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In this paper, we propose to tackle egocentric action recognition by suppressing background distractors and enhancing action-relevant interactions. The existing approaches usually utilize two independent branches to recognize egocentric actions, i.e., a verb branch and a noun branch. However, the mechanism to suppress distracting objects and exploit local human-object correlations is missing. To this end, we introduce two extra sources of information, i.e., the candidate objects' spatial location and their discriminative features, to enable concentration on the occurring interactions. We design a Symbiotic Attention withObject-centric featureAlignmentframework (SAOA) to provide meticulous reasoning between the actor and the environment. First, we introduce an object-centric feature alignment method to inject the local object features to the verb branch and noun branch. Second, we propose a symbiotic attention mechanism to encourage the mutual interaction between the two branches and select the most action-relevant candidates for classification. The framework benefits from the communication among the verb branch, the noun branch, and the local object information. Experiments based on different backbones and modalities demonstrate the effectiveness of our method. Notably, our framework achieves the state-of-the-art on the largest egocentric video dataset.

We present JRDB, a novel egocentric dataset collected from our social mobile manipulator JackRabbot. The dataset includes 64 minutes of annotated multimodal sensor data including stereo cylindrical 360$^\circ$ RGB video at 15 fps, 3D point clouds from two Velodyne 16 Lidars, line 3D point clouds from two Sick Lidars, audio signal, RGB-D video at 30 fps, 360$^\circ$ spherical image from a fisheye camera and encoder values from the robot's wheels. Our dataset incorporates data from traditionally underrepresented scenes such as indoor environments and pedestrian areas, all from the ego-perspective of the robot, both stationary and navigating. The dataset has been annotated with over 2.3 million bounding boxes spread over 5 individual cameras and 1.8 million associated 3D cuboids around all people in the scenes totaling over 3500 time consistent trajectories. Together with our dataset and the annotations, we launch a benchmark and metrics for 2D and 3D person detection and tracking. With this dataset, which we plan on extending with further types of annotation in the future, we hope to provide a new source of data and a test-bench for research in the areas of egocentric robot vision, autonomous navigation, and all perceptual tasks around social robotics in human environments.

We present a solution to egocentric 3D body pose estimation from monocular images captured from downward looking fish-eye cameras installed on the rim of a head mounted VR device. This unusual viewpoint leads to images with unique visual appearance, with severe self-occlusions and perspective distortions that result in drastic differences in resolution between lower and upper body. We propose an encoder-decoder architecture with a novel multi-branch decoder designed to account for the varying uncertainty in 2D predictions. The quantitative evaluation, on synthetic and real-world datasets, shows that our strategy leads to substantial improvements in accuracy over state of the art egocentric approaches. To tackle the lack of labelled data we also introduced a large photo-realistic synthetic dataset. xR-EgoPose offers high quality renderings of people with diverse skintones, body shapes and clothing, performing a range of actions. Our experiments show that the high variability in our new synthetic training corpus leads to good generalization to real world footage and to state of theart results on real world datasets with ground truth. Moreover, an evaluation on the Human3.6M benchmark shows that the performance of our method is on par with top performing approaches on the more classic problem of 3D human pose from a third person viewpoint., 14 pages. arXiv admin note: substantial text overlap with arXiv:1907.10045

In this work, we consider transferring the structure information from large networks to compact ones for dense prediction tasks in computer vision. Previous knowledge distillation strategies used for dense prediction tasks often directly borrow the distillation scheme for image classification and perform knowledge distillation for each pixel separately, leading to sub-optimal performance. Here we propose to distill structured knowledge from large networks to compact networks, taking into account the fact that dense prediction is a structured prediction problem. Specifically, we study two structured distillation schemes: i) pair-wise distillation that distills the pair-wise similarities by building a static graph; and ii) holistic distillation that uses adversarial training to distill holistic knowledge. The effectiveness of our knowledge distillation approaches is demonstrated by experiments on three dense prediction tasks: semantic segmentation, depth estimation and object detection. Code is available at: https://git.io/StructKD, Comment: v1:10 pages cvpr2019 accepted; v2:15 pages for a journal version; Code is available at: https://github.com/irfanICMLL/structure_knowledge_distillation; fix typos

We propose a novel logic, called Frame Logic (FL), that extends first-order logic (with recursive definitions) using a construct Sp(.) that captures the implicit supports of formulas -- the precise subset of the universe upon which their meaning depends. Using such supports, we formulate proof rules that facilitate frame reasoning elegantly when the underlying model undergoes change. We show that the logic is expressive by capturing several data-structures and also exhibit a translation from a precise fragment of separation logic to frame logic. Finally, we design a program logic based on frame logic for reasoning with programs that dynamically update heaps that facilitates local specifications and frame reasoning. This program logic consists of both localized proof rules as well as rules that derive the weakest tightest preconditions in FL., This manuscript is an extended and revised version of the publication with the same title that appeared at ESOP 2022 (https://doi.org/10.1007/978-3-030-44914-8_19). It is currently under review

This paper studies the coordinated use of smart inverters with legacy voltage regulating devices to help increase the energy savings from conservation voltage reduction (CVR) in distribution systems with high photovoltaic penetration. Two voltage optimization algorithms are developed to evaluate the CVR benefit by considering two types of smart inverter functions: aggregated reactive power control and autonomous volt/VAR control. In each algorithm, smart inverters are coordinately controlled with the operation of legacy voltage regulating devices including load tap changers and capacitor banks. Both algorithms are tested on representative utility distribution system models. Simulation results demonstrate that the proposed algorithms can achieve around 1.8-3.6% energy savings when only using legacy voltage regulating devices and an additional 0.3-0.9% when adding smart inverters.

In this paper, a long-term workload management problem for multi-server edge computing with server collaboration is studied. In the considered model, mobile users computation-intensive tasks are generated dynamically over the time and offloaded to associated edge servers according to pre-determined subscription agreements. Upon receiving the subscribed workload, each edge server can then decide to whether participate in server collaboration for enabling workload re-allocation (i.e., workload exchange) with other heterogeneously configured edge servers. Unlike most of the existing work, this paper takes into account both competitions and collaborations among strategic edge servers in sharing their computing capacities. To achieve the equilibrium for each edge server in minimizing its expected cost (including energy consumption, delay, transmission, configuration and pricing costs), a joint optimization is formulated for determining i) its amount of workload to undertake, ii) compensation price charged from peers, and iii) computing speed to adopt. To efficiently solve this problem, we propose a novel cooperative queueing game approach, which integrates a convex optimization, a core cost sharing scheme and a mapping rule. Theoretical analyses and extensive simulations are conducted to evaluate the performance of the proposed solution, and demonstrate its superiority over counterparts.

Mobile Edge Computing (MEC) has emerged as a promising paradigm catering to overwhelming explosions of mobile applications, by offloading the compute-intensive tasks to an MEC network for processing. The surging of deep learning brings new vigor and vitality to shape the prospect of intelligent Internet of Things (IoT), and edge intelligence arises to provision real-time deep neural network (DNN) inference services for users. In this paper, we study a novel delay-aware DNN inference throughput maximization problem by accelerating each DNN inference through jointly exploring DNN partitioning and multi-thread parallelism. Specifically, we consider the problem under both offline and online request arrival settings: a set of DNN inference requests is given in advance, and a sequence of DNN inference requests arrives one by one without the knowledge of future arrivals, respectively. We first show that the defined problems are NP-hard. We then devise a novel constant approximation algorithm for the problem under the offline setting. We also propose an online algorithm with a provable competitive ratio for the problem under the online setting. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising.

Temporal point process is widely used for sequential data modeling. In this paper, we focus on the problem of modeling sequential event propagation in graph, such as retweeting by social network users, news transmitting between websites, etc. Given a collection of event propagation sequences, conventional point process model consider only the event history, i.e. embed event history into a vector, not the latent graph structure. We propose a Graph Biased Temporal Point Process (GBTPP) leveraging the structural information from graph representation learning, where the direct influence between nodes and indirect influence from event history is modeled respectively. Moreover, the learned node embedding vector is also integrated into the embedded event history as side information. Experiments on a synthetic dataset and two real-world datasets show the efficacy of our model compared to conventional methods and state-of-the-art., Comment: 9 pages, 6 figures, 2 tables

In this article, we investigate the problem of social link inference in a target location-aware social network (LSN), which aims at predicting the unobserved links between users within the network. This problem is critical for downstream applications, including network completion and friend recommendation. In addition to the network structures commonly used in general link prediction, the studies tailored for social link inference in an LSN leverage user trajectories from the spatial aspect. However, the temporal factor lying in user trajectories is largely overlooked by most of the prior studies, limiting the capabilities of capturing the temporal relevance between users. Moreover, effective user matching by fusing different views, i.e., social, spatial, and temporal factors, remains unresolved, which hinders the potential improvement of link inference. To this end, this article devises a novel multiview matching network (MVMN) by regarding each of the three factors as one view of any target user pair. MVMN enjoys the flexibility and completeness of modeling each factor by developing its suitable matching module: 1) location matching module; 2) time-series matching module; and 3) relation matching module. Each module learns a view-specific representation for matching, and MVMN fuses them for final link inference. Extensive experiments on two real-world data sets demonstrate the superiority of our approach against several competitive baselines for link prediction and sequence matching, validating the contribution of its key components.

The adaptive neurofuzzy inference system (ANFIS) is a structured multioutput learning machine that has been successfully adopted in learning problems without noise or outliers. However, it does not work well for learning problems with noise or outliers. High-accuracy real-time forecasting of traffic flow is extremely difficult due to the effect of noise or outliers from complex traffic conditions. In this study, a novel probabilistic learning system, probabilistic regularized extreme learning machine combined with ANFIS (probabilistic R-ELANFIS), is proposed to capture the correlations among traffic flow data and, thereby, improve the accuracy of traffic flow forecasting. The new learning system adopts a fantastic objective function that minimizes both the mean and the variance of the model bias. The results from an experiment based on real-world traffic flow data showed that, compared with some kernel-based approaches, neural network approaches, and conventional ANFIS learning systems, the proposed probabilistic R-ELANFIS achieves competitive performance in terms of forecasting ability and generalizability.

Introduction. To represent our national goods at the Western market it is necessary to ensure, first of all, the competitiveness of local products through complex mechanization of technological processes, labour costs reduction, increasing yields and quality of received products. The aim of the research is to provide the comparative analysis of technology and machines for potato harvesting, to determine the main factors that influence the agricultural technical indicators of harvesting equipment, to develop technological process patterns and to examine the strategies for improving the potato root-and-harvest machines. Methods and methodology. The research was carried out on the basis of technological and constructional analysis of technology and machines for potato harvesting. The methods of comparison and mathematical modelling of technological processes were used in the study. The research studies of Ukrainian and foreign scientists on technology and machines for potato harvesting were used as information base of our paper. Results. A comparative analysis of technology and machines for potato harvesting made it possible to determine the main factors that influence the agricultural performance of harvesting machinery and to develop the technological process for a digger, a double-loader digger and a harvester (harvesting machines). A theoretical model of the technological processes of harvesting machines includes both uncontrolled and controlled factors that influence the choice of a potato harvesting technology of a digger, a double-loader digger and a harvester. Nevertheless, the first factors are connected, primarily, with the conditions of potato growth and its biological properties, and the second factors deal with the organizational and technical measures, including the improvement of the working bodies of machines (cutting, dirt digging through, top separation, longitudinal hump with cut roller and other constructive and kinematic parameters), quality of received products, length and terms of potato storage, available farming human resources, free vehicle in the period of harvesting, farm potato storage, farm equipment for potato cleaning and sorting.

Unit square visibility graphs (USV) are described by axis-parallel visibility between unit squares placed in the plane. If the squares are required to be placed on integer grid coordinates, then USV become unit square grid visibility graphs (USGV), an alternative characterisation of the well-known rectilinear graphs. We extend known combinatorial results for USGV and we show that, in the weak case (i.e., visibilities do not necessarily translate into edges of the represented combinatorial graph), the area minimisation variant of their recognition problem is $${{\,\mathrm{{\textsf{N}}{\textsf{P}}}\,}}$$ N P -hard. We also provide combinatorial insights with respect to USV, and as our main result, we prove their recognition problem to be $${{\,\mathrm{{\textsf{N}}{\textsf{P}}}\,}}$$ N P -hard, which settles an open question.

Exciting recent advances in genome sequencing, coupled with greatly reduced storage and computation costs, make genomic testing increasingly accessible to individuals. Already today, one’s digitized DNA can be easily obtained from a sequencing lab and later used to conduct numerous tests by engaging with a testing facility. Due to the inherent sensitivity of genetic material and the often-proprietary nature of genomic tests, privacy is a natural and crucial issue. While genomic privacy received a great deal of attention within and outside the research community, genomic security has not been sufficiently studied. This is surprising since the usage of fake or altered genomes can have grave consequences, such as erroneous drug prescriptions and genetic test outcomes. Unfortunately, in the genomic domain, privacy and security (as often happens) are at odds with each other. In this article, we attempt to reconcile security with privacy in genomic testing by designing a novel technique for a secure and private genomic range query protocol between a genomic testing facility and an individual user. The proposed technique ensures authenticity and completeness of user-supplied genomic material while maintaining its privacy by releasing only the minimum thereof. To confirm its broad usability, we show how to apply the proposed technique to a previously proposed genomic private substring matching protocol. Experiments show that the proposed technique offers good performance and is quite practical. Furthermore, we generalize the genomic range query problem to sparse integer sets and discuss potential use cases.

Problem definition: Fulfillment flexibility, the ability of distribution centers (DCs) to fulfill demand originating from other DCs, can help e-retailers reduce lost sales and improve service quality. Because the cost of full flexibility is prohibitive, we seek to understand the value of partially flexible fulfillment networks under simple and effective fulfillment policies. Academic/practical relevance: We propose a general method for understanding the practical value of (partial) fulfillment flexibility using a data-driven model, theoretical analysis, and numerical simulations. Our method applies to settings with local fulfillment (i.e., order fulfillment from the originating DC) prioritization and possible customer abandonment, two features that are new to the fulfillment literature. We then apply this method for a large e-retailer. We also introduce a new class of spillover limit fulfillment policies with attractive theoretical and practical features. Methodology: Our analysis uses dynamic and stochastic optimization, applied probability, and numerical simulations. Results: We derive optimal fulfillment policies in stylized settings, as well as bounds on the performance under an optimal policy using theoretical analysis, to provide guidelines on which policies to test in numerical simulations. We then use simulations to estimate for our industrial partner that a proposed fulfillment network with additional flexibility equates to a profit improvement on the order of tens of millions of U.S. dollars. Managerial implications: We provide an approach for e-retailers to understand when fulfillment flexibility is most valuable. We find that fulfillment flexibility provides the most benefit for our collaborator when gross profits are high relative to fulfillment costs or centrally held inventory is low. Also, we identify the risks of myopic fulfillment with additional flexibility and demonstrate that an effective spillover limit policy mitigates these risks. History: This paper has been accepted as part of the 2019 Manufacturing & Service Operations Management Practice-Based Research Competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0981 .

Tree fruit architecture results from combination of the training system and pruning and thinning processes across multiple growth and development years. Further, the tree fruit architecture contributes to the light interception and improves tree growth, fruit quality, and fruit yield, in addition to easing the process of orchard management and harvest. Currently tree architectural traits are measured manually by researchers or growers, which is labor-intensive and time-consuming. In this study, the remote sensing techniques were evaluated to phenotype critical architectural traits with the final goal to assist tree fruit breeders, physiologists and growers in collecting architectural traits efficiently and in a standardized manner. For this, a consumer-grade red–green–blue (RGB) camera was used to collect apple tree side-images, while an unmanned aerial vehicle (UAV) integrated RGB camera was programmed to image tree canopy at 15 m above ground level to evaluate multiple tree fruit architectures. The sensing data were compared to ground reference data associated with tree orchard blocks within three training systems (Spindle, V-trellis, Bi-axis), two rootstocks (‘WA 38 trees grafted on G41 and M9-Nic29) and two pruning methods (referred as bending and click pruning). The data were processed to extract architectural features from ground-based 2D images and UAV-based 3D digital surface model. The traits extracted from sensing data included box-counting fractal dimension (DBs), middle branch angle, number of branches, trunk basal diameter, and tree row volume (TRV). The results from ground-based sensing data indicated that there was a significant (P

In molecular communications, the direct detection of signaling molecules may be challenging due to a lack of suitable sensors and interference in the environment. Motivated by research in molecular biology, we investigate an indirect detection mechanism using chemical reactions between the signaling molecules and a molecular probe to produce an easy-to-measure product at the receiver. We consider two implementations of the proposed detection mechanism, i.e., unrestricted probe movement and probes restricted to a volume around the receiver. The reaction-diffusion equations describing the concentrations of the reactant and product molecules in the system are non-linear and coupled, and cannot be solved in closed form. Therefore, we develop an efficient iterative algorithm by discretizing the time variable and solving for the space variables of the equations in each time step. Our results show that the concentrations of the product molecules and the signalling molecules share a similar characteristic over time, i.e., a single peak and a long tail. The peak and tail values of the product molecule concentration can be controlled by choosing probes with suitable parameters. By carefully choosing the molecular probe and optimizing the decision threshold, the BER can be improved significantly and outperform that of a direct detection system., 13 pages, 11 figures, 1 table. This journal version was submitted in April 2022 to the IEEE for possible publication. A part of this article was presented at the IEEE Wireless Communications and Networking Conference 2020 and stored in the previous version on arXiv

Monitoring the degradation of induction motors is an essential concern for industries, especially when there are high costs related to maintenance and their stops. Nowadays, there are many non-invasive ways to monitor the health status of induction motors, including sensors to measure temperature, magnetic field, vibration, current, and power. However, installing these sensors may not be a simple task since intrusive procedures such as engine disassembling, wire separation, and engine interruption are commonly observed. Thus, we propose MUCLA (Multiphase Clamp and Tracking - patent pending) as a new device and methodology for monitoring motor degradation by analyzing its stator current. MUCLA introduces a non-invasive current sensors array on a proposed clamp in the hardware perspective, while a spectral tracking methodology refers to our novelty in the software viewpoint. For end-users, the main MUCLA contribution relies on the combination of the simplicity of installation and the possibility of using the proposed device even when considering Variable-Frequency Drive powered engines. The results obtained with both simulation and practical tests show encouraging results over different input workloads and emulated degradation. Compared to the current initiatives, we can classify MUCLA as a better solution for industrial plants in terms of installation time, installation complexity, and cost.

Today there is a rapid development taking place in phenotyping of plants using non-destructive image based machine vision techniques. Machine vision based plant phenotyping ranges from single plant trait estimation to broad assessment of crop canopy for thousands of plants in the field. Plant phenotyping systems either use single imaging method or integrative approach signifying simultaneous use of some of the imaging techniques like visible red, green and blue (RGB) imaging, thermal imaging, chlorophyll fluorescence imaging (CFIM), hyperspectral imaging, 3-dimensional (3-D) imaging or high resolution volumetric imaging. This paper provides an overview of imaging techniques and their applications in the field of plant phenotyping. This paper presents a comprehensive survey on recent machine vision methods for plant trait estimation and classification. In this paper, information about publicly available datasets is provided for uniform comparison among the state-of-the-art phenotyping methods. This paper also presents future research directions related to the use of deep learning based machine vision algorithms for structural (2-D and 3-D), physiological and temporal trait estimation, and classification studies in plants.

This is the first work that incorporates recent advancements in "explainability" of machine learning (ML) to build a routing obfuscator called ObfusX. We adopt a recent metric---the SHAP value---which explains to what extent each layout feature can reveal each unknown connection for a recent ML-based split manufacturing attack model. The unique benefits of SHAP-based analysis include the ability to identify the best candidates for obfuscation, together with the dominant layout features which make them vulnerable. As a result, ObfusX can achieve better hit rate (97% lower) while perturbing significantly fewer nets when obfuscating using a via perturbation scheme, compared to prior work. When imposing the same wirelength limit using a wire lifting scheme, ObfusX performs significantly better in performance metrics (e.g., 2.4 times more reduction on average in percentage of netlist recovery).

For cloud network performance profiling, network tomography based on end-to-end measurement is often used in deducing the network performance for its efficiency. However, most tomography problems are under-constrained, which require additional assumptions or probing monitors planted among network switches, which are often unavailable in software-defined networking (SDN) environment. On the other hand, SDN based flow mirroring could provide accurate flow information, but the cost of both gathering and analysing the packet traces is tremendous that it is impossible to cover the whole network. We propose ScoutFlow, a method combining SDN flow measurement and end-to-end performance tomography, to achieve accurate performance profiling for cloud network while keeping low monitoring overhead. We evaluate ScoutFlow in our campus data center cloud, the experiment shows good scalability and accuracy.

This paper proposes a comprehensive steady-state analysis for a front-end Modular Multi-Parallel Rectifiers (MMR) system with a shared DC-link. A generalised mathematical representation for n number of parallel rectifiers is proposed. The MMR is analysed for both Continuous Conduction Mode (CCM) and Discontinuous Conduction Mode (DCM). In addition, the CCM and DCM boundary of the MMR is identified mathematically. A 60kW Simulink model for the MMR is utilised to confirm the proposed steady-state mathematical modelling equations.

TruthSift is a cloud-based platform that logically combines members' contributions into a collective intelligence. Members add statements and directed connectors to diagrams. TruthSift monitors which statements have been logically established by demonstrations for which every challenge has beenrefuted by an established refutation, and the complement: which statements have been refuted by established refutations. When members run out of rational objections the result is a converged diagramsuccinctly representing the state of knowledge about a topic, including plausible challenges and how they were refuted. Previous computer systems for collaborative intelligence did not have a qualitatively better solution for combining contributions than voting, and are subject to group think, interest group capture, and inability to follow a multi-step logical argument. They did not settle issues automatically point by point and propagate the consequences up. I review indications that many practically important statements most people believe to be firmly established will be revealed to be firmly refuted upon computer assisted scrutiny. TruthSift also supports construction of powerful probabilistic models over networks of causes, implications, tests, and necessary factors.

Multi-view classification with limited sample size and data augmentation is a very common machine learning (ML) problem in medicine. With limited data, a triplet network approach for two-stage representation learning has been proposed. However, effective training and verifying the features from the representation network for their suitability in subsequent classifiers are still unsolved problems. Although typical distance-based metrics for the training capture the overall class separability of the features, the performance according to these metrics does not always lead to an optimal classification. Consequently, an exhaustive tuning with all feature-classifier combinations is required to search for the best end result. To overcome this challenge, we developed a novel nearest-neighbor (NN) validation strategy based on the triplet metric. This strategy is supported by a theoretical foundation to provide the best selection of the features with a lower bound of the highest end performance. The proposed strategy is a transparent approach to identify whether to improve the features or the classifier. This avoids the need for repeated tuning. Our evaluations on real-world medical imaging tasks (i.e., radiation therapy delivery error prediction and sarcoma survival prediction) show that our strategy is superior to other common deep representation learning baselines [i.e., autoencoder (AE) and softmax]. The strategy addresses the issue of feature's interpretability which enables more holistic feature creation such that the medical experts can focus on specifying relevant data as opposed to tedious feature engineering.

With the rapid progress of wireless communication technologies along with their digital revolutions, the quantity of Internet of Things (IoT) has been increased by manifolds resulting in a huge increase in data volume and network traffic. It became easier for an intruder to pretend as a valid service provider, and generate different types of network attacks. This becomes even more severe when the service involves digital financial transactions. This paper proposes an intrusion detection system (IDS) based on a stacked autoencoder (AE) and a deep neural network (DNN). The stacked AE learns the features of the input network record in an unsupervised manner to decrease the feature width. Then, the DNN is trained in a supervised manner to extract deep-learned features for the classifier. In the proposed system, the stacked AE has two latent layers and the DNN has two or three layers, where each layer has a fully-connected layer, a batch normalization, and a dropout. The system was evaluated on three publicly available datasets: KDDCup99, NSL-KDD, and AWID. Experimental results exhibited that the proposed IDS achieved 94.2%, 99.7%, 99.9% accuracy, respectively, for multiclass classification.

Federated Learning (FL) has attracted significant interest given its prominent advantages and applicability in many scenarios. However, it has been demonstrated that sharing updated gradients/weights during the training process can lead to privacy concerns. In the context of the Internet of Things (IoT), this can be exacerbated due to Intrusion Detection Systems (IDS), which are intended to detect security attacks by analyzing the devices' network traffic. Our work provides a comprehensive evaluation of Differential Privacy (DP) techniques, which are applied during the training of an FL-enabled IDS for Industrial IoT (IIoT). Unlike previous approaches, we deal with non-iid data over the recent ToN_IoT dataset and compare the accuracy obtained considering different privacy requirements and aggregation functions, namely FedAvg and the recently proposed Fed+. According to our evaluation, the use of Fed+ in our setting provides similar results even when noise is included in the federated training process.

This paper aims to predict a set of apps a user will open on her mobile device in the next time slot. Such an information is essential for many smartphone operations, e.g., app pre-loading and content pre-caching, to improve user experience. However, it is hard to build an explicit model that accurately captures the complex environment context and predicts a set of apps at one time. This paper presents a deep reinforcement learning framework, named as DeepAPP, which learns a model-free predictive neural network from historical app usage data. Meanwhile, an online updating strategy is designed to adapt the predictive network to the time-varying app usage behavior. To transform DeepAPP into a practical deep reinforcement learning system, several challenges are addressed by developing a context representation method for complex contextual environment, a general agent for overcoming data sparsity and a lightweight personalized agent for minimizing the prediction time. Extensive experiments on a large-scale anonymized app usage dataset reveal that DeepAPP provides high accuracy (precision 70.6% and recall of 62.4%) and reduces the prediction time of the state-of-the-art by 6.58 times. A field experiment of 29 participants demonstrates DeepAPP can effectively reduce launch time of apps.

In this paper, we propose and analyze SPARQ-SGD, an event-triggered and compressed algorithm for decentralized training of large-scale machine learning models over a graph. Each node can locally compute a condition (event) which triggers a communication where quantized and sparsified local model parameters are sent. In SPARQ-SGD, each node first takes a fixed number of local gradient steps and then checks if the model parameters have significantly changed compared to its last update; it communicates further compressed model parameters only when there is a significant change, as specified by a (design) criterion. We prove that SPARQ-SGD converges as $O\left( {\frac{1}{{nT}}} \right)$ and $O\left( {\frac{1}{{\sqrt {nT} }}} \right)$ in the strongly-convex and non-convex settings, respectively, demonstrating that aggressive compression, including event-triggered communication, model sparsification and quantization does not affect the overall convergence rate compared to uncompressed decentralized training; thereby theoretically yielding communication efficiency for ‘free’. We evaluate SPARQ-SGD over real datasets to demonstrate significant savings in communication bits over the state-of-the-art.

Projection techniques are often used to visualize high-dimensional data, allowing users to better understand the overall structure of multi-dimensional spaces on a 2D screen. Although many such methods exist, comparably little work has been done on generalizable methods of inverse-projection -- the process of mapping the projected points, or more generally, the projection space back to the original high-dimensional space. In this paper we present NNInv, a deep learning technique with the ability to approximate the inverse of any projection or mapping. NNInv learns to reconstruct high-dimensional data from any arbitrary point on a 2D projection space, giving users the ability to interact with the learned high-dimensional representation in a visual analytics system. We provide an analysis of the parameter space of NNInv, and offer guidance in selecting these parameters. We extend validation of the effectiveness of NNInv through a series of quantitative and qualitative analyses. We then demonstrate the method's utility by applying it to three visualization tasks: interactive instance interpolation, classifier agreement, and gradient visualization.

The assets in Industry 4.0 are categorised into physical, virtual and human. The innovation and popularisation of ubiquitous computing enhance the usage of smart devices: RFID tags, QR codes, LoRa tags, etc. for assets identification and tracking. The generated data from Industrial Internet of Things (IIoT) eases information visibility and process automation in Industry 4.0. Virtual assets include the data produced from IIoT. One of the applications of the industrial big data is to predict the failure of manufacturing equipment. Predictive maintenance enables the business owner to decide such as repairing or replacing the component before an actual failure which affects the whole production line. Therefore, Industry 4.0 requires an effective asset management to optimise the tasks distributions and predictive maintenance model. This paper presents the Genetic Algorithm (GA) based resource management integrating with machine learning for predictive maintenance in fog computing. The time, cost and energy performance of GA along with MinMin, MaxMin, FCFS, RoundRobin are simulated in the FogWorkflowsim. The predictive maintenance model is built in two-class logistic regression using real-time datasets. The results demonstrate that the proposed technique outperforms MinMin, MaxMin, FCFS, RoundRobin in execution time, cost and energy usage. The execution time is 0.48% faster, 5.43% lower cost and energy usage is 28.10% lower in comparison with second-best results. The training and testing accuracy of the prediction model is 95.1% and 94.5%, respectively.

Energy management strategy is gaining much popularity due to the involvement of conventional/non-conventional power sources with an efficient energy storing solution to offer un-interruptible power demand. Thus, in this work a simpler logic control circuit based nonisolated bi-directional DC-DC converter with both battery and super capacitor topology is proposed. For the proposed topology, first its mathematical modelling in ideal-case with parasitic in every mode of actions is presented. To control the bidirectional DC-DC converter topology, its small signal model for individual buck and boost operation is obtained by using averaging and linearization technique. Then, a unified logic circuit-based controller is designed for the obtained systems to analyze the performance of the converter in the buck and boost mode operation. The performance of the proposed topology is verified through MATLAB/Simulink environment.

Issuing a disaster certificate, which is used to decide the contents of a victim’s support, requires accuracy and rapidity. However, in Japan at large, issuing of damage certificates has taken a long time in past earthquake disasters. Hence, the government needs a more efficient mechanism for issuing damage certificates. This study developed an estimation system of roof-damaged buildings to obtain an overview of earthquake damage based on aero-photo images using deep learning. To provide speedy estimation, this system utilized the trimming algorithm, which automatically generates roof image data using the location information of building polygons on GIS (Geographic Information System). Consequently, the proposed system can estimate, if a house is covered with a blue sheet with 97.57 % accuracy and also detect whether a house is damaged, with 93.51 % accuracy. It would therefore be worth considering the development of an image recognition model and a method of collecting aero-photo data to operate this system during a real earthquake.

The future communication technologies like 6G are capable to provide higher mobility and better quality of service requirements to Internet of Things (IoT). To ensure mobility, the 6G technologies need more reliable and scalable solutions, which are capable to integrate large scale heterogeneous IoT networks. In a heterogeneous environment, seamless mobility along with the demands of IP addresses requires a proxy mobile IPv6 (PMIPv6) protocol that provides cost-effective solutions in next generation IoT networks. The PMIPv6 has been exploited for resource efficiency in IoT-enabled next generation networks. In this paper, we have proposed a demand-based resource-efficient location-aware PMIPv6 extension for seamless mobility in the next generation IoT networks. The proposed approach efficiently utilizes the network resources using location information and received signal strength (RSS). This solution enhances the performance of PMIPv6 protocol in terms of signaling cost, and load on network entities. Furthermore, mathematical models are derived in terms of signaling cost, load distribution. The proposed solution is compared with the existing RSS-based PMIPv6 extension protocols. The results show that the proposed scheme enhances the performance and is a resource-friendly for the next generation large scale IoT networks.

One of the combinatorial optimization problems is Knapsack problem, which aims to maximize the benefit of objects whose weight not exceeding the capacity of knapsack. This paper introduces artificial bee colony algorithm to select a subset of project and represented by knapsack problem to put the best investment plan which achieve the highest profits within a determined costs, this plan is one of the applications of the financial field. The result from the proposed algorithm implemented by matlab (8.3) show the ability to find best solution with precisely and rapidity compared to genetic algorithm http://dx.doi.org/10.25130/tjps.23.2018.039

To utilize a huge amount of observation data based on real-world events, a data assimilation process is needed to estimate the state of the system behind the observed data. The Kalman filter is a very commonly used technique in data assimilation, but it has a problem in terms of practical use from the viewpoint of processing efficiency and estimating the deterioration in precision when applied to particularly large-scale datasets. In this paper, we propose a method that simultaneously addresses these problems and demonstrate its usefulness. The proposed method improves the processing efficiency and suppresses the deterioration in estimation precision by introducing correction processes focusing on the non-negative nature and sparseness of data in wavelet space. We show that the proposed method can accurately estimate population dynamics (MAE ≤ 3, RMSE ≤ 7) on the basis of an evaluation done using population data generated from cellular networks. In addition, the possibility of wide area abnormality detection using the proposed method is shown from a situation analysis of when Category 5 typhoon Hagibis made landfall in Japan. The proposed method has been deployed in a commercial service to estimate real-time population dynamics in Japan.

The parsing of floorplans has been an issue for a long time in automated document processing and with algorithmic methods until recent years. This problem has also improved output with the emergence of convolutionary neural networks (CNN). The job here is to obtain spatial and geometrical data from floorplans as accurately as possible. The aim of this project is to extract the most information from a floor plan image around instance segmentation models like Cascade Mask R-CNN. A new style of key point CNN is being implemented to supplement the segmentation to find correct corner positions. Then the resulting segmentation is combined in a post-processing stage. With a mean IoU of 72.7 percent versus 57.5 percent, the resulting segmentation scores surpass the existing baseline of the CubiCasa5k floorplan data base. Moreover, for almost every class, the mean IoU for each class is increased. Cascade Mask R-CNN has also been shown to be better suited to this role than Mask R-CNN.

Gesture Recognition which is an inevitable part of human computer interaction is an ever evolving active research area. This has been in use under varied applications like smart home systems, sign language recognition, augment reality and in device controls. The acquisition of hand gestures are usually through optical sensors, in this work a radar based hand gesture data set is used for classifying the gestures. Micro doppler signatures were used as the input to the model. Radar dataset has fewer data samples compared to optical based data sets. The proposed work uses separable convolutional neural networks model which does depth wise convolution followed by point wise convolution to reduce overfitting effect of the training data. The proposed model was built in such a way that the model is capable of classifying any unseen data without exactly mimicking the training samples. The proposed model has achieved 94.56% as the testing accuracy which is certainly better than the previous work on this Dop Net data set. Moreover the model has also minimized the computational hours of the model using separable convolutions.

Content Based Image Retrieval is ever growing technology for many applications such as medical, remote sensing, social media search engines and surveillance monitoring, etc,. Representing image content with appropriate features is tedious task using traditional low level feature extraction methods. Deep learning models achieved high precision in classification and object detection algorithms by extracting automated high level feature extraction process. This paper proposed a hybrid feature extraction technique by combining the high level features and low level features to improve the robustness of the feature vector. The proposed model used pre-trained Googlenet model as feature extractor and combined with Gabor multiscale texture features. The final feature vector will be used for retrieving the relevant image data from the large scale image dataset. It has achieved the precision of 91 percent which shows better than state of art methods.

Prediction of the facts about any topic always sounds fascinating. It becomes more important and useful when it can predict the facts about healthcare information. In computer science, machine learning techniques/models can be used to predict important health care facts, further the study of the sentiments related to health care information can also play a significant role in the generation of important information. There are many international and national organizations like United States Centers for Disease Control and Prevention (CDC), World Health Organization (WHO), etc. that publish important healthcare data. These data sets can be used to predict important facts about the disease. Further, the datasets related to sentiments of the people like tweets on Twitter, posts on Facebook, and blogs are useful to study the sentiments of the people on a particular topic. Sentiments related to health care information, diseases, and epidemics are very useful. The study of these sentiments can help make better prediction systems and to generate fruitful health care facts. Further, the real datasets related to infectious diseases like COVID-19 can be used for regression analysis for making predictions regarding the upcoming cases. In this paper, we have presented a survey on the application of different machine learning techniques, that are used in the classification and regression analysis related to health care predictions and the study of sentiments. We have presented a review of major research studies related to healthcare between the years 2010 to 2020.

According to the latest news, the 3rd Generation cooperation Project has been taken the inventiveness of creating an interworking architecture involving cellular and WLAN as an additional feature to the cellular network requirements of 3GPP. This article provides an overview of an interworking infrastructure that allows mobile operators to provide the public WLAN dependent entrance service to various 3GPP network subscribers. The enabling features include 3GPP membership reuse, network availability, authentications relying on 3GPP systems, security keys agreement, USIM /SIM card authorization, user data service access and routing, as well as end-user paying. The function of interworking is accomplished without setting any particular 3GPP specifications for the new WLAN entrance networks, but instead by depend on existing features available in the traditional WLAN entrance networks centered on the IEEE 802.11 principles. In this paper, authors have focused on two key aspects of MTC devices, as currently discussed in 3GPP-Release 10. These are named as “the control of subscriptions” and the “congestion/overload control of the network”. A new solution is proposed for the latter one based on the handling of the bulk signals.

Many organizations view themselves as market driven in this digitalized society, where the decision making depends solely on the customer feedbacks and suggestions. This massive amount of unstructured data produced, affects query processing performance adding to the difficulty in managing or retrieving the data. Many organizations are progressively exploring ways to comprehend and analyse this huge unstructured data to infer operational insights from it. We could solve many of the storage issues of unstructured data using the document-based NoSQL databases. But applying artificial intelligence to make exact matching predictions with the text data is remaining as a challenge. With the new method proposed here, the intelligent processing of the textual data stored in document-based NoSQL databases is made possible, and it is really an added benefit to those, who opt or use NoSQL databases for unstructured data storage.

The data surveying this study mines methods for extracting informative and interesting knowledge from learning student datasets. The amount of data stored in education big databases is growing at an alarming rate. As such, data processing and knowledge discovery techniques can be used to discover interesting relationships between different characteristics and assessments of students. Initially to propose a novel self-analysis model which performs analysis about the different big data learning services. From the result provided by the user, the service will be ranked and analyze about the knowledge from daily activity gathered by the learner. The proposed model performs analysis to provide efficient Educational Data Mining (EDM) E-Learning system based on machine learning to ranking the users and performance evaluation in their activities. The existing method does not consider the learner activities, ranking and it focus on single feature based classification so the system doesn’t provide a better classification result. These issues to overcome introduce an Extended Multi-Labeled Gradient Boosting (XMGB) improve the classification performance both of imbalanced dataset. The method initial stage apply the Adaptive Expectation-Maximization Mixture (AEMM) method cluster the data various group and extract the E-learning multiple feature set. In this method to compute the classification time and memory provide an efficient result. Then introduce an academic activity based weight score analysis model to evaluate the learner daily activates and exam score its help to the ranking and cluster the data groups. In this proposed method analysis result to evaluate the following performance matrix: Recall, precision, F measure, classification accuracy. In this simulation analysis of the proposed method XMGB are provide an efficient classification with less time complexity compared to other existing methods.

The primary objective of university education is to provide students with the required information and skills to enable them to make a successful contribution to the national development effort. In order to evaluate the level of expertise and skill of students, this preparation includes frequent examination and appraisal in the form of tests. Manual exams are carried out by most universities; Post Graduate and Under Graduate in India. Due to multiple uses made of the outcomes of the students, these examinations have high stakes. Consequently, the experiments became subject to different types of malpractice. Test malpractice affects the success of students in exams and leaves the scores/grades earned inaccurate, hence a challenge to examination bodies and the nation's education system (India). These are all disturbing signs that pose a danger to the nation. Examination malpractice has eaten deep into the Indian culture of education. Therefore, the object of this paper is to explore the problem of examination malpractice in India. The paper covered relevant measures, procedure setting/implementation and suggestions for this action. And finally, a model is proposed to stop malpractices in the examination. This IoT based model is used for detecting any metallic object or object in a range more than normal IR proximity sensor range. The block diagram of proposed model and circuit diagram for that model discussed in detail. This proposed model may be used for any investigation in the universities examinations.

At present, the economic benefits of double inlet and double outlet ball mills, which are commonly used in coal-fired power plants, need to be improved. Based on this, this paper explores the factors influencing the output of double inlet and double outlet ball mill by establishing the output system of double inlet and double outlet ball mill. By analyzing the influence factors of the output of double inlet and double outlet ball mill, and combining with system dynamics, the dynamic model of double inlet and double outlet ball mill output system is established, and SPSS is used to fit the model data with the real data. The fitting degree is above 0.8, and the authenticity is high. The simulation results show that the impact energy of barrel grinding, the grinding powder fineness, and the pressure ratio in the ball mill affect the output of double inlet and double outlet ball mill according to certain relations. Finally, the research in this paper provides theoretical and technical support for improving the economic benefits of double inlet and double outlet ball mills.

Chatbots are becoming increasingly popular as virtual assistants, many businesses are launching If-This-Then-That programs to help them get started. Such systems, on the other hand, often generate chatbots that are stagnant and difficult to handle. The market for chatbots is increasing. Computer systems are often required, and online connections are improved by allowing users to express their needs, desires, or questions naturally and clearly by speaking, tapping, and talking. They're easy to use, perfect for people of all ages, and have the most detailed responses to questions. The majority of today's education is distributed through e-learning. A chatbot is one of the most powerful ways for students to read, as it can answer questions at any time without the need for human interaction. This chatbot is highly capable of overcoming student uncertainty without the need for human interaction. The IF THIS Then THAT paradigm is used by the bulk of chatbots. Natural language processing and deep learning technologies were used to build this chatbot. As a consequence, the chatbot will comprehend questions at a higher level.

One-level optimization methods have been proposed to optimize a single user’s load profile or a cluster of users in the smart grids. In this work, two two-level optimization methods are studied, one case considering technical requirements (case 1) and another considering economic criterion (case 2). In the upper level, the supplier optimizes the objective function. Meanwhile, at the lower level, users optimize their electrical costs. The proposed methods are based on Genetic Algorithm methods. In this sense, an indirect control is established in which users react to a price signal. Simulation results illustrate that both cases improve the demand profile and increase the retailer profit concerning an unscheduled case. However, when the supplier tries to maximize the profit, some users receive benefits to detriment of others, concluding that the technical approach is preferable to the economic one.

In the recent past, the cloud materials computing plays a vital role for keeping higher range of information in state of art of cloud environment. The information has to be kept in cloud may have large size that occupies more memory space in the cloud environment. The service provider have limitations in getting cloud space and providing space to the users to store more data limited and available space of cloud. We have proposed in this paper, an accurate and efficient video/image materials compression algorithm to minimize the data size to store in the cloud with less memory space. The novel algorithm for compression of image materials using block compressive sensing double density wavelet transform (BCS-DDWT) algorithm is proposed to compress materials. Compression of the image materials could be prepared based on various wavelets at variouspositions of decomposition. The concert investigation of BCS-DDWT with wavelets at 4 various positions of decomposition using BCS-DDWTcould be shown in the results. The performance of BCS-DDWTcompression is balanced by calculating parameters such as peak signal to noise ratio, mean square error, and bits per pixeland compression ratio.