Reviewerly/exHarmony huggingface dataset:
Zero Shot Learning on Simulated Robots

In this work we present a method for leveraging data from one source to learn how to do multiple new tasks. Task transfer is achieved using a self-model that encapsulates the dynamics of a system and serves as an environment for reinforcement learning. To study this approach, we train a self-models on various robot morphologies, using randomly sampled actions. Using a self-model, an initial state and corresponding actions, we can predict the next state. This predictive self-model is then used by a standard reinforcement learning algorithm to accomplish tasks without ever seeing a state from the "real" environment. These trained policies allow the robots to successfully achieve their goals in the "real" environment. We demonstrate that not only is training on the self-model far more data efficient than learning even a single task, but also that it allows for learning new tasks without necessitating any additional data collection, essentially allowing zero-shot learning of new tasks.
Behind the Scenes of Researching Android 9 Pie's System Navigation

The Android operating system runs on more than 2 billion smartphones, tablets, and other devices. In this case study, we go behind the scenes of the user experience research process at Google that led to the design of Android 9 Pie's system navigation. We describe the type of research we conducted over a 9 month period and what we learned about smartphone system navigation and gestures.
Spectral sparsification of matrix inputs as a preprocessing step for quantum algorithms

We study the potential utility of classical techniques of spectral sparsification of graphs as a preprocessing step for digital quantum algorithms, in particular, for Hamiltonian simulation. Our results indicate that spectral sparsification of a graph with $n$ nodes through a sampling method, e.g.\ as in \cite{Spielman2011resistances} using effective resistances, gives, with high probability, a locally computable matrix $\tilde H$ with row sparsity at most $\mathcal{O}(\text{poly}\log n)$. For a symmetric matrix $H$ of size $n$ with $m$ non-zero entries, a one-time classical runtime overhead of $\mathcal{O}(m||H||t\log n/ε)$ expended in spectral sparsification is then found to be useful as a way to obtain a sparse matrix $\tilde H$ that can be used to approximate time evolution $e^{itH}$ under the Hamiltonian $H$ to precision $ε$. Once such a sparsifier is obtained, it could be used with a variety of quantum algorithms in the query model that make crucial use of row sparsity. We focus on the case of efficient quantum algorithms for sparse Hamiltonian simulation, since Hamiltonian simulation underlies, as a key subroutine, several quantum algorithms, including quantum phase estimation and recent ones for linear algebra. Finally, we also give two simple quantum algorithms to estimate the row sparsity of an input matrix, which achieve a query complexity of $\mathcal{O}(n^{3/2})$ as opposed to $\mathcal{O}(n^2)$ that would be required by any classical algorithm for the task.
Low Data Dialogue Act Classification for Virtual Agents during Debugging

A “dialogue act” is a written or spokenaction during a conversation. Dialogue acts are usually only a fewwords long, and are divided by researchers into a relatively smallset (often less than 10) of dialogue act types, such as elicitinginformation, expressing an opinion, or making a greeting. Researchinterest into automatic classification of dialogue acts has grownrecently due to the proliferation of Virtual Agents (VA) e.g. Siri,Cortana, Alexa. But unfortunately, the gains made into VAdevelopment in one domain are generally not applicable to otherdomains, since the composition of dialogue acts differs indifferent conversations. In this thesis, I target the problem ofdialogue act classification for a VA assistant to softwareengineering repairing bugs in a low data setting. A problem in theSE domain is that very little sample data exists. Therefore, Ipresent a transfer-learning approach to learn on a much largerdataset for general business conversations, and apply the knowledgeto a manually created corpus of debugging conversations collectedfrom 30 professional developers in a “Wizard of Oz” experiment andmanually annotated with a predetermined dialogue act set. Inexperiments, we observe between 8% and 20% improvements over twokey baselines. Additionally, I present a separate dialogue actclassifier on the manually collected dataset that uses a manuallydiscovered SE specific dialogue act set which achieves on average69% precision and 50% recall over 5-fold crossvalidation.
Alternative Vision of Living with IoT

In this submission, I discuss my research on values, norms and practices of subcultures formed as an "alternative" to the dominant way of life. In particular, I explore how the Internet of Things (IoT) or intelligent agents relates to alternative forms of interaction or be understood and reconstructed through alternative concepts or frameworks. For the past three years I have been conducting fieldwork on communities pursuing alternative lifestyles. This work considers how those alternative lifestyles may contribute to an understanding of objects, spaces in future smart home. Through my fieldwork and research through design, I hope to offer an alternative vision to living with IoT and envision future domesticity in a unique and even groundbreaking way.
User experiences of garment-based dynamic compression for novel haptic applications.

Compression is a haptic stimulus used in medical interventions (e.g., compression stockings) and has the potential to be integrated into new research areas (e.g., immersive VR experiences, distributed notification mechanisms), yet remains largely understudied. This work investigates the user experience of compression garment technologies that are dynamic, remotely controllable, and low mass, to better address this research gap. Shape memory alloy-based compression garments, capable of creating spatially- and temporally- dynamic on-body compression, were designed and deployed in a user study (n=17, 8M/9F) to understand the effects of compression, and to draw insights for future compression-based applications. The major takeaways are: (1) importance of and sizing/fit, (2) individual/gender preferences and need for customizability, and (3) the relationship between context-specific stimulation and perception.
Pemeriksaan Kerusakan Track Adjuster Pada Excavator XGMA TYPE XG822EL

Track adjuster is the main component of heavy equipment which has functioned as undercarriage excavator suspension system. If track adjuster doesn’t work properly, there will many problems rise and damaged will occur on the undercarriage.The study method are the collecting data from many literatures, the observation of track adjuster damages, the experienced mechanics and foreman. It could help investigate the problems.There are many operasional problems of track adjuster which causes many damages on track adjuster, especially on its critical points. The problems and damages could be minimalized by apply proper maintenance of track adjuster like cleaning before reassembling it, and it’s important to keep cleans the hydraulic oil and all the support tools. The work area should be clean alsoKey Note : Excavator, Track Adjuster,XGMA,XG822EL,Undercarriage.
Call Management Application for Android Mobile Phones

In day to day life everyone uses mobile phones for sharing important information and emergency work with another person. So frequency of calling is increases and sometimes get headache when continuously talking with family or friends. Call Management Application provides availability of call type and manual message function on caller side. Also availability of emergency contact on lock screen and detailed call logs. When caller has emergency work with receiver person and in case, receiver phone on silent mode then automatically default ringtone will be set at receiver side to get alert of emergency work. To reduce mobile interruption through effective management of incoming phone calls, to make real time decisions to handle important calls and improve users satisfaction, the proposed system provides different features like get alert of important calls even if receiver's mobile is on silent mode, make list of emergency number on lock screen etc.
An Adversorial Approach to Enable Re-Use of Machine Learning Models and Collaborative Research Efforts Using Synthetic Unstructured Free-Text Medical Data.

We leverage Generative Adversarial Networks (GAN) to produce synthetic free-text medical data with low re-identification risk, and apply these to replicate machine learning solutions. We trained GAN models to generate free-text cancer pathology reports. Decision models were trained using synthetic datasets reported performance metrics that were statistically similar to models trained using original test data. Our results further the use of GANs to generate synthetic data for collaborative research and re-use of machine learning models.
The Intercultural Conversation: Theoretical Perspectives and Proposal for an Ideal and Pragmatical Model

Abstract The present study aims to make a pragmatic model conversation between people of different cultures from the point of view of the Intercultural Linguistic Pragmatics. To complete our purposes, we use a specific methodology in which the connection between language and social anthropology is expressed: ethnomethodology. To achieve that, we have previously pointed a series of theoretical considerations related to how affect different cultural influences in the generation of distinct discourses.In the same way, we think about the functions that cultural mediator, endowed with intercultural competence, performs in order to make communicative competence successful. Keywords: Linguistics; cultural communication; communication strategies; intercultural dialogue.
Implicit Trajectory Planning for Feedback Linearizable Systems: A Time-varying Optimization Approach

We develop an optimization-based framework for joint real-time trajectory planning and feedback control of feedback-linearizable systems. To achieve this goal, we define a target trajectory as the optimal solution of a time-varying optimization problem. In general, however, such trajectory may not be feasible due to , e.g., nonholonomic constraints. To solve this problem, we design a control law that generates feasible trajectories that asymptotically converge to the target trajectory. More precisely, for systems that are (dynamic) full-state linearizable, the proposed control law implicitly transforms the nonlinear system into an optimization algorithm of sufficiently high order. We prove global exponential convergence to the target trajectory for both the optimization algorithm and the original system. We illustrate the effectiveness of our proposed method on multi-target or multi-agent tracking problems with constraints.
Object Segmentation Tracking from Generic Video Cues

We propose a light-weight variational framework for online tracking of object segmentations in videos based on optical flow and image boundaries. While high-end computer vision methods on this task rely on sequence specific training of dedicated CNN architectures, we show the potential of a variational model, based on generic video information from motion and color. Such cues are usually required for tasks such as robot navigation or grasp estimation. We leverage them directly for video object segmentation and thus provide accurate segmentations at potentially very low extra cost. Our simple method can provide competitive results compared to the costly CNN-based methods with parameter tuning. Furthermore, we show that our approach can be combined with state-of-the-art CNN-based segmentations in order to improve over their respective results. We evaluate our method on the datasets DAVIS 16,17 and SegTrack v2.
Watch Spaces

We present a platform to prototype spatial user interfaces for smart watches relative to the user's body. We show the general feasibility and present 2 applications. The first application scenario enable users "pin" applications in the air around them and get back to them by moving the smart watch display at the same position again. The second application shows a zoom use case for maps or other larger displays/visualizations. The smart watch acts like a digital "magnifying glass" enabling the user to see details he cares about.
Bob Schatz: Agile and Transformation Leader

Bob is a leader in Agile Product Development, Process Improvement, Organizational Fitness, Scrum Training, and Lean Management at Agile Infusion LLC. Bob specializes in training, consulting, and coaching in the practice of successfully using agile project management techniques to transform organizations and improve the performance of their software development project teams. He is a longtime Certified Scrum Trainer (CST) with Scrum Alliance. He began his practice as a result of leading the first large agile transition at Primavera Systems in 2002.
Experiential Learning English Perceived By 3rd Level Cadet At Military Academy, Magelang

AbstractThis study aims to describe the experiential learning English perceived by 3th level cadet at Military Academy Magelang. Especially this research has a qualitative approach that can be used in response the research. The researcher focused in chosen one student which is having many experience at his school. The researcher used an interview that can be used to explore the views, experience, beliefs, and motivations of individual participant. The process to gathering data are asked to the participant some questions which divided into three categories, they are the general condition of the habit in Magelang Military Academy, the strategy by the participant in learning English, and their perception of learning English. The results showing that the most answering from the participant agglomerate into three categories such as; Learning English practice is necessary, participant tend to enjoy the learning process, and participant tend to learning English are important for career. The present study can be employed as reference for other prospective studies that are interested in conducting similar topic.Keywords: perception, learning english, academy military magelang
ME2: A Scalable Modular Meta-heuristic for Multi-modal Multi-dimension Optimization

Map, Explore & Exploit (ME2) is a scalable meta-heuristic for problems in the field of multi-modal, multi-dimension optimization. It has a modular design with three phases, as reflected by its name. Its first phase (Map) generates a set of samples that is mostly uniformly distributed over the search space. The second phase (Explore) explores the neighbourhood of each sample point using an evolutionary strategy, to find a good - not necessarily optimal - set of neighbours. The third phase (Exploit) optimizes the results of the second phase. This final phase applies a simple gradient descent algorithm to find the local optima for each and all of the neighbourhoods, with the objective of finding a/the global optima of the whole space. The performance of ME2 is compared, on a fair basis, with the performance of benchmark optimization algorithms: Genetic Algorithms, Particle Swarm Optimization, Simulated Annealing and Covariance Matrix Adaptation Evolution Strategy. In most test cases it finds the global optima earlier than the other algorithms. It also scales-up, without loss of performance, to higher dimensions. Due to the distributed nature of ME2’s second and third phase, it can be comprehensively parallelized. The search & optimization process during these two phases can be applied to each sample point independently of all the others. A multi-threaded version of ME2 was written and compared to its single-threaded version, resulting in a near-linear speed-up as a function of the number of cores employed.
Overview and Architecture of the Query Store

In order for the Query Store to collect and maintain a baseline on query performance, it needs an architecture that has minimal impact on the overall performance of SQL Server. This chapter will review how Query Store operates to collect data. We’ll also cover the various data sets that make up the Query Store. This information should make it possible for you to both understand what the Query Store is doing and have faith that it’s performing these actions in the safest manner possible for your databases.
Application of Neural Network Machine Translation in College Translation Teaching

Currently the booming development of machine translation based on neural networks causes great concerns in teachers and students who focus on linguistics and translation studies. They are eager to know the answers to these questions: How good results can the machine translation, especially neural network machine translation achieve? What are the advantages and disadvantages of machine translation? To what level can the quality of machine translation be developed in the future? Can it replace human translators? How does college translation teaching adapt to the development of machine translation and embark on the corresponding teaching reforms? This paper attempts to answer the above questions based on a comprehensive technical analysis.
Characterization of rare earth elements by coupling multivariate analysis, factor analysis, and geostatistical simulation; case-study of Gazestan deposit, central Iran

The traditional approaches of modeling and estimation of highly skewed deposits have led to incorrect evaluations, creating challenges and risks in resource management. The low concentration of the rare earth element (REE) deposits, on one hand, and their strategic importance, on the other, enhances the necessity of multivariate modeling of these deposits. The wide variations of the grades and their relation with different rock units increase the complexities of the modeling of REEs. In this work, the Gazestan Magnetite-Apatite deposit was investigated and modeled using the statistical and geostatistical methods. Light and heavy REEs in apatite minerals are concentrated in the form of fine monazite inclusions. Using 908 assayed samples, 64 elements including light and heavy REEs from drill cores were analyzed. By performing the necessary pre-processing and stepwise factor analysis, and taking into account the threshold of 0.6 in six stages, a mineralization factor including phosphorus with the highest correlation was obtained. Then using a concentration-number fractal analysis on the mineralization factor, REEs were investigated in various rock units such as magnetite-apatite units. Next, using the sequential Gaussian simulation, the distribution of light, heavy, and total REEs and the mineralization factor in various realizations were obtained. Finally, based on the realizations, the analysis of uncertainty in the deposit was performed. All multivariate studies confirm the spatial structure analysis, simulation and analysis of rock units, and relationship of phosphorus with mineralization.
A Decomposition-based Approach for Constrained Large-Scale Global Optimization

Many real-world global optimization problems are too complex for comprehensive analysis and are viewed as “black-box” (BB) optimization problems. Modern BB optimization has to deal with growing dimensionality. Large-scale global optimization (LSGO) is known as a hard problem for many optimization techniques. Nevertheless, many efficient approaches have been proposed for solving LSGO problems. At the same time, LSGO does not take into account such features of real-world optimization problems as constraints. The majority of state-of-the-art techniques for LSGO are based on problem decomposition and use evolutionary algorithms as the core optimizer. In this study, we have investigated the performance of a novel decomposition-based approach for constrained LSGO (cLSGO), which combines cooperative coevolution of SHADE algorithms with the e-constraint handling technique for differential evolution. We have introduced some benchmark problems for cLSGO, based on scalable separable and non-separable problems from IEEE CEC 2017 benchmark for constrained real parameter optimization. We have tested SHADE with the penalty approach, regular e-SHADE and e-SHADE with problem decomposition. The results of numerical experiments are presented and discussed.
Pelatihan Akuntansi Berbasis Myob Bagi Koperasi Simpan Pinjam/Unit Simpan Pinjam di Kota Surakarta

The purpose of this service is because the Savings and Loan Cooperative / Savings and Loan Unit is a financial institution that serves funding needs for members and prospective members, must be able to present financial reports accurately and in a timely manner.GenerallyKSP / USP in Surakarta City records transactionsmanually,so that the financial statements consisting of Balance Sheet and Calculation of Operating Results cannot present accurately and on time. This has an impact on decision making and the function of financial management cannot run properly. One solution implemented is to use a Myob-based accounting program, so that the cooperative can present financial reports quickly and accurately. As for the training material, participants are taught the basics of accounting, financial accounting standards, how to install Accounting Myob software version 18, and create an account that is in accordance with the accounts in each KSP / USM, how to fill in the balance / initial balance along with how to operate it , and hands-on practice. The results after participating in this direct training are being able to apply it in the cooperative of each participant.
A new family of APN quadrinomials.

The binomial $B(x) = x^{3} + \beta x^{36}$ (where $\beta $ is primitive in $\mathbb {F}_{2^{2}}$ ) over $\mathbb {F}_{2^{10}}$ is the first known example of an Almost Perfect Nonlinear (APN) function that is not CCZ-equivalent to a power function, and has remained unclassified into any infinite family of APN functions since its discovery in 2006. We generalize this binomial to an infinite family of APN quadrinomials of the form $x^{3} + a (x^{2^{i}+1})^{2^{k}} + b x^{3 \cdot 2^{m}} + c (x^{2^{i+m}+2^{m}})^{2^{k}}$ from which $B(x)$ can be obtained by setting $a = \beta $ , $b = c = 0$ , $i = 3$ , $k = 2$ . We show that for any dimension $n = 2m$ with $m$ odd and $3 \nmid m$ , setting $(a,b,c) = (\beta, \beta ^{2}, 1)$ and $i = m-2$ or $i = (m-2)^{-1} \mod n$ yields an APN function, and verify that for $n = 10$ the quadrinomials obtained in this way for $i = m-2$ and $i = (m-2)^{-1} \mod n$ are CCZ-inequivalent to each other, to $B(x)$ , and to any other known APN function over $\mathbb {F}_{2^{10}}$ .
Pelatihan Pembuatan Media Pembelajaran Berbasis Mobile Menggunakan PowerPoint Bagi Guru-guru SMP, SMK dan SMA Di Kabupaten Cilacap

E-learning or electronic learning is a concept of learning carried out through electronic media networks. The development of highly advanced technology in the modern era and globalization enables various activities to be carried out quickly and efficiently. The development of technology has a lot of influence on our way of life, one of which is in the field of education with the use of e-learning. One of the developments in the telecommunications sector that is growing rapidly is cellular phones. Java is the standard language for making Android applications. If you have never studied java, you will have difficulty learning android. PowerPoint applications that are part of Microsoft Office are tools that can be manipulated to design a learning application that is quite attractive. Grafting ispring and saktibuider tools on powerpoint then the process of making an android application does not need to think about coding which is quite boring especially for beginners. The learning model will be made easier if every subject teacher can make learning by summarizing the subject book being taught using the Android-based application model. The application can be accessed by students as handbooks that can be read at any time using an Android smartphone, of course this will facilitate students in learning, especially elementary, middle and high school students