Reviewerly/exHarmony huggingface dataset:
Like-tasted user groups to predict ratings in recommender systems

Recommendation systems have gained the intention of many researchers due to the growth of the business of personalizing, sorting and suggesting products to customers. Most of the rating prediction in recommendation systems are based on customer preferences or on the historical behavior of similar customers. The similarity between customers is generally measured by the number of times customers liked or disliked the same item. Given the huge number and the variety of items, many customers cannot be considered as similar, as they did not evaluate the same items, even if they have similar tastes. This paper presents a new method of rating prediction in recommendation systems. The proposed method starts by identifying the taste directions or the interest centers based on the users’ demographic information combined with their previous evaluations. Thus, it uses the principal component analysis to retrieve the major taste orientations. According to these orientations, user groups are created. Then, for each group, it generates a prediction model that will be used to predict unknown rates of users within the corresponding group. In order to assess the accuracy of the proposed method, we compare its results with four baseline methods, namely: RegSVD, BiasedMF, SVD++ and MudRecS. The results prove that the proposed algorithm is more accurate than the baseline algorithms.
Quantum-Computation and Applications

In this research notebook on quantum computation and applications for quantum engineers, researchers, and scientists, we will discuss and summarized the core principles and practical application areas of quantum computation. We first discuss the historical prospect from which quantum computing emerged from the early days of computing before the dominance of modern microprocessors. And the re-emergence of that quest with the sunset of Moore's law in the current decade. The mapping of computation onto the behavior of physical systems is a historical challenge vividly illustrate by considering how quantum bits may be realized with a wide variety of physical systems, spanning from atoms to photons, using semiconductors and superconductors. The computing algorithms also change with the underline variety of physical systems and the possibility of encoding the information in the quantum systems compared to the ordinary classical computers because of these new abilities afforded by quantum systems. We will also consider the emerging engineering, science, technology, business, and social implications of these advancements. We will describe a substantial difference between quantum and classical computation paradigm. After we will discuss and understand engineering challenges currently faced by developers of the real quantum computation system. We will evaluate the essential technology required for quantum computers to be able to function correctly. Later on, discuss the potential business application, which can be touch by these new computation capabilities. We utilize the IBM Quantum Experience to run the real-world problem, although on a small scale.
Innovations into industrial technology programmes of Nigerian Universities for effective application of mobile communication technologies and quality assurance of graduates

&lt;p&gt;The study innovated the industrial technology programmes of Nigerian universities for effective application of mobile&lt;br /&gt;communication technologies and quality assurance of graduates. Five research questions guided the study while three null&lt;br /&gt;hypotheses formulated were tested at .05 level of significance. The study made use of descriptive survey design and was&lt;br /&gt;carried out in Enugu State, Nigeria. The participants for the study were 120 comprising of 67 lecturers of&lt;br /&gt;electrical/electronic technology and 53 technologists of industrial technical education. A 97-item questionnaire was used as&lt;br /&gt;instrument for data collection. One hundred and eighteen copies of the questionnaire were retrieved. The data collected were&lt;br /&gt;analyzed using mean, factor analysis, standard deviation and improvement need index (INI) to answer the four research&lt;br /&gt;questions while analysis of variance was employed to test the hypotheses at .05 level of significance. Results revealed that&lt;br /&gt;33 innovations for inclusion into programme of electrical/electronic technology. Furthermore, the results of the study&lt;br /&gt;revealed that teachers need improvement in implementing the innovated contents of electrical/electronic technology aspect&lt;br /&gt;of industrial technical education programme. Hence, there is need to package the findings of the study into training&lt;br /&gt;programme and use it for building the capacity of teachers of electrical/electronic technology in Nigerian universities.&lt;/p&gt;
A Unified Network Architecture for Semi-Structured Deep Distributional Regression

We propose a unified network architecture for deep distributional regression in which entire distributions can be learned in a general framework of interpretable regression models and deep neural networks. Our approach combines advanced statistical models and deep neural networks within a unifying network, contrasting previous approaches that embed the neural network part as a predictor in an additive regression model. To avoid identifiability issues between different model parts, we construct an orthogonalization cell that projects the deep neural network part into the orthogonal complement of the statistical model predictor, facilitating both estimation and interpretability in high-dimensional settings. We identify appropriate default penalties that can also be understood as prior distribution assumptions in the Bayesian version of our network architecture. We consider several use-cases in experiments with synthetic data and real world applications to illustrate special merits of our approach.
Picket: Self-supervised Data Diagnostics for ML Pipelines

Data corruption is an impediment to modern machine learning deployments. Corrupted data can severely bias the learned model and can also lead to invalid inference. We present, Picket, a first-of-its-kind system that enables data diagnostics for machine learning pipelines over tabular data. Picket can safeguard against data corruptions that lead to degradation either during training or deployment. For the training stage, Picket identifies erroneous training examples that can result in a biased model, while for the deployment stage, Picket flags corrupted query points to a trained machine learning model that due to noise will result to incorrect predictions. Picket is built around a novel self-supervised deep learning model for mixed-type tabular data. Learning this model is fully unsupervised to minimize the burden of deployment, and Picket is designed as a plugin that can increase the robustness of any machine learning pipeline. We evaluate Picket on a diverse array of real-world data considering different corruption models that include systematic and adversarial noise. We show that Picket offers consistently accurate diagnostics during both training and deployment of various models ranging from SVMs to neural networks, beating competing methods of data quality validation in machine learning pipelines.
Fast and Accurate Non-Projective Dependency Tree Linearization

We propose a graph-based method to tackle the dependency tree linearization task. We formulate the task as a Traveling Salesman Problem (TSP), and use a biaffine attention model to calculate the edge costs. We facilitate the decoding by solving the TSP for each subtree and combining the solution into a projective tree. We then design a transition system as post-processing, inspired by non-projective transition-based parsing, to obtain non-projective sentences. Our proposed method outperforms the state-of-the-art linearizer while being 10 times faster in training and decoding.
Sparsified Linear Programming for Zero-Sum Equilibrium Finding

Computational equilibrium finding in large zero-sum extensive-form imperfect-information games has led to significant recent AI breakthroughs. The fastest algorithms for the problem are new forms of counterfactual regret minimization [Brown and Sandholm, 2019]. In this paper we present a totally different approach to the problem, which is competitive and often orders of magnitude better than the prior state of the art. The equilibrium-finding problem can be formulated as a linear program (LP) [Koller et al., 1994], but solving it as an LP has not been scalable due to the memory requirements of LP solvers, which can often be quadratically worse than CFR-based algorithms. We give an efficient practical algorithm that factors a large payoff matrix into a product of two matrices that are typically dramatically sparser. This allows us to express the equilibrium-finding problem as a linear program with size only a logarithmic factor worse than CFR, and thus allows linear program solvers to run on such games. With experiments on poker endgames, we demonstrate in practice, for the first time, that modern linear program solvers are competitive against even game-specific modern variants of CFR in solving large extensive-form games, and can be used to compute exact solutions unlike iterative algorithms like CFR.
Gaussian-Based Visualization of Gaussian and Non-Gaussian-Based Clustering

A generic method is introduced to visualize in a “Gaussian-like way,” and onto $\mathbb {R}^{2}$ , results of Gaussian or non-Gaussian–based clustering. The key point is to explicitly force a visualization based on a spherical Gaussian mixture to inherit from the within cluster overlap that is present in the initial clustering mixture. The result is a particularly user-friendly drawing of the clusters, providing any practitioner with an overview of the potentially complex clustering result. An entropic measure provides information about the quality of the drawn overlap compared with the true one in the initial space. The proposed method is illustrated on four real data sets of different types (categorical, mixed, functional, and network) and is implemented on the r package ClusVis.
A Sequence-to-sequence Approach for Numerical Slot-filling Dialog Systems

Dialog systems capable of filling slots with numerical values have wide applicability to many task-oriented applications. In this paper, we perform a particular case study on the “number_of_guests” slot-filling in hotel reservation domain, and propose two methods to improve current dialog system model on 1. numerical reasoning performance by training the model to predict arithmetic expressions, and 2. multi-turn question generation by introducing additional context slots. Furthermore, because the proposed methods are all based on an end-to-end trainable sequence-to-sequence (seq2seq) neural model, it is possible to achieve further performance improvement on increasing dialog logs in the future.
Extraction of Iron Oxide Nanoparticles from 3 Dimensional MRI Images Using<i>K</i>-Mean Algorithm

Nanomedicine targeted drug delivery is one of the emerging techniques for diagnosis and treatment of complex diseases. Medical image processing of High-Resolution Magnetic Resonance Imaging (HR-MRI), when combined with iron oxide nanoparticles (IO-NPs), provides a precious tool to monitor diagnosis and treatment processes. The challenge is to detect the nanoparticles inside the HR-MRI images. This is due to the low resolution of the images and the small size of the nanoparticles. In this paper, we study the drug delivery efficiency using a mouse with an inflamed calf, with IO-NPs attached to the therapeutic drug and injected into the mouse's eye. Our aim is to know how much of the drug injected will reach the inflamed region of the calf. A high-resolution MRI system was used to take images of the inflamed calf region. Knowing that iron oxide has a strong magnetic intensity on MRI images, image processing techniques were used to identify the location and quantity of IO-NPs attached to the drug. By knowing the location and quantity of IO-NPs we can estimate the quantity of drug delivered to the region of interest. In our project, K-mean algorithm , an automatic clustering algorithm was used to detect the iron oxide NPs in the MRI images. This then extracts them from the 3D model of the femoral region of interest. Extraction of NPs permits an estimation of the number of NPs clustered in the region and furthermore estimates the quantity of the drug delivered to the region of interest. The results obtained of nanoparticle detection and extraction seem to be a promising way to estimate the amount of delivered drug to a targeted area.
A Case Study of User Communication Styles with Customer Service Agents versus Intelligent Virtual Agents

We investigate differences in user communication with live chat agents versus a commercial Intelligent Virtual Agent (IVA). This case study compares the two types of interactions in the same domain for the same company filling the same purposes. We compared 16,794 human-to-human conversations and 27,674 conversations with the IVA. Of those IVA conversations, 8,324 escalated to human live chat agents. We then investigated how human-to-human communication strategies change when users first communicate with an IVA in the same conversation thread. We measured quantity, quality, and diversity of language, and analyzed complexity using numerous features. We find that while the complexity of language did not significantly change between modes, the quantity and some quality metrics did vary significantly. This fair comparison provides unique insight into how humans interact with commercial IVAs and how IVA and chatbot designers might better curate training data when automating customer service tasks.
A unifying framework for modeling acoustic/prosodic entrainment: definition and evaluation on two large corpora

Acoustic/prosodic (a/p) entrainment has been associated with multiple positive social aspects of human-human conversations. However, research on its effects is still preliminary, first because how to model it is far from standardized, and second because most of the reported findings rely on small corpora or on corpora collected in experimental setups. The present article has a twofold purpose: 1) it proposes a unifying statistical framework for modeling a/p entrainment, and 2) it tests on two large corpora of spontaneous telephone interactions whether three metrics derived from this framework predict positive social aspects of the conversations. The corpora differ in their spoken language, domain, and positive social outcome attached. To our knowledge, this is the first article studying relations between a/p entrainment and positive social outcomes in such large corpora of spontaneous dialog. Our results suggest that our metrics effectively predict, up to some extent, positive social aspects of conversations, which not only validates the methodology, but also provides further insights into the elusive topic of entrainment in human-human conversation.
Sparse Approach in Filtering of Color Images Corrupted by Mixture Noises

A novel filtering approach is presented in denoising in the color images contaminated by mixture of additive-impulsive noises. Novel framework consists of three principal stages: impulsive noise suppression that is performed detecting pixels corrupted by impulsive noise and then, filtering found spikes by a variant of median filter; during second stage, original additive noise suppression filter is employed in Wavelet transform domain via a sparse representation and 3D-filtering; finally, nondesirable effects obtained in an image during previous stages are processed to correct fine details. In case of multiplicative noise suppression, the designed denoising scheme uses 3D homomorphic sparse processing stage and post-filtering procedure. Evaluation of novel approach in denoising complex distortions has been performed using objective criteria (PSNR and SSIM measures) and subjective perception via human visual system confirming their better performance in comparison with state-of-theart techniques.
Privacy-Preserving Visual Feature Descriptors through Adversarial Affine Subspace Embedding

Many computer vision systems require users to upload image features to the cloud for processing and storage. Such features can be exploited to recover sensitive information about the scene or subjects, e.g., by reconstructing the appearance of the original image. To address this privacy concern, we propose a new privacy-preserving feature representation. The core idea of our work is to drop constraints from each feature descriptor by embedding it within an affine subspace containing the original feature as well as one or more adversarial feature samples. Feature matching on the privacy-preserving representation is enabled based on the notion of subspace-to-subspace distance. We experimentally demonstrate the effectiveness of our method and its high practical relevance for applications such as crowd-sourced 3D scene reconstruction and face authentication. Compared to the original features, our approach has only marginal impact on performance but makes it significantly more difficult for an adversary to recover private information.
An end-to-end approach for the verification problem: learning the right distance

In this contribution, we augment the metric learning setting by introducing a parametric pseudo-distance, trained jointly with the encoder. Several interpretations are thus drawn for the learned distance-like model’s output. We first show it approximates a likelihood ratio which can be used for hypothesis tests, and that it further induces a large divergence across the joint distributions of pairs of examples from the same and from different classes. Evaluation is performed under the verification setting consisting of determining whether sets of examples belong to the same class, even if such classes are novel and were never presented to the model during training. Empirical evaluation shows such method defines an end-to-end approach for the verification problem, able to attain better performance than simple scorers such as those based on cosine similarity and further outperforming widely used downstream classifiers. We further observe training is much simplified under the proposed approach compared to metric learning with actual distances, requiring no complex scheme to harvest pairs of examples.
From web to SMS: A text summarization of Wikipedia pages with character limitation

Wikipedia is one of the main sources of information on the Web.But the access to this content may be difficult especially when using a basic telephone without browsing capability and only a GSM network.The only means of text-based communication remains through SMS.Due to the limitation of the number of characters, a Wikipedia page cannot always be sent through SMS.This work raises the issue of text summarization with character limitation.To solve this issue, two extractive approaches have been combined: LSA and TextRank algorithms.Generated summaries have been evaluated using ROUGE metrics.Since ROUGE metrics do not consider character limitation, a new threshold named Threshold of Acceptability for Character-Oriented Summaries (TACOS) has been proposed to appreciate ROUGE metrics.The evaluation showed the relevance of the approach for pages of at most 2000 characters.The system has been tested using the SMS simulator of RapidSMS without a GSM gateway to simulate the deployment in a real environment.To the best of our knowledge, this is the first work tackling text summarization issue with character limitation.
Interactive Construction of User-Centric Dictionary for Text Analytics

We propose a methodology to construct a term dictionary for text analytics through an interactive process between a human and a machine.The interactive approach helps the creation of flexible dictionaries with precise granularity required in text analysis.This paper introduces the first formulation of interactive dictionary construction to address this issue.To optimize the interaction, we propose a new algorithm that effectively captures an analyst's intention starting from only a small number of sample terms.Along with the algorithm, we also design an automatic evaluation framework that provides a systematic assessment of any interactive method for the dictionary creation task.Experiments using real scenario based corpora and dictionaries show that our algorithm outperforms baseline methods, and works even with a small number of interactions.Also, we provide our dataset for future studies 1 .
ArtPDGAN: Creating Artistic Pencil Drawing with Key Map Using Generative Adversarial Networks

A lot of researches focus on image transfer using deep learning, especially with generative adversarial networks (GANs). However, no existing methods can produce high quality artistic pencil drawings. First, artists do not convert all the details of the photos into the drawings. Instead, artists tend to use strategies to magnify some special parts of the items and cut others down. Second, the elements in artistic drawings may not be located precisely. What’s more, the lines may not relate to the features of the items strictly. To address above challenges, we propose ArtPDGAN, a novel GAN based framework that combines an image-to-image network to generate key map. And then, we use the key map as an important part of input to generate artistic pencil drawings. The key map can show the key parts of the items to guide the generator. We use a paired and unaligned artistic drawing dataset containing high-resolution photos of items and corresponding professional artistic pencil drawings to train ArtPDGAN. Results of our experiments show that the proposed framework performs excellently against existing methods in terms of similarity to artist’s work and user evaluations.
Innovative Analysis of Higher Vocational Education Model Based on Virtual Reality Technology

Abstract Higher vocational colleges are an important part of China’s higher education system. They pay attention to the cultivation of students’ ability to analyze and solve problems, and play an important role in promoting students’ understanding and mastery of knowledge. At the present stage, due to the influence of many historical and realistic factors, the educational and teaching conditions of China’s higher vocational colleges are relatively weak, and the supporting charity of teaching is slightly outdated, so many advanced teaching practice models are difficult to play their roles. Virtual reality technology is the combination of information technology and artificial intelligence, it represents the forefront of scientific and technological development. With the popularization and application of this technology in various industries, more and more people have noticed that innovative education in vocational colleges can be improved from the perspective of technological innovation and teaching mode innovation. The purpose of this paper is to explore how to realize the innovation of higher vocational education model with the intervention of virtual reality technology. This paper starts with the application status of virtual reality technology, analyzes the specific measures of this technology in the innovation practice of higher vocational education, and simulates and analyzes the effect of this technology on practical teaching innovation. The research results of this paper show that virtual reality technology can greatly improve the formalization, inpertinence and imperfect training system of innovation education in higher vocational colleges, break the limitation of time and space, reduce the cost, and improve the status quo of innovation education in higher vocational colleges.