Reviewerly/exHarmony huggingface dataset:
Knowledge-based Context-aware Multi-turn Conversational Model with Hierarchical Attention

We study response generation in multi-turn open- domain dialogue systems. Background knowledge based response generation has been developed to make dialogue models generate more informative and appropriate responses. However, these knowledge-based dialogue models are limited to the domain of single round conversation, and fail to consider the role of dialogue context in the selection of relevant knowledge and response generation. As a result, these models might lose some useful information in the dialogue context and generate irrelevant responses. We argue that both dialogue context and relevant knowledge play important roles in the response generation of multiturn open-domain dialogue systems. We propose a Knowledge- based Context-aware Multi-turn Conversational (KCMC) model to consider both dialogue context and relevant knowledge in a unified framework. The Knowledge Fusion module is designed to augment the semantic representation of dialogue context with associated knowledge triples. And we introduce hierarchical encoders to model the hierarchy of dialogue context and to capture important information in the dialogue context. Furthermore, a hierarchical attention mechanism attends to important parts of knowledge triples, which facilitates better knowledge selection and response generation. Through extensive experiments on two datasets, we demonstrate that the proposed model is capable of generating more informative and appropriate responses than baseline models.
BiG-Transformer: Integrating Hierarchical Features for Transformer via Bipartite Graph

Self-attention based models like Transformer have achieved great success on kinds of Natural Language Processing tasks. However, the traditional fixed fully-connected structure faces many challenges in practice, such as computing redundancy, fixed granularity, and inexplicable. In this paper, we present BiG-Transformer, which employs attention with bipartite-graph structure to replace the fully-connected self-attention mechanism in Transformer. Specifically, two parts of the graph are designed for integrating hierarchical semantic information, and two types of connection are proposed to fuse information from different positions. Experiments on four tasks show the BiG-Transformer achieves better performance compared to Transformer liked models and Recurrent Neural Networks.
Convolutional networks for motion detection in smart gadgets

The excursion by individuals towards a world, where the correspondences between people and PC is accomplished through the movements or activities done by the people. It is occurring at a higher rate than anticipated. In a significant number of the fields, where human pictures are utilized as an order to a PC, hand involves just a fourth of the territory in the picture while the rest of the region is loaded up with the body and the earth encompassing the object of the intrigue. Restriction of the hand which is required for the order is difficult to use in such conditions. Our thought in this paper is to make a framework with the assistance of convolutional neural systems that can separate the hand signals provided as an order by the client, with no pre-preparing methods.
Dynamically Generated Compact Neural Networks for Task Progressive Learning

Task progressive learning is often required where the training data become available in batches over the time. Such learning has the characteristic of using an existing model trained over a set of tasks to learn a new task while maintaining the accuracy of older tasks. Artificial Neural Networks (ANNs) have a higher capacity for progressive learning than other traditional machine learning models due to the availability of a large number of ANN parameters. A progressive model that uses a fully connected ANN suffers from long training time, overfitting, and excessive resource usage. It is therefore necessary to generate the ANN incrementally as new tasks arrive and new training is needed. In this paper, an incremental algorithm is presented to dynamically generate a compact neural network by pruning and expanding the synaptic weights based on the learning requirements of the new tasks. The algorithm is implemented, analyzed, and validated using the cloud network security datasets, UNSW and AWID, as well as the image dataset, MNIST.
Soft Colour Segmentation On Light Fields

In this work we explore methods for allowing advanced colour editing on light field images to be performed. This investigation is twofold. First we look at soft colour algorithms to decompose images into colour layers and the various ways it could be applied to light field data in order to ensure spatially consistent results. Then, with the purpose of colour editing in mind, we present an object-based layer separation method so that editing a layer does not wrongly affect specific objects. We further discuss the advantages and drawbacks that light field data present over regular single-view images for this purpose. Finally we present some editing results to show that our methods allow us to obtain visually appealing images that remain consistent across all light field views and minimise the colour artefacts inherent to layer decomposition methods.
Language-Facilitated Human-Robot Cooperation within a Human Cognitive Modeling Infrastructure: A Case in Space Exploration Task

It is natural and efficient to use natural language for transferring knowledge from a human to a robot. The inconsistency of human-robot spatial cognitive style, the high frequency communication and low-cognition-level symbol matching control have greatly affected the operational efficiency in spatial-cognition-demanding tasks such as positioning and exploring. To fill the knowledge gap, this study applies ACT-R cognitive theory to establish a new way of knowledge representation and processing for the robots with a purpose to improve the flexibility of natural language facilitated humanrobot cooperation. This idea is specifically validated in the task of human-robot teaming space exploration.
Simplifying Neural Networks via Look up Tables and Product of Sums Matrix Factorizations

We study 2 approaches, called TableNet and Prod-SumNet respectively, to simplify the implementation of neural networks. First we look at using Look Up Tables (LUT) to remove the multiplication operations and obviate the need of a multiplier. We compare the different tradeoffs of this approach in terms of accuracy versus LUT size and the number of operations and show that similar performance can be obtained with a comparable memory footprint as a full precision linear classifier, but without the use of any multipliers. Secondly we reduce the number of trainable model parameters by decomposing linear operators in neural networks as a product of sums of simpler linear operators which generalizes recently proposed deep learning architectures such as CNN, KFC, Dilated CNN, etc. We show that good accuracy on MNIST and Fashion MNIST can be obtained using a relatively small number of trainable parameters. In addition, since implementation of the convolutional layer is resource-heavy, we also consider an implementation in the transform domain that obviates the need for convolutional layers. We illustrate the tradeoff of varying the number of trainable variables and the corresponding error rate. As an example, by using this decomposition on a reference CNN architecture for MNIST with over 3 · 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> trainable parameters, we are able to obtain an accuracy of 98.44% using only 3554 trainable parameters. Furthermore, the generality of the framework allows it to be be suitable for general problems unlike CNN which performs best in the image processing or other shift-invariant domains.
Multi-label learning for dynamic model type recommendation

Dynamic selection techniques aim at selecting the local experts around each test sample in particular for performing its classification. While generating the classifier on a local scope may make it easier for singling out the locally competent ones, as in the online local pool (OLP) technique, using the same base-classifier model in uneven distributions may restrict the local level of competence, since each region may have a data distribution that favors one model over the others. Thus, we propose in this work a problem-independent dynamic base-classifier model recommendation for the OLP technique, which uses information regarding the behavior of a portfolio of models over the samples of different problems to recommend one (or several) of them in a per-instance manner. Our proposed framework builds a multi-label meta-classifier responsible for recommending a set of relevant base-classifier models based on the local data complexity of the region surrounding each test sample. The OLP technique then produces a local pool with the model that yields the highest probability score of the meta-classifier. Experimental results show that different data distributions favored different model types on a local scope. Moreover, based on the performance of an ideal model type selector, it was observed that there is a clear advantage in choosing a relevant base-classifier model for each test instance in particular. Overall, the proposed model type recommender system yielded a statistically similar performance to the original OLP with fixed base-classifier model. However, the proposed framework struggled to recommend at least one relevant model type specially for the samples with low labelset cardinality. Given the novelty of the approach and the gap in performance between the proposed framework and the ideal selector, we regard this as a promising research direction. Code available at github.com/marianaasouza/dynamic-model-recommender.
Assessing Grassroots Engineering Applications in Brazil

Engineering (University of Campinas, 1999). My research area encompasses philosophy of technology and of engineering and engineering education.I am now studying grassroots engineering (GE) and social/solidarity technology (ST), as well as engineering education, focusing, on one hand, on the ethicalpolitical, aesthetics, and epistemic aspects that both characterize and make GE and ST possible, and, on the other hand, on the challenges the engineering education must face in order to train/develop the capabilities or skills engineers must possess so to be able of doing GE
On extracting keywords from long‐and‐difficult English sentences for smart sentiment analysis

With the population of social multi‐media and website, smart sentiment analysis from sentences or language becomes a new interesting application in the field of smart cities. In this paper, the sentiment is automatically analyzed by extracting the keywords from the long‐and‐difficult English sentences. In supervised scenario, we extract the keywords that are more relevant to express emotions according to syntactic relationship and logical structure, and then assign these keywords weights. In semi‐supervised scenario, we combine the key sentence extraction and classifier fusion algorithm to extract key sentences that contain more sentiment words. During extracting key sentences, we consider sentiment word attribute, position attribute, punctuation attribute, and keywords attribute. During classifier fusion stage, we utilize the classifier with highest confidence to decide the final classification result. The experiments, performed on IMDB and Rotten Tomatoes, show that the proposed method performs better than previous ones. Therefore, extracting keywords via syntactic relationship and logical structure is helpful for smart sentiment analysis.
Study of Mean Square Errors of Receding Horizon Unbiased FIR Filters

This work considers the statistical properties of the receding horizon unbiased filter with finite impulse response. Our approach is based on analysis of proposed new representations for the filter mean square error (MSE) during sliding windows. We focus on developing algorithms for selecting the optimal length of the sliding window in sense of minimum of the MSE trace. The main idea is to construct asymptotic approximation of the MSE trace for large finite values of the sliding window size. This allows to evaluate the impact of different parameters on the estimation error and find the optimal length of the sliding window. If only ranges for noises matrices are specified, we propose an adaptive version of its evaluation in the learning cycle.
Estimation of the Complexity of the Classification Task Based on the Analysis of Variational Autoencoders

The problem of constructing a criterion, that allows to evaluate the complexity of the problem to solve by evaluating the inner layer of the variational autoencoder, is considered. A variational autoencoder was modeled for image classification tasks of several levels of complexity. A complexity estimate, based on the study of the inner layer of the autoencoder in the form of measuring distances between distributions was formed. Calculated complexity estimates allow you to rank classification tasks according to the expert rating.
IoT-Based Smart Gallery to Promote Museum in Ambon

The happening COVID-19 pandemic has a significant impact on the tourism visit of the Museum in Ambon. Creativity is indispensable for the maintainer to attract many tourists to visit the Museum after the COVID-19 pandemic has passed. This research intends to design an application that can guide tourists who will attend the Museum. The goal is to be able to know a lot of information about the Museum. The app is an IoT based smart gallery that is accessed using QRcode. The application designed to showcase various museum collections in the form of text, images, audio, and video. The app is expected to increase tourists' visits to museums.
Stylometrics Features Under Domain Shift: Do They Really “Context-Independent”?

The problem of determining the author of a text, i.e. authorship attribution (AA), is based on the assumption on the stability and uniqueness of the authorial style. Since it is obvious that a lot of content words bear topical information, researchers widely use the so-called “context-independent” style markers which are believed to be resistant to the shift of topic, genre, mode, etc. (“domain”), e.g., character n-grams, part-of-speech n-grams, function words, readability indices, etc. However, there is a lack of systematic studies on the stability of such markers in idiolects under “context” (domain) change, mainly due to the lack of appropriate corpora. This paper aims to partially fill this gap by revealing the effect of domain shift (simultaneous change of genre and topic) on several groups of stylometric markers using a carefully constructed author and domain-controlled corpus. The effect of domain shift was studied separately for each group of markers (function words, lexical complexity indices, lexical and morphological diversity markers, readability indices, morphological markers, combined n-grams of part-of-speech and punctuation marks) using a combination of unsupervised and supervised methods: principal component analysis (PCA), ANOVA on PCA, joint PCA and cluster analysis, class (domain) prediction by nearest shrunken centroid. Only the markers of lexical complexity, lexical and morphological diversity were revealed to be untouched by domain shift, which stresses the necessity to choose the markers for cross-domain AA with special cautiousness due to the risk of domain, not authorship detection.
A novel recommendation algorithm based on product life cycle theory

A large number of practical applications of the recommendation system found that the novelty of the recommendation results and the user satisfaction are more closely related, making the novelty recommendation recently widely concerned and studied. Many novelty recommendation algorithms used the popularity of the item to measure novelty, but this method is too simple, and the change of item popularity is more reflective of its novelty. According to the product life cycle theory (PLC), this study proposed a novelty recommendation algorithm that recommends item that be not popular now and may be popular in the future to improve the novelty of the recommendation results, The time change of the popularity of the items to be recommended is analyzed, and the future popularity of the items are predicted by analogy. Two strategies for selecting recommended selection are selecting future popular items (the predicting popularity-based filtering Algorithm, PP algorithm) and excluding future recession items (the Excluding Recession-based filtering algorithm, ER algorithm), according to the definition of novelty of the item, recommended the novelty items to the target user. The effectiveness of the proposed algorithm was verified through an offline experiment. Results indicate that PP algorithm can significantly improve the accuracy and novelty, but seriously sacrifice the coverage and reduce the ability of the recommendation system to mine the long tail items when the number of alternative items N is small, the novelty of the recommendation list of the ER algorithm is remarkably higher than that of traditional algorithms, the novelty is high when the quantity of alternative sets reaches around 350, where the average popularity of the recommendation list declines by 40%, and the coverage is elevated by 150%, thereby improving the ability of the proposed system to extract all kinds of items. This study serves as reference for the improvement of user satisfaction with recommendation systems.
Ai, on the Law of the Elephant: Toward Understanding Artificial Intelligence [Abstract]

Machine learning and other artificial intelligence (“AI”) systems are changing our world in profound, exponentially rapid, and likely irreversible ways. Although AI may be harnessed for great good, it is capable of and is doing great harm at scale to people, communities, societies, and democratic institutions. The dearth of AI governance leaves unchecked AI’s potentially existential risks. Whether sounding urgent alarm or merely jumping on the bandwagon, law scholars, law students, and lawyers at bar are contributing volumes of AI policy and legislative proposals, commentaries, doctrinal theories, and calls to corporate and international organizations for ethical AI leadership.Profound concerns exist about AI and the actual and potential crises of societal, democratic, and individual harm that it causes or may cause in future. Compounding those deep concerns is lawyers’ lack of sufficient AI knowledge and technological competence, despite ethical mandates for diligence and competence. As a result, legal discussions and law and policy recommendations may be fundamentally flawed because they are constructed upon erroneous, uninformed, or misconceived understandings of AI technologies, inputs, and processes.
Training With Cache: Specializing Object Detectors From Live Streams Without Overfitting

Online distillation can dynamically adapt to changes of distribution in a target domain by continuously updating a smaller student model from a live video stream. This ensures the accuracy of the student model even if distribution changes occur due to a change of content. However, online distillation degrades the overall accuracy because it causes overfitting to “current” distribution not to “recent” distribution. The student model is trained on sequential incoming data, and its model parameters are overwritten with the “current” distribution. As a result, the student model forgets the “recent” distribution. To overcome this problem, we propose a new training framework using cache. Our framework temporarily stores incoming frames and teacher model outputs in a cache and trains a student model with data selected from the cache. Since our approach trains the student model with not only incoming data but also past data, it can improve the overall accuracy while adapting to changes of distribution without overfitting. To use limited cache size efficiently, we also propose a loss-aware cache algorithm that chooses training data prioritized by its loss value. Our experiments show that training with cache improves the accuracy compared with online distillation, and the loss-aware cache algorithm outperforms a cache algorithm modeled on traditional offline training.
IoT Based Virtual Reality Game for Physio-therapeutic Patients

Biofeedback therapy trains the patient to control voluntarily the involuntary process of their body. This non-invasive and non-drug treatment is also used as a means to rehabilitate the physical impairments that may follow a stroke, a traumatic brain injury or even in neurological aspects within occupational therapy. The idea behind this study is based on using immersive gaming as a tool for physical rehabilitation that combines the idea of biofeedback and physical computing to get a patient emotionally involved in a game that requires them to do the exercises in order to interact with the game. This game is aimed towards addressing the basic treatment for ‘Frozen Shoulder’. In this work, the physical motions are captured by the wearable ultrasonic sensor attached temporarily to the various limbs of the patient. The data received from the sensors are then sent to the game via serial wireless communication. There are two main aspects to this study: motion capturing and game design. The current status of the application is a single ultrasonic detector. The experimental result shows that physio-therapeutic patients are benefited through the IoT based virtual reality game.
WW-Nets: Dual Neural Networks for Object Detection

We propose a new deep convolutional neural network framework that uses object location knowledge implicit in network connection weights to guide selective attention in object detection tasks. Our approach is called What-Where Nets (WW-Nets), and it is inspired by the structure of human visual pathways. In the brain, vision incorporates two separate streams, one in the temporal lobe and the other in the parietal lobe, called the ventral stream and the dorsal stream, respectively. The ventral pathway from primary visual cortex is dominated by "what" information, while the dorsal pathway is dominated by "where" information. Inspired by this structure, we have proposed an object detection framework involving the integration of a "What Network" and a "Where Network". The aim of the What Network is to provide selective attention to the relevant parts of the input image. The Where Network uses this information to locate and classify objects of interest. In this paper, we compare this approach to state-of-the-art algorithms on the PASCAL VOC 2007 and 2012 and COCO object detection challenge datasets. Also, we compare out approach to human "ground-truth" attention. We report the results of an eye-tracking experiment on human subjects using images from PASCAL VOC 2007, and we demonstrate interesting relationships between human overt attention and information processing in our WW-Nets. Finally, we provide evidence that our proposed method performs favorably in comparison to other object detection approaches, often by a large margin. The code and the eye-tracking ground-truth dataset can be found at: https://github.com/mkebrahimpour.