Reviewerly/exHarmony huggingface dataset:
Guiding Variational Response Generator to Exploit Persona

Leveraging persona information of users in Neural Response Generators (NRG) to perform personalized conversations has been considered as an attractive and important topic in the research of conversational agents over the past few years. Despite of the promising progresses achieved by recent studies in this field, persona information tends to be incorporated into neural networks in the form of user embeddings, with the expectation that the persona can be involved via the End-to-End learning. This paper proposes to adopt the personality-related characteristics of human conversations into variational response generators, by designing a specific conditional variational autoencoder based deep model with two new regularization terms employed to the loss function, so as to guide the optimization towards the direction of generating both persona-aware and relevant responses. Besides, to reasonably evaluate the performances of various persona modeling approaches, this paper further presents three direct persona-oriented metrics from different perspectives. The experimental results have shown that our proposed methodology can notably improve the performance of persona-aware response generation, and the metrics are reasonable to evaluate the results.
The Development of Secure Protocol in Smart Technologies for Schema-based One-time Pads Using the System of Near-field Actions

Interacting with modern technology using the Internet provides a large number of remote control mechanisms. All of them somehow work with packet data transmission, but differ from each other by algorithms of packet formation, their transmission, as well as encryption methods. Remote access tools are mechanisms for communication both locally and globally (on the Internet). The purpose of this article is to develop a secure Protocol for “smart home” remote control technology. In the course of the study, a method was developed to control the devices of the “Internet of things” using the channel of information transmission in the form of instant messaging system on the Internet. The result of this work is a developed mechanism for secure data exchange between the client and the “smart home” server. This Protocol is a unique solution for protecting information exchange channels in the field of “Internet of things” devices, as well as the whole concept of “smart home”. The implementation of the tasks is fully achieved, and this Protocol can be used in real conditions. The relevance of protecting information in “smart homes” is a fundamental point in developing this technology. Developing this Protocol allows you to bring the information security of homes to a new level. The project can be used in mass use, and the safe mechanism can become an integral part in the homes of the future.
HyPar-Flow: Exploiting MPI and Keras for Scalable Hybrid-Parallel DNN Training using TensorFlow

To reduce training time of large-scale DNNs, scientists have started to explore parallelization strategies like data-parallelism, model-parallelism, and hybrid-parallelism. While data-parallelism has been extensively studied and developed, several problems exist in realizing model-parallelism and hybrid-parallelism efficiently. Four major problems we focus on are: 1) defining a notion of a distributed model across processes, 2) implementing forward/back-propagation across process boundaries that requires explicit communication, 3) obtaining parallel speedup on an inherently sequential task, and 4) achieving scalability without losing out on a model's accuracy. To address these problems, we create HyPar-Flow --- a model-size/-type agnostic, scalable, practical, and user-transparent system for hybrid-parallel training by exploiting MPI, Keras, and TensorFlow. HyPar-Flow provides a single API that can be used to perform data, model, and hybrid parallel training of any Keras model at scale. We create an internal distributed representation of the user-provided Keras model, utilize TF's Eager execution features for distributed forward/back-propagation across processes, exploit pipelining to improve performance and leverage efficient MPI primitives for scalable communication. Between model partitions, we use send and recv to exchange layer-data/partial-errors while allreduce is used to accumulate/average gradients across model replicas. Beyond the design and implementation of HyPar-Flow, we also provide comprehensive correctness and performance results on three state-of-the-art HPC systems including TACC Frontera (#5 on Top500.org). For ResNet-1001, an ultra-deep model, HyPar-Flow provides: 1) Up to 1.6x speedup over Horovod-based data-parallel training, 2) 110x speedup over single-node on 128 Stampede2 nodes, and 3) 481x speedup over single-node on 512 Frontera nodes.
PERANCANGAN SISTEM INFORMASI REKAM MEDIS DI RS KELUARGA HUSADA BATAM BERBASIS WEB

Information technology is growing rapidly in the development of even all walks of life. Every development makes it easy for the public to receive information quickly. An example in the health sector is in the medical record information system. A medical record is a unit of data from facts or evidence about a patient's history, patient's condition and previous treatments, and written by medical staff who provide health services to patients. Recording medical records by recording in the patient's book is an old method used, and a problem often encountered when using the old medical record system is found to be difficult in managing patient files. This medical record will later be used by medical personnel to add or view patient health records that have been treated. In this study a medical record system was designed using the waterfall method. Waterfall is a systematic and sequential model for information system development. Therefore the researchers developed the system using the waterfall method. The purpose of this medical record information system is to design a medical record information system that can manage patient history data and monthly reports. The system designed is very important because to prevent errors in processing patient data. This application is made based on web using the programming language HTML, PHP, and MYSQL database. So as to produce a web medical record that can be used to make the most disease reports every month, and the number of monthly patient visits using the web-based waterfall method.
Analisis Trend Topik Pengembangan Rekayasa Perangkat Lunak dalam mendukung Strategi Kurikulum Perguruan Tinggi

The development of software becomes important now and almost every point of view of our lives, many trends in various countries of the world in 2018 and will be interesting to follow in the following year for educational software-related developers. This study aims to analyze the relevance of the subject matter to the trend of device development research topics in order to produce sequential research between software development practicum subjects and trending topics in the world. The methodology used is a qualitative descriptive method used in analyzing the compatibility between courses, with trends in computer learning topics at the world level. Data collection is carried out through documents related to the outline categorized into 2 (two) groups. The first group deals with information relating to research trends related to publications in the journal Web of Science, SINTA, and Final Projects (TA) for Students. AMIK Indonesia. Relevance between AMIK Indonesia subject matter material and trends in relevant and supporting research topics for practicum material As a qualification of relevance for the category of high relevance with a value of 75% and above only 4 subjects, 7 moderate categories with a value of 55% to 74%, and 21 subjects with a low level of relevance, the findings that can produce material for the development of course material by supporting lecturers in each subject are specific to trending topics that are irrelevant or not supported by practicum courses that will have a positive influence on improvement quality.Keywords:Analysis, Trend Topics, Software Engineering Development, Strategies, Higher Education Curriculum.
Research Guides: Art of STEM Competition & Exhibit: Home

Homepage for the Art of STEM Competition at Kelvin Smith Library, Case Western Reserve Library.
Assessment of the accordance between sonography and mammography reports with pathology report based on sonographic guided core needle biopsy in diagnosis of breast masses

Background: Screening programs for the early diagnosis of breast cancer lead to perform several biopsies in order to determine the breast abnormalities found in sonography and mammography. According to gradually replacement of core needle biopsy instead of more invasive methods like open surgical biopsy, it seems necessary to study the concordance between biopsy results and non-invasive diagnostic methods in order to clarify ambiguous results and confirm the reliability of non-invasive methods.Objective: The objective of this study was to match the sonography and mammography reports with core needle biopsy in patients referred to Sahand core needle biopsy center.Materials and methods: This prospective cross-sectional study was performed on 240 patients referred to Sahand Center with sonography or mammography reports or by a surgeon for core needle biopsy. All the patients were examined by a gynecologist assistant and were asked about demographic information and their recent diseases. The matched sonography or mammography cases with pathology reports and positive predictive value based on comparison between them and pathology report were determined as standard diagnostic criteria. Then, data were statistically analyzed by SPSS v.21 software.Results: In this study, there was no statistically significant difference between sonography or mammography and pathology reports for categorizing patients in two distinct malignant and benign groups (p>0.05). There was also no difference observed in two age groups (p>0.05). In both sonography and mammography groups, the sensitivity and specificity were 60.32% and 92.96%, respectively. Moreover, the positive and negative predictive values of sonography and mammography were the same in both methods and were 70.37% and 86.56%, respectively.Conclusion: According to the significant concordance between sonographyand mammography with core needle biopsy, these non-invasive methods can be helpful in diagnosing malignant lesions and differentiating them from benign lesions; they are also affordable and available. The attention to underlying variables such as age, can improve sensitivity and specificity of non-invasive methods in comparison to invasive diagnostic methods.g.
Dynamics of Risk Degree of Innovation-Driven Growth at Higher Education Institutions

In this article, the analysis of dynamics of risk degree of innovation-driven growth at higher education institutions is discussed.The survey has been employed in this paper and the data analyzed by SPSS software.Results revealed that the resulting risks are regularly encountered in a modern Russian higher educational institution: inability to provide necessary financial support for educational activities; inability to provide required modern material and technical basis; challenges and failures in collaboration with enterprises for organizing practical training for students and the absence of practical training programs at the university.It is also indicated that the risk event might lead to different results: negative, positive, or null (if everything went according to the plan).
Text to features for Swedish text

In text mining, texts are usually transformed into numerical vectors or feature vectors, before they are given to a machine learning algorithm for text classification. In this project, a set of fea ...
Rancang Bangun Sistem Informasi Berbasis Web Ditoko Buku Andalusia

The purpose of this study is to produce a new information system design on information systems in the Andalusian Bookstore. so that it can help the transaction process and data processing of books at the Andalusian Bookstore and simplify the process of making reports. The method used is the FAST (Framework for Application System Thinking) consisting of phases of Scope Definition, Problem Analysis, Requirements Analysis, Logical Design and Physical Design. The design of the Information System uses PHP and HTML and MYSQL as the database. The results of this research are the implementation of a web-based information system that can help overcome the problems that have occurred in the Andalusian Bookstore. With this Web-based Information System it will facilitate the processing of data and making timely reports at the Andalusian Bookstore so that problems with the manual system can be overcome.
Research Guides: Applied Geophysics - Spring 2019: Evaluating Resources

This guide was created specifically for Dr. Kristina Keating's class - Spring 2019 How do you know if it’s a *good* source?
Compiling Arguments in an Argumentation Framework into Three-valued Logical Expressions

In this paper, we propose a new method for computing general allocators directly from completeness conditions. A general allocator is an abstraction of all complete labelings for an argumentation framework. Any complete labeling is obtained from a general allocator by assigning logical constants to variables. We proved the existence of the general allocators in our previous work. However, the construction requires us to enumerate all complete labelings for the framework, which makes the computation prohibitively slow. The method proposed in this paper enables us to compute general allocators without enumerating complete labelings. It also provides the solutions of local allocation that yield semantics for subsets of the framework. We demonstrate two applications of general allocators, stability, and a new concept for frameworks, termed arity. Moreover, the method, including local allocation, is applicable to broad extensions of frameworks, such as argumentation frameworks with set-attacks, bipolar argumentation frameworks, and abstract dialectical frameworks.
Image Quality Constrained GAN for Super-Resolution

As one of the most important research topics in image processing, super-resolution aims to estimate high resolution images from single or multiple low resolution images taken from the same scene. With the advent of deep learning techniques, generative adversarial networks are widely adopted for solving various image processing problems including super resolution. We investigate the effect of introducing image quality constraints into the training objective function of a generative adversarial network for super resolution. Experiment results demonstrate that network performance has great potential to be improved with such constraints.
Weakly-Supervised Temporal Activity Localization and Classification With Web Videos

Author(s): Dougherty, Thomas | Advisor(s): Roy-Chowdhury, Amit K | Abstract: In this thesis, weakly-supervised temporal activity localization and classification is considered with the use of web videos. Most activity localization methods depend on the availability of frame-wise annotation, which is a burdensome task to collect. To reduce the effort of manual labeling, learning from weak labels may be used as a potential solution. Recently there has been a substantial influx of tagged videos on the Internet. These can potentially be used as a rich source of data for weakly-supervised training. The following problem is considered. Given only the keyword of an action, can videos be retrieved online and be used to train the Weakly-supervised Temporal Activity Localization and Classification (W-TALC) network? Then, can a re-ranking method be implemented to filter out noisy video data? Action categories of the Thumos14 dataset are used to search for videos online with Youtube Data API. These videos are used as a training set for the W-TALC network. Given only the video labels, the W-TALC network learns to both localize and classify actions in videos. Using a re-ranking strategy, noisy video data is removed and shows an increase in detection performance versus using the original web video dataset. Analysis of the web video dataset and results of the detection performance shows promise for the reliable use of web videos for training.
DepDist: Surface realization via regex and learned dependency-distance tolerance

This paper describes a method of inflecting and linearizing a lemmatized dependency tree by: (1) determining a regular expression and substitution to describe each productive wordform rule; (2) learning the dependency distance tolerance for each head-dependent pair, resulting in an edge-weighted directed acyclic graph (DAG); and (3) topologically sorting the DAG into a surface realization based on edge weight.The method's output for 11 languages across 18 treebanks is competitive with the other submissions to the Second Multilingual Surface Realization Shared Task (SR'19).
A Message Passing Approach for Multiple Maneuvering Target Tracking

This paper considers the problem of detecting and tracking multiple maneuvering targets, which suffers from the intractable inference of high-dimensional latent variables that include target kinematic state, target visibility state, motion mode-model association, and data association. A unified message passing algorithm that combines belief propagation (BP) and mean-field (MF) approximation is proposed for simplifying the intractable inference. By assuming conjugate-exponential priors for target kinematic state, target visibility state, and motion mode-model association, the MF approximation decouples the joint inference of target kinematic state, target visibility state, motion mode-model association into individual low-dimensional inference, yielding simple message passing update equations. The BP is exploited to approximate the probabilities of data association events since it is compatible with hard constraints. Finally, the approximate posterior probability distributions are updated iteratively in a closed-loop manner, which is effective for dealing with the coupling issue between the estimations of target kinematic state and target visibility state and decisions on motion mode-model association and data association. The performance of the proposed algorithm is demonstrated by comparing with the well-known multiple maneuvering target tracking algorithms, including interacting multiple model joint probabilistic data association, interacting multiple model hypothesis-oriented multiple hypothesis tracker and multiple model generalized labeled multi-Bernoulli.
Aplib: Tactical Programming of Intelligent Agents

This paper presents aplib, a Java library for programming intelligent agents, featuring BDI and multi agency, but adding on top of it a novel layer of tactical programming inspired by the domain of theorem proving. Aplib is also implemented in such a way to provide the fluency of a Domain Specific Language (DSL). Compared to dedicated BDI agent programming languages such as JASON, 2APL, or GOAL,aplib's embedded DSL approach does mean that \aplib\ programmers will still be limited by Java syntax, but on other hand they get all the advantages that Java programmers get: rich language features (object orientation, static type checking, $λ$-expression, libraries, etc), a whole array of development tools, integration with other technologies, large community, etc.
Research Guides: Tips for Using Library Search: Browsing

Provides some tips and strategies for effectively using the University of Michigan Library search engine.
Table-to-Text Natural Language Generation with Unseen Schemas

Traditional table-to-text natural language generation (NLG) tasks focus on generating text from schemas that are already seen in the training set. This limitation curbs their generalizabilities towards real-world scenarios, where the schemas of input tables are potentially infinite. In this paper, we propose the new task of table-to-text NLG with unseen schemas, which specifically aims to test the generalization of NLG for input tables with attribute types that never appear during training. To do this, we construct a new benchmark dataset for this task. To deal with the problem of unseen attribute types, we propose a new model that first aligns unseen table schemas to seen ones, and then generates text with updated table representations. Experimental evaluation on the new benchmark demonstrates that our model outperforms baseline methods by a large margin. In addition, comparison with standard data-to-text settings shows the challenges and uniqueness of our proposed task.
Microsoft Research Asia's Systems for WMT19

We Microsoft Research Asia made submissions to 11 language directions in the WMT19 news translation tasks. We won the first place for 8 of the 11 directions and the second place for the other three. Our basic systems are built on Transformer, back translation and knowledge distillation. We integrate several of our rececent techniques to enhance the baseline systems: multi-agent dual learning (MADL), masked sequence-to-sequence pre-training (MASS), neural architecture optimization (NAO), and soft contextual data augmentation (SCA).
Electronic Control Unit Discrimination Using Wired Signal Distinct Native Attributes

A controller area network bus is a communications system used in modern automobiles to connect the electronic control units that implement normal vehicular operations as well as advanced autonomous safety and driver comfort features. However, these advancements come at the expense of vehicle security – researchers have shown that automobiles can be hacked by compromising electronic control units or by connecting unauthorized devices to the controller area network bus. Physical layer device fingerprinting is a promising approach for implementing vehicle security. This chapter presents a fingerprinting method and classification algorithm for electronic control unit discrimination. Cross-lot discrimination is assessed using four Toyota Avalon electronic control units with different lot numbers as authorized devices, and a BeagleBoard, Arduino and CANable as rogue devices. The experiments yielded perfect rejection rates for rogue devices with false credentials and access denial rates exceeding 98% for authorized electronic control units with false credentials. Additionally, an average correct classification of approximately 99% was obtained for authorized devices.
Generative Adversarial Exploration for Reinforcement Learning

Exploration is crucial for training the optimal reinforcement learning (RL) policy, where the key is to discriminate whether a state visiting is novel. Most previous work focuses on designing heuristic rules or distance metrics to check whether a state is novel without considering such a discrimination process that can be learned. In this paper, we propose a novel method called generative adversarial exploration (GAEX) to encourage exploration in RL via introducing an intrinsic reward output from a generative adversarial network, where the generator provides fake samples of states that help discriminator identify those less frequently visited states. Thus the agent is encouraged to visit those states which the discriminator is less confident to judge as visited. GAEX is easy to implement and of high training efficiency. In our experiments, we apply GAEX into DQN and the DQN-GAEX algorithm achieves convincing performance on challenging exploration problems, including the game Venture, Montezuma's Revenge and Super Mario Bros, without further fine-tuning on complicate learning algorithms. To our knowledge, this is the first work to employ GAN in RL exploration problems.