Reviewerly/exHarmony huggingface dataset:
Deep Credible Metric Learning for Unsupervised Domain Adaptation Person Re-identification

The trained person re-identification systems fundamentally need to be deployed on different target environments. Learning the cross-domain model has great potential for the scalability of real-world applications. In this paper, we propose a deep credible metric learning (DCML) method for unsupervised domain adaptation person re-identification. Unlike existing methods that directly finetune the model in the target domain with pseudo labels generated by the source pre-trained model, our DCML method adaptively mines credible samples for training to avoid the misleading from noise labels. Specifically, we design two credibility metrics for sample mining including the k-Nearest Neighbor similarity for density evaluation and the prototype similarity for centrality evaluation. As the increasing of the pseudo label credibility, we progressively adjust the sampling strategy in the training process. In addition, we propose an instance margin spreading loss to further increase instance-wise discrimination. Experimental results demonstrate that our DCML method explores credible and valuable training data and improves the performance of unsupervised domain adaptation.
Double-Uncertainty Weighted Method for Semi-supervised Learning

Though deep learning has achieved advanced performance recently, it remains a challenging task in the field of medical imaging, as obtaining reliable labeled training data is time-consuming and expensive. In this paper, we propose a double-uncertainty weighted method for semi-supervised segmentation based on the teacher-student model. The teacher model provides guidance for the student model by penalizing their inconsistent prediction on both labeled and unlabeled data. We train the teacher model using Bayesian deep learning to obtain double-uncertainty, i.e. segmentation uncertainty and feature uncertainty. It is the first to extend segmentation uncertainty estimation to feature uncertainty, which reveals the capability to capture information among channels. A learnable uncertainty consistency loss is designed for the unsupervised learning process in an interactive manner between prediction and uncertainty. With no ground-truth for supervision, it can still incentivize more accurate teacher’s predictions and facilitate the model to reduce uncertain estimations. Furthermore, our proposed double-uncertainty serves as a weight on each inconsistency penalty to balance and harmonize supervised and unsupervised training processes. We validate the proposed feature uncertainty and loss function through qualitative and quantitative analyses. Experimental results show that our method outperforms the state-of-the-art uncertainty-based semi-supervised methods on two public medical datasets.
Ferret: Fast Extension for Correlated OT with Small Communication

Correlated oblivious transfer (COT) is a crucial building block for secure multi-party computation (MPC) and can be generated efficiently via OT extension. Recent works based on the pseudorandom correlation generator (PCG) paradigm presented a new way to generate random COT correlations using only communication sublinear to the output length. However, due to their high computational complexity, these protocols are only faster than the classical IKNP-style OT extension under restricted network bandwidth. In this paper, we propose new COT protocols in the PCG paradigm that achieve unprecedented performance. \em With $50$ Mbps network bandwidth, our maliciously secure protocol can produce one COT correlation in $22$ nanoseconds. More specifically, our results are summarized as follows: \beginenumerate \item We propose a semi-honest COT protocol with sublinear communication and linear computation. This protocol assumes primal-LPN and is built upon a recent VOLE protocol with semi-honest security by Schoppmann et al. (CCS 2019). We are able to apply various optimizations to reduce its communication cost by roughly $15\times$, not counting a one-time setup cost that diminishes as we generate more COT correlations. \item We strengthen our COT protocol to malicious security with no loss of efficiency. Among all optimizations, our new protocol features a new checking technique that ensures correctness and consistency essentially for free. In particular, our maliciously secure protocol is only \em $1-3$ nanoseconds slower for each COT. \item We implemented our protocols, and the code will be publicly available at EMP toolkit. We observe at least $9\times$ improvement in running time compared to the state-of-the-art protocol by Boyle et al. (CCS 2019) in both semi-honest and malicious settings under any network faster than $50$ Mbps. \endenumerate With this new record of efficiency for generating COT correlations, we anticipate new protocol designs and optimizations will flourish on top of our protocol.
Roto-translation equivariant convolutional networks: Application to histopathology image analysis

Rotation-invariance is a desired property of machine-learning models for medical image analysis and in particular for computational pathology applications. We propose a framework to encode the geometric structure of the special Euclidean motion group SE(2) in convolutional networks to yield translation and rotation equivariance via the introduction of SE(2)-group convolution layers. This structure enables models to learn feature representations with a discretized orientation dimension that guarantees that their outputs are invariant under a discrete set of rotations. Conventional approaches for rotation invariance rely mostly on data augmentation, but this does not guarantee the robustness of the output when the input is rotated. At that, trained conventional CNNs may require test-time rotation augmentation to reach their full capability. This study is focused on histopathology image analysis applications for which it is desirable that the arbitrary global orientation information of the imaged tissues is not captured by the machine learning models. The proposed framework is evaluated on three different histopathology image analysis tasks (mitosis detection, nuclei segmentation and tumor detection). We present a comparative analysis for each problem and show that consistent increase of performances can be achieved when using the proposed framework.
Presentation of a recommender system with ensemble learning and graph embedding: a case on MovieLens

Information technology has spread widely, and extraordinarily large amounts of data have been made accessible to users, which has made it challenging to select data that are in accordance with user needs. For the resolution of the above issue, recommender systems have emerged, which much help users go through the process of decision-making and selecting relevant data. A recommender system predicts users’ behavior to be capable of detecting their interests and needs, and it often uses the classification technique for this purpose. It may not be sufficiently accurate to employ single classification, where not all cases can be examined, which makes the method inappropriate to specific problems. In this research, group classification and the ensemble learning technique were used for increasing prediction accuracy in recommender systems. Another issue that is raised here concerns user analysis. Given the large size of the data and a large number of users, the process of user needs analysis and prediction (using a graph in most cases, representing the relations between users and their selected items) is complicated and cumbersome in recommender systems. Graph embedding was also proposed for resolution of this issue, where all or part of user behavior can be simulated through the generation of several vectors, resolving the problem of user behavior analysis to a large extent while maintaining high efficiency. In this research, individuals most similar to the target user were classified using ensemble learning, fuzzy rules, and the decision tree, and relevant recommendations were then made to each user with a heterogeneous knowledge graph and embedding vectors. This study was performed on the MovieLens datasets, and the obtained results indicated the high efficiency of the presented method.
Machine learning assisted quantum state estimation

Abstract We build a general quantum state tomography framework that makes use of machine learning techniques to reconstruct quantum states from a given set of coincidence measurements. For a wide range of pure and mixed input states we demonstrate via simulations that our method produces functionally equivalent reconstructed states to that of traditional methods with the added benefit that expensive computations are front-loaded with our system. Further, by training our system with measurement results that include simulated noise sources we are able to demonstrate a significantly enhanced average fidelity when compared to typical reconstruction methods. These enhancements in average fidelity are also shown to persist when we consider state reconstruction from partial tomography data where several measurements are missing. We anticipate that the present results combining the fields of machine intelligence and quantum state estimation will greatly improve and speed up tomography-based quantum experiments.
Enhancing Blockchain security in cloud computing with IoT environment using ECIES and cryptography hash algorithm

Blockchain is one of the fast growing technologies that has significant role in the area of criminal investigation. The security is becoming a great threat to all the industries, Electronic Health Record (EHR), Banking, Smart Applications (SA), Supply Chain Management (SCM) and IoT environment in recent years. In this research, we have developed a novel framework that will monitor the activities that takes place on particular data evidence, We create a Cloud based Software Defined Network (SDN), its consists of 100 - mobile Nodes (IOT devices), open flow switch and Blockchain based controllers, cloud server, Authentication Server (AS) and investigator. Initially all users are registered with AS and obtain their secret key from AS based on the Harmony Search Optimization (HSO). In the mobile nodes the packets are encrypted by using Elliptic Curve Integrated Encryption Scheme (ECIES) algorithm and transfer to the cloud server. SDN controller maintains blockchain to preserve evidences collected from data and signature of the users based on the SHA-256 Cryptographic Hash Algorithm. Authorized investigator performs following processes: identification, evidence collection, evidence analysis, and report generation based on the Logical Graph of Evidences (LGoE). we plot the resultant graph for Response time versus Number of users, Evidence insertion time versus number of users, Evidence verification time versus Number of users, Computational overhead versus number of users, Total change rate versus Number of users, Hash computation time versus number of users, Key generation time versus number of users, Encryption time versus number of users and Decryption time versus number of users. Finally, the investigator or other legal people can obtain the evidences from the controller and construct as a Logical Graph of Evidence (LGoE). The experimental analysis revealed the fact that the proposed system obtained better performance in terms of response time, accuracy, increasing throughput and total change security parameters.
Human Activity Recognition with Convolutional Neural Networks

The problem of automatic identification of physical activities performed by human subjects is referred to as Human Activity Recognition (HAR). There exist several techniques to measure motion characteristics during these physical activities, such as Inertial Measurement Units (IMUs). IMUs have a cornerstone position in this context, and are characterized by usage flexibility, low cost, and reduced privacy impact. With the use of inertial sensors, it is possible to sample some measures such as acceleration and angular velocity of a body, and use them to learn models that are capable of correctly classifying activities to their corresponding classes. In this paper, we propose to use Convolutional Neural Networks (CNNs) to classify human activities. Our models use raw data obtained from a set of inertial sensors. We explore several combinations of activities and sensors, showing how motion signals can be adapted to be fed into CNNs by using different network architectures. We also compare the performance of different groups of sensors, investigating the classification potential of single, double and triple sensor systems. The experimental results obtained on a dataset of 16 lower-limb activities, collected from a group of participants with the use of five different sensors, are very promising.
MeTRAbs: Metric-Scale Truncation-Robust Heatmaps for Absolute 3D Human Pose Estimation

Heatmap representations have formed the basis of human pose estimation systems for many years, and their extension to 3D has been a fruitful line of recent research. This includes 2.5D volumetric heatmaps, whose X and Y axes correspond to image space and Z to metric depth around the subject. To obtain metric-scale predictions, 2.5D methods need a separate post-processing step to resolve scale ambiguity. Further, they cannot localize body joints outside the image boundaries, leading to incomplete estimates for truncated images. To address these limitations, we propose metric-scale truncation-robust (MeTRo) volumetric heatmaps, whose dimensions are all defined in metric 3D space, instead of being aligned with image space. This reinterpretation of heatmap dimensions allows us to directly estimate complete, metric-scale poses without test-time knowledge of distance or relying on anthropometric heuristics, such as bone lengths. To further demonstrate the utility our representation, we present a differentiable combination of our 3D metric-scale heatmaps with 2D image-space ones to estimate absolute 3D pose (our MeTRAbs architecture). We find that supervision via absolute pose loss is crucial for accurate non-root-relative localization. Using a ResNet-50 backbone without further learned layers, we obtain state-of-the-art results on Human3.6M, MPI-INF-3DHP and MuPoTS-3D. Our code is publicly available. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> https://vision.rwth-aachen.de/metrabs.
Annealing by simulating the coherent Ising machine

The coherent Ising machine (CIM) enables efficient sampling of low-lying energy states of the Ising Hamiltonian with all-to-all connectivity by encoding the spins in the amplitudes of pulsed modes in an optical parametric oscillator (OPO). The interaction between the pulses is realized by means of measurement-based optoelectronic feedforward which enhances the gain for lower-energy spin configurations. We present an efficient method of simulating the CIM on a classical computer that outperforms the CIM itself as well as the noisy mean-field annealer in terms of both the quality of the samples and the computational speed. It is furthermore advantageous with respect to the CIM in that it can handle Ising Hamiltonians with arbitrary real-valued node coupling strengths. These results illuminate the nature of the faster performance exhibited by the CIM and may give rise to a new class of quantum-inspired algorithms of classical annealing that can successfully compete with existing methods.
Credit Risk Analysis Using Quantum Computers

We present and analyze a quantum algorithm to estimate credit risk more efficiently than Monte Carlo simulations can do on classical computers. More precisely, we estimate the economic capital requirement, i.e. the difference between the Value at Risk and the expected value of a given loss distribution. The economic capital requirement is an important risk metric because it summarizes the amount of capital required to remain solvent at a given confidence level. We implement this problem for a realistic loss distribution and analyze its scaling to a realistic problem size. In particular, we provide estimates of the total number of required qubits, the expected circuit depth, and how this translates into an expected runtime under reasonable assumptions on future fault-tolerant quantum hardware.
What do hotel employees think about service robots? A qualitative study in Turkey

This study aims to determine Turkish hotel employees' perceptions towards service robots. Data were collected through 40 in-depth interviews with hotel employees from different departments. Data were analysed through thematic analysis. The results revealed that the word “robot” evoked negative emotions for hotel employees. While the hotel employees think that service robots may provide different benefits and advantages for employees and businesses, they also believe that service robots may create some problems during communication with the customers. The hotel employees also think that service robots would lead to an increased unemployment rate in the future.
A Blockchain-Based Deep Learning Approach for Cyber Security in Next Generation Industrial Cyber-Physical Systems

With the recent development of Internet of Things (IoT) in the next generation cyber-physical system (CPS) such as autonomous driving, there is a significant requirement of big data analysis with high accuracy and low latency. For efficient big data analysis, deep learning (DL) supports strong analytic capability; it has been applied at the cloud and edge layers by extensive research to provide accurate data analysis at low latency. However, existing researches failed to address certain challenges, such as centralized control, adversarial attacks, security, and privacy. To this end, we propose DeepBlockIoTNet, a secure DL approach with blockchain for the IoT network wherein the DL operation is carried out among the edge nodes at the edge layer in a decentralized, secure manner. The blockchain provides a secure DL operation and removes the control from a centralized authority. The experimental evaluation demonstrates that the proposed approach supports higher accuracy.
UC Non-Interactive, Proactive, Threshold ECDSA with Identifiable Aborts

Building on the Gennaro & Goldfeder and Lindell & Nof protocols (CCS '18), we present two threshold ECDSA protocols, for any number of signatories and any threshold, that improve as follows over the state of the art: -- For both protocols, only the last round requires knowledge of the message, and the other rounds can take place in a preprocessing stage, lending to a non-interactive threshold ECDSA protocol. -- Both protocols withstand adaptive corruption of signatories. Furthermore, they include a periodic refresh mechanism and offer full proactive security. -- Both protocols realize an ideal threshold signature functionality within the UC framework, in the global random oracle model, assuming Strong RSA, DDH, semantic security of the Paillier encryption, and a somewhat enhanced variant of existential unforgeability of ECDSA. -- Both protocols achieve accountability by identifying corrupted parties in case of failure to generate a valid signature. The two protocols are distinguished by the round-complexity and the identification process for detecting cheating parties. Namely: -- For the first protocol, signature generation takes only 4 rounds (down from the current state of the art of 8 rounds), but the identification process requires computation and communication that is quadratic in the number of parties. -- For the second protocol, the identification process requires computation and communication that is only linear in the number of parties, but signature generation takes 7 rounds. These properties (low latency, compatibility with cold-wallet architectures, proactive security, identifiable abort and composable security) make the two protocols ideal for threshold wallets for ECDSA-based cryptocurrencies.
i3DMM: Deep Implicit 3D Morphable Model of Human Heads

We present the first deep implicit 3D morphable model (i3DMM) of full heads. Unlike earlier morphable face models it not only captures identity-specific geometry, texture, and expressions of the frontal face, but also models the entire head, including hair. We collect a new dataset consisting of 64 people with different expressions and hairstyles to train i3DMM. Our approach has the following favorable properties: (i) It is the first full head morphable model that includes hair. (ii) In contrast to mesh-based models it can be trained on merely rigidly aligned scans, without requiring difficult non-rigid registration. (iii) We design a novel architecture to decouple the shape model into an implicit reference shape and a deformation of this reference shape. With that, dense correspondences between shapes can be learned implicitly. (iv) This architecture allows us to semantically disentangle the geometry and color components, as color is learned in the reference space. Geometry is further disentangled as identity, expressions, and hairstyle, while color is disentangled as identity and hairstyle components. We show the merits of i3DMM using ablation studies, comparisons to state-of-the-art models, and applications such as semantic head editing and texture transfer. We will make our model publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Effectiveness of VR crane training with head-mounted display: Double mediation of presence and perceived usefulness

Virtual reality (VR) has been shown to provide an immersive experience that can promote effective learning. The use of VR with head-mounted displays can be especially ideal for training operators of heavy equipment such as cranes, resulting in cost savings and enhanced safety of apprentices. In this study, a set of VR training systems is introduced for three different types of cranes (i.e., overhead, container, and tower crane), and an experiment is conducted to test their effectiveness. A total of 108 students participated in the experiment. The findings revealed that the VR crane training system significantly enhanced the students' self-efficacy when operating a crane. The underlying mechanism of how the VR crane training improves trainees' level of self-efficacy is also investigated. Double mediation analysis revealed that the change in self-efficacy is driven by usability mediated by the feeling of presence and perceived usefulness.
Making smart manufacturing smarter – a survey on blockchain technology in Industry 4.0

Blockchain is distributed digital ledger technology that facilitates reliable and transparent transactions among the participants of a system. Blockchain has been applied to various manufacturing applications and helps to create an automatic, decentralised smart manufacturing system with a high level of efficiency and productivity. This paper presents a comprehensive survey on blockchain in Industry 4.0 – applications, architectures, techniques, and research challenges. We propose a blockchain reference architecture for smart manufacturing, which guides our discussions on applying the blockchain technology to various applications of the smart factory and smart supply chain.
Improved RRT-Connect Algorithm Based on Triangular Inequality for Robot Path Planning

This paper proposed a triangular inequality-based rewiring method for the rapidly exploring random tree (RRT)-Connect robot path-planning algorithm that guarantees the planning time compared to the RRT algorithm, to bring it closer to the optimum. To check the proposed algorithm’s performance, this paper compared the RRT and RRT-Connect algorithms in various environments through simulation. From these experimental results, the proposed algorithm shows both quicker planning time and shorter path length than the RRT algorithm and shorter path length than the RRT-Connect algorithm with a similar number of samples and planning time.