Reviewerly/exHarmony huggingface dataset:
Privacy-preserving tax-case processing

Tax-case processing is based on a classification of common taxable conditions, e.g. retirement benefits, social security benefits, income brackets. Based on those, groups of people fall under certain categorizations and should be treated equally. However, to maintain privacy, people from the same group might be handled anonymously by different case handlers. Consequently, tax-case processing for different individuals from the same group categorization may end up with different results, depending on the region in which the case is dealt with and which case manager treats the given case. This situation maintain privacy but violates fairness. On the other hand, some of the tax calculation is personal since there is no isolation between the taxable conditions data and the individual data. Often, taxable data provides personally identifiable queues. Furthermore, tax processing cannot take advantage of previous tax-case processing that has been already conducted; thus, reducing the system's overall efficiency for cases that has been already processed. To improve efficiency and transparency of the taxation process, we have investigated and explored architectural designs for privacy-preserving tax-case processing prototypes with the use of blockchain technology. In this article, we provide an overview of the available technologies, classify the architecture, and discuss a proof-of-concept implementation that can facilitate those goals. Based on the findings, a number of frameworks is considered and a proof-of-concept is made to showcase the design intent. We established, that in some cases it is possible to use blockchain technology for tax-case processing to enhance the privacy and improving the transparency at the same time.
Wall Deadlock Evasion Control Based on Rotation Radius Adjustment

This letter describes a wall deadlock evasion method for tracked vehicles. Wall deadlock is a phenomenon where the robot cannot rotate to the commanded direction when it collides with a wall, because the motion is restricted by the wall. The key idea behind solving this problem involves an adjustment of the rotation radius to generate sufficient rotational moment. There are several approaches to generate a rotational moment; however, no previous solution has been established to address this problem by adjusting the rotation radius based on the dynamics of wall deadlock. In this letter, the authors propose a new wall deadlock evasion method based on the sufficient rotation radius estimation. Experimental results show that the robot can generate rotational motion that satisfies conditions expected by the model. The wall deadlock evasion method is implemented and shows improved performance in terms of reproducibility of motion compared with the different approach proposed in our previous work. Wall deadlock evasion provides more choices of motion such as being as close to the obstacles as possible and ensures that the robot can continue locomotion after such motion. By handling wall deadlock, the robots can utilize surrounding walls for motion in situations such as relative positioning or driving in fixed lanes.
Reconstruction of 3D Media with Special Feature Based on Analysis of Morphological Completeness

The Use of computer-based mathematical modeling method has been proven effective for reconstructing 3D structures from a 2D training image (TI). In order to reconstruct the 3D structure of heterogeneous media from a single TI with special morphological characteristics, a 3D reconstruction algorithm based on morphological completeness analysis of TI is proposed in this paper. By analyzing the completeness of TI, this algorithm obtains a matching 3D reconstruction method. For TI with local special morphological features, the morphological completeness should adopt a 3D reconstruction method that only controls one section. In the specific process of reconstruction, this paper presents concrete measures including feature extraction, image restoration and specific reconstruction, etc. At last, the quantitative analysis using four functions including two-point correlation function, linear path function, chord length distribution function and local porosity function shows that the reconstruction structure is well similar to TI (referring to the 3D structure) in terms of quantitative features, and the 3D reconstruction method proposed in this paper is very effective.
An Algorithm of Dynamically-Connected Deep Neural Network

Deep neural network is an important and effective approach to discover patterns inside data. Most existing neural networks, like deep neural network and convolutional neural network, have a fixed network connection among neurons. This will cause overfitting problems and long training time. This article discusses an algorithm to combat these problems, through creating and removing connections during training, leading to faster training and fewer symmetric problems. This enables the network to adapt itself to the proper number of neurons according to the dataset. It greatly speeds up training process, so it is useful for large datasets and deep networks. This method is simple to implement and requires less memory and computing power than existing machine learning algorithm. It also has good results in machine learning problems.
Striking the Balance Between Novelty and Accuracy in Location-Based Recommendation System

With widespread popularity of Location-Based Social Networks (LSBNs), the recommendation problem in this domain has led to significant amount of research regarding its practical applications. Despite extensive studies on recommendation systems based on parameters such as GPS trajectories, user-item ratings and check-in data, few methodologies take novelty of recommendations as a significant parameter. In this paper, we attempt to provide an improved approach to recommend points of interest (POIs) to users using a graph based approach which is in accordance with their personal interests and preferences. The proposed algorithm provides users with a personalized ranked list of venues based on their past check-in data and social relationships, which is novel yet accurate at the same time. It takes into account the existing challenges and is based on two key components: User Preferences and Social Relationships that are inferred from their past check-in history and Entropy of Venues which determines the novelty of recommendations provided. In short, it returns a ranked list of `k' venues which are most likely to suit the personal taste of the user. Experimental results demonstrate that the proposed methodology outperforms the baseline methods in terms of novelty and accuracy.
Adversarial Demotion of Bias in Natural Language Generation

Natural Language Generation models have been a critical area of research in application-oriented artificial intelligence tasks, such as dialogue systems, machine translation, and question answering. The next crucial step in this direction is to ensure quality of generated text. This work proposes a novel method based on adversarial training to mitigate gender bias in generation systems, and can be extended to remove any unwanted characteristics in the generated text.
A Systematic Survey, Classification and Analysis of Stegano-Encryption Techniques for Medical Images

Right information at the right time and only to the right recipient is a pivotal requirement of modern-day communication systems. Classified information, being an important asset, must be protected against snooping, alteration and, fabrication. This is even more important for personally identifiable medical records, owing to the sensitive and private nature of patients information. Secure storage and exchange of medical information are often challenged by evolving threat landscape, which is consistently being nurtured by sophisticated attack-vectors, ever-rising count of security vulnerabilities and un-educated and un-aware users handling this critical information. Traditional approaches of cryptography and steganography are normally deployed to protect/hide medical records; however, they often suffer from implementation flaws. Steganography can shield the data by hiding it in a cover object but once the presence and encoding scheme of data is revealed, it no longer remains secure. Thus, steganography alone cannot guarantee data privacy and is preferably used in conjunction with an appropriate encryption technique. This research surveys and critically analyzes the security techniques deployed for privacy protection of medical images, in particular, those using a combination of steganography and encryption techniques. The paper scrutinizes the effectiveness, coverage and implementation viability of some of the recent techniques over medical records and discusses their strengths and weaknesses in a systematic fashion. The survey follows a thematic approach to classify available approaches with regards to different methods used and is further grouped in chronological order to develop insight regarding the research contributions made in this field over the time scale. These classifications are further used to identify research gaps and pinpoint future research directions.
Multiple Description Image Coding Based on Compression-Guided Optimization

In this paper, we design a new multiple description coding scheme for image signals based on our proposed compression-guided optimization. Firstly, we propose a compression-constrained adaptive filtering method to produce two descriptions for the source image, where the proposed filtering algorithm works not only to guarantee a high-quality side decoding but also make a high-efficient central decoding. Secondly, we design a compression-dependent deblocking algorithm based on the transform coefficients which are decoded from both descriptions to improve the performance for the cental decoding. Experimental results demonstrate that our proposed method achieves impressive performance gains when it is applied to image signals.
An Optimal Trajectory Planning for Autonomous Vehicles Based on Vehicle Dynamics Constraints

In order to improve the safety and comfort of the unmanned vehicle, an optimal local trajectory planning algorithm based on the constraints of vehicle dynamics equation is proposed in this paper, which can also conveniently satisfy the constraints at the starting and end point of the trajectory. In order to prove the superiority of this algorithm, this paper compares it with another two commonly used trajectories described by trigonometric and polynomial function between the same starting and end point, and contrastive analysis of the tracking deviation under a same control strategy. The tracking results show that zero error tracking can be achieved for the optimal trajectory because the optimal trajectory and corresponding steering angle are obtained at the same time in our algorithm and the vehicle dynamic constraints also have been taken into consideration. The polynomial path tracking error is the largest and the trigonometric path is moderate comparatively. In the whole tracking process, the optimal path proposed in this paper can obtain the minimum input absolute value and energy value of the steering angle while the vehicle's side angle, heading angle and yaw rate are relatively small, so that the safety and comfort of the vehicle are ensured.
Domain Specific and Idiom Adaptive Video Summarization

As short videos become an increasingly popular form of storytelling, there is a growing demand for video summarization to convey information concisely with a subset of video frames. Some criteria such as interestingness and diversity are used by existing efforts to pick appropriate segments of content. However, there lacks a mechanism to infuse insights from cinematography and persuasion into this process. As a result, the results of the video summarization sometimes deviate from the original. In addition, the exploration of the vast design space to create customized video summaries is costly for video producer. To address these challenges, we propose a domain specific and idiom adaptive video summarization approach. Specifically, our approach first segments the input video and extracts high-level information from each segment. Such labels are used to represent a collection of idioms and summarization metrics as submodular components which users can combine to create personalized summary styles in a variety of ways. In order to identify the importance of the idioms and metrics in different domains, we leverage max margin learning. Experimental results have validated the effectiveness of our approach. We also plan to release a dataset containing over 600 videos with expert annotations which can benefit further research in this area.
Towards Swarm Intelligence Architectural Patterns

The Internet of Things (IoT) is exploding. It is made up of billions of smart devices -from minuscule chips to mammoth machines - that use wireless technology to talk to each other (and to us). IoT infrastructures can vary from instrumented connected devices providing data externally to smart, and autonomous systems. To accompany data explosion resulting, among others, from IoT, Big data analytics processes examine large data sets to uncover hidden patterns, unknown correlations between collected events, either at a very technical level (incident/anomaly detection, predictive maintenance) or at business level (customer preferences, market trends, revenue opportunities) to provide improved operational efficiency, better customer service, competitive advantages over rival organizations, etc. In order to capitalize business value of the data generated by IoT sensors, IoT, Big Data Analytics/IA need to meet in the middle. One critical use case for IoT is to warn organizations when a product or service is at risk. The aim of this paper is to present a first proposal of IoT-Big Data-IA architectural patterns catalogues with a Blockchain implementation perspective in seek of design methodologies artifacts.
Benchmarking different deep regression models for predicting image rotation angle and robot's end effector's position

Deep visual regression models have an important role to find how much the learning model fits the relationship between the visual data (images) and the predicted continuous output. Recently, deep visual regression has been utilized in different applications such as age prediction, digital holography, and head-pose estimation. Deep learning has recently been cutting-edge research. Most of the research papers have focused on utilizing deep learning in classification tasks. There is still a lack of research that use deep learning for regression. This paper utilizes different deep learning models for two regression tasks. The first one is the prediction of the image rotation angle. The second task is to predict the position of the robot's end-effector in 2D space. Efficient features were learned or extracted in order to perform good regression. The paper demonstrates and compares various models such as a local Receptive Field-Extreme Learning Machine (LRF-ELM), Hierarchical ELM, Supervised Convolutional Neural Network (CNN), and pre-trained CNN such as AlexNet. Each model was trained to learn or extract features and map them to specific continuous output. The results show that all models gave good performance in terms of RMSE and accuracy. H-ELM was found to outperform other models in term of training speed.
The Container Selection Policy Based on Brownout in Edge Computing

When the holistic containerized edge cloud data center is overloaded, VM consolidation and Dynamic Voltage Frequency Scaling cannot function well. Brownout has shown to be a promising technique to handle overloads through dynamically deactivating optimal containers. However, it cannot guarantee the efficiency of handling data center overload and the profit of edge service provider. Therefore, we propose a container selection policy based on penalty and resource utilization equilibrium PU-CSP. Considering the profit of edge service provider, it also can minimize the overload time of data center. The results show that our approach reduces about 26.14% penalty and 33.33% overload time than the approach with lowest utilization container first selection policy LUCF.
Rule Based Method for Terrorism, Violence and Threat Classification: Application to Arabic Tweets

In this paper, we present a rule based method to classify Tweets under three main categories; terrorism, violence and threat classes. Given that Arabic is a morphologically complex language, we build a linguistic module to identify a set of patterns for each class. Our proposed method requires three fundamental steps: First, we create our reference corpus collected from Arabic tweets. From the study of this corpus, we identify a set of linguistic rules. Finally, these patterns will be rewritten into local grammar within the linguistic platform NooJ. The evaluation of our system achieved encouraging results to obtaining 84%, 86.8% and 84.7% in terms of recall, precision and f-score respectively, when applied to test corpus.
Enhancing Neural Sentiment Analysis with Aspect Weights

Sentiment analysis is a challenging task and has impactful applications, including analyzing customer feedback on social media. In this paper, we propose a novel approach which enhances a neural architecture to predict the overall sentiment of restaurant reviews which may contain multiple aspect-level sentiments. We calculate the weights of different aspects of a restaurant and incorporate them in a neural architecture. We also compare our results with the current state-of-the-art approach (ULMFiT [1]) and show an absolute improvement of 7% in the F-score and 6% in the accuracy. To the best of our knowledge, this is the first work in the line of research investigating the incorporation of aspect weights into a neural architecture for sentiment analysis, culminating in a detector thereof.
HiFI: A Hierarchical Framework for Incremental Learning using Deep Feature Representation

The presented work focuses on automatic recognition of object classes while ensuring near real-time training required for recognizing a new object not seen previously. This is achieved by proposing a two-stage hierarchical deep learning framework which first learns object categories using a Nearest Class Mean (NCM) classifier applied directly to CNN features and then, uses a two-layer artificial neural network to learn the object labels within each category. In order to recognize a new object not seen earlier, the category is identified first and then the second stage neural network is incrementally trained with the features of the new object without forgetting previously learnt labels. The proposed hierarchical framework is shown to provide comparable recognition accuracy with significant reduction in overall computational time in recognizing new objects compared to methods that use end-to-end re-training. The efficacy of the approach is demonstrated through comparison with existing state-of-the-art methods on the publicly available CORe50 dataset.
Prototype Selection and Dimensionality Reduction on Multi-Label Data

Multi-label classification problem is one of the most general and relevant problems in the area of classification, where each item of the evaluated dataset is associated with more than one label. This paper discusses novel algorithms for prototype selection and dimensionality reduction on multi-label data. We have extended CNN (Condensed Nearest Neighbor) algorithm for multi-label data. We have also worked on an extension of the Class Augmented PCA(CA-PCA) method for multi-label data. These methods have been implemented on benchmark multi-label datasets and found to give good results.
Innovation of Financial Shared Service Center Based on Artificial Intelligence

With the development of the global economy and the further improvement of artificial intelligence research, the relationship between artificial intelligence and finance is increasingly close. The booming financial sharing services have greatly improved the efficiency of relevant financial work. At present, the global financial sharing service platform has excellent performance in reducing labor cost and improving operation efficiency, but it has not fully exerted the function of creating value of financial work. The establishment of financial sharing requires a large amount of data research, which requires the help of artificial intelligence to carry out relevant data statistics. Using big data and artificial intelligence technology to improve the financial sharing service platform can further liberate human resources and promote capital integration, which is an important way for the financial sharing service center to play its role in creating value. Finally, the improvement work should properly deal with the balance between centralization and decentralization, actively extend the business and expand the professional skills of financial personnel. However, we must see its two sides in the development process and avoid the problems in the research.
Performance Evaluation for Four Supervised Classifiers in Internet Traffic Classification

Supervised machine learning is a method to predict a class for labeled data, to improve different QoS metrics of several scopes such as educational, industrial and medical etc. This paper presents in-deep study focusing on four supervised classifiers were used widely to distinguish or categorize TCP/IP network traffic model and how they can be employed, these four are Naïve Bayes, Probabilistic Neural Network, Support Vector Machine and C4.5 decision tree. The classifiers are compared with regard to three significant metrics namely classification accuracy, classification speed and memory consumption. The implementation results of simulation and comparisons show that C4.5 decision tree introduce best results with high accuracy up to 99.6% using the benchmark dataset consist of 24863 packets compared to the rest three tested classifiers.
Performance Assessment of Equipment Management Based on Cloud Model

Equipment management performance assessment is a multi-criteria decision-making process. In view of the randomness and fuzziness of the factors affecting the equipment management performance and the difficulties to evaluate objectively and quantitatively, this paper improves and perfects the model though weight determination and assessment method selection. To avoid the information loss and distortion, the combination weighting method is established and the cloud model is introduced into equipment management performance assessment. So this paper establishes an assessment model based on combination weighting and cloud model. The approach is tested by evaluating an example and comparing with fuzzy comprehensive evaluation method. The comparison verifies the feasibility and effectiveness of the algorithm.
A New Differential Evolution Searching Strategy With Neighbourhood Search For Global optimization Problem

In this paper, we propose a sequential DE searching strategy algorithm, called STDE. This approach combines two searching strategies in the early and late evolution stage. In the first evolution stage that including nine-tenth iterations, the main idea is to devote effort to global search. In the remaining iterations of second stage that would make use of local search to explore better solution around the current best one.
Mechanizing Bisimulation Theorems for Relation-Changing Logics in Coq

Over the last years, the study of logics that can modify a model while evaluating a formula has gained in interest. Motivated by many examples in practice such as hybrid logics, separation logics and dynamic epistemic logics, the ability of updating a model has been investigated from a more general point of view. In this work, we formalize and verify in the proof assistant Coq, the bisimulation theorems for a particular family of dynamic logics that can change the accessibility relation of a model. The benefits of this formalization are twofold. First, our results apply for a wide variety of dynamic logics. Second, we argue that this is the first step towards the development of a modal logic library in Coq, which allows us to mechanize many relevant results in modal logics.