Reviewerly/exHarmony huggingface dataset:
Landmark‐free clothes recognition with a two‐branch feature selective network

In this Letter, the authors present a ‘landmark-free’ clothes recognition approach. Recent studies have shown that the use of landmark information has achieved great success in the task of clothes recognition. However, the landmark annotation is very labour intensive and time consuming. It also suffers from inter- and intra-individual variability. To overcome these problems, the authors propose a two-branch feature selective network for category classification and attribute prediction. Note that, in this Letter, they prove that the proposed network has an excellent ability to effectively learn a discriminative feature representation of a ‘clothing image’. Experimental results on the benchmark data set show that the proposed network yields comparable performance to the state-of-the-art methods, which strongly depend on the fashion landmark.
Thinking Outside the Box: Generation of Unconstrained 3D Room Layouts

We propose a method for room layout estimation that does not rely on the typical box approximation or Manhattan world assumption. Instead, we reformulate the geometry inference problem as an instance detection task, which we solve by directly regressing 3D planes using an R-CNN. We then use a variant of probabilistic clustering to combine the 3D planes regressed at each frame in a video sequence, with their respective camera poses, into a single global 3D room layout estimate. Finally, we showcase results which make no assumptions about perpendicular alignment, so can deal effectively with walls in any alignment.
Remote Monitoring of Children With Chronic Illness Using Wearable Vest

This paper presents the design and development of a textile-based wearable smart vest that can be used to monitor children who are suffering from chronic illness. This system provides essential information for real-time monitoring of such children so that immediate actions can be taken in case of an emergency. Parents/Caretakers are continuously notified about the health conditions and the activities performed by their children using a smart android application. Three different sensors were fabricated on the vest that includes Lilypad accelerometer, pulse oximeter, and Lilypad temperature sensors. The activities performed by children with chronic illness need to be monitored continuously because certain activities like run if performed for a long duration might result in serious fatigue conditions. Hence in this work, the Lilypad accelerometer is used to identify the actions performed by the children. A new sparse representation-based classification algorithm was used to classify activities like walk, run, jump, hop, sit and stand. The proposed classifier achieved a high recognition rate of about 97.49%. Pulse oximeter provided the values of oxygen saturation level (in %) and heart rate (in bpm). Lilypad temperature sensor was used to monitor the body temperature level of the child. The identified activity along with the values of oxygen saturation level, heart rate and temperature are indicated to the parents/caretakers using a customized mobile application. This aids in taking immediate action in case of abnormal conditions.
Information filtering based on eliminating redundant diffusion and compensating balance

In statistical physics, researchers concentrate on mass diffusion and heat conduction-based information filtering models, which effectively facilitate recommendation accuracy and diversity. There are many improved methods combining mass diffusion with heat conduction theories. Research results show that the best results are achieved when the combination of mass diffusion and heat conduction reaches equilibrium. With elaborative analysis, we find that similarity redundancies derive from the attribute correlations of objects, and deduce the similarity estimation deviation. Considering the former deficiencies, we propose a novel model through eliminating redundant diffusion and compensating balance (shortly ERD-CB), which symmetrically combines mass diffusion with heat conduction process through balance compensation. Three benchmark datasets from Movielens, Amazon and Netflix are used in our extensive experiments. Experiment results show that the ERD-CB model outperforms the benchmarkbaselines for accuracy, diversity and novelty.
Absorbing Microbursts Without Headroom for Data Center Networks

In data center networks, handling microbursts is crucial to user experience because the microburst causes excessive packet losses in shallow buffered switches. Explicit congestion notification (ECN) enables the switch to absorb microbursts by leaving buffer headroom, but has a fundamental trade-off between latency and throughput. In this letter, we propose LossPass, a switch port management scheme that achieves low latency and high throughput simultaneously. LossPass breaks the trade-off of ECN by absorbing microbursts without headroom. Intensive benchmark results demonstrate that LossPass reduces the average and tail latency of small flows while maintaining line-rate throughput.
What Sort of Asset? Bitcoin Analysed

Early analysis of Bitcoin concluded that it did not meet the economic conditions to be classified as a currency. Since this analysis interest in bitcoin has increased substantially. We investigate whether the introduction of futures trading in bitcoin is able to resolve the issues that stopped bitcoin from being considered a currency. Our analysis shows that spot volatility has increased following the announcement of the futures contracts, the futures contracts are not an effective hedging instrument and that price discovery is driven by uninformed investors in the spot market. The conclusion that bitcoin is a speculative asset rather than a currency is not altered by the introduction of futures trading.
Selective sampling for trees and forests

In this paper we describe selective sampling algorithms for decision trees and random forests and their contribution to the classification accuracy. In our selective sampling algorithms, the instance that yields the highest expected utility is chosen to be labeled by the expert. We show that it is possible to obtain the most valuable unlabeled instance to be labeled by the expert and added to the training dataset of the decision tree simply by depicting the influence of this new instance on the class probabilities of the leaves. All the unlabeled instances that fall into the same leaf will have the same class probabilities. As a result, we can compute the expected accuracy of the decision tree according to its leaves instead for each individual unlabeled instance. An extension for random forests is also presented. Moreover, we show that the selective sampling classifier has to belong to the same family as the classifier whose accuracy we wish to improve but need not be identical to it. For example, a random forest classifier can be used for the selective sampling process, and the results can be used to improve the classification accuracy of a decision tree. Likewise, a random forest classifier consisting of three trees can be used in the selective sampling algorithm to improve the classification accuracy of a random forest consisting of ten trees. Our experiments show that the proposed selective sampling algorithms achieve better accuracy than the standard random sampling, uncertainty sampling and the active belief decision tree learning approach (ABC4.5) for several real-world datasets. We also show that our selective sampling algorithms improve significantly the classification performance of several state-of-the-art classifiers such as the random rotation forest classifier for real-world large-scale datasets.
How Museums Made (and Re-made) Their Digital User

Surveying archival material from across fifty years of practice, and leveraging multi-disciplinary theoretical perspectives from digital studies, design studies and disabilityDisability studies, this chapter uses a form of ideational history, to evidence shifting linguisticLinguistics habits and frames of reference around the construct of the ‘digital userDigital user’, at once both sequential and enduring: from the user as ‘operator’Operator (set within a system-orientated context of organizational efficiency); to the user as ‘individual’ (amidst priorities of usability and experience design); to—emerging today—the user as ‘actant’Actant (against which social value and agency are the new indices of success). In these different constructions, the ‘digital userDigital user’ moves from being part of the system, to outside system, to in the world, whilst its principal capability shifts from automation, to personalizationPersonalization to empowermentEmpowerment. Crucially, it is proposed here that it has been the role of accessible designAccessible design (in particular, the design of digital with disabilityDisability in mind) that has acted both as a key informant and agitator of this change, but also the practice through which this development can most usefully be seen.
High-speed video salient object detection with temporal propagation using correlation filter

It is challenging for video salient object detection in the pursuit of high accuracy and fast speed with large amount of calculation in spatiotemporal domain. Most of existing methods use complex models with massive number of parameters to detect salient regions in video and cost a lot of time. In this paper, we propose a high-speed video salient object detection method at 0.5s each frame (including average 0.32 s for optical flow computation). It mainly consists of two modules, the initial spatiotemporal saliency module and the correlation filter based salient temporal propagation module. The former one integrates the spatial saliency by robust minimum barrier distance and boundary contrast cue with temporal saliency information from motion field. The latter one incorporates correlation filters to keep the saliency consistency between neighboring frames. The above two modules are finally fused together in an adaptive way. Comprehensive experiments on four benchmarks: SegTrack v1, SegTrack v2, FBMS and Visal dataset, clearly demonstrate that our algorithm shows better performance than the other state-of-art methods.
Heterogeneous deniable authentication and its application to e-voting systems

An electronic voting (e-voting) system allows voters to cast their votes secretly and securely over a public channel. Security is the critical issue that should be considered in such a system. In this paper, we propose a heterogeneous deniable authentication (HDA) protocol for e-voting systems. The proposed protocol allows a sender in a certificateless cryptography (CLC) environment to transmit a message to a receiver in a public key infrastructure (PKI)environment. Our protocol admits security proof in the random oracle model under the bilinear Diffie–Hellman (BDH) and computational Diffie–Hellman (CDH) assumptions. Additionally, our protocol provides batch verification, which can accelerate the verification of authenticators. Comprehensive analysis shows that our protocol is highly suitable for practical e-voting systems.
A classifying ubiquitous clinic recommendation approach for forming patient groups and recommending suitable clinics

Existing ubiquitous clinic recommendation systems assume that patients’ preferences are alike and thus apply the same recommendation mechanism to all mobile patients. However, this assumption may be unreasonable and must be relaxed. Accordingly, this study proposes a classifying ubiquitous recommendation approach. The classifying ubiquitous recommendation approach divides patients into multiple groups by mining their unknown preferences before recommending them suitable clinics. To tune the recommendation mechanism of each patient group, an integer nonlinear programming problem is solved in a rolling manner. When a new patient accesses the ubiquitous clinic recommendation system, the recommendation mechanisms of all groups are applied to recommend the suitable clinic to the new patient. The results of a regional experiment indicated that the classifying ubiquitous recommendation approach improved the successful recommendation rate by up to 52%. Therefore, patients’ unknown preferences are different and affect their behaviors in choosing clinics, which should be considered in grouping patients and in tailoring the clinic recommendation mechanism.
A SAT-Based System for Consistent Query Answering

An inconsistent database is a database that violates one or more integrity constraints, such as functional dependencies. Consistent Query Answering is a rigorous and principled approach to the semantics of queries posed against inconsistent databases. The consistent answers to a query on an inconsistent database is the intersection of the answers to the query on every repair, i.e., on every consistent database that differs from the given inconsistent one in a minimal way. Computing the consistent answers of a fixed conjunctive query on a given inconsistent database can be a coNP-hard problem, even though every fixed conjunctive query is efficiently computable on a given consistent database. We designed, implemented, and evaluated CAvSAT, a SAT-based system for consistent query answering. CAvSAT leverages a set of natural reductions from the complement of consistent query answering to SAT and to Weighted MaxSAT. The system is capable of handling unions of conjunctive queries and arbitrary denial constraints, which include functional dependencies as a special case. We report results from experiments evaluating CAvSAT on both synthetic and real-world databases. These results provide evidence that a SAT-based approach can give rise to a comprehensive and scalable system for consistent query answering.
Hybrid Spiral STC-Hedge Algebras Model in Knowledge Reasonings for Robot Coverage Path Planning and Its Applications

Robotics is a highly developed field in industry, and there is a large research effort in terms of humanoid robotics, including the development of multi-functional empathetic robots as human companions. An important function of a robot is to find an optimal coverage path planning, with obstacle avoidance in dynamic environments for cleaning and monitoring robotics. This paper proposes a novel approach to enable robotic path planning. The proposed approach combines robot reasoning with knowledge reasoning techniques, hedge algebra, and the Spiral Spanning Tree Coverage (STC) algorithm, for a cleaning and monitoring robot with optimal decisions. This approach is used to apply knowledge inference and hedge algebra with the Spiral STC algorithm to enable autonomous robot control in the optimal coverage path planning, with minimum obstacle avoidance. The results of experiments show that the proposed approach in the optimal robot path planning avoids tangible and intangible obstacles for the monitoring and cleaning robot. Experimental results are compared with current methods under the same conditions. The proposed model using knowledge reasoning techniques in the optimal coverage path performs better than the conventional algorithms in terms of high robot coverage and low repetition rates. Experiments are done with real robots for cleaning in dynamic environments.
On the Reliability of LSTM-MDL Models for Pedestrian Trajectory Prediction

Recurrent neural networks, like the LSTM model, have been applied to various sequence learning tasks with great success. Following this, it seems natural to use LSTM models for predicting future locations in object tracking tasks. In this paper, we evaluate an adaption of a LSTM-MDL model and investigate its reliability in the context of pedestrian trajectory prediction. Thereby, we demonstrate the fallacy of solely relying on prediction metrics for evaluating the model and how the models capabilities can lead to suboptimal prediction results. Towards this end, two experiments are provided. Firstly, the models prediction abilities are evaluated on publicly available surveillance datasets. Secondly, the capabilities of capturing motion patterns are examined. Further, we investigate failure cases and give explanations for observed phenomena, granting insight into the models reliability in tracking applications. Lastly, we give some hints how demonstrated shortcomings may be circumvented.
Spatial Temporal Balanced Generative Adversarial AutoEncoder for Anomaly Detection

For video anomaly detection, anomalies are usually detected based on the dissimilarity between testing samples and training samples. However, existing methods model the normal pattern in an unbalanced way with incomplete spatial or temporal information. In this paper, we propose a framework to better combine spatial and temporal information to model the input normal pattern. We cascade two AutoEncoders: one is trained to learn spatial features and the other trained to learn temporal features. We stack the output of spatial network with input to feed the temporal information network. An optical flow door is added to the temporal information network for better prediction. Anomaly is detected by the difference between a prediction of future frame and its ground truth. Experiments on two datasets validate the effectiveness of our method.
Evolutionary Machine Learning for Multi-Objective Class Solutions in Medical Deformable Image Registration

Current state-of-the-art medical deformable image registration (DIR) methods optimize a weighted sum of key objectives of interest. Having a pre-determined weight combination that leads to high-quality results for any instance of a specific DIR problem (i.e., a class solution) would facilitate clinical application of DIR. However, such a combination can vary widely for each instance and is currently often manually determined. A multi-objective optimization approach for DIR removes the need for manual tuning, providing a set of high-quality trade-off solutions. Here, we investigate machine learning for a multi-objective class solution, i.e., not a single weight combination, but a set thereof, that, when used on any instance of a specific DIR problem, approximates such a set of trade-off solutions. To this end, we employed a multi-objective evolutionary algorithm to learn sets of weight combinations for three breast DIR problems of increasing difficulty: 10 prone-prone cases, 4 prone-supine cases with limited deformations and 6 prone-supine cases with larger deformations and image artefacts. Clinically-acceptable results were obtained for the first two problems. Therefore, for DIR problems with limited deformations, a multi-objective class solution can be machine learned and used to compute straightforwardly multiple high-quality DIR outcomes, potentially leading to more efficient use of DIR in clinical practice.
LCBC-XTEA: High Throughput Lightweight Cryptographic Block Cipher Model for Low-Cost RFID Systems

The RFID Technology is widely used in many authentications and a sophisticated application, still having many prospective issues includes privacy and security issues. To resolve these issues, this paper presents an efficient Lightweight cryptographic Block cipher algorithm. The hardware architecture of Tiny Encryption Algorithm (TEA) is designed and which is simple, flexible, fewer computations required and simple key scheduling. To overcome the security attacks in key scheduling on TEA, an Extended TEA (XTEA) is designed, which is having pipelined architecture with parallel computation to improve the throughput and provide better security. The proposed XTEA is in reconfigurable nature, by changing the mode to process encryption or decryption. The TEA and XTEA simulation results are obtained from Xilinx ISE tool on ModelSim 6.5f simulator and implemented on FPGA Platform-Artix-7 with resource constraints like Area, time and power are tabulated. The proposed XTEA is compared with similar existing research approaches like AES-8bit, TinyXTEA1, and Tiny XTEA-3 with an improvement of Area, throughput and Efficiency on same FPGA platform. The proposed XTEA works at the high throughput of 81 Mbps and Efficiency of 0.34 Mbps/Slice.
Two‐dimensional encryption system for secure free‐space optical communication of time‐series data streams

The importance of encryption in free-space optical communication systems has been increasing due to their widespread use. Herein, for the first time, the authors propose a novel two-dimensional encryption system for free-space optical communication using two spatial patterns as encryption keys. A data stream is physically encrypted with optical signals to reduce the risk of unauthorised third-party intrusion into communication. The encryption and restoration of a binary data stream using the proposed encryption system are confirmed by simulation results. The authors believe that, in the future, the proposed system could be used as an encryption method at the physical layer level to achieve the desired level of safety determined by the user.
A Blockchain-Based Scheme for Secure Sharing of X-Ray Medical Images

This paper proposes a secure sharing and trading scheme of X-Ray medical image data based on block chain, as it can be used for further scientific research processing. The specific representation includes the following steps: i. The original X-Ray data of medical instruments are transmitted to the cloud platform through the MQTT protocol; ii. The patient information (name, medical card number, etc.) of the image data is encrypted by hashing algorithm to protect privacy; iii. Applying a watermark to the image data; iv. Generating blocks through the consensus mechanism of block chain. The scheme proposed in this paper overcomes the security challenges faced by the traditional cloud-based image data management solution: user privacy disclosure, illegal tampering, and the risk of data being stolen and sold, and realizes a secure transaction system connecting users with data needs, which makes the huge image data on clinical medicine have higher scientific research value.
On the application of sequential pattern mining primitives to process discovery: Overview, outlook and opportunity identification

Abstract Sequential pattern mining (SPM) is a well‐studied theme in data mining, in which one aims to discover common sequences of item sets in a large corpus of temporal itemset data. Due to the sequential nature of data streams, supporting SPM in streaming environments is commonly studied in the area of data stream mining as well. On the other hand, stream‐based process discovery (PD), originating from the field of process mining, focusses on learning process models on the basis of online event data. In particular, the main goal of the models discovered is to describe the underlying generating process in an end‐to‐end fashion. As both SPM and PD use data that are comparable in nature, that is, both involve time‐stamped instances, one expects that techniques from the SPM domain are (partly) transferable to the PD domain. However, thus far, little work has been done in the intersection of the two fields. In this focus article, we therefore study the possible application of SPM techniques in the context of PD. We provide an overview of the two fields, covering their commonalities and differences, highlight the challenges of applying them, and, present an outlook and several avenues for future work. This article is categorized under: Algorithmic Development &gt; Spatial and Temporal Data Mining Fundamental Concepts of Data and Knowledge &gt; Key Design Issues in Data Mining Fundamental Concepts of Data and Knowledge &gt; Big Data Mining