Reviewerly/exHarmony huggingface dataset:
Design and Implementation of an e-Voting System Based on Paillier Encryption

With the rapid development of cloud computing technology, cloud services are gaining wider application space. It gives users the advantage of computing power and storage space that were beyond the reach of the past. However, user privacy and data security are the main problems in the application and promotion of cloud system. How to ensure the privacy of data and ensure its availability in the process of calculating data is a major problem. Ensuring both the privacy and the usability of the data in the process of calculation remains a major challenge. As a promising tool for solving such problem, homomorphic encrytion is a hot topic in both academia and industry in recent years. The purpose of this research is to demonstrate the effectiveness of the Paillier encryption and its homomorphic properties implemented in an electronic voting system. We describe an e-voting system based on Paillier homomorphic encryption along with other cryptographic tools such as blind signatures and zero-knowledge proof. The proposed scheme guarantees the general voting system requirements such as eligibility, accuracy, simplicity, privacy, robustness and verifiability. The scheme is implemented in C++ using GMP, an arithmetic multiprecision library, and the "Paillier" library. This implementation uses the CPU to make the calculations necessary during encryption, decryption, vote validation, and tallying. Some portions of the proposed e-voting scheme such as signing the blinded ballots, checking for valid votes and counting up the votes could be made to run in parallel in order to improve the e-voting system performance. We also implement a "small" version of the voting system using CUDA and demonstrate that it is possible to use the GPU's processing power to accelerate the speed of this e-voting system.
‘User-Home relationship’ regarding user experience of smart home products

Technological developments in recent years have radically advanced the design of smart home products. With the help of artificial intelligence, the interaction between user and product has been raised to a new level. While AI has been changing the type of interaction, the relationships between user, product and home have gained importance to provide useful and meaningful outputs for smart home systems. This paper proposes a new approach by focusing on a specific relationship. The aim of the study is to explore the relationship between the user and the home to obtain insight into improving the user experience quality. The research explores specific characteristics such as home-occupancy status, home type and number of members of a household. This study consists of a literature review on the concept of smart home and a field study. The field study was conducted with 48 participants in the Bay Area, San Francisco. Following this, multiple-choice questionnaires and semi-structured interviews were conducted. With the findings of the study, the impact of the user-home relationship on the user experience of smart home products is discussed. We propose that this relationship should be taken into consideration as a new dynamic dimension for developing user experience strategies.
Multi-Agent Reinforcement Learning Based on Clustering in Two-Player Games

Non-stationary environment is general in real environment, including adversarial environment and multi-agent problem. Multi-agent environment is a typical non-stationary environment. Each agent of the shared environment must learn a efficient interaction for maximizing the expected reward. Independent reinforcement learning (InRL) is the simplest form in which each agent treats other agents as part of environment. In this paper, we present Max-Mean-Learning-Win-or-Learn-Fast (MML-WoLF), which is an independent on-policy learning algorithm based on reinforcement clustering. A variational auto-encoder method based on reinforcement learning is proposed to extract features for unsupervised clustering. Based on clustering results, MML-WoLF uses statistics and the dominated factor to calculate the values of the states that belong to a certain category. The agent policy is iteratively updated by the value. We apply our algorithm to multi-agent problems including matrix-game, grid world, and continuous world game. The clustering results are able to show the strategies distribution under the agent's current policy. The experiment results suggest that our method significantly improves average performance over other independent learning algorithms in multi-agent problems.
Self-Attention-Masking Semantic Decomposition and Segmentation for Facial Attribute Manipulation

Many face attribute manipulation methods can only provide global attribute manipulation according to the attribute labels. In this paper, we propose a self-attention-masking semantic decomposition method which is able to learn an attribute attention mask for each attribute. User can adjust the strength and color of each attribute smoothly and more freely. We decouple the attention of different attributes and overcome the disadvantage of overlap between different attribute attention masks by an attention weighting module. Thanks to the attribute attention masks, our method allows manipulate facial attribute without generator after only once generation. Moreover, we can perform facial semantic segmentation without pixel level semantic labels. Experiments show that our method simultaneously improves the freedom of attribute manipulation and the authenticity of synthetic face. The mean intersection over union of semantic segmentation is over 65% for hair and skin. Our code is available at github.com/flyfeatherok/SAMSD.
Toward Ciphertext Policy Attribute Based Encryption Model: A Revocable Access Control Solution in Cloud Computing

Cloud Computing is the most promising paradigm in recent times. It offers on-demand services to individuals and industries. However, outsourcing sensitive data to entrusted Cloud servers impedes the adoption of Cloud concept. Security presents the most important issue. Consequently, Cloud service provider should implement fine grained access control models. Ciphertext Policy Attribute Based Encryption (CPABE) is considered as one of the most appropriate approach in Cloud Computing environment. However, it suffers from revocation, data owner overhead and computational cost limitations. In this work, we propose a Revocable algorithm (R-CPABE). The main idea of this work is to divide the original data after publishing in cloud server. In case of user revocation, one single slice is affected. Data owner need to retrieve, re-encrypt and re-publish it. To confirm the safety of our solution, we provide a security analysis. To evaluate its efficiency, a performance evaluation is performed.
Controlling Emergency Vehicles During Road Congestion—A Survey and Solution

A substantial amount of time is wasted away during traffic which occurs due to the unsteadiness of traveling circumstances. Traffic congestion is both recurring and non-recurring. Recurring occurs on a daily basis owing to the lack of road capacity as more vehicles are communicating at a certain time than what the road can accommodate at that instance of time. Non-recurring congestion arises due to transitory interference in travel and obstacles such as terrible weather, double parking, vehicle collision, or accidents. Big data analytics and IoT can jointly be used to regulate the traffic in a transport management system. Information may be gathered in real time using cameras, sensors, various wearable gadgets, and smart devices under IoT devices. Sensors linked to traffic signal junction keep transferring information to a central server. The behavior of traffic over time can be illustrated by drawing an analysis of the heavy congestion periods. Data analysis models are assisted cities to operate effortlessly in terms of traffic management. An endeavor has been taken to propose a method that helps traffic congestion under the smart city. Here, we propose to use the concept of IoT that suggests a sensor node and camera be fitted on every junction of the road to capture real-time traffic data. In this regard, the concept of the central server may also be used for storing traffic data and forwarding the status and the best feasible alternate route to high-priority vehicles to avoid real-time congestion on either event-driven or demand-based.
Asymmetric label switching resists binary imbalance

In this correspondence, an asymmetric version of the label switching technique to build binary classification ensembles is introduced. The new version presents one more design parameter, the degree of asymmetry, and, consequently, it is more flexible to adapt to the problem under study. In particular, asymmetric switching allows designs that resist to class imbalance. A Bayesian analysis serves to establish how to deal with datasets for carrying out a principled rebalancing, which can be combined with other principled procedures according to the relative advantages of asymmetric switching. A number of simple experiments support the low sensitivity to imbalance and the validity of the analysis for this method of constructing ensembles.
Prototyping and Preliminary Evaluation of Mind Monitoring Service for Elderly People at Home

In order to support sustainable in-home long-term care, it is essential to monitor mental states of elderly people at home, as well as to encourage their ability of self-care. However, the technical challenges include the limitations on human interventions and sensor-based monitoring, as well as daily recording and externalization of mental states. In this research, we propose Mind Monitoring Service, which aims to monitor mental states and promote self-care of elderly people at home. In the proposed service, an agent asks a user specific questions to acquire his/her mental state. Based on the answers, the service then assesses the mental state and sends feedback. We implement a prototype service, and evaluate the feasibility of the service through a preliminary experiment. The results show that data characterizing mental states of individual subjects was obtained successfully, and that some subjects externalized their minds by feedback from the service.
AAT Meets Virtual Reality

Smoking is still one of the main causes of premature mortality and is associated with a variety of diseases. Nevertheless, a large part of the society smokes. Many of them do not seek treatment, and the path to smoking cessation is often abandoned. For this reason, we have developed a VR application that uses the AAT procedure, which has already achieved positive results in the detection and treatment of addiction disorders, as a basic procedure. We want to support the classical therapies by increasing the motivation of the affected patients through immersion, embodiment and game design elements. For this purpose, we have initially developed and evaluated a first demonstrator. Based on the results and findings, a completely revised VR application was programmed, which also eliminates the identified errors and problems of the first demonstrator. In addition, a mobile application will be developed to support the treatment. Our results show that the transfer of the AAT procedure into virtual reality, and thus into three-dimensional space, is possible and promising. We also found that three-dimensional stimuli should be preferred, since the interaction with them was more intuitive and entertaining for the participants. The benefits of game design elements in combination with the representation of interactions in the form of a hand with gripping animations also proved to be of great value, as this increased immersion, embodiment, and therefore motivation.
The Prototype of Anthropomorphic Robot Arm Work Space Design for Self-reliant Patients

The overall bedridden patients increased by 40 million in this year, however there aren't enough care workers to service them. This research aims to design the prototype workspace of anthropomorphic robot arm for self-service patient, the elderly, and those that have disability. The robotic arm workspace is designed based on kinematics theory. The structure of the robot arm compounds with 6 servo motors, 6 DOF, with 4 links. The peripheral is commanded by computer program, Arduino, via GUI. The robot arm performance was measured by time series and users' satisfaction. The result shows that testers gained the skill to command the robot arm when they repeated the test many times. They were satisfied with the prototype because the prototype would help them to service themselves.
Ensemble framework for concept-drift detection in multidimensional streaming data

The potential objective of data mining (DM) over the data streaming is the detection of concept-drift. Concept-Drift signifies a diversity among the data tuples streamed in the sequence. The concept-drift often appears as incremental or abrupt. The incremental drift denotes the gradual increment of the drift between the tuples of streaming data. The other format of the drift is abrupt, which signifies the drift between tuples of data streaming in sequence. The proposed method is an Ensemble Framework for Concept-Drift Detection in Multidimensional Streaming Data (EFCDD). In addition, the proposed method EFCDD deals with the recurrent drift of the concept in streaming data. To state the drift, the projection diversity of the values representing the field positions or field-IDs, which are in use for framing the structure of the records streaming form the intended sources. The experimental study was carried out by mocking the streams of those transmitting records of the benchmark datasets often used in DM. The outcomes of the experimental study evince the scalability and prominence of EFCDD toward the detection of drift in concept. The proposal performance is measured by comparing simulation outcomes with the other existing model.
A Seamless Image-Stitching Method Based on Human Visual Discrimination and Attention

Stitching gaps and misalignments in mosaic images can severely degrade the human visual perception of mosaic effects. Image stitching plays a key role in eliminating these unpleasant defects. In this paper, an image-stitching method for mosaic images with invisible seams is proposed, according to the research on the human visual system (HVS). By quantifying the human visual attention of images and visual discrimination about luminance difference and fine dislocations, each pixel in the stitching region is given a priority value for tracing a stitching line. Coupled with the processing of an optimal stitching line locating method and the multi-band blending algorithm, the pixels of discontinuous items in mosaic images decrease significantly and the stitching line is almost invisible. This study provides a new insight into the image-stitching field, and the experiments show that the results of the proposed method are more consistent with the human visual system in creating high-quality image mosaics.
Implementation of energy‐efficient fast convolution algorithm for deep convolutional neural networks based on FPGA

The state-of-the-art convolutional neural networks (CNNs) have been widely applied to many deep neural networks models. As the model becomes more accurate, both the number of computation and the data accesses are significantly increased. The proposed design uses the row stationary with network-on-chip and the fast convolution algorithm in process elements to reduce the number of computation and data accesses simultaneously. The experimental evaluation which using the CNN layers of VGG-16 with a batch size of three shows that the proposed design is more energy-efficient than the state-of-the-art work. The proposed design improves the total GOPs of the algorithm by 1.497 times and reduces the on-chip memory and off-chip memory accesses by 1.07 and 1.46 times than prior work, respectively.
RAPID: Rapid and Precise Interpretable Decision Sets

Interpretable Decision Sets (IDS) is an approach to building transparent and interpretable supervised machine learning models. Unfortunately, IDS does not scale to most commonly encountered big data sets. In this paper, we propose Rapid And Precise Interpretable Decision Sets (RAPID), a faster alternative to IDS. We use the existing formulation of decision set learning and propose a time-efficient learning framework. RAPID has two major improvements over IDS. First, it uses a linear-time randomized Unconstrained Submodular Maximization algorithm to optimize the objective function. Second, we design special data structures, based on Frequent-Pattern (FP) trees to achieve better computational efficiency. In this work, we first perform a time complexity analysis of IDS and RAPID, and show the significant advantages of the proposed method. Next we run our algorithm, along with baselines, on three public datasets. We show comparable accuracy for RAPID, with 10, 000x improvement in running time over IDS. Additionally, due to the significant improvements in running time of RAPID, we can run more extensive hyperparameter search algorithms, leading to comparable accuracy with competitive baseline models.
Shilling Attack Detection Scheme in Collaborative Filtering Recommendation System Based on Recurrent Neural Network

With the prosperity of the modern electronic business, merchandise recommendation system has become an important tool for online shopping. However, the threat of shilling attack caused by injecting fake rating records into the system cannot be ignored. To deal with shilling attacks, many methods especial user profile-based detection methods have been proposed. But there are still remainder challenging problems in those methods: (1) detection attributes need to be designed in advance; (2) instability of detection effect when faced with variety of attack models; (3) other aspects such as low accuracy, high computing cost and failure in detecting some special shilling attacks. Therefore, a shilling attack detection scheme based on neural network is proposed in this paper in order to address these challenging problems. In this scheme, the LSTM model is used to learn the historical rating records, and then predict the ratings for the next period when the desired accuracy is achieved. Chi-square test is used to determine whether the item is under attacks by comparing the predicted ratings and the actual ones within the time period. The simulation of experimental results on MovieLens 20M dataset show that our proposal is feasible and effective, and it improves the detection performance.
Zero-Shot Pronunciation Lexicons for Cross-Language Acoustic Model Transfer

Existing acoustic models can be transferred to any language with a pronunciation lexicon (lexicon) that uses the same set of sub-word units as in training. Unfortunately such lexicons are not readily available in many low-resource languages. We bypass this requirement and create lexicons by training a grapheme-to-phoneme (G2P) transducer on a subset of words from other languages for which pronunciations are available. The subset of words is selected based on how representative it is of target language text. We find that cross-language acoustic model transfer using our selection strategy outperforms selection based on language similarity, and results in ASR performance approaching that of hand-crafted rule based lexicons in the majority of cases.
A Comparative Analysis of Unsupervised Learning Techniques for Anomaly Detection in Railway Systems

Anomaly detection is a fundamental Data Mining activity. It is largely applied to identify data with potential to contaminate or bias the results of an analysis. On the other hand, anomalies can also indicate faults in real-world systems. In the railway context, anomalous data can occur due to faults on its assets (elements). Thus, it is possible to use the anomaly detection task both to monitor the system health and to avoid future consequences, especially accidents. Due to the immense volume of data contained in a railway system, unsupervised learning techniques are commonly used for this purpose. However, no studies were found that evaluate the efficiency of these techniques in anomaly detection, mainly applied to large real datasets from real railway systems. This paper thus presents a comparative analysis among unsupervised learning techniques applied to anomaly detection in the behavior of track switches, an availability and safety critical asset in a railway system. The K-means, Self-Organizing Maps, and Auto-encoders techniques were applied to real dataset from a railway in Brazil to detect anomalies in their track switches. A comparison of the technique performances in detecting anomalies in a real dataset were developed, resulting in a ranking. These results can be used as a basis for developing predictive methods for fault detection and diagnosis in railway systems.
Deep Learning Approach to Trademark International Class Identification

A trademark may be a word, phrase, symbol, sound, color, scent or design, or combination of these, that identifies and distinguishes the goods or services of a particular source from those of others. Obtaining a trademark is a complex, time intensive and costly process that involves varied steps before the trademark can be registered including searching prior trademarks, filing of the trademark application, review of the trademark application and final publication for opposition by the public. One of the crucial aspect in the review of the trademark application is determining the international classes of the filed marks based on their goods and services description. Currently, the process of identifying the international classes of a filed mark is performed manually in the United States Patent and Trademark Office (USPTO) and takes significant amount of time. Recently, word embeddings and deep neural networks (DNNs) have revolutionized the field of computer vision and demonstrated excellent performance in various natural language processing (NLP) tasks such as machine translation, speech recognition, sentence and document classification etc. to name a few. In this study, we explored fastText and different neural networks such as Convolution Neural Networks (CNN), Long Short Term Memory (LSTM), bidirectional versions of both LSTM and Gated Recurrent Unit (GRU) and Recurrent Convolutional Neural Network (RCNN) to automate the international class identification for the filed marks based on their goods and services description. Overall, it was found that the trademark word embeddings with RCNN model outperformed the other models. Our study thereby seeks to provide a solution towards the manual, time intensive and laborious process of identifying international classes for the trademarks.
Fusing hotel ratings and reviews with hesitant terms and consensus measures

People have come to refer to reviews for valuable information on products before making a purchase. Digesting relevant opinions regarding a product by reading all the reviews is challenging. An automated methodology which aggregates opinions across all the reviews for a single product to help differentiate any two products having the same overall rating is defined. In order to facilitate this process, rating values, which capture the overall satisfaction, and written reviews, which contain the sentiment of the experience with a product, are fused together. In this manner, each reviewer’s opinion is expressed as an interval rating by means of hesitant fuzzy linguistic term sets. These new expressions of opinion are then aggregated and expressed in terms of a central opinion and degree of consensus representing the agreement among the sentiment of the reviewers for an individual product. A real case example based on 2506 TripAdvisor reviews of hotels in Rome during 2017 is provided. The efficiency of the proposed methodology when discriminating between two hotels is compared with the TripAdvisor rating and median of reviews. The proposed methodology obtains significant differentiation between product rankings.