Reviewerly/exHarmony huggingface dataset:
Evolving Ramp Secret Sharing with a Small Gap

Evolving secret-sharing schemes, introduced by Komargodski, Naor, and Yogev (TCC 2016b), are secret-sharing schemes in which there is no a-priory upper bound on the number of parties that will participate. The parties arrive one by one and when a party arrives the dealer gives it a share; the dealer cannot update this share when other parties arrive. Motivated by the fact that when the number of parties is known, ramp secret-sharing schemes are more efficient than threshold secret-sharing schemes, we study evolving ramp secret-sharing schemes. Specifically, we study evolving (b(j), g(j))-ramp secret-sharing schemes, where $$g,b: \mathbb {N}\rightarrow \mathbb {N}$$ are non-decreasing functions. In such schemes, any set of parties that for some j contains g(j) parties from the first parties that arrive can reconstruct the secret, and any set such that for every j contains less than b(j) parties from the first j parties that arrive cannot learn any information about the secret. We focus on the case that the gap is small, namely $$g(j)-b(j)=j^{\beta }$$ for $$0<\beta <1$$. We show that there is an evolving ramp secret-sharing scheme with gap $$t^{\beta }$$, in which the share size of the j-th party is $$\tilde{O}(j^{4-\frac{1}{\log ^2 {1/\beta }}})$$. Furthermore, we show that our construction results in much better share size for fixed values of $$\beta $$, i.e., there is an evolving ramp secret-sharing scheme with gap $$\sqrt{j}$$, in which the share size of the j-th party is $$\tilde{O}(j)$$. Our construction should be compared to the best known evolving g(j)-threshold secret-sharing schemes (i.e., when $$b(j)=g(j)-1$$) in which the share size of the j-th party is $$\tilde{O}(j^4)$$. Thus, our construction offers a significant improvement for every constant $$\beta $$, showing that allowing a gap between the sizes of the authorized and unauthorized sets can reduce the share size. In addition, we present an evolving (k/2, k)-ramp secret-sharing scheme for a constant k (which can be very big), where any set of parties of size at least k can reconstruct the secret and any set of parties of size at most k/2 cannot learn any information about the secret. The share size of the j-th party in our construction is $$O(\log k\log j)$$. This is an improvement over the best known evolving k-threshold secret-sharing schemes in which the share size of the j-th party is $$O(k\log j)$$.
Outlier accommodation in moving‐horizon state estimation: A risk‐averse performance‐specified approach

Summary This paper presents a novel state estimation approach for linear dynamic systems when measurements are corrupted by outliers. Since outliers can degrade the performance of state estimation, outlier accommodation is critical. The standard approach combines outlier detection utilizing Neyman‐Pearson (NP) type tests with a Kalman filter (KF). This approach ignores all residuals greater than a designer‐specified threshold. When measurements with outliers are used (ie, missed detections), both the state estimate and the error covariance matrix become corrupted. This corrupted state and covariance estimate are then the basis for all subsequent outlier decisions. When valid measurements are rejected (ie, false alarms), potentially using the corrupted state estimate and error covariance, measurement information is lost. Either using invalid information or discarding too much valid information can result in divergence of the KF. An alternative approach is moving‐horizon (MH) state estimation, which maintains all recent measurement data within a moving window with a time horizon of length L . In MH approaches, the number of measurements available for state estimation is affected by both the number of measurements per time step and the number of time steps L over which measurements are retained. Risk‐averse performance‐specified (RAPS) state estimation works within an optimization setting to choose a set of measurements that achieves a performance specification with minimum risk of outlier inclusion. This paper derives and formulates the MH‐RAPS solution for outlier accommodation. The paper also presents implementation results. The MH‐RAPS application uses Global Navigation Satellite Systems measurements to estimate the state of a moving platform using a position, velocity, and acceleration model. In this application, MH‐RAPS performance is compared with MH‐NP state estimation.
Accurate and Real-Time Object Detection Based on Bird's Eye View on 3D Point Clouds

Aiming at accurate and real-time object detection on 3D point clouds, we proposed a single-stage deep neural network which includes new solutions in three aspects: network architecture, loss function design and data augmentation. Firstly, the point clouds are directly voxelized to build a binary bird's eye view (BEV) map. The network is specially designed to combine the semantic and position information on point clouds to output a final feature map. When regressing the bounding boxes of objects from the bird's eye view, an extra prediction error regression is considered in the loss function to achieve the convergence with higher precision. In training process, a special data augmentation is adopted by mixing 3D point clouds from different frames to improve generalization performance of the network. Experimental results show that our approach achieves higher performance than the state-of-the-art methods on the KITTI BEV object detection benchmark at a frame rate of 20Hz, using only the position information of LIDAR point clouds.
Reconfigurable Architecture to Speed-up Modular Exponentiation

Diffie-Hellman and RSA encryption/decryption involve computationally intensive cryptographic operations such as modular exponentiation. Computing modular exponentiation using appropriate pre-computed pairs of bases and exponents was first proposed by Boyko et al. In this paper, we present a reconfigurable architecture for pre-computation methods to compute modular exponentiation and thereby speeding up RSA and Diffie-Hellman like protocols. We choose Diffie-Hellman key pair (a, g <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup> mod p) to illustrate the efficiency of Boyko et al's scheme in hardware architecture that stores pre-computed values a <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> and corresponding g <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> in individual block RAM. We use a Pseudo-random number generator (PRNG) to randomly choose a <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> values that are added and corresponding g <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> values are multiplied using modular multiplier to arrive at a new pair (a, g <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup> mod p). Further, we present the advantage of using Montgomery and interleaved methods for batch multiplication to optimise time and area. We show that a 1024-bit modular exponentiation can be performed in less than 73μs at a clock rate of 200MHz on a Xilinx Virtex 7 FPGA.
Approaches for increasing patient’s engagement and motivation in exer-games-based autonomous telerehabilitation

In the latest years at-home autonomous rehabilitation platforms have grown in popularity. Some of them allow patients to recover from their physical damages by playing exer-games, which are more enjoyable than classic rehabilitation. One of the main problems of this approach is the repetitive structure of the exer-games, since rehabilitation requires intensive training each day and patients could lose motivation if they are not enough entertained. We describe here few methods we recently implemented that can be used alongside with exer-games to make the platform more compelling and entertain the patient during the day-by-day rehabilitation cycle without losing motivation.
ShiftMod Cipher: A Symmetrical Cryptosystem Scheme

The security of storing and sending files and digital messages needs to be improved along with technological developments. Encryption using cryptography is a solution that is widely used to overcome this. Cryptographic schemes must be safe and fast computational. This research proposes a new symmetrical cryptosystem scheme using a combination of shift and modulus operations. This scheme is included in the category of symmetric cryptography that uses the same key during the encryption and decryption process. Based on the testing of the proposed method, the encryption process requires a very fast time which is less than 0.05 seconds with a relatively good level of security as evidenced by the value of BER and the Avalanche effect.
Compound worlds and metaphor landscapes: Affixoids, allostructions, and higher-order generalizations

This paper investigates the use of German -landschaft ‘landscape’ and -welt ‘world’ as compound constituents. Both occur frequently in metaphorical uses such as Korpuslandschaft ‘corpus landscape’ or Arbeitswelt ‘labor world’. The high productivity of both compound types raises the question of whether [N-landschaft] and [N-welt] form constructions in their own right, both with a collectivizing meaning, and if so, how they relate to their respective higher-order schemas. More specifically, the question arises how the metaphorical-collectivizing uses relate to non-metaphorical ones. As both metaphorical and non-metaphorical reading variants coexist, these patterns show interesting similarities to those semantically bleached compound constituents that have been discussed under the label of “affixoids” in the morphological literature and in recent constructionist approaches. Drawing on synchronic data from the DWDS reference corpus of the 20 th century and from the webcorpus DECOW16B as well as diachronic data from the German Text Archive, I argue that they form part of a family of collectivizing constructions. On a more theoretical note, I discuss how the higher-order generalizations connecting this family of constructions can be conceptualized.
Comparative Analysis of Sign Language Notation Systems for Indian Sign Language

Hearing impaired or deaf people convey their ideas through body movement is called Sign language gesture. Sign language is a visual language and notation system is used to transcribe the gesture in iconic form. The notation is a set of symbols that make any writing system visible based on a specific structure. Sign language notation structure is based on the information extracted from the signing space, shape of hands, bodily movement, facial expression. In this research paper analysis of notation systems: Stokoe, SignWriting, HamNoSys, Gloss are explored and compared for Indian sign language.
Spiking Reservoir Networks: Brain-Inspired Recurrent Algorithms That Use Random, Fixed Synaptic Strengths

A class of brain-inspired recurrent algorithms known as <;i>reservoir computing (RC) networks<;/i> reduces the computational complexity and cost of training machine-learning models by using random, fixed synaptic strengths. This article offers insights about a spiking reservoir network, the liquid state machine (LSM), the inner workings of the algorithm, the design metrics, and neuromorphic designs. The discussion extends to variations of the LSM that incorporate local plasticity mechanisms and hierarchy to improve performance and memory capacity.
DyCOCo: A Dynamic Computation Offloading and Control Framework for Drone Video Analytics

Unmanned aerial vehicles (UAV) or drone systems equipped with cameras are extensively used in different surveillance scenarios and often require real-time control and high-quality video transmission. However, unstable network situations and various transport protocols may result in impairments during video streaming, which in turn negatively impacts user's quality of experience (QoE). In this paper, we propose a dynamic computation offloading and control framework, named DyCOCo, based on image impairment detection under various available network bandwith conditions. Our DyCOCo framework demo features IoT devices in a testbed setup on the GENI infrastructure. Our demo results show that our DyCOCo approach can efficiently choose the suitable networking protocols and orchestrate both the camera control on the drone, and the computation offloading of the video analytics over limited edge computing/networking resources.
Lattice Reduction for Modules, or How to Reduce ModuleSVP to ModuleSVP

This is the extended abstract of [MS20]. See the full version at eprint:2019/1142 . We show how to generalize lattice reduction algorithms to module lattices. Specifically, we reduce $$\gamma $$ -approximate ModuleSVP over module lattices with rank $$k \ge 2$$ to $$\gamma '$$ -approximate ModuleSVP over module lattices with rank $$2 \le \beta \le k$$ . To do so, we modify the celebrated slide-reduction algorithm of Gama and Nguyen to work with module filtrations, a high-dimensional generalization of the ( $$\mathbb {Z}$$ -)basis of a lattice. The particular value of $$\gamma $$ that we achieve depends on the underlying number field K, the order $$R \subseteq \mathcal {O}_K$$ , and the embedding (as well as, of course, k and $$\beta $$ ). However, for reasonable choices of these parameters, the resulting value of $$\gamma $$ is surprisingly close to the one achieved by “plain” lattice reduction algorithms, which require an arbitrary SVP oracle in the same dimension. In other words, we show that ModuleSVP oracles are nearly as useful as SVP oracles for solving higher-rank instances of approximate ModuleSVP. Our result generalizes the recent independent result of Lee, Pellet-Mary, Stehlé, and Wallet, which works in the important special case when $$\beta = 2$$ and $$R = \mathcal {O}_K$$ is the ring of integers of K under the canonical embedding, while our reduction works. Indeed, at a high level our reduction can be thought of as a generalization of theirs in roughly the same way that block reduction generalizes LLL reduction. In this extended abstract, we present a special case of the more general result to appear in the full version [MS20].
Dynamic Replication Scheduling for Cloud Datacenters Based on Workload Statistics

Cloud providers often achieve high service reliability and availability by using replication technologies. Typical replication mechanisms usually adopt fixed schedules for replicators by transferring data to their replicas at a specified time. While replications and applications are performed simultaneously, they may cause resource contention and result in replication failures. Once a replication fails, its replicator will retry the replication to extend the transmission time and cause additional CPU overhead in the system. As a result, the response time of the replication is increased. In this paper, we present a mechanism of dynamic replication scheduling based on workload statistics (DRSWS) to dynamically adjust the size of each replication batch according to the workload statistics. The experimental results show that DRSWS can efficiently alleviate system overhead and reduce response time.
Smart Contracts and Contract Law

This chapter acts as an introduction to the book by discussing the intersection of law and technology. The topics examined include formalism versus contextualism, form versus context, enforceability, and regulation.
Container Orchestration by Kubernetes for RDMA Networking

With the widespread usage of containerized virtualization in data centers and clouds, it is important to enabling high-throughput and zero-copy data transfer between those containers. Remote Direct Memory Access (RDMA) allows bypassing the kernel for packet processing by offloading it to specific RDMA-enabled NICs. The existing solutions enabling RDMA with containers are either based on custom container orchestrators (e.g., FreeFlow) or lack the ability for the control plane to manage the underlying RDMA traffic (e.g., Kubernetes RDMA plug-in via SR-IOV). The work in this paper builds off of previous work in Kubernetes to make an architecture that allows control over bandwidth requirements of RDMA within a Kubernetes cluster.
Solar Irradiance Fluctuation Prediction Methodology Using Artificial Neural Networks

Abstract Renewable energy is an attractive alternative source of energy to fossil fuels, as it can help prevent global warming and air pollution. Solar energy, one of the most promising renewable energy sources, can be converted into electricity using photovoltaic power generation systems. Anywhere on the Earth, solar irradiance generally fluctuates during the day but depends on atmospheric conditions. Thus, when a photovoltaic power generation system is connected to a conventional electricity network, predicting near-future global solar irradiance, especially its drastic increases and decreases, is critical to stabilize the network. In this research, a simple method utilizing artificial neural networks to predict large increases and decreases in global solar irradiance is developed. The red–blue ratio (RBR) values, which are extracted from a set of sampling points in images of the sky, as well as the corresponding global solar irradiance values, are used as the artificial neural network inputs. The direction of the movement of clouds is predicted using RBR data at the sampling points. Then, solar irradiance is predicted using the RBR values along the axis closest to the predicted cloud movement direction and the corresponding solar irradiance measurements. The proposed methodology is able to predict both large increases and decreases in solar irradiance greater than 50 through 100 W/m2 1 min in advance with a 40% prediction error. A significant reduction in computational effort is achieved compared to existing sky image-based methodologies using limited sky image data.
Distributed Data Anonymization

Data generalization is a widely-used privacy technique, in which an accurate value of sensitive information is replaced with a more general representation. The problem of data generalization becomes challenging when data is distributed among several agents, who are interested in releasing their table of data to shape a data mining algorithm on the whole of their data. The main issue originates from the fact that when each agent generalizes her own dataset locally, the released tables of data suffer from non-homogeneity. To sole the issue, all agents can generalize their data to the widest range of generalization. However, this approach causes utility loss. To optimally address this problem, in this study we present a framework that serves as a tool for data owners to generalize their data homogeneously before being published. The effectiveness of the proposed mechanism is validated through an experimental analysis on a benchmark dataset.
Using a Priori Knowledge to Improve Scene Understanding

Semantic segmentation algorithms that can robustly segment objects across multiple camera viewpoints is crucialfor assuring navigation and safety in emerging applicationssuch as autonomous driving. Existing algorithms treat eachimage in isolation, but autonomous vehicles often revisit thesame locations. We propose leveraging this a priori knowledge to improve semantic segmentation of images from se-quential driving datasets. We examine several methods tofuse these temporal scene priors, and introduce a prior fusion network that is able to learn how to transfer this information. Our model improves the accuracy of dynamic object classes from 69.1% to 73.3%, and static classes from 88.2% to 89.1%.
An automatic approach for classification and categorisation of lip morphological traits

Classification of facial traits (e.g., lip shape) is an important area of medical research, for example, in determining associations between lip traits and genetic variants which may lead to a cleft lip. In clinical situations, classification of facial traits is usually performed subjectively directly on the individual or recorded later from a three-dimensional image, which is time consuming and prone to operator errors. The present study proposes, for the first time, an automatic approach for the classification and categorisation of lip area traits. Our approach uses novel three-dimensional geometric features based on surface curvatures measured along geodesic paths between anthropometric landmarks. Different combinations of geodesic features are analysed and compared. The effect of automatically identified categories on the face is visualised using a partial least squares method. The method was applied to the classification and categorisation of six lip shape traits (philtrum, Cupid's bow, lip contours, lip-chin, and lower lip tone) in a large sample of 4747 faces of normal British Western European descents. The proposed method demonstrates correct automatic classification rate of up to 90%.
A Near Optimal Multi-Faced Job Scheduler for Datacenter Workloads

As data-parallel applications process more complex data, the dependencies between computation jobs in a multi-stage job also become more complicated. However, most of the existing scheduling solutions primarily rely on total bytes sent (job size) to differentiate jobs where jobs with fewer? bytes sent are prioritized over the larger ones. This approach overlooks the fact that jobs may consist of multiple computation stages, and that the completion of a computation job stage depends on the completion of other jobs' stage. In this paper, we present a coflow scheduler of multi-stage jobs that minimizes the average job completion time. Our solution prioritizes jobs based on the multi-faceted characteristics of multi-stage job structure per stage, instead of total bytes sent. Our experiments show that our approach provides twice the performance of existing solutions on average and by four times in bursty traffic scenario.
Reco: Efficient Regularization-Based Coflow Scheduling in Optical Circuit Switches

To improve the application-level data efficiency, the scheduling of coflows, defined as a collection of parallel flows sharing the same objective, is prevailing in recent data centers. Meanwhile, optical circuit switches (OCS) are gradually applied to provide high data rate with low power consumption. However, so far few research outputs have covered the flow scheduling in the context of OCS, let alone the coflow scheduling problems. In this paper, we investigate coflow scheduling in the OCS-based data centers. We first derive a novel operation called regularization processed respectively on the flow traffic demands and the flow start times. Regularization can be efficiently implemented and reduce the circuit reconfiguration frequency dramatically. We then propose a 2-approximation algorithm, called Reco-Sin, for single coflow scheduling to minimize the coflow completion time (CCT). For multiple coflows, we derive another approximation algorithm, called Reco-Mul, to minimize the total weighted CCT, which can transform any non-preemptive multi-coflow scheduling in packet switches to that in OCS. Extensive simulations based on Facebook data traces show that Reco-Sin and Reco-Mul outperform state-of-the-art schemes significantly, i.e., one single coflow can be finished up to 2.72× faster with Reco-Sin, and multiple coflows can be completed up to 3.44× faster with Reco-Mul.