Reviewerly/exHarmony huggingface dataset:
A Human-Like Semantic Cognition Network for Aspect-Level Sentiment Classification

In this paper, we propose a novel Human-like Semantic Cognition Network (HSCN) for aspect-level sentiment classification, motivated by the principles of human beings’ reading cognitive process (pre-reading, active reading, post-reading). We first design a word-level interactive perception module to capture the correlation between context words and the given target words, which can be regarded as pre-reading. Second, to mimic the process of active reading, we propose a targetaware semantic distillation module to produce the targetspecific context representation for aspect-level sentiment prediction. Third, we further devise a semantic deviation metric module to measure the semantic deviation between the targetspecific context representation and the given target, which evaluates the degree we understand the target-specific context semantics. The measured semantic deviation is then used to fine-tune the above active reading process in a feedback regulation way. To verify the effectiveness of our approach, we conduct extensive experiments on three widely used datasets. The experiments demonstrate that HSCN achieves impressive results compared to other strong competitors.
GRAM

The performance of graph processing for real-world graphs is limited by inefficient memory behaviours in traditional systems because of random memory access patterns. Offloading computations to the memory is a promising strategy to overcome such challenges. In this paper, we exploit the resistive memory (ReRAM) based processing-in-memory (PIM) technology to accelerate graph applications. The proposed solution, GRAM, can efficiently executes vertex-centric model, which is widely used in large-scale parallel graph processing programs, in the computational memory. The hardware-software co-design used in GRAM maximizes the computation parallelism while minimizing the number of data movements. Based on our experiments with three important graph kernels on seven real-world graphs, GRAM provides 122.5X and 11.1x speedup compared with an in-memory graph system and optimized multithreading algorithms running on a multi-core CPU. Compared to a GPU-based graph acceleration library and a recently proposed PIM accelerator, GRAM improves the performance by 7.1X and 3.8X respectively.
Automatic Image Segmentation With Superpixels and Image-Level Labels

Automatically and ideally segmenting the semantic region of each object in an image will greatly improve the precision and efficiency of subsequent image processing.We propose an automatic image segmentation algorithm based on superpixels and image-level labels.The proposed algorithm consists of three stages.At the stage of superpixel segmentation, we adaptively generate the initial number of superpixels using the minimum spatial distance and the total number of pixels in the image.At the stage of superpixel merging, we define small superpixels and directly merge the most similar superpixel pairs without considering the adjacency, until the number of superpixels equals the number of groupings contained in image-level labels.Furthermore, we add a stage of reclassification of disconnected regions after superpixel merging to enhance the connectivity of segmented regions.On the widely used Microsoft Research Cambridge data set and Berkeley segmentation data set, we demonstrate that our algorithm can produce high-precision image segmentation results compared with the state-of-the-art algorithms.
Hybrid Image-Retrieval Method for Image-Splicing Validation

Recently, the task of validating the authenticity of images and the localization of tampered regions has been actively studied. In this paper, we go one step further by providing solid evidence for image manipulation. If a certain image is proved to be the spliced image, we try to retrieve the original authentic images that were used to generate the spliced image. Especially for the image retrieval of spliced images, we propose a hybrid image-retrieval method exploiting Zernike moment and Scale Invariant Feature Transform (SIFT) features. Due to the symmetry and antisymmetry properties of the Zernike moment, the scaling invariant property of SIFT and their common rotation invariant property, the proposed hybrid image-retrieval method is efficient in matching regions with different manipulation operations. Our simulation shows that the proposed method significantly increases the retrieval accuracy of the spliced images.
Personalized Dialogue Generation with Diversified Traits

Endowing a dialogue system with particular personality traits is essential to deliver more human-like conversations. However, due to the challenge of embodying personality via language expression and the lack of large-scale persona-labeled dialogue data, this research problem is still far from well-studied. In this paper, we investigate the problem of incorporating explicit personality traits in dialogue generation to deliver personalized dialogues. To this end, firstly, we construct PersonalDialog, a large-scale multi-turn dialogue dataset containing various traits from a large number of speakers. The dataset consists of 20.83M sessions and 56.25M utterances from 8.47M speakers. Each utterance is associated with a speaker who is marked with traits like Age, Gender, Location, Interest Tags, etc. Several anonymization schemes are designed to protect the privacy of each speaker. This large-scale dataset will facilitate not only the study of personalized dialogue generation, but also other researches on sociolinguistics or social science. Secondly, to study how personality traits can be captured and addressed in dialogue generation, we propose persona-aware dialogue generation models within the sequence to sequence learning framework. Explicit personality traits (structured by key-value pairs) are embedded using a trait fusion module. During the decoding process, two techniques, namely persona-aware attention and persona-aware bias, are devised to capture and address trait-related information. Experiments demonstrate that our model is able to address proper traits in different contexts. Case studies also show interesting results for this challenging research problem.
A Taxonomy and Survey of Semantic Approaches for Query Expansion

Conventional approaches to query expansion (QE) rely on the integration of an unstructured corpus and probabilistic rules for the extraction of candidate expansion terms. These methods do not consider search query semantics, thereby resulting in ineffective retrieval of information. The semantic approaches for QE overcome this limitation, whereby a search query is expanded with meaningful terms that accord with user information needs. This paper surveys recent approaches to semantic QE that employ different models and strategies and leverages various knowledge structures. We organize these approaches into a taxonomy that includes linguistic methods, ontology-based methods, and mixed-mode methods. We also discuss the strengths and limitations of each type of semantic QE method. In addition, we evaluate various semantic QE approaches in terms of knowledge structure utilization, corpus collection, baseline model adaptation, and retrieval performance. Finally, future directions in exploiting personalized social information and multiple ontologies for semantic QE are suggested.
Augmented Reality Improves Procedural Work on an International Space Station Science Instrument

Objective: The purpose of the current study was to determine whether an augmented reality instruction method would result in faster task completion times, lower mental workload, and fewer errors for simple tasks in an operational setting. Background: Prior research on procedural work that directly compared augmented reality instructions to traditional instruction methods (e.g., paper) showed that augmented reality instructions can enhance procedural work, but this was not true for simple tasks in an operational setting. Method: Participants completed simple procedural tasks on spaceflight hardware using an augmented reality instruction method and a paper instruction method. Results: Our results showed that the augmented reality instruction method resulted in faster task completion times and lower levels of mental and temporal demand compared with paper instructions. When participants used the augmented reality instruction method before the paper instruction method, there was a transfer of training that improved a subsequent procedure using the paper instruction method. Conclusion: An off-the-shelf augmented reality head-mounted display (HoloLens) can enhance procedural work for simple tasks in an operational setting. Application: The ability of augmented reality to enhance procedural work for simple tasks in an operational setting can help in reducing costs and mitigating risks that could ultimately lead to accidents and critical failures.
Thermal comfort measurement using thermal-depth images for robotic monitoring

This paper describes an application of thermal-depth images to human thermal comfort measurement. A mobile monitoring of the elderly and residents of care houses is one of the promising applications of mobile assistive robots. Monitoring if a person feels comfortable is an important task of such robots. We rely on an established comfort measure in the architecture domain, namely, predicted mean vote (PMV). PMV is calculated mainly by six factors and one of which is the clothing insulation or clo-value. Clo-values are usually measured by a thermal mannequin, a specially-designed apparatus for the purpose. We apply human recognition techniques in thermal-depth images to efficiently measure clo-values, thereby enabling on-line assessment of thermal comfort. We evaluate the method and develop a mobile robot system for experimental testing.
Peer-to-peer Federated Learning on Graphs

We consider the problem of training a machine learning model over a network of nodes in a fully decentralized framework. The nodes take a Bayesian-like approach via the introduction of a belief over the model parameter space. We propose a distributed learning algorithm in which nodes update their belief by aggregate information from their one-hop neighbors to learn a model that best fits the observations over the entire network. In addition, we also obtain sufficient conditions to ensure that the probability of error is small for every node in the network. We discuss approximations required for applying this algorithm to train Deep Neural Networks (DNNs). Experiments on training linear regression model and on training a DNN show that the proposed learning rule algorithm provides a significant improvement in the accuracy compared to the case where nodes learn without cooperation.
Convolutional Neural Network on Embedded Platform for People Presence Detection in Low Resolution Thermal Images

Detection of human presence is a key feature in Human Computer Interaction. Solutions based on cameras are attractive, but require computer vision techniques to extract meaningful data, which can be expensive from a computational point of view. In this work, we present a new system that merges a low resolution thermal camera with advanced feature extraction techniques such as Convolutional Neural Networks. We demonstrate the possibility to adapt their execution to resource-constrained platform without significant loss of performance, by processing data on a 32-bit low power microcontroller, performing the classification on thermal video stream. It achieve 76.7% of accuracy in the microcontroller, requiring only 16.5 mW in continuous classification mode and using 6 kB of RAM.
Prediction of diffuse horizontal irradiance using a new climate zone model

Knowledge on the diffuse horizontal irradiance (DHI), and direct normal irradiance (DNI) is crucial for the estimation of the irradiance on tilted surfaces, which in turn is critical for photovoltaic (PV) applications and for designing and simulating concentrated solar power (CSP) plants. Since global horizontal irradiance (GHI) is the most commonly measured solar radiation variable, it is advantageous for establishing a suitable method that uses it to compute DHI and DNI. In this way, a new model for predicting the diffuse fraction (Kd) based on the climate zone is proposed, using only the clearness index (Kt) as the predictor and 1-min resolution GHI data. A review of the literature on models that use hourly and sub-hourly Kt values to compute Kd was also carried out, and an extensive performance assessment of both the proposed model and the models from the literature was conducted using ten statistical indicators and a global performance index (GPI). A set of model parameters was determined for each climate zone considered in this study (arid, high albedo, temperate and tropical) using 48 worldwide radiometric stations. It was found that the best overall performing model was the model proposed in this work.
Control of fraud on mobile money services in Ghana: an exploratory study

Purpose Fraud is a global economic menace which threatens the survival of individuals, firms, industries and economies, and the mobile money service is no exception. This paper aims to explore the main causes of fraud in the mobile money services in Ghana and the measures to combat the menace by the key stakeholders connected to the mobile money services. The paper is motivated by recent reports of numerous fraudulent transactions on the mobile money platform, and the need to clamp down these nefarious transactions with effective and practical measures to sustain the service. Design/methodology/approach A thorough review of existing studies on fraud risk relating to mobile money services was done revealing a paucity of literature on the subject. Primary data were gathered using an interview guide to explore the magnitude of the problem based on the views of employees of mobile money operators, mobile money agents, banking supervisors from Bank of Ghana, employees of partnering banks, employees of National Communications Authority and mobile money subscribers. Findings The study revealed that fraud in mobile money services is caused by weak internal controls and systems, lack of sophisticated information technology tools to detect the menace, inadequate education and training and the poor remuneration of employees. These factors disrupt the growth, and the smooth-running of the services. To curb this menace, a detailed legal code and internal fraud policy should be developed and used by mobile money operators and partner banks. Adequate training for mobile money agents should be encouraged coupled with public awareness campaigns to educate stakeholders especially the mobile money subscribers on the tricks of the fraudsters. Research limitations/implications With the chosen research methodology and limited sample size, the findings may not reflect the views of all the stakeholders connected to the mobile money services. Therefore, future studies on this subject are entreated to use research methods which embrace larger samples to get more details about this menace. Practical implications The study will assist in tackling the mobile money fraud to sustain the service in the foreseeable future. Originality/value This paper contributes to scanty literature on fraud relating to the mobile money services by drawing lessons from a middle-income country.
Annotating a broad range of anaphoric phenomena, in a variety of genres: the ARRAU Corpus

Abstract This paper presents the second release of arrau , a multigenre corpus of anaphoric information created over 10 years to provide data for the next generation of coreference/anaphora resolution systems combining different types of linguistic and world knowledge with advanced discourse modeling supporting rich linguistic annotations. The distinguishing features of arrau include the following: treating all NPs as markables, including non-referring NPs, and annotating their (non-) referentiality status; distinguishing between several categories of non-referentiality and annotating non-anaphoric mentions; thorough annotation of markable boundaries (minimal/maximal spans, discontinuous markables); annotating a variety of mention attributes, ranging from morphosyntactic parameters to semantic category; annotating the genericity status of mentions; annotating a wide range of anaphoric relations, including bridging relations and discourse deixis; and, finally, annotating anaphoric ambiguity. The current version of the dataset contains 350K tokens and is publicly available from LDC. In this paper, we discuss in detail all the distinguishing features of the corpus, so far only partially presented in a number of conference and workshop papers, and we also discuss the development between the first release of arrau in 2008 and this second one.
Energy Efficient Virtual Machine Placement With an Improved Ant Colony Optimization Over Data Center Networks

Virtualization has achieved widespread adoption in various fields. As one of the most significant technologies in network virtualization, an effective virtual machine placement can facilitate to improve resource utilization in data center networks and cut down the enterprises' operation cost. In this paper, we proposed an energy efficient virtual machine placement scheme, which pursued to reduce communication cost and power consumption over traffic-aware data center networks. To solve such an optimization problem, an improved ant colony optimization with adaptive parameter setting was presented to balance its fast convergence and robust search capability. Compared with existing algorithms, simulation results demonstrated that our scheme achieved improvements in power consumption and communication cost, and had a significant reduction in run time under different traffic patterns and configurations.
Multi-Keyword Searchable and Data Verifiable Attribute-Based Encryption Scheme for Cloud Storage

In a data sharing system, it is a basic requirement for a user, who has an appropriate privilege to perform keyword retrieval for encrypted documents stored in the cloud.Although traditional searchable encryption technology can provide data protection and retrieval characteristic, there are some main issues should also be considered.First, most existing attribute-based searchable encryption schemes only support single-keyword search, which may return abundant irrelevant search results, resulting in a waste of computational and broadband resources.Second, the user often needs to seek some data related to some particular keywords but his attributes may be altered frequently.Third, the cloud server is not completely loyal which sometimes returns a fraction of erroneous search results.Focus on these issues, a practical multi-keyword searchable encryption scheme is proposed for data integrity verification and attribute revocation by combining the ciphertext policy attribute-based encryption (CP-ABE) and auditing ideas.The scheme on one hand supports multi-keyword search which avoids the cloud server yield ample irrelevant documents by narrowing the search scope, and the other hand can implement effectively attribute revocation by entrusting ciphertext updates to the powerful cloud server, thereby preventing access by illegal users.Furthermore, third-party audits use verification algorithms to ensure the correctness of search results and reduce the amount of computing by end users.The most critically, the scheme proved to be resistant to selective plaintext attacks and selective keyword attacks under the general group model.The extensive experimental results demonstrate that the scheme is more expressive, efficient, and feasible in the practical applications.
Managing Smart Home Appliances with Proof of Authority and Blockchain

With the advance in technology and growth in standard of living, smart homes have become a reality. Smart homes consist of home appliances and devices that communicate with each other to address the needs of the residents. These appliances generate, share and consume lots of data which are private and sometimes safety critical to the residents. Managing them is a challenging task. The current frameworks for managing home appliances are centralized in nature. Such frameworks force smart home residents to trust the service providers or a third party. These frameworks are also prone to hacking, compromise of data and a single point of failure. Availability of services can also never be guaranteed with such frameworks. Technologies such as blockchain and smart contracts can help to manage these appliances. In this paper, we study the scope of blockchain technology in smart homes. We propose, implement and evaluate a blockchain based approach using Proof-of-Authority as the consensus mechanism for managing appliances in smart homes. In addition, we compare the performance of our system with the traditional Proof-of-Work based system.
Decision tree–based classifier in providing telehealth service

Although previous research showed that telehealth services can reduce the misuse of resources and urban-rural disparities, most healthcare insurers do not include telehealth services in their health insurance schemes. Therefore, no target variable exists for the classification approaches to learn from or train with. The problem of identifying the potential recipients of telehealth services when introducing telehealth services into health welfare or health insurance schemes becomes an unsupervised classification problem without a target variable.We propose a HDTTCA approach, which is a systematic approach (the main process of HDTTCA involves (1) data set preprocessing, (2) decision tree model building, and (3) predicting and explaining of the most important attributes in the data set for patients who qualify for telehealth service) to identify those who are eligible for telehealth services.This work uses data from the NHIRD provided by the NHIA in Taiwan in 2012 as our research scope, which consist of 55,389 distinct hospitals and 653,209 distinct patients with 15,882,153 outpatient and 135,775 inpatient records. After HDTTCA produces the final version of the decision tree, the rules can be used to assign the values of the target variables in the entire NHIRD. Our data indicate that 3.56% (23,262 out of 653,209) of the patients are eligible for telehealth services in 2012. This study verifies the efficiency and validity of HDTTCA by using a large data set from the NHI of Taiwan.This study conducts a series of experiments 30 times to compare the HDTTCA results with the logistic regression findings by measuring their average performance and determining which model addresses the telehealth patient classification problem better. Four important metrics are used to compare the results. In terms of sensitivity, the decision trees generated by HDTTCA and the logistic regression model are on equal grounds. In terms of accuracy, specificity, and precision, the decision tree generated by HDTTCA provides a better performance than that of the logistic regression model. When HDTTCA is applied, the decision tree model generates a competitive performance and provides clear, easily understandable rules. Therefore, HDTTCA is a suitable choice in solving telehealth service classification problems.