Reviewerly/exHarmony huggingface dataset:
Secure, fair and instant data trading scheme based on bitcoin

Data trading scheme based on Bitcoins faces many challenges. One of them is the transactions’ fairness. That is, the distrust between the unfamiliar parties involved in the transaction is likely to cause unfair trade. For example, the party can provid inveracious data or refuse to pay after receiving the authentic data. Another one is the Bitcoin transaction latency, which causes the inability to reach an instant transaction. The third one is the illegal trading of data, for example, malicious agencies buy and sell users’ private data to make a profit. In order to address these challenges, we propose a secure, fair and instant data trading scheme. In this paper, we adopt a special Bitcoin script to reduce transaction latency. The script requires the payee to provide the payer the encryption key of data or he will not get Bitcoin. This effectively ensures fairness. If the same account is spent twice, the transaction’s script will expose the private key of the account. This allows Bitcoin transactions are completed instantly without waiting. In addition, by using identity-based signature and attribute-based encryption, we achieve the goal that only legitimate users can buy and sell data. What’s more, the data seller stores the data in the cloud, and the cloud server performs data auditing to ensure data integrity. Security analysis shows that our scheme is not only fair and reasonable, but also resistant to collusion attacks and double-spending attacks. Performance analysis shows that our scheme has a shorter execution time and is more suitable for real scenarios than other existing schemes.
Optimized highway deep learning network for fast single image super-resolution reconstruction

With the success of the deep residual network for image recognition tasks, the residual connection or skip connection has been widely used in deep learning models for various vision tasks, including single image super-resolution (SISR). Most existing SISR approaches pay particular attention to residual learning, while few studies investigate highway connection for SISR. Although skip connection can help to alleviate the vanishing gradient problem and enable fast training of the deep network, it still provides the coarse level of approximation in both forward and backward propagation paths and thus challenging to recover high-frequency details. To address this issue, we propose a novel model for SISR by using highway connection (HNSR), which composes of a nonlinear gating mechanism to further regulate the information. By using the global residual learning and replacing all local residual learning with designed gate unit in highway connection, HNSR has the capability of efficiently learning different hierarchical features and recovering much more details in image reconstruction. The experimental results have validated that HNSR can provide not only improved quality but also less prone to a few common problems during training. Besides, the more robust and efficient model is suitable for implementation in real-time and mobile systems.
Analysis of Ex-YOLO Algorithm with Other Real-Time Algorithms for Emergency Vehicle Detection

This paper highlights the topic of object recognition and its algorithms, used in detection of different objects. By using the concept of object recognition, emergency vehicles like ambulances, fire trucks and ambulances can be identified on the way so that a clear path can be made for them. This can be used in autonomous vehicles as an algorithm to identify emergency vehicles. This paper is an extension of a previous paper (Goel et al. in Intelligent communication, control and devices. Springer, Singapore [1]), highlighting present techniques related to object detection. In this paper, the focus is to develop a machine learning model that is built on YOLO algorithm and is able to recognize emergency vehicles. Model proposed is Extended-YOLO (Ex-YOLO), an extension of YOLO. It is basically an ML pipeline with two phases, one is the YOLO algorithm that creates bounding boxes across objects and the other would be the classifier that would detect emergency vehicles. The output from the YOLO algorithm is cropped out and then preprocessed. Image tensors are then passed to Phase II classifiers that are pretrained and classified the images to further categories. Classifiers present in Phase II of the pipeline work independently.
Recognition of Different Emergency Situation through Body Gesture Using Microsoft Kinect Sensor

This study aim to recognize gestures that may serve as an indication for an emergency situation. Different gesture that indicates an emergency condition has been observe and studied that will be used as comparison and recognition for recognition. In this study emergency situation are classified into three category namely medical, life threating condition and disaster. The acquisition of gestures was done using the latest camera technology of today which is the MS Kinect sensor. The researchers used the skeletal tracking capability of the sensor to acquire data that will represent the movement or gesture of the person. The recognition of this gesture with respect to an actual medical emergency will be done using Decision tree algorithm.
CoIn: Accelerated CNN Co-Inference through data partitioning on heterogeneous devices

In Convolutional Neural Networks (CNN), the need for low inference time per batch is crucial for real-time applications. To improve the inference time, we present a method (CoIn) that benefits from the use of multiple devices that execute simultaneously. Our method achieves the goal of low inference time by partitioning images of a batch on diverse micro-architectures. The strategy for partitioning is based on offline profiling on the target devices. We have validated our partitioning technique on CPUs, GPUs and FPGAs that include memory-constrained devices in which case, a re-partitioning technique is applied. An average speedup of 1.39x and 1.5x is seen with CPU-GPU and CPU-GPU-FPGA co-execution respectively. In comparison with the approach of the state-of-the-art, CoIn has an average speedup of 1.62x across all networks.
A sound abstract interpreter for dynamic code

Dynamic languages, such as JavaScript, employ string-to-code primitives to turn dynamically generated text into executable code at run-time. These features make standard static analysis extremely hard if not impossible because its essential data structures, i.e., the control-flow graph and the system of recursive equations associated with the program to analyze, are themselves dynamically mutating objects. Hence, the need to handle string-to-code statements approximating what they can execute, and therefore allowing the analysis to continue (even in presence of string-to-code statements) with an acceptable degree of precision. In order to reach this goal, we propose a static analysis allowing us to collect string values and allowing us to soundly over-approximate and analyze the code potentially executed by a string-to-code statement.
Holistic Specifications for Robust Programs

Functional specifications describe what program components can do: the sufficient conditions to invoke components’ operations. They allow us to reason about the use of components in a closed world setting, where components interact with known client code, and where the client code must establish the appropriate pre-conditions before calling into a component. Sufficient conditions are not enough to reason about the use of components in an open world setting, where components interact with external code, possibly of unknown provenance, and where components may evolve over time. In this open world setting, we must also consider the necessary conditions, i. e. what are the conditions without which an effect will not happen. In this paper we propose the $${\mathcal {C}}$$ hainmail specification language for writing holistic specifications that focus on necessary conditions (as well as sufficient conditions). We give a formal semantics for $${\mathcal {C}}$$ hainmail, and discuss several examples. The core of $${\mathcal {C}}$$ hainmail has been mechanised in the Coq proof assistant.
E-Voting System In Smart Phone Using Mobile Application

The development in the web technologies given growth to the new application that will make the voting process very easy and proficient. The E-voting helps in providing convenient, capture and count the votes in an election. This project provides the description about e-voting using an Android platform. The proposed e-voting system helps the user to cast the vote without visiting the polling booth. The application provides authentication measures in order to avoid fraud voters using the OTP. Once the voting process is finished the results will be available within a fraction of seconds. All the casted vote count is encrypted using AES256 algorithm and stored in the database in order to avoid any outbreaks and revelation of results by third person other than the administrator.
Digital Watermarking Technique using Dual Tree Complex Wavelet Transform

With the growing advent of data in day to day life, prevention of illegal data distribution is becoming very important. Digital watermarking is an effective way of ensuring the data is not distributed illegally and its being used effectively to stop the malpractices. Hence, the watermark needs to be imperceptible as well as robust from attacks like rotation, cropping and compression. We propose a method of digital watermarking using a novel technique of Dual Tree- Complex Wavelet Transform. The watermark is embedded into the Chrominance levels by converting the RGB image into YUV channels. This is done because the chrominance levels are less perceptible to the human visual system. A perceptual mask is generated using a predefined low pass filter, which adds to the imperceptibility of the watermark. During the watermark extraction process, the inverse of the perceptual mask generated is used. By using DTCWT it is possible to exploit features like directionality and shift invariance which is not possible in other frequency domain techniques like DWT, DCT and DFT.
Using an Innovative Standard-setting Approach to Align Integrated and Independent Writing Tasks to the CEFR

Claudia Harscha* & Voula Paraskevi Kanistraba University of Bremen, Bremen, Germanyb Trinity College London, London, UK
Opinion Mining on Food Services using Topic Modeling and Machine Learning Algorithms

The development in technologies and the use of social media on a wide scale, has created opportunities for growth in analytics in obtaining useful insights from data with no proper schema. The use of opinion mining in the field of big data is used for categorizing the opinions of people or customers into different sentiments and to evaluate the mood of the customers. Opinion mining has gained significant importance over time due to the steep increase in amount of opinions available online. People share their opinions on products, services, movies, restaurants, schools, hospitals, holiday destinations etc. Social media has made it simple for users to share their opinion and make it available for any person on the Internet. The companies improve their strategy, products, marketing and services in order to detect latest trends and opportunities or to gauge how effective their marketing is. Several research attempts are being made at sentiment analysis to detect sentiment polarity and to perform predictive analytic. It is a research field which uses computer science and psychology. Natural Language Processing studies how machines can be used to comprehend a language and make it do meaningful things. The process of collecting public opinion and analyzing huge amount of social data has drawn great importance because of its real time behavior. A real time McDonald's dataset has been used for this work to determine the positive, negative and neutral reviews about the policies followed or violated by the restaurant. The people have posted their reviews as feedback of the service provided at McDonalds. McDonald's gets an enormous amount of customer comments on their website daily. Their employees don't have time to read every single comment, but they do want to read a subset of comments that they are most interested in. In particular, the media has recently portrayed their employees as being rude, and so they want to review comments about rude service. The objective of this workis to analyze every review of the user and classify if it is positive, negative or neutral in nature, which is called as opinion mining, using Machine Learning algorithms. Furthermore, topic modeling is done on the customer's reviews on the McDonald's services using LDA to find the frequently mentioned topics in their reviews. The classification of the emotions in the reviews are happy, sad, angry etc.
Resource-Efficient and High-Throughput VLSI Design of Global Optical Flow Method for Mobile Systems

Most very-large-scale integration (VLSI) designs of the global optical flow method focus on reducing external memory accesses due to global and iterative processes for low-power operation in mobile systems. However, to achieve this goal, considerable amounts of resources are used and the throughput is decreased. To address these issues, we propose a multirow-based propagation approach and an efficient VLSI architecture. This approach divides an image into multiple small subimages. The flow of each subimage is then estimated sequentially using a small amount of internal memory without accessing any external memory. To avoid discontinuity artifacts stemming from the boundaries of the subimages, boundary conditions are imposed based on the smoothness of the optical flow. In addition, the main equations of the global optical flow method are simplified to boost the processing speed. The experimental results demonstrate that the external memory bandwidth is reduced by 96.7% with negligible accuracy degradation. Furthermore, the proposed design uses 63.4%-98% less internal memory and achieves $1.9\times $ -$18.8\times $ higher throughput-per-watt compared to state-of-the-art designs of the global optical flow method.
Issues and Trends in Information Security Policy Compliance

In the era of Industry 4.0 (IR 4.0), information leakage has become a critical issue for information security. The basic approach to addressing information leakage threats is to implement an information security policy (ISP) that defines the standards, boundaries, and responsibilities of users of information and technology of an organization. ISPs are one of the most commonly used methods for controlling internal user security behaviours, which include, but not limited to, computer usage ethics; organizational system usage policies; Internet and email usage policies; and the use of social media. Human error is the main security threat to information security, resulting from negligence, ignorance, and failure to adhere to organizational information security policies. Information security incidents are a problem related to human behaviour because technology is designed and operated by humans, presenting the opportunities and spaces for human error. In addition to the factor of human error as the main source of information leakage, this study aims to systematically analyse the fundamental issues of information security policy compliance. An analysis of these papers identifies and categories critical factor that effect an employee's attitude toward compliance with ISP. The human, process, technology element and information governance should be thought as a significant scope for more efficiency of information security policy compliance and in any further extensive studies to improve on information security policy compliance. Therefore, to ensure these are properly understood, further study is needed to identity the information governance that needs to be included in organizations and current best practices for developing an information security policy compliance within organizations.
Comparative Study of Feature-based Algorithms and Classifiers in Face Recognition for Automated Attendance System

Attendance plays an indispensable role in any educational system. In this research work, an automated attendance recording systems has been proposed to update the attendance records of the students by recognizing their physical presence in the classroom with the help of face detection and recognition techniques. Face recognition mimics the operations of object recognition in computer vision and image processing domain. PCA and LDA are the two widely used models for feature extraction in face recognition algorithms to extract the low dimensional and more discriminating features from face. The purpose of this paper is to present an autonomous, relative study of three state-of-the-art appearance-based feature extraction methods (PCA, LDA and Hybrid approach) in completely even conditions regarding processing and algorithms execution. The experiments were performed on face databases known as SDB (Student Data Base), where the images were collected by using Truevision HP laptop camera with different illumination conditions at different angles with different facial expressions and poses etc. The hybrid approach improves the face recognition rate when compared with PCA and LDA using SVM as a classifier.
Target selection by gaze pointing and manual confirmation: performance improved by locking the gaze cursor

Eye movement-based human-computer interactions are emerging in diverse scenarios. When selecting targets on a user interface, the method of combining fast gaze pointing with reliable manual action is becoming increasingly popular. However, this method suffers from noise in gaze pointing caused by eye jitters and users' habitual early move-away of gaze before manual actions. Here we propose a novel solution to mitigate these problems by locking the gaze cursor at the target for imminent manual selection. We compared this gaze-lock cursor with a conventional gaze cursor in a typing task with varying key sizes and key gaps. Results show that typing performance was significantly better with larger key size and gap. More importantly, the gaze-lock cursor significantly increased speed and decreased errors when compared to a conventional gaze cursor. Our findings demonstrate that the gaze-lock cursor is a promising tool for gaze interactions involving frequent target selections. Practitioner summary: Target selection by gaze pointing and manual confirmation suffers from eye jitters and users' habitual early move-away of gaze before manual actions. The performance of this method can be improved by applying the gaze-lock cursor we proposed, increasing target size or increasing the target gap. Abbreviations: WTC: warping to target center; ALCM: automatic lock of cursor movement; LCD: liquid crystal display; EWMA: exponential weighted moving average; ER: error rate; ET: execution time; ED: edit distance; CV: coefficients of variation; ANOVA: analysis of variance; GUIs: general user interfaces
Environment Modeling and Abstraction of Network States for Cognitive Functions

Cognitive Autonomous Networks (CANs) promise to overcome the shortcomings of current Self-Organizing Network (SON) implementations, i.e., the limited flexibility and adaptability to changing environments, by applying cognition. In CAN, intelligent network automation functions, herein called Cognitive Functions (CFs), apply machine learning techniques to learn context-specific behavioral policies with which to automate network operations. For proper operation, the CAN system needs to learn the environment in which the functions are operating and to abstract the environment and performance observations into states to which the CFs must respond. This paper proposes a design and implementation of an Environmental-state Modeling and Abstraction (EMA) engine that could be tasked to learn the required abstract states in a consistent way across multiple CFs.
Machine Learning approaches for Nastaliq style Urdu handwritten recognition: A survey

In day to day life handwriting is a mean of communication and recording the information regardless of the new technologies. The handwritten information is converted into digital form that is used as an input. Recognition is an attractive area of interest in research in recent years. There are good recognition system available for alphabetical and symbolic languages but for the language based on Arabic script like Urdu no such recognition system exist. The recognition is done either online or offline. For an online recognition system the input is taken through stylus or tablet whereas offline recognition is done by taking images through scanner. With the development in electronic tablets the movements of pen are captured accurately. Recognition of Urdu handwritten text is very complex task due to challenges in Urdu script. In this paper a survey is carried out on different Deep learning and statistical techniques for handwritten text recognition.
A Facial Expression Recognition Based on Improved Convolutional Neural Network

In order to solve the problems of low recognition rate and complex algorithm of traditional facial expression recognition methods, an improved facial expression recognition algorithm based on convolutional neural network (CNN) was proposed. The convolutional neural network uses batch regularization and ReLU activation function to solve the problem of gradient disappearance. The Dropout technology is introduced to solve the problem of network overfitting. Experimental results show that the improved convolutional neural network can improve the accuracy of face expression image recognition.
RepeatPadding: Balancing words and sentence length for language comprehension in visual question answering

Visual question answering (VQA) is a complicated Turing-AI task which needs not only to understand the multi-modality inputs but also reason to provide correct answer. Nowadays, there are complicated and sophisticated modules for reasoning in popular works. However, the language representation which is frequently treated as the guider of VQA hasn’t been fully explored in current researches, leading to insufficient reasoning and unsatisfactory answer. In this work, two types of method including VieAns and RepeatPadding which focus on language processing are proposed to balance the sentence by cropping and padding the question, where the language information is transformed to different expressions and further pushes the language model to grab more representative features for further boosting the accuracy of predicted answers. Experiments on the benchmark COCO-QA and VQA2.0 datasets are conducted to demonstrate the effectiveness of the proposed method. Particularly, the proposed RepeatPadding method is more suitable for different language models.
Unsupervised Blockchain for Safeguarding Confidential Information in Vehicle Assets Transfer

Blockchain is extensively used for transaction management for digital assets. Vehicle assets transfer is a challenging task. Several participants are involved in the vehicle assets transfer, so it leads to the risk of modifying the vehicle information, disclosing relevant information to the public, and several faults. In such a situation, necessary data can get very powerless against fakes and information altering or even become non-discernible. Here the attempt is to entail an unsupervised confidential information management system assuring the users possessing and governing their information. The protocol is implemented, which converts the blockchain into computerized access governance manager that does not possibly need to believe the third party. Distinct from bitcoins, the transactions are not purely financial as they usually carry procedures like storing, querying, and distribution of information.