Reviewerly/exHarmony huggingface dataset:
A framework to make charity collection transparent and auditable using blockchain technology

Charity is considered as a moral obligation throughout the world, and a huge amount of money comes into circulation in the name of charity. In most of the cases, the charity collection processes are not transparent, and due to this the charitable organizations struggle to gain donors’ trust and interest. This article presents a blockchain-based charity management platform that aims to provide a transparent, secure, auditable, and efficient system. The proposed platform comprehensively covers charity collection process using crypto wallets, Initial Coin Offering (ICO), economic model, and introduces CharityCoin (CC) as a digital currency. Furthermore, smart-contracts for pertinent use cases have also been provided, which include exchanging fiat currency to CC, buying and selling CC, transferring CC to organizations and individuals, and call for donations. Finally, the performance evaluation shows that proposed architecture scales well for large data size.
A Rotational Libra R-CNN Method for Ship Detection

Recently, ship detection methods based on deep learning have attracted significant attention due to their superior accuracy over traditional methods. However, there still exist two problems affecting its robustness in practical application. 1) The size of ships in one image varies greatly, i.e., different sizes; 2) Numerous ships gather in limited field-of-view, i.e., dense distribution. To address these problems, we propose a rotational Libra R-convolutional neural network (CNN) method. Our idea is to balance the three levels of neural networks for predicting the location of ships with rotational angle information, which refers to the feature level, sample level, and objective level. First, to extract a discriminative feature and improve its robustness against the impact of different sizes of ships, the concept of balanced feature pyramid is introduced. Second, to generate reliable proposals for feature pyramid and efficiently mine hard negative samples, we employ intersection over union (IoU)-balanced sampling. Finally, to eliminate the redundant background and detect densely distributed ships, we bring in a rotational region detection branch with balanced L1 loss. In general, we develop the balanced learning with rotational region detection to achieve consistent improvement on accuracy and visualization. Experimental results on DOTA data set show that the proposed method achieves the state-of-the-art accuracy.
Designing Anthropomorphic Enterprise Conversational Agents

Abstract The increasing capabilities of conversational agents (CAs) offer manifold opportunities to assist users in a variety of tasks. In an organizational context, particularly their potential to simulate a human-like interaction via natural language currently attracts attention both at the customer interface as well as for internal purposes, often in the form of chatbots. Emerging experimental studies on CAs look into the impact of anthropomorphic design elements, so-called social cues, on user perception. However, while these studies provide valuable prescriptive knowledge of selected social cues, they neglect the potential detrimental influence of the limited responsiveness of present-day conversational agents. In practice, many CAs fail to continuously provide meaningful responses in a conversation due to the open nature of natural language interaction, which negatively influences user perception and often led to CAs being discontinued in the past. Thus, designing a CA that provides a human-like interaction experience while minimizing the risks associated with limited conversational capabilities represents a substantial design problem. This study addresses the aforementioned problem by proposing and evaluating a design for a CA that offers a human-like interaction experience while mitigating negative effects due to limited responsiveness. Through the presentation of the artifact and the synthesis of prescriptive knowledge in the form of a nascent design theory for anthropomorphic enterprise CAs, this research adds to the growing knowledge base for designing human-like assistants and supports practitioners seeking to introduce them into their organizations.
Privacy-Preserving Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids Using Functional Encryption

Advanced smart grid technologies enable energy prosumers to trade surplus energy from their distributed renewable energy sources with other peer prosumers through peer-to-peer (P2P) energy trading. In many previous works, P2P energy trading was facilitated by blockchain technology through blockchain’s distributive nature and capacity to run smart contracts. However, the feature that all the data and transactions on a blockchain are visible to all blockchain nodes may significantly threaten the privacy of the parties participating in P2P energy trading. There are many previous works that have attempted to mitigate this problem. However, all these works focused on the anonymity of participants but did not protect the data and transactions. To address this issue, we propose a P2P energy trading system on a blockchain where all bids are encrypted and peer matching is performed on the encrypted bids by a functional encryption-based smart contract. The system guarantees that the information encoded in the encrypted bids is protected, but the peer matching transactions are performed by the nodes in a publicly verifiable manner through smart contracts. We verify the feasibility of the proposed system by implementing a prototype composed of smart meters, a distribution system operator (DSO) server, and private Ethereum blockchain.
Grandpa, Grandpa, Tell Me the One About Bitcoin Being a Safe Haven: New Evidence From the COVID-19 Pandemic

Bitcoin being a safe haven asset is one of the traditional stories in the cryptocurrency community. However, during its existence and relevant presence, i.e. approximately since 2013, there has been no severe situation on the financial markets globally to prove or disprove this story until the COVID-19 pandemics. We study the quantile correlations of Bitcoin and two benchmarks -- S\&P500 and VIX -- and we make comparison with gold as the traditional safe haven asset. The Bitcoin safe haven story is shown and discussed to be unsubstantiated and far-fetched, while gold comes out as a clear winner in this contest.
A multiple k-means clustering ensemble algorithm to find nonlinearly separable clusters

Cluster ensemble is an important research content of ensemble learning, which is used to aggregate several base clusterings to generate a single output clustering with improved robustness and quality. Since clustering is unsupervised, where the “accuracy” does not have a clear meaning, most of existing ensemble methods try to obtain the most consistent clustering result with base clusterings. However, it is difficult for these methods to realize “Multi-weaks equal to a Strong”. For example, on a data set with nonlinearly separable clusters, if the base clusterings are produced by some linear clusterers, these methods generally cannot integrate them to obtain a good nonlinear clustering. In this paper, we select k-means as a base clusterer and provide an ensemble clusterer (algorithm) of multiple k-means clusterings based on a local hypothesis. In the new algorithm, we study the extraction of the local-credible labels from a base clustering, the production of different base clusterings, the construction of cluster relation and the final assignment of each object. The proposed ensemble clusterer not only inherits the scalability of k-means but also overcomes its limitation that it only can find linearly separable clusters. Finally, the experimental results illustrate its effectiveness and efficiency.
Evaluation of low-cost depth cameras for agricultural applications

Low-cost depth-cameras have been used in many agricultural applications with reported advantages of low cost, reliability and speed of measurement. However, some problems were also reported and seem to be technology-related, so understanding the limitations of each type of depth camera technology could provide a basis for technology selection and the development of research involving its use. The cameras use one or a combination of two of the three available technologies: structured light, time-of-flight (ToF), and stereoscopy. The objectives were to evaluate these different technologies for depth sensing, including measuring accuracy and repeatability of distance data and measurements at different positions within the image, and cameras usefulness in indoor and outdoor settings. Then, cameras were tested in a swine facility and in a corn field. Five different cameras were used: (1) Microsoft Kinect v.1, (2) Microsoft Kinect v.2, (3) Intel® RealSense™ Depth Camera D435, (4) ZED Stereo Camera (StereoLabs), and (5) CamBoard Pico Flexx (PMD Technologies). Results indicate that there were significant camera to camera differences for ZED Stereo Camera and Kinect v.1 camera (p < 0.05). All cameras showed an increase in the standard deviation as the distance between camera and object increased; however, the Intel RealSense camera had a larger increase. Time-of-flight cameras had the smallest error between different sizes of objects. Time-of-flight cameras had non-readable zones on the corners of the images. The results indicate that the ToF technology is the best to be used for indoor applications and stereoscopy is the best technology for outdoor applications.
A Quantum-Like multimodal network framework for modeling interaction dynamics in multiparty conversational sentiment analysis

Sentiment analysis in conversations is an emerging yet challenging artificial intelligence (AI) task. It aims to discover the affective states and emotional changes of speakers involved in a conversation on the basis of their opinions, which are carried by different modalities of information (e.g., a video associated with a transcript). There exists a wealth of intra- and inter-utterance interaction information that affects the emotions of speakers in a complex and dynamic way. How to accurately and comprehensively model complicated interactions is the key problem of the field. To fill this gap, in this paper, we propose a novel and comprehensive framework for multimodal sentiment analysis in conversations, called a quantum-like multimodal network (QMN), which leverages the mathematical formalism of quantum theory (QT) and a long short-term memory (LSTM) network. Specifically, the QMN framework consists of a multimodal decision fusion approach inspired by quantum interference theory to capture the interactions within each utterance (i.e., the correlations between different modalities) and a strong-weak influence model inspired by quantum measurement theory to model the interactions between adjacent utterances (i.e., how one speaker influences another). Extensive experiments are conducted on two widely used conversational sentiment datasets: the MELD and IEMOCAP datasets. The experimental results show that our approach significantly outperforms a wide range of baselines and state-of-the-art models.
Analysis of Gender Inequality In Face Recognition Accuracy

We present a comprehensive analysis of how and why face recognition accuracy differs between men and women. We show that accuracy is lower for women due to the combination of(1) the impostor distribution for women having a skew toward higher similarity scores, and (2) the genuine distribution for women having a skew toward lower similarity scores. We show that this phenomenon of the impostor and genuine distributions for women shifting closer towards each other is general across datasets of African-American, Caucasian, and Asian faces. We show that the distribution of facial expressions may differ between male/female, but that the accuracy difference persists for image subsets rated confidently as neutral expression. The accuracy difference also persists for image subsets rated as close to zero pitch angle. Even when removing images with forehead partially occluded by hair/hat, the same impostor/genuine accuracy difference persists. We show that the female genuine distribution improves when only female images without facial cosmetics are used, but that the female impostor distribution also degrades at the same time. Lastly, we show that the accuracy difference persists even if a state-of-the-art deep learning method is trained from scratch using training data explicitly balanced between male and female images and subjects.
FADB: A Fine-Grained Access Control Scheme for VANET Data Based on Blockchain

Vehicular Ad Hoc Network (VANET) is an important foundation of intelligent transportation system and is widely used in traffic management, automatic driving, and road optimization. With the gradual popularization and further development of VANET, a large amount of VANET data has been produced. However, it poses huge challenges to the security and privacy when using VANET data provides services for users. In this paper, combining the technologies of blockchain and ciphertext-based attribute encryption (CP-ABE), we propose a fine-grained access control scheme for VANET data based on blockchain (FADB). In FADB, we employ the blockchain to replace the third-party service providers for user identity management and data storage. And different VANET data access rights can be established according to user attribute. By improving the CP-ABE, the lightweight VANET devices can outsource complex encryption and decryption operations to powerful RSUs and further improve the efficiency of data access. Final, we carry out a series of simulation tests and security analysis, proving that the FADB can provide effective data security and low performance overhead.
NI-MWMOTE: An improving noise-immunity majority weighted minority oversampling technique for imbalanced classification problems

Oversampling techniques have been favored by researchers because of their simplicity and versatility in dealing with imbalanced classification problems. For oversampling techniques appeared in recent years (e.g. Majority Weighted Minority Oversampling Technique (MWMOTE)), noise processing plays an important role. This is because the processing of noise directly affects the distribution of new synthetic instances. MWMOTE and many other oversampling techniques use knn based noise processing method. While the knn method can effectively handle partial noise when the neighborhood parameter k value is reasonable, it may lead to under-recognition or over-recognition without prior experience. Therefore, we propose an improving noise-immunity majority weighted minority oversampling technique abbreviated NI-MWMOTE. NI-MWMOTE uses an adaptive noise processing scheme, which combines Euclidean distance and neighbor density to rank the probability that suspected noise (knn method) is true noise, and then adaptively selects the best noise processing scheme through iteration and misclassification error. Then, aggregative hierarchical clustering (AHC) method is used to cluster minority instances. And, in each sub-cluster, the sampling size of new samples is adaptively determined by classification complexity and cross-validation. NI-MWMOTE not only avoids the generation of new noise, but also effectively overcomes both between-class imbalances and within-class imbalances. Results demonstrate that NI-MWMOTE achieves significantly better results in most imbalanced datasets than eight popular oversampling algorithms.
Retracted: Gesture recognition algorithm based on multi‐scale feature fusion in RGB‐D images

With the rapid development of sensor technology and artificial intelligence, the video gesture recognition technology under the background of big data makes human–computer interaction more natural and flexible, bringing the richer interactive experience to teaching, on-board control, electronic games etc. To perform robust recognition under the conditions of illumination change, background clutter, rapid movement, and partial occlusion, an algorithm based on multi-level feature fusion of two-stream convolutional neural network is proposed, which includes three main steps. Firstly, the Kinect sensor obtains red–green–blue-depth (RGB-D) images to establish a gesture database. At the same time, data enhancement is performed on the training set and test set. Then, a model of multi-level feature fusion of a two-stream convolutional neural network is established and trained. Experiments show that the proposed network model can robustly track and recognise gestures under complex backgrounds (such as similar complexion, illumination changes, and occlusion), and compared with the single-channel model, the average detection accuracy is improved by 1.08%, and mean average precision is improved by 3.56%.
Stochastic Gradient Coding for Straggler Mitigation in Distributed Learning

We consider distributed gradient descent in the presence of stragglers. Recent work on gradient coding and approximate gradient coding have shown how to add redundancy in distributed gradient descent to guarantee convergence even if some workers are stragglers-that is, slow or non-responsive. In this work we propose an approximate gradient coding scheme called Stochastic Gradient Coding (SGC), which works when the stragglers are random. SGC distributes data points redundantly to workers according to a pair-wise balanced design, and then simply ignores the stragglers. We prove that the convergence rate of SGC mirrors that of batched Stochastic Gradient Descent (SGD) for the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> loss function, and show how the convergence rate can improve with the redundancy. We also provide bounds for more general convex loss functions. We show empirically that SGC requires a small amount of redundancy to handle a large number of stragglers and that it can outperform existing approximate gradient codes when the number of stragglers is large.
Efficient Bitruss Decomposition for Large-scale Bipartite Graphs

Cohesive subgraph mining in bipartite graphs becomes a popular research topic recently. An important structure k-bitruss is the maximal cohesive subgraph where each edge is contained in at least k butterflies (i.e., (2,2)-bicliques). In this paper, we study the bitruss decomposition problem which aims to find all the k-bitrusses for k ≥ 0. The existing bottom-up techniques need to iteratively peel the edges with the lowest butterfly support. In this peeling process, these techniques are time-consuming to enumerate all the supporting butterflies for each edge. To relax this issue, we first propose a novel online index - the BE-Index which compresses butterflies into k-blooms (i.e., (2,k)-bicliques). Based on the BE-Index, the new bitruss decomposition algorithm BiT-BU is proposed, along with two batch-based optimizations, to accomplish the butterfly enumeration of the peeling process in an efficient way. Furthermore, the BiT-PC algorithm is devised which is more efficient against handling the edges with high butterfly supports. We theoretically show that our new algorithms significantly reduce the time complexities of the existing algorithms. Also, we conduct extensive experiments on real datasets and the results demonstrate that our new techniques can speed up the state-of-the-art techniques by up to two orders of magnitude.
A novel energy-aware resource management technique using joint VM and container consolidation approach for green computing in cloud data centers

Cloud computing is being rapidly adopted for managing IT services as a notable solution due to diverse beneficiaries such as automatically optimized resource management as well as modern service delivery models. The container as a service has been recently introduced by cloud providers as a new service apart from traditional cloud services. Containers enable applications to run and deploy on isolated virtual space, and the operating system kernel is shared among them. Also, containerization has some attributes such as scalability, highly portable properties, and lightweight, for those reasons, it is applied for running isolated applications. Reducing energy consumption, as well as their CO2 emissions, are great deals for cloud providers. In this direction, consolidation is recommended as a vital energy-aware approach in cloud data centers. Previously, independent virtual machine migration or container migration was proposed in the literature for green computing in cloud data centers. However, this paper proposes a new cloud resource management procedure based on a multi-criteria decision-making method that takes advantage of a joint virtual machine and container migration approach concurrently. The results of simulations using ContainerCloudsim simulator validates the applicability of the proposed approach which shows notable reductions in energy consumption, SLA violation, and number of migrations in comparison with the state-of-the-art algorithms.
Neural CRF Model for Sentence Alignment in Text Simplification

The success of a text simplification system heavily depends on the quality and quantity of complex-simple sentence pairs in the training corpus, which are extracted by aligning sentences between parallel articles.To evaluate and improve sentence alignment quality, we create two manually annotated sentence-aligned datasets from two commonly used text simplification corpora, Newsela and Wikipedia.We propose a novel neural CRF alignment model which not only leverages the sequential nature of sentences in parallel documents but also utilizes a neural sentence pair model to capture semantic similarity.Experiments demonstrate that our proposed approach outperforms all the previous work on monolingual sentence alignment task by more than 5 points in F1.We apply our CRF aligner to construct two new text simplification datasets, NEWSELA-AUTO and WIKI-AUTO, which are much larger and of better quality compared to the existing datasets.A Transformer-based seq2seq model trained on our datasets establishes a new state-of-the-art for text simplification in both automatic and human evaluation. 1
Channel Attention Based Iterative Residual Learning for Depth Map Super-Resolution

Despite the remarkable progresses made in deep learning based depth map super-resolution (DSR), how to tackle real-world degradation in low-resolution (LR) depth maps remains a major challenge. Existing DSR model is generally trained and tested on synthetic dataset, which is very different from what would get from a real depth sensor. In this paper, we argue that DSR models trained under this setting are restrictive and not effective in dealing with realworld DSR tasks. We make two contributions in tackling real-world degradation of different depth sensors. First, we propose to classify the generation of LR depth maps into two types: non-linear downsampling with noise and interval downsampling, for which DSR models are learned correspondingly. Second, we propose a new framework for real-world DSR, which consists of four modules : 1) An iterative residual learning module with deep supervision to learn effective high-frequency components of depth maps in a coarse-to-fine manner; 2) A channel attention strategy to enhance channels with abundant high-frequency components; 3) A multi-stage fusion module to effectively reexploit the results in the coarse-to-fine process; and 4) A depth refinement module to improve the depth map by TGV regularization and input loss. Extensive experiments on benchmarking datasets demonstrate the superiority of our method over current state-of-the-art DSR methods.