Reviewerly/exHarmony huggingface dataset:
Dynamic Multiworkflow Deadline and Budget Constrained Scheduling in Heterogeneous Distributed Systems

Dynamic scheduling of multiworkflow in a heterogeneous distributed system has become a crucial issue in recent days due to user-defined deadlines and budget constraints. In order to settle down this problem, we present a dynamic multiworkflow deadline and budget constrained heterogeneous scheduling (DMW-HDBS) algorithm. DMW-HDBS owns some unique features: in the task selection phase, an optimized priority selection strategy is applied to determine the priority scheduling tasks; in the stage of resource allocation, the cost and budget ratio is utilized to better balance therelationship between deadline and budget constraint; and the discard judgment mechanism is used to improve the success rate of multiworkflow scheduling. Real-world application experiments are conducted to verify that our algorithm has better scheduling performance than other competing algorithms.
“I'm in his belly!”: Children's Responses to Different Types of Characters in Virtual Reality

Immersive technology, such as virtual reality (VR), has become more integrated in children's lives transforming how they experience education, medical treatment, and entertainment. In VR, children are likely to engage with interactive and socially real characters. To examine children's experience of virtual characters in VR, we studied 5-to 9-year old's (N= 25) spontaneous reactions towards three virtual character types (human, anthropomorphized fictional Muppet, animal). Results showed children engaged in four major behavioral interactions: they tried to touch the characters, embodied the characters, talked directly to the characters, and referred to themselves in regard to the virtual environment. These results suggest that children test concepts of realism through touch and verbalizations and physically examine social boundaries. Additionally, children consider self-representation while in a virtual environment. We discuss the implications of these results for future work and provide design considerations when creating VR content with and for children.
Named Entity Recognition in Chinese Judicial Domain Based on Self-attention mechanism and IDCNN

Chinese named entity recognition (CNER) in the judicial domain is an important basic task for intelligent analysis and processing of massive documents. This domain entity has more complicated structure than the common named entity, and its entity category is more abundant. However, the general method can not solve the problem of domain specific identification. In this paper, we combine self-attention mechanism and iteration dilated convolution neural network (IDCNN) for CNER in judicial domain. The bidirectional gate recurrent unit (BiGRU) model is used to automatically learn the context semantic information of the text and solve the long-distance dependence of the sequence. The model introduce the IDCNN to extract the key features of context semantic information, and capture finer-grained semantic information in underlying texts. The self-attention mechanism is used to analyze the relationship between characters, and the problem of long sequence semantic dilution is effectively solved by means of dynamic weight, and the optimal tag sequence is calculated by integrating conditional random fields (CRF), which further improves the recognition ability of the model. Finally, by analyzing the characteristics of legal documents, the new data set is annotated and the fine-grained named entity recognition is realized. The experimental results on our corpus show that the proposed method can effectively identify the entities in legal documents, and improve performance in the judicial field.
Prior Based Human Completion

We study a very challenging task, human image completion, which tries to recover the human body part with a reasonable human shape from the corrupted region. Since each human body part is unique, it is infeasible to restore the missing part by borrowing textures from other visible regions. Thus, we propose two types of learned priors to compensate for the damaged region. One is a structure prior, it uses a human parsing map to represent the human body structure. The other is a structure-texture correlation prior. It learns a structure and a texture memory bank, which encodes the common body structures and texture patterns, respectively. With the aid of these memory banks, the model could utilize the visible pattern to query and fetch a similar structure and texture pattern to introduce additional reasonable structures and textures for the corrupted region. Besides, since multiple potential human shapes are underlying the corrupted region, we propose multi-scale structure discriminators to further restore a plausible topological structure. Experiments on various large-scale benchmarks demonstrate the effectiveness of our proposed method.
Attribute-Based Keyword Search over the Encrypted Blockchain

To address privacy concerns, data in the blockchain should be encrypted in advance to avoid data access from all users in the blockchain. However, encrypted data cannot be directly retrieved, which hinders data sharing in the blockchain. Several works have been proposed to deal with this problem. However, the data retrieval in these schemes requires the participation of data owners and lacks finer-grained access control. In this paper, we propose an attribute-based keyword search scheme over the encrypted blockchain, which allows users to search encrypted files over the blockchain based on their attributes. In addition, we build a file chain structure to improve the efficiency of searching files with the same keyword. Security analysis proves the security of the proposed scheme. Theoretical analysis and experimental results in performance evaluation show that our scheme is feasible and efficient.
Highlight-Transformer: Leveraging Key Phrase Aware Attention to Improve Abstractive Multi-Document Summarization

Abstractive multi-document summarization aims to generate a comprehensive summary covering salient content from multiple input documents.Compared with previous RNNbased models, the Transformer-based models employ the self-attention mechanism to capture the dependencies in input documents and can generate better summaries.Existing works have not considered key phrases in determining attention weights of self-attention.Consequently, some of the tokens within key phrases only receive small attention weights.It can affect completely encoding key phrases that convey the salient ideas of input documents.In this paper, we introduce the Highlight-Transformer, a model with the highlighting mechanism in the encoder to assign greater attention weights for the tokens within key phrases.We propose two structures of highlighting attention for each head and the multihead highlighting attention.The experimental results on the Multi-News dataset show that our proposed model significantly outperforms the competitive baseline models.
Wikipedia ORES Explorer: Visualizing Trade-offs For Designing Applications With Machine Learning API

With the growing industry applications of Artificial Intelligence (AI) systems, pre-trained models and APIs have emerged and greatly lowered the barrier of building AI-powered products. However, novice AI application designers often struggle to recognize the inherent algorithmic trade-offs and evaluate model fairness before making informed design decisions. In this study, we examined the Objective Revision Evaluation System (ORES), a machine learning (ML) API in Wikipedia used by the community to build anti-vandalism tools. We designed an interactive visualization system to communicate model threshold trade-offs and fairness in ORES. We evaluated our system by conducting 10 in-depth interviews with potential ORES application designers. We found that our system helped application designers who have limited ML backgrounds learn about in-context ML knowledge, recognize inherent value trade-offs, and make design decisions that aligned with their goals. By demonstrating our system in a real-world domain, this paper presents a novel visualization approach to facilitate greater accessibility and human agency in AI application design.
A Lightweight Ship Detection Method in Optical Remote Sensing Image under Cloud Interference

Ship detection in optical remote sensing image is faced with challenges of high detection false alarm caused by cloud interference, and the contradiction between detection accuracy and computation workload. In this paper, a lightweight anti-cloud ship detection method is proposed. The framework of subgraph classification and mapping reduces the computation workload, and the classifier based on joint feature of gray level co-occurrence matrix, local binary pattern and support vector machine achieves a low detection false alarm. Compared with YOLOv3, SSD and MobileNet-SSD methods, experimental results show that the proposed method outperforms in terms of false alarm rate and computation workload.
Teaching Robotics with Virtual Reality: Developing Curriculum for the 21st Century Workforce

The nature of work is changing. The ongoing shift to automated manufacturing and fabrication in the 21st Century will require a workforce with a radically different set of skills focused less on manual dexterity for the performance of repetitive tasks and more on interaction with robotic systems. It is expected that both Industrial Manufacturing and the Architecture Engineering and Construction (AEC) sector will begin to adopt more automation technology for the production of manufactured goods as well as the design and construction of the built environment. Teaching the fundamental skills required for 21st century workers in the Industrial Manufacturing and AEC sectors may no longer be feasible or efficient using on the job training or apprenticeship as a primary educational modality. Virtual Reality (VR) environments offer a possible solution to train workers in an immersive, interactive, and safe environment where they are less likely to be injured, slow down production, or cause unintentional damage to products or projects in construction. Developing curriculum for these workers requires a modular approach to pedagogy that draws on skills and strategies of game design. Assessment tools for evaluating knowledge and skills acquisition can be structured using a reward system drawn from game design and game theory. Pedagogic sequence can thus be more self-directed as a learner explores their developing knowledge, skills, and mastery of specific tasks. The addition of biometric sensors integrated with the VR Headset promises to add a powerful tool for testing curriculum in real time and providing feedback that can influence the duration, sequence, and progression of a learner’s exploration of the learning environment. In this paper we will describe the development of our own AI-powered VR learning environment for teaching fundamentals of robotics, discuss the curriculum and assessment tools we have developed for in-person workshops and VR environment training, and present our aspirations for the further development of this educational tool.
Neural semi-Markov CRF for Monolingual Word Alignment

Monolingual word alignment is important for studying fine-grained editing operations (i.e., deletion, addition, and substitution) in textto-text generation tasks, such as paraphrase generation, text simplification, neutralizing biased language, etc.In this paper, we present a novel neural semi-Markov CRF alignment model, which unifies word and phrase alignments through variable-length spans.We also create a new benchmark with human annotations that cover four different text genres to evaluate monolingual word alignment models in more realistic settings.Experimental results show that our proposed model outperforms all previous approaches for monolingual word alignment as well as a competitive QA-based baseline, which was previously only applied to bilingual data.Our model demonstrates good generalizability to three out-of-domain datasets and shows great utility in two downstream applications: automatic text simplification and sentence pair classification tasks. 1
LogLog: A Blockchain Solution for Tracking and Certifying Wood Volumes

When it comes to sustainability, many certification systems live and die by their capacity to track a volume from source to destination. This is because customers (and consequently, auditors) are increasingly concerned with where products are sourced ("Are these local materials?") and what is their environmental impact is ("How much CO2 did this generate?"). The Forest Sustainability Council (FSC) created various frameworks to meet these demands. The FSC is responsible for tracking and labelling all the wood volumes that its members use. Though widely respected, their certifications rely on trusted third parties and extensive record keeping. Thus, they do not efficiently address problems like bribery and "creative" inventory practices.Blockchain technology is an attractive solution because it increases traceability, auditability and access to information. However, blockchain systems have limitations. Notably, they are inefficient for storing large amounts of data. The standard workaround is to use a multiple-database approach where large files are kept off-chain, creating a trade-off between lower transaction fees and lower availability, among other factors.In contrast with these approaches, we propose LogLog: an entirely on-chain system for tracking wood volumes throughout a supply chain and enforcing FSC certification standards. LogLog is ERC-1155 compliant and meets the base requirements for a forestry supply chain.We created two reference implementations of our design using Solidity and deployed them over the Ethereum blockchain. Our evaluation uses realistic parameters and suggests our prototype is a viable alternative to current multiple-database systems in contexts where data integrity is a strict requirement.
Fast and Error-Free Negacyclic Integer Convolution Using Extended Fourier Transform

With the rise of lattice cryptography, (negacyclic) convolution has received increased attention. E.g., the NTRU scheme internally employs cyclic polynomial multiplication, which is equivalent to the standard convolution, on the other hand, many Ring-LWE-based cryptosystems perform negacyclic polynomial multiplication. A method by Crandall implements an efficient negacyclic convolution over a finite field of prime order using an extended Discrete Galois Transform (DGT) – a finite field analogy to Discrete Fourier Transform (DFT). Compared to DGT, the classical DFT runs faster by an order of magnitude, however, it suffers from inevitable rounding errors due to finite floating-point number representation. In a recent Fully Homomorphic Encryption (FHE) scheme by Chillotti et al. named TFHE, small errors are acceptable (although not welcome), therefore we decided to investigate the application of DFT for negacyclic convolution. The primary goal of this paper is to suggest a method for fast negacyclic convolution over integer coefficients using an extended DFT. The key contribution is a thorough analysis of error propagation, as a result of which we derive parameter bounds that can guarantee even error-free results. We also suggest a setup that admits rare errors, which allows to increase the degree of the polynomials and/or their maximum norm at a fixed floating-point precision. Finally, we run benchmarks with parameters derived from a practical $$\mathsf {TFHE}$$ setup. We achieve around $$24{\times }$$ better times than the generic NTL library (comparable to Crandall's method) and around $$4{\times }$$ better times than a naïve approach with DFT, with no errors.
An Improved Ant Colony Optimization with Correlation and Gini Importance for Feature Selection

Accurate classification of examples depends upon identification of informative attributes and removal of redundant attributes. Attribute selection helps reduce the noise and increase classification accuracy. In computational biology and bioinformatics, feature selection facilitates identification of most relevant attributes thereby facilitating valuable domain information. In this project, we have employed a synergistic filter-wrapper methodology for simultaneous classification and attribute selection. Ant colony optimization employs correlation and Gini ranking information along with pheromone learning, for providing better and better attributes as iterations proceed. The robust random forest classifier employed classifies the data to maximize fivefold accuracy. We evaluated the performance of our algorithm with eight benchmark datasets.
On the Distribution of "Simple Stupid Bugs" in Unit Test Files: An Exploratory Study

A key aspect of ensuring the quality of a software system is the practice of unit testing. Through unit tests, developers verify the correctness of production source code, thereby verifying the system's intended behavior under test. However, unit test code is subject to issues, ranging from bugs in the code to poor test case design (i.e., test smells). In this study, we compare and contrast the occurrences of a type of single-statement-bug-fix known as "simple stupid bugs" (SStuBs) in test and non-test (i.e., production) files in popular open-source Java Maven projects. Our results show that SStuBs occur more frequently in non-test files than in test files, with most fix-related code associated with assertion statements in test files. Further, most test files exhibiting SStuBs also exhibit test smells. We envision our findings enabling tool vendors to better support developers in improving the maintenance of test suites.
Communication Efficient SGD via Gradient Sampling with Bayes Prior

Gradient compression has been widely adopted in data-parallel distributed training of deep neural networks to reduce communication overhead. Some literatures have demonstrated that large gradients are more important than small ones because they contain more information, such as Top-k compressor. Other mainstream methods, like random-k compressor and gradient quantization, usually treat all gradients equally. Different from all of them, we regard large and small gradients selection as the exploitation and exploration of gradient information, respectively. And we find taking both of them into consideration is the key to boost the final accuracy. So, we propose a novel gradient compressor: Gradient Sampling with Bayes Prior in this paper. Specifically, we sample important/large gradients based on the global gradient distribution, which is periodically updated across multiple workers. Then we introduce Bayes Prior into distribution model to further explore the gradients. We prove the convergence of our method for smooth non-convex problems in the distributed system. Compared with methods that running after high compression ratio at the expense of accuracy, we pursue no loss of accuracy and the actual acceleration benefit in practice. Experimental comparisons on a variety of computer vision tasks (e.g. image classification and object detection) and backbones (ResNet, MobileNetV2, InceptionV3 and AlexNet) show that our approach outperforms the state-of-the-art techniques in terms of both speed and accuracy, with the limitation of 100× compression ratio.
Concentration of Contractive Stochastic Approximation and Reinforcement Learning

Using a martingale concentration inequality, concentration bounds “from time n 0 on” are derived for stochastic approximation algorithms with contractive maps and both martingale difference and Markov noises. These are applied to reinforcement learning algorithms, in particular to asynchronous Q-learning and TD(0). Funding: V. S. Borkar was supported in part by a S. S. Bhatnagar Fellowship from the Council of Scientific and Industrial Research, Government of India.
Analysis and design of secure quantum communication systems utilizing electromagnetic Schrodinger coherent states

We develop a general mathematical formalism and computational method suitable for analyzing the performance of quantum communication systems utilizing coherent radiation states (Schrodinger states). The key motivation is to demonstrate how the analysis and design of quantum communication systems may benefit from the integration of the physical structure (here, the optical quantum coherent state) with the quantum signal processing aspects (here, quantum estimation and prediction theory). As an application, we show how the quantum-electromagnetic signature of optical radiation emitted by quantum antennas, the Schrodinger's coherent state structure, may be exploited to jointly design the transmitter and receiver of a K-ary digital communication link. We present a concrete example comprised of a quantum quadrature-amplitude modulation (QAM) system (with ), including a complete description of the required general design principles developed from quantum mechanics and the quantum antenna's radiated coherent state structure. The simulation results illustrate improvement in spectral efficiency due to the use of over smaller values of K. We also compare the quantum antenna-based link performance with classical QAM utilizing classical antenna-based communication systems and report superiority of the quantum link over the classical version. To provide for security protection, our system is equipped with a quantum encryption protocol for quantum key distribution, and a demonstration of the complete quantum QAM system with encryption is presented. The main message of the article is the fruitfulness of incorporating a multidisciplinary approach to our thinking about electromagnetic wireless systems through the joint deployment of electromagnetic, quantum, and communication theories in the design and development process of current and future advanced technologies.
Designing Mobile Applications to Minimize Disorientation in Informal Learning Environments

The popularity of mobile devices for learning outside conventional classrooms necessitates the exploration of the many challenges which can impede the learning process for children using them. Key among these is the possibility of disorientation, occurring when children are faced with stimuli external to their learning task which can redirect their attention elsewhere. This paper explores the several factors which can contribute towards disorienting children from their learning task in an informal learning environment (ILE) using science festivals as a context. We detail the design and deployment of a mobile leaf identification application used by attendees at a child-focused science festival in a setting filled with distractions. Participating children used the app to watch an educational video about leaves, and were asked to identify the leaves around the exhibit correctly by taking a picture using the app. From our observations, we derive the key factors which can disorient young learners in an ILE and the ones which help them reorient towards the learning task. We conclude by providing considerations for designing mobile applications for children to be used in ILEs which can promote informal learning while minimizing disorientation.
Construction of Ternary Plateaued Functions from Quadratic Forms for Ternary Bent Functions

The paper presents a method to generate plateaued functions by applying spectral invariance operations to a given plateaued function specified by an particular disjoint quadratic form. The disjoint quadratic form of the starting function is generated by reducing the number of terms in the homogeneous disjoint quadratic forms of bent functions. Selecting different spectral invariance operations and applying them in different order allows fast producing of a large number of plateaued functions. To increase the degree of the starting functions the generalized disjoint spectral translation is appropriately modified and used.