Reviewerly/exHarmony huggingface dataset:
Deviation based clustering for unsupervised person re-identification

Recently, unsupervised person re-identification (re-ID) has gained a lot of attention, since it does not depend on intensive manual annotation and is more practical to deploy in the real world directly. An inspiring method, the Bottom-up Clustering (BUC), achieves the state-of-the-art among unsupervised re-ID methods and outperforms most semi-supervised and transfer learning algorithms. The BUC utilizes the minimum-distance between samples in different clusters as the merging criterion, and the number of samples as the penalization term. However, the minimum-distance criterion only considers one pair of samples between two clusters, and cannot exploit the information of all samples in clusters. The penalization, intuitively, is unsuitable for the dataset with irregular sample quantity. To relieve this problem, we propose a deviation based clustering re-ID approach, which takes the inter- and intra-cluster deviation into consideration. The inter-cluster deviation denotes the increase of deviation after merging two clusters, considering all samples between the two clusters to merge. The intra-cluster deviation, working as penalization, denotes the distance between samples and the center in each cluster, and thus it can help to mitigate the side-effect of irregular datasets. The two criterions can reflect the inter- and intra- dispersion precisely. Based on these criterions, we group similar samples into clusters and utilize the cluster identities as a pseudo annotation to train our model. To evaluate our proposed approach, we implement abundant experiments on two popular re-ID datasets, where one has irregular sample quality (i.e., Market-1501) and the other has regular sample quality (i.e., DukeMTMC-reID). Evaluations show that our method outperforms BUC by 1.8% on Rank-1 (i.e., 68.0% accuracy) and 2.1% on mAP (i.e., 40.4% accuracy) for the Market-1501 dataset, and well maintains the benefit of BUC on the DukeMIMC-reID dataset with the accuracy of 47.8% in Rank-1 and 27.0% in mAP.
Hybrid Reciprocal Recommender Systems: Integrating Item-to-User Principles in Reciprocal Recommendation

Reciprocal Recommender Systems (RRS) recommend users to other users in a personalised manner, in scenarios where both sides of the preference relation must be considered. Existing RRS approaches based on collaborative filtering or content-based filtering, have been used for enhancing user experience in online dating and other online services aimed at connecting users with each other. However, some of these services e.g. skill sharing platforms, are still pervaded by content published, shared and consumed by users, consequently there is a valuable source of item-to-user preferential information not captured by existing RRS models. We present a novel hybrid RRS framework that integrates user preferences towards content in reciprocal recommendation, and we instantiate and evaluate it using data from Cookpad, a recipe sharing social media platform. As part of our model, we also implement a novel content-based extension of Jaccard similarity measure that operates on word embeddings.
Improving the Training Pattern in Back-Propagation Neural Networks Using Holt-Winters’ Seasonal Method and Gradient Boosting Model

In this paper, we propose an improved training pattern in back-propagation neural networks using Holt-Winters’ seasonal method and gradient boosting model (NHGB). It removes the errors that cause disabilities in the hidden layers of BPNN and further improves the predictive performance. It increases the weights and decays the error using Holt-Winters’ seasonal method and gradient boosting model, which reduces longer convergence time. The NHGB method is compared with other existing methods against average initial error, root mean square error, accuracy, sensitivity, and specificity metrics. The result shows that NHGB method is effective in terms of reduced RMSE and increased accuracy in classifying the datasets.
Text or Non-text Image Classification using Fully Convolution Network (FCN)

The semantic information in a natural scene plays a vital role in image understanding. One of the semantic information present in a natural image is the text. It can be utilized for analyzing several computer vision applications. The proposed work focuses on the new task of classifying the text images from a bulk of natural images. A model is intended to address this issue; it uses a lightweight, fully convolution network (FCN) which classifies the image as the text or non-text image. The effectiveness of the proposed model is demonstrated on the standard dataset, experimental evaluation and comparison with the baseline method show that the proposed model outperforms.
Secure V2X Environment using Blockchain Technology

Vehicle to everything (V2X) is the new and revolutional phenomena of the recent era. In V2X environment, the data security of data and information is the most significant parameter, as the information is shared on the shared network. To secure the information in V2X environment; the blockchain technology can play a critical role as it consists of the powerful data encryption and decryption algorithms. To implement the characteristics of blockchain in V2X environment, the practitioners face various challenges. The key aim of this study is to develop a hypothetical framework that renders the impact of challenging factors on the implementation of blockchain in V2X paradigm. We have conducted systematic literature review approach and identified the 10 challenges that are critical to address for the successful implementation of blockchain technology in V2X network. We believe that the findings of this study serves as knowledge base for the researchers and practitioners to develop the effective strategies for the successful implementation of blockchain characteristics in V2X environment.
Set-valued data publication with local privacy

Most user-generated data in online services are presented as set-valued data, e.g., visited website URLs, recently used Apps by a person, and etc. These data are of great value to service providers, but also bring privacy concerns if collected and analyzed directly. To tackle potential privacy threatens, local differential privacy (LDP) attracts increasing attention nowadays. However, existing approaches only provide sub-optimal error bound for set-valued data distribution estimation with LDP. Besides, it is computational expensive and communication expensive to use for high dimensional set-valued data, considering large domains in real scenarios. Thus, existing approaches are unpractical to use on resource-constrained user-side devices (e.g., smartphones and wearable devices). In this paper, we propose a utility-optimal and efficient set-valued data publication method (i.e., wheel mechanism ). On the user side, each user contributes only one numerical value to represent their privatized data. The computational complexity is O (min{ m log m , me ɛ }) and communication cost is O (log( me ɛ )) bits, while existing approaches usually depend on O ( d ) or O (log d ), where m is the number of items in the set-valued data ( m ≡ 1 for categorical data), d is the domain size (usually d ≫ m ) and ɛ is the privacy budget. On the server side, the estimator takes numerical values from users as input and derives an unbiased distribution estimation. Theoretical results show that estimation error bounds are improved from previously known [EQUATION] to the optimal rate [EQUATION]. Results on extensive experiments demonstrate that our proposed wheel mechanism is 3-100× faster than existing approaches, meanwhile has optimal statistical efficiency.
Efficiency investigation and optimization of contract management business processes in a workwear rental and laundry service company

The paper presents business processes business process models in the area of contract management in a workwear rental and laundry service company. The presented diagrams are the result of the analytical work performed by the multidisciplinary team of experts. The team consists of IT specialist, subject-matter experts and employees of the rental company. Business models presented in the article are described in accordance with the assumptions of BPMN (Business Process Model and Notation). The models of the rental company's activities were created in the ARIS software environment, where the simulation of all necessary operations was carried out. As the result of simulation experiments a number of interesting parameters were obtained. There is no possibility to obtain these parameters in the static analysis of a business process. They can be only determined by the dynamic analysis. A sample results show the frequency of various significant events, accumulated time corresponding to activities performance, number of actions being performed by different people, overall working time for employees, the degree of the human factor use, and the global cost connected with different activities. The following process models have been developed, simulated and optimized: Release of an employee, Locker room change request, Clothing size change request for an employee, Cabinet location change request, Clothing sets amount change request, Personal data change, Employment process, Registration of the own clothing for an employee, New cabinet order, New set of keys set up, Transfer of an employee, Clothing repair request.
Iterative Soft Decoding of Reed-Solomon Codes Based on Deep Learning

In this letter, a deep learning based iterative soft decision decoding algorithm for Reed-Solomon codes is proposed. This algorithm takes advantage of deep neural network and Stochastic Shifting based Iterative Decoding (SSID). By assigning weights to every edge in the Tanner graph and changing the architecture of SSID method, the proposed neural network decoder achieves better decoding performance. Compared with BM decoding, HDD-LCC and traditional SSID, simulation results show that for RS (15, 11), this algorithm provides coding gain up to 1.5dB, 1.1dB and 0.5dB, respectively when FER = 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> .
Energy- and Time-Aware Data Acquisition for Mobile Robots Using Mixed Cognition Particle Swarm Optimization

In mobile data acquisition, mobile robots usually face challenging tasks when collecting information in an undetermined environment with energy limitation and time-sensitive requirements. We formulate the task of data acquisition as a multiobjective optimization problem under energy and time constraints. In our investigation, three objectives for data acquisition are considered, including collecting the largest amount of information, moving along a path with the smallest probability of encountering obstacles, and traveling with shortest possible overall distance. To resolve the formulated problem which yields the best path for a mobile robot, we propose a mixed cognition particle swarm optimization (MCPSO) algorithm, which adopts the min-max normalization to calculate the fitness, and we transform the multiobjective optimization problem into a single-objective optimization problem by summation after normalization. The efficiency of the MCPSO algorithm is evaluated for mobile data acquisition in several well-known benchmarks by simulation. The simulation results demonstrate that the proposed MCPSO algorithm can achieve higher accuracy and faster convergence compared with other particle swarm optimization algorithms.
Power output enhancement of grid-connected PV system using dual-axis tracking

The current paper proposes an augmentation of power output production of a single-phase grid-connected photovoltaic (PV) system using dual-axis solar tracking (DAST). Solarius PV software was applied to design and predict the energy demand for a single-family house over one year under the climate condition of Basra city, Iraq. The performance of the DAST-PV system was compared to another stationary PV system has the same capacity in terms of power output and the reduction of generated emissions over 20 years lifetime. The results showed that the stationary PV system can overcome up to 84.8% of the total electricity demand of the house while DAST-PV system can overcome up to 100% with surplus energy of 9886.55 kWh. Moreover, the greenhouse gases (GHGs) emissions avoided by the DAT-PV system were about 34.8% more than the stationary system over the system lifetime.
Exploring the role of asymmetric-pulse modulation in quantum thermal machines and quantum thermometry

We explore the consequences of periodically modulating a quantum two-level system (TLS) with an asymmetric pulse when the system is in contact with thermal baths. By adopting the Floquet-Lindblad formalism for our analysis, we find that the unequal "up" and "down" time duration of the pulse has two main ramifications. First, the energy gap of the multiple sidebands or photon sectors created as a result of the periodic modulation are renormalized by a term which is dependent on both the modulation strength as well as the fraction of up (or down) time duration. Second, the weights of the different sidebands are no longer symmetrically distributed about the central band or zero photon sector. We illustrate the advantages of these findings in the context of applications in quantum thermal machines and thermometry. For a thermal machine constructed by coupling the TLS to two thermal baths, we demonstrate that the asymmetric pulse provides an extra degree of control over the mode of operation of the thermal machine. Further, by appropriately tuning the weight of the subbands, we also show that an asymmetric pulse may provide superior optimality in a recently proposed protocol for quantum thermometry, where dynamical control has been shown to enhance the precision of measurement.
Distributed Training of Support Vector Machine on a Multiple-FPGA System

Support Vector Machine (SVM) is a supervised machine learning model for classification tasks. Training SVM on a large number of data samples is challenging due to the high computational cost and memory requirement. Hence, model training is supported on a high-performance server which typically runs a sequential training algorithm on centralized data. However, as we move towards massive workloads, it will be impossible to store all the data in a centralized manner and expect such sequential training algorithms to scale on traditional processors. Moreover, with the growing demands of real-time machine learning for edge analytics, it is imperative to devise an efficient training framework with relatively cheaper computations and limited memory. Therefore, we propose and implement a first-of-its-kind system of multiple FPGAs as a distributed computing framework comprising up to eight FPGA units on Amazon F1 instances with negligible communication overhead to fully parallelize, accelerate, and scale the SVM training on decentralized data. Each FPGA unit has a pipelined SVM training IP logic core operating at 125 MHz with a power dissipation of 39 Watts for accelerating its allocated computations in the overall training process. We evaluate and compare the performance of the proposed system on five real SVM benchmarks.
Lightweight privacy-Preserving data classification

Internal attacks are of a huge concern, because they are usually delicately masqueraded under harmless-looking activities, which are very difficult to detect. Machine learning techniques have been successfully applied to identify insider threats. However, they may violate user privacy since they can legally access user's sensitive information. To preserve user privacy, encryption algorithms have been lately exploited as a powerful tool, to hide private data in a multiple-party collaboration. A combination of encryption and data mining techniques raises high computational complexity. Hence, in order to improve the system's performance while securing both user's private data and the classifier, we propose a new secure data analysis protocol, namely SmartClass, by adopting the garbled circuit technique to speed-up the system performance. We developed an efficient encryption step that exploits the additive homomorphism and best properties of the binary Elliptic Curve Cryptography (ECC) algorithm, while keeping the protocol highly secure. We implemented the proposed system and study its effectiveness. Experimental results show the proposed approach is very promising.
Analysis of Interaction Spaces for VR in Public Transport Systems

Virtual reality (VR) is becoming increasingly integrated into people’s lives. However, current applications are usually limited to a certain fixed location. With the introduction of wireless and mobile head mounted displays (HMD) there is no longer a need for a workstation. In this way it is possible to introduce VR experiences in public spaces. Some airlines have already started to use VR as an alternative entertainment system on flights.
Unified Integration of Smart Contracts Through Service Orientation

In this article, we introduce a set of technologies that lay the foundation for a service-oriented integration of smart contracts into generic software applications. In particular, we showcase the use of three specifications using a typical supply chain scenario.
State involvement in cryptocurrencies. A potential world money?

This article is about the role that states play in the research and development of cryptocurrencies and their underlying technology. Some states, for instance China, are about to launch their own state-backed cryptocurrency perhaps due to the potential of this new type of digital money to become world money. To support this argument, Marxist monetary theory is deployed to show that cryptocurrencies could be conceived as potential digital commodity money, a new and incorporeal type of commodity money with intrinsic value but without use value. Lacking a natural form, it could potentially have only a "formal" use value: direct exchangeability with all other commodities. If states manage to actualise this potential by issuing their own cryptocurrencies and making them legal tender money, cryptocurrencies could function as international means of payments and means of hoarding perhaps more efficiently than credit money. In the case of China, this means that this new digital money would have a chance of competing with the US dollar as international reserve currency.
Hand Motion with Eyes-free Interaction for Authentication in Virtual Reality

Designing an authentication method is a crucial component to secure privacy in information systems. Virtual Reality (VR) is a new interaction platform, in which the users can interact with natural behaviours (e.g. hand, gaze, head, etc.). In this work, we propose a novel authentication method in which user can perform hand motion in an eyes-free manner. We evaluate the usability and security between eyes-engage and eyes-free input with a pilot study. The initial result revealed our purposed method can achieve a trade-off between usability and security, showing a new way to behaviour-based authentication in VR.
Towards Self-Adapting Data Migration in the Context of Schema Evolution in NoSQL Databases

When NoSQL database systems are used in an agile software development setting, data model changes occur frequently and thus, data is routinely stored in different versions. This leads to an overhead affecting the software development and in particular, the management of data accesses. In this context, different data migration strategies exist, which are characterized by certain advantages and disadvantages. Using exactly that strategy whose characteristics match the according migration scenario, depends on the query workload, the changes in the data model caused by schema evolution, and the requirements for the application in terms of migration costs and latency during data accesses. In this paper we present a methodology of selfadapting data migration, which automatically adjusts migration strategies and its parameters accordingly, thereby supporting the agile software development.
Geometric Image Correspondence Verification by Dense Pixel Matching

This paper addresses the problem of determining dense pixel correspondences between two images and its application to geometric correspondence verification in image retrieval. The main contribution is a geometric correspondence verification approach for re-ranking a shortlist of retrieved database images based on their dense pair-wise matching with the query image at a pixel level. We determine a set of cyclically consistent dense pixel matches between the pair of images and evaluate local similarity of matched pixels using neural network based image descriptors. Final re-ranking is based on a novel similarity function, which fuses the local similarity metric with a global similarity metric and a geometric consistency measure computed for the matched pixels. For dense matching our approach utilizes a modified version of a recently proposed dense geometric correspondence network (DGC-Net), which we also improve by optimizing the architecture. The proposed model and similarity metric compare favourably to the state-of-the-art image retrieval methods. In addition, we apply our method to the problem of long-term visual localization demonstrating promising results and generalization across datasets.
Active Stereo 3-D Surface Reconstruction Using Multistep Matching

Precise 3-D surface reconstruction plays an important role in automated manipulation, industrial inspection, robotics, and so on. In this article, we present a novel 3-D surface reconstruction framework for stereo vision systems assisted with structured light projection. In the framework, a multistep matching scheme is proposed to establish a reliable correspondence between image pairs with high computation efficiency and accuracy. The successive matching steps can find the most precise correspondence through a step-by-step filtering procedure. To further enhance the precision, a correspondence refinement algorithm is presented. Phase maps with different frequencies are utilized as the code words for the multistep matching due to their high encoding accuracy and robustness to noise. This method does not require phase unwrapping or projector calibration, which improves the reconstruction precision and simplifies the operation. Selection strategies for the number of matching steps, the pattern frequencies, and the matching threshold are proposed. Furthermore, various 3-D reconstruction experiments are conducted using the proposed framework. Comparative experiments verify the advantages of the proposed framework compared with existing 3-D reconstruction methods regarding the accuracy and precision. The adaptability to scenarios with different motion speeds is demonstrated. Robustness and limitations of the framework are also revealed by conducting experiments in challenging scenarios. Note to Practitioners-This article is motivated by the precise 3-D surface reconstruction problem in automated robotic systems. In different scenarios, such as the reconstruction of the static objects or moving objects, the errors induced by sensor noise and motion should be taken into consideration. To enhance the measurement precision under these occasions, selection of pattern number and fringe frequencies has been a problem. To overcome these problems, this article proposes a novel framework for active stereo 3-D surface reconstruction. The framework utilizes multifrequency phase-shifting fringes to encode the reconstructed target. Then, a multistep matching method filters the candidates step by step to obtain the most precise corresponding pixel and avoid noise error accumulation. A refinement method is introduced to further improve the precision. Selection strategies of the number of matching steps, the fringe frequencies, and matching thresholds enable the 3-D reconstruction framework to be utilized on different occasions. In applications, limitations of the proposed method should be noted.