Reviewerly/exHarmony huggingface dataset:
A $t$-Distribution Based Operator for Enhancing Out of Distribution Robustness of Neural Network Classifiers

Neural Network (NN) classifiers can assign extreme probabilities to samples that have not appeared during training (out-of-distribution samples) resulting in erroneous and unreliable predictions. One of the causes for this unwanted behaviour lies in the use of the standard softmax operator which pushes the posterior probabilities to be either zero or unity hence failing to model uncertainty. The statistical derivation of the softmax operator relies on the assumption that the distributions of the latent variables for a given class are Gaussian with known variance. However, it is possible to use different assumptions in the same derivation and attain from other families of distributions as well. This allows derivation of novel operators with more favourable properties. Here, a novel operator is proposed that is derived using $t$-distributions which are capable of providing a better description of uncertainty. It is shown that classifiers that adopt this novel operator can be more robust to out of distribution samples, often outperforming NNs that use the standard softmax operator. These enhancements can be reached with minimal changes to the NN architecture.
A note on the growth of the dimension in complete simple games

The remoteness from a simple game to a weighted game can be measured by the concept of the dimension or the more general Boolean dimension. It is known that both measures can be exponential in the number of voters. For complete simple games it was only recently shown in O’Dwyer and Slinko (2017) that the dimension can also be exponential. Here we show that this is also the case for complete simple games with two types of voters and for the Boolean dimension of general complete simple games, which was posed as an open problem in O’Dwyer and Slinko (2017).
Systematic Attack Surface Reduction for Deployed Sentiment Analysis Models

This work proposes a structured approach to baselining a model, identifying attack vectors, and securing the machine learning models after deployment.This method for securing each model post deployment is called the BAD (Build, Attack, and Defend) Architecture.Two implementations of the BAD architecture are evaluated to quantify the adversarial life cycle for a black box Sentiment Analysis system.As a challenging diagnostic, the Jigsaw Toxic Bias dataset is selected as the baseline in our performance tool.Each implementation of the architecture will build a baseline performance report, attack a common weakness, and defend the incoming attack.As an important note: each attack surface demonstrated in this work is detectable and preventable.The goal is to demonstrate a viable methodology for securing a machine learning model in a production setting.
k-SDPP: Fixed-Size Video Summarization via Sequential Determinantal Point Processes

With the explosive growth of video data, video summarization which converts long-time videos to key frame sequences has become an important task in information retrieval and machine learning. Determinantal point processes (DPPs) which are elegant probabilistic models have been successfully applied to video summarization. However, existing DPP-based video summarization methods suffer from poor efficiency of outputting a specified size summary or neglecting inherent sequential nature of videos. In this paper, we propose a new model in the DPP lineage named k-SDPP in vein of sequential determinantal point processes but with fixed user specified size k. Our k-SDPP partitions sampled frames of a video into segments where each segment is with constant number of video frames. Moreover, an efficient branch and bound method (BB) considering sequential nature of the frames is provided to optimally select k frames delegating the summary from the divided segments. Experimental results show that our proposed BB method outperforms not only k-DPP and sequential DPP (seqDPP) but also the partition and Markovian assumption based methods.
Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs

The main approach to defining equivalence among acyclic directed causal graphical models is based on the conditional independence relationships in the distributions that the causal models can generate, in terms of the Markov equivalence. However, it is known that when cycles are allowed in the causal structure, conditional independence may not be a suitable notion for equivalence of two structures, as it does not reflect all the information in the distribution that is useful for identification of the underlying structure. In this paper, we present a general, unified notion of equivalence for linear Gaussian causal directed graphical models, whether they are cyclic or acyclic. In our proposed definition of equivalence, two structures are equivalent if they can generate the same set of data distributions. We also propose a weaker notion of equivalence called quasi-equivalence, which we show is the extent of identifiability from observational data. We propose analytic as well as graphical methods for characterizing the equivalence of two structures. Additionally, we propose a score-based method for learning the structure from observational data, which successfully deals with both acyclic and cyclic structures.
You Don’t Have Time to Read This: An Exploration of Document Reading Time Prediction

Predicting reading time has been a subject of much previous work, focusing on how different words affect human processing, measured by reading time.However, previous work has dealt with a limited number of participants as well as word level only predictions (i.e.predicting the time to read a single word).We seek to extend these works by examining whether or not document level predictions are effective, given additional information such as subject matter, font characteristics, and readability metrics.We perform a novel experiment to examine how different features of text contribute to the time it takes to read, distributing and collecting data from over a thousand participants.We then employ a large number of machine learning methods to predict a user's reading time.We find that despite extensive research showing that word level reading time can be most effectively predicted by neural networks, larger scale text can be easily and most accurately predicted by one factor, the number of words.
Selecting Voting Locations for Fun and Profit

While manipulative attacks on elections have been well-studied, only recently has attention turned to attacks that account for geographic information, which are extremely common in the real world. The most well known in the media is gerrymandering, in which district border-lines are changed to increase a party's chance to win, but a different geographical manipulation involves influencing the election by selecting the location of polling places, as many people are not willing to go to any distance to vote. In this paper we initiate the study of this manipulation. We find that while it is easy to manipulate the selection of polling places on the line, it becomes difficult already on the plane or in the case of more than two candidates. Moreover, we show that for more than two candidates the problem is inapproximable. However, we find a few restricted cases on the plane where some algorithms perform well. Finally, we discuss how existing results for standard control actions hold in the geographic setting, consider additional control actions in the geographic setting, and suggest directions for future study.
Learning Bayesian Networks Under Sparsity Constraints: A Parameterized Complexity Analysis

We study the problem of learning the structure of an optimal Bayesian network when additional structural constraints are posed on the network or on its moralized graph. More precisely, we consider the constraint that the moralized graph can be transformed to a graph from a sparse graph class Π by at most k vertex deletions. We show that for Π being the graphs with maximum degree 1, an optimal network can be computed in polynomial time when k is constant, extending previous work that gave an algorithm with such a running time for Π being the class of edgeless graphs [Korhonen &amp; Parviainen, NIPS 2015]. We then show that further extensions or improvements are presumably impossible. For example, we show that when Π is the set of graphs in which each component has size at most three, then learning an optimal network is NP-hard even if k=0. Finally, we show that learning an optimal network with at most k edges in the moralized graph presumably is not fixed-parameter tractable with respect to k and that, in contrast, computing an optimal network with at most k arcs can be computed is fixed-parameter tractable in k.
The project of a deeply tagged parallel corpus of Middle Russian translations from Latin

Tagged parallel corpora are powerful tools for the analysis of natural language.Moreover, for historical linguistics, whose most peculiar shortcoming is lack of living native speakers, corporaas paper or electronic collections of written texts -are the main source of linguistic information.Old and Middle Russian are well-documented languages, and a host of manuscripts in both idioms -including those containing numerous translations -are available for investigation.Nevertheless, up to now there is no parallel translational corpus of Middle Russian.Thus, a number of important written sources containing information valuable for linguists, literary scholars and historians cannot be studied properly.This article provides a preliminary account of the project of a deeply tagged parallel corpus of Middle Russian translations from Latin.Such corpus may prove useful in the formal description of the translation techniques of the time, which may help with dividing the anonymous texts of the time into several groups based on their language features.Such grouping may help with authorship attribution and, consequently, with incorporating each translation into a proper cultural landscape.From the linguistic point of view, such corpus could provide researchers with crucial information on the vocabulary, morphology and syntax of Middle Russian with an emphasis on the argument structure of the verbs, usage of borrowed lexical items and set expressions and professional skills of the ancient translators.The article gives an outline of the crucial features of the prospective Middle Russian translational corpus, its possible primary contents, text standardization and annotation principles, as well as the reasons for not using a theory-neutral syntactic apparatus, characteristic of the existing historical corpora of ancient Indo-European languages, such as TOROT or PROIEL.An explanation of how the potential users of this corpus could benefit from our non-standard tagging principles is given.
Entanglement for any definition of two subsystems

The notion of entanglement of quantum states is usually defined with respect to a fixed bipartition. Indeed, a global basis change can always map an entangled state to a separable one. The situation is however different when considering a set of states. In this work we define the notion of an "absolutely entangled set" of quantum states: for any possible choice of global basis, at least one of the states in the set is entangled. Hence, for all bipartitions, i.e. any possible definition of the subsystems, the set features entanglement. We present a minimum example of this phenomenon, with a set of four states in $\mathbb{C}^4 = \mathbb{C}^2 \otimes \mathbb{C}^2$. Moreover, we propose a quantitative measure for absolute set entanglement. To lower-bound this quantity, we develop a method based on polynomial optimization to perform convex optimization over unitaries, which is of independent interest.
Analysing the Works of a Machine Translation

Machine translation has been around for years. This application is now used frequently in social media with texts which are usually short and simple and found in daily lives. The machine translation has undergone a great progress from the time it was introduced to public in early 2000’s. However, there has been no research to see the performance of the machine translation work.The present research is intended to analyse the works of machine translation with the objects which are complicated scientific texts as found in journal articles. It is intended to see if the result of machine translation is comprehensible, accurate and reliable in translating documents. The result suggests that the works of machine translation are found to be comprehensible and reliable for obtaining the meaning of the translated texts, but it does not conform to the standards for publishable criteria. Further works are needed if the translated texts are aimed for publication.&#x0D;
Speeding up Very Fast Decision Tree with Low Computational Cost

Very Fast Decision Tree (VFDT) is one of the most widely used online decision tree induction algorithms, and it provides high classification accuracy with theoretical guarantees. In VFDT, the split-attempt operation is essential for leaf-split. It is computation-intensive since it computes the heuristic measure of all attributes of a leaf. To reduce split-attempts, VFDT tries to split at constant intervals (for example, every 200 examples). However, this mechanism introduces split-delay for split can only happen at fixed intervals, which slows down the growth of VFDT and finally lowers accuracy. To address this problem, we first devise an online incremental algorithm that computes the heuristic measure of an attribute with a much lower computational cost. Then a subset of attributes is carefully selected to find a potential split timing using this algorithm. A split-attempt will be carried out once the timing is verified. By the whole process, computational cost and split-delay are lowered significantly. Comprehensive experiments are conducted using multiple synthetic and real datasets. Compared with state-of-the-art algorithms, our method reduces split-attempts by about 5 to 10 times on average with much lower split-delay, which makes our algorithm run faster and more accurate.
Medical Predictions: Naive Bayes Classifier vs Artificial Neural Networks

The aim of this research is to provide arguments for choosing the most appropriate artificial intelligence mechanism for specific medical predictions. Naive Bayes classifier and artificial neural networks are used to make suggestions regarding the evolution of patients infected with hepatitis B virus. A detailed analysis of their features and performances is provided. Both methods offer reliable results and are suitable to assist medical decision-making.
Arduino-Based Mini Reed Switch Magnetic Sensor Media: Implementation in Physics Learning to Improve Students’ Analyzing Ability

This article was to improve students' ability to analyze the viscosity material in implementing the Arduino-based MRSM (Mini Reed Switch Magnetic) teaching media. The researchers used a quasi experimental design in the form of non equivalent pre-test post-test group design. The instruments used were tests and observation sheets to obtain data. Data collection on theability to analyze through giving tests to students. Data were analyzed using descriptive statistics. The results showed an increase in the ability to analyze by 20%. The highest assessment aspect in the first aspect is the ability of students to find problems encountered and be able to distinguish problems that fit the theme. The results of this trial produce data on the implementation of learning by 98% with a reliable category. Hence, MRSM teaching media is able to improve the ability to analyze students.&#x0D; Keywords:Mini Reed Switch Magnetic, Sensor Media, Physics Learning
Scanning Imaging Restoration of Moving or Dynamically Deforming Objects

The raster scanning imaging mode is widely used in scanning electron microscopes (SEMs), transmission electron microscopes (TEM), and atomic force microscopes (AFM), and can achieve subatomic resolution. However, only a point on the shallow surface of an object can be imaged at one time using the raster scanning imaging mode, whereas the entire surface of the object can be imaged in the image plane once and instantaneously using the optical imaging mode, which is a parallel imaging mode. Therefore, the image distortion and blur for the scanning imaging mode are different from the optical imaging. In this paper, we propose a theory to describe the mechanism of the scanning imaging process and restore the degraded image (distorted and blurred image) obtained using an SEM. The theory consists of a scanning equation, motion or deformation equations, and an assumption called the intensity-invariant hypothesis. Numerical simulations of the scanning imaging process and restoration of the degraded images are performed using the scanning imaging formulas, spatial non-uniform point spread function, and inverse restoration algorithms, including algebraic, interpolation, and their hybrid methods to verify the feasibility of our theory. In situ experiments on uniform linear motion, uniaxial tensile, and fatigue were also conducted to demonstrate the validity and efficiency of the proposed scanning imaging theory and restoration methods. We anticipate that this imaging and restoration theory will enable the scanning imaging mode to be used in in situ dynamic imaging and for mechanical property measurement of materials.
Treebank Embedding Vectors for Out-of-Domain Dependency Parsing

A recent advance in monolingual dependency parsing is the idea of a treebank embedding vector, which allows all treebanks for a particular language to be used as training data while at the same time allowing the model to prefer training data from one treebank over others and to select the preferred treebank at test time.We build on this idea by 1) introducing a method to predict a treebank vector for sentences that do not come from a treebank used in training, and 2) exploring what happens when we move away from predefined treebank embedding vectors during test time and instead devise tailored interpolations.We show that 1) there are interpolated vectors that are superior to the predefined ones, and 2) treebank vectors can be predicted with sufficient accuracy, for nine out of ten test languages, to match the performance of an oracle approach that knows the most suitable predefined treebank embedding for the test set.
Test-Cost Sensitive Ensemble of Classifiers Using Reinforcement Learning

The use of classification methods in real-world problems has costs that are usually neglected in the early algorithms which cause inefficiencies in practice.One of these costs, which is significant in many cases, is the cost of obtaining feature values for each instance, named Test-Cost.The Ensemble of classifiers as a common and practical classification method, is also considered and used in this perspective.Each classifier needs a number of features to classify the sample; if instead of using all classifiers, the best arrange of classifiers with the aim of minimizing the needed features is found, an effective solution for lowering the test-cost is obtained.In this paper, a method is proposed which uses reinforcement learning to construct such a Classifier Ensemble.The proposed method learns to find the best sequence of classifiers for each sample to minimize the test-cost.Two problems, an easy one and a hard one, are considered for testing the proposed method, in both of which yields very good results.
Neighbor Combinatorial Attention for Critical Structure Mining

Graph convolutional networks (GCNs) have been widely used to process graph-structured data. However, existing GNN methods do not explicitly extract critical structures, which reflect the intrinsic property of a graph. In this work, we propose a novel GCN module named Neighbor Combinatorial ATtention (NCAT) to find critical structure in graph-structured data. NCAT attempts to match combinatorial neighbors with learnable patterns and assigns different weights to each combination based on the matching degree between the patterns and combinations. By stacking several NCAT modules, we can extract hierarchical structures that is helpful for down-stream tasks. Our experimental results show that NCAT achieves state-of-the-art performance on several benchmark graph classification datasets. In addition, we interpret what kind of features our model learned by visualizing the extracted critical structures.
Method to implement K-NN machine learning to classify data privacy in IoT environment

&lt;span&gt;Internet of Things technology allows many devices to connect with each other. The interaction could be between humans and devices or between devices itself. In fact, the data are traveling between the devices through the media within the boundary, and it could be traveling outside the boundary when it required to be analyzed or stored in the cloud through the internet. Due the transmission media and internet, the data are vulnerable to attacks. Thus, the data need to be encrypted strongly for the purpose of protection. Usually, most of the encryption techniques will consume computer resources. In this work, we divide the data that are used in the IoT environment into three levels of sensitivity which are low, medium and high sensitive data to leverage the computer resources such as time of encryption and decryption, battery usage and so on. A framework is proposed in this work to encrypt the data depends on the level of sensitivity using the machine learning K nearest neighbors (K-NN).&lt;/span&gt;
PENGEMBANGAN TAMPILAN ANTARMUKA APLIKASI SURVEI BERBASIS WEB DENGAN METODE USER CENTERED DESIGN

A survey is a data collection activity that uses for analysis needs for the organization.But in the process they meet some difficulties, so a web-based survey application was built for easily collecting respondents' data.This research focuses on how developing user interface by involving users directly.A method that can be focus on user role in the system development is User-Centered Design (UCD).this research will discuss about developing user interface from the context of use, user requirement, developing design solution and evaluating design solution.The result of this research showed that user interface design had a better usability score after using the UCD method.For the effectivity aspect, the result was 100% users can use the system.For the efficiency aspect, the average time result was increased rather than the first design.For the user satisfaction aspect, the result was 85,6% users satisfied with the current user interface.
Structured Weight Unification and Encoding for Neural Network Compression and Acceleration

We investigate structured joint weight unification and weight encoding to compress deep neural network models for reduced storage and computation. A structured weight unification method is proposed, where weight coefficients are unified according to a hardware-friendly structure, so that the unified weights can be effectively encoded and the inference computation can be accelerated. Our method can be seen as a generalization of structured weight pruning, where we unify weights of a selected structure to share some value instead of removing them. A 3D pyramid-based encoding method is further proposed to team up with the structurally learned weights, providing a systematic solution for compressing neural network models while preserving the network capacity and the original prediction performance. Also, we develop a training framework to iteratively optimize the subproblems of weight unification and target prediction, which ensures the unification rate with little prediction loss. Experiments over several benchmark models and datasets of different tasks demonstrate the effectiveness of our approach.