Reviewerly/exHarmony huggingface dataset:
A Configurable Architecture for Running Hybrid Convolutional Neural Networks in Low-Density FPGAs

Convolutional neural networks have become the state of the art of machine learning for a vast set of applications, especially for image classification and object detection. There are several advantages to running inference on these models at the edge, including real-time performance and data privacy. The high computing and memory requirements of convolutional neural networks have been major obstacles to the broader deployment of CNNs on edge devices. Data quantization is an optimization method that reduces the number of bits used to represent weights and activations of a network model, minimizing storage requirements and computing complexity. Quantization can be applied at the layer level, by using different bit widths in different layers: this is called hybrid quantization. This article proposes a new efficient and configurable architecture for running CNNs with hybrid quantization in low-density Field-Programmable Gate Arrays (FPGAs) targeting edge devices. The architecture has been implemented on the Xilinx ZYNQ7020/45 devices and is running the AlexNet and VGG16 networks. Running AlexNet, the architecture has a throughput up to 508 images per second on the ZYNQ7020 device, and 1639 images per second on the ZYNQ7045 device. Considering VGG16, the architecture delivers up to 43 images per second on the ZYNQ7020 device, and 81 images per second on the ZYNQ7045 device. The proposed hybrid architecture achieves up to 13.7x improvement in performance compared to state-of-the-art solutions, with small accuracy degradation.
X-Stance: A Multilingual Multi-Target Dataset for Stance Detection

We extract a large-scale stance detection dataset from comments written by candidates of elections in Switzerland. The dataset consists of German, French and Italian text, allowing for a cross-lingual evaluation of stance detection. It contains 67 000 comments on more than 150 political issues (targets). Unlike stance detection models that have specific target issues, we use the dataset to train a single model on all the issues. To make learning across targets possible, we prepend to each instance a natural question that represents the target (e.g. Do you support X?). Baseline results from multilingual BERT show that zero-shot cross-lingual and cross-target transfer of stance detection is moderately successful with this approach.
ALigN: A Highly Accurate Adaptive Layerwise Log_2_Lead Quantization of Pre-Trained Neural Networks

Deep Neural Networks are one of the machine learning techniques which are increasingly used in a variety of applications. However, the significantly high memory and computation demands of deep neural networks often limit their deployment on embedded systems. Many recent works have considered this problem by proposing different types of data quantization schemes. However, most of these techniques either require post-quantization retraining of deep neural networks or bear a significant loss in output accuracy. In this paper, we propose a novel and scalable technique with two different modes for the quantization of the parameters of pre-trained neural networks. In the first mode, referred to as log_2_lead, we use a single template for the quantization of all parameters. In the second mode, denoted as ALigN, we analyze the trained parameters of each layer and adaptively adjust the quantization template to achieve even higher accuracy. Our technique significantly maintains the accuracy of the parameters and does not require retraining of the networks. Moreover, it supports quantization to an arbitrary bit-size. For example, compared to the single-precision floating-point numbers-based implementation, our proposed 8-bit quantization technique generates only ~ 0.2% and ~ 0.1%, loss in the Top-1 and Top-5 accuracies respectively for VGG-16 network using ImageNet dataset. We have observed similar minimal losses in the Top-1 and Top-5 accuracies for AlexNet and Resnet-18 using the proposed quantization scheme for the 8-bit range. Our proposed quantization technique also provides a higher mean intersection over union for semantic segmentation when compared with state-of-the-art quantization techniques. The proposed technique represents parameters in powers of 2, thereby eliminating the need for resource-computationally intensive multiplier units for the hardware accelerators of the neural networks. We also present a design for implementing the multiplication operation using bit-shifts and addition for the proposed quantization technique.
Characterizing the Expressive Power of Invariant and Equivariant Graph Neural Networks.

Various classes of Graph Neural Networks (GNN) have been proposed and shown to be successful in a wide range of applications with graph structured data. In this paper, we propose a theoretical framework able to compare the expressive power of these GNN architectures. The current universality theorems only apply to intractable classes of GNNs. Here, we prove the first approximation guarantees for practical GNNs, paving the way for a better understanding of their generalization. Our theoretical results are proved for invariant GNNs computing a graph embedding (permutation of the nodes of the input graph does not affect the output) and equivariant GNNs computing an embedding of the nodes (permutation of the input permutes the output). We show that Folklore Graph Neural Networks (FGNN), which are tensor based GNNs augmented with matrix multiplication are the most expressive architectures proposed so far for a given tensor order. We illustrate our results on the Quadratic Assignment Problem (a NP-Hard combinatorial problem) by showing that FGNNs are able to learn how to solve the problem, leading to much better average performances than existing algorithms (based on spectral, SDP or other GNNs architectures). On a practical side, we also implement masked tensors to handle batches of graphs of varying sizes.
Usability and Design Issues of Mobile Assisted Language Learning Application

This paper aims to look at teachers, government officials, and students for Literacy & Numeracy Drive (LND), a smartphone app for students in Punjab province, Pakistan, to teach languages and math.Furthermore, to recognize LND usability and design problems while its use for grade three in schools.As the usability and design issues of LND were not discussed since the launch of this application.The methodology for this study is the questionnaire for teachers and semistructured interviews for government officials of District Sheikhupura and students.The result shows that LND has various usability and design problems in its current form, i.e., buttons, icons, color schemes, sluggish performance, and fonts.Besides, teachers, government officials, and students suggested that game-based learning consists of an interactive interface, phonics, key animations to be created and adopted.Highly engaging and appealing delivery of the curriculum and improvements in the appraisal will improve the participation of students and deliver better outcomes.
Path length statistics in reservoir computers

Because reservoir computers are high dimensional dynamical systems, designing a good reservoir computer is difficult. In many cases, the designer must search a large nonlinear parameter space, and each step of the search requires simulating the full reservoir computer. In this work, I show that a simple statistic based on the mean path length between nodes in the reservoir computer is correlated with better reservoir computer performance. The statistic predicts the diversity of signals produced by the reservoir computer, as measured by the covariance matrix of the reservoir computer. This statistic by itself is not sufficient to predict reservoir computer performance because not only must the reservoir computer produce a diverse set of signals, it must be well matched to the training signals. Nevertheless, this path length statistic allows the designer to eliminate some network configurations from consideration without having to actually simulate the reservoir computer, reducing the complexity of the design process.
On physical and social distancing

column On physical and social distancing: reflections on moving just about everything online amid Covid-19 Share on Author: Mikael Wiberg Umeå University Umeå UniversityView Profile Authors Info & Claims InteractionsVolume 27Issue 4July - August 2020 pp 38–41https://doi.org/10.1145/3404213Online:09 July 2020Publication History 2citation536DownloadsMetricsTotal Citations2Total Downloads536Last 12 Months353Last 6 weeks52 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
On a projection-based class of uniformity tests on the hypersphere

We introduce a projection-based class of uniformity tests on the hypersphere. The class employs statistics that integrate, along all possible directions, a weighted quadratic discrepancy between the empirical cumulative distribution of the projections and the projected uniform distribution. Simple expressions for several test statistics are obtained for the circle and the sphere, as well as relatively tractable forms for higher dimensions. Despite their different origins, variants of the proposed class are shown to contain and be contained in variants of the Sobolev class of uniformity tests. Our new class proves itself advantageous by allowing the derivation of new tests that neatly extend the circular-only tests by Watson, Ajne, and Rothman, and by introducing the first instance of an Anderson–Darling-like test for directional data. We obtain usable asymptotic distributions and the local asymptotic optimality against certain alternatives of the new tests. A simulation study evaluates the theoretical findings and provides evidence that the new testing proposals are competitive. An application to the study of the crater distribution on Rhea illustrates the usage of the new tests.
Bangla language textual image description by hybrid neural network model

Automatic image captioning task in different language is a challenging task which has not been well investigated yet due to the lack of dataset and effective models. It also requires good understanding of scene and contextual embedding for robust semantic interpretation of images for natural language image descriptor. To generate image descriptor in Bangla, we created a new Bangla dataset of images paired with target language label, named as Bangla Natural Language Image to Text (BNLIT) dataset. To deal with the image understanding, we propose a hybrid encoder-decoder model based on encoder-decoder architecture and the model is evaluated on our newly created dataset. This proposed approach achieves significance performance improvement on task of semantic retrieval of images. Our hybrid model uses the Convolutional Neural&lt;br /&gt;Network as an encoder whereas the Bidirectional Long Short Term Memory is used for the sentence representation that decreases the computational complexities without trading off the exactness of the descriptor. The model yielded benchmark accuracy in recovering Bangla natural language and we also conducted a thorough numerical analysis of the model performance on the BNLIT dataset.
The effect of predictor variables on employee engagement and organizational commitment and employee performance

The purpose of this study was to examine and analyze the influence of personality, motivation, job satisfaction, employee engagement, organizational commitment on employee performance in PT.PLN (Persero) The Power Generation Unit of Mahakam, Indonesia.There were 167 employees participated in this survey using a saturated sample in which all populations were sampled.The research model used in this study was a structural model to test the hypotheses proposed by the SEM (Structure Equation Modeling) analysis technique using the Warp PLS 6.0 program approach.The results show that personality and job satisfaction had a positive and significant effect on employee engagement.Motivation was not significant to employee engagement.Personality and motivation were not significant to organizational commitment.Job satisfaction had positive and significant effect on organizational commitment.Personality, motivation, and job satisfaction did not have significant effect on employee performance.Employee engagement was positive and significant for organizational commitment.Employee engagement and organizational commitment were positive and significant for employee performance.
Blockchain-Based Service Recommendation Supporting Data Sharing

With the rapid development of cloud computing, massive web services have appeared quickly with a heavy burden for user to choose services that they preferred. To cope with the stress, many recommendation algorithms have been proposed. Nonetheless, the recommendation effects were not ideal due to the insufficient data in a cloud platform. For this situation, it is necessary for cloud platforms to cooperate with each other to share data. However, the cloud platforms generally do not intend share the data because of the uses’ privacy. Meanwhile, the traditional recommendation exist a series of challenges such as security and privacy, data tampering and so on. In order to address the above problems, we propose a blockchain-based service recommendation scheme (BPDS–SR) which can achieve a higher accuracy and lower cost with more profits.
SHECS-PIR: Somewhat Homomorphic Encryption-Based Compact and Scalable Private Information Retrieval

A Private Information Retrieval (PIR) protocol allows a client to retrieve arbitrary elements from a database stored in a server without revealing to the server any information about the requested element. PIR is an important building block of many privacy-preserving protocols, and its efficient implementation is therefore of prime importance. Several concrete, practical PIR protocols have been proposed and implemented so far, particularly based on very low-depth somewhat homomorphic encryption. The main drawback of these protocols, however, is their large communication cost, especially in terms of the server’s reply, which grows like $$O(d\root d \of {n})$$ for an n-element database, where d is a parameter typically chosen as 2 or 3. In this paper, we describe an efficient PIR protocol called SHECS-PIR, based on deeper circuits and GSW-style homomorphic encryption. SHECS-PIR reduces the communication cost down to $$O(\log n)$$ removing all other factors apart from database size while maintaining a high level of efficiency. In fact, for large databases, we achieve faster server processing time in addition to more compact queries.
Anticipating Human Intention for Full-Body Motion Prediction in Object Grasping and Placing Tasks

Motion prediction in unstructured environments is a difficult problem and is essential for safe and efficient human-robot space sharing and collaboration. In this work, we focus on manipulation movements in environments such as homes, workplaces or restaurants, where the overall task and environment can be leveraged to produce accurate motion prediction. For these cases we propose an algorithmic framework that accounts explicitly for the environment geometry based on a model of affordances and a model of short-term human dynamics both trained on motion capture data. We propose dedicated function networks for graspability and placebility affordances and we make use of a dedicated RNN [1] for short-term motion prediction. The prediction of grasp and placement probability densities are used by a constraint-based trajectory optimizer to produce a full-body motion prediction over the entire horizon. We show by comparing to ground truth data that we achieve similar performance for full-body motion predictions as using oracle grasp and place locations.
Zero‐anaphora resolution in Korean based on deep language representation model: BERT

It is necessary to achieve high performance in the task of zero anaphora resolution (ZAR) for completely understanding the texts in Korean, Japanese, Chinese, and various other languages. Deep-learning-based models are being employed for building ZAR systems, owing to the success of deep learning in the recent years. However, the objective of building a high-quality ZAR system is far from being achieved even using these models. To enhance the current ZAR techniques, we fine-tuned a pre-trained bidirectional encoder representations from transformers (BERT). Notably, BERT is a general language representation model that enables systems to utilize deep bidirectional contextual information in a natural language text. It extensively exploits the attention mechanism based upon the sequence-transduction model Transformer. In our model, classification is simultaneously performed for all the words in the input word sequence to decide whether each word can be an antecedent. We seek end-to-end learning by disallowing any use of hand-crafted or dependency-parsing features. Experimental results show that compared with other models, our approach can significantly improve the performance of ZAR.
A new metaheuristic approach based on agent systems principles

Agent-based modeling is a relatively new approach to model complex systems composed of agents whose behavior is described using simple rules. As a consequence of the agent interactions emerges a complex global behavioral pattern not explicitly programmed. In the last decade, an increasing number of metaheuristic techniques have been reported in the literature where authors claim their novelty and their abilities to perform as powerful optimization methods. Although these schemes emulate very different processes or systems, the rules used to model individual behavior are very similar. The idea behind the design of many metaheuristic methods is to configure a recycled set of rules that has demonstrated to be successful in previous approaches for producing new optimization schemes. Such common rules have been designed without considering the final global result obtained by the individual interactions. On the other hand, agent-based systems provide a solid theory and a set of consistent models that allow characterizing global behavioral patterns produced by the collective interaction of the individuals from a set of simple rules. Under this perspective, several agent-based concepts and models that generate very complex global search behaviors can be used to produce or improve efficient optimization algorithms. In this paper, a new metaheuristic algorithm based on agent systems principles is presented. The proposed method is based on the agent-based model known as “Heroes and Cowards”. This model involves a small set of rules to produce two emergent global patterns that can be considered in terms of the metaheuristic literature as exploration and exploitation stages. To evaluate its performance, the proposed algorithm has been tested in a set of representative benchmark functions, including multimodal, unimodal, and hybrid benchmark formulations. The competitive results demonstrate the promising association between both paradigms.
Breast cancer classification using machine learning techniques: a comparative study

Background: The second leading deadliest disease affecting women worldwide, after lung cancer, is breast cancer. Traditional approaches for breast cancer diagnosis suffer from time consumption and some human errors in classification. To deal with this problems, many research works based on machine learning techniques are proposed. These approaches show their effectiveness in data classification in many fields, especially in healthcare. Methods: In this cross sectional study, we conducted a practical comparison between the most used machine learning algorithms in the literature. We applied kernel and linear support vector machines, random forest, decision tree, multi-layer perceptron, logistic regression, and k-nearest neighbors for breast cancer tumors classification. The used dataset is Wisconsin diagnosis Breast Cancer. Results: After comparing the machine learning algorithms efficiency, we noticed that multilayer perceptron and logistic regression gave the best results with an accuracy of 98% for breast cancer classification. Conclusion: Machine learning approaches are extensively used in medical prediction and decision support systems. This study showed that multilayer perceptron and logistic regression algorithms are performant ( good accuracy specificity and sensitivity) compared to the other evaluated algorithms.
Constrained Mixed-Variable Design Optimization Based on Particle Swarm Optimizer with a Diversity Classifier for Cyclically Neighboring Subpopulations

In this research, an easy-to-use particle swarm optimizer (PSO) for solving constrained engineering design problems involving mixed-integer-discrete-continuous (MIDC) variables that adopt two kinds of diversity-enhancing mechanisms to achieve superior reliability and validity was developed. As an initial diversity-boosting tool, the local neighborhood topology of each particle is set up such that information exchange is restricted to a limited number of consecutively numbered particles. This topological mechanism forces each particle to move in the search space while interacting only with its neighboring subpopulation. The second diversity-enhancing task is to ensure that the exploration behavior of each particle in the search space is governed such that it follows the diversity classifier decision applied to its subpopulation. This diversity classification iteratively adjusts the three-phase velocity-related mechanism of each particle such that it approaches or retreats from its previous best position/the current best position among the subpopulation. In summary, this PSO tool not only introduces the social interaction of the particle within its cyclically neighboring subpopulation but also exploits the three-phase velocity behavior law governed by the distributed diversity measures categorized for each neighboring subpopulation. This scheme has superior reliability, as well as high practicality for engineering optimization problems involving MIDC variables, which are handled by the widely adopted straightforward rounding-off technique used in most swarm-inspired metaheuristic search technologies.
Multi-hop Inference for Question-driven Summarization

Question-driven summarization has been recently studied as an effective approach to summarizing the source document to produce concise but informative answers for nonfactoid questions.In this work, we propose a novel question-driven abstractive summarization method, Multi-hop Selective Generator (MSG), to incorporate multi-hop reasoning into question-driven summarization and, meanwhile, provide justifications for the generated summaries.Specifically, we jointly model the relevance to the question and the interrelation among different sentences via a human-like multi-hop inference module, which captures important sentences for justifying the summarized answer.A gated selective pointer generator network with a multi-view coverage mechanism is designed to integrate diverse information from different perspectives.Experimental results show that the proposed method consistently outperforms stateof-the-art methods on two non-factoid QA datasets, namely WikiHow and PubMedQA.