Reviewerly/exHarmony huggingface dataset:
Resource Utilization-Aware Collaborative Optimization of IaaS Cloud Service Composition for Data-Intensive Applications

Recently, growing cloud services (CSs) have been leased by organizations for high-performance computation and massive data storage of data-intensive applications (DiAs). To improve the resource utilization of leased CSs, it has become a challenging task to optimize infrastructure as a service CS composition for DiAs (ICSCDs) from the user side. This paper proposes a resource utilization-aware collaborative optimization approach. Targeting the collaboration features of tasks in a DiA, the environments-classes, agents, roles, groups, and objects model is used to formalize the ICSCD problem from the perspective of role-based collaboration. Aiming at the dynamic characteristics of the cloud environment, an integrated method is presented to evaluate the qualification of CSs via the interval numbers with multiple parameters. Based on the exact qualification values, the ICSCD can be optimized for improving the resource utilization of the CSs. A solution using the IBM ILOG CPLEX optimization package is put forward to solve the problem. The experimental results demonstrate that the approach can provide high precision, performance, stability, resource utilization, and low usage cost for the resource utilization-aware ICSCD from the user side.
Collusion Resistant Broadcast and Trace from Positional Witness Encryption

An emerging trend is for researchers to identify cryptography primitives for which feasibility was first established under obfuscation and then move the realization to a different setting. In this work we explore a new such avenue—to move obfuscation-based cryptography to the assumption of (positional) witness encryption. Our goal is to develop techniques and tools, which we will dub "witness encryption friendly" primitives and use these to develop a methodology for building advanced cryptography from positional witness encryption. We take a bottom up approach and pursue our general agenda by attacking the specific problem of building collusion-resistant broadcast systems with tracing from positional witness encryption. We achieve a system where the size of ciphertexts, public key and private key are polynomial in the security parameter $$\lambda $$ and independent of the number of users N in the broadcast system. Currently, systems with such parameters are only known from indistinguishability obfuscation.
Towards Design Space Exploration and Optimization of Fast Algorithms for Convolutional Neural Networks (CNNs) on FPGAs

Convolutional Neural Networks (CNNs) have gained widespread popularity in the field of computer vision and image processing. Due to huge computational requirements of CNNs, dedicated hardware-based implementations are being explored to improve their performance. Hardware platforms such as Field Programmable Gate Arrays (FPGAs) are widely being used to design parallel architectures for this purpose. In this paper, we analyze Winograd minimal filtering or fast convolution algorithms to reduce the arithmetic complexity of convolutional layers of CNNs. We explore a complex design space to find the sets of parameters that result in improved throughput and power-efficiency. We also design a pipelined and parallel Winograd convolution engine that improves the throughput and power-efficiency while reducing the computational complexity of the overall system. Our proposed designs show up to 4.75× and 1.44× improvements in throughput and power-efficiency, respectively, in comparison to the state-of-the-art design while using approximately 2.67× more multipliers. Furthermore, we obtain savings of up to 53.6% in logic resources compared with the state-of-the-art implementation.
A systems perspective of quantum computing

Quantum architects, with knowledge of both physics and computer engineering, are key to developing scalable quantum computers.
Concurrent-learning-based visual servo tracking and scene identification of mobile robots

Purpose The purpose of this paper is to present a visual servo tracking strategy for the wheeled mobile robot, where the unknown feature depth information can be identified simultaneously in the visual servoing process. Design/methodology/approach By using reference, desired and current images, system errors are constructed by measurable signals that are obtained by decomposing Euclidean homographies. Subsequently, by taking the advantage of the concurrent learning framework, both historical and current system data are used to construct an adaptive updating mechanism for recovering the unknown feature depth. Then, the kinematic controller is designed for the mobile robot to achieve the visual servo trajectory tracking task. Lyapunov techniques and LaSalle’s invariance principle are used to prove that system errors and the depth estimation error converge to zero synchronously. Findings The concurrent learning-based visual servo tracking and identification technology is found to be reliable, accurate and efficient with both simulation and comparative experimental results. Both trajectory tracking and depth estimation errors converge to zero successfully. Originality/value On the basis of the concurrent learning framework, an adaptive control strategy is developed for the mobile robot to successfully identify the unknown scene depth while accomplishing the visual servo trajectory tracking task.
Maximising motivators for technology-enhanced learning for further education teachers: moving beyond the early adopters in a time of austerity

Technology-enhanced learning (TEL) has become a prominent issue in further education (FE) since the publication of the Further Education Learning Technology Action Group (FELTAG) report in 2014, but many initiatives have concentrated on digital competence without investigating the role of staff attitudes and motivation in extending their use of new technologies. This research explored the views and experiences of FE staff using technology to support learning and the impact of these on their motivation to develop a technology-enhanced curriculum in their subject. The aim was to identify any common themes or factors linked to positive engagement with TEL which could inform institutional efforts to increase the extent and effectiveness of TEL use. This research used a mixed-methods approach to attempt to provide a broader and more reliable view of attitudes and also considered the similarities and differences between the experiences of further and higher education teachers through comparison with Bennett's (2014) Digital Practitioner Framework and the particular barriers found in the resource-constrained environment of FE. It summarises the key factors identified as likely to influence staff engagement with TEL, and recommends how such motivating factors could be maximised and how potential barriers could be addressed.
Safe Artificial General Intelligence via Distributed Ledger Technology

Artificial general intelligence (AGI) progression metrics indicate AGI will occur within decades. No proof exists that AGI will benefit humans and not harm or eliminate humans. A set of logically distinct conceptual components is proposed that are necessary and sufficient to (1) ensure various AGI scenarios will not harm humanity, and (2) robustly align AGI and human values and goals. By systematically addressing pathways to malevolent AI we can induce the methods/axioms required to redress them. Distributed ledger technology (DLT, “blockchain”) is integral to this proposal, e.g., “smart contracts” are necessary to address the evolution of AI that will be too fast for human monitoring and intervention. The proposed axioms: (1) Access to technology by market license. (2) Transparent ethics embodied in DLT. (3) Morality encrypted via DLT. (4) Behavior control structure with values at roots. (5) Individual bar-code identification of critical components. (6) Configuration Item (from business continuity/disaster recovery planning). (7) Identity verification secured via DLT. (8) “Smart” automated contracts based on DLT. (9) Decentralized applications—AI software modules encrypted via DLT. (10) Audit trail of component usage stored via DLT. (11) Social ostracism (denial of resources) augmented by DLT petitions. (12) Game theory and mechanism design.
Precursory Pattern Based Feature Extraction Techniques for Earthquake Prediction

Earthquake prediction is an important and complex task in the real world.Although many data mining-based methods have been proposed to solve this problem, the prediction accuracy is still far from satisfactory due to the deficiency of feature extraction techniques.To this end, in this paper, we propose a precursory pattern-based feature extraction method to enhance the performance of earthquake prediction.Especially, the raw seismic data is firstly divided into fixed day time periods, and the magnitude of the largest earthquake in each fixed time period is labeled as the main shock.The precursory pattern is a part of the seismic sequence before the main shock, on which the existing mathematical statistic features can be directly generated as seismic indicators.Based on these precursory pattern-based features, a simple yet effective classification and regression tree algorithm is adopted to predict the label of the main shock in a predefined future time period.The experimental results on two historical earthquake records of the Changding-Garzê and Wudu-Mabian seismic zones of China demonstrate the effectiveness of the proposed precursory pattern-based features with the selected CART algorithm for earthquake prediction.
Proof of Sincerity: A New Lightweight Consensus Approach for Mobile Blockchains

In this paper, we present the idea of proof of sincerity for a new consensus method for mobile-user-friendly blockchains. Ever since the birth of Bitcoin, there have been many distributed consensus methods studied to replace the central (or third-party) trusts to protect the integrity of the distributed ledgers. Existing methods show pros and cons but none of them showed a proper balance between the imbalance of wealth and the security of the ledgers. In proof of sincerity, by allowing each and every entity even with smaller computing power to get rewarded by mining, we can effectively prevent the extreme imbalance of the wealth (often caused by the mining that is dominated by a small number of huge resource owners). At the same time, by maintaining the appropriate level of sincerity in terms of the combination of the number of miners and the level of each mining work, we can effectively maintain the security level of the distributed ledgers.
A solution to reconstruct cross-cut shredded text documents based on constrained seed K-means algorithm and ant colony algorithm

The reconstruction of cross-cut shredded text documents (RCCSTD) is an important problem in forensics and is a real, complex and notable issue for information security and judicial investigations. It can be considered a special kind of greedy square jigsaw puzzle and has attracted the attention of many researchers. Clustering fragments into several rows is a crucial and difficult step in RCCSTD. However, existing approaches achieve low clustering accuracy. This paper therefore proposes a new clustering algorithm based on horizontal projection and a constrained seed K-means algorithm to improve the clustering accuracy. The constrained seed K-means algorithm draws upon expert knowledge and has the following characteristics: 1) the first fragment in each row is easy to distinguish and the unidimensional signals that are extracted from the first fragment can be used as the initial clustering center; 2) two or more prior fragments cannot be clustered together. To improve the splicing accuracy in the rows, a penalty coefficient is added to a traditional cost function. Experiments were carried out on 10 text documents. The accuracy of the clustering algorithm was 99.1% and the overall splicing accuracy was 91.0%, according to our measurements. The algorithm was compared with two other approaches and was found to offer significantly improved performance in terms of clustering accuracy. Our approach obtained the best results of RCCSTD problem based on our experiment results. Moreover, a more complex and real problem – reconstruction of cross-cut shredded dual text documents (RCCSDTD) problem – was tried to solve. The satisfactory results for RCCSDTD problems in some cases were obtained, to authors’ best knowledge, our method is the first feasible approach for RCCSDTD problem. On the other hand, the developed system is fundamentally an expert system that is being specifically applied to solve RCCSTD problems.
CAM-ADX: A New Genetic Algorithm with Increased Intensification and Diversification for Design Optimization Problems with Real Variables

Summary This paper presents a modified genetic algorithm (GA) using a new crossover operator (ADX) and a novel statistic correlation mutation algorithm (CAM). Both ADX and CAM work with population information to improve existing individuals of the GA and increase the exploration potential via the correlation mutation. Solution-based methods offer better local improvement of already known solutions while lacking at exploring the whole search space; in contrast, evolutionary algorithms provide better global search in exchange of exploitation power. Hybrid methods are widely used for constrained optimization problems due to increased global and local search capabilities. The modified GA improves results of constrained problems by balancing the exploitation and exploration potential of the algorithm. The conducted tests present average performance for various CEC’2015 benchmark problems, while offering better reliability and superior results on path planning problem for redundant manipulator and most of the constrained engineering design problems tested compared with current works in the literature and classic optimization algorithms.
Unconstraint Optimal Selection of Side Information for Histogram Shifting Based Reversible Data Hiding

Histogram shifting (HS) as a typical reversible data hiding (RDH) scheme is widely researched due to its high quality of stego-image. During HS process, the selected side information, i.e., peak and zero bins, usually greatly affects the performance of stego-image. Due to the massive solution space and burden in distortion computation, conventional HS-based schemes commonly utilize some empirical criterions associated with many artificial-designed constraints to determine side information, which could not lead to a global optimal performance for HS-based RDH. Later, our previous work proposed an adaptive information selection scheme for “multiple embedding” method by removing lots of constraints employed in conventional schemes. However, those constraints could not be completely avoided. Those chosen peak and zero bins in “multiple embedding” process are required to be different with each other, since they are commonly determined at one time as side information. In this paper, we employ “multilevel embedding” method and contrive to get rid of these above-mentioned constraints, which are called “unnecessary constraints” in this paper, so as to search essential global optimal side information around the entire solution space for HS-based RDH. Apparently, the process will dramatically increase the solution space and computation complexity. To effectively control the time cost, two novel approaches are proposed: 1) A pattern-based rapid performance evaluation method is designed to compute the rate and distortion; 2) Spirited by our previous work, a problem-oriented designed evolutionary algorithm, i.e., transfer learning-based genetic algorithm, is proposed to perform high-efficiency search around the entire huge solution space. For a given data payload, the proposed scheme could adaptively determine the optimal combination of peak and zero bins without any unnecessary constraint. The experimental results demonstrate the superiority of the proposed scheme compared with other state-of-the-art methods.
Remote implementation of single-qubit operations via hyperentangled states with cross-Kerr nonlinearity

Quantum information processing via hyperentanglement can improve the quantum channel capacity for long-distance quantum communication and large-scale quantum communication networks. Some important works have been presented in quantum remote state preparation with hyperentangled states. However, the protocols for quantum operation remote implementation are based on quantum systems encoded in one degree of freedom. We present a quantum operation remote implementation protocol for arbitrary single-qubit operations with two-photon hyperentangled states via cross-Kerr nonlinearities. Moreover, we discuss the scheme for parallel remote implementation of partially unknown quantum operations via two-photon hyperentangled states.
Spill the load: Mixed evidence for a foveal load effect, reliable evidence for a spillover effect in eye-movement control during reading

It has been hypothesized that the processing difficulty of the fixated word (i.e., "foveal load") modulates the amount of parafoveal preprocessing of the next word. Evidence for the hypothesis has been provided by the application of parafoveal masks within the boundary paradigm. Other studies that applied alternative means of manipulating the parafoveal preview (i.e., visual degradation) could not replicate the effect of foveal load. The present study examined the effect of foveal load by directly comparing the application of parafoveal masks (Exp. 1) with the alternative manipulation of visually degrading the parafoveal preview (Exp. 2) in adult readers. Contrary to expectation, we did not find the foveal-load interaction in the first experiment with traditional letter masks. We did, however, find the expected interaction in the second experiment with visually degraded previews. Both experiments revealed a spillover effect indicating that the processing of a word is not (always) fully completed when the reader already fixates the next word (i.e., processing "spills over" to the next word). The implications for models of eye-movement control in reading are discussed.
Detecting Emotions in English and Arabic Tweets

Assigning sentiment labels to documents is, at first sight, a standard multi-label classification task. Many approaches have been used for this task, but the current state-of-the-art solutions use deep neural networks (DNNs). As such, it seems likely that standard machine learning algorithms, such as these, will provide an effective approach. We describe an alternative approach, involving the use of probabilities to construct a weighted lexicon of sentiment terms, then modifying the lexicon and calculating optimal thresholds for each class. We show that this approach outperforms the use of DNNs and other standard algorithms. We believe that DNNs are not a universal panacea and that paying attention to the nature of the data that you are trying to learn from can be more important than trying out ever more powerful general purpose machine learning algorithms.
Side-Channel-Based Code-Execution Monitoring Systems: A Survey

Embedded devices, such as programmable logic controllers (PLCs) and Internet of Things (IoT) devices are becoming targets of malware attacks with increasing frequency and catastrophic results. Physical side-channel analysis is one way to monitor a device without accessing its software and, thus, without imposing on its resources. In this article, we present a tutorial-level discussion about detecting anomalies in embedded devices during code execution using side-channel analysis. We provide the necessary background on side-channel analysis and detail the important phases of side-channel-based monitoring in existing approaches. We further provide experimental results of electromagnetic (EM)-emanation and power-consumption side channels. We expect that this article will enable signal processing researchers to better understand the issues involved in extracting side-channel signals, determine how these signals are related to the code, and leverage them for air-gapped intrusion detection. We hope that this understanding will inspire new research on side-channel modeling and characterization.
Deep recurrent neural network for geographical entities disambiguation on social media data

A particular challenge in Natural Language Processing is the disambiguation of polysemic words. The great availability, diversity and the speed of changing of the data from on-line sources force the development of disambiguation systems with a reduced dependency on linguistic resources. We argue that the contextual neural encoding of a specific entity avoids the need of using external linguistic resources like knowledge bases. Hence, we propose a neural network architecture grounded in the use of Long Short-Term Memory Recurrent Neural Network for encoding the context of a target geographical entity, specifically Two k-Contextual Windows model for the disambiguation of the geographical entity Granada. We generate two annotated corpora of texts from social media written in English and Spanish, which we use to evaluate our proposal. The results show that our claim holds.
Unifying Temporal Context and Multi-Feature With Update-Pacing Framework for Visual Tracking

Model drifting is one of the knotty problems that seriously restricts the accuracy of discriminative trackers in visual tracking. Most existing works usually focus on improving the robustness of the target appearance model. However, they are prone to suffer from model drifting due to the inappropriate model updates during the tracking-by-detection. In this paper, we propose a novel update-pacing framework to suppress the occurrence of model drifting in visual tracking. Specifically, the proposed framework first initializes an ensemble of trackers, each of which updates the model in a different update interval. Once the forward tracking trajectory of each tracker is determined, the backward trajectory will also be generated by the current model to measure the difference with the forward one, and the tracker with the smallest deviation score will be selected as the most robust tracker for the remaining tracking. By performing such self-examination on trajectory pairs, the framework can effectively preserve the temporal context consistency of sequential frames to avoid learning corrupted information. To further improve the performance of the proposed method, a multi-feature extension framework is also proposed to incorporate multiple features into the ensemble of the trackers. The extensive experimental results obtained on large-scale object tracking benchmarks demonstrate that the proposed framework significantly increases the accuracy and robustness of the underlying base trackers, such as DSST, Struck, KCF, and CT, and achieves superior performance compared with the state-of-the-art methods without using deep models.
An In-Depth and Black-Box Characterization of the Effects of Laser Pulses on ATmega328P

Laser fault injection is a complex, physical process with many parameters that influence the success of the injection. Some parameters are difficult to control. While many works have established that focused laser light can inject seemingly random faults in electronic devices such as microcontrollers, FPGAs and ASICs, only few works explain precisely why a specific fault can be observed. We narrow this gap and characterize in detail the effects of laser pulses on an 8-bit microcontroller in a black-box fashion, with access to only public documentation. With our setup and settings we can inject faults only in the read-out circuitry for the on-chip flash memory. As result of our analysis we are able to inject bit-reset faults in individual bits of opcodes and data words stored in flash with 100% accuracy and repeatability. This allows us to easily demonstrate well known attacks on cryptographic software, e.g. manipulation of a block cipher implementation’s number of rounds. At the same time our study informs the targeted development of countermeasures.