Query: Not All Dialogues are Created Equal: Instance Weighting for Neural Conversational Models

Relevant passages:
A Persona-Based Neural Conversation Model <=SEP=> We present persona-based models for handling the issue of speaker consistency in neural response generation. A speaker model encodes personas in distributed embeddings that capture individual characteristics such as background information and speaking style. A dyadic speakeraddressee model captures properties of interactions between two interlocutors. Our models yield qualitative performance improvements in both perplexity and BLEU scores over baseline sequence-to-sequence models, with similar gains in speaker consistency as measured by human judges.

Discriminative learning for differing training and test distributions <=SEP=> We address classification problems for which the training instances are governed by a distribution that is allowed to differ arbitrarily from the test distribution---problems also referred to as classification under covariate shift. We derive a solution that is purely discriminative: neither training nor test distribution are modeled explicitly. We formulate the general problem of learning under covariate shift as an integrated optimization problem. We derive a kernel logistic regression classifier for differing training and test distributions.

Unsupervised Modeling of Twitter Conversations <=SEP=> We propose the first unsupervised approach to the problem of modeling dialogue acts in an open domain. Trained on a corpus of noisy Twitter conversations, our method discovers dialogue acts by clustering raw utterances. Because it accounts for the sequential behaviour of these acts, the learned model can provide insight into the shape of communication in a new medium. We address the challenge of evaluating the emergent model with a qualitative visualization and an intrinsic conversation ordering task. This work is inspired by a corpus of 1.3 million Twitter conversations, which will be made publicly available. This huge amount of data, available only because Twitter blurs the line between chatting and publishing, highlights the need to be able to adapt quickly to a new medium.

Neural Responding Machine for Short-Text Conversation <=SEP=> We propose Neural Responding Machine (NRM), a neural networ k-based response generator for Short-Text Conversation. NRM takes the general encoder-de coder framework: it formalizes the generation of response as a decoding process based on the lat ent representation of the input text, while both encoding and decoding are realized with recurren t n ural networks (RNN). The NRM is trained with a large amount of one-round conversation dat a collected from a microblogging service. Empirical study shows that NRM can generate gramma tically correct and content-wise appropriate responses to over 75% of the input text, outperf orming state-of-the-arts in the same setting, including retrieval-based and SMT-based models.

OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles <=SEP=> We present a new major release of the OpenSubtitles collection of parallel corpora. The release is compiled from a large database of movie and TV subtitles and includes a total of 1689 bitexts spanning 2.6 billion sentences across 60 languages. The release also incorporates a number of enhancements in the preprocessing and alignment of the subtitles, such as the automatic correction of OCR errors and the use of meta-data to estimate the quality of each subtitle and score subtitle pairs.

Development of an OCV prediction mechanism for lithium-ion battery system <=SEP=> This paper investigates the development of an open circuit voltage (OCV) prediction technique for lithium cells. The underlying principal of the technique described in this paper employs one simple equation to predict the equilibrated cell voltage after a small rest period. The technique was tested and verified by using results obtained from experiments conducted at the Centre for Automotive and Power System Engineering (CAPSE) battery laboratories at the University of South Wales. Lithium-ion battery system analysis was carried out with reference to the standard battery system models available in the literature.

'Just What the Doctor Ordered': A Revised UTAUT for EMR System Adoption and Use by Doctors <=SEP=> Electronic medical record (EMR) systems can deliver many benefits to healthcare organizations and the patients they serve. However, one of the biggest stumbling blocks in garnering these benefits is the limited adoption and use by doctors. We employ the unified theory of acceptance and use of technology (UTAUT) as the theoretical foundation and adapt the theory to the context of EMR system adoption and use by doctors. Specifically, we suggest that age will be the only significant moderator, and gender, voluntariness and experience will not play significant moderating roles. We tested our model in a longitudinal study over a 7-month period in a hospital implementing a new EMR system. We collected 3 waves of survey data from 141 doctors and used system logs to measure use. While the original UTAUT only predicted about 20% of the variance in intention, the modified UTAUT predicted 44%. Both models were comparable in their prediction of use. In addition to contributing to healthcare IT and UTAUT research, we hope this work will serve as a foundation for future work that integrates UTAUT with other theoretical perspectives.

Optical mouse sensor for detecting height variation and translation of a surface <=SEP=> A new sensing system based on an optical mouse sensor is developed. The optical mouse sensor is traditionally used as a 2D motion sensor, and in this study it is creatively used for detecting the height variation and translation of the object surfaces at the same time. Based on the experimental characteristics of the sensing system, it can be seen that the sensing system is useful in many fields such as robot odometry. The sensing system is based on a low-cost photoelectric sensor, which is a commercial LED-based optical mouse sensor, and thus it is easily available and generalized. Specially, the outputs of the sensor are intuitively displayed on the computer screen and recorded on the hard disk, which greatly improves the convenience of the system. A periodically-square-shaped rubber surface and a periodically-arc-shaped rubber surface are studied in the experiments. Experiments show that the shape information of the object surfaces and the translation direction of the object surfaces can be obtained at the same time. Besides, in the experiments, it is found that the sensitivity of the optical mouse sensor in different translation direction is different.

Human-level intelligence or animal-like abilities? <=SEP=> What just happened in artificial intelligence and how it is being misunderstood.

BCS: A Binary Cuckoo Search algorithm for feature selection <=SEP=> Feature selection has been actively pursued in the last years, since to find the most discriminative set of features can enhance the recognition rates and also to make feature extraction faster. In this paper, the propose a new feature selection called Binary Cuckoo Search, which is based on the behavior of cuckoo birds. The experiments were carried out in the context of theft detection in power distribution systems in two datasets obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques.

A new miniaturized antenna for ISM 433 MHz frequency band <=SEP=> A new miniaturized antenna for 433MHz frequency band is introduced for sensor networks. The antenna is a magnetic monopole loaded with a capacitor, which can significantly decrease the resonant frequency of the structure. It is designed to operate in the ISM 433MHz band on a small PCB (103mm⋆55mm) and has at least 3MHz bandwidth. Moreover, its radiation efficiency is suitable for the possible applications and its pattern is omnidirectionnal.

Controllable Procedural Content Generation via Constrained Multi-Dimensional Markov Chain Sampling <=SEP=> Statistical models, such as Markov chains, have recently started to be studied for the purpose of Procedural Content Generation (PCG). A major problem with this approach is controlling the sampling process in order to obtain output satisfying some desired constraints. In this paper we present three approaches to constraining the content generated using multi-dimensional Markov chains: (1) a generate and test approach that simply resamples the content until the desired constraints are satisfied, (2) an approach that finds and resamples parts of the generated content that violate the constraints, and (3) an incremental method that checks for constraint violations during sampling. We test our approaches by generating maps for two classic video games, Super Mario Bros. and Kid Icarus.

On-Line Multi-View Video Summarization for Wireless Video Sensor Network <=SEP=> Battery lifetime is critical for wireless video sensors. To enable battery-powered wireless video sensors, low-power design is required. In this paper, we consider applying multi-view summarization to wireless video sensors to remove redundant contents such that the compression and transmission power can be reduced. A low-complexity online multi-view video summarization scheme is proposed. Experiments show that the proposed summarization method successfully reduces the video content while keeping important events. A power analysis of the system also shows that a significant amount of energy can be saved.

Chatbot for IT Security Training: Using Motivational Interviewing to Improve Security Behaviour <=SEP=> We conduct a pre-study with 25 participants on Mechanical Turk to find out which security behavioural problems are most important for online users. These questions are based on motivational interviewing (MI), an evidence-based treatment methodology that enables to train people about different kinds of behavioural changes. Based on that the chatbot is developed using Artificial Intelligence Markup Language (AIML). The chatbot is trained to speak about three topics: passwords, privacy and secure browsing. These three topics were ’most-wanted’ by the users of the pre-study. With the chatbot three training sessions with people are conducted.

Prometheus: User-Controlled P2P Social Data Management for Socially-Aware Applications <=SEP=> Recent Internet applications, such as online social networks and user-generated content sharing, produce an unprecedented amount of social information, which is further augmented by location or collocation data collected from mobile phones. Unfortunately, this wealth of social information is fragmented across many different proprietary applications. Combined, it could provide a more accurate representation of the social world, and it could enable a whole new set of socially-aware applications. We introduce Prometheus, a peer-to-peer service that collects and manages social information from multiple sources and implements a set of social inference functions while enforcing user-defined access control policies. Prometheus is socially-aware: it allows users to select peers that manage their social information based on social trust and exploits naturallyformed social groups for improved performance. We tested our Prometheus prototype on PlanetLab and built a mobile social application to test the performance of its social inference functions under real-time constraints. We showed that the social-based mapping of users onto peers improves the service response time and high service availability is achieved with low overhead.

Design pattern implementation in Java and aspectJ <=SEP=> AspectJ implementations of the GoF design patterns show modularity improvements in 17 of 23 cases. These improvements are manifested in terms of better code locality, reusability, composability, and (un)pluggability.The degree of improvement in implementation modularity varies, with the greatest improvement coming when the pattern solution structure involves crosscutting of some form, including one object playing multiple roles, many objects playing one role, or an object playing roles in multiple pattern instances.

A Semantic Framework for the Security Analysis of Ethereum smart contracts <=SEP=> Smart contracts are programs running on cryptocurrency (e.g., Ethereum) blockchains, whose popularity stem from the possibility to perform financial transactions, such as payments and auctions, in a distributed environment without need for any trusted third party. Given their financial nature, bugs or vulnerabilities in these programs may lead to catastrophic consequences, as witnessed by recent attacks. Unfortunately, programming smart contracts is a delicate task that requires strong expertise: Ethereum smart contracts are written in Solidity, a dedicated language resembling JavaScript, and shipped over the blockchain in the EVM bytecode format. In order to rigorously verify the security of smart contracts, it is of paramount importance to formalize their semantics as well as the security properties of interest, in particular at the level of the bytecode being executed. In this paper, we present the first complete small-step semantics of EVM bytecode, which we formalize in the F* proof assistant, obtaining executable code that we successfully validate against the official Ethereum test suite. Furthermore, we formally define for the first time a number of central security properties for smart contracts, such as call integrity, atomicity, and independence from miner controlled parameters. This formalization relies on a combination of hyperand safety properties. Along this work, we identified various mistakes and imprecisions in existing semantics and verification tools for Ethereum smart contracts, thereby demonstrating once more the importance of rigorous semantic foundations for the design of security verification techniques.

Axe UX: Exploring long-term user experience with iScale and AttrakDiff <=SEP=> Positive user experience (UX) is an important goal in product design. Positive long-term UX is believed to improve customer loyalty, therefore being vital for continuous commercial success. Most UX research investigates momentary or short-term UX although the relationship between the user and the product evolves over time. There is a need to develop methods for measuring long-term UX and evaluate their feasibility in different product contexts. In this explorative study, 18 customers reported their experiences during the first three months of use of a non-interactive design tool, an axe. UX was evaluated with retrospective iScale tool and monthly repeated AttrakDiff questionnaire. iScale demonstrated the long-term trend of the attractiveness of the product well and provided information about the causes of the change in the experience. The AttrakDiff questionnaire was a good indicator of attractiveness during a longer period of time and is also well suited to remote studies.

kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels <=SEP=> Many kinds of memory safety vulnerabilities have been endangering software systems for decades. Amongst other approaches, fuzzing is a promising technique to unveil various software faults. Recently, feedback-guided fuzzing demonstrated its power, producing a steady stream of security-critical software bugs. Most fuzzing efforts—especially feedback fuzzing—are limited to user space components of an operating system (OS), although bugs in kernel components are more severe, because they allow an attacker to gain access to a system with full privileges. Unfortunately, kernel components are difficult to fuzz as feedback mechanisms (i.e., guided code coverage) cannot be easily applied. Additionally, non-determinism due to interrupts, kernel threads, statefulness, and similar mechanisms poses problems. Furthermore, if a process fuzzes its own kernel, a kernel crash highly impacts the performance of the fuzzer as the OS needs to reboot. In this paper, we approach the problem of coverageguided kernel fuzzing in an OS-independent and hardware-assisted way: We utilize a hypervisor and Intel’s Processor Trace (PT) technology. This allows us to remain independent of the target OS as we just require a small user space component that interacts with the targeted OS. As a result, our approach introduces almost no performance overhead, even in cases where the OS crashes, and performs up to 17,000 executions per second on an off-the-shelf laptop. We developed a framework called kernel-AFL (kAFL) to assess the security of Linux, macOS, and Windows kernel components. Among many crashes, we uncovered several flaws in the ext4 driver for Linux, the HFS and APFS file system of macOS, and the NTFS driver of Windows.

High-Efficiency Slim Adapter With Low-Profile Transformer Structure <=SEP=> This paper presents the implementation of a high-efficiency slim adapter using an resonant converter. A new structure of the slim-type transformer is proposed, which is composed of copper wire as the primary winding and printed-circuit-board winding on the outer layer as the secondary winding. The proposed structure is suitable for a slim and high-efficiency converter because it has advantages of easy utilization and wide conductive cross-sectional area. In addition, the voltage-doubler rectifier is applied to the secondary side due to its simple structure of secondary winding, and a filter is adopted to reduce the output filter size. The validity of this study is confirmed by the experimental results from an 85-W prototype.

Thing broker: a twitter for things <=SEP=> In the Web of Things, standard web technologies and protocols are used to represent and communicate with physical and virtual things. One challenge toward this vision is integrating things with different characteristics, protocols, interfaces and constraints while maintaining the simplicity and flexibility required for a variety of applications. In this paper we present the Thing Broker, a core platform for Web of Things that provides RESTFul interfaces to things using a Twitter-based set of abstractions and communication model. We present the key abstractions, a reference implementation and explain how a typical WoT application can be created using Thing Broker. We finish with a preliminary evaluation and draw some lessons from our experiences.

Research Paper: Automatic Detection of Acute Bacterial Pneumonia from Chest X-ray Reports <=SEP=> OBJECTIVE
To evaluate the performance of a natural language processing system in extracting pneumonia-related concepts from chest x-ray reports.


METHODS



DESIGN
Four physicians, three lay persons, a natural language processing system, and two keyword searches (designated AAKS and KS) detected the presence or absence of three pneumonia-related concepts and inferred the presence or absence of acute bacterial pneumonia from 292 chest x-ray reports. Gold standard: Majority vote of three independent physicians. Reliability of the gold standard was measured.


OUTCOME MEASURES
Recall, precision, specificity, and agreement (using Finn's R: statistic) with respect to the gold standard. Differences between the physicians and the other subjects were tested using the McNemar test for each pneumonia concept and for the disease inference of acute bacterial pneumonia.


RESULTS
Reliability of the reference standard ranged from 0.86 to 0.96. Recall, precision, specificity, and agreement (Finn R:) for the inference on acute bacterial pneumonia were, respectively, 0.94, 0.87, 0.91, and 0.84 for physicians; 0.95, 0.78, 0.85, and 0.75 for natural language processing system; 0.46, 0.89, 0.95, and 0.54 for lay persons; 0.79, 0.63, 0.71, and 0.49 for AAKS; and 0.87, 0.70, 0.77, and 0.62 for KS. The McNemar pairwise comparisons showed differences between one physician and the natural language processing system for the infiltrate concept and between another physician and the natural language processing system for the inference on acute bacterial pneumonia. The comparisons also showed that most physicians were significantly different from the other subjects in all pneumonia concepts and the disease inference.


CONCLUSION
In extracting pneumonia related concepts from chest x-ray reports, the performance of the natural language processing system was similar to that of physicians and better than that of lay persons and keyword searches. The encoded pneumonia information has the potential to support several pneumonia-related applications used in our institution. The applications include a decision support system called the antibiotic assistant, a computerized clinical protocol for pneumonia, and a quality assurance application in the radiology department.

Studying depression using imaging and machine learning methods <=SEP=> Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies.

Detection of Surface Crack in Concrete Using Measurement Technique With Laser Displacement Sensor <=SEP=> Detection of crack in concrete is an important issue as concrete is one of the principal material used in civil infrastructures. Cracks often occur first on the surface of concrete structure under load and provide an indication for further degradation. In this brief, an efficient noncontact method of the detection of such cracks in concrete using a measurement system with a laser displacement sensor (LDS) is presented. The proposed system consists of an LDS mounted on the scanner performing a raster scan. During the scanning process, the system gives the reading of displacement value from the sensor head to the laser spot on the target surface of the specimen. The proof of concept is given by testing with two different concrete specimens, namely, a concrete slab with a 0.7-mm-width through crack and a cylindrical concrete specimen with multiple cracks on the surface caused by loading effect. It is shown that a characteristic response of crack as a sharp distortion of the displacement reading occurs when the laser spot crosses the crack. The negligible standard deviation proves the repeatability and accuracy of the measurement and shows that this technique can be applied in a real-life scenario. A root-mean-square deviation in the displacement reading of the LDS is used as an index of crack. The effect of the shape of the specimen is investigated by detecting same-width cracks at different points along the circumference of a cylindrical specimen with different incident angles.

Unified temporal and spatial calibration for multi-sensor systems <=SEP=> In order to increase accuracy and robustness in state estimation for robotics, a growing number of applications rely on data from multiple complementary sensors. For the best performance in sensor fusion, these different sensors must be spatially and temporally registered with respect to each other. To this end, a number of approaches have been developed to estimate these system parameters in a two stage process, first estimating the time offset and subsequently solving for the spatial transformation between sensors. In this work, we present on a novel framework for jointly estimating the temporal offset between measurements of different sensors and their spatial displacements with respect to each other. The approach is enabled by continuous-time batch estimation and extends previous work by seamlessly incorporating time offsets within the rigorous theoretical framework of maximum likelihood estimation. Experimental results for a camera to inertial measurement unit (IMU) calibration prove the ability of this framework to accurately estimate time offsets up to a fraction of the smallest measurement period.

Critical review on video game evaluation heuristics: social games perspective <=SEP=> This paper presents the first step in creating design and evaluation heuristics for social games which emerge from the domain of social media. Initial high level heuristics for social games are offered by reviewing four existing video game heuristic models and analyzing two social games design frameworks.

An empirical analysis of the distribution of unit test smells and their impact on software maintenance <=SEP=> Unit testing represents a key activity in software development and maintenance. Test suites with high internal quality facilitate maintenance activities, such as code comprehension and regression testing. Several guidelines have been proposed to help developers write good test suites. Unfortunately, such rules are not always followed resulting in the presence of bad test code smells (or simply test smells). Test smells have been defined as poorly designed tests and their presence may negatively affect the maintainability of test suites and production code. Despite the many studies that address code smells in general, until now there has been no empirical evidence regarding test smells (i) distribution in software systems nor (ii) their impact on the maintainability of software systems. This paper fills this gap by presenting two empirical studies. The first study is an exploratory analysis of 18 software systems (two industrial and 16 open source) aimed at analyzing the distribution of test smells in source code. The second study, a controlled experiment involving twenty master students, is aimed at analyzing whether the presence of test smells affects the comprehension of source code during software maintenance. The results show that (i) test smells are widely spread throughout the software systems studied and (ii) most of the test smells have a strong negative impact on the comprehensibility of test suites and production code.

Regenerative braking control method and optimal scheme for electric motorcycle <=SEP=> This paper describes an optimal regenerative braking control scheme for a permanent magnet brushless DC motor of an electric motorcycle to achieve dual goals of the electric brake and the maximal power harvest in two cases of the road, downhill and flat, without any additional changes on the hardware. Based on regenerative braking of brushless DC motor in half bridge modulation mode, the relationship between average regenerative current on the DC bus and PWM duty cycle under different speeds was studied intensively from the three aspects of theoretical deduction, simulation based on Matlab/Simulink and platform experiments, and then a simple but effective scheme considering the actual application was proposed tentatively to achieve the dual goals. Since the braking kinetic energy is harvested in the form of the electric energy stored up in battery, the scheme could improve the driving range, safeness, noise preventability and cost effective of electric motorcycles.

Design and optimisation of a voice coil motor (VCM) with a rotary actuator for an ultrasound scanner. IEEE Transactions on Industrial Electronics <=SEP=> This paper proposes a new application for the rotary VCM. In developing a low cost ultrasound scanner for the developing world an oscillating transducer is required to sweep over the skin. The ultrasound scanner must operate from a USB power supply in remote locations. The application requires a 3.3N force on the coils of the motor to overcome the inertia of the skin. A proof of concept prototype motor with electronics has been designed, simulated and tested. The VCM optimisation is discussed in detail with the unique separation of the magnets being critical to reduce the axial bearing forces for this application.

Design and Modeling of a Textile Pressure Sensor for Sitting Posture Classification <=SEP=> A textile pressure sensor has been designed for measuring pressure distribution on the human body. Electrodes built with conductive textiles are arranged on both sides of a compressible spacer, forming a variable capacitor. The use of textiles makes unobtrusive, comfortable, lightweight, and washable sensors possible. This simplifies the goal to integrate such sensors into clothing in the future, to be simple, and fast to mount just as getting dressed. Hysteresis induced by the spacer of the sensor has been modeled with the Preisach model to reduce the measurement error from 24% to 5% on average and the maximal error from above 50% to below 10%. Standard textiles that are not specially optimized for low hysteresis can be used for designing the sensor due to the modeling. The model may also be used for other pressure or even strain sensors to reduce their hysteresis. The modeling enhances the accuracy of the textile sensor to those of commercial, nontextile pressure sensing mats. Furthermore, we used the sensor system for the classification of sitting postures in a chair. The data of nine subjects have been classified with Naive Bayes classifier, achieving an average recognition rate of 82%. We show that the textile sensor performs similarly to a commercial, nontextile pressure sensing mat for this application.