Reviewerly/exHarmony huggingface dataset:
Exploring Cross-Modal Training via Touch to Learn a Mid-Air Marking Menu Gesture Set

While mid-air gestures are an attractive modality with an extensive research history, one challenge with their usage is that the gestures are not self-revealing. Scaffolding techniques to teach these gestures are difficult to implement since the input device, e.g. a hand, wand or arm, cannot present the gestures to the user. In contrast, for touch gestures, feedforward mechanisms (such as Marking Menus or OctoPocus) have been shown to effectively support user awareness and learning. In this paper, we explore whether touch gesture input can be leveraged to teach users to perform mid-air gestures. We show that marking menu touch gestures transfer directly to knowledge of mid-air gestures, allowing performance of these gestures without intervention. We argue that cross-modal learning can be an effective mechanism for introducing users to mid-air gestural input.
A Knowledge Base System for Operation Optimization: Design and Implementation Practice for the Polyethylene Process

Abstract Setting up a knowledge base is a helpful way to optimize the operation of the polyethylene process by improving the performance and the efficiency of reuse of information and knowledge—two critical elements in polyethylene smart manufacturing. In this paper, we propose an overall structure for a knowledge base based on practical customer demand and the mechanism of the polyethylene process. First, an ontology of the polyethylene process constructed using the seven-step method is introduced as a carrier for knowledge representation and sharing. Next, a prediction method is presented for the molecular weight distribution (MWD) based on a back propagation (BP) neural network model, by analyzing the relationships between the operating conditions and the parameters of the MWD. Based on this network, a differential evolution algorithm is introduced to optimize the operating conditions by tuning the MWD. Finally, utilizing a MySQL database and the Java programming language, a knowledge base system for the operation optimization of the polyethylene process based on a browser/server framework is realized.
A Study of Learning Data Structure Invariants Using Off-the-shelf Tools

Data structure invariants play a key role in checking correctness of code, e.g., a model checker can use an invariant, e.g., acyclicity of a binary tree, that is written in the form of an assertion to search for program executions that violate it, e.g., erroneously introduce a cycle in the structure. Traditionally, the properties are written manually by the users. However, writing them manually can itself be error-prone, which can lead to false alarms or missed bugs. This paper presents a controlled experiment on applying a suite of off-the-shelf machine learning (ML) tools to learn properties of dynamically allocated data structures that reside on the program heap. Specifically, we use 10 data structure subjects, and systematically create training and test data for 6 ML methods, which include decision trees, support vector machines, and neural networks, for binary classification, e.g., to classify input structures as valid binary search trees. The study reveals two key findings. One, most of the ML methods studied – with off-the-shelf parameter settings and without fine tuning – achieve at least 90% accuracy on all of the subjects. Two, high accuracy is achieved even when the size of the training data is significantly smaller than the size of the test data. We believe future work can utilize the learnt invariants to automate dynamic and static analyses, thereby enabling advances in machine learning to further enhance software testing and verification techniques.
A Sustainable Strategy for Multi-Cloud Service Composition Based on Formal Concept Analysis

Multi-cloud composed by multiple clouds environment can take advantages of cloud computing which is becoming increasingly popular among new scientific and technological undertakings. The requested services of users could often be distributed across some providers hosted by several clouds. The multi-cloud services composition problem requires new scalable approaches to efficiently perform searching to reduce the energy consumption and monetary cost for fulfilling user's request. In this paper, we present a sustainable strategy for multi-cloud service composition, using Formal Concept Analysis and social network by considering some critical factors such as energy consumption between clouds, monetary cost and trust between users and clouds. In addition, a novel algorithm is proposed regarding to the sustainable multi-cloud service composition. We also evaluated the proposed approach through experiments and the results reveal the feasibility and effectiveness of our approach. Finally, we point out future directions for the selection of cloud providers.
Author Disambiguation through Adversarial Network Representation Learning

Many persons share with the same name. Distinguishing different persons with the same name is important but challenging. Albeit much work has been proposed for author disambiguation, most of them do not adequately consider the heterogeneous relationships among authors and papers. In our work, ambiguous names and their related information, such as papers, conferences, titles, abstracts, etc., are constructed into a heterogeneous network which consists of different edge types. To fully incorporate all the information of the constructed network, we use Generative Adversarial Networks (GAN) to learn the network representation of the heterogeneous network. Although GAN has been used in many fields such as image generation, it hasn't been used to obtain representations for the heterogeneous network. As far as we know, our work is the first work which use adversarial training to learn heterogeneous network representation. After the representations are learned, they are partitioned into different groups each representing distinct authors. After extensive experiments on three major author disambiguation datasets, we demonstrate that our method outperforms several state-of-the-art baselines in author disambiguation problem.
Mixing Update Q-value for Deep Reinforcement Learning

The value-based reinforcement learning methods are known to overestimate action values such as deep Q-learning, which could lead to suboptimal policies. This problem also persists in an actor-critic algorithm. In this paper, we propose a novel mechanism to minimize its effects on both the critic and the actor. Our mechanism builds on Double Q-learning, by mixing update action value based on the minimum and maximum between a pair of critics to limit the overestimation. We then propose a specific adaptation to the Twin Delayed Deep Deterministic policy gradient algorithm (TD3) and show that the resulting algorithm not only reduces the observed overestimations, as hypothesized, but that this also leads to much better performance on several tasks.
Chinese Clinical Named Entity Recognition with Word-Level Information Incorporating Dictionaries

Electronic Medical Records (EMRs) are the digital equivalent of paper records, which include treatment and medical history about a patient. At present, the main research goal of Chinese EMRS is to accurately recognize the body parts, drugs, illnesses and other information in the Chinese medical process. Implementing EMRs can boost both the quality and safety of patient care. In Chinese EMRs, how to accurately recognize named entities is important because it is useful to predict the disease risk, therapeutic method and recovery probability. This paper proposes a novel deep learning framework, which uses character-word joint embedding and combines different feature information based on the dictionary. Compared with the predecessors, we incorporate word-level information based on the basic Bi-LSTM model. In addition, we propose an improved n-gram feature encoding method and compare it with PDET feature and PIET feature. Our experimental results demonstrate that our proposed model performs the best in predicting named entities in Chinese EMRs.
Unsupervised Meta-Learning for Clustering Algorithm Recommendation

In this work, the goal is to use clustering algorithms as recommender in a meta-learning system and, thus, to propose an unsupervised meta-learning approach. Meta-learning has been successfully used for recommendation of Machine Learning algorithms in several Data Mining tasks. Meta-learning can rank algorithms according to their adequacy for a new dataset and use this ranking to recommend algorithms. The recommendations are usually made by predictive meta-models induced by supervised Machine Learning techniques, therefore needing a target attribute. In many situations, the target attribute is not available or has a high computational cost. In these situations, the use of unsupervised meta-models (as clustering algorithms) could provide important insights from Machine Learning experiments, like the interpretation of the partitions found by these clustering algorithms. Here, clustering algorithms are used as unsupervised meta-models. Experimental results show that the proposed approach achieved better clustering quality.
A Set Space Model to Capture Structural Information of a Sentence

The context of a sentence is composed of a limited number of words. This leads to the feature sparsity problem whereby the sentence's meaning is easily influenced by language phenomena such as polysemy, ambiguity and puns. To resolve these problems, the set space model (SSM) uses language characteristics to group features of a sentence into different sets. Afterwards, the proposed feature calculus is used to capture the structural information of the sentence. Experiments have shown that this approach to the relation recognition task is effective. However, at least three weaknesses remain. First, due to the lack of a probabilistic explanation, several aspects of SSM (e.g., filter selection) have not yet been covered. Second, the existing studies have only provided an outline of SSM, and many issues remain unclear. To understand this approach, it is necessary to discuss a suitable example in detail. Third, SSM has been applied only to the task of relation recognition. Case studies of more typical topics (e.g., named entity recognition) will help illustrate the use of SSM's methodology to manipulate features. This paper develops SSM to cover these problems. It describes a systematic and novel approach to manipulating features of a sentence. In the experimental part, two typical information extraction tasks are performed to demonstrate SSM's capabilities. Two case studies are considered, and favorable improvements are observed. All of the obtained results surpass those of compared approaches. The experiments also show the influence of sentence structural information on information extraction.
Analyzing Minimal Complexity Machines

The minimal complexity machine (MCM) minimizes the maximum distance between training data and the separating hyperplane and is shown to generalize better than the conventional support vector machine. In this paper, we analyze the MCM and clarify the conditions that the solution of MCM is nonunique and unbounded. To resolve the unboundedness, we propose the minimal complexity linear programming support vector machine (MLP SVM), in which the minimization of the maximum distance between training data and the separating hyperplane is added to the linear programming support vector machine (LP SVM). By computer experiments we show that the solution of the MCM is unbounded under some conditions and that the MLP SVM generalizes better than the LP SVM for most of the two-class and multiclass problems.
A Deadline-Constrained Scheduling Algorithm for Scientific Workflows in Clouds

Scheduling scientific workflows in clouds under deadline constraint is a challenging problem since public clouds usually provide various types of instances that are rented on-demand and charged based on time period. To solve this problem, the paper proposes DCWS, a deadline-constrained workflow scheduling algorithm for cost-effective execution of scientific workflows in clouds. DCWS is a list-based scheduling algorithm that uses several strategies to reduce the monetary cost under deadline constraint: i) sub-deadlines are assigned for individual tasks by considering the probabilities that tasks are placed together; ii) instance type upgrading and downgrading strategies are designed to accelerate workflow execution and reduce the total cost respectively; iii) task backfilling and sub-deadline violation penalizing are used to improve resource utilization and ensure that the sub-deadlines of the individual tasks are satisfied. Experimental results demonstrate that in comparison with two state-of-the-art algorithms, DCWS is effective on reducing monetary cost under deadline constraint.
The hardness of resilience for nested aggregation query

Resilience problem is defined on a database d, given a boolean query q where q(d) is initially true, and an integer k, it is to find the tuple set d′ of smallest size such that the query result q(d∖d′) becomes false. As a potential explanation of a specified query, resilience plays a fundamental and important role in query explanation, database debugging and error tracing. Complexity results of resilience decision on relational algebraic queries have been studied previously. In this paper, we investigate the resilience decision problem on aggregation queries. New results on the hardness are provided. We show that, this problem is polynomially intractable on nested COUNT and SUM query both under data complexity and parametric complexity, and even it is NP-hard to approximate it within a constant ratio under the active domain constraint.
SMARTKT: A Search Framework to Assist Program Comprehension using Smart Knowledge Transfer

Regardless of attempts to extract knowledge from code bases to aid in program comprehension, there is an absence of a framework to extract and integrate knowledge to provide a near-complete multifaceted understanding of a program. To bridge this gap, we propose SMARTKT (Smart Knowledge Transfer) to extract and transfer knowledge related to software development and application-specific characteristics and their interrelationships in form of a knowledge graph. For an application, the knowledge graph provides an overall understanding of the design and implementation and can be used by an intelligent natural language query system to convert the process of knowledge transfer into a developer-friendly Google-like search. For validation, we develop an analyzer to discover concurrency-related design aspects from runtime traces in a machine learning framework and obtain a precision and recall of around 97% and 95% respectively. We extract application-specific knowledge from code comments and obtain 72% match against human-annotated ground truth.
Analysis of Epochs in Environment based Neural Networks Speech Recognition System

Epochs play an important role in the training of the datasets. The number of epochs decides whether the data is overtrained or not. The results depend on the training of these datasets. Speech recognition is widely used application these days. Speech Recognition with deep learning has changed the perspective of the world to look at the technology. Our project is based on deep learning speech recognition system where there are audio files and text transcripts in the datasets. The audio files are trained with the recognition model, and text transcripts are trained with a language model. The dataset used for this project consists of audio clips recorded in three different environments, namely clean, white noise and continuous noise. It was proved that in a clean environment and more epochs, as the model gets trained more, gives the least Word Error Rate of 14.41%.
A characterization methodology for candidates and recruiters interaction in online recruitment services

Online recruitment services have attracted an increasing number of candidates and recruiters who are looking for better job opportunities and the best professionals in their respective areas. These services, through search and recommendation systems, explore candidates and job profiles to identify the ideal candidates for each job vacancy. There are many challenges when we follow this scenario, such as reciprocal matches between vacancies and candidates, temporal dynamics (candidate/vacancy relationship varies over time) and imbalances between demand and supply between areas. Modeling the preferences and behavior of candidates and recruiters is an essential task for which improvements can be proposed by these services to mitigate their challenges. We present in this work a methodology that aims to help answer questions that may be asked about users preferences and behavior, extracting information that leads to improvements in existing functionality and the creation of new ones. We applied our methodology to actual data and questions, which were provided by Catho, the leading Latin American market company within this segment. In the analysis of results, we present opportunities for improvement in online recruitment services, such as the creation of a tool to help register job vacancies and resumes.
A Novel Image Tamper Detection and Self-Recovery Algorithm Based on Watermarking and Chaotic System

With the development of image editing software techniques, the content integrity and authenticity of original digital images become more and more important in digital content security. A novel image tampering detection and recovery algorithm based on digital watermarking technology and a chaotic system is proposed, and it can effectively locate the tampering region and achieve the approximate recovery of the original image by using the hidden information. The pseudo-random cyclic chain is realized by the chaotic system to construct the mapping relationship between the image subblocks. It can effectively guarantee the randomness of the positional relationship between the hidden information and the original image block for the better ergodicity of the pseudo-random chain. The recovery value optimization algorithm can represent image information better. In addition to the traditional Level-1 recovery, a weight adaptive algorithm is designed to distinguish the original block from the primary recovery block, allowing 3 × 3 neighbor block recovery to achieve better results. The experimental results show that the hierarchical tamper detection algorithm makes tamper detection have higher precision. When facing collage attacks and large general tampering, it will have higher recovery image quality and better resistance performance.
Guided Unsupervised Desmoking of Laparoscopic Images Using Cycle-Desmoke

The generation of smoke in laparoscopic surgery due to laser ablation and cauterization causes deterioration in the visual quality of the operative field. In order to reduce the effect of smoke, the present paper proposes an end-to-end network, called Cycle-Desmoke. The network enhances the CycleGAN framework by adoption of a new generator architecture and addition of new Guided-Unsharp Upsample loss in combination to adversarial and cycle-consistency loss. The Atrous Convolution Feature Extraction Module present in the encoder blocks of the generator helps distinguishing smoke by capturing features at multiple scales by the use of kernels with different receptive fields. Further, the use of Guided-Unsharp Upsample loss supervises the upsampling process of the feature maps and helps improve the contrast of the desmoked image. The network performs robust unsupervised Image-to-Image Translation from smoke domain to smoke-free domain. The public Cholec80 dataset is used to evaluate the performance of the proposed method. Quantitative and qualitative comparative analysis of the proposed method over the state-of-the-methods reveals the effectiveness of the method at the task of smoke removal and enhancement of the image.
A Secured One Time Password Authentication Technique using (3, 3) Visual Cryptography Scheme

Abstract The world is vastly becoming a completely digital one with most transactions carried out over the internet. This has exposed the internet to increasing threats and attacks and has necessitated the need for an internet-based authentication and authorization services. One Time Passwords (OTP) have been introduced to overcome the limitations of static passwords. However, research has shown that OTP are not completely free from middle man attacks. This paper presents a (3, 3) Visual Cryptographic Scheme (VCS) technique for OTP security. The technique secures the generated OTP image by dividing it into three shares, of which one of the shares will be sent to the user while the remaining shares will be stored at different locations on the server. Before the original OTP image will be recovered, all the shares must the retrieved and stacked together. To avoid pixel expansion problem and loss of image quality that have characterized existing VCS technique, progressive visual cryptography was adopted to decrypt the retrieved OTP shares. A high quality OTP image was recovered as revealed by the peak signal to noise ratio values and there was no change in size of the OTP images.
Challenges of Constructing a Railway Knowledge Graph

International railway networks are an important means for passenger and freight transport. Railway software systems often must be supported and maintained for decades. Different systems cover different aspects such as train protection, signalling, infrastructure hardware and software. Usage of different standards and regulations, both international (e.g., European Train Control System) and national, for these aspects leads to a large number of incompatible systems.
Isolated Sign Recognition with a Siamese Neural Network of RGB and Depth Streams

Sign recognition is a challenging problem due to high variance of the signs among different signers and multiple modalities of the input information. In addition, the challenges that exist in the action classification problems in computer vision are similar in this domain too, such as variations in illumination and background. In this work, we propose a Siamese Neural Network (SNN) architecture that is used to extract features from the RGB and the depth streams of a sign frame in parallel. We use a pretrained model for the SNN without any finetuning to our training data. We then apply global feature pooling to the depth and color features that the SNN generates and feed the concatenation of the selected features to a recurrent neural network (RNN) to discriminate the signs. We trained our model parameters with the Montalbano dataset and achieved 93.19% test accuracy with ResNet-50 and 91.61% with VGG-16 Network Models.