that OTT bypass and its effects are poorly understood by users.


title: Through-wall UWB radar operating within FCC's mask for sensing heart beat and breathing rate

abstract: Non-invasive sensing of heart beat and breathing rate using UWB radar within the FCC's mask between 3.1 to 10.6 GHz and operating within -41 dbm/MHz has been developed. Compared to existing radars, the EIRP of UWB is much lower and it can also achieve comparable performance with the unique capability of through the wall environment. The UWB radar transceiver system architecture is presented and the results in monitoring breathing rate and heart beat of the human object is discussed


title: EEG signal denoising based on wavelet transform

abstract: Low amplitude EEG signal are easily affected by various noise sources. This work presents de-noising methods based on the combination of stationary wavelet transform (SWT), universal threshold, statistical threshold and Discrete Wavelet Transform (DWT) with symlet, haar, coif, and bior4.4 wavelets. The results show significant improvement in performance parameter such as Signal to Artifacts ratio (SAR), Correlation Coefficient (CC) and Normalized Mean Squared error (NMSE). Simulink has been used to model DWT based de noising of EEG signal implementable on FPGA with Xilinx System Generator.


title: Stability of a trotting quadruped robot with passive, underactuated legs

abstract: A 2-D dynamic simulation of a quadruped robot with passive, directionally compliant legs has shown that with the proper leg configuration, increased stability of the robot can be achieved passively. Increased stability is defined as decreased pitching motion of the robot. A limited search of the design space of the legs resulted in the finding the leg parameters that minimized the pitching. Steady state trotting, changing speeds, and disturbances such as steps and holes were tested.


title: Quranic-based concepts: Verse relations extraction using Manchester OWL syntax

abstract: In recent years, there is global demand for Islamic knowledge by both Muslims and non-Muslims. This has brought about number of automated applications that ease the retrieval of knowledge from the holy books. However current retrieval methods lack semantic information they are mostly base on keywords matching approach. In this paper we have proposed a Model that will make use of semantic Web technologies (ontology) to model Quran domain knowledge. The system will enhance Quran knowledge by enabling queries in natural language.


title: Reactive Resource Provisioning Heuristics for Dynamic Dataflows on Cloud Infrastructure

abstract: The need for low latency analysis over high-velocity data streams motivates the need for distributed continuous dataflow systems. Contemporary stream processing systems use simple techniques to scale on elastic cloud resources to handle variable data rates. However, application QoS is also impacted by variability in resource performance exhibited by clouds and hence necessitates autonomic methods of provisioning elastic resources to support such applications on cloud infrastructure. We develop the concept of “dynamic dataflows” which utilize alternate tasks as additional control over the dataflow’s cost and QoS. Further, we formalize an optimization problem to represent deployment and runtime resource provisioning that allows us to balance the application’s QoS , value , and the resource cost . We propose two greedy heuristics, centralized and sharded , based on the variable-sized bin packing algorithm and compare against a Genetic Algorithm (GA) based heuristic that gives a near-optimal solution. A large-scale simulation study, using the linear road benchmark and VM performance traces from the AWS public cloud, shows that while GA-based heuristic provides a better quality schedule, the greedy heuristics are more practical, and can intelligently utilize cloud elasticity to mitigate the effect of variability, both in input data rates and cloud resource performance, to meet the QoS of fast data applications.


title: On the Inductive Bias of Dropout

abstract: Dropout is a simple but effective technique for learning in neural networks and other settings. A sound theoretical understanding of dropout is needed to determine when dropout should be applied and how to use it most effectively. In this paper we continue the exploration of dropout as a regularizer pioneered by Wager et al. We focus on linear classification where a convex proxy to the misclassification loss (i.e. the logistic loss used in logistic regression) is minimized. We show: • when the dropout-regularized criterion has a unique minimizer, • when the dropout-regularization penalty goes to infinity with the weights, and when it remains bounded, • that the dropout regularization can be non-monotonic as individual weights increase from 0, and • that the dropout regularization penalty may not be convex. This last point is particularly surprising because the combination of dropout regularization with any convex loss proxy is always a convex function. ::: ::: In order to contrast dropout regularization with L2 regularization, we formalize the notion of when different random sources of data are more compatible with different regularizers. We then exhibit distributions that are provably more compatible with dropout regularization than L2 regularization, and vice versa. These sources provide additional insight into how the inductive biases of dropout and L2 regularization differ. We provide some similar results for L1 regularization.


title: Enticing online consumers: an extended technology acceptance perspective

abstract: The business-to-consumer aspect of electronic commerce (EC) is the most visible business use of the World Wide Web (www). A virtual store allows companies to provide product information and offer direct sales to their customers through an electronic channel. The fundamental problem motivating this study is that: in order for a virtual store to compete effectively with both physical stores and other online retailers, there is an urgent need to understand the factors that entice consumers to use it. This research attempted to provide both theoretical and empirical analyses to explain consumers' use of a virtual store and its antecedents.By applying the technology acceptance model (TAM) and innovation diffusion theory (IDT), this research took an extended perspective to examine consumer behavior in the virtual store context. The data from a survey of online consumers was used empirically to test the proposed research model. Confirmatory factor analysis (CFA) was performed to examine the reliability and validity of the measurement model, and the structural equation modeling (SEM) technique was used to evaluate the causal model. The implication of the work to both researchers and practitioners is discussed.


title: Energy detection spectrum sensing on DPSK modulation transceiver using GNU radio

abstract: Cognitive radio (CR) can facilitate the issue of range shortage by permitting secondary users to coincide with incumbent clients in authorized range groups, while it causes no interference to occupant interchanges. Spectrum sensing is the essential in enabling innovation of CR to detect the presence of primary users (PUs) or authorized user signals and adventure the spectrum holes. Henceforth, this paper addresses the testing of spectrum sensing method for Differential Phase Shift Keying (DPSK) transceiver by utilizing an open source programming, GNU Radio. Moreover, the effect of various reproduction channels, for example, Dynamic channel demonstrate with Rayleigh fading, dynamic channel display with Rician fading, frequency selective faded channel show with Rayleigh and frequency selective faded channel show with Rician fading are exhibited.


title: MULTI-LEVEL MARKETING - A TOOL OF RELATIONSHIP MARKETING

abstract: This paper aims to analyse the opportunity of using multi-level marketing (MLM) as a tool of relationship marketing. The research is firstly based on an analysis regarding the issues about the legality of MLM techniques in the context of EU and US regulation systems. The outcomes of this research stress the main characteristics of legal network marketing and how a person which wants to become independent distributor could avoid the cooperation with an illegal pyramid scheme. The second research is based on a case study at the level of an insurance broker, which emphasizes the benefits that all parties involved in a transaction (broker, distributor and customer) could obtain by using an MLM scheme.


title: Hierarchical attribute‐based encryption with continuous auxiliary inputs leakage

abstract: The continuous auxiliary inputs leakage is more strong side-channel attacks. In this article, we first propose a continuous auxiliary inputs leakage model for the hierarchical attribute-based encryption scheme. Under the security model, an adversary has ability to gain partial updated master keys and updated secret keys continually by certain leakage attacks. Moreover, a resilient-leakage hierarchical attribute-based encryption scheme is constructed. The security proof for this scheme is provided under the standard model. Furthermore, we give the performance comparison between our scheme and relevant scheme. Copyright © 2016 John Wiley & Sons, Ltd.


title: Machine Learning for Plant Phenotyping Needs Image Processing

abstract: We found the article by Singh et al. [1] extremely interesting because it introduces and showcases the utility of machine learning for high-throughput data-driven plant phenotyping. With this letter we aim to emphasize the role that image analysis and processing have in the phenotyping pipeline beyond what is suggested in [1], both in analyzing phenotyping data (e.g., to measure growth) and when providing effective feature extraction to be used by machine learning. Key recent reviews have shown that it is image analysis itself (what the authors of [1] consider as part of pre-processing) that has brought a renaissance in phenotyping [2].


title: Toward developing a systematic approach to generate benchmark datasets for intrusion detection

abstract: In network intrusion detection, anomaly-based approaches in particular suffer from accurate evaluation, comparison, and deployment which originates from the scarcity of adequate datasets. Many such datasets are internal and cannot be shared due to privacy issues, others are heavily anonymized and do not reflect current trends, or they lack certain statistical characteristics. These deficiencies are primarily the reasons why a perfect dataset is yet to exist. Thus, researchers must resort to datasets that are often suboptimal. As network behaviors and patterns change and intrusions evolve, it has very much become necessary to move away from static and one-time datasets toward more dynamically generated datasets which not only reflect the traffic compositions and intrusions of that time, but are also modifiable, extensible, and reproducible. In this paper, a systematic approach to generate the required datasets is introduced to address this need. The underlying notion is based on the concept of profiles which contain detailed descriptions of intrusions and abstract distribution models for applications, protocols, or lower level network entities. Real traces are analyzed to create profiles for agents that generate real traffic for HTTP, SMTP, SSH, IMAP, POP3, and FTP. In this regard, a set of guidelines is established to outline valid datasets, which set the basis for generating profiles. These guidelines are vital for the effectiveness of the dataset in terms of realism, evaluation capabilities, total capture, completeness, and malicious activity. The profiles are then employed in an experiment to generate the desirable dataset in a testbed environment. Various multi-stage attacks scenarios were subsequently carried out to supply the anomalous portion of the dataset. The intent for this dataset is to assist various researchers in acquiring datasets of this kind for testing, evaluation, and comparison purposes, through sharing the generated datasets and profiles.


Editorial: Survey and Experimental Analysis of Event Detection Techniques for Twitter


Speech Recognition Using Artificial Neural Network – A Review


title: Am. J. Hum. Genet. 75:128–137, 2004 Report Phylogeography of Y-Chromosome Haplogroup I Reveals Distinct Domains of Prehistoric Gene Flow in Europe

abstract: To investigate which aspects of contemporary human Y-chromosome variation in Europe are characteristic of primary colonization, late-glacial expansions from refuge areas, Neolithic dispersals, or more recent events of gene flow, we have analyzed, in detail, haplogroup I (Hg I), the only major clade of the Y phylogeny that is widespread over Europe but virtually absent elsewhere. The analysis of 1,104 Hg I Y chromosomes, which were identified in the survey of 7,574 males from 60 population samples, revealed several subclades with distinct geographic distributions. Subclade I1a accounts for most of Hg I in Scandinavia, with a rapidly decreasing frequency toward both the East European Plain and the Atlantic fringe, but microsatellite diversity reveals that France could be the source region of the early spread of both I1a and the less common I1c. Also, I1b*, which extends from the eastern Adriatic to eastern Europe and declines noticeably toward the southern Balkans and abruptly toward the periphery of northern Italy, probably diffused after the Last Glacial Maximum from a homeland in eastern Europe or the Balkans. In contrast, I1b2 most likely arose in southern France/Iberia. Similarly to the other subclades, it underwent a postglacial expansion and marked the human colonization of Sardinia ∼9,000 years ago.


title: IEC 61850 based breaker failure protection scheme design for TNB double busbar substation

abstract: IEC 61850 Communication Networks and System for Power Utility Automation is an open standard which enable the information exchange between Intelligent Electronic Devices (IEDs) from different manufacturers. The standard has introduced the standard communication services such as clientserver communication, server-to-server communication named Generic Object Oriented Substation Event (GOOSE)/Generic Substation Status Event (GSSE) message and sampled value communication service for the current and voltage measurement transmission. These communication services provide seamless communication for information exchange between IEDs from different vendors. IEC 61850 has also standardized the engineering process for the Protection and Control (P&C) scheme communication configuration. With the utilization of Ethernet communication technology by IEC 61850 for substation P&C, it can simplify TNB protection and control scheme architecture with less hardwires connection. Based on the advantages of the standard which can be gained by TNB for protection and control scheme, this paper is intended to provide the practical design for IEC 61850 based Breaker Failure Protection (BFP) scheme which can be adopted in TNB's double busbar substation. This paper also specifies the GOOSE message configuration for this scheme design that can be considered for future implementation in TNB IEC 61850 Substation.


title: Corrective 3D reconstruction of lips from monocular video

abstract: In facial animation, the accurate shape and motion of the lips of virtual humans is of paramount importance, since subtle nuances in mouth expression strongly influence the interpretation of speech and the conveyed emotion. Unfortunately, passive photometric reconstruction of expressive lip motions, such as a kiss or rolling lips, is fundamentally hard even with multi-view methods in controlled studios. To alleviate this problem, we present a novel approach for fully automatic reconstruction of detailed and expressive lip shapes along with the dense geometry of the entire face, from just monocular RGB video. To this end, we learn the difference between inaccurate lip shapes found by a state-of-the-art monocular facial performance capture approach, and the true 3D lip shapes reconstructed using a high-quality multi-view system in combination with applied lip tattoos that are easy to track. A robust gradient domain regressor is trained to infer accurate lip shapes from coarse monocular reconstructions, with the additional help of automatically extracted inner and outer 2D lip contours. We quantitatively and qualitatively show that our monocular approach reconstructs higher quality lip shapes, even for complex shapes like a kiss or lip rolling, than previous monocular approaches. Furthermore, we compare the performance of person-specific and multi-person generic regression strategies and show that our approach generalizes to new individuals and general scenes, enabling high-fidelity reconstruction even from commodity video footage.


title: Diffusion and success factors of mobile marketing

abstract: Mobile marketing offers direct communication with consumers, anytime and anyplace. This paper reviews mobile marketing and then investigates the most successful form of mobile communication, short message services (SMS), via a quantitative content analysis of the Fortune Global 500 Web sites and qualitative interviews with European experts. The content analysis explores the diffusion of SMS technology and sheds light on mobile marketing campaigns of large multinational organizations. Combining a literature review with results from the qualitative survey leads to a conceptual model of successful SMS advertising. The paper closes with future research avenues for this emerging marke ting tool.


title: BabelNet: Building a Very Large Multilingual Semantic Network

abstract: In this paper we present BabelNet -- a very large, wide-coverage multilingual semantic network. The resource is automatically constructed by means of a methodology that integrates lexicographic and encyclopedic knowledge from WordNet and Wikipedia. In addition Machine Translation is also applied to enrich the resource with lexical information for all languages. We conduct experiments on new and existing gold-standard datasets to show the high quality and coverage of the resource.


title: Toward Real Event Detection

abstract: News agencies and other news providers or consumers are confronted with the task of extracting events from news articles. This is done i) either to monitor and, hence, to be informed about events of specific kinds over time and/or ii) to react to events immediately. In the past, several promising approaches to extracting events from text have been proposed. Besides purely statistically-based approaches there are methods to represent events in a semantically-structured form, such as graphs containing actions (predicates), participants (entities), etc. However, it turns out to be very difficult to automatically determine whether an event is real or not. In this paper, we give an overview of approaches which proposed solutions for this research problem. We show that there is no gold standard dataset where real events are annotated in text documents in a fine-grained, semantically-enriched way. We present a methodology of creating such a dataset with the help of crowdsourcing and present preliminary results.


title: Simple-Structured Dual-Slot Broadband Circularly Polarized Antenna

abstract: A simple-structured single-fed dual-slot broadband antenna with circularly polarized waves is presented. Two semicircular slots etched in the ground plane were used as the radiators. The slots were proximity-coupled to a 50 Ω microstrip feedline printed on the other side of the substrate. The fabricated antenna with an overall size of 101 × 64 × 1.52 mm3 yielded a very wide –10 dB impedance bandwidth of 86.9% (3.63–9.21 GHz) and a 3 dB axial-ratio bandwidth of 74.3% (4.18–9.12 GHz). The measured antenna peak gain in the broadside direction over the axial-ratio bandwidth ranged from 3.9 to 7.3 dB. Good agreement between the simulated and measured results validates the proposed antenna.


title: Securing Untrusted Full-Duplex Relay Channels in the Presence of Multiple External Cluster-Based Eavesdroppers

abstract: We investigate the physical layer security of a wireless cooperative network where communication between Alice and Bob is assisted by a full-duplex (FD) untrusted relay, Ray, in the presence of multiple external eavesdroppers, Eves. A cluster-based colluding eavesdropping setting is considered, where illegitimate nodes with common interests are grouped in a cluster. To confuse the different eavesdropping clusters, we employ artificial-noise (AN) aided beamforming at the source node. Moreover, FD relay jamming is used to improve the system's security. To maintain secure communications against the untrusted relay node, an FD destination jamming scheme is adopted. Our proposed scheme is designed based on the channel state information of the legitimate nodes only. We optimize the transmit power allocation factors between data and AN signals, the data and AN precoders at Alice, the AN precoder at Bob, and the data and AN precoders at Ray. Numerical results show that the optimal power allocation factor between data and AN depends on the total number of antennas of the different colluding eavesdropping clusters.


title: Towards better understanding of gradient-based attribution methods for Deep Neural Networks

abstract: Understanding the flow of information in Deep Neural Networks (DNNs) is a challenging problem that has gain increasing attention over the last few years. While several methods have been proposed to explain network predictions, there have been only a few attempts to compare them from a theoretical perspective. What is more, no exhaustive empirical comparison has been performed in the past. In this work we analyze four gradient-based attribution methods and formally prove conditions of equivalence and approximation between them. By reformulating two of these methods, we construct a unified framework which enables a direct comparison, as well as an easier implementation. Finally, we propose a novel evaluation metric, called Sensitivity-n and test the gradient-based attribution methods alongside with a simple perturbation-based attribution method on several datasets in the domains of image and text classification, using various network architectures.


Team DEEP-HRI Moments in Time Challenge 2018 Technical Report


title: Integrated, Decentralized Wastewater Management for Resource Recovery in Rural and Peri-Urban Areas

abstract: Collection and treatment of wastewater have a huge impact on the environment and economy, both at the local and global levels. Eco-innovation may play a paramount role in the reduction of the environmental impact of such systems, and in their greater sustainability in economic, environmental, and social terms. Decentralization appears as a logical solution to tackle sustainability problems of wastewater management systems, as it focuses on the on-site treatment of wastewater and on local recycling and reuse of resources contained in domestic wastewater (in primus, water itself). This paper analyses the needs, technological options and contribution to water management of decentralized systems. Decentralized solutions in general will tend to be compatible with local water use and reuse requirements, where locally treated water could support agricultural productivity or (in more urban areas) be used as a substitute for drinking-quality supply water for compatible uses. In analyzing sustainability of technology, different dimensions should be taken into account (in particular, local issues). There is no fixed or universal solution to the technological issue; to the contrary, all relevant studies demonstrated there are varying degrees of sustainability in the way a technology is selected and operated, to avoid exporting problems over time or space.


A Proposed Maneuver to Guide Transseptal Puncture Using Real-Time Three-Dimensional Transesophageal Echocardiography: Pilot Study


Parallelizing Big Data Machine Learning Algorithms with Model Rotation


title: Musical groove modulates motor cortex excitability: A TMS investigation

abstract: Abstract Groove is often described as a musical quality that can induce movement in a listener. This study examines the effects of listening to groove music on corticospinal excitability. Musicians and non-musicians listened to high-groove music, low-groove music, and spectrally matched noise, while receiving single-pulse transcranial magnetic stimulation (TMS) over the primary motor cortex either on-beat or off-beat. We examined changes in the amplitude of the motor-evoked potentials (MEPs), recorded from hand and arm muscles, as an index of activity within the motor system. Musicians and non-musicians rated groove similarly. MEP results showed that high-groove music modulated corticospinal excitability, whereas no difference occurred between low-groove music and noise. More specifically, musicians’ MEPs were larger with high-groove than low-groove music, and this effect was especially pronounced for on-beat compared to off-beat pulses. These results indicate that high-groove music increasingly engages the motor system, and the temporal modulation of corticospinal excitability with the beat could stem from tight auditory–motor links in musicians. Conversely, non-musicians’ MEPs were smaller for high-groove than low-groove music, and there was no effect of on- versus off-beat pulses, potentially stemming from suppression of overt movement. In sum, high-groove music engages the motor system, and previous training modulates how listening to music with a strong groove activates the motor system.


Selectional Preference And Sense Disambiguation


title: Property Graph vs RDF Triple Store: A Comparison on Glycan Substructure Search

abstract: Resource description framework (RDF) and Property Graph databases are emerging technologies that are used for storing graph-structured data. We compare these technologies through a molecular biology use case: glycan substructure search. Glycans are branched tree-like molecules composed of building blocks linked together by chemical bonds. The molecular structure of a glycan can be encoded into a direct acyclic graph where each node represents a building block and each edge serves as a chemical linkage between two building blocks. In this context, Graph databases are possible software solutions for storing glycan structures and Graph query languages, such as SPARQL and Cypher, can be used to perform a substructure search. Glycan substructure searching is an important feature for querying structure and experimental glycan databases and retrieving biologically meaningful data. This applies for example to identifying a region of the glycan recognised by a glycan binding protein (GBP). In this study, 19,404 glycan structures were selected from GlycomeDB (www.glycome-db.org) and modelled for being stored into a RDF triple store and a Property Graph. We then performed two different sets of searches and compared the query response times and the results from both technologies to assess performance and accuracy. The two implementations produced the same results, but interestingly we noted a difference in the query response times. Qualitative measures such as portability were also used to define further criteria for choosing the technology adapted to solving glycan substructure search and other comparable issues.


title: From Argument Diagrams to Argumentation Mining in Texts: A Survey

abstract: In this paper, the authors consider argument mining as the task of building a formal representation for an argumentative piece of text. Their goal is to provide a critical survey of the literature on both the resulting representations i.e., argument diagramming techniques and on the various aspects of the automatic analysis process. For representation, the authors also provide a synthesized proposal of a scheme that combines advantages from several of the earlier approaches; in addition, the authors discuss the relationship between representing argument structure and the rhetorical structure of texts in the sense of Mann and Thompsons 1988 RST. Then, for the argument mining problem, the authors also cover the literature on closely-related tasks that have been tackled in Computational Linguistics, because they think that these can contribute to more powerful argument mining systems than the first prototypes that were built in recent years. The paper concludes with the authors' suggestions for the major challenges that should be addressed in the field of argument mining.


GIPSY: Automated Geographic Indexing of Text Documents


title: proSQLite: Prolog File Based Databases via an SQLite Interface

abstract: We present a succinct yet powerful interface library to the SQLite database system. The single file, server-less approach of SQLite along with the natural integration of relational data within Prolog, render the library a useful addition to the existing database libraries in modern open-source engines. We detail the architecture and predicates of the library and provide example deployment scenarios. A simple bioinformatics example is presented throughout to illustrate proSQLitet's main functions. Finally, this paper discusses the strengths of the system and highlights possible extensions.


title: Autonomous Underwater Navigation and Optical Mapping in Unknown Natural Environments

abstract: We present an approach for navigating in unknown environments while, simultaneously, gathering information for inspecting underwater structures using an autonomous underwater vehicle (AUV). To accomplish this, we first use our pipeline for mapping and planning collision-free paths online, which endows an AUV with the capability to autonomously acquire optical data in close proximity. With that information, we then propose a reconstruction pipeline to create a photo-realistic textured 3D model of the inspected area. These 3D models are also of particular interest to other fields of study in marine sciences, since they can serve as base maps for environmental monitoring, thus allowing change detection of biological communities and their environment over time. Finally, we evaluate our approach using the Sparus II, a torpedo-shaped AUV, conducting inspection missions in a challenging, real-world and natural scenario.


title: Emotion recognition for human-machine communication

abstract: The ability to recognize emotion is one of the hallmarks of emotion intelligence. This paper proposed to recognize emotion using physiological signals obtained from multiple subjects. IAPS images were used to elicit target emotions. Five physiological signals: Blood volume pulse (BVP), Electromyography (EMG), Skin Conductance (SC), Skin Temperature (SKT) and Respiration (RESP) were selected to extract 30 features for recognition. Two pattern classification methods, Fisher discriminant and SVM method are used and compared for emotional state classification. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance as 92% over six emotional states.


title: A Noncooperative Game-Theoretic Framework for Radio Resource Management in 4G Heterogeneous Wireless Access Networks

abstract: Fourth generation (4G) wireless networks will provide high-bandwidth connectivity with quality-of-service (QoS) support to mobile users in a seamless manner. In such a scenario, a mobile user will be able to connect to different wireless access networks such as a wireless metropolitan area network (WMAN), a cellular network, and a wireless local area network (WLAN) simultaneously. We present a game-theoretic framework for radio resource management (that is, bandwidth allocation and admission control) in such a heterogeneous wireless access environment. First, a noncooperative game is used to obtain the bandwidth allocations to a service area from the different access networks available in that service area (on a long-term basis). The Nash equilibrium for this game gives the optimal allocation which maximizes the utilities of all the connections in the network (that is, in all of the service areas). Second, based on the obtained bandwidth allocation, to prioritize vertical and horizontal handoff connections over new connections, a bargaining game is formulated to obtain the capacity reservation thresholds so that the connection-level QoS requirements can be satisfied for the different types of connections (on a long-term basis). Third, we formulate a noncooperative game to obtain the amount of bandwidth allocated to an arriving connection (in a service area) by the different access networks (on a short-term basis). Based on the allocated bandwidth and the capacity reservation thresholds, an admission control is used to limit the number of ongoing connections so that the QoS performances are maintained at the target level for the different types of connections.


title: A comparison of social, learning, and financial strategies on crowd engagement and output quality

abstract: A significant challenge for crowdsourcing has been increasing worker engagement and output quality. We explore the effects of social, learning, and financial strategies, and their combinations, on increasing worker retention across tasks and change in the quality of worker output. Through three experiments, we show that 1) using these strategies together increased workers' engagement and the quality of their work; 2) a social strategy was most effective for increasing engagement; 3) a learning strategy was most effective in improving quality. The findings of this paper provide strategies for harnessing the crowd to perform complex tasks, as well as insight into crowd workers' motivation.


title: Discovering Structured Variations Via Template Matching

abstract: Understanding patterns of variation from raw measurement data remains a central goal of shape analysis. Such an understanding reveals which elements are repeated, or how elements can be derived as structured variations from a common base element. We investigate this problem in the context of 3D acquisitions of buildings. Utilizing a set of template models, we discover geometric similarities across a set of building elements. Each template is equipped with a deformation model that defines variations of a base geometry. Central to our algorithm is a simultaneous template matching and deformation analysis that detects patterns across building elements by extracting similarities in the deformation modes of their matching templates. We demonstrate that such an analysis can successfully detect structured variations even for noisy and incomplete data.


title: Orthogonal Weight Normalization: Solution to Optimization over Multiple Dependent Stiefel Manifolds in Deep Neural Networks

abstract: Orthogonal matrix has shown advantages in training Recurrent Neural Networks (RNNs), but such matrix is limited to be square for the hidden-to-hidden transformation in RNNs. In this paper, we generalize such square orthogonal matrix to orthogonal rectangular matrix and formulating this problem in feed-forward Neural Networks (FNNs) as Optimization over Multiple Dependent Stiefel Manifolds (OMDSM). We show that the rectangular orthogonal matrix can stabilize the distribution of network activations and regularize FNNs. We also propose a novel orthogonal weight normalization method to solve OMDSM. Particularly, it constructs orthogonal transformation over proxy parameters to ensure the weight matrix is orthogonal and back-propagates gradient information through the transformation during training. To guarantee stability, we minimize the distortions between proxy parameters and canonical weights over all tractable orthogonal transformations. In addition, we design an orthogonal linear module (OLM) to learn orthogonal filter banks in practice, which can be used as an alternative to standard linear module. Extensive experiments demonstrate that by simply substituting OLM for standard linear module without revising any experimental protocols, our method largely improves the performance of the state-of-the-art networks, including Inception and residual networks on CIFAR and ImageNet datasets. In particular, we have reduced the test error of wide residual network on CIFAR-100 from 20.04% to 18.61% with such simple substitution. Our code is available online for result reproduction.


title: Development of an automatic arc welding system using an adaptive sliding mode control

abstract: This paper develops an automatic welding control scheme for alternating current shielded metal arc welding (SMAW) system. A mathematical model of the welding control system is derived and the system parameters identified. An adaptive sliding mode controller is designed to estimate the bound of the system uncertainties and to modulate the electrode feed rate in such a way that the desired arc length and arc current are maintained as the electrode melts during the welding process. The proposed control method is suitable for any consumed electrode welding technique. The simulation and experimental results show that the automatic welding control system successfully maintains the magnitude of the arc current at the desired value and preserves the arc stability, thereby obtaining an enhanced SMAW control system performance.


title: Lane change trajectory prediction by using recorded human driving data

abstract: Being able to predict the trajectory of a human driver's potential lane change behavior in urban high way scenario is crucial for lane change risk assessment task. A good prediction of the driver's lane change trajectory makes it possible to evaluate the risk and warn the driver beforehand. Rather than generating such a trajectory only using a mathematical model, this paper develops a lane change trajectory prediction approach based on real human driving data stored in a database. In real-time, the system generates parametric trajectories by interpolating k human lane change trajectory instances from the pre-collected database that are similar to the current driving situation. In order to build this real lane change database, a human lane change data collection vehicle platform is developed. Extensive experiments have been carried out in urban highway environments to build a significant database with more than 200 lane changes. Real results show that this approach produces lane change trajectories that are quite similar to real ones which makes it suitable to predict humanlike lane change maneuvers.


Making Things Hard on Yourself , But in a Good Way : Creating Desirable Difficulties to Enhance Learning


title: PerFallD: A pervasive fall detection system using mobile phones

abstract: Falls are a major health risk that diminish the quality of life among elderly people. With the elderly population surging, especially with aging “baby boomers”, fall detection becomes increasingly important. However, existing commercial products and academic solutions struggle to achieve pervasive fall detection. In this paper, we propose utilizing mobile phones as a platform for pervasive fall detection system development. To our knowledge, we are the first to do so. We design a detection algorithm based on mobile phone platforms. We propose PerFallD, a pervasive fall detection system implemented on mobile phones. We implement a prototype system on the Android G1 phone and conduct experiments to evaluate our system. In particular, we compare PerFallD's performance with that of existing work and a commercial product. Experimental results show that PerFallD achieves strong detection performance and power efficiency.


title: Properties of 50–110-GHz Waveguide Components Fabricated by Metal Additive Manufacturing

abstract: This paper presents several V-band (50–75 GHz) WR-15 and W-band (75–110 GHz) WR-10 waveguide components fabricated using both direct metal laser sintering in aluminum, nickel-chromium, maraging steel (MS), and copper alloys and stereolithography in copper-coated plastic. Printing resolution is analyzed with respect to feature creation. The RF performance and surface roughness are measured, and the loss due to surface roughness quantified. The measured loss for WR-10 at 92.5-GHz ranges from 5.5 dB/m for the copper-plated plastic waveguides to 36.9 dB/m for the nickel alloy and for WR-15 at 62.5-GHz ranges from 11.0 dB/m for the aluminum alloy to 14.4 dB/m for the MS alloy. From a loss budget study, it is found that standard models do not accurately predict loss due to surface roughness for rough surfaces where the height variation is much larger than skin depth $\delta$ . It is also found that for internally complex printing, the MS alloy versions maintain the closest structure to designed components and expected RF performance. This paper presents the current state-of-the-art in available additive-manufactured waveguide components at V- and W-bands.


title: Distalization pattern of the maxillary arch depending on the number of orthodontic miniscrews.

abstract: OBJECTIVE ::: To determine the effects of linear force vector(s) from interradicular miniscrews on the distalization pattern of the maxillary arch in adult Class II patients. ::: ::: ::: MATERIALS AND METHODS ::: Twenty-five adult patients with mild to moderate Class II dentition and minimal crowding were collected. Either single (group A, n = 12) or dual (group B, n = 13) miniscrews were inserted on the posterior interradicular area to deliver a distalizing force to the main archwire. The displacement patterns of maxillary incisors and molars were measured and compared. ::: ::: ::: RESULTS ::: Significant distalization in the molars and incisors was shown in both groups. Significantly greater distalization and intrusion of the first molar and intrusive displacement of the incisor, together with significant reduction of the mandibular plane, were noted in group B, in contrast to the rotation of the occlusal plane in group A. ::: ::: ::: CONCLUSIONS ::: Interradicular miniscrews predictably induced total arch distalization, leading to the correction of Class II. Additional miniscrews in the premolar area appear to facilitate intrusion and distalization of the entire arch according to the position of the force vectors.


title: Design of low-density parity-check (ldpc) codes for deep-space applications

abstract: This article summarizes a comparison of numerous low-density parity-check (LDPC) codes using a set of metrics relevant to deep-space communications. These metrics are the error-correcting capability of the code, the computational complexity of the decoder, the architectural complexities of the encoder and decoder, and the ease of generating a family of structurally related codes at different code rates and block lengths. Based on these metrics, we select a family of LDPC codes that is a good candidate for inclusion in a new Consultative Committee for Space Data Systems (CCSDS) telemetry channel coding standard for deep-space applications.


title: Integrated employment or sheltered workshops: Preferences of adults with intellectual disabilities, their families, and staff

abstract: Despite national and state policies promoting integrated employment, the majority of adults with intellectual or developmental disabilities (76%) are served in facility-based programs. This article focuses on whether or not this gap between policy and practice is in part due to the lack of interest of adults with intellectual disabilities and their families for employment outside facility-based programs. Results are based on the answers given by 210 adults with intellectual disabilities in 19 sheltered workshops, their respective families or caregivers (N = 185), and staff members in these workshops (N = 224). Results show that the majority of respondents would either like employment outside sheltered workshops or at least consider it an option. Moreover, the majority of respondents believe that adults with intellectual disabilities can perform outside workshops, if support is made available if needed. It is noteworthy that the preference for employment outside of workshops is not associated with the severity of the disability. Based on these findings, this study supports the literature that advocates for system change policy promoting the employment of adults with intellectual disabilities in the general labor market.


title: When More Pain Is Preferred to Less: Adding a Better End

abstract: Subjects were exposed to two aversive experiences: in the short trial, they immersed one hand in water at 14 °Cfor 60 s; in the long trial, they im- mersed the other hand at 14 "C for 60 s, then kept the hand in the water 30 s longer as the temperature of the water was gradually raised to 15 °C, still pain- ful but distinctly less so for most sub- jects. Subjects were later given a choice of which trial to repeat. A significant majority chose to repeat the long trial, apparently preferring more pain over less. The results add to other evidence suggesting that duration plays a small role in retrospective evaluations of aver- sive experiences; such evaluations are often dominated by the discomfort at the worst and at the final moments of epi- sodes. you enjoying this?" or "Does it hurt?" This confidence could be unwarranted because two fallible mental processes separate retrospective assessments from the sequence of experiences that consti- tuted the original episode; an operation of memory and an act of evaluation. Some recent research has called into question the accuracy of people's mem- ories for their hedonic and affective ex- periences (Kent, 1985; Rachman & Eyrl, 1989; Thomas & Diener, 1990). This ar- ticle focuses on the process of evaluating


title: Contours and Barriers: What Is It to Draw the Limits of Moral Language?

abstract: Does language limit the moral thoughts we can have? To answer that, I distinguish between two kinds of limits: (1) Boundaries or barriers fence things out. Identification and erection of linguistic barriers, defines, diagnoses, or places restrictions on what language can in principle grasp or be, and often involves abstraction from actual linguistic behavior. This is typically preformed by remarks I call 'theses'; (2) Contours or outlines give real-life portrayals. Drawing the contours of a linguistic activity involves a certain attention to reality: to detail and particularity (‘Was this greeting contrived or genuine?’), and we typically draw contours by using remarks I call ‘helpers’. I examine the possibility that confusion can be diagnosed in Sabina Lovibond's attempt to apply the idea that moral language has necessary boundaries, and explain the alternative of drawing linguistic contours. I then examine Richard Rorty's position according to which the fact that we can shape our language indicates that the boundaries of language do not encompass all possible sense, and compare Rorty's discussion with Sarah Bachelard's discussion of euthanasia. My claim here is that trying to improve on the language we have might be part of drawing its contours, rather than redefining its boundaries. The discussion reveals a difference between two kinds of contour drawing, and thus between two kinds of helpers: ones that help to draw the contours of the actuality of linguistic activities, and ones that help to draw the contours of their potentiality. Finally, I argue that the value of drawing contours instead of barriers is that the former better reveal the fact that we care about our ways of making sense.


RECURRENTNEURAL NETWORK REGULARIZATION


Design and Analysis of Low Power Level Shifter


title: Power grids reliability appraisal with intrusion tolerant capability in SCADA systems

abstract: By intruding into the substations and control center of the supervisory control and data acquisition (SCADA) system, trip commands can be sent to intelligent electronic devices (IEDs) which control the system breakers. Reliability of the power system can be impacted through the cyber attacks. In this paper, a modified semi-Markov process (SMP) model is used to describe the procedures of normal and penetration attacks against the intrusion tolerant system. By modeling the transition probabilities between the SMP states and the sojourn time of each SMP state, the mean times to compromise (MTTCs) of the normal and penetration attacks are calculated. With increased probabilities of breaker trips resulted from the cyber attacks, the loss of load probabilities (LOLP) are evaluated based on IEEE reliability test system 79 (RTS79). When the level of attack increases or the level of defense in the system decreases, the simulation results demonstrate that the power system becomes less reliable.


title: Conditional End-to-End Audio Transforms

abstract: We present an end-to-end method for transforming audio from one style to another. For the case of speech, by conditioning on speaker identities, we can train a single model to transform words spoken by multiple people into multiple target voices. For the case of music, we can specify musical instruments and achieve the same result. Architecturally, our method is a fully-differentiable sequence-to-sequence model based on convolutional and hierarchical recurrent neural networks. It is designed to capture long-term acoustic dependencies, requires minimal post-processing, and produces realistic audio transforms. Ablation studies confirm that our model can separate speaker and instrument properties from acoustic content at different receptive fields. Empirically, our method achieves competitive performance on community-standard datasets.


title: A conceptual model and process for client-driven agile requirements prioritization

abstract: Continuous customer-centric requirements reprioritization is essential in successfully performing agile software development. Yet, in the agile RE literature, very little is known about how agile reprioritization happens in practice. Generic conceptual models about this process are missing, which in turn, makes it difficult for both practitioners and researchers to reason about requirements decision-making at inter-iteration time. This paper presents a Grounded Theory study on agile requirements prioritization methods to yield a conceptual model for understanding the inter-iteration prioritization process in terms of inputs and outcomes. The latter is derived by using qualitative empirical data, published earlier by other authors. Such a conceptual model makes explicit the concepts that are used tacitly in different agile requirements prioritization methods and can be used for structuring future empirical investigations about this topic.


title: Ongoing and online: Children and youth's perceptions of cyber bullying ☆

abstract: The use of online technology is exploding worldwide and is fast becoming a preferred method of interacting. While most online interactions are neutral or positive the Internet provides a new means through which children and youth are bullied. The aim of this grounded theory approach was to explore technology, virtual relationships and cyber bullying from the perspectives of students. Seven focus groups were held with 38 students between fifth and eighth grades. The participants considered cyber bullying to be a serious problem and some characterized online bullying as more serious than 'traditional' bullying because of the associated anonymity. Although the students depicted anonymity as integral to cyber bullying, the findings suggest that much of the cyber bullying occurred within the context of their social groups and relationships. Findings revealed five major themes: technology embraced at younger ages and becoming the dominant medium for communication; definitions and views of cyber bullying; factors unique to cyber bullying; types of cyber bullying; and telling adults. The findings highlight the complexity of the perceived anonymity provided by the Internet and how this may impact cyber bullying. The study offers greater awareness of the meanings of online relationships for children and youth.


title: Scalable Hypergraph Learning and Processing

abstract: A hypergraph allows a hyperedge to connect more than two vertices, using which to capture the high-order relationships, many hypergraph learning algorithms are shown highly effective in various applications. When learning large hypergraphs, converting them to graphs to employ the distributed graph frameworks is a common approach, yet it results in major efficiency drawbacks including an inflated problem size, the excessive replicas, and the unbalanced workloads. To avoid such drawbacks, we take a different approach and propose HyperX, which is a thin layer built upon Spark. To preserve the problem size, HyperX directly operates on a distributed hypergraph. To reduce the replicas, HyperX replicates the vertices but not the hyperedges. To balance the workloads, we investigate the hypergraph partitioning problem aiming at minimizing the space and the communication cost subject to two separate constraints on the hyperedge and the vertex workloads. With experiments on both real and synthetic datasets, we verify that HyperX significantly improves the efficiency of the learning algorithms when compared with the graph conversion approach.


title: Asynchronous BCI control using high-frequency SSVEP

abstract: BackgroundSteady-State Visual Evoked Potential (SSVEP) is a visual cortical response evoked by repetitive stimuli with a light source flickering at frequencies above 4 Hz and could be classified into three ranges: low (up to 12 Hz), medium (12-30) and high frequency (> 30 Hz). SSVEP-based Brain-Computer Interfaces (BCI) are principally focused on the low and medium range of frequencies whereas there are only a few projects in the high-frequency range. However, they only evaluate the performance of different methods to extract SSVEP.MethodsThis research proposed a high-frequency SSVEP-based asynchronous BCI in order to control the navigation of a mobile object on the screen through a scenario and to reach its final destination. This could help impaired people to navigate a robotic wheelchair. There were three different scenarios with different difficulty levels (easy, medium and difficult). The signal processing method is based on Fourier transform and three EEG measurement channels.ResultsThe research obtained accuracies ranging in classification from 65% to 100% with Information Transfer Rate varying from 9.4 to 45 bits/min.ConclusionsOur proposed method allows all subjects participating in the study to control the mobile object and to reach a final target without prior training.


title: Automated classification of Pap smear images to detect cervical dysplasia

abstract: An automated Pap smear classifier is proposed to detect cervical dysplasia.Study performed on both cell as well as smear level indigenous real images collected from two diagnostic centers.Analysis is being performed on shape, texture and color features which includes 121 total numbers of features.An ensemble classifier is designed using LSSVM, MLP and Random Forest using weighted majority voting.Classification reflects the established Bethesda pathological classification of cervical cancer. Background and objectivesThe present study proposes an intelligent system for automatic categorization of Pap smear images to detect cervical dysplasia, which has been an open problem ongoing for last five decades. MethodsThe classification technique is based on shape, texture and color features. It classifies the cervical dysplasia into two-level (normal and abnormal) and three-level (Negative for Intraepithelial Lesion or Malignancy, Low-grade Squamous Intraepithelial Lesion and High-grade Squamous Intraepithelial Lesion) classes reflecting the established Bethesda system of classification used for diagnosis of cancerous or precancerous lesion of cervix. The system is evaluated on two generated databases obtained from two diagnostic centers, one containing 1610 single cervical cells and the other 1320 complete smear level images. The main objective of this database generation is to categorize the images according to the Bethesda system of classification both of which require lots of training and expertise. The system is also trained and tested on the benchmark Herlev University database which is publicly available. In this contribution a new segmentation technique has also been proposed for extracting shape features. Ripplet Type I transform, Histogram first order statistics and Gray Level Co-occurrence Matrix have been used for color and texture features respectively. To improve classification results, ensemble method is used, which integrates the decision of three classifiers. Assessments are performed using 5 fold cross validation. ResultsExtended experiments reveal that the proposed system can successfully classify Pap smear images performing significantly better when compared with other existing methods. ConclusionThis type of automated cancer classifier will be of particular help in early detection of cancer.


title: Choosing a Selection Technique for a Virtual Environment

abstract: Bearing in mind the difficulty required to create virtual environments, a platform for Setting-up Interactive Virtual Environments pSIVE was created to help non-specialists benefit from virtual applications involving virtual tours where users may interact with elements of the environment to extract contextual information. The platform allows creating virtual environments and setting up their aspects, interaction methods and hardware to be used. The construction of the world is done by loading 3D models and associating multimedia information videos, texts or PDF documents to them. ::: ::: A central interaction task in the envisioned applications of pSIVE is the selection of objects that have associated multimedia information. Thus, a comparative study between two variants of the ray-tracing selection technique was performed. The study also demonstrates the flexibility of the platform, since it was easily adapted to serve as a test environment.


title: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients

abstract: This paper introduces a novel parameter automation strategy for the particle swarm algorithm and two further extensions to improve its performance after a predefined number of generations. Initially, to efficiently control the local search and convergence to the global optimum solution, time-varying acceleration coefficients (TVAC) are introduced in addition to the time-varying inertia weight factor in particle swarm optimization (PSO). From the basis of TVAC, two new strategies are discussed to improve the performance of the PSO. First, the concept of "mutation" is introduced to the particle swarm optimization along with TVAC (MPSO-TVAC), by adding a small perturbation to a randomly selected modulus of the velocity vector of a random particle by predefined probability. Second, we introduce a novel particle swarm concept "self-organizing hierarchical particle swarm optimizer with TVAC (HPSO-TVAC)". Under this method, only the "social" part and the "cognitive" part of the particle swarm strategy are considered to estimate the new velocity of each particle and particles are reinitialized whenever they are stagnated in the search space. In addition, to overcome the difficulties of selecting an appropriate mutation step size for different problems, a time-varying mutation step size was introduced. Further, for most of the benchmarks, mutation probability is found to be insensitive to the performance of MPSO-TVAC method. On the other hand, the effect of reinitialization velocity on the performance of HPSO-TVAC method is also observed. Time-varying reinitialization step size is found to be an efficient parameter optimization strategy for HPSO-TVAC method. The HPSO-TVAC strategy outperformed all the methods considered in this investigation for most of the functions. Furthermore, it has also been observed that both the MPSO and HPSO strategies perform poorly when the acceleration coefficients are fixed at two.


title: Issues in multinational ERP implementation

abstract: Enterprise resource planning (ERP) systems have been credited with providing competitive advantage in multinational business operations, to include supply chains. Multinational implementations of ERP involve added complexity. This paper uses case studies and other reports of multinational ERP implementation to identify implementation factors of importance. Four technical factors were identified (business process reengineering, federalism and customisation, supply chain features and outsourcing). Additionally, more general issues were compared (culture/language, management style, political factors and labour skills). Multinational ERP implementations radically change organisational information systems. Careful planning of how to implement ERP systems is needed in multinational environments in order to identify the best ERP design and the best redesign of business processes.


title: Permanent Magnet Synchronous Motor Magnet Designs with Skewing for Torque Ripple and Cogging Torque Reduction

abstract: This paper examines the torque ripple and cogging torque variation in surface mounted permanent magnet synchronous motors with skewed rotor. The effect of slot/pole combinations and magnet shapes on the magnitude and harmonic content of torque waveforms in a PMSM drive has been studied. Finite element analysis (FEA) and experimental results show that for certain magnet designs and configurations the skewing with steps does not necessarily reduce the ripple in the electromagnetic torque, but may cause it to increase. The electromagnetic torque waveforms including cogging torque have been analyzed for four different PMSM configurations having the same envelop dimensions and output characteristics.


title: The future of mobile cloud computing: Integrating cloudlets and Mobile Edge Computing

abstract: Extending the coverage area of mobile cloud computing services will allow new services to be provisioned to the mobile users. The main obstacle for achieving this goal is related to the deployments challenges and limitations of the Cloudlets system. Mobile Edge Computing (MEC) system emerged recently providing an opportunity to fill the gap of the Cloudlets system by providing resources-rich computing resources with proximity to the end users. In this paper, we are proposing a hierarchical model that is composed of MEC servers and Cloudlets infrastructures. The objective of the proposed model is to increase the coverage area for the mobile users in which the users can accomplish their requested services with minimal costs in terms of power and delay. An extensive experimental evaluation is conducted showing the superiority of the proposed model.


title: Transformational Leadership, Creativity, and Organizational Innovation

abstract: This study proposes a model of the impact of transformational leadership both on followers' creativity at the individual level and on innovation at the organizational level. The model is tested on 163 R&D personnel and managers at 43 micro- and small-sized Turkish software development companies. The results suggest that transformational leadership has important effects on creativity at both the individual and organizational levels. At the individual level, the results of hierarchical linear modeling show that there is a positive relationship between transformational leadership and employees' creativity. In addition, transformational leadership influences employees' creativity through psychological empowerment. At the organizational level, the results of regression analysis reveal that transformational leadership positively associates with organizational innovation, which is measured with a market-oriented criterion developed specifically for developing countries and newly developing industries. The implications of the findings along with some potential practical applications are discussed.


title: An Introduction to Least Commitment Planning

abstract: Recent developments have clarified the process of generating partially ordered, partially specified sequences of actions whose execution will achieve an agent's goal. This article summarizes a progression of least commitment planners, starting with one that handles the simple STRIPS representation and ending with UCPOP, a planner that manages actions with disjunctive precondition, conditional effects, and universal quantification over dynamic universes. Along the way, I explain how Chapman's formulation of the modal truth criterion is misleading and why his NP-completeness result for reasoning about plans with conditional effects does not apply to UCPOP.


title: Robust Feature Selection Using Ensemble Feature Selection Techniques

abstract: Robustness or stability of feature selection techniques is a topic of recent interest, and is an important issue when selected feature subsets are subsequently analysed by domain experts to gain more insight into the problem modelled. In this work, we investigate the use of ensemble feature selection techniques, where multiple feature selection methods are combined to yield more robust results. We show that these techniques show great promise for high-dimensional domains with small sample sizes, and provide more robust feature subsets than a single feature selection technique. In addition, we also investigate the effect of ensemble feature selection techniques on classification performance, giving rise to a new model selection strategy.


title: Mapping Agile Practices to CMMI-DEV Level 3 in Web Development Environments

abstract: Agile approaches formally appeared ten years ago and nowadays are a valid alternative for organizations developing software. Agile methodologies are especially interesting to those developing Web environments applications, as they can fit properly the special characteristics of this type of developments. In addition, maturity models like CMMI-DEV (Capability Maturity Model Integration for Development) focus on assessing the maturity level of organizations developing or acquiring software. These models are well established and can increment quality of development processes to enhance costumers’ satisfaction. CMMI-DEV level 3 provides a good compromise on maturity gained and effort needed. The feasibility of getting it through a combination of Agile methods can be very useful to organizations developing systems in Web environments, as they can keep the adaptability of Agile together with a more mature development process. This paper proposes a set of Agile methods so as to reach all CMMI-DEV maturity level 3 generic and specific goals. Based on this analysis, the paper proposes further research lines.


Learning Factorial Codes by Predictability Minimization


title: Robust watermarking based on DWT and nonnegative matrix factorization

abstract: This paper presents a novel blind robust digital image watermarking scheme using nonnegative matrix factorization (NMF) in DWT domain. Firstly, the original image is transformed into some subband coefficients using discrete wavelet transformation (DWT), and then a Gaussian pseudo-random watermark sequence is embedded in the factorized decomposition coefficients using NMF. Because of the multiresolution decomposition for DWT and physically meaningful factorization for NMF, the proposed scheme can achieve good robustness, which is also demonstrated in the following experiments.


title: Scalable GMP compliant suspension culture system for human ES cells.

abstract: Abstract Suspension bioreactors are an attractive alternative to static culture of human embryonic stem cells (hESCs) for the generation of clinically relevant cell numbers in a controlled system. In this study, we have developed a scalable suspension culture system using serum-free defined media with spinner flasks for hESC expansion as cell aggregates. With optimized cell seeding density and splitting interval, we demonstrate prolonged passaging and expansion of several hESC lines with overall expansion, yield, viability and maintenance of pluripotency equivalent to adherent culture. Human ESCs maintained in suspension as aggregates can be passaged at least 20 times to achieve over 1 × 10 13 fold calculated expansion with high undifferentiation rate and normal karyotype. Furthermore, the aggregates are able to differentiate to cardiomyocytes in a directed fashion. Finally, we show that the cells can be cryopreserved in serum-free medium and thawed into adherent or suspension cultures to continue passaging and expansion. We have successfully used this method under cGMP or cGMP-equivalent conditions to generate cell banks of several hESC lines. Taken together, our suspension culture system provides a powerful approach for scale-up expansion of hESCs under defined and serum-free conditions for clinical and research applications.


title: Socially Nudged: A Quasi-Experimental Study of Friends' Social Influence in Online Product Ratings

abstract: Social-networking functions are increasingly embedded in online rating systems. These functions alter the rating context in which consumer ratings are generated. In this paper, we empirically investigate online friends’ social influence in online book ratings. Our quasi-experimental research design exploits the temporal sequence of social-networking events and ratings and offers a new method for identifying social influence while accounting for the homophily effect. We find that rating similarity between friends is significantly higher after the formation of the friend relationship, indicating that with social-networking functions, online rating contributors are socially nudged when giving their ratings. Exploration of contingent factors suggests that social influence is stronger for older books and for users who have smaller networks, and that relatively more recent and extremely negative ratings cast more salient influence. The online appendix is available at https://doi.org/10.1287/isre.2017.0741.


title: Smart Itinerary Recommendation Based on User-Generated GPS Trajectories

abstract: Traveling to unfamiliar regions require a significant effort from novice travelers to plan where to go within a limited duration. In this paper, we propose a smart recommendation for highly efficient and balanced itineraries based on multiple user-generated GPS trajectories. Users only need to provide a minimal query composed of a start point, an end point and travel duration to receive an itinerary recommendation. To differentiate good itinerary candidates from less fulfilling ones, we describe how we model and define itinerary in terms of several characteristics mined from user-generated GPS trajectories. Further, we evaluated the efficiency of our method based on 17,745 user-generated GPS trajectories contributed by 125 users in Beijing, China. Also we performed a user study where current residents of Beijing used our system to review and give ratings to itineraries generated by our algorithm and baseline algorithms for comparison.


title: PeerFlow: Secure Load Balancing in Tor

abstract: Abstract We present PeerFlow, a system to securely load balance client traffic in Tor. Security in Tor requires that no adversary handle too much traffic. However, Tor relays are run by volunteers who cannot be trusted to report the relay bandwidths, which Tor clients use for load balancing. We show that existing methods to determine the bandwidths of Tor relays allow an adversary with little bandwidth to attack large amounts of client traffic. These methods include Tor’s current bandwidth-scanning system, TorFlow, and the peer-measurement system EigenSpeed. We present an improved design called PeerFlow that uses a peer-measurement process both to limit an adversary’s ability to increase his measured bandwidth and to improve accuracy. We show our system to be secure, fast, and efficient. We implement PeerFlow in Tor and demonstrate its speed and accuracy in large-scale network simulations.


Energy Analysis of Public-Key Cryptography on Small Wireless Devices


Neural EEG-Speech Models


title: Latent aspect rating analysis on review text data: a rating regression approach

abstract: In this paper, we define and study a new opinionated text data analysis problem called Latent Aspect Rating Analysis (LARA), which aims at analyzing opinions expressed about an entity in an online review at the level of topical aspects to discover each individual reviewer's latent opinion on each aspect as well as the relative emphasis on different aspects when forming the overall judgment of the entity. We propose a novel probabilistic rating regression model to solve this new text mining problem in a general way. Empirical experiments on a hotel review data set show that the proposed latent rating regression model can effectively solve the problem of LARA, and that the detailed analysis of opinions at the level of topical aspects enabled by the proposed model can support a wide range of application tasks, such as aspect opinion summarization, entity ranking based on aspect ratings, and analysis of reviewers rating behavior.


title: Big data algorithms beyond machine learning

abstract: The availability of big data sets in research, industry and society in general has opened up many possibilities of how to use this data. In many applications, however, it is not the data itself that is of interest but rather we want to answer some question about it. These answers may sometimes be phrased as solutions to an optimization problem. We survey some algorithmic methods that optimize over large-scale data sets, beyond the realm of machine learning.


title: Finite element analysis of an axial flux hysteresis motor based on complex permeability concept considering the saturation of the hysteresis loop

abstract: Axial structures of the hysteresis motors, similar to the other axial types of electrical machines, have some outstanding characteristics with regard to radial structures, such as higher torque-to-weight ratio, excellent efficiency, adjustable airgap, and balanced rotor–stator attractive forces. Since the accurate measurement of the hysteresis machine characteristics by prototyping and testing is expensive and time consuming, in this paper, the finite-element analysis is adopted for the prediction and analysis of an axial flux hysteresis motor. The analysis is conducted based on the elliptical approximation of the hysteresis loop. Since the elliptical approximation is not able to model the saturation of the hysteresis loop, an iteration-based method is proposed to overcome this problem and increase the analysis accuracy. Finally, in order to investigate the proposed model accuracy, the device is prototyped, whose experimental results agree well with those extracted from the analytical model.


title: Predicting the Present with Bayesian Structural Time Series

abstract: This article describes a system for short term forecasting based on an ensemble prediction that averages over different combinations of predictors. The system combines a structural time series model for the target series with regression component capturing the contributions of contemporaneous search query data. A spike-and-slab prior on the regression coefficients induces sparsity, dramatically reducing the size of the regression problem. Our system averages over potential contributions from a very large set of models and gives easily digested reports of which coefficients are likely to be important. We illustrate with applications to initial claims for unemployment benefits and to retail sales. Although our exposition focuses on using search engine data to forecast economic time series, the underlying statistical methods can be applied to more general short term forecasting with large numbers of contemporaneous predictors.


title: Object Recognition for the Internet of Things

abstract: We present a system which allows to request information on physical objects by taking a picture of them. This way, using a mobile phone with integrated camera, users can interact with objects or "things" in a very simple manner. A further advantage is that the objects themselves don't have to be tagged with any kind of markers. At the core of our system lies an object recognition method, which identifies an object from a query image through multiple recognition stages, including local visual features, global geometry, and optionally also metadata such as GPS location. We present two applications for our system, namely a slide tagging application for presentation screens in smart meeting rooms and a cityguide on a mobile phone. Both systems are fully functional, including an application on the mobile phone, which allows simplest point-and-shoot interaction with objects. Experiments evaluate the performance of our approach in both application scenarios and show good recognition results under challenging conditions.


title: Lyric-Based Music Recommendation

abstract: Traditional music recommendation systems rely on collaborative filtering to recommend songs or artists. This is computationally efficient and performs well method but is not effective when there is limited or no user input. For these cases, it may be useful to consider content-based recommendation. This paper considers a content-based recommendation system based on lyrical data. We compare a complex network of lyrical recommendations to an equivalent collaborative filtering network. We used user generated tag data from Last.fm to produce 23 subgraphs of each network based on tag categories representing musical genre, mood, and gender of vocalist. We analyzed these subgraphs to determine how recommendations within each network tend to stay within tag categories. Finally, we compared the lyrical recommendations to the collaborative filtering recommendations to determine how well lyrical recommendations perform. We see that the lyrical network is significantly more clustered within tag categories than the collaborative filtering network, particularly within small musical niches, and recommendations based on lyrics alone perform 12.6 times better than random recommendations.


title: GeomRDF: A Geodata Converter with a Fine-Grained Structured Representation of Geometry in the Web

abstract: In recent years, with the advent of the web of data, a growing number of national mapping agencies tend to publish their geospatial data as Linked Data. However, differences between traditional GIS data models and Linked Data model can make the publication process more complicated. Besides, it may require, to be done, the setting of several parameters and some expertise in the semantic web technologies. In addition, the use of standards like GeoSPARQL (or ad hoc predicates) is mandatory to perform spatial queries on published geospatial data. In this paper, we present GeomRDF, a tool that helps users to convert spatial data from traditional GIS formats to RDF model easily. It generates geometries represented as GeoSPARQL WKT literal but also as structured geometries that can be exploited by using only the RDF query language, SPARQL. GeomRDF was implemented as a module in the RDF publication platform Datalift. A validation of GeomRDF has been realized against the French administrative units dataset (provided by IGN France).


Automated Cancer Diagnosis Based on Histopathological Images: A Systematic Survey