Reviewerly/exHarmony huggingface dataset:
Simultaneous Semantic Segmentation and Depth Completion with Constraint of Boundary

As the core task of scene understanding, semantic segmentation and depth completion play a vital role in lots of applications such as robot navigation, AR/VR and autonomous driving. They are responsible for parsing scenes from the angle of semantics and geometry, respectively. While great progress has been made in both tasks through deep learning technologies, few works have been done on building a joint model by deeply exploring the inner relationship of the above tasks. In this paper, semantic segmentation and depth completion are jointly considered under a multi-task learning framework. By sharing a common encoder part and introducing boundary features as inner constraints in the decoder part, the two tasks can properly share the required information from each other. An extra boundary detection sub-task is responsible for providing the boundary features and constructing cross-task joint loss functions for network training. The entire network is implemented end-to-end and evaluated with both RGB and sparse depth input. Experiments conducted on synthesized and real scene datasets show that our proposed multi-task CNN model can effectively improve the performance of every single task.
Breast Cancer Histopathological Image Classification using Deep Learning

The paper employs deep learning to classify breast cancer histopathological image into benign and malignant categories. The Inception v1 convolutional neural network is mainly adopted. Spatial Pyramid Pooling and special global average pooling are added to the network to ensure that images can be imported in the original aspect ratio format. The experimental results show that the convolutional neural network performs well in breast cancer image classification, and the Global Average Pooling effect is slightly better than the Spatial Pyramid Pooling.
Area-efficient low-latency polynomial basis finite field GF(2 ) systolic multiplier for a class of trinomials

Many security and data reliability algorithms rely heavily on finite field GF(2m) arithmetic computations, in particular, multiplication. The design of a field multiplier employing systolic architecture is very much suited for very large scale integration (VLSI) implementation. Though systolic architecture gives higher throughput, it usually requires area overhead and high-latency. In this paper, we propose a systolic architecture for polynomial basis finite field GF(2m) multiplier based on a class of trinomials. Analysis shows that the proposed multiplier achieves low-area and low-latency compared to similar multipliers available in the literature. The proposed multiplier achieves 10% reduction in area complexity when compared with the best existing area-efficient multiplier, for m = 409. Application specific integrated circuit (ASIC) implementation of the proposed multiplier together with the two most comparable multipliers confirms that the proposed multiplier outperforms the other two multipliers with improvements in area and latency. The proposed area-efficient multiplier is suitable for Internet of Things edge-devices.
A Genetic Asexual Reproduction Optimization Algorithm for Imputing Missing Values

In this paper, we suggest a new technique that significantly improve the computational time of the genetic algorithm for imputing missing values. Data contain noise and missing values, which made them unreliable for scientific purposes. Due to this, we are required to preprocess these data before using them. Researchers either avoid or impute missing data. It is necessary to choose an appropriate imputation method, and it is based on several factors such as datatypes and numbers of missing data. For a higher missing value rate, missing value imputation (MVI) can be suitable way for imputing missing data in incomplete dataset. One of the MVI methods is the genetic algorithm; although genetic algorithm may produce good results, the computational time is very high. The proposed algorithm is a combination of the genetic and Asexual Reproduction Optimization (ARO) algorithm. We present an experimental evaluation of Pima and mammographic mass dataset that collected from UCI repository. In the small percentage of missing values, those instances can be imputed by the ARO algorithm, but in the case of large amounts, our approach illustrates much better results. This proposed technique works even better when the rate of missing values is higher. The accuracy and computational time of our proposed algorithm are compared with another techniques like Mean, K-Nearest Neighbor, and SVM. On average our approach 8% improved the accuracy and 4% improved the ROC, and it requires less computational time than a basic genetic algorithm.
The agencement of taskification: On new forms of reading and writing in BYOD schools

The proliferation of digital devices in educational settings has contributed to the decentralization of knowledge from teachers and established textbooks to fluid online personalized resources, and from blackboards as spaces of materialization of this knowledge to personal screens. In this new constellation, school practices such as reading, writing and note-taking take new forms. How are these practices reconfigured when centered around mobile digital devices? In this paper, we investigate routine lesson practices of a Bring Your Own Device (BYOD) school by means of an ethnographic observation. Adopting the term agencement as a heuristic device, we explore what practices are being constituted and how they are always in connection with one another. Moreover, we show that by practicing they produce different forms of action. We argue that in such a hybrid constellation, tasks find a different status beyond isolated and self-contained units for enhancing learning in different lessons. We introduce task-agencement, through which the task always entails a process of taskification that formats BYOD schools. With taskification, we mean that the task-agencement expands the domain in which tasks not only influence the organization and structure of lessons, but equally the very organization of the BYOD school by enacting a particular form of schooling. Finally, we propose a taskified form as a form of BYOD schooling that templates and formats how practices such as reading and writing, contextualization and introduction of the lesson are conducted.
Image Colorization with Convolutional Neural Networks

Previous approaches to the colorization of grayscale images rely on human manual labor and often produce desaturated results that are not likely to be true colorizations. Inspired by [15], we proposed an automatic approach based on deep neural networks to color the image in grayscale. We have studied several models, approaches and loss function to understand the best practices to produce a plausible colorization. We trained a convolutional neural network by noting that some loss functions work better than others. We used the VGG-16 CNN model based on the classification with the loss of cross entropy that is very well to produce a plausible colorization.
Semantic Probabilistic Traversable Map Generation For Robot Path Planning

Probabilistic traversable map plays a critical role for mobile robot in safe and reliable navigation. Different from structured environment, traversable region in unstructured environment such as grass and sidewalk is relatively more complex. Traditional elevation-based traversable map cannot represent such complex environment well. Thus, it may cause navigation failure. To address this limitation, this paper proposes a novel semantic-elevation mapping approach for navigation task. We first build a multi-layer semantic map from continuous semantic segmentation images. Then, this multi-layer semantic map is fused and converted into a probabilistic map by a distance transform approach. Generated semantic probabilistic map is then fused to an elevation map at path planning stage. The proposed approach is tested on an Unmanned Ground Vehicle (UGV) platform. The results show that our semantic-elevation mapping approach works more reliably and safely than that only with elevation-based approach.
Color Image Encryption Technique Based on Chaos

Modern information systems' security is heavily reliant on data encryption techniques. Chaos and image encryption techniques have garnered a great deal of attention with several recently developed models. In this study, we propose a novel hybrid encryption technique utilizing three distinct chaos maps: Baker, Arnold and Henon; each applied to a single channel of Red-Green-Blue. Unlocking encryptions in the proposed model requires three separate keys, thus elevating information security. MATLAB was used for implementation and assessment of the encryption quality metrics. A comparison applied against other techniques provided promising results in visual inspection, correlation coefficient and signal-to-noise ratio. Our findings indicate that future application of this hybrid encryption technique will serve to efficiently enhance transmitted information security, and safeguard sensitive data against unauthorized attack.
Hierarchical Orchestration of Disaggregated Memory

This article presents XMemPod, a hierarchical disaggregated memory orchestration system. XMemPod virtualizes cluster wide memory to scale large memory workloads in virtualized clouds. It makes three novel contributions: (1) XMemPod offers efficient, transparent, and dynamic sharing of available memory that is disaggregated across VMs on the same host or in the cluster. (2) XMemPod provides a hierarchical memory expansion framework, which enables memory-intensive workloads on a VM to expand its memory demand over virtualized host memory first, and remote memory next, before resorting to external disk. (3) XMemPod provides a suite of optimization techniques to further improve the utilization and access latency of disaggregated memory. XMemPod is deployed on a virtualized RDMA cluster without any modifications to user applications and the OSes. Evaluated with multiple workloads on unmodified Spark, Apache Hadoop, Memcached, Redis and VoltDB, using XMemPod, throughputs of these applications improve by 11× to 612× over conventional Linux, and by 1.7× to 14× over the existing representative remote memory paging systems, and yet the total amount of network traffic consumed by XMemPod is only 24 percent of the existing approaches.
DACH: Domain Adaptation Without Domain Information

Domain adaptation is becoming increasingly important for learning systems in recent years, especially with the growing diversification of data domains in real-world applications, such as the genetic data from various sequencing platforms and video feeds from multiple surveillance cameras. Traditional domain adaptation approaches target to design transformations for each individual domain so that the twisted data from different domains follow an almost identical distribution. In many applications, however, the data from diversified domains are simply dumped to an archive even without clear domain labels. In this article, we discuss the possibility of learning domain adaptations even when the data does not contain domain labels. Our solution is based on our new model, named domain adaption using cross-domain homomorphism (DACH in short), to identify intrinsic homomorphism hidden in mixed data from all domains. DACH is generally compatible with existing deep learning frameworks, enabling the generation of nonlinear features from the original data domains. Our theoretical analysis not only shows the universality of the homomorphism, but also proves the convergence of DACH for significant homomorphism structures over the data domains is preserved. Empirical studies on real-world data sets validate the effectiveness of DACH on merging multiple data domains for joint machine learning tasks and the scalability of our algorithm to domain dimensionality.
Enhanced Face Recognition System Based on Deep CNN

Facial recognition techniques are considered the most important fields of research in biometric technology. In this research paper, we present a Face Recognition (FR) system divided into three steps: The Viola-Jones face detection algorithm, facial image enhancement using Adaptive Histogram Equalization algorithm (AHE), and feature learning for classification. For learning the features followed by classification we used VGG16, ResNet50 Convolutional Neural Networks (CNN) architectures for the proposed system. Our experimental work was performed on the Extended Yale B database and CMU PIE face database. Finally, the comparison with the other methods on both databases shows the robustness and effectiveness of the proposed approach. Where the Resnet50 architecture has achieved a rate of 97, 23% and 98, 38% respectively.
K‐PSO: An improved PSO‐based container scheduling algorithm for big data applications

Summary In recent years, Docker container technology is being applied in the field of cloud computing at an explosive speed. The scheduling of Docker container resources has gradually become a research hotspot. Existing big data computing and storage platforms apply with traditional virtual machine technology, which often results in low resource utilization, a long time for flexible scaling and expanding clusters. In this paper, we propose an improved container scheduling algorithm for big data applications named Kubernetes‐based particle swarm optimization(K‐PSO). Experimental results show that the proposed K‐PSO algorithm converges faster than the basic PSO algorithm, and the running time of the algorithm is cut in about half. The K‐PSO container scheduling algorithm and algorithm experiment for big data applications are implemented in the Kubernetes container cloud system. Our experimental results show that the node resource utilization rate of the improved scheduling strategy based on K‐PSO algorithm is about 20% higher than that of the Kube‐scheduler default strategy, balanced QoS priority strategy, ESS strategy, and PSO strategy, while the average I/O performance and average computing performance of Hadoop cluster are not degraded.
Accurate Cost Estimation of Memory Systems Utilizing Machine Learning and Solutions from Computer Vision for Design Automation

Hardware/software co-designs are usually defined at high levels of abstractions at the beginning of the design process in order to provide a variety of options on how to realize a system. This allows for design exploration which relies on knowing the costs of different design configurations (with respect to hardware usage and firmware metrics). To this end, methods for cost estimation are frequently applied in industrial practice. However, currently used methods oversimplify the problem and ignore important features, leading to estimates which are far off from real values. In this article, we address this problem for memory systems. To this end, we borrow and re-adapt solutions based on Machine Learning (ML) which have been found suitable for problems from the domain of Computer Vision (CV). Based on that, an approach is proposed which outperforms existing methods for cost estimation. Experimental evaluations within an industrial context show that, while the accuracy of the state-of-the-art approach is frequently off by more than 20 percent for area estimation and more than 15 percent for firmware estimation, the method proposed in this article comes rather close to the actual values (just 5-7 percent off for both area and firmware). Furthermore, our approach outperforms existing methods for scalability, generalization, and decrease in manual effort.
A logic for Lawson compact algebraic L-domains

In this paper, we build a logic which is named N-sequent calculus. Based on this logic, we provide two kinds of logical representations of Lawson compact algebraic L-domains: one in terms of logical algebras and the other in terms of logical syntax. The first representation takes the corresponding logical algebras as research objects. The use of prime filters achieves the connection between our logic and Lawson compact algebraic L-domains. This approach is inspired by Abramsky's SFP domain logic and the disjunctive propositional logic on algebraic L-domains introduced by Yixiang Chen and Achim Jung. However, there are essential differences between them at the morphisms part. For the second representation, we directly adopt N-sequent calculi themselves as objects instead of the logical algebras. Then we establish the category of N-sequent calculi with consequence relations equivalent to that of Lawson compact algebraic L-domains with Scott continuous maps. This demonstrates the capability of the syntax of the logic in representing domains.
Deep Learning Based Approach for Historical Manuscript Dating

Digitization of historical manuscripts from premodern eras, has captivated the document analysis and pattern recognition community in recent years. Estimation of the period of production of such documents is a challenging yet favored research problem. In this paper, we present a deep learning based approach to effectively characterize the year of production of sample documents from the Medieval Paleographical Scale (MPS) dataset. By employing transfer learning on a number of popular pre-trained Convolutional Neural Network (CNN) models, we have significantly reduced the Mean Absolute Error (MAE) reported in previous studies.
Simitate: A Hybrid Imitation Learning Benchmark

We present Simitate - a hybrid benchmarking suite targeting the evaluation of approaches for imitation learning. A dataset containing 1938 sequences where humans perform daily activities in a realistic environment is presented. The dataset is strongly coupled with an integration into a simulator. RGB and depth streams with a resolution of 960×540 at 30Hz and accurate ground truth poses for the demonstrator's hand, as well as the object in 6 DOF at 120Hz are provided. Along with our dataset we provide the 3D model of the used environment, labeled object images and pre-trained models. A benchmarking suite that aims at fostering comparability and reproducibility supports the development of imitation learning approaches. Further, we propose and integrate evaluation metrics on assessing the quality of effect and trajectory of the imitation performed in simulation. Simitate is available on our project website: https://agas.uni-koblenz.de/simitate/.
An End-to-End Trainable Framework for Joint Optimization of Document Enhancement and Recognition

Recognizing text from degraded and low-resolution document images is still an open challenge in the vision community. Existing text recognition systems require a certain resolution and fails if the document is of low-resolution or heavily degraded or noisy. This paper presents an end-to-end trainable deep-learning based framework for joint optimization of document enhancement and recognition. We are using a generative adversarial network (GAN) based framework to perform image denoising followed by deep back projection network (DBPN) for super-resolution and use these super-resolved features to train a bidirectional long short term memory (BLSTM) with Connectionist Temporal Classification (CTC) for recognition of textual sequences. The entire network is end-to-end trainable and we obtain improved results than state-of-the-art for both the image enhancement and document recognition tasks. We demonstrate results on both printed and handwritten degraded document datasets to show the generalization capability of our proposed robust framework.
Matchability of heterogeneous networks pairs

We consider the problem of graph matchability in non-identically distributed networks. In a general class of edge-independent networks, we demonstrate that graph matchability can be lost with high probability when matching the networks directly. We further demonstrate that under mild model assumptions, matchability is almost perfectly recovered by centering the networks using universal singular value thresholding before matching. These theoretical results are then demonstrated in both real and synthetic simulation settings. We also recover analogous core-matchability results in a very general core-junk network model, wherein some vertices do not correspond between the graph pair.
Recognising decorations in archaeological finds through the analysis of characteristic curves on 3D models

In the analysis of archaeological finds, it is important for archaeologists to identify their style, origin, period, etc. to allow their correct classification. In the digital era, the development of automatic techniques to measure the peculiar characteristics of archaeological finds would be of great help in this activity. Considering that ancient artefacts are very often incomplete, consumed, degraded, if not consisting of simple fragments, geometric details, such as decorations, visual motifs, patterns, are more useful for their analysis than global characteristics. These patterns are usually composed by characteristic curves arranged in a regular way, as in a Greek fret or a floral band. Here we propose the recognition of characteristic curves on 3D models of archaeological artefacts, identified by a set of characteristic points. We approximate these curves with known curves to provide localisation and quantitative measurement of the characteristic features used as decorations or patterns of the digital models of ancient objects. To solve this problem, we adopt a generalised version of the Hough Transform (HT). In addition, we introduce new rules of composition and automatic aggregation of the characteristic curves, not limiting the recognition to a single curve at a time and supporting an automatic annotation of the fragment digital model.
Indoor Roaming Activity Detection and Analysis of Elderly People using RFID Technology

Smart Home (SH) is a house or an apartment equipped with advanced automation technologies to provide the occupants with intelligent monitoring and actionable information that can be situation specific. Recent research indicates that the population over the age of 60 is growing at an alarming rate, which is estimated that by 2050 this particular group will have globally increased by over 50%. With such an increase, the sense of eldercare is being emphasized among nowadays and Smart Home is undoubtedly a promising solution to the problem. This research paper involves the SH architecture that includes sensors, data communication, and data integration. The system collects movement based activity data from the elderly using Radio Frequency Identification (RFID) technology. The research also discusses why RFID is chosen among other sensors. RFID is the use of radio waves to read and capture information stored on a tag attached to an object. A tag can be read from up to several feet away and does not need to be within direct line-of-sight of the reader to be tracked. The RFID system is made up of two parts such as a label and a reader. This paper presents a study of why movement based activity detection can be used in the longterm to help elderly people living alone in a Smart Home. The work focuses on the need to manage the data and how the system must be maintained.