Reviewerly/exHarmony huggingface dataset:
Projecting impacts of global climate and land‐use scenarios on plant biodiversity using compositional‐turnover modelling

Nations have committed to ambitious conservation targets in response to accelerating rates of global biodiversity loss. Anticipating future impacts is essential to inform policy decisions for achieving these targets, but predictions need to be of sufficiently high spatial resolution to forecast the local effects of global change. As part of the intercomparison of biodiversity and ecosystem services models of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, we present a fine-resolution assessment of trends in the persistence of global plant biodiversity. We coupled generalized dissimilarity models, fitted to >52 million records of >254 thousand plant species, with the species-area relationship, to estimate the effect of land-use and climate change on global biodiversity persistence. We estimated that the number of plant species committed to extinction over the long term has increased by 60% globally between 1900 and 2015 (from ~10,000 to ~16,000). This number is projected to decrease slightly by 2050 under the most optimistic scenario of land-use change and to substantially increase (to ~18,000) under the most pessimistic scenario. This means that, in the absence of climate change, scenarios of sustainable socio-economic development can potentially bring extinction risk back to pre-2000 levels. Alarmingly, under all scenarios, the additional impact from climate change might largely surpass that of land-use change. In this case, the estimated number of species committed to extinction increases by 3.7-4.5 times compared to land-use-only projections. African regions (especially central and southern) are expected to suffer some of the highest impacts into the future, while biodiversity decline in Southeast Asia (which has previously been among the highest globally) is projected to slow down. Our results suggest that environmentally sustainable land-use planning alone might not be sufficient to prevent potentially dramatic biodiversity loss, unless a stabilization of climate to pre-industrial times is observed.
Effective reversible data hiding in encrypted image with adaptive encoding strategy

Recently, reversible data hiding in encrypted image have attracted extensive attentions, which can be applied in secure cloud computing and privacy-preserving image processing. In this paper, a reversible data hiding scheme in encrypted image based on the adaptive encoding strategy is proposed. On the content-owner side, block permutation and stream cipher encryption are applied to mask the contents of original image. Through analyzing the distribution of MSB layers, embeddable blocks are first determined and auxiliary data are then generated by data hider. In order to vacate room for data accommodation, MSB layers of embeddable blocks are adaptively compressed according to occurrence frequency of MSB. Thus, additional data can be embedded into MSB layers of encrypted image together with reversed Huffman codewords and auxiliary data. Based on the availability of encryption key and data-hiding key, the receiver can realize separable operations of data extraction, image decryption and image recovery efficiently. Experimental results demonstrate that, our scheme not only can achieve satisfactory rate-distortion performance, but also can obtain greater embedding rate compared with the state-of-the-art schemes.
A Secure Healthcare System Design Framework using Blockchain Technology

Blockchain, the technology of the future neutrally facilitated the financial transactions in cryptocurrencies by strictly eliminating the need for a governing authority or a management that was required to authorize the transactions based on trust and transparency. The Blockchain Network also follows the principle of absolute privacy and anonymity on the identification of the users associated in a transaction. Since the time of its inception, the Blockchain Technology has undergone research that has demonstrated some various kinds of methods to sort out the access control system of the conventional system. In recent years Blockchain has also shown optimum reliability in multiple sectors such as Smart Home, Healthcare, Banking, Information Storage Management, Security and etc. This work in terms is further concerned to the sector of Smart Healthcare, which has grown to a much affluence regarding the efficient technique of serving and dictating medical health care to the patients with the point of maintaining privacy of the patients' data and also the process of laying out real time accurate and trusted data to the medical practitioners. But in the scenario of Smart Healthcare, the primary concern arises in the fact of Privacy and Security of the data of the patients due to the interoperability of multiple stakeholders in the process. Also, there has been a fact of determining accurate and proper data to the doctors if the concerned subject is out of reach from the in hand medical service. Therefore, this Concern of privacy and also mitigation of the accurate data has been very much managed in the work by regulating, a monitoring and sensing paradigm with accordance to the IOT and the Blockchain as a transaction and access management system and also an appropriate medium for laying out accurate and trusted data for serving with deliberate medical care and benefits to the patients across.
A Compact Bat Algorithm for Unequal Clustering in Wireless Sensor Networks

Everyday, a large number of complex scientific and industrial problems involve finding an optimal solution in a large solution space. A challenging task for several optimizations is not only the combinatorial operation but also the constraints of available devices. This paper proposes a novel optimization algorithm, namely the compact bat algorithm (cBA), to use for the class of optimization problems involving devices which have limited hardware resources. A real-valued prototype vector is used for the probabilistic operations to generate each candidate for the solution of the optimization of the cBA. The proposed cBA is extensively evaluated on several continuous multimodal functions as well as the unequal clustering of wireless sensor network (uWSN) problems. Experimental results demonstrate that the proposed algorithm achieves an effective way to use limited memory devices and provides competitive results.
Blockchain-Based Trust Mechanism for IoT-Based Smart Manufacturing System

Integrated and collaborative manufacturing system develops as massive data are obtained by the Internet of Things (IoT) technology. However, the “trust tax” imposed on manufacturers during their countless collaborations with customers, suppliers, distributors, governments, service providers, and other manufacturers is very high. Blockchain is an emerging technology that can lead to more transparent, secure, and efficient transactions. It represents a new paradigm, as well as new thinking, of how data can be securely stored, integrated, and communicated among different stakeholders, organizations, and systems that unnecessarily trust each other. Blockchain is greatly useful for reducing the “trust tax,” especially beneficial for the small- and medium-sized enterprises that must tolerate much heavier trust tax than the established manufacturers. This paper investigates the blockchain-based security and trust mechanism and elaborates a particular application of blockchain for quality assurance, which is one of the strategic priorities of smart manufacturing. Data generated in a smart manufacturing process can be leveraged to retrieve material provenance, facilitate equipment management, increase transaction efficiency, and create a flexible pricing mechanism. The dairy industry is used to instantiate the value propositions of blockchain for quality assurance.
Light Field Messaging With Deep Photographic Steganography

We develop Light Field Messaging (LFM), a process of embedding, transmitting, and receiving hidden information in video that is displayed on a screen and captured by a handheld camera. The goal of the system is to minimize perceived visual artifacts of the message embedding, while simultaneously maximizing the accuracy of message recovery on the camera side. LFM requires photographic steganography for embedding messages that can be displayed and camera-captured. Unlike digital steganography, the embedding requirements are significantly more challenging due to the combined effect of the screen's radiometric emittance function, the camera's sensitivity function, and the camera-display relative geometry. We devise and train a network to jointly learn a deep embedding and recovery algorithm that requires no multi-frame synchronization. A key novel component is the camera display transfer function (CDTF) to model the camera-display pipeline. To learn this CDTF we introduce a dataset (Camera-Display 1M) of 1,000,000 camera-captured images collected from 25 camera-display pairs. The result of this work is a high-performance real-time LFM system using consumer-grade displays and smartphone cameras.
Using Facebook for Qualitative Research: A Brief Primer

As Facebook continues to grow its number of active users, the potential to harness data generated by Facebook users also grows. As much of Facebook users' activity consists of creating (and commenting on) written posts, the potential use of text data for research is enormous. However, conducting a content analysis of text from Facebook users requires adaptation of research methods used for more traditional sources of qualitative data. Furthermore, best practice guidelines to assist researchers interested in conducting qualitative studies using data derived from Facebook are lacking. The purpose of this primer was to identify opportunities, as well as potential pitfalls, of conducting qualitative research with Facebook users and their activity on Facebook and provide potential options to address each of these issues. We begin with an overview of information obtained from a literature review of 23 studies published between 2011 and 2018 and our own research experience to summarize current approaches to conducting qualitative health research using data obtained from Facebook users. We then identify potential strategies to address limitations related to current approaches and propose 5 key considerations for the collection, organization, and analysis of text data from Facebook. Finally, we consider ethical issues around the use and protection of Facebook data obtained from research participants. In this primer, we have identified several key considerations that should aid health researchers in the planning and execution of qualitative studies involving content analysis of text data from Facebook users.
PhaseCam3D — Learning Phase Masks for Passive Single View Depth Estimation

There is an increasing need for passive 3D scanning in many applications that have stringent energy constraints. In this paper, we present an approach for single frame, single viewpoint, passive 3D imaging using a phase mask at the aperture plane of a camera. Our approach relies on an end-to-end optimization framework to jointly learn the optimal phase mask and the reconstruction algorithm that allows an accurate estimation of range image from captured data. Using our optimization framework, we design a new phase mask that performs significantly better than existing approaches. We build a prototype by inserting a phase mask fabricated using photolithography into the aperture plane of a conventional camera and show compelling performance in 3D imaging.
“Short-Dot”: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products

We consider the problem of computing a matrix-vector product Ax using a set of P parallel or distributed processing nodes prone to “straggling,” i.e., unpredictable delays. Every processing node can access only a fraction (s/N) of the N-length vector x, and all processing nodes compute an equal number of dot products. We propose a novel error correcting code-that we call “Short-Dot”-that introduces redundant, shorter dot products such that only a subset of the nodes' outputs are sufficient to compute Ax. To address the problem of straggling in computing matrix-vector products, prior work uses replication or erasure coding to encode parts of the matrix A, but the length of the dot products computed at each processing node is still N. The key novelty in our work is that instead of computing the long dot products as required in the original matrix-vector product, we construct a larger number of redundant and short dot products that only require a fraction of x to be accessed during the computation. Short-Dot is thus useful in a communication-constrained scenario as it allows for only a fraction of x to be accessed by each processing node. Further, we show that in the particular regime where the number of available processing nodes is greater than the total number of dot products, Short-Dot has lower expected computation time under straggling under an exponential model compared to existing strategies, e.g. replication, in a scaling sense. We also derive fundamental limits on the trade-off between the length of the dot products and the recovery threshold, i.e., the required number of processing nodes, showing that Short-Dot is near-optimal.
Advanced Simulation of Quantum Computations

Quantum computation is a promising emerging technology which, compared to conventional computation, allows for substantial speed-ups, e.g., for integer factorization or database search. However, since physical realizations of quantum computers are in their infancy, a significant amount of research in this domain still relies on simulations of quantum computations on conventional machines. This causes a significant complexity which current state-of-the-art simulators try to tackle with a rather straight forward array-based representation and by applying massive hardware power. There also exist solutions based on decision diagrams (i.e., graph-based approaches) that try to tackle the exponential complexity by exploiting redundancies in quantum states and operations. However, these existing approaches do not fully exploit redundancies that are actually present. In this paper, we revisit the basics of quantum computation, investigate how corresponding quantum states and quantum operations can be represented even more compactly, and, eventually, simulated in a more efficient fashion. This leads to a new graph-based simulation approach which outperforms state-of-the-art simulators (array-based as well as graph-based). Experimental evaluations show that the proposed solution is capable of simulating quantum computations for more qubits than before, and in significantly less run-time (several magnitudes faster compared to previously proposed simulators). An implementation of the proposed simulator is publicly available online at http://iic.jku.at/eda/research/quantum_simulation.
Addressing the Item Cold-Start Problem by Attribute-Driven Active Learning

In recommender systems, cold-start issues are situations where no previous events, e.g., ratings, are known for certain users or items. In this paper, we focus on the item cold-start problem. Both content information (e.g., item attributes) and initial user ratings are valuable for seizing users' preferences on a new item. However, previous methods for the item cold-start problem either (1) incorporate content information into collaborative filtering to perform hybrid recommendation, or (2) actively select users to rate the new item without considering content information and then do collaborative filtering. In this paper, we propose a novel recommendation scheme for the item cold-start problem by leveraging both active learning and items' attribute information. Specifically, we design useful user selection criteria based on items' attributes and users' rating history, and combine the criteria in an optimization framework for selecting users. By exploiting the feedback ratings, users' previous ratings and items' attributes, we then generate accurate rating predictions for the other unselected users. Experimental results on two real-world datasets show the superiority of our proposed method over traditional methods.
Learning short-text semantic similarity with word embeddings and external knowledge sources

We present a novel method based on interdependent representations of short texts for determining their degree of semantic similarity. The method represents each short text as two dense vectors: the former is built using the word-to-word similarity based on pre-trained word vectors, the latter is built using the word-to-word similarity based on external sources of knowledge. We also developed a preprocessing algorithm that chains coreferential named entities together and performs word segmentation to preserve the meaning of phrasal verbs and idioms. We evaluated the proposed method on three popular datasets, namely Microsoft Research Paraphrase Corpus, STS2015 and P4PIN, and obtained state-of-the-art results on all three without using prior knowledge of natural language, e.g., part-of-speech tags or parse tree, which indicates the interdependent representations of short text pairs are effective and efficient for semantic textual similarity tasks.
An adaptive inertia weight teaching-learning-based optimization algorithm and its applications

This paper presents an effective metaheuristic algorithm called teaching learning-based optimization which is widely applied to solve the various real-world optimization problems. However, teaching learning-based optimization is rapidly trapped into local optima. To handle this kind of problem, we proposed an improved teaching learning-based optimization algorithm using adaptive exponential distribution inertia weight and altering the position-updating equation. In addition, the logistic map is applied to generate a uniformly distributed population to enhance the quality of the initial populations. The performance of the proposed method is evaluated on a suite of benchmark functions with different characteristics. The efficiency of the proposed technique is also evaluated on six gene expression datasets with the help of three classifiers. The experimental result demonstrates that the proposed method is comparatively useful in adapting the inertia weight in comparison to the existing inertia weight strategies with regards to the quality of solutions, convergence rate along with classification accuracy. In particular gene selection, the proposed method has achieved up to 98% classification accuracy for three out of six datasets with optimal gene subsets for all six datasets and maximum accuracy is achieved as 100% in small round blue-cell tumor dataset.
Multiple People Tracking Using Body and Joint Detections

Most multiple people tracking systems compute trajectories based on the tracking-by-detection paradigm. Consequently, the performance depends to a large extent on the quality of the employed input detections. However, despite an enormous progress in recent years, partially occluded people are still often not recognized. Also, many correct detections are mistakenly discarded when the non-maximum suppression is performed. Improving the tracking performance thus requires to augment the coarse input. Well-suited for this task are fine-graded body joint detections, as they allow to locate even strongly occluded persons. Thus in this work, we analyze the suitability of including joint detections for multiple people tracking. We introduce different affinities between the two detection types and evaluate their performances. Tracking is then performed within a near-online framework based on a min cost graph labeling formulation. As a result, our framework can recover heavily occluded persons and solve the data association efficiently. We evaluate our framework on the MOT16/17 benchmark. Experimental results demonstrate that our framework achieves state-of-the-art results.
Dynamic opposite learning enhanced teaching–learning-based optimization

The teaching–learning-based optimization (TLBO) algorithm has been one of most popular bio-inspired meta-heuristic algorithms due to the competitive converging speed and high accuracy. A batch of TLBO variants has been proposed to enhance the exploitation ability and accelerate the exploration process. However, they still suffer from premature convergence in solving complex non-linear problems. In the study, a novel TLBO variant named dynamic-opposite learning TLBO (DOLTLBO) is proposed, which employs a new dynamic-opposite learning (DOL) strategy to overcome premature convergence. The search space of DOL has the characteristics of asymmetry and dynamically adjusting along with a random opposite number. The asymmetric search space significantly increase the probability for the population in obtaining the global optimum, which holistically improves the exploitation capability of DOLTLBO. Meanwhile, the dynamically changing characteristic enriches the diversity of the search space, thus enhancing the exploration ability. To validate the proposed DOL operator and DOLTLBO algorithm, shifted and rotated benchmark functions from CEC 2014, multiextremal functions and constrained engineering problems have been experimented upon. Comprehensive numerical results with the comparisons with the state-of-the-art counterparts show that DOLTLBO has significant advantages of converging to the global optimum on most benchmarks and engineering problems, which also validates the superiority of the novel DOL operator.
DeepPAR and DeepDPA: Privacy Preserving and Asynchronous Deep Learning for Industrial IoT

Industrial Internet of Things (IIoT) is significant of building powerful industrial systems and applications. Deep learning has provided a promising opportunity to extract useful knowledge by utilizing vast amounts of data in IIoT. However, lacking of massive public datasets will lead to low performance and overfitting of the learned model. Therefore, the federated deep learning over distributed datasets has been proposed. Whereas, it inevitably introduces some new security challenges, i.e., disclosing participant's data privacy. However, existing methods cannot guarantee each participant's data privacy in a learning group. In this article, we propose two privacy-preserving asynchronous deep learning schemes [privacy-preserving and asynchronous deep learning via re-encryption (DeepPAR) and dynamic privacy-preserving and asynchronous deep learning (DeepDPA)]. Compared to the state-of-the-art work, DeepPAR protects each participant's input privacy while preserving dynamic update secrecy inherently. Meanwhile, DeepDPA enables to guarantee backward secrecy of group participants in a lightweight manner. Security analysis and performance evaluations on real dataset show that our proposed schemes are secure, efficient and effective.