Reviewerly/exHarmony huggingface dataset:
ACTION RECOGNITION USING UNDECIMATED DUAL TREE COMPLEX WAVELET TRANSFORM FROM DEPTH MOTION MAPS / DEPTH SEQUENCES

Abstract. Accumulating the motion information from a video sequence is one of the highly challenging and significant phase in Human Action Recognition. To achieve this, several classical and compact representations are proposed by the research community with proven applicability. In this paper, we propose a compact Depth Motion Map based representation methodology with hastey striding, consisely accumulating the motion information. We extract Undecimated Dual Tree Complex Wavelet Transform features from the proposed DMM, to form an efficient feature descriptor. We designate a Sequential Extreme Learning Machine for classifying the human action secquences on benchmark datasets, MSR Action 3D dataset and DHA Dataset. We empirically prove the feasability of our method under standard protocols, achieving proven results.
Unsupervised Reinforcement Learning For Video Summarization Reward Function

We propose a new reward function based on Deep Summarization Network (DSN), which is used to synthesize short video summaries to facilitate large-scale browsing of videos. The DSN uses the video summarization as a process of sequential decision making, predicting the probability of each video frame to indicate the likelihood that the video frame is selected, and then selecting the frame based on the probability distribution to form video summaries. By designing a new DSN reward function, the rewards for representative and diversity rewards are higher, and a large number of experiments are performed on the two benchmark datasets, demonstrating that our summary network is significantly better than existing unsupervised video summaries.
On Solving Linear Systems in Sublinear Time.

We study \emph{sublinear} algorithms that solve linear systems locally. In the classical version of this problem the input is a matrix $S\in \mathbb{R}^{n\times n}$ and a vector $b\in\mathbb{R}^n$ in the range of $S$, and the goal is to output $x\in \mathbb{R}^n$ satisfying $Sx=b$. For the case when the matrix $S$ is symmetric diagonally dominant (SDD), the breakthrough algorithm of Spielman and Teng [STOC 2004] approximately solves this problem in near-linear time (in the input size which is the number of non-zeros in $S$), and subsequent papers have further simplified, improved, and generalized the algorithms for this setting. Here we focus on computing one (or a few) coordinates of $x$, which potentially allows for sublinear algorithms. Formally, given an index $u\in [n]$ together with $S$ and $b$ as above, the goal is to output an approximation $\hat{x}_u$ for $x^*_u$, where $x^*$ is a fixed solution to $Sx=b$. Our results show that there is a qualitative gap between SDD matrices and the more general class of positive semidefinite (PSD) matrices. For SDD matrices, we develop an algorithm that approximates a single coordinate $x_{u}$ in time that is polylogarithmic in $n$, provided that $S$ is sparse and has a small condition number (e.g., Laplacian of an expander graph). The approximation guarantee is additive $| \hat{x}_u-x^*_u | \le \epsilon \| x^* \|_\infty$ for accuracy parameter $\epsilon>0$. We further prove that the condition-number assumption is necessary and tight. In contrast to the SDD matrices, we prove that for certain PSD matrices $S$, the running time must be at least polynomial in $n$. This holds even when one wants to obtain the same additive approximation, and $S$ has bounded sparsity and condition number.
Reading Customer Reviews to Answer Product-related Questions

The e-commerce websites are ready to build the community question answering (CQA) service, as it can facilitate questioners (potential buyers) to obtain satisfying answers from experienced customers and furthermore stimulate consumption.Given that more than 50% product-related questions only anticipate a binary response (i.e., "Yes" or "No"), the research on productrelated question answering (PQA), which aims to automatically provide instant and correct replies to questioners, emerges rapidly.The mainstream approaches on PQA generally employ customer reviews as the evidence to help predict answers to the questions which are product-specific and concerned more about subjective personal experiences.However, the supportive features either extracted by heuristic rules or acquired from unsupervised manners are not able to perform well on PQA.In this paper, we contribute an end-to-end neural architecture directly fed by the raw text of productrelated questions and customer reviews to predict the answers.Concretely, it teaches machines to generate and to synthesize multiple question-aware review representations in a reading comprehension fashion to make the final decision.We also extract a real-world dataset crawled from 9 categories in Amazon.comfor PQA to assess the performance of our neural reading architecture (NRA) and other mainstream approaches such as COR-L [12], MOQA [12], and AAP [21].Experimental results show that our NRA sets up a new state-of-theart performance on this dataset, significantly outperforming existing algorithms.
On the Feasibility of Automated Detection of Allusive Text Reuse

The detection of allusive text reuse is particularly challenging due to the sparse evidence on which allusive references rely-commonly based on none or very few shared words.Arguably, lexical semantics can be resorted to since uncovering semantic relations between words has the potential to increase the support underlying the allusion and alleviate the lexical sparsity.A further obstacle is the lack of evaluation benchmark corpora, largely due to the highly interpretative character of the annotation process.In the present paper, we aim to elucidate the feasibility of automated allusion detection.We approach the matter from an Information Retrieval perspective in which referencing texts act as queries and referenced texts as relevant documents to be retrieved, and estimate the difficulty of benchmark corpus compilation by a novel inter-annotator agreement study on query segmentation.Furthermore, we investigate to what extent the integration of lexical semantic information derived from distributional models and ontologies can aid retrieving cases of allusive reuse.The results show that (i) despite low agreement scores, using manual queries considerably improves retrieval performance with respect to a windowing approach, and that (ii) retrieval performance can be moderately boosted with distributional semantics.
Staging Bodies, Performing Ramps

This paper proposes a cultural, aesthetic disability technoscience that is manifested in the embodied wisdom of disabled people.
The mechanism of strain influence on interpolation induced systematic errors in digital image correlation method

Digital image correlation methods are widely used in displacement and strain measurements nowadays. Although the influence of strain on measurement accuracy has been extensively discussed, its mechanism is still unknown. In this work, numerical simulations using first-order shape functions are carried out to calculate the displacement of uniform tensile and compressive deformation, and the systematic errors corresponding to different strain values are illustrated and compared. An obvious reduction in the systematic errors can be observed when the strain value is greater than 0.01. The subset size also has an impact on the systematic errors; in particular, systematic errors can be eliminated using a suitable subset size. Furthermore, the mechanism of the influence of strain and subset size on systematic errors is revealed: the sub-pixel interpolation errors in deformed subsets bring forth systematic errors, and the existence of strain contributes to the sizes of the induced systematic errors. An analytical formula describing the relationship between systematic errors and strain/subset size is deduced and verified. This work can help researchers predict systematic errors for certain strains and subset sizes, while furthering our understanding of the role of strain in correlation calculations.
Calibrating Probability Estimation Trees using Venn-Abers Predictors

Class labels output by standard decision trees are not very useful for making informed decisions, e.g., when comparing the expected utility of various alternatives.In contrast, probability estimation trees (PETs) output class probability distributions rather than single class labels.It is well known that estimating class probabilities in PETs by relative frequencies often lead to extreme probability estimates, and a number of approaches to provide more well-calibrated estimates have been proposed.In this study, a recent modelagnostic calibration approach, called Venn-Abers predictors is, for the first time, considered in the context of decision trees.Results from a large-scale empirical investigation are presented, comparing the novel approach to previous calibration techniques with respect to several different performance metrics, targeting both predictive performance and reliability of the estimates.All approaches are considered both with and without Laplace correction.The results show that using Venn-Abers predictors for calibration is a highly competitive approach, significantly outperforming Platt scaling, Isotonic regression and no calibration, with respect to almost all performance metrics used, independently of whether Laplace correction is applied or not.The only exception is AUC, where using non-calibrated PETs together with Laplace correction, actually is the best option, which can be explained by the fact that AUC is not affected by the absolute, but only relative, values of the probability estimates.
Effect of an incoherent pump on two-mode entanglement in optical parametric generation

Pumping a nonlinear crystal by an intense radiation results in the optical parametric generation of photons in two modes (the signal and the idler). The quantized electromagnetic field in these modes is described by a continuous-variable quantum state, which is entangled if the pump is a coherent state produced by a laser. The signal and the idler modes remain populated by photons even if the pump becomes incoherent (dephased by a medium, superposed with a thermal state, or produced by an alternative source such as the superluminescent diode). However, the incoherent pump does effect the entanglement and purity of the signal and the idler modes, which is of vital importance for quantum information applications and interferometry. Here we develop an approach to infer the signal-idler entanglement and purity for a general quantum incoherent pump with the given Glauber-Sudarshan function. We show that the signal-idler entanglement is extremely sensitive to the phase distribution of the pump and illustrate our findings by physically relevant examples of the incoherent pump: the noisy coherent state, slightly dephased and phase-averaged coherent states, the thermal state, and states modulated by the Kerr medium. The effect of an incoherent pump on the combined quadratures is discussed as well.
Triplet Loss with Channel Attention for Person Re-identification

The triplet loss function has seen extensive use within person re-identification.Most works focus on either improving the mining algorithm or adding new terms to the loss function itself.Our work instead concentrates on two other core components of the triplet loss that have been under-researched.First, we improve the standard Euclidean distance with dynamic weights, which are selected based on the standard deviation of features across the batch.Second, we exploit channel attention via a squeeze and excitation unit in the backbone model to emphasise important features throughout all layers of the model.This ensures that the output feature vector is a better representation of the image, and is also more suitable to use within our dynamically weighted Euclidean distance function.We demonstrate that our alterations provide significant performance improvement across popular reidentification data sets, including almost 10% mAP improvement on the CUHK03 data set.The proposed model attains results competitive with many state-of-the-art person re-identification models.
Belief Graphical Models for Uncertainty Representation and Reasoning

Many real world problems and applications require to exploit incomplete, complex and uncertain information. Belief graphical models encompass a wide range of graphical formalisms for representing and reasoning with uncertain and complex information. They generally involve a graphical component which can be directed or undirected and a numerical one depending on the considered uncertainty setting. The graphical component encodes a set of independence statements while the numerical one quantifies the uncertainty regarding variables. The main use of belief graphical models is knowledge representation, reasoning and decision making for multivariate problems. Belief graphical models can be built either by eliciting the uncertain knowledge of an expert or automatically learnt from data using machine learning techniques. Many types of inference algorithms exist and many platforms are now available allowing modeling and reasoning with belief graphical models in many application areas such as diagnosis, forecasting, decision making and classification. This chapter provides an overview of the most common belief graphical models. In particular, it gives an overview on various aspects related to graphical models for uncertainty: representation, inference, learning and finally applications.
JavaScript Code Suggestion Based on Deep Learning

Code suggestion system is widely used in integrated development environments (IDEs) for generating code recommendations while editing to improve program efficiency. Current most common systems focus on the settings that complete a single code unit or predict likely next single unit. In this paper, we describe a code suggestion prototype system for JavaScript based on Jupyter Notebook [1] (an IDE) to provide multiple successive code units completion. Our main work is as follows: 1. Provide a JavaScript pre-processing solution for feature extraction; 2. Apply several deep learning technologies, including LSTM [2], attention mechanism (AM) [3] and sparse point network (SPN) [4] to support system performance; 3. Design a solution for model deployment and provide post-processing methods to improve user experience. Offline model performance shows that the LSTM + SPN has achieved a 79.73% all-token accuracy rate and 44.34% identifier accuracy rate among top 5 predictions respectively. Online evaluation shows that the demo system fits practical application experience.
AN ANALYSIS OF STUDENTS’ ABILITY AND DIFFICULTIES IN WRITING DESCRIPTIVE TEXT

AbstractThe title of this paper is “An Analysis of Students’ Ability and Difficulties in Writing Descriptive Text”. The researcher did the research at vocational high school. This research focused on the students’ ability and difficulties in writing descriptive text. Even though descriptive text is not too difficult, but many students still found it difficult. The aims of the research are to know the ability of students in writing descriptive text, to know the difficulties of students in writing descriptive text, and to know how the teacher teaches descriptive text. This is a qualitative research. In collecting the data, the researcher used text analysis, observation and questionnaire. The components of writing that are analyzed are developing the ideas, organizing of ideas, grammar, vocabulary and punctuation. The results of this study illustrate that student faced difficulties in organizing and developing ideas. They were not able to develop ideas on the texts they wrote. Whereas, in term of grammar there was no significant difficulty, they can write descriptive text by using the correct grammar. In term of vocabulary, it was difficult for students to choose appropriate vocabulary even some of them still used Bahasa Indonesia. In term of punctuation marks, there is no significant error. Based on the results of this study, it is suggested for teachers to pay attention to the teaching structure of descriptive text in the development of ideas, arrangement of ideas, grammar, vocabulary and punctuation. Teachers are also expected to provide examples and practice about the descriptive text to the students so they can achieve the purpose of education, especially in English subject.
A Status Quo Biased Multistage Decision Model for Regional Agricultural Socioeconomic Planning Under Fuzzy Information

We proposed a novel fuzzy multistage control model of sustainable regional agricultural development which better reflects specific features of human stakeholders. First, we use fuzzy logic for the modeling of imprecision in human judgments, intentions, preferences, evaluations, etc. Second, we propose to reflect in the model the so called status quo bias of the humans which basically stands for a common propensity of the humans to stay with known and already employed procedures and courses of action, avoiding larger changes. We develop therefore a human centric model. We also indicate that the inclusion of the status quo bias can be viewed as a way to mitigate risk which is crucial, notably in the case of agriculture. We present some simple example of how a best (optimal) investment policy can be obtained under different development scenarios, and indicate what change the inclusion of the status quo bias brings.
Experience-based Causality Learning for Intelligent Agents

Understanding causality in text is crucial for intelligent agents. In this article, inspired by human causality learning, we propose an experience-based causality learning framework. Comparing to traditional approaches, which attempt to handle the causality problem relying on textual clues and linguistic resources, we are the first to use experience information for causality learning. Specifically, we first construct various scenarios for intelligent agents, thus, the agents can gain experience from interaction in these scenarios. Then, human participants build a number of training instances for agents of causality learning based on these scenarios. Each instance contains two sentences and a label. Each sentence describes an event that an agent experienced in a scenario, and the label indicates whether the sentence (event) pair has a causal relation. Accordingly, we propose a model that can infer the causality in text using experience by accessing the corresponding event information based on the input sentence pair. Experiment results show that our method can achieve impressive performance on the grounded causality corpus and significantly outperform the conventional approaches. Our work suggests that experience is very important for intelligent agents to understand causality.
Toward Robust Policy Summarization

agents are being developed to help people with high stakes decision-making processes from driving cars to prescribing drugs. It is therefore becoming increasingly important to develop explainable AI methods that help people understand the behavior of such agents. Summaries of agent policies can help human users anticipate agent behavior and facilitate more effective collaboration. Prior work has framed agent summarization as a machine teaching problem where examples of agent behavior are chosen to maximize reconstruction quality under the assumption that people do inverse reinforcement learning to infer an agent's policy from demonstrations. We compare summaries generated under this assumption to summaries generated under the assumption that people use imitation learning. We show through simulations that in some domains, there exist summaries that produce high-quality reconstructions under different models, but in other domains, only matching the summary extraction model to the reconstruction model produces high-quality reconstructions. These results highlight the importance of assuming correct computational models for how humans extrapolate from a summary, suggesting human-in-the-loop approaches to summary extraction.
S-Box on Subgroup of Galois Field

In substitution–permutation network as a cryptosystem, substitution boxes play the role of the only nonlinear part. It would be easy for adversaries to compromise the security of the system without them. 8-bit S-boxes are the most used cryptographic components. So far, cryptographers were constructing 8-bit S-boxes used in cryptographic primitives by exhaustive search of permutations of order 256. However, now for cryptographic techniques with 8-bit S-boxes as confusion layers, researchers are trying to reduce the size of S-box by working with a small unit of data. The aim is to make the techniques compact, fast and elegant. The novelty of this research is the construction of S-box on the elements of the multiplicative subgroup of the Galois field instead of the entire Galois field. The sturdiness of the proposed S-box against algebraic attacks was hashed out by employing the renowned analyses, including balance, nonlinearity, strict avalanche criterion, and approximation probabilities. Furthermore, the statistical strength of the S-box was tested by the majority logic criterion. The fallouts show that the S-box is appropriate for applications for secure data communications. The S-box was also used for watermarking of grayscale images with good outcomes.
Electronic Banking and Performance of Small and Medium Scale Enterprises in Anambra State, Nigeria

This study aims to identify the relationship between electronic banking and performance of small and medium scale enterprises in Anambra state; one of the states with the highest number of SMEs, entrepreneurial skills, informal enterprises and a suitable business environment in Nigeria. To achieve this, the researcher examined performance of SMEs and their association with components of electronic banking; automated teller machine, point of sale services, transaction alerts via short message services (SMS) and mobile banking, through a questionnaire. Three hundred and seventy (73.1% response rate) copies of questionnaire issued to five hundred and six sampled respondents of 50 SMEs in Anambra state were properly filled and found relevant to the study. The study used SPSS and Excel to identify the descriptive characteristics of the variables of the study and analyze the data. Regression analysis was used to test the hypotheses of the study. Study results concluded that there is positive relationship between; automated teller machine, point of sale services, transaction alerts via short message services (SMS), mobile banking and performance of SMEs in Anambra State, Nigeria. The result also showed that SMEs continuous usage of electronic banking services could be attributed to cost effectiveness, convenience, security, accessibility and diversity of the services. The study recommends among others that, security of electronic banking services should be upgraded by a conjunction between SMEs and banks and that government should provide adequate regulatory framework to protect customers and security of transaction. Furthermore, policy makers in Nigeria should take advantage of the positive revelation of this study to make policies that will increase number of SMEs in Anambra state and Nigeria at large especially from the large number of informal enterprises in Anambra state and Nigeria.