Reviewerly/exHarmony huggingface dataset:
High-Speed and High-Security Hybrid AES-ECC Cryptosystem Based on FPGA

In the age of information, high-speed and high-security cryptosystems have become essential. This paper introduces a hybrid AES-ECC cryptosystem which takes advantages of Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC). In this cryptosystem, plaintext is encrypted by AES, which guarantees high encryption speed; meanwhile, by applying ECC to encrypt AES keys, the security of key distribution over insecure channels is greatly improved and key management becomes easier. To shorten latency, a six-clock-cycle Montgomery Ladder is applied in ECC core. In AES core, key expansion unit and SBOX are shared to conserve resources. The proposed crypto system is implemented on Xilinx Virtex-5 and the synthesize results shows that the cryptosystem is a cutting-edge design. The latency of the proposed architecture is optimized to 10.04 μs (2,070 clock cycles) for encryption while 10.44μs (2,153 clock cycles) for decryption with 206.2 MHz clock frequency at the cost of 23,764 LUTs. The throughput rates for encryption and decryption are 61.45 Mbps and 62.45 Mbps.
Path planning of mobile robots based on an improved A*algorithm

When an AGV (Automated Guided Vehicle) performs navigation tasks, it needs to run the path planning algorithm to obtain an optimal path in a current environment. In this paper, Dijkstra algorithm and A*algorithm with different heuristic functions are applied to static environment modeling with various types of obstacles. To solve the problem that there are many redundant points and inflection points in the search process of the A*algorithm, an improved A*algorithm with Manhattan distance as a heuristic function is selected as the path planning algorithm. In addition, a calculation method of optimizing a past cost function is proposed, and the weight of heuristic function is optimized simultaneously. Simulation results show that the improved algorithm has a higher efficiency and less path inflection points than the traditional A*algorithm has.
Scientific Software Engineering: Mining Repositories to gain insights into BACARDI

For Space Situational Awareness, the German Aerospace Center (DLR) develops the software system “Back-bone Catalogue of Relational Debris Information” (BACARDI), which allows for keeping track of resident space objects. BACARDI's key features are automated processing services which produce orbit information and products like collision warnings. We present how we applied new methods of software analytics to the BACARDI project. BACARDI is an example of a complex software system with large development effort carried out by a team of various specialists. Our goal is to design and implement an efficient software development process, balancing the explorative character of a research project and operational requirements (i.e. tailored from official standards in the aerospace domain). Therefore, we established a software development process for the project where we focus on software quality. We applied methods to structure, communicate, and utilize the diverse skills, knowledge, and experience in the team concisely and precisely. After one year of practical utilization, we analyzed the process based on the repository data. By analyzing these data, we assess and prove the effects of the introduced process on the development of a software, which is used in the aerospace domain.
Efficient Algorithms for Kernel Aggregation Queries

Kernel functions support a broad range of applications that require tasks like density estimation, classification, regression or outlier detection. For these tasks, a common online operation is to compute the weighted aggregation of kernel function values with respect to a set of points. However, scalable aggregation methods are still unknown for typical kernel functions (e.g., Gaussian kernel, polynomial kernel, sigmoid kernel and additive kernels) and weighting schemes. In this paper, we propose a novel and effective bounding technique, by leveraging index structures, to speed up the computation of kernel aggregation. In addition, we extend our technique to additive kernel functions, including <inline-formula><tex-math notation="LaTeX">$\chi ^2$</tex-math></inline-formula> , intersection, JS and Hellinger kernels, which are widely used in different communities, e.g., computer vision, medical science, Geoscience etc. To handle the additive kernel functions, we further develop the novel and effective bound functions to efficiently evaluate the kernel aggregation. Experimental studies on many real datasets reveal that our proposed solution KARL achieves at least one order of magnitude speedup over the state-of-the-art for different types of kernel functions.
Iterative Decoding of Non-Binary Cyclic Codes Using Minimum-Weight Dual Codewords

This paper proposes a novel shift-sum decoding scheme for non-binary cyclic codes. Using minimum-weight dual codewords and their cyclic shifts, a reliability measure can be yielded as an indicator for the error position and the error magnitude. Based on this shift-sum decoding concept, a harddecision iterative decoding algorithm is proposed, which can correct errors beyond half of the code’s minimum Hamming distance. By utilizing reliability information from the channel, a soft-decision iterative decoding algorithm is further introduced to improve the decoding performance. These two shift-sum based iterative decoding algorithms are realized with polynomial multiplication and integer (or real number) comparisons, which are hardware-friendly. Simulation results on Reed-Solomon codes and non-binary BCH codes show the decoding potential of the proposed algorithms.
Multi-component LFM signal detection based on DIRFAT algorithm

A novel algorithm based on Discrete-Improved-Radon-Fourier-Ambiguity-Transform (DIRFAT) is proposed for linear frequency modulated (LFM) signal detection and parameter estimation in this paper. By the proposed algorithm, the components can be coherently integrated into distinct peaks on DIRFAT plane, and their parameters can be estimated simultaneously. The proposed algorithm consists of three stages. Firstly, eliminate the linear chirp rate of the every component on discrete ambiguity function (DAF) plane by axis rotation (AR). Afterward, compensate the phase item caused by AR. Otherwise, the energy of the components may spread. Finally, coherently integrate the components and realize components detection. Compared with several existing algorithms, DIRFAT algorithm has better parameter estimation accuracy. Both simulations and real data processing demonstrate that DIRFAT algorithm can detect and analyze Multi-component LFM signals effectively without the affection of the cross-terms.
Tutorial on Human-Centered Explainability for Healthcare

In recent years, the rapid advances in Artificial Intelligence (AI) techniques along with an ever-increasing availability of healthcare data have made many novel analyses possible. Significant successes have been observed in a wide range of tasks such as next diagnosis prediction, AKI prediction, adverse event predictions including mortality and unexpected hospital re-admissions. However, there has been limited adoption and use in the clinical practice of these methods due to their black-box nature. A significant amount of research is currently focused on making such methods more interpretable or to make post-hoc explanations more accessible. However, most of this work is done at a very low level and as a result, may not have a direct impact at the point-of-care. This tutorial will provide an overview of the landscape of different approaches that have been developed for explainability in healthcare. Specifically, we will present the problem of explainability as it pertains to various personas involved in healthcare viz. data scientists, clinical researchers, and clinicians. We will chart out the requirements for such personas and present an overview of the different approaches that can address such needs. We will also walk-through several use-cases for such approaches. In this process, we will provide a brief introduction to explainability, charting its different dimensions as well as covering some relevant interpretability methods spanning such dimensions. We will touch upon some practical guides for explainability and provide a brief survey of open source tools such as the IBM AI Explainability 360 Open Source Toolkit.
Impact of Stalling on QoE for 360-degree Virtual Reality Videos

Currently, there has been significant attention towards 360-degree videos delivery and immersive technology like Virtual Reality (VR). Streaming 360○ video in tile-based method faces stalling and border effect of mixed bitrate tile due to small playback buffer and spatial partition of video with different bitrates tile respectively, which tend to deteriorates the end users Quality of Experience (QoE) significantly. In this paper, we study the impact of various stalling events occurs in 360○ VR video on users QoE in virtual reality. We also investigate the interaction between the stalling event and bitrate level. Besides, we analyze the impact of stalling on the users presence and immersion in a virtual environment. To aim this, we created a database of 360○ video with 4K resolutions and encoded the videos in three different bitrate levels (1Mbps, 5Mbps, and 15Mbps). We then simulate the stalling events at the start and in the middle of the video to conduct a subjective experiment. The subjective study finds that there is a strong dependency between stalling and bitrate. Stalling always impact the QoE of 360-degree videos but the strength of this negative impact depends on the video bitrate level, and the adverse effect of stalling event is more profound when the bitrate level approaches to the high and low end.
Distribution of the Minimum Distance of Random Linear Codes

In this paper, we study the distribution of the minimum distance (in the Hamming metric) of a random linear code of dimension k in $\mathbb{F}_q^n$. We provide quantitative estimates showing that the distribution function of the minimum distance is close (superpolynomially in n) to the cumulative distribution function of the minimum of (q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> -1)/(q-1) independent binomial random variables with parameters $\frac{1}{q}$ and n. The latter, in turn, converges to a Gumbel distribution at integer points when $\frac{k}{n}$ converges to a fixed number in (0, 1). In a sense, our result shows that apart from identification of the weights of parallel codewords, the probabilistic dependencies introduced by the linear structure of the random code, produce a negligible effect on the minimum code weight. As a corollary of the main result, we obtain an asymptotic improvement of the Gilbert-Varshamov bound for 2 < q < 49.
Design of Remote Sensing Image Sharing Service System Based on Block Chain Technology

Block chain is a newly emerging technology for data sharing and application. It can exchange de-centralized information in distributed systems without mutual trust by means of data encryption, timestamp and distributed consensus, so as to improve the efficiency of data sharing and application. This technology can be fully utilized in the large data remote sensing image system, and the multi-system shared node storage system can be managed efficiently and uniformly, so as to improve the economic efficiency of the system. This paper designs the shared architecture based on block chain technology, proposes key research technologies, and provides theoretical basis for nonengineering practice.
Improving Industrial Safety Gear Detection through Re-ID conditioned Detector

Industrial safety gears such as hardhats, vests, gloves and goggles are vital in safety of workers. With the advancement of vision technologies, most industries are moving towards automatic safety monitoring systems for its enforcement. However, most of the industrial safety monitoring systems are plagued by the following problems. To begin with, object detection which is the principal component of this system suffers from the problem of false detections and missed detections which are extremely costly resulting in wrong safety monitoring alerts and safety hazards. Further, while video object detection has seen a large traction through ImagenetDet and MOT17Det challenges, to the best of our knowledge there is no work till date in the context of industrial safety. Finally, unlike existing areas of object detection where there is the availability of large datasets, best of existing research works in detecting industrial safety gears is restricted to mostly hardhats due to lack of large datasets. In this work, we address these previously mentioned challenges by presenting a unified industrial safety system. As part of this developed system, we firstly introduce safety gear detection dataset consisting of 5k images with the previously mentioned classes of safety gears and present exhaustive benchmark on state-of-the-art single frame object detection. Secondly, to address wrong/missed detections we propose to exploit temporal information from contiguous frames by conditioning the object detection in the current frame on results of re-identification of objects computed in prior frames. Finally, we conduct extensive experiments using the developed Re-ID conditioned object detection system with various state-of-the-art object detectors to show that the proposed system produces mAP of 85%, 87%, 92% and 78% with average improvements of 5% mAP across the previously mentioned safety gears under complex conditions of illumination, posture and occlusions.
BSTN: An Effective Framework for Compressed Video Quality Enhancement

In this paper, we propose a boundary-guided spatial-temporal network (BSTN) to enhance the quality of HEVC compressed videos. We first adopt the advanced optical flow algorithm to estimate the motion between the current frame and its frames, these adjacent frames are pre-warped by the estimated motion flow. Then, a bi-directional residual convolutional LSTM (BRCLSTM) is designed to implicitly discover frame variations over time between the compensated frames and current frame. In addition, we generate a guided map by utilizing the partition information of transform units (TUs) to guide our network to concentrate more on the boundaries of TUs, and the guided map is fused into our network by a boundary attention fusion module to enhance the reconstructed quality of current frame. Finally, some stacked convolutional layers with ReLU are used to reconstruct the desired frame. We evaluate our method under different configurations. Experimental results show that our method can produce superior results than the state-of-the-arts, both objectively and subjectively.
Finite Key Analysis of the Extended B92 Protocol

In this paper we derive a key rate expression for the extended version of the B92 quantum key distribution protocol that takes into account, for the first time, the effects of operating with finite resources. With this expression, we conduct an analysis of the protocol in a variety of different noise and key- length settings, and compare to previous bounds on comparable protocols.
A Point-of-Interest Recommendation Algorithm Combining Social Influence and Geographic Location Based on Belief Propagation

The location-based social network (LBSN) contains a large amount of user check-in data informations, in order to better improve the recommendation performance and avoid the impact of user check-in data sparsity. It is proposed to mine the time-category informations in the user's check-in data to carry out network modeling; by using the belief propagation algorithm on the time-category Markov network to obtain the user's social influence set. Calculating the similarity and familiarity of the social users in the collection, linearly integrate the unified social influence factors and geographical location influences, to recommend locations. Experimental analysis in the Foursquare dataset, compared with other algorithms, the recommendation algorithm performance of combining social influence and geographic location based on belief propagation has improved.
Commodity Text Classification Based E-Commerce Category and Attribute Mining

This paper proposes a commodity text classification-based e-commerce category and attribute mining method. The fastText is utilized to classify the commodity text data and establish the first-level and leaf category mapping between two different e-commerce platforms. Based on the category mapping, the system can mine the attributes and attributes' values that the target leaf category does not have, while its corresponding leaf category from another platform does. Additionally, the longest common subsequence (LCS) algorithm is utilized to calculate the attribute similarity. The experiments on two different e-commerce platforms show that the proposed method not only can help the e-commerce platform establish and improve the category architecture but also enhances user experience.
Local generalization and bucketization technique for personalized privacy preservation

Anonymization technique has been extensively studied and widely applied for privacy-preserving data publishing. In most previous approaches, a microdata table consists of three categories of attributes, namely explicit-identifier, quasi-identifier (QI), and sensitive attribute. In general, individuals may have different views on the sensitivity of different attributes. Therefore, there is another type of attribute that contains both QI values and sensitive values, termed semi-sensitive attribute. In this paper, we propose a new anonymization technique, called Local Generalization and Bucketization, to prevent identity disclosure and protect the sensitive values on each semi-sensitive attribute and sensitive attribute. The rationale is to use local generalization and local bucketization to divide the tuples into local equivalence groups and partition the sensitive values into local buckets, respectively. The protections of local generalization and local bucketization are independent, so that they can be implemented by appropriate algorithms without weakening other protection. Besides, the protection of local bucketization for each semi-sensitive attribute and sensitive attribute is also independent. Consequently, local bucketization can comply with various principles in different attributes according to the actual requirements of anonymization. We conducted extensive experiments to illustrate the effectiveness of the proposed approach.
A TD-CF preprocessing method of FMCW radar for Dynamic Hand Gesture Recognition

Radar-based dynamic gesture recognition has attracted great interest of researchers in the field of human-computer interaction. A very important problem in gesture recognition based on radar is radar echo data preprocessing technique, it can significantly improve hand recognition accuracy. In this paper, we present a Threshold-Denoising and CA-CFAR (TD-CF) method of FMCW radar echo for hand gesture recognition. We first perform a 2D-FFT with a window function on the echo data matrix to deal with spectrum leakage problem and get a Range Doppler Map. An improved wavelet threshold algorithm is designed for noise removal and then a CA-CFAR detector is used to suppress echo data clutter. Experimental results validate that this preprocessing method can effectively remove noise and suppress clutter, which greatly reduce the difficulty of subsequent recognition. We can find obvious movement trend of gestures after TD-CF preprocessing.
Data placement in distributed data centers for improved SLA and network cost

Large-scale data-intensive applications provide services to users by routing service requests to geographically distributed data centers interconnected by Internet links. In order to achieve good reliability and data access latency performance, cloud service providers often simultaneously place multiple copies of the data in different data centers. The network communication required for updating the multiple data copies incurs an operational cost. At the same time, the penalty incurred by the Service Level Agreement (SLA) violation for data access from the data centers also imposes an operational cost on the service providers. In this paper, we tackle the problem of data placement in distributed data centers with the aim to minimize the operational cost incurred by delay SLA violation penalty and inter-data center network communication, assuming each data has K data replicas. We propose a K-level Cluster-based Data Placement algorithm (K-CDP) for the problem. The algorithm solves the linear programming relaxation and dual programming problems corresponding to the problem of minimizing SLA violation penalty cost caused by placing a replica of each data in a data center. Based on the obtained solutions, the algorithm clusters the data so that the data with similar placeable data centers form a data cluster. For the data in each cluster, the algorithm selects K data centers to minimize the operational cost. We prove that algorithm K-CDP is 2-approximation to the data placement problem. Our simulation results demonstrate that the proposed algorithm can effectively reduce the penalty cost incurred by delay SLA violation, the network communication cost, and the operational cost of data centers.
Impact of coupling topology upon noise robustness of small optical reservoirs

Abstract In this work, we perform the numerical investigation of the performance of the small optical reservoir computing (RC) systems with four neurons using the commercial software for optical fiber communication system. The small optical RC system consists of the components of the optical fiber communication. The nonlinear function which is required in RC is provided by the erbium-doped optical fiber amplifiers (EDFA). We demonstrate that the EDFA should be operated in the saturated or non-linear regime to obtain a better performance of the small optical RC system. The performance of the small optical RC systems for different topological neuron structures is investigated. The results show that the interconnection between the neurons could offer a better performance than the systems without interconnection between the neurons. Moreover, the input signals with different noise levels are launched into the systems. The results show that the small optical RC system can classify the noisy input optical waveforms even when the signal-to-noise ratio is as low as − 2.55 dB.
Distribution Privacy Under Function Recoverability

A user generates n independent and identically distributed data random variables with a probability mass function that must be guarded from a querier. The querier must recover, with a prescribed accuracy, a given function of the data from each of n independent and identically distributed user-devised query responses. The user chooses the data pmf and the random query responses to maximize distribution privacy as gauged by the divergence between the pmf and the querier's best estimate of it based on the n query responses. A general lower bound is provided for distribution privacy; and, for the case of binaryvalued functions, upper and lower bounds that converge to said bound as n grows. Explicit strategies for the user and querier are identified.