Reviewerly/exHarmony huggingface dataset:
Cognitive Semantics For Dynamic Planning In Human-Robot Teams

Robots are making their way into our society and are foreseen to become an important part in our everyday life, at work or at home. Industrial factory layouts are moving robots out of enclosures bringing them side by side with human workers. As for service robots they are by definition meant to perform tasks in our immediate proximity. To be performed successfully, these tasks, also referred to as joint actions, require coordination and trust. Coordination implies that the robot needs to account for his actions and their effects on the environment but also for changes that the user introduces. Therefore, flexible planning capacities allowing on-the-fly adaptation to what a human is requesting or doing, together with a shared mental representation of the task, are needed. In this paper we present (i) a symbolic knowledge system and the way it translates into simple temporal networks (STN) to generate actions plans, and (ii) interaction models based on natural language. First results indicate the robot can build plans for a joint action according to several parameters given its conceptual semantic description. Furthermore, a human can interactively either modify the plan or ask for explanations about it. By several experiments we demonstrate the generation and adaptation of these dynamic human-robot collaboration plans.
Automatic Streamlining for Constrained Optimisation

Augmenting a base constraint model with additional constraints can strengthen the inferences made by a solver and therefore reduce search effort. We focus on the automatic addition of streamliner constraints, which trade completeness for potentially very significant reduction in search. Recently an automated approach has been proposed, which produces streamliners via a set of streamliner generation rules. This existing automated approach to streamliner generation has two key limitations. First, it outputs a single streamlined model. Second, the approach is limited to satisfaction problems. We remove both limitations by providing a method to produce automatically a portfolio of streamliners, each representing a different balance between three criteria: how aggressively the search space is reduced, the proportion of training instances for which the streamliner admitted at least one solution, and the average reduction in quality of the objective value versus the unstreamlined model. In support of our new method, we present an automated approach to training and test instance generation, and provide several approaches to the selection and application of the streamliners from the portfolio. Empirical results demonstrate drastic improvements both to the time required to find good solutions early and to prove optimality on three problem classes.
Developing Logistic Software Platforms: E-Market Place, a Case Study

This paper describes a framework for software development with emphasis on logistics platforms. Specifically, a case study is presented regarding the Logport program in Colombia’s Caribbean coast. The software development is based on the merging of modeling, design, and agile development techniques aimed towards software production. The agile methodologies discussed herein include Scrum, XP, and Crystal among others, and specific verification and validation aspects of CMMI 1.3 are evaluated. Furthermore, the integration of emerging technologies including elastic computing in cloud computing that allows scaled integration, security, load balancing, process speed, and high concurrency among other features are discussed. Finally, the proposed solutions cover actual and emergent needs in a B2B or B2C electronic commerce dynamic environment where suppliers and clients offer and demand transport, storage, and customs services. In this environment, their goal is to ensure there is added value to their logistics processes starting at the inputs and all the way to the outputs of the commercial business processes. Colombia’s Caribbean region is one such multimodal and multiport environment in which there is constant demand for low cost, time and storage optimization, and customs reliability for the businesspersons of this region, which serves as a Hub for the American Caribbean.
Orienteering Timing System Based on Sub-G Band Wireless Sensor Network

Orienteering is a long-distance outdoor sport, in which electronic timing system plays an important role. However, the current production of electronic timing equipment technology on the Chinese market is still less developed in weight, volume, power consumption and other technical indicators compared with the international manufacturers in advanced level. Non-contact timer still has a big gap, and the price is too expensive, bulky and lack of portability. In order to solve this problems and improve the quality of directional movement of electronic timing equipment, this article adopted the new SoC as a master plan, design and implement a set of low power consumption, low cost, portable and reliable directional movement of the timing system, innovating the NFC tags and Sub-G communications functions.
Side Channel Attacks in Computation Offloading Systems with GPU Virtualization

The Internet of Things (IoT) and mobile systems nowadays are required to perform more intensive computation, such as facial detection, image recognition and even remote gaming, etc. Due to the limited computation performance and power budget, it is sometimes impossible to perform these workloads locally. As high-performance GPUs become more common in the cloud, offloading the computation to the cloud becomes a possible choice. However, due to the fact that offloaded workloads from different devices (belonging to different users) are being computed in the same cloud, security concerns arise. Side channel attacks on GPU systems have been widely studied, where the threat model is the attacker and the victim are running on the same operating system. Recently, major GPU vendors have provided hardware and library support to virtualize GPUs for better isolation among users. This work studies the side channel attacks from one virtual machine to another where both share the same physical GPU. We show that it is possible to infer other user's activities in this setup and can further steal others deep learning model.
Short-term Photovoltaic Power Forecasting Using Deep Convolutional Networks

Uncertain factors such as weather conditions make the photovoltaic power forecasting challenging. Therefore, some advanced deep learning (DL) techniques have been introduced into the field. However, the exploitation of historical data by existing DL-based models is usually limited to one-dimensional power arrays, and the information describing power varies over time is also ignored. Differently, this work proposes a model based on a deep convolutional network (DCN), which attempts to improve forecast performance by combining feature learning and multi-dimensional arrays. To match the DCN paradigm along with digging the power information indepth, we use the historical daily data to build a power tensor. We train and test the proposed model using data sets from the real world, Self-evaluation and comparison with several state-of-the-art models demonstrate the superiority of our proposal.
PINFER: Privacy-Preserving Inference

The foreseen growing role of outsourced machine learning services is raising concerns about the privacy of user data. This paper proposes a variety of protocols for privacy-preserving regression and classification that (i) only require additively homomorphic encryption algorithms, (ii) limit interactions to a mere request and response, and (iii) that can be used directly for important machine-learning algorithms such as logistic regression and SVM classification. The basic protocols are then extended and applied to simple feed-forward neural networks.
The Application of Big Data to Improve Pronunciation and Intonation Evaluation in Foreign Language Learning

The key to Foreign Language Learning lies in the accurate mastery of pronunciation and intonation. However, there exists a lack of learning environment and awareness of pronunciation practice in China. Most oral foreign language learning software can only provide fuzzy evaluation for learners. And learners cannot get detailed feedback to correct their pronunciation errors. Our objective in this study is to improve the evaluation function of pronunciation and intonation in the oral English learning system with the help of Big Data on education. To provide detailed feedback for learners, we set five evaluation indexes: pronunciation, intonation, fluency, sentence stress and rhythm. Our program used GOP (Goodness of Pronunciation), Duration Score and spectrogram from Praat to evaluate learner’s oral English. The experimental results suggest that the evaluation function proposed in this paper is more effective than the traditional one. The accuracy of learners’ pronunciation and intonation improves more effectively.
Whither Specifications as Programs

Unifying theories distil common features of programming languages and design methods by means of algebraic operators and their laws. Several practical concerns—e.g., improvement of a program, conformance of code with design, correctness with respect to specified requirements—are subsumed by the beautiful notion that programs and designs are special forms of specification and their relationships are instances of logical implication between specifications. Mathematical development of this idea has been fruitful but limited to an impoverished notion of specification: trace properties. Some mathematically precise properties of programs, dubbed hyperproperties, refer to traces collectively. For example, confidentiality involves knowledge of possible traces. This article reports on both obvious and surprising results about lifting algebras of programming to hyperproperties, especially in connection with loops, and suggests directions for further research. The technical results are: a compositional semantics, at the hyper level, of imperative programs with loops, and proof that this semantics coincides with the direct image of a standard semantics, for subset closed hyperproperties.
Virtual Reality Technologies in the Training of Professional Drivers. Comparison of the 2D and 3D Simulation Application

During recent years virtual reality (VR) technology has been gaining popularity in many fields, including the acknowledgement given in professional driver training. Currently, the implementation of methodologically correct, ICT-based (including both the driving simulators and e-learning) training is both resource- and time-demanding. It is also strongly connected with high personnel costs. Due to their low implementation costs and impact made on a trainee, VR-based tools could play a complementary role in the training process and could support the real life training, as well as provide an alternative for traditional educational platforms. The article introduces the comparative analysis on how the virtual reality systems influence the simulation quality in relations to the 2D screens used in low-class simulators. The dynamic parameters of driving, intensity of the simulation sickness symptoms occurrence, immersion level, registered during three research drives were analyzed in a group of 30 drivers. Both the benefits and inconveniences deriving from the characteristics of certain virtual environment projection methods were specified.
Bilingual Machine Translation System Between Hindi and Sanskrit Languages

Machine translation (MT) is increasingly becoming popular today as it reduces a great deal of efforts when it comes to the task of translation from one language to other. Not only is it time saving, but secure and cost-effective in the long run. A lot of work has been done in the field of MT lately. Various machine translation systems (MTS) have been developed for numerous languages. Sanskrit is one of the oldest languages in the world and is still in existence. There is loads of literary, scientific and mathematical work written in Sanskrit during ancient times. Despite of its important part in Indian culture and history, not much work has been done for translation to or from Sanskrit language. This proposed work is an effort to bridge the language gap between Sanskrit and Hindi, one of the most widely spoken languages of the world. The idea is to develop an MTS which will serve as a tool to perform two-way translation between Hindi and Sanskrit language. The system once developed can be used for learning and understanding the grammar and structure of both the involved languages.
Logic-Based Benders Decomposition for Super Solutions: An Application to the Kidney Exchange Problem

When optimising under uncertainty, it is desirable that solutions are robust to unexpected disruptions and changes. A possible formalisation of robustness is given by super solutions. An assignment to a set of decision variables is an (a, b, c) super solution if any change involving at most a variables can be repaired by changing at most b other variables; the repair solution should have a cost of at most c units worse than a non-robust optimum. We propose a method exploiting Logic Based Benders Decomposition to find super solutions to an optimisation problem for generic disruptions. The master deals with the original problem, while subproblems try to find repair solutions for each possible disruption. As a case study, we consider the Kidney Exchange Problem, for which our method scales dramatically better on realistic instances than a reformulation-based approach from the literature.
Link Prediction Based on Node Embedding and Personalized Time Interval in Temporal Multi-relational Network

Link prediction on temporal networks has a wide range of applications, such as facilitating individual relationship mining, user recommendation, and user behavior analysis. The traditional link prediction methods on temporal network only considered the structure of single-relational networks, which ignored the diversity of network link types and the influence between different link types. This paper proposes a temporal multi-relational network link prediction method combining personalized time interval and node embedding. Firstly, the node embedding is generated according to the structure of target network and auxiliary network which overcomes the defect of single network information sparseness; then, considering the diversity of link types, we construct the relationship formation sequence based on personalized time interval and the influence between different relationships for each link; next, the relationship formation sequence is modeled based on the Hawkes process, which takes product of the node embedding as the initial value to calculate the possibility of link formation. The method captures the dynamic characteristics and multi-relational property of the network, which is helpful to improve the accuracy of link prediction. Experimental results find that the proposed method has better performance and can be applied to large-scale networks.
Predicting an Effective Android Application Release Based on User Reviews and Ratings

Android applications' acceptance rate is growing rapidly, which causes a huge competition among the developers. To develop acceptable and successful application, user reviews are very important source of information. Especially for a better release of new version apps, it is necessary to know the users' demand. However, it is very difficult to analyze and maintain all the reviews manually due to limited resource and time. Only a little solution domain have discovered to categorize user reviews automatically. However, those works have only categorized the reviews without giving any suggestion regarding which category is more significant. This paper proposed an automatic category prioritization technique by analyzing the corresponding ratings and the reviews. This approach has been experimented on Opera and Firefox reviews by collecting data from Google play store. Experimental results reported that this approach can effectively categorized and prioritized user reviews, where the proposed prioritized categories play the significant role in next version of android application release.
Design of a Horizontal Data Fragmentation, Allocation and Replication Method in the Cloud

At present, the demand for information in distributed database systems is large and growing day by day. While this is happening, new challenges arise to improve the performance of the databases. Data fragmentation and replication methods have a leading role in distributed systems over the cloud, which is why this paper presents the design of a horizontal fragmentation, allocation and replication method in the cloud. This research proposes an algorithm for solving the problem of overlapping horizontal fragments in a data fragmentation and replication method in the cloud. The design of a Web application using the aforementioned method is also presented, this allows the fragmentation, assignment and replication scheme proposed by the method to be directly applied on a distributed database.
Selecting Paragraphs to Answer Questions for Multi-passage Machine Reading Comprehension

This paper addresses the problem of question answering style multi-passage Machine Reading Comprehension (MRC) and suggests that paragraph-level segments are suitable to answer questions in real Web query scenario. We propose to combine a learning to rank framework with an attention-based neural network to select the best-matching paragraph for a specific question. To estimate the quality of a paragraph with respect to a given query, its largest ROUGE-L score compared against the annotated answers is used as the ranking indicator. Experimental results on a real-world dataset demonstrate that the proposed method obtains a significant improvement compared to the state-of-the-art baselines.
Three-dimensional THT solder joint reconstruction for inline inspection systems

Automatic Optical Inspection (AOI) is an important part of modern electronics manufacturing. While profilometric technologies are very present in inspection systems specialized on surface mounted technologies (SMT), through hole technology (THT) oriented inspection still mostly relies on two-dimensional data. Solder joints of THT connections have highly reflective metallic surfaces and thus are a very challenging object for typical light based profilometric measurement systems. A new approach is presented, which uses specular reflections of angled illuminations to reconstruct the surface of THT solder joints for inline inspection. The observed reflections depend on the position of the angled light sources, as well as the surface angle of the inspected object and its relative position to the observing camera. The reflections of each angled illumination are observed separately and processed as a stack of images. To reduce the complexity the image stack is transformed into a polar coordinate system leveraging the rotation symmetry of THT solder connections. The surface angle of every observed pixel is estimated based on the observation stack and corrected for its position relative to the camera and illumination. The surface angle then gets integrated along the transformed coordinate axis. This results in the estimation of the surface profile of the solder connection. After retransformation into the source coordinate system, this can be used to classify the solder joint for defects and insufficient solder volume.
Abstractive Sentence Compression with Event Attention

Sentence compression aims at generating a shorter sentence from a long and complex source sentence while preserving the important content of the source sentence. Since it provides enhanced comprehensibility and readability to readers, sentence compression is required for summarizing news articles in which event words play a key role in delivering the meaning of the source sentence. Therefore, this paper proposes an abstractive sentence compression with event attention. In compressing a sentence of news articles, event words should be preserved as important information for sentence compression. For this, event attention is proposed which focuses on the event words of the source sentence in generating a compressed sentence. The global information in the source sentence is as significant as event words, since it captures the information of a whole source sentence. As a result, the proposed model generates a compressed sentence by combining both attentions. According to experimental results, the proposed model outperforms both the normal sequence-to-sequence model and the pointer generator on three datasets, namely the MSR dataset, Filippova dataset, and Korean sentence compression dataset. In particular, it shows 122% higher BLEU score than the sequence-to-sequence model. Therefore, the proposed model is effective in sentence compression.
Single Image Dehazing Using Non-local Total Generalized Variation

Single image dehazing has been a challenging problem due to its ill-posed nature. In this paper, a novel single image dehazing approach is proposed to accurately model the transmission map and suppress artifacts in the recovered haze-free image. Firstly, a coarse transmission is estimated using the patch based haze-line model. After that, a non-local Total Generalized Variation regularization is introduced to refine the transmission while preserving the local smoothness property and depth discontinuities. In addition, a regularized optimization is proposed for recovering the scene radiance without bringing artifacts boosting. Compared with the state-of-the-art dehazing methods, both quantitative and qualitative experimental results indicate that the proposed method is capable of obtaining an accurate transmission map and a visually plausible dehazed image.
How Do Outstanding Users Differ From Other Users in Q&amp;A Communities?

This paper reports on an investigation into outstanding and ordinary users of two Question & Answer (Q&A) communities. Considering some behavior perspectives such as participation, linguistic traits, social ties, influence, and focus, we found that outstanding users (i) are more likely to engage in discussions; (ii) tend to use more sophisticated linguistic traits; (iii) generate longer debates; (iv) value the diversity of their connections; and (v) participate in several topics, rather than one specialist niche. These findings allow us to use behavioral patterns to predict if a given user is outstanding and predict which answer gives a definitive solution for a question. Then, we present two feature learning methods to automatically generate the inputs for the prediction model to classify users as outstanding or ordinary. Our feature learning approaches outperformed related methods and generated competitive results when compared to feature engineering based on behavioral patterns.
BiRNN-DKT: Transfer Bi-directional LSTM RNN for Knowledge Tracing

In recent years, online education is transforming from mobile education to intelligent education, and the rapid development of machine learning breathe into intelligent education with powerful energy. Deep Knowledge Tracing (DKT) is a state of the art method for modeling students' abilities which is changing by time. It can accurately predict students' mastery of knowledge or skill as well as their future performance. In this paper, we study the structure of the DKT model and proposed a new deep knowledge tracing model based on Bidirectional Recurrent Neural Network (BiRNN-DKT). We have also optimized the incorporating of data preprocessing and external features to improve model performance. Experiments show that the model can not only predict students' performance by capturing their history performance, but also get more accurate learning status simulation by integrating past and future context sequence information into the model multiple knowledge concepts. Compared with the traditional model, the proposed BiRNN-DKT gets an improvement in predicting students' knowledge ability and performance, and has great advantages in measuring the level of knowledge acquired by students.