Reviewerly/exHarmony huggingface dataset:
Fusion Approaches of Heterogeneous Multichannel CNN and LSTM Models for Human Activity Recognition using Wearable Sensors

Human activity recognition (HAR) is an emerging filed to classify daily activities and is applicable to many applications including healthcare. Several machine learning models and approaches have been investigated to improve the classification performances. In this paper, we investigate several fusion approaches of heterogeneous multichannel models where the channels use different kinds of deep learning (DL) algorithms to better the HAR performances. The DL algorithms exploited in those channels are convolutional neural networks (CNNs), long short-term memory networks (LSTMs), CNN-LSTMs, and convolutional LSTMs. Six different fusion approaches based on feature fusion and decision fusion are applied to combine the outputs of the channels. Their classification performances are evaluated and compared. The best fusion approaches achieve 98.96% and 96.35% of accuracy when using the PAMAP2 and DaLiAc datasets, respectively.
The Ikshana Hypothesis of Human Scene Understanding

In recent years, deep neural networks (DNNs) achieved state-of-the-art performance on several computer vision tasks. However, the one typical drawback of these DNNs is the requirement of massive labeled data. Even though few-shot learning methods address this problem, they often use techniques such as meta-learning and metric-learning on top of the existing methods. In this work, we address this problem from a neuroscience perspective by proposing a hypothesis named Ikshana, which is supported by several findings in neuroscience. Our hypothesis approximates the refining process of conceptual gist in the human brain while understanding a natural scene/image. While our hypothesis holds no particular novelty in neuroscience, it provides a novel perspective for designing DNNs for vision tasks. By following the Ikshana hypothesis, we design a novel neural-inspired CNN architecture named IkshanaNet. The empirical results demonstrate the effectiveness of our method by outperforming several baselines on the entire and subsets of the Cityscapes and the CamVid semantic segmentation benchmarks.
Representing point sets on the plane as permutations

Let Σ={σ1,…,σn} be a permutation of the set [n]={1,…,n}. A subsequence σi1,…,σim of Σ is increasing (respectively decreasing) if σi1<…<σim (respectively σi1>…>σim). It is well known that any permutation of [n] contains an increasing or a decreasing subsequence of at least n elements. A classical proof of this result is the following [6]: To every σi assign the coordinates f(σi)=(xi,yi) on the plane, where xi is the length of a longest increasing subsequence of Σ ending at σi, and yi is the length of a longest decreasing subsequence of Σ starting at σi. Then it is easy to see that at least one point in f(Σ)={f(σi):i=1,…,n} has its x or y-coordinate greater than or equal to n. In this paper, we characterize the point sets S in N2 such that S=f(Σ) for some permutation Σ. Furthermore, given a set S of n points in N2, we give an O(nlog⁡n)-time algorithm to obtain, if it exists, a permutation Σ such that f(Σ)=S. Additionally, we show that by slightly modifying S, the permutation Σ always exists and can be found in O(nlog⁡n) time. Our results can be used, for example, to encrypt keywords by generating permutations from which the keywords can be recovered.
Dynamic Network-Assisted D2D-Aided Coded Distributed Learning

Today, numerous machine learning (ML) applications offer continuous data processing and real-time data analytics at the edge of wireless networks. Distributed real-time ML solutions are highly susceptible to the so-called straggler effect caused by resource heterogeneity, which can be mitigated by various computation offloading mechanisms that severely impact communication efficiency, especially in large-scale scenarios. To reduce the communication overhead, we leverage device-to-device (D2D) connectivity, which enhances spectrum utilization and allows for efficient data exchange between proximate devices. In particular, we design a novel D2D-aided coded distributed learning method named D2D-CDL for efficient load balancing across devices. The proposed solution captures system dynamics, including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data</i> (time-varying learning model, irregular intensity of data arrivals), <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">device</i> (diverse computational resources and volume of training data), and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deployment</i> (different locations and D2D graph connectivity). To decrease the number of communication rounds, we derive an optimal compression rate, which minimizes the processing time. The resulting optimization problem provides suboptimal compression parameters that improve the total training time. Our proposed method is particularly beneficial for real-time collaborative applications, where users continuously generate training data thus yielding a model drift.
Genetic Programming for Fiber-Threading for Fiber-Reinforced Plastics

Setting up fiber-threading for a pultrusion line is tedious, error prone and takes a long time. Between 100 and 1000 fibers have to be arranged into a two-dimensional shape, which have to be threaded between several support plates without causing crossovers. When manually planning this process based on intuition, it is hard to keep track of the complexity. This slows the process down to where it can take several hours or several days, and shortening this duration reduces the cost considerably. As planning the setup takes up a large chunk of time, we are proposing a simulation and an algorithm to automatically calculate how the fiber bundles need to be threaded from the creels through the support plates to result in the desired shape. Using a three-dimensional simulation for collision detection in conjunction with a genetic algorithm, we are able to shorten the planning of the fibers to around 10 minutes on a modern 8-core personal computer. Based on this data, further work can be done to further improve, visualize or permanently store the data in a digitized company.
DTB-Net: A Detection and Tracking Balanced Network for Fast Video Object Detection in Embedded Mobile Devices

Recently, object detection is of great significance and full of challenges in the task of video analysis with the continuous breakthrough of convolutional neural networks. However, detecting video objects in embedded mobile devices still remains challenging. The two major reasons limit the video detection performance of existing methods in embedded mobile devices: 1) The computing capability of embedded mobile devices is limited; 2) The motion and change of the interested object causes a lot of noise and blur. To address these problems, we propose a novel method for fast video object detection in embedded mobile devices, called a Detection and Tracking Balanced Network (DTB-Net). DTB-Net performs an accurate and fast object detection in embedded mobile devices by a balance module to regulate the working state of tracker and decide on the final output of network. In addition, we build a new Robot Detection Dataset for video object detection, called the FIST -RD. A series of detailed evaluation experiments in Nvidia Jetson TX2 platform based on FIST-RD dataset demonstrate the competitive performance of proposed DTB-Net.
Level set method for image segmentation model based on improved signed pressure force function

A new level set model is proposed to solve the problems of selective binary and gaussian filtering regularized level set (SBGFRLS), which cannot segment the image with weak boundary or uneven gray scale. A new signed pressure force function (SPF) which fuses global and local statistical information is constructed by introducing the generalized global mean gray value and adding local image gray value. In order to speed up the curve evolution, the velocity function is improved. The experimental results show that the new model can effectively segment targets with fuzzy boundaries and multi-target images with different gray scales, and has some anti-noise properties.
A Cost-Aware Scheduling Algorithm for Reliable Workflow in IaaS Clouds

With the development of cloud computing, cloud services have been widely used to deal with various large-scale complex workflow applications with the powerful computing capability. However, it is very challenging for cloud service providers to effectively schedule and deploy workflow applications while guaranteeing quality-of service (QoS) for different users. In this paper, a novel cost-aware reliable workflow scheduling algorithm (C RW S) is designed to minimize workflow scheduling cost and satisfy the workflow reliability constraints. In C RW S algorithm, we rank tasks to form a scheduling order and decomposes the workflow reliability constraint into tasks sub-reliability constraints. Virtual machines (VMs) are heuristically selected to scheduling workflow tasks for reducing cost while meeting the task sub-reliability constraint. Simulation experimental results demonstrate that C RW S achieves the lowest cost for workflow with the reliability constraint, as compared to the state-of-the-art algorithms
Addressing the cold-start problem with a two-branch architecture for fair tweet recommendation

In this paper we describe our solution for the RecSys Challenge 20211 focused on tweet recommendation to increase the number of user interactions. A large database with 800 million tweets produced over four weeks is used as training to predict four types of interactions: Like, Reply, Retweet and Quote. The proposed challenge is very similar to last year’s but incorporates a new fairness metric. Authors are splited into groups based on their number of followers and the final score is computed by averaging PRAUC and RCE of these groups. To satisfy this new restriction, we present a two-branch architecture that separates authors according to their total number of interactions in the dataset. In this way, authors who appear a few number of times (cold-start users) are predicted using similar users. The same is true for users with many interactions (active users). Each of the branches consists of a concatenation of four LightGBM models, one per target. They all use features we extracted from the interaction but they also use the output of the previous model. We first predict Like and use the output to predict Retweet. Then we predict Reply using Like and Retweet and so on. The users’ popularity, as well as the first and last words of the tweet text, turned out to be the best features for our method. Our solution obtained the 5th place in the final ranking and won the 2nd prize in the academic category. All the source code is available online2.
Research on the Fine Management Platform of Scientific Research Activities under the Information Condition

How to realize fine management of scientific research activities in the in-formation age. Scientific research activities staff urgently needs to solve this practical problem. Based on the cur-rent situation of scientific research activities in universities, this paper puts forward the overall requirements for comprehensive and fine management of scientific research activities, and expounds the specific content and module design. We should further promote the reform of the management system, and strive to strengthen the information construction of scientific research activities center by means of modernization and informatization. By means of precise management, we will improve the contribution of scientific research activities to the information construction and teaching environment of colleges. We will comprehensively improve the quality and efficiency of the use of resources.
On Mixed Teaching Model Based on Rain Classroom in the Background of Big Data—— based on the course Introduction to Linguistics

With the rapid development of digital technology, mobile phones and other terminal mobile devices as the carrier of a variety of interactive platforms continue to innovate, which has greatly promoted the reform of curriculum teaching mode. In the context of “big data”, “Rain Classroom” is used to carry out blended linguistics teaching through audio, video, text, image, etc. Its main characteristics are flexibility, interaction and efficiency. It can promote students' independent learning, active classroom atmosphere, broaden students' vision, broaden students' scope of knowledge, cultivate rational thinking ability and innovative inquiry learning ability.
Hardware and Software Structure for a Social Robot Capable of Situation Analysis

Abstract This paper discusses structuring of hardware and software for an autonomous social robot. It shows that the real-world social robot operations face the challenge of environmental variability and uncertainty of the objective parameters. Thus, a social robot must be capable of situation analysis for better autonomy. We propose a modular distributed structure of the control system. Separate modules monitor the status and control the subsystems of the robot. General coordination of subsystems is provided by the Supervisor module. For the robot to function autonomously, the Supervisor must be capable of situation analysis and its key functions: objective retrieval and analysis, situation description, configuring and strategizing the solution. The robot’s sensory inputs help acquire the objective and its parameters to describe the situation. Description relies on the database of a priori knowledge of the environment and its objects. Analysis is linked to a reduction in the uncertainty of the objective parameters and situation description. For a case study, the paper demonstrates a maze-solving strategy as affected by the situation.
Reducing repetition in convolutional abstractive summarization

Abstract Convolutional sequence to sequence (CNN seq2seq) models have met success in abstractive summarization. However, their outputs often contain repetitive word sequences and logical inconsistencies, limiting the practicality of their application. In this paper, we find the reasons behind the repetition problem in CNN-based abstractive summarization through observing the attention map between the summaries with repetition and their corresponding source documents and mitigate the repetition problem. We propose to reduce the repetition in summaries by attention filter mechanism (ATTF) and sentence-level backtracking decoder (SBD), which dynamically redistributes attention over the input sequence as the output sentences are generated. The ATTF can record previously attended locations in the source document directly and prevent the decoder from attending to these locations. The SBD prevents the decoder from generating similar sentences more than once via backtracking at test. The proposed model outperforms the baselines in terms of ROUGE score, repeatedness, and readability. The results show that this approach generates high-quality summaries with minimal repetition and makes the reading experience better.
Fusion for Tile-based Deconvolution Layers

Recently, various deep learning accelerators are being studied through data flow structure improvement and memory access optimization. Among them, the encoder-decoder model is widely used in object detection and semantic segmentation showing good performance. However, due to the deconvolution operation that outputs a high-resolution feature map from the decoder, the memory access and computational complexity are higher than that of the existing encoder-only structure. Thus, it is a big obstacle to the implementation of encoder-decoder accelerators. Most of the previous studies have focused only on the encoder part. This paper attempts to apply the fusion approach, which was effective for the convolution layer of the encoder, to the deconvolution of the decoder and shows the possibility of reducing the processing time and hardware complexity.
A note on no-free-lunch theorem

The No-Free-Lunch theorem is an interesting and important theoretical result in machine learning. Based on philosophy of No-Free-Lunch theorem, we discuss extensively on the limitation of a data-driven approach in solving NP-hard problems.
Movie Genre Classification from RGB Movie Poster Image Using Deep Feed-forward Network

A movie poster image is one of the important media in the filmmaking process, providing valuable information about the movie, such as movie titles, characters, and genres. Identifying a movie genre from a poster can be a daunting task, as it can relate to multiple genres. To solve this problem, this paper uses a deep feedforward neural network to classify movie genres from movie poster images. In this regard, we used and trained a state-of-the-art InceptionV3 deep neural network. The network is trained on our dataset consisting of 36,423 movie poster images taken from the IMDB website, which is categorized into 28 genres. The model predicts the top three classes with the highest probability of a particular movie poster.
Research and design of blended teaching mode based on smart learning environment

The blended teaching model integrates the dual experience of traditional classroom and online learning, helping teachers adopt appropriate teaching strategies and methods to apply to teaching practice, thereby improving the quality of teaching. Innovative application of blended teaching mode can help us solve many stubborn problems in current education. In recent years, the construction and application of smart learning environment has also gradually emerged in the field of education, becoming an inevitable direction for realizing the leap-forward development of education. The function of recording, monitoring and analyzing the learning process of the intelligent learning environment system; the function of perceiving the learning situation and the recognition of learner characteristics; the function of satisfying the learner's learning at any time, any place, and any way. Each function enables learners in the blended learning mode to obtain a comprehensive and personalized learning experience. This article briefly describes the connotation and functional characteristics of the smart learning environment and blended learning, analyzes the teaching link composition of the blended teaching mode, and based on the current problems in classroom teaching, combined with the characteristics of learners, conducts a smart learning environment-based the blended teaching model is designed and explained this model.
Research on Modelling and Analysis of Electrical Virtual Simulation SCM Experimental Platform

Abstract The thesis establishes a single-chip virtual simulation experiment platform, which mainly completes the related experiments of the single-chip principal course, and can be used for single-chip curriculum teaching, curriculum design, graduation design and related competition training for college students. The simulation platform has the characteristics of friendly interface and high degree of simulation, which truly breaks through the limitations of geography and time. The policy modelling platform realizes a teaching development model that centres on cultivating students’ application ability and takes teacher construction and laboratory construction as two wings. At the same time, a project-based driving form is adopted to encourage students to participate in a variety of after-school practice links to achieve the unity of classroom theory and experimental teaching.
Perceived Trust and Confidence for Cryptocurrency Adoption

This chapter seeks to examine the influence of public perceptions of trust in people and confidence in institutions on cryptocurrency adoption, taking into account the individual-level demographic factors and the regional-level contextual factors. Data is obtained from three large-scale international surveys and national databases and analyzed using R software. The multivariate results demonstrate that individuals' public perceptions of trust and confidence significantly contribute to cryptocurrency adoption. Lower perceived trust in people and higher perceived confidence in civil service and international regulatory bodies increase cryptocurrency adoption, while perceived confidence in political and financial institutions discourages cryptocurrency adoption. Additionally, the univariate results find significant comparisons of gender and perceived trust differences on the predictors of cryptocurrency adoption. This chapter discusses and provides insights on the social impact and future of cryptocurrency adoption, particularly among the upper- and lower-middle-income countries.
Fuzzy AHP based Ranking of Cryptography Indicators

The progression of cryptographic attacks in the ICT era doubtless leads to the development of new cryptographic algorithms and assessment, and evaluation of the existing ones. In this paper, the artificial intelligence application, through the fuzzy analytic hierarchy process (FAHP) implementation, is used to rank criteria and sub-criteria on which the algorithms are based to determine the most promising criteria and optimize their use. Out of fifteen criteria, security soundness, robustness and hardware failure distinguished as significant ones.
Smartphone-Based Human Activity Pattern Identification Using Unsupervised Learning

Thakur, Dipanwita Biswas, SuparnaTo avail smart healthcare facilities, it is essential to accumulate meaningful information on a patient. Study of human activity patterns is one of the magnificent areas to accumulate such meaningful information. Supervised learning methods are widely used to classify various human activity patterns. In supervised learning, the generation of training dataset is laborious and repetitive work. In this paper, to avoid this labor-intensive work, we investigate a new approach using unsupervised learning method for smartphone sensor-based human activity pattern identification (HAPI). In this work, hierarchical clustering method is used to cluster similar pattern activities using in-built accelerometer and gyroscope sensors of smartphones. Experimental results using our self-collected dataset show that hierarchical clustering achieves more than 90% accuracy to identify human activity patterns without generating the training dataset manually.
Dynamic Flow Entry Timeouts Based Packet Nonuniform Sampling for Elephant Flow Detection

This paper proposes a controller-based elephant detection approach based on packet statistics sampled by dynamic timeouts of flow entries. It avoids transporting statistics generated at switches to controllers by directly counting statistics using the packets forwarded to controllers for the installation and reactivation of entries. By initializing entries with hard timeout of 0.008s, and keeping doubling it whenever the entries timed out until flows are detected as elephants or out of life spans (the timeouts of flow entries of flows that have been detected as elephants are switched to idle timeout of 5s), the approach achieves a detection accuracy around 90% while reducing the bandwidth usage to less than 30% of the original over packet traces caught from the same network. By giving various initial timeout adjustment strategy to TCP and UDP flows, the approach can maintain the same high detection accuracy and low bandwidth usage over various networks.
Consideration of Generating Suitable Parameters for Constructing type (h, m) Gauss Period Normal Basis

In this research, the authors consider and evaluate a method to find a prime number that satisfies the condition to construct type(h, m) Gauss period Normal Basis. More precisely, we use methods for prime factorization, elliptic curve method, to determine the suitability of a prime number for the construction. In addition, we consider the condition of parameters that easy to find such a prime.