Reviewerly/exHarmony huggingface dataset:
Time-aware Multi-interest Capsule Network for Sequential Recommendation

In recent years, sequential recommendation has been widely researched, which aims to predict the next item of interest based on user's previously interacted item sequence. Many works use RNN to model the user interest evolution over time. However, they typically compute a single vector as the user representation, which is insufficient to capture the variation of user diverse interests. Some non-RNN models employ the dynamic routing mechanism to automatically vote out multiple capsules that represent user's diverse interests, but they are ignorant of the temporal information of user's historical behaviors, thus yielding suboptimal performance. In this paper, we aim to establish a time-aware dynamic routing algorithm to effectively extract temporal user multiple interests for sequential recommendation. We observe that the significance of an item to user interests may change monotonically over time, and user interests may fluctuate periodically. Following the intuitive temporal patterns of user interests, we propose a novel time-aware multi-interest capsule network named TAMIC that leverages two kinds of time-aware voting gates, i.e., monotonic gates and periodic gates, to control the influence of each interacted item on user's current interests during the routing procedure. We further employ an aggregation module to form a temporal multi-interest user representation which is used for next item prediction. Extensive experiments on real-world datasets verify the effectiveness of the time gates and the superior performance of our TAMIC approach on sequential recommendation, compared with the state-of-the-art methods.
Hybrid Tokenization and Datasets for Solving Mathematics and Science Problems Using Transformers

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)Hybrid Tokenization and Datasets for Solving Mathematics and Science Problems Using TransformersPratik Mandlecha, Snehith Kumar Chatakonda, Neeraj Kollepara, and Pawan KumarPratik Mandlecha, Snehith Kumar Chatakonda, Neeraj Kollepara, and Pawan Kumarpp.289 - 297Chapter DOI:https://doi.org/10.1137/1.9781611977172.33PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Transformers, which were introduced for solving the task of machine translation, have expanded their utility in multiple domains. A recent application of transformers is in solving elementary mathematics problems. In this paper, we use a hybrid tokenization technique for encoding the mathematics and science problems and answers, which is used to train the transformer. We compare the performance of our tokenization with that of the char-to-char tokenzation in solving various types of mathematics and science problems. We discuss the accuracy, memory usage, and time to train the model with proposed tokenization. The proposed tokenization shows higher accuracy for some problems, and requires lesser memory compared to char-to-char tokenization. We propose an extended dataset of science and mathematics problems that consists of billions of samples in question-answer format in raw text. Code and Dataset: https://github.com/misterpawan/scimat2. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-717-2 https://doi.org/10.1137/1.9781611977172Book Series Name:ProceedingsBook Code:PRDT22Book Pages:1-737
Small and medium enterprises' business sustainability based on the industry 4.0 internet of things adoption: A malaysian bumiputera case study

Companies of all sizes, including Small and Medium Enterprises, have been left with few choices for preparing for and implementing changes. This research gathered information to provide the theoretical framework on the determinants that Malaysian Bumiputera SMEs are relying on for their decision to adopt or not to adopt IoT in their business operations. Bumiputera refers to the indigenous people of Malaysia. The study utilised a cross-disciplinary study design. The data was obtained from company proprietors in Malaysia that operate in the category of Bumiputera SMEs and was statistically analysed using IBM's SPSS and AMOS software. According to the initial findings, it can be seen that respondents are very excited about the benefits of IoT, but lack the imagination to pioneer the arrangement. Furthermore, even though respondents are unconcerned about IoT, it gives the appearance of being unsure, leading them to think further on whether to trust it or not. It was also shown that top organizations have an increasingly good intuition of IoT, but also a progressively negative intuition of it. It is recommended that SMEs holistically, especially Bumiputera SMEs, acquire more knowledge and be prepared to expand the grasp of productive factors of IoT in Malaysia.
Quantum communication with SU(2) invariant separable $$2\times N$$ level systems

Information is encoded in a qubit in the form of its Bloch vector. In this paper, we propose protocols for remote transfers of information in a known and an unknown qubit to qudits using SU(2)-invariant $$2 \times N$$ -level separable discordant states as quantum channels. These states have been identified as separable equivalents of the two-qubit entangled Werner states in Bharath and Ravishankar (Phys Rev A 89:062110, 2014). Due to $$SU(2) \times SU(2)$$ invariance of these states, the remote qudit can be changed by performing appropriate measurements on the qubit. We also propose a protocol for transferring information of a family of unknown qudits to remote qudits using $$2 \times N$$ -level states as channels. Finally, we propose a protocol for swapping of quantum discord from $$2\times N$$ -level systems to $$N \times N$$ -level systems. All the protocols proposed in this paper involve separable states as quantum channels.
Verifying opacity by abstract interpretation

Nowadays, preventing sensitive information leakage is a crucial issue. Indeed, in order to deploy secure computing systems, data protection is an aspect that cannot be ignored. In this respect, opacity is a security policy aiming at hiding the truth value of a predicate during computation. Unfortunately, despite its simple intended meaning, opacity is a quite difficult program property (indeed it is actually an hyperproperty) to guarantee. In this paper, we propose a verification mechanism, based on abstract interpretation, for opacity. Indeed, while studying the relation between opacity and abstract non-interference, a weakening of non-interference observing properties of program computations instead of concrete values, we noticed that under particular conditions opacity is implied by abstract non-interference. Hence, by exploiting the recently proposed static approach for verifying non-interference, based on hypersemantics, we can show how to verify abstract non-interference and therefore opacity.
Effective shortcut technique for generative adversarial networks

The generator in the generative adversarial network (GAN) learns image generation in a coarse-to-fine manner in which earlier layers learn the overall structure of the image and the latter ones refine the details. To propagate the coarse information well, recent works usually build their generators by stacking up multiple residual blocks. Although the residual block can produce a high-quality image as well as be trained stably, it often impedes the information flow in the network. To alleviate this problem, this brief introduces a novel generator architecture that produces the image by combining features obtained through two different branches: the main and auxiliary branches. The goal of the main branch is to produce the image by passing through the multiple residual blocks, whereas the auxiliary branch is to convey the coarse information in the earlier layer to the later one. To combine the features in the main and auxiliary branches successfully, we also propose a gated feature fusion module that controls the information flow in those branches. To prove the superiority of the proposed method, this brief provides extensive experiments using various standard datasets including CIFAR-10, CIFAR-100, LSUN, CelebA-HQ, AFHQ, and tiny-ImageNet. Furthermore, we conducted various ablation studies to demonstrate the generalization ability of the proposed method. Quantitative evaluations prove that the proposed method exhibits impressive GAN performance in terms of Inception score (IS) and Frechet inception distance (FID). For instance, the proposed method boosts the FID and IS scores on the tiny-ImageNet dataset from 35.13 to 25.00 and 20.23 to 25.57, respectively.
How Does a Spontaneously Speaking Conversational Agent Affect User Behavior?

People treat conversational agents as mere tools and not as human-like social actors. While there has been much research on human-like agents, few studies have approached the realization of a conversational agent as a social actor from the viewpoint of speech synthesis. This study investigated the effect of synthetic voice of conversational agent trained with spontaneous speech on human interactants. Specifically, we hypothesized that humans will exhibit more social responses when interacting with conversational agent that has a synthetic voice built on spontaneous speech. Typically, speech synthesizers are built on a speech corpus where voice professionals read a set of written sentences. The synthesized speech is clear as if a newscaster were reading a news or a voice actor were playing an anime character. However, this is quite different from spontaneous speech we speak in everyday conversation. Recent advances in speech synthesis enabled us to build a speech synthesizer on a spontaneous speech corpus, and to obtain a near conversational synthesized speech with reasonable quality. By making use of these technologies, we examined whether humans produce more social responses to a spontaneously speaking conversational agent. We conducted a large-scale conversation experiment with a conversational agent whose utterances were synthesized with the model trained either with spontaneous speech or read speech. The result showed that the subjects who interacted with the agent whose utterances were synthesized from spontaneous speech tended to show shorter response time and a larger number of backchannels. The result of a questionnaire showed that subjects who interacted with the agent whose utterances were synthesized from spontaneous speech tended to rate their conversation with the agent as closer to a human conversation. These results suggest that speech synthesis built on spontaneous speech is essential to realize a conversational agent as a social actor.
Code authorship identification via deep graph CNNs

Code authorship identification (CAI) is an emerging field useful to identify code plagiarism, settle copyright disputes, and it is in general, an essential tool for code forensics. However, scaling CAI has always been limited by domain knowledge and the burden of feature engineering. Recent works in CAI have demonstrated the utility of using Convolutional Neural Networks (CNN) to avoid this problem. Unfortunately, the use of CNNs for CAI imposes a rigid representation of programs, limiting expressions of dependency and semantic relationships. In this paper, we explore the use of Graph Deep CNN for CAI. Here, programs are represented by graphs which allow to express their complex relationships. We use a subset of Google Code Jam as a dataset and a methodology for transforming these programs into graphs. After extensive simulations, we show our methodology performs well in terms of scalability, accuracy, and loss.
Slow Feature Preprocessing in Deep Neural Networks for Wearable Sensor-Based Locomotion Recognition

This paper demonstrates how Slow Feature Analysis (SFA) can be used to transform sensor data before it is classified using a deep neural network. Slow features is concept originating from neurosciences which posits that a function can be regressed to fast changing sensory input to obtain a slower output, commensurate to the dynamics of the physical phenomena. Such dimensionality reduction is a common preprocessing technique. We explore this in the recognition of modes of locomotion and transportation from mobile phone motion sensors. Using a well-established deep learning activity recognition algorithm (DeepConvLSTM), we systematically investigate combinations of slow features as an additional layer, or substituting a layer in the deep network. The data used consists of 5s windows captured by a smartphone acceleration sensor carried at the hips. The results show that SFA can be used to improve the classification performance of the DeepConvLSTM network on an acceleration signal by up to 2.2%.
Image denoising to enhance character recognition using deep learning

Image denoising is an important decisive preliminary stage in character recognition. A character image properties are enhanced to make it easier to recognize. In particular, to optical character recognition, an efficient image denoising technique instantaneously increases character recognition accuracy. In this paper, we proposed implementing a deep convolutional neural network (DCNN). A relationship between a noisy character image to its clean counter-part are mapped using DCNN. The overall process is divided into two stages: noise type classification and image denoising. Firstly, the noise type classification identifies the types of noise, and based on this noise type, a particular denoising model is selected, which increases the image denoising performance. The denoising network inputs a noisy image and a target of its clean corresponding image during the training. After the mapping function is trained, the generated model performs character image denoising. To increase the denoising efficiency, the noisy color image is decomposed into its RGB band. Then, on each band, a trained mapping function perform image denoising irrespective of the other band. Finally, each block is assembled to generate a clean image. In this paper, the MNIST and Char74K dataset of handwritten digits diluted with artificial noise divided into ten types are used for experimentation. The superiority of our strategy over counter-parts is the adequate minimization of image deterioration image denoising. Our experimental results show that the proposed techniques perform better image denoising as compared to the existing methods, both in terms of image noise type classification and image denoising. The overall character recognition accuracy increased by 66% after performing the proposed denoising technique.
The Evolution of Data and Freedom of Expression and Hate Speech Concerns with Artificial Intelligence

Abstract This opinion article, by Channarong Intahchomphoo and Christian Tschirhart, explains the evolution of data and how it becomes useful information and then insightful knowledge. In the current era we are witnessing a high increase in the development and adaptation of artificial intelligence (AI) in society. AI technologies have the ability to process large volumes of data and information to help in finding insightful knowledge. However, AI is not perfect and there are ethical concerns, particularly when unintended negative consequences result from it; this paper also discusses ethical concerns currently confronting our society related to the freedom of expression and hate speech issues with AI. Importantly, this paper notes that governments are working to find ways to regulate social media and internet companies through legal channels as governments are no longer confident in the ability of social media and internet companies to self-regulate and thereby to guide society on what content is right or wrong. This is a critical new development in internet and AI governance that information and technology professionals and public and private organizations need to monitor closely the situation as it evolves.
Numerical solution for high-order ordinary differential equations using H-ELM algorithm

Purpose This paper aims to introduce a novel algorithm to solve initial/boundary value problems of high-order ordinary differential equations (ODEs) and high-order system of ordinary differential equations (SODEs). Design/methodology/approach The proposed method is based on Hermite polynomials and extreme learning machine (ELM) algorithm. The Hermite polynomials are chosen as basis function of hidden neurons. The approximate solution and its derivatives are expressed by utilizing Hermite network. The model function is designed to automatically meet the initial or boundary conditions. The network parameters are obtained by solving a system of linear equations using the ELM algorithm. Findings To demonstrate the effectiveness of the proposed method, a variety of differential equations are selected and their numerical solutions are obtained by utilizing the Hermite extreme learning machine (H-ELM) algorithm. Experiments on the common and random data sets indicate that the H-ELM model achieves much higher accuracy, lower complexity but stronger generalization ability than existed methods. The proposed H-ELM algorithm could be a good tool to solve higher order linear ODEs and higher order linear SODEs. Originality/value The H-ELM algorithm is developed for solving higher order linear ODEs and higher order linear SODEs; this method has higher numerical accuracy and stronger superiority compared with other existing methods.
Spider monkey based metaheuristic tuning of PID controllers for stability landing of UAV'S with SMP-Auxetic landing gears

The proposed work deals with the study of automatic tuning of PID controllers for perched landing of UAV'S with shape memory This modeling considerably enhanced the range of feasible structures for perch and rest compared with avian-inspired influencers. Though not nature-inspired, and far easier than a foot from a bird, stiff fingers and contact modules were easier to create than avian-inspired gripers with several joint joints per finger and stronger and more durable. Start and landing in flight phases are critical phases.polymer based auxetic landing gears. A metaheuristic tuning is implemented through spider monkey approach for PID controllers in drone perching mechanism. Trials were conducted with open loop for drone perching conditions in measuring the error rates pitch, yaw and roll moment. Fitness function is calculated through regression analysis for the observed experiments. Spider monkey based optimization algorithm is implemented for the fitness function to find the optimal data of Kp, Ki and Kd for minimal error rate of pitch, yaw and roll moment to balance the drone at various perching angles. The provided results have been compared with model predictive controller (MPC) and Generic model control (GMC). It has been noted that SM based PID controller reduces the maximum error rates with 34.6% when compared with MPC and 24.8% when compared with GMC.
End-to-End Conversational Search for Online Shopping with Utterance Transfer

Successful conversational search systems can present natural, adaptive and interactive shopping experience for online shopping customers.However, building such systems from scratch faces real word challenges from both imperfect product schema/knowledge and lack of training dialog data.In this work we first propose ConvSearch, an end-to-end conversational search system that deeply combines the dialog system with search.It leverages the text profile to retrieve products, which is more robust against imperfect product schema/knowledge compared with using product attributes alone.We then address the lack of data challenges by proposing an utterance transfer approach that generates dialogue utterances by using existing dialog from other domains, and leveraging the search behavior data from e-commerce retailer.With utterance transfer, we introduce a new conversational search dataset for online shopping.Experiments show that our utterance transfer method can significantly improve the availability of training dialogue data without crowd-sourcing, and the conversational search system significantly outperformed the best tested baseline.
Visual Modeling of Data using Convolutional Neural Networks

Artificial Intelligence has been showing monumental growth in filling the gap between the capabilities of humans and machines. Researchers and scientists work on many aspects to make new things happen. Computer Vision is one of them. To make the system to visualize, neural networks are used. Some of the well-known Neural Networks include CNN, Feedforward Neural Networks (FNN), and Recurrent Neural Networks (RNN) and so on. Among them, CNN is the correct choice for computer vision because they learn relevant features from an image or video similar to the human brain. In this paper, the dataset used is CIFAR-10 (Canadian Institute for Advanced Research) which contains 60,000 images in the size of 32x32. Those images are divided into 10 different classes which contains both training and testing images. The training images are 50,000 and testing images are 10,000. The ten different classes contain airplanes, automobiles, birds, cat, ship, truck, deer, dog, frog and horse images. This paper was mainly concentrated on improving performance using normalization layers and comparing the accuracy achieved using different activation functions like ReLU and Tanh.
Intruder model for generating attack scenarios in computer systems

In this paper, a new technique for automatic generation of complex intruder attacks is proposed. In this approach, the generation of attack scenarios is guided by an intruder strategy. Thus, only attack scenarios achieving the intruder objectives are generated. Therefore, the generated attack graph do not contain redundant nodes or edges, and enables a network administrator to have a better visualisation and apprehension of different attack scenarios for a computer network. In this way, our approach is made as a candidate for increasing accuracy of the attack graph generated based on the use of the intruder strategy.
A Generalization of the Cylinder Conjecture for Divisible Codes

We extend the original cylinder conjecture on point sets in affine three-dimensional space to the more general framework of divisible linear codes over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${ {\mathbb {F}}_{q}}$ </tex-math></inline-formula> and their classification. Through a mix of linear programming, combinatorial techniques and computer enumeration, we investigate the structural properties of these codes. In this way, we can prove a reduction theorem for a generalization of the cylinder conjecture, show some instances where it does not hold and prove its validity for small values of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> . In particular, we correct a flawed proof for the original cylinder conjecture for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q = 5$ </tex-math></inline-formula> and present the first proof for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q = 7$ </tex-math></inline-formula> .