Reviewerly/exHarmony huggingface dataset:
Can Sequence-to-Sequence Models Crack Substitution Ciphers?

Decipherment of historical ciphers is a challenging problem.The language of the target plaintext might be unknown, and ciphertext can have a lot of noise.State-of-the-art decipherment methods use beam search and a neural language model to score candidate plaintext hypotheses for a given cipher, assuming the plaintext language is known.We propose an end-to-end multilingual model for solving simple substitution ciphers.We test our model on synthetic and real historical ciphers and show that our proposed method can decipher text without explicit language identification while still being robust to noise.
A Graphical User Interface Framework for Formal Verification.

We present the framework for implementing general user interfaces (UIs) within an interactive theorem prover. The framework uses web technology and functional reactive programming, as well as metaprogramming features of advanced interactive theorem proving (ITP) systems to allow users to create arbitrary interactive UIs for representing the goal state. Users of the framework can create GUIs declaratively within the ITP’s metaprogramming language, without having to develop in multiple languages and without coordinated changes across multiple projects, which improves development time for new designs of UI. The ProofWidgets framework also allows UIs to make use of the full context of the theorem prover and the specialised libraries that ITPs offer, such as methods for dealing with expressions and tactics. The framework includes an extensible structured pretty-printing engine that enables advanced interaction with expressions such as interactive term rewriting. We exemplify the framework with an implementation for the https://leanprover-community.github.io. The framework is already in use by hundreds of contributors to the Lean mathematical library.
Modeling and Evaluation of k-anonymization Metrics

The k-anonymity concept introduced in (Samarati and Sweeney 1998) proposes a good trade-off between the privacy and the utility of the data published for exploitation. However, minimizing the loss of information throughout the k-anonymization of a database is known to be NP-Hard (Meyerson and Williams 2004). Several previous works defined metrics to measure and to optimize this process according to different priorities or different ways of looking at things. In this paper, we first present a unified modeling of the optimization metrics for the k-anonymization of a database. Then, we propose different new metrics for this optimization problem. Finally, we evaluate three metrics of the literature and our new metrics using a greedy algorithm along the anonymization process for 21 values of k.
Uncertainty-Aware Multi-View Representation Learning

Learning from different data views by exploring the underlying complementary information among them can endow the representation with stronger expressive ability. However, high-dimensional features tend to contain noise, and furthermore, the quality of data usually varies for different samples (even for different views), i.e., one view may be informative for one sample but not the case for another. Therefore, it is quite challenging to integrate multi-view noisy data under unsupervised setting. Traditional multi-view methods either simply treat each view with equal importance or tune the weights of different views to fixed values, which are insufficient to capture the dynamic noise in multi-view data. In this work, we devise a novel unsupervised multi-view learning approach, termed as Dynamic Uncertainty-Aware Networks (DUA-Nets). Guided by the uncertainty of data estimated from the generation perspective, intrinsic information from multiple views is integrated to obtain noise-free representations. Under the help of uncertainty, DUA-Nets weigh each view of individual sample according to data quality so that the high-quality samples (or views) can be fully exploited while the effects from the noisy samples (or views) will be alleviated. Our model achieves superior performance in extensive experiments and shows the robustness to noisy data.
Recommendations for an Open Science approach to welding process research data

Abstract The increasing adoption of Open Science principles has been a prevalent topic in the welding science community over the last years. Providing access to welding knowledge in the form of complex and complete datasets in addition to peer-reviewed publications can be identified as an important step to promote knowledge exchange and cooperation. There exist previous efforts on building data models specifically for fusion welding applications; however, a common agreed upon implementation that is used by the community is still lacking. One proven approach in other domains has been the use of an openly accessible and agreed upon file and data format used for archiving and sharing domain knowledge in the form of experimental data. Going into a similar direction, the welding community faces particular practical, technical, and also ideological challenges that are discussed in this paper. Collaboratively building upon previous work with modern tools and platforms, the authors motivate, propose, and outline the use of a common file format specifically tailored to the needs of the welding research community as a complement to other already established Open Science practices. Successfully establishing a culture of openly accessible research data has the potential to significantly stimulate progress in welding research.
Filter Distribution Templates in Convolutional Networks for Image Classification Tasks

Neural network designers have reached progressive accuracy by increasing models depth, introducing new layer types and discovering new combinations of layers. A common element in many architectures is the distribution of the number of filters in each layer. Neural network models keep a pattern design of increasing filters in deeper layers such as those in LeNet, VGG, ResNet, MobileNet and even in automatic discovered architectures such as NASNet. It remains unknown if this pyramidal distribution of filters is the best for different tasks and constrains. In this work we present a series of modifications in the distribution of filters in three popular neural network models and their effects in accuracy and resource consumption. Results show that by applying this approach, some models improve up to 8.9% in accuracy showing reductions in parameters up to 54%.
Representing the Unification of Text Featurization using a Context-Free Grammar

We propose a novel context-free grammar to represent text embeddings in conjunction with their various transformations. We show how this grammar can serve as a unification layer on top of different featurization techniques, and their hybridization thereof. The approach is embodied in an open-source library, called TextWiser, with a high-level user interface to serve researchers and practitioners. The goal of TextWiser is to enable rapid experimentation with various featurization methods and to serve as a building block within AI applications consuming unstructured data. We highlight several key benefits that are desirable especially in industrial settings where rapid experimentation, reusability, reproducibility, and time to market are of great interest. Finally, we showcase a deployed service powered by TextWiser as a proof-of-concept enterprise application.
Emphasis on thinking in elementary grade mathematics lessons

The article considers the problems of personality activity in teaching as one of the topical issues in psycho- pedagogical science, and in educational practice. The issues of personal activity in learning as the leading factor in achieving the goals of education and the overall development of the individual. Mathematical education occupies one of the leading places in the system of general human education, since mathematical models describing the interrelation of the quantitative characteristics of various phenomena and processes are now an integral part of research in any field of knowledge.
Sampling heuristics for active function learning

People are capable of learning diverse functional relationships from data; nevertheless, they are most accurate when learning linear relationships, and deviate further from estimating the true relationship when presented with non-linear functions. We investigate whether, when given the opportunity to learn actively, people choose samples in an efficient fashion, and whether better sampling policies improve their ability to learn linear and non-linear functions. We find that, across multiple different function families, people make informative sampling choices consistent with a simple, low-effort policy that minimizes uncertainty at extreme values without requiring adaptation to evidence. While participants were most accurate at learning linear functions, those who more closely adhered to the simple sampling strategy also made better predictions across all non-linear functions. We discuss how the use of this heuristic might reflect rational allocation of limited cognitive resources.
CSPM: A Secure Cloud Computing Performance Management Model

Cloud computing offers an innovative organization and user ability share, store and retrieve data anytime and from anywhere. The critical aspect in cloud computing is security due to privacy and sensitive information. As a lot of companies and users share confidential and critical data on a cloud so the important part is how the data be secured. The worried question about confidentiality, availability and integrity of data in the cloud from attacks and damage of cloud services. This paper discusses the concerns associated to the data security, performance and management sides in cloud computing. The worry side in security such as CIA security which is data Confidentiality, data integrity, and service availability will be given. This paper proposes a cloud security performance management (CSPM) model which ensures different aspect such as, cloud computing security, cloud performance, cloud management. In addition, it discusses the construction of the proposed CSPM model. It defines the factors and layers of CSPM model.  Gets the outcome of the new proposed model and then implementation that's to illustrate the factors of security in cloud computing, such as data confidentiality, data integrity, and service availability. Data management and time performance will be discussed during experimentation part of this work.
Several Remarks on Grover's Quantum Search Algorithm with a Single Marked Element

The Grover's quantum search algorithm is one of the most important quantum algorithms.In this article, we mainly discuss related properties of the Grover's quantum search algorithm with a single marked element.We show that the range of constant c when the probability of measured value 0x is p .
Direct Answer Threshold Optimization in Dialogue Systems

The presence of dialogue systems is rising in a wide array of industries.While complex, human-like conversational flow and turn-taking have been the focus of recent research advances, we make the point that direct answers are equally important in humanbot interactions.This is especially true in an information seeking task where prompt, correct answers with minimal back-and-forth are desirable.We define a direct answer as a response given to a user query without requiring further clarifications from the user.To determine whether a direct answer is to be given or not, a threshold is applied to the to the confidence level of the predicted intent; in the case where the confidence is higher than the threshold, the user receives a direct answer.This threshold is often set intuitively or on the basis of a few observations, usually between 50% -75%.In this paper, we propose a method to estimate this threshold based on the intent classification confidence level combined with several intent volumetrics.The goal of our method is to maximize the number of correct direct responses for as many intents as possible in order to minimize user frustration from unnecessary requests for clarifications.Moreover, our method is applicable in the earlier stages of a dialogue system when real interaction logs are scarce.We show that our method improves the accuracy of directly answered queries by 3 to 14% while maximizing the number of accurately answered intents on two dialogue system datasets of 32 and 152 intents.
Privacy of Edge Computing and IoT

The pervasiveness of IoT promises numerous benefits to society by making our environment and even our bodies ‘smart’. Coupled with AI, data collected from IoT can help us manage our health and well-being, provide care to the aged at home and bring about efficiency and productivity improvements in all aspects of our lives. At the same time, the ubiquitous collection of highly sensitive data presents a real threat to privacy and security. Our chapter reviews the potential threats to privacy and security and proposes a new way of thinking of IoT as operating within Privacy Spaces. We define a Privacy Space as the space that a collection of IoT operates in to collect data through the range of their sensors. The boundary of the space is defined by one or more edge devices that are responsible for collecting data from these particular sensors. Edge devices operating on the boundary of a privacy space are also responsible for the security of the devices inside that space. We discuss the technologies involved in Privacy Spaces and go on to show how data collected within a Privacy Space can be managed and shared with other Privacy Spaces and with other external systems while maintaining control and privacy guarantees.
On the Complexity of Finding Justifications for Collective Decisions

In a collective decision-making process, having the possibility to provide non-expert agents with a justification for why a target outcome is a good compromise given their individual preferences, is an appealing idea. Such questions have recently been addressed in the computational social choice community at large---whether it was to explain the outcomes of a specific rule in voting theory or to seek transparency and accountability in multi-criteria decision making. Ultimately, the development of real-life applications based on these notions depends on their practical feasibility and on the scalability of the approach taken. In this paper, we provide computational complexity results that address the problem of finding and verifying justifications for collective decisions. In particular, we focus on the recent development of a general notion of justification for outcomes in voting theory. Such a justification consists of a step-by-step explanation, grounded in a normative basis, showing how the selection of the target outcome follows from the normative principles considered. We consider a language in which normative principles can be encoded---either as an explicit list of instances of the principles (by means of quantifier-free sentences), or in a succinct fashion (using quantifiers). We then analyse the computational complexity of identifying and checking justifications. For the case where the normative principles are given in the form of a list of instances, verifying the correctness of a justification is DP-complete and deciding on the existence of such a justification is complete for Sigma 2 P. For the case where the normative principles are given succinctly, deciding whether a justification is correct is in NEXP wedge coNEXP, and NEXP-hard, and deciding whether a justification exists is in EXP with access to an NP oracle and is NEXP-hard.
Hand Gesture Controlled Nursing Bed Using FMCW Radar

Abstract The nursing bed has become an important tool for medical and elder care, as a well-designed bed can prevent users from bedsores. To improve the user convenience, a user-friendly human-computer interface is also necessary. In this paper, we design a new nursing bed containing a FMCW millimeter wave radar for gesture recognition. A control system can help user frictionless back-up, integral turning over and leg raising when lying in the bed. Besides, we adopt the convolutional recurrent neural network for recognition of 6 gestures. Experiments with the nursing bed show that the control success rate of the bed reaches 93.75% and the response time achieves 1.03 s.
Automated Lay Language Summarization of Biomedical Scientific Reviews

Health literacy has emerged as a crucial factor in making appropriate health decisions and ensuring treatment outcomes. However, medical jargon and the complex structure of professional language in this domain make health information especially hard to interpret. Thus, there is an urgent unmet need for automated methods to enhance the accessibility of the biomedical literature to the general population. This problem can be framed as a type of translation problem between the language of healthcare professionals, and that of the general public. In this paper, we introduce the novel task of automated generation of lay language summaries of biomedical scientific reviews, and construct a dataset to support the development and evaluation of automated methods through which to enhance the accessibility of the biomedical literature. We conduct analyses of the various challenges in solving this task, including not only summarization of the key points but also explanation of background knowledge and simplification of professional language. We experiment with state-of-the-art summarization models as well as several data augmentation techniques, and evaluate their performance using both automated metrics and human assessment. Results indicate that automatically generated summaries produced using contemporary neural architectures can achieve promising quality and readability as compared with reference summaries developed for the lay public by experts (best ROUGE-L of 50.24 and Flesch-Kincaid readability score of 13.30). We also discuss the limitations of the current attempt, providing insights and directions for future work.
Self-training Converts Weak Learners to Strong Learners in Mixture Models

We consider a binary classification problem when the data comes from a mixture of two rotationally symmetric distributions satisfying concentration and anti-concentration properties enjoyed by log-concave distributions among others. We show that there exists a universal constant $C_{\mathrm{err}}&gt;0$ such that if a pseudolabeler $\boldsymbolβ_{\mathrm{pl}}$ can achieve classification error at most $C_{\mathrm{err}}$, then for any $\varepsilon&gt;0$, an iterative self-training algorithm initialized at $\boldsymbolβ_0 := \boldsymbolβ_{\mathrm{pl}}$ using pseudolabels $\hat y = \mathrm{sgn}(\langle \boldsymbolβ_t, \mathbf{x}\rangle)$ and using at most $\tilde O(d/\varepsilon^2)$ unlabeled examples suffices to learn the Bayes-optimal classifier up to $\varepsilon$ error, where $d$ is the ambient dimension. That is, self-training converts weak learners to strong learners using only unlabeled examples. We additionally show that by running gradient descent on the logistic loss one can obtain a pseudolabeler $\boldsymbolβ_{\mathrm{pl}}$ with classification error $C_{\mathrm{err}}$ using only $O(d)$ labeled examples (i.e., independent of $\varepsilon$). Together our results imply that mixture models can be learned to within $\varepsilon$ of the Bayes-optimal accuracy using at most $O(d)$ labeled examples and $\tilde O(d/\varepsilon^2)$ unlabeled examples by way of a semi-supervised self-training algorithm.
PERANCANGAN APLIKASI PELAYANAN CLEANING SERVICE BERBASIS WEB

Problems that occur in a service can be seen in a business process carried out. Customer service, management of orders, promotions, and other services, whether it is a main process or a supporting process. In service business this service still uses social media. In business reporting, running is still done manually. To provide good results on this service, the system requires web-based e-commerce to help all who will run the service business process. With the existence of e-commerce services can facilitate existing promotions. This e-commerce system service uses the MVC model (Model, View, Controller). The programming language used is the programming language PHP (PHP Hypertext Preprocessor) and uses the Laravel framework and MySQL as a Database Management System (DBMS) and Sublime Text 3 as supporting editors. Business processes that exist in this service can cover a wider area in order to convey information to consumers and facilitate customer access in ordering services.Keywords :Laravel Framework, MySQL, E-Commerce.
CoLecTer KIN: Mobile Application for Collaborative Bilingual Glossary Compilation in the ESP Classroom

The ubiquitous use of smartphones has shown to offer great advantages in language learning, which still remain to be harnessed in the field of mobile-assisted language learning (MALL), ESP teaching and translator training in higher education. Language teachers are predominantly the users of existing mobile applications and not their producers, which may narrow their range of choices and options in using mobile technology in teaching. This paper presents the results of a student survey on the usability, perceived benefits, and feedback on the features of an Android mobile application built by the author. The CoLecTer KIN application was used by first-year undergraduate students of Applied Kinesiology in a collaborative project of compiling a Slovene-English bilingual glossary of topic-specific terminology. The design of the application makes it suitable for use in the ESP and translation classroom and in collaborative translation projects.
Early Prediction of Children’s Task Completion in a Tablet Tutor using Visual Features (Student Abstract)

Intelligent tutoring systems could benefit from human teachers’ ability to monitor students’ affective states by watching them and thereby detecting early warning signs of disengagement in time to prevent it. Toward that goal, this paper describes a method that uses input from a tablet tutor’s user-facing camera to predict whether the student will complete the current activity or disengage from it. Training a disengagement predictor is useful not only in itself but also in identifying visual indicators of negative affective states even when they don’t lead to non-completion of the task. Unlike prior work that relied on tutor-specific features, the method relies solely on visual features and so could potentially apply to other tutors. We present a deep learning method to make such predictions based on a Long Short Term Memory (LSTM) model that uses a target replication loss function. We train and test the model on screen capture videos of children in Tanzania using a tablet tutor to learn basic Swahili literacy and numeracy. We achieve balanced-class-size prediction accuracy of 73.3% when 40% of the activity is still left.