Reviewerly/exHarmony huggingface dataset:
Integrating machine learning techniques in quantum communication to characterize the quantum channel

Free-space quantum communication technology has made significant advancements over the years. However, to achieve a global quantum network there are still obstacles to overcome. To date, free-space quantum communication channels have been faced with challenges related to losses in the quantum channel, security, and low data rates. Classical machine learning techniques provide a resourceful method for determining the properties of the free-space quantum channel. In this paper, we used supervised machine learning to predict the atmospheric strength of a free-space quantum channel in the form of the Strehl ratio. We show that using the ensemble technique called random forest we could make predictions of the Strehl ratio of the quantum channel with a mean absolute percentage error of 4.44%. By adding the feature of fidelity to the training set before and after the quantum channel, we were able to predict the Strehl ratio with an improved mean absolute percentage error of 3.86%. For comparison we used other machine learning techniques such as linear regression and support vector machine with various kernels.
A Practical Attribute-Based Document Collection Hierarchical Encryption Scheme in Cloud Computing

Ciphertext-policy attribute-based encryption can provide fine-grained access control and secure data sharing to the data users in cloud computing.However, the encryption/decryption efficiency of existing schemes can be further improved when encrypting a large document collection.In this paper, we propose a practical Ciphertext-Policy Attribute-Based Hierarchical document collection Encryption scheme named CP-ABHE.By practical, we mean that CP-ABHE is more efficient in both computation and storage space without sacrificing data security.In CP-ABHE, we first construct a set of integrated access trees based on the documents' attribute sets.We employ the greedy strategy to build the trees incrementally and grow the trees dynamically by combining the small ones.Then, all the documents on an integrated access tree are encrypted together.Different to existing schemes, the leaves in different access trees with the same attribute share the same secret number, which is employed to encrypt the documents.This greatly improves the performance of CP-ABHE.The security of our scheme is theoretically proved based on the decisional bilinear Diffie-Hellman assumption.The simulation results illustrate that CP-ABHE performs very well in terms of security, efficiency, and the storage size of the ciphertext. INDEX TERMSCloud computing, attribute-based document collection encryption, encryption/decryption efficiency, information security.Attribute-based encryption (ABE) schemes can provide complicated systems to diversify the data users' access paths.In ABE schemes, each document is encrypted individually and a data user can decrypt a document if her attribute set matches the access structure of the document.Existing ABE schemes can be divided into Key-Policy ABE (KP-ABE) schemes [11], [12], [15], [16], [20], [24], [25], [28] and Ciphertext-Policy ABE (CP-ABE) schemes [1], [7], [10], [19], [21]-[23], [27], [34].Compared with KP-ABE schemes, CP-ABE schemes are more flexible and suitable for general applications.In the following, we first analyze the existing ABE schemes in detail and further present the novelty and innovation of the CP-ABHE scheme proposed in this paper.For convenience, we choose the schemes in [1] and [11] as typical examples of KP-ABE scheme and CP-ABE scheme, respectively.
Machines and Artificial Intelligence

The machine has become the Other against which we compare ourselves. Aided by artificial intelligence, computers and robots are starting to surpass humans in the physical, linguistic, and intellectual skills that we once thought defined us as the dominant species. One response by science and technology has been to attempt to enhance humans as cyborgs who are able to keep up with our machine Others. More extreme responses are envisioned by transhumanists who anticipate that we will become a near-immortal transhuman species, even if it means transferring our consciousness to a robot or computer. At the same time, some are working on Distributed Autonomous Organizations (DAOs) that are run by autonomous software, cryptocurrencies, and smart contracts. DAOs and increasingly autonomous robots raise additional questions of whether these entities can become legal non-human persons who might have rights and responsibilities similar to human beings and corporations. This prospect raises further issues about who or what controls the global economy and what will be the fate of humans in various occupations. The paper concludes with a consideration of the implications of these developments for consumer research.
Employment of Vocational Graduates: Still a Slough or Already a Ford?

The study is devoted to employment of recent vocational graduates. The pro­portion of middle-school graduates in vocational enrollment has increased es­sentially over the past decade, which indicates that the choice of vocational trajectories, on average, is now made at lower age. It was established based on the Monitoring of Education Markets and Organizations that on average 44 percent of students combined work and study in 2010–2015. Vocational students mostly combine and work and study because of financial constraints, their study-work rarely being related to their major. Later on, when making a transition from education to the labor market, vocational graduates have to ac­cept one of the first job offers as they cannot afford a longer job search. The second part of the study draws upon the findings from the 2010–2015 sam­pling survey of graduate employment administered by the Federal State Statistics Service (Rosstat). It is shown that combining work and study has posi­tive effects on employability of graduates as well as on the size of their starting salaries. In addition, self-funded students and those who combine study with major-related work are more likely to get employed in their field of study af­ter graduation. Education-job mismatch among graduates is found to entail income “penalties”.
Hypercomplex extreme learning machine with its application in multispectral palmprint recognition

An extreme learning machine (ELM) is a novel training method for single-hidden layer feedforward neural networks (SLFNs) in which the hidden nodes are randomly assigned and fixed without iterative tuning. ELMs have earned widespread global interest due to their fast learning speed, satisfactory generalization ability and ease of implementation. In this paper, we extend this theory to hypercomplex space and attempt to simultaneously consider multisource information using a hypercomplex representation. To illustrate the performance of the proposed hypercomplex extreme learning machine (HELM), we have applied this scheme to the task of multispectral palmprint recognition. Images from different spectral bands are utilized to construct the hypercomplex space. Extensive experiments conducted on the PolyU and CASIA multispectral databases demonstrate that the HELM scheme can achieve competitive results. The source code together with datasets involved in this paper can be available for free download at https://figshare.com/s/01aef7d48840afab9d6d.
Saliency and spatial information-based landmark selection for mobile robot navigation in natural environments

In this paper, a landmark selection and tracking approach is presented for mobile robot navigation in natural environments, using textural distinctiveness-based saliency detection and spatial information acquired from stereo data. The presented method focuses on achieving high robustness of tracking rather than self-positioning accuracy. The landmark selection method is designed to select a small amount of the most salient feature points in a wide variety of sparse unknown environments to ensure successful matching. Landmarks are selected by an iterative algorithm from a textural distinctiveness-based saliency map extended with spatial information, where a repulsive potential field is created around the position of each already selected landmark for better distribution in order to increase robustness. The template matching of landmarks is aided with visual odometry-based motion estimation. Other robustness increasing strategies includes estimating landmark positions by unscented Kalman filters as well as from surrounding landmarks. Experimental results show that the introduced method is robust and suitable for natural environments.
Neural Collaborative Subspace Clustering

We introduce the Neural Collaborative Subspace Clustering, a neural model that discovers clusters of data points drawn from a union of low-dimensional subspaces. In contrast to previous attempts, our model runs without the aid of spectral clustering. This makes our algorithm one of the kinds that can gracefully scale to large datasets. At its heart, our neural model benefits from a classifier which determines whether a pair of points lies on the same subspace or not. Essential to our model is the construction of two affinity matrices, one from the classifier and the other from a notion of subspace self-expressiveness, to supervise training in a collaborative scheme. We thoroughly assess and contrast the performance of our model against various state-of-the-art clustering algorithms including deep subspace-based ones.
In the Wake of Discovery: Student Perceptions, Integration, and Instructional Design

Academic libraries have increasingly adopted web-scale discovery tools in order to meet the expectations of students who want immediate results, efficient algorithms, and a single search box. As they have become the de facto search tool at many libraries, librarians and patrons alike have been forced to confront the way they think about research processes and in some cases, modulate ingrained habits. In this article, the authors explore student perceptions of efficacy, relevancy, and ease of use of a library discovery tool through a 2017 mixed-methods user experience study conducted at three City University of New York (CUNY) campuses. The results from this user study will be useful to other institutions that already have implemented discovery layers within their library and help inform website design, discovery layer design, and pedagogy. By combining student interviews with a task analysis, the authors were able to learn more about student search behaviors, how they respond to challenges, and what they expect from search tools. Identifying these barriers to student use of the Primo discovery tool has helped us articulate best practices for instruction and interface customization that may address these barriers and has illuminated implications for website integration.
Intelligent Service Recommendation for Cold-Start Problems in Edge Computing

Memory-based collaborative filtering (i.e., MCF) is regarded as an effective technique to recommend appropriate services to target users. However, if recommendation data are very sparse in the edge environment, traditional MCF-based recommendation methods probably cannot output any recommended item (or service), i.e., a cold-start recommendation problem occurs. To cope with this cold-start problem, we propose an intelligent recommendation method named Inverse_CF_Rec. Concretely, for a target user, we first search for his/her opposite users (together referred to as “enemy” hereafter); afterward, we infer the possible friends of the target user indirectly according to Social Balance Theory; finally, optimal services are recommended to the target user based on the derived possible friends of the target user. The experiments are conducted on a real-world dataset WS-DREAM to validate the effectiveness and efficiency of our proposal. The experiment results show the advantages of our recommendation method in terms of recommendation accuracy and efficiency.
A Blockchain based Credit Analysis Framework for Efficient Financial Systems

Blockchain the technology of the decade which has disrupted the complete technological sector and moreover has plotted its jurisdiction over almost all other industrial sectors with a significant coefficient. The credit industry in terms of credit cards and also the credit system of the financial supply chain management proves to be one of the competent use cases for the blockchain technology to be tendered upon. In the credit card-based system the structure terms to be quite complicated and also there are a lot of challenges in order to pass a credit fund to a particular customer and also there are huge processing and validation charges that are levied upon. Moreover, the financial organization which is responsible for dedicating a card to the user for smooth processing of the credit funds, also has to fulfil some tenacious check on the customers where the verification procedure lies into multiple segments to determine the pay term history, CIBIL Score and also the capability of the customer to pay out the credit funds. Also, in the financial supply chain management, the credit system is indeed one of the toughest systems to be managed as in Financial Supply Chain Management (FSCM), the transactions are not in lower dimensions of few hundred thousand dollars rather are in millions and billions. Also, in the credit system of the FSCM, the credit exposure or the limit of a particular client is dynamic and the credit limit or exposure depends upon a wide variance of factors such as previous pay term history, average days past due, average term revenue, and revenue insurance. Moreover, the analysis and the validation of the capability of the customer to pay a certain amount is quite a time intensive and depends upon variables from numerous sectors. Therefore, a blockchain based environment may allow to assess the eligibility of a customer over the order limit or credit limit that have been demanded. Hence a blockchain based credit analysis framework can be very useful for gathering information about the customers from multiple financial sectors, analysis of the eligibility of the customer to stand the credit and to calculate the credit score of the customers based on the consensus of multiple stakeholders in the network including banks, insurance agencies and other third-party financial organizations
Strategic Responsibility Under Imperfect Information

A central issue in the specification and verification of autonomous agents and multiagent systems is the ascription of responsibility to individual agents and groups of agents. When designing a (multi)agent system, we must specify which agents or groups of agents are responsible for bringing about a particular state of affairs. Similarly, when verifying a multiagent system, we may wish to determine the responsibility of agents or groups of agents for a particular state of affairs, and the contribution of each agent to bringing about that state of affairs. In this paper, we discuss several aspects of responsibility, including strategic ability of agents, their epistemic properties, and their relationship to the evolution of the system behavior. We introduce a formal framework for reasoning about the responsibility of individual agents and agent groups in terms of the agents' strategies and epistemic properties, and state some properties of the framework.
Author Correction: Automated Gleason grading of prostate cancer tissue microarrays via deep learning

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.
Distributed Environmental Modeling and Adaptive Sampling for Multi-Robot Sensor Coverage

We consider the problem of online distributed environmental modeling and adaptive sampling for multi-robot sensor coverage, where a team of robots spread out over the workspace in order to optimize the sensing performance over environmental phenomena, whose distribution is often referred to as a density function. Unlike most existing works that either assume certain knowledge of the density function beforehand or centrally learn the density function assuming global knowledge of collected data from all the robots, we propose a fully distributed adaptive sampling approach to allow robots to efficiently learn the unknown density function online. In particular, we developed adaptive coverage controllers based on the learned density functions for minimizing the sensing cost. To capture significantly different components of the environmental phenomenon with only locally collected data for each robot when global knowledge is not available, we propose a distributed mixture of Gaussian Processes algorithm that enables robots to collaboratively learn the global density function by exchanging only model-related parameters. We empirically demonstrate the effectiveness of our algorithm via evaluation on real-world data gathered from agricultural field robot and indoor static sensors.
BitNet: Learning-Based Bit-Depth Expansion

Bit-depth is the number of bits for each color channel of a pixel in an image. Although many modern displays support unprecedented higher bit-depth to show more realistic and natural colors with a high dynamic range, most media sources are still in bit-depth of 8 or lower. Since insufficient bit-depth may generate annoying false contours or lose detailed visual appearance, bit-depth expansion (BDE) from low bit-depth (LBD) images to high bit-depth (HBD) images becomes more and more important. In this paper, we adopt a learning-based approach for BDE and propose a novel CNN-based bit-depth expansion network (BitNet) that can effectively remove false contours and restore visual details at the same time. We have carefully designed our BitNet based on an encoder-decoder architecture with dilated convolutions and a novel multi-scale feature integration. We have performed various experiments with four different datasets including MIT-Adobe FiveK, Kodak, ESPL v2, and TESTIMAGES, and our proposed BitNet has achieved state-of-the-art performance in terms of PSNR and SSIM among other existing BDE methods and famous CNN-based image processing networks. Unlike previous methods that separately process each color channel, we treat all RGB channels at once and have greatly improved color restoration. In addition, our network has shown the fastest computational speed in near real-time.
A General Framework to Identify Software Components from Execution Data

Restructuring an object-oriented software system into a component-based one allows for a better understanding of the software system and facilitates its future maintenance. A component-based architecture structures a software system in terms of components and interactions where each component refers to a set of classes. In reverse engineering, identifying components is crucial and challenging for recovering the component-based architecture. In this paper, we propose a general framework to facilitate the identification of components from software execution data. This framework is instantiated for various community detection algorithms, e.g., the Newman's spectral algorithm, Louvain algorithm, and smart local moving algorithm. The proposed framework has been implemented in the open source (Pro)cess (M)ining toolkit ProM. Using a set of software execution data containing around 1.000.000 method calls generated from four real-life software systems, we evaluated the quality of components identified by different community detection algorithms. The empirical evaluation results demonstrate that our approach can identify components with high quality, and the identified components can be further used to facilitate future software architecture recovery tasks.
Facial Image Inpainting With Deep Generative Model and Patch Search Using Region Weight

Facial image inpainting is a challenging task because the missing region needs to be filled by the new pixels with semantic information (e.g., noses and mouths). The traditional methods that involve searching for similar patches are mature but it is not suitable for semantic inpainting. Recently, the deep generative model-based methods have been able to implement semantic image inpainting although inpainting results are blurry or distorted. In this paper, through analyzing the advantages and disadvantages of the two methods, we propose a novel and efficient method that combines these two methods by a series connection, which searches for the most reasonable similar patch using the coarse image generated by the deep generative model. When training model, adding Laplace loss to standard loss accelerates model convergence. In addition, we define region weight (RW) when searching for similar patches, which makes edge connection more natural. Our method addresses the problem of blurred results in the deep generative model and dissatisfactory semantic information in the traditional methods. Our experiments, which used the CelebA dataset, demonstrate that our method can achieve realistic and natural facial inpainting results.
Attention over Heads: A Multi-Hop Attention for Neural Machine Translation

In this paper, we propose a multi-hop attention for the Transformer.It refines the attention for an output symbol by integrating that of each head, and consists of two hops.The first hop attention is the scaled dot-product attention which is the same attention mechanism used in the original Transformer.The second hop attention is a combination of multi-layer perceptron (MLP) attention and head gate, which efficiently increases the complexity of the model by adding dependencies between heads.We demonstrate that the translation accuracy of the proposed multi-hop attention outperforms the baseline Transformer significantly, +0.85 BLEU point for the IWSLT-2017 German-to-English task and +2.58 BLEU point for the WMT-2017 German-to-English task.We also find that the number of parameters required for a multi-hop attention is smaller than that for stacking another self-attention layer and the proposed model converges significantly faster than the original Transformer.
Evaluating ESOP Optimization Methods in Quantum Compilation Flows

Exclusive-or sum-of-products (ESOP) expressions are used as intermediate representations in quantum circuit synthesis flows, and their complexity impacts the number of gates of the resulting circuits. Many state-of-the-art techniques focus on minimizing the number of product terms in a ESOP expression, either exactly or in a heuristic fashion. In this paper, we investigate into ESOP optimization considering two recent quantum compilation flows with opposite requirements. The first flow generates Boolean functions with a small number of Boolean variables, which enables the usage of methods from exact synthesis; the second flow generates Boolean functions with many Boolean variables, such that heuristics are more effective. We focus on the reduction of the number of T gates, which are expensive in fault-tolerant quantum computing and integrate ESOP optimization methods into both flows. We show an average reductions of 36.32% in T-count for the first flow, while in the second flow an average reduction of 28.23% is achieved.