Reviewerly/exHarmony huggingface dataset:
Group Knowledge Transfer: Collaborative Training of Large CNNs on the Edge.

Scaling up the convolutional neural network (CNN) size (e.g., width, depth, etc.) is known to effectively improve model accuracy. However, the large model size impedes training on resource-constrained edge devices. For instance, federated learning (FL) on edge devices cannot tackle large CNN training demands, even though there is a strong practical need for FL due to its privacy and confidentiality properties. To address the resource-constrained reality, we reformulate FL as a group knowledge transfer (GKT) training algorithm. GKT designs a variant of the alternating minimization approach to train small CNNs on edge nodes and periodically transfer their knowledge by knowledge distillation to a large server-side CNN. GKT consolidates several advantages in a single framework: reduced demand for edge computation, lower communication cost for large CNNs, and asynchronous training, all while maintaining model accuracy comparable to FL. To simplify the edge training, we also develop a distributed training system based on our GKT. We train CNNs designed based on ResNet-56 and ResNet-110 using three distinct datasets (CIFAR-10, CIFAR-100, and CINIC-10) and their non-IID variants. Our results show that GKT can obtain comparable or even slightly higher accuracy. More importantly, GKT makes edge training affordable. Compared to the edge training using FedAvg, GKT demands 9 to 17 times less computational power (FLOPs) on edge devices and requires 54 to 105 times fewer parameters in the edge CNN.
Schrödinger cat states in continuous variable non-Gaussian networks

We show how continuous variable network with embedded non-Gaussian element can effectively prepare Schrödinger cat state using cubic phase state as elementary non-Gaussian resource, an entangling Gaussian gate, and homodyne measurement. The gate prepares superposition of two "copies" of an arbitrary input state well separated on the phase plane. A key feature of the cat-breeding configuration is that the measurement outcome provides multivalued information about the target system variables, which makes irrelevant the Heisenberg picture as it is applied to Gaussian networks. We present an intuitively clear interpretation of the emerging cat state, extendable to the circuits with other non-Gaussian elements.
A Semantic-based Scene segmentation using convolutional neural networks

Semantic segmentation is a crucial operation in the computer vision field. One of the promising techniques is the convolutional neural network (CNN). It can be utilized with both single and multidimensional arrays and is useful for processing 2D arrays in computer vision tasks. In this paper, a new model for semantic scene segmentation is proposed. In order to enhance the segmentation results, the model starts with classifying the input scene as either indoor or outdoor scenes. In this context, the MobileNet is used as it provides better results when compared to Inception-v3 and Inception-ResNet-v2 networks. The next step, two models based on Pyramid Scene Parsing Network (PSPNet) are used for image segmentation (indoor images are segmented by the indoor model and outdoor images are segmented by the outdoor model). Experimental results prove the concept that a specific scene model can achieve higher accuracy than general scene models on the semantic segmentation task.
Enabling Incremental Knowledge Transfer for Object Detection at the Edge

Object detection using deep neural networks (DNNs) involves a huge amount of computation which impedes its implementation on resource/energy-limited user-end devices. The reason for the success of DNNs is due to having knowledge over all different domains of observed environments. However, we need a limited knowledge of the observed environment at inference time which can be learned using a shallow neural network (SHNN). In this paper, a systemlevel design is proposed to improve the energy consumption of object detection on the user-end device. An SHNN is deployed on the user-end device to detect objects in the observing environment. Also, a knowledge transfer mechanism is implemented to update the SHNN model using the DNN knowledge when there is a change in the object domain. DNN knowledge can be obtained from a powerful edge device connected to the user-end device through LAN or Wi-Fi. Experiments demonstrate that the energy consumption of the user-end device and the inference time can be improved by 78% and 40% compared with running the deep model on the user-end device.
Context Quality Impact in Context-Aware Data Mining for Predicting Soil Moisture

Nowadays research has shown that including context-awareness, in a classic data mining (CDM) process can improve the overall results. The current work investigates the impact of context completeness and accuracy over predictive forecasting for soil moisture in a context-aware data mining (CADM) system. Experiments with different levels of noise and missing data in the context were performed using several machine learning algorithms for both CDM and CADM scenarios. The results show that the soil moisture prediction results are improved when using CADM, even if the quality standards are not completely met.
Network perception task migration in cloud-edge fusion computing

Abstract With the development of cloud computing, edge computing has been proposed to provide real-time and low-delay services to users. Current research usually integrates cloud computing and edge computing as cloud-edge fusion computing for more personalized services. However, both cloud computing and edge computing suffer from high network consumption, which remains a key problem yet to be solved in cloud-edge fusion computing environments. The cost of network consumption can be divided into two parts: migration costs and communication costs. To solve the high network consumption problem, some virtual machines can be migrated from overloaded physical machines to others with the help of virtualization technology. Current network perception migration strategies focus more on the communication cost by optimizing the communication topology. Considering both communication and migration costs, this paper addresses the high network consumption problem in terms of the communication correlations of virtual machines and the network traffic of the migration process. It proposes three heuristic virtual machine migration algorithms, LM, mCaM and mCaM2, to balance communication costs and migration costs. The performance of these algorithms is compared with those of existing virtual machine migration algorithms through experiments. The experimental results show that our virtual machine migration algorithms clearly optimize the communication cost and migration cost. These three algorithms have a lower network cost than AppAware, an existing algorithm, by 20% on average. This means that these three algorithms improve the network performance and reduce the network consumption in cloud-edge fusion computing environments. They also outperform existing algorithms in terms of operation time by 70% on average.
Lightweight fog based solution for privacy-preserving in IoT using blockchain

Internet of things (IoT) mainly depends on clouds to process and store their data. Clouds cannot handle the volume and velocity of data generated by IoT. IoT is delay-sensitive and resources limited. Fog computing proposed endorsing the internet of things (IoT) demands. Fog computing extends the cloud computing service to the edge of the network. Fog utilization reduces response time and network overhead while maintaining security aspects. isolation and operating system (OS) dependency achieved by using virtualization. Blockchain proposed to solve the security and privacy of fog computing. Blockchain is a decentralized, immutable ledger. fog computing with blockchain proposed as an IoT infrastructure. Fog computing adopted with lightweight blockchain in this proposed work. This adaptation endorses the IoT demands for low response time with limited resources. This paper explores system applicability. Varies from other papers that focus on one factor such as privacy or security-applicability of the proposed model achieved by concentration different IoT needs and limits. Response time and ram usage with 1000 transactions did not encroach 100s and 300MiB in the proposed model.
Hierarchical Document Classification as a Sequence Generation Task

Hierarchical classification schemes are an effective and natural way to organize large document collections. However, complex schemes make the manual classification time-consuming and require domain experts. Current machine learning approaches for hierarchical classification do not exploit all the information contained in the hierarchical schemes. During training, they do not make full use of the inherent parent-child relation of classes. For example, they neglect to tailor document representations, such as embeddings, to each individual hierarchy level. Our model overcomes these problems by addressing hierarchical classification as a sequence generation task. To this end, our neural network transforms a sequence of input words into a sequence of labels, which represents a path through a tree-structured hierarchy scheme. The evaluation uses a patent corpus, which exhibits a complex class hierarchy scheme and high-quality annotations from domain experts and comprises millions of documents. We re-implemented five models from related work and show that our basic model achieves competitive results in comparison with the best approach. A variation of our model that uses the recent Transformer architecture outperforms the other approaches. The error analysis reveals that the encoder of our model has the strongest influence on its classification performance.
A Blockchain Consensus Protocol Based on Dedicated Time-Memory-Data Trade-Off

A problem of developing the consensus protocols in public blockchain systems which spend a combination of energy and space resources is addressed. A technique is proposed that provides a flexibility for selection of the energy and space resources which should be employed by a player participating in the consensus procedure. The technique originates from the cryptographic time-memory-data trade-off approaches for cryptanalysis. The proposed technique avoids the limitations of Proof-of-Work (PoW) and Proof-of-Memory (PoM) which require spending of only energy and space, respectively. Also, it provides a flexibility for adjusting the resources spending to the system budget. The proposed consensus technique is based on a puzzle where the problem of inverting one-way function is solved employing a dedicated Time-Memory-Data Trade-Off (TMD-TO) paradigm. The algorithms of the consensus protocol are proposed which employ certain unconstrained and constrained TMD-TO based inversions. Security of the proposed technique is considered based on the probability that the honest pool of nodes generate a longer extension of the blockchain before its update, and a condition on the employed parameters in order to achieve desired security has been derived. Implementation of the proposed technique in Go language and its inclusion as an alternative consensus option in Ethereum platform are shown. Implementation complexity and performance of the proposed consensus protocol are discussed and compared with the ones when PoW and PoM are employed.
Delivering Software- and Systems-Engineering Standards for Small Teams

Reports on the ISO/IEC 29110-series standards and guides that are designed to help very small entities develop software or systems to improve their competitiveness. A recent survey showed that they provide many benefits to developers and their customers.
MoWIE

Cloud-based, adaptive applications such as video streaming and interactive cloud gaming are important to existing 4G and emerging 5G and beyond networks, as they attract a large number of users and bring significant revenues to both carriers and application service providers. These applications are also known to consume significant cellular network resources. To maintain good quality-of-experience (QoE) while minimizing network resource consumption, these applications employ adaptive mechanisms based on the changing network condition. Although various methods have been deployed to convey network information to applications running on user equipment (UEs) or cloud servers, the existing mechanisms are limited in generality and flexibility. In this paper, we propose MoWIE (Mobile and Wireless Information Exposure), a framework that uses systematic, flexible information channels to decide "what" information to expose between networks and applications and advocate an integrated in-band and out-band transport to realize "how" exposed information can be provided to applications with adaptive information exposure rate optimization. We conduct three case studies over real-world operational networks - through collaboration between one of the largest application developers and one of the largest cellular carriers in the world. These case studies, while still preliminary and ongoing, illuminate the potential benefits of systematic, adaptive network information exposure in mobile networks. We conclude by discussing remaining important yet challenging issues in implementing and deploying MoWIE in practice.
Truck and Trailer Classification With Deep Learning Based Geometric Features

In this paper, we present a novel and effective approach to truck and trailer classification, which integrates deep learning models and conventional image processing and computer vision techniques. The developed method groups trucks into subcategories by carefully examining the truck classes and identifying key geometric features for discriminating truck and trailer types. We also present three discriminating features that involve shape, texture, and semantic information to identify trailer types. Experimental results demonstrate that the developed hybrid approach can achieve high accuracy with limited training data, where the vanilla deep learning approaches show moderate performance due to over-fitting and poor generalization. Additionally, the models generated are human-understandable.
Design and Control of Soft Unmanned Aerial Vehicle ‘S-CLOUD’

Short time-of-flight and safety concerns owing to low efficiency and fast rotation of propellers, respectively, are hurdles in the usage of drones. Usually, drones are carrying out missions that distant from the people like aerial photography, measurement, delivery, and surveillance, and, thus, it is not allowed to deploy drones above crowds. In this article, we present a novel soft drone called S-CLOUD that has longer time-of-flight (more than an hour) and higher user safety. It is composed of a torus-shaped envelope made of soft material, filled with Helium gas, and a center flow control unit mounted at the center of the envelope for producing translational (X-, Y-, and Z-axes) and rotational (Z-axis) motions. In this article, the control unit and envelope design are described in detail, and the characteristics of the drone are explained. Moreover, sensor-based feedback control algorithms and their experimental results are described. Finally, applications with S-CLOUD exhibiting its feasibility are also presented.
Multisensor-Based 3D Gesture Recognition for a Decision-Making Training System

This article demonstrates a gesture recognition method using multiple Inertial Measurement Unit (IMU) sensors, which can record acceleration and rotation information for hand joints. The proposed gesture recognition method comprises frequency ConvNet and TemporalNet to extract the representative features within a sliding window of IMU signals for recognizing various types of hand gestures. To validate the proposed gesture recognition method, basketball official referee signals (ORSs), which comprise sixty-five types of gestures including both large motion hand movement and subtle motion hand movement, are utilized as the main recognition task to evaluate the proposed method. The evaluation results reveal the proposed recognition model can achieve convinced performance, which outperforms other existing works. In addition, the satisfied performance of the proposed recognition model encourages us to develop a decision-making training (DMT) system for cultivating basketball referees. The results of subjective evaluations by the recruited 20 participants indicate the training system based on the proposed gestures recognition method can efficiently strengthen the decision-making skills of users.
Data stream event prediction based on timing knowledge and state transitions

We study a practical problem of predicting the upcoming events in data streams using a novel approach. Treating event time orders as relationship types between event entities, we build a dynamic knowledge graph and use it to predict future event timing. A unique aspect of this knowledge graph embedding approach for prediction is that we enhance conventional knowledge graphs with the notion of "states"---in what we call the ephemeral state nodes---to characterize the state of a data stream over time. We devise a complete set of methods for learning relevant events, for building the event-order graph stream from the original data stream, for embedding and prediction, and for theoretically bounding the complexity. We evaluate our approach with four real world stream datasets and find that our method results in high precision and recall values for event timing prediction, ranging between 0.7 and nearly 1, significantly outperforming baseline approaches. Moreover, due to our choice of efficient translation-based embedding, the overall throughput that the stream system can handle, including continuous graph building, training, and event predictions, is over one thousand to sixty thousand tuples per second even on a personal computer---which is especially important in resource constrained environments, including edge computing.
Mining arguments in scientific abstracts with discourse-level embeddings

Argument mining consists in the automatic identification of argumentative structures in texts. In this work we leverage existing discourse-level annotations to facilitate the identification of argumentative components and relations in scientific texts, which has been recognized as a particularly challenging task. We propose a new annotation schema and use it to augment a corpus of computational linguistics abstracts that had previously been annotated with discourse units and relations. Our initial experiments with the enriched corpus confirm the potential value of incorporating discourse information in argument mining tasks. In order to tackle the limitations posed by the lack of corpora containing both discourse and argumentative annotations we explore two transfer learning approaches in which discourse parsing is used as an auxiliary task when training argument mining models. In this case, as no discourse information is used as input, the resulting models could be used to predict the argumentative structure of unannotated texts.
Node Classification and Link Prediction in Social Graphs using RLVECN

Node classification and link prediction problems in Social Network Analysis (SNA) remain open research problems with respect to Artificial Intelligence (AI). Inherent representations about social network structures can be effectively harnessed for training AI models in a bid to detect clusters via classification of actors as well as predict ties with regard to a given social network. In this paper, we have proposed a unique hybrid model: Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN). Our proposition is designed for analyzing and extracting expressive feature representations from social network structures to aid in link prediction, node classification and community detection tasks. RLVECN utilizes an edge sampling technique for exploiting features of a given social network via learning the context of each actor with respect to its associate actors.
The role of information and communication technology (internet penetration) on Asian stock market efficiency: Evidence from quantile‐on‐quantile cointegration and causality approach

Abstract The objective of the study is to evaluate the long‐run and causal relationship between information and communication technology (ICT) and Asian stock market efficiency by using quantile‐on‐quantile cointegration and causality approach, which analysed the different slope coefficients at median and the lower tails of conditional quantiles distribution. The study selected 10 Asian countries that experienced massive capital flows and e‐financing to verify efficient market hypothesis. The study utilizing a monthly time series data ranging from 2000 M01 to 2016 M12, where pre‐ and post‐global financial crisis time period is certainly induced, which affected through trade and financial channels in many Asian countries. The results confirmed the positive linkages between stock market efficiency and internet use in the 8 Asian countries while this result is averted in the two Asian countries (i.e., China and Russia) in a given time period. The heterogeneity between the countries in the stock‐ICT nexus could be endorsed to the multiple factors that need to be included in the multivariate framework.
Human activity of daily living recognition in presence of an animal pet using thermal sensor array

The recognition of human activities of daily living has gained increasing attention in recent years due to its potential to promote autonomy for elderly people in their own homes and its usage for security surveillance in scenarios such as supermarkets, banks, etc. Infrared thermal array has been proposed as a non-invasive device for human activity detection, which has the advantages of low-cost, low-power, and high-performance capabilities. However, most of the ambient-based sensor research has not considered animal pets, whose bodies have similar temperature to human body, that may live with the elderly people in a single inhabitant environment. This has led to a gap in the usability and deployability of such systems on a broader range. This paper proposes a filtering method for removing thermal noises, including those radiated by pets, from the acquired heat-map. Therefore, only the presence of a human in the thermal scene is considered for further activity recognition. This paper shows the possibility of using an entropy point estimate for a one-dimensional heat-map histogram as a distinctive attribute to distinguish between heat-maps of the human and animal pet with 100% achieved accuracy.
Preliminary study of applied binary neural networks for neural cryptography

Adversarial neural cryptography is deemed as an encouraging area of research that could provide different perspective in the post-quantum cryptography age, specially for secure transmission of information. Nevertheless, it is still under explored with a handful of publications on the subject. This study proposes the theoretical implementation of a neuroevolved binary neural network based on boolean logic functions only (BiSUNA), with the purpose of encrypting/decrypting a payload between two agents, hiding information from a competitor.
Tight Bounds for Simon’s Algorithm

Simon's algorithm is the first example of a quantum algorithm exponentially faster than any classical algorithm, and has many applications in cryptanalysis. While these quantum attacks are often extremely efficient, they are generally missing some precise cost estimate. This article aims at resolving this issue by computing precise query costs for the different use cases of Simon's algorithm in cryptanalysis.