Reviewerly/exHarmony huggingface dataset:
Person Re-Identification With Deep Kronecker-Product Matching and Group-Shuffling Random Walk

Person re-identification (re-ID) aims to robustly measure visual affinities between person images. It has wide applications in intelligent surveillance by associating same persons' images across multiple cameras. It is generally treated as an image retrieval problem: given a probe person image, the affinities between the probe image and gallery images (P2G affinities) are used to rank the retrieved gallery images. There exist two main challenges for effectively solving this problem. 1) Person images usually show significant variations because of different person poses and viewing angles. The spatial layouts and correspondences between person images are therefore vital information for tackling this problem. State-of-the-art methods either ignore such spatial variation or utilize extra pose information for handling the challenge. 2) Most existing person re-ID methods rank gallery images considering only P2G affinities but ignore the affinities between the gallery images (G2G affinity). Such affinities could provide important clues for accurate gallery image ranking but were only utilized in post-processing stages by current methods. In this article, we propose a unified end-to-end deep learning framework to tackle the two challenges. For handling viewpoint and pose variations between compared person images, we propose a novel Kronecker Product Matching operation to match and warp feature maps of different persons. Comparing warped feature maps results in more accurate P2G affinities. To fully utilize all available P2G and G2G affinities for accurately ranking gallery person images, a novel group-shuffling random walk operation is proposed. Both Kronecker Product Matching and Group-shuffling Random Walk operations are end-to-end trainable and are shown to improve the learned visual features if integrated in the deep learning framework. The proposed approach outperforms state-of-the-art methods on Market-1501, CUHK03 and DukeMTMC datasets, which demonstrates the effectiveness and generalization ability of our proposed approach. Code is available at https://github.com/YantaoShen/kpm_rw_person_reid.
A Systematic Review of Blockchain Hardware Acceleration Architectures

The aim of this paper is to provide a systematic literature review of blockchain hardware acceleration.Blockchain technology has achieved significant attention in recent years particularly in the area of cryptocurrency however it is gaining popularity in other applications such as supply chain management and e-government.Based on a structured, systematic review of the relevant literature, we present a classification of the primary areas in blockchain technology that make use of heterogeneous hardware for accelerating certain blockchain functions.Based on these findings, we identify various research gaps and future exploratory directions that are anticipated to be of significant value both for academics and industry practitioners.
Performance of Training Sparse Deep Neural Networks on GPUs

Deep neural networks have revolutionized the field of machine learning by dramatically improving the state-of-the-art in various domains. The sizes of deep neural networks (DNNs) are rapidly outgrowing the capacity of hardware to fast store and train them. Over the past few decades, researches have explored the prospect of sparse DNNs before, during, and after training by pruning edges from the underlying topology. After the above operation, the generated neural network is known as a sparse neural network. More recent works have demonstrated the remarkable results that certain sparse DNNs can train to the same precision as dense DNNs at lower runtime and storage cost. Although existing methods ease the situation that high demand for computation resources severely hinders the deployment of large-scale DNNs in resource-constrained devices, DNNs can be trained at a faster speed and lower cost. In this work, we propose a Fine-tune Structured Sparsity Learning (FSSL) method to regularize the structures of DNNs and accelerate the training of DNNs. FSSL can: (1) learn a compact structure from large sparse DNN to reduce computation cost; (2) obtain a hardware-friendly to accelerate the DNNs evaluation efficiently. Experimental results of the training time and the compression rate show that superior performance and efficiency than the Matlab example code. These speedups are about twice speedups of non-structured sparsity.
Order-LWE and the Hardness of Ring-LWE with Entropic Secrets

We propose a generalization of the celebrated Ring Learning with Errors (RLWE) problem (Lyubashevsky, Peikert and Regev, Eurocrypt 2010, Eurocrypt 2013), wherein the ambient ring is not the ring of integers of a number field, but rather an order (a full rank subring). We show that our Order-LWE problem enjoys worst-case hardness with respect to short-vector problems in invertible-ideal lattices of the order. The definition allows us to provide a new analysis for the hardness of the abundantly used Polynomial-LWE (PLWE) problem (Stehlé et al., Asiacrypt 2009), different from the one recently proposed by Rosca, Stehlé and Wallet (Eurocrypt 2018). This suggests that Order-LWE may be used to analyze and possibly design useful relaxations of RLWE. We show that Order-LWE can naturally be harnessed to prove security for RLWE instances where the “RLWE secret” (which often corresponds to the secret-key of a cryptosystem) is not sampled uniformly as required for RLWE hardness. We start by showing worst-case hardness even if the secret is sampled from a subring of the sample space. Then, we study the case where the secret is sampled from an ideal of the sample space or a coset thereof (equivalently, some of its CRT coordinates are fixed or leaked). In the latter, we show an interesting threshold phenomenon where the amount of RLWE noise determines whether the problem is tractable. Lastly, we address the long standing question of whether high-entropy secret is sufficient for RLWE to be intractable. Our result on sampling from ideals shows that simply requiring high entropy is insufficient. We therefore propose a broad class of distributions where we conjecture that hardness should hold, and provide evidence via reduction to a concrete lattice problem.
The Analysis of KMP Algorithm and its Optimization

Knuth-Morris-Pratt (KMP) algorithm is an essential manifestation of matching algorithms. This paper presents and discusses the KMP algorithm and some of its optimization. Calculating and searching in a letter numbered table and a new data structure called last-identical array propose a new string searching algorithm L-I-KMP based on the KMP algorithm. This new method is faster than KMP in practice of specific situations.
Smart Gesture-based Control in Human Computer Interaction Applications for Special-need People

Gesture recognition has been recognized as a natural way for the communication especially for elder or impaired people. Hand gesture recognition is an important research issue in the field of human-computer interaction (HCI), because of its extensive applications in virtual reality, sign language recognition, and computer games. There is a number of algorithms addressing different aspects of the Gesture recognition problem have been proposed. While image-based techniques have been widely studied, it may be affected by lighting conditions, large variations of the hand gesture and textures. Recently, with the developments of new technologies and the large availability of inexpensive depth sensors, real time gesture recognition has been faced by using depth information and avoiding the limitations due to complex background and lighting situations. This paper introduces an enhanced automated model for hand gesture recognition using convolution neural network (CNN). In this paper, a hand gesture recognition model with Kinect sensor has been proposed, which operates robustly in uncontrolled environments and is insensitive to hand variations and distortions. The proposed model uses both the depth and color information from Kinect sensor to detect the hand shape, which ensures the robustness in cluttered environments. The proposed model consists of two major modules, namely, hand detection and gesture recognition. Experiments have been conducted on large dataset to demonstrate the efficiency of the proposed model. The experimental results show an outstanding performance in the terms of accuracy, recall and precision.
Decoupling Constraints from Sampling-Based Planners

We present a general unifying framework for sampling-based motion planning under kinematic task constraints which enables a broad class of planners to compute plans that satisfy a given constraint function that encodes, e.g., loop closure, balance, and end-effector constraints. The framework decouples a planner’s method for exploration from constraint satisfaction by representing the implicit configuration space defined by a constraint function. We emulate three constraint satisfaction methodologies from the literature, and demonstrate the framework with a range of planners utilizing these constraint methodologies. Our results show that the appropriate choice of constrained satisfaction methodology depends on many factors, e.g., the dimension of the configuration space and implicit constraint manifold, and number of obstacles. Furthermore, we show that novel combinations of planners and constraint satisfaction methodologies can be more effective than previous approaches. The framework is also easily extended for novel planners and constraint spaces.
End-To-End Detection Of Attacks To Automatic Speaker Recognizers With Time-Attentive Light Convolutional Neural Networks

In this contribution, we introduce convolutional neural network architectures aiming at performing end-to-end detection of attacks to voice biometrics systems, i.e. the model provides scores corresponding to the likelihood of attack given general purpose time-frequency features obtained from speech. Microphone level attackers based on speech synthesis and voice conversion techniques are considered, along with presentation replay attacks. While the convolutional models yield a sequence of representations corresponding to different parts of the input at varying time steps, concatenated first and second-order statistics pooled from the outputs of a self-attention layer are used as a fixed-dimension representations of utterances of varying length, which are then input into a set of fully connected layers to finally yield scores. Evaluation of the proposed framework is performed with data from ASVspoof 2019 challenge yielding relative improvements higher than one order of magnitude in terms of equal error rate over two baseline systems provided by ASVspoof 2019's organizers, and significant improvements over the benchmark systems we evaluated.
A generic framework for a Peer to Peer Blockchain based Fog Architecture in Industrial Automation

Industrial Internet of things (IIoT) is referred exclusively to smart and cheap sensor based devices lying on top of machines or machine parts, to collect and process massive raw data and therefore to make intelligent decisions autonomously, without human intervention, according to the Industry 4.0 requirements. However, there are still challenges to address, such as the massive amount of data generated in high speeds and the fast processing requirements and also challenges in the security domain, such as data integrity and ID authentication. Currently decentralized IoT/Fog network architectures would come in handy, in order to release data processing “burden” from isolated Cloud servers. On the other hand, Blockchain has emerged as a key technology for handling manufacturing, raw or processed data, in safety and security environment, while distributing it to the physical world, consisting of heterogeneous entities, such as manufacturers, retailers, suppliers, logistics agents or simple clients, thus reinforcing the IT and automation potentials of product supply chain. In this paper we propose an abstraction layer architecture that solves all that issues. On the one hand, there is a decentralized IOT/Fog and cloud framework for real time machine condition monitoring (MCM) and fault prediction, where computational demanding tasks are distributed across fog nodes and decision fusion rules are set and controlled by the Cloud. On the other hand, a peer to peer Blockchain ledger is also proposed for integrating the IOT/Fog, MCM network architecture along with other entities of the physical world, making supply chain network more efficient and intelligent. The proposed generic architecture can be applied to other application areas as well.
Streaming Feature Selection for Multi-Label Data with Dynamic Sliding Windows and Feature Repulsion Loss

In recent years, there has been a growing interest in the problem of multi-label streaming feature selection with no prior knowledge of the feature space. However, the algorithms proposed to handle this problem seldom consider the group structure of streaming features. Another shortcoming arises from the fact that few studies have addressed atomic feature models, and particularly, few have measured the attraction and repulsion between features. To remedy these shortcomings, we develop the streaming feature selection algorithm with dynamic sliding windows and feature repulsion loss (SF-DSW-FRL). This algorithm is essentially carried out in three consecutive steps. Firstly, within dynamic sliding windows, candidate streaming features that are strongly related to the labels in different feature groups are selected and stored in a fixed sliding window. Then, the interaction between features is measured by a loss function inspired by the mutual repulsion and attraction between atoms in physics. Specifically, one feature attraction term and two feature repulsion terms are constructed and combined to create the feature repulsion loss function. Finally, for the fixed sliding window, the best feature subset is selected according to this loss function. The effectiveness of the proposed algorithm is demonstrated through experiments on several multi-label datasets, statistical hypothesis testing, and stability analysis.
Two-Lane Path Planning of Autonomous Vehicles in 2.5D Environments

We present a two-lane path planning of autonomous vehicles in two-and-a-half-dimensional (2.5D) environments. Our method builds on collision-free two-dimensional (2D) path-planning techniques from our previous work, and extends these techniques to 2.5D by mapping a 2D path in the parameter space onto a terrain graph surface, as a curve on the surface in the object space. Based on this path on the terrain surface, we propose a novel method of designing cross slopes in high curvature regions of the path, such that vehicles need not slow down in these regions. Furthermore, we apply time-optimal control techniques developed in robotics to compute the minimal-time speed control along the specified path. Examples are provided to demonstrate the effectiveness of the proposed algorithm.
Continuous Learning without Forgetting for Person Re-Identification

Deep learning-based person re-identification faces a scalability challenge when the target domain requires continuous learning. Service environments, such as airports, need to recognize new visitors and add new cameras over time. Training-at-once is not enough to make the model robust to new tasks and domain variations. A well-known approach is fine-tuning, which suffers forgetting problem on old tasks when learning new tasks. Joint-training can alleviate the problem but requires old datasets, which is unobtainable in some cases. Recently, Learning without forgetting (LwF) shows its ability to mitigate the problem without old datasets. This paper extends the benefit of LwF from image classification to person re-identification with further challenges. Comprehensive experiments are based on Market1501 and DukeMTMC4ReID to evaluate and benchmark LwF to other approaches. The results confirm that LwF outperforms fine-tuning in preserving old knowledge and joint-training in faster training.
Server Consolidation Energy-Saving Algorithm Based on Resource Reservation and Resource Allocation Strategy

Energy consumption has overtaken equipment costs given the growth of computing power, thereby becoming the dominant cost to data centers. Constrained by ensuring that peak resource requirements of VMs are met, the effective use of resource fragments is an effective means of energy conservation. We apply resource reservation to the resource-utilization-aware energy efficient server consolidation algorithm (RUAEE), and propose a server consolidation energy-saving algorithm (SCES). We adjust the allocations of VMs by computing the resource reservation and resource allocation ratio dynamically. This technique maintains the host resource utilization to within a reasonable range, reduces resource fragmentation and reserves resources to satisfy the peak resource requests simultaneously. Compared with the RUAEE algorithm, our algorithm can reduce the risk of system overload and improve the stability of the system. The experimental results based on the actual workload of the Google cluster show that the algorithm is effective at reducing resource waste, improving system stability, and achieving energy savings.
Real-time obstacle avoidance with deep reinforcement learning Three-Dimensional Autonomous Obstacle Avoidance for UAV

At present, drones are rapidly developing in the aviation industry and are applied to all aspects of life. However, letting drones autonomously avoid obstacles is still the focus of research by aviation scholars at this stage. However, the current automation is mostly based on human experience to determine the obstacle avoidance strategy of UAV. And the method only rely on the machine to avoid obstacle is very few. In this paper, the UAV collect visual and distance sensor information to make autonomous obstacle avoidance decision through the deep reinforcement learning algorithm, and the algorithm is tested in the v-rep simulation environment.
Comparison of Fuzzy Controller Optimization with Dynamic Parameter Adjustment Based on of Type-1 and Type-2 Fuzzy Logic

This paper presents the comparison of fuzzy controller optimization results using dynamic parameter adjustment Type-1 (T1) and Interval Type-2 (T2) fuzzy logic to the Firefly Algorithm (FA). The FA is used for optimizations parameters of the membership functions in the fuzzy controllers. The dynamic adjustment is applied to the randomness parameter of the search space, which represents the exploration of the method, avoiding stagnation or premature convergence. The FA generates the values that the parameters of the membership functions take for optimization use in the fuzzy systems for control. The control plants have one or more input variables that are processed and result in one or more output variables, it would be very difficult to model the human reasoning in equations to achieve a machine acquires the knowledge acquired by humans. For that reason the fuzzy logic that generates that insertity is used as if it were human reasoning.
The influence of assessment on learning motivation

This paper considers methodological approaches to the assessment of learning achievement, analysing the impact of the mark on learning motivation of students. External testing and subsequent questionnaires data may serve as an indicator of increasing learning motivation of students due to their confidence in the assessment results. Reliability of assessment is an incentive to enhance learning activities of students and increase interest in a given academic discipline. We observed difficulties of obtaining an objective assessment in schools and possible ways of overcoming them. The research provides the basic provisions of the assessment using the design pattern of assessment tools and processes on the basis of evidence-based arguments modelled on Evidence Centered Design which provides assistance in the development of high-quality assessment tools. The use of patterns may allow educators to make greater use of methods for classifying, organising and planning the assessment of cognitive and competency-based learning outcomes.
Detecting Video Anomaly with a Stacked Convolutional LSTM Framework

Automatic anomaly detection in real-world video surveillance is still challenging. In this paper, we propose an autoencoder architecture based on a stacked convolutional LSTM framework that highlights both spatial and temporal aspects in detecting anomalies of surveillance videos. The spatial component(i.e. spatial encoder/decoder) uses Convolutional Neural Network (CNN) and carries information about scenes and objects. The temporal component(i.e. temporal encoder/decoder) uses stacked convolutional LSTM and conveys object movement. Specifically, we integrate CNN and the stacked convolutional LSTM to learn normal patterns from the training data, which contains only normal events. With the integrated approach, our method can better model spatio-temporal information than many others. We train our models in an unsupervised manner, and labels are required only in the testing phase. Our method is evaluated on the datasets of Avenue, UCSD and ShanghaiTech Campus. The results show that the accuracy of our method rivals state-of-the-art methods with a faster detection speed.
Automated Speech Recognition Systems and Older Adults: A Literature Review and Synthesis

The number of older adults is growing significantly worldwide. At the same time, technological developments are rapidly evolving, and older populations are expected to interact more frequently with such sophisticated systems. Automated speech recognition (ASR) systems is an example of one technology that is increasingly present in daily life. However, age-related physical changes may alter speech production and limit the effectiveness of ASR systems for older individuals. The goal of this paper was to summarize the current knowledge on ASR systems and older adults. The PRISMA method was employed and 17 studies were compared on the basis of word error rate (WER). Overall, WER was found to be influenced by age, gender, and the number of speech samples used to train ASR systems. This work has implications for the development of future human-machine technologies that will be used by a wide range of age groups.
Deep Kalman Filter with Optical Flow for Multiple Object Tracking

Deep matching and Kalman filter-based multiple object tracking (DK-tracking) have been demonstrated to be promising. However, most of existing DK-tracking trackers assume that objects are slow-varying movement with a constant velocity. The assumption is hard to be satisfied in the real world, especially in the image space due to the sight distance. In this paper, we propose a novel multiple object tracking method combining deep feature matching, Kalman filter and flow information, which is called DK-flow-tracking, to improve tracking performance. In DK-flow-tracking, optical flow in consecutive frames is used to provide accurate object motion information for guiding Kalman filter to track objects. Experiments are performed on public datasets: MOT2016, MOT2017, and the proposed method achieves better performances compared to the DK-tracking with the assumption of a constant velocity movement.