Reviewerly/exHarmony huggingface dataset:
Visual IoT Security: Data Hiding in AMBTC Images Using Block-Wise Embedding Strategy

This study investigates combining the property of human vision system and a 2-phase data hiding strategy to improve the visual quality of data-embedded compressed images. The visual Internet of Things (IoT) is indispensable in smart cities, where different sources of visual data are collected for more efficient management. With the transmission through the public network, security issue becomes critical. Moreover, for the sake of increasing transmission efficiency, image compression is widely used. In order to respond to both needs, we present a novel data hiding scheme for image compression with Absolute Moment Block Truncation Coding (AMBTC). Embedding secure data in digital images has broad security uses, e.g., image authentication, prevention of forgery attacks, and intellectual property protection. The proposed method embeds data into an AMBTC block by two phases. In the intra-block embedding phase, a hidden function is proposed, where the five AMBTC parameters are extracted and manipulated to embed the secret data. In the inter-block embedding phase, the relevance of high mean and low mean values between adjacent blocks are exploited to embed additional secret data in a reversible way. Between these two embedding phases, a halftoning scheme called direct binary search is integrated to efficiently improve the image quality without changing the fixed parameters. The modulo operator is used for data extraction. The advantages of this study contain two aspects. First, data hiding is an essential area of research for increasing the IoT security. Second, hiding in compressed images instead of original images can improve the network transmission efficiency. The experimental results demonstrate the effectiveness and superiority of the proposed method.
Analysis of color image features extraction using texture methods

A digital color images are the most important types of data currently being traded; they are used in many vital and important applications. Hence, the need for a small data representation of the image is an important issue. This paper will focus on analyzing different methods used to extract texture features for a color image. These features can be used as a primary key to identify and recognize the image. The proposed discrete wave equation DWE method of generating color image key will be presented, implemented and tested. This method showed that the percentage of reduction in the key size is 85% compared with other methods.
Conversational Data Exploration

This paper presents a framework for the design of chatbots for data exploration. With respect to conversational virtual assistants (such as Amazon Alexa or Apple Siri), this class of chatbots exploits structured input to retrieve data from known data sources. The approach is based on a conceptual representation of the available data sources, and on a set of modeling abstractions that allow designers to characterize the role that key data elements play in the user requests to be handled. Starting from the resulting specifications, the framework then generates a conversation for exploring the content exposed by the considered data sources.
Hand Gesture Recognition Based on Faster-RCNN Deep Learning

The hand gesture recognition is an effective way to apply human computer interaction which should aim at good recognition accuracy and high speed.To solve the problem of hand gesture recognition in complex scenes, we propose a new approach for hand gesture recognition which is based on the faster regional convolutional neural network (Faster-RCNN) deep learning algorithm with five layers of neural network.A benchmark database with gestures is used, several general characters under the complex environmental background are chosen to as the processing object.The model is established with a certain number samples for training.The results of model checking show that the algorithm can identify the gesture recognition categories effectively, quickly and accurately with low computational cost.The accuracy can reach highly up to 99.2% which is great significance for human computer interaction application.
The Efficacy of Predictive Methods in Financial Statement Fraud

The existence and persistence of financial statement fraud (FSF) are detrimental to the financial health of global capital markets. A number of detective and predictive methods have been used to prevent, detect, and correct FSF, but their practicability has always been a big challenge for researchers and auditors, as they do not address real-world problems. In this paper, both supervised and unsupervised approaches are employed for analysing the financial data obtained from China’s stock market in detecting FSF. The variables used in this paper are 18 financial datasets, representing a fraud triangle. Additionally, this study examined the properties of five widely used supervised approaches, namely, multi-layer feed forward neural network (MFFNN), probabilistic neural network (PNN), support vector machine (SVM), multinomial log-linear model (MLM), and discriminant analysis (DA), applied in different real-life situations. The empirical results show that MFFNN yields the best classification results in detection of fraudulent data presented in financial statement. The outcomes of this study can be applied to different types of financial statement datasets, as they present a practical way for constructing predictive models using a combination of supervised and unsupervised approaches.
Towards a Taxonomy for In-Vehicle Interactions Using Wearable Smart Textiles: Insights from a User-Elicitation Study

Textiles are a vital and indispensable part of our clothing that we use daily. They are very flexible, often lightweight, and have a variety of application uses. Today, with the rapid developments in small and flexible sensing materials, textiles can be enhanced and used as input devices for interactive systems. Clothing-based wearable interfaces are suitable for in-vehicle controls. They can combine various modalities to enable users to perform simple, natural, and efficient interactions while minimizing any negative effect on their driving. Research on clothing-based wearable in-vehicle interfaces is still underexplored. As such, there is a lack of understanding of how to use textile-based input for in-vehicle controls. As a first step towards filling this gap, we have conducted a user-elicitation study to involve users in the process of designing in-vehicle interactions via a fabric-based wearable device. We have been able to distill a taxonomy of wrist and touch gestures for in-vehicle interactions using a fabric-based wrist interface in a simulated driving setup. Our results help drive forward the investigation of the design space of clothing-based wearable interfaces for in-vehicle secondary interactions.
A New Variant of Teaching Learning Based Optimization Algorithm for Global Optimization Problems

This paper proposes a new variant of teaching learning based optimization (TLBO) algorithm for solving global optimization problems to improve the shortcoming of TLBO. The proposed algorithm uses the genetic crossover and mutation strategies for improving the search mechanism and convergence rate. Genetic mutation strategy is applied in teacher phase of TLBO algorithm for improving the mean knowledge of leaners. While, Crossover strategy is applied in learner phase of TLBO algorithm to find good learner. The results are taken on six well known benchmark test functions. From results, it is observed that the proposed algorithm provides more optimized results in comparison to same class of algorithms.
Low Cost Constant Round MPC Combining BMR and Oblivious Transfer

In this work, we present two new actively secure, constant-round multi-party computation (MPC) protocols with security against all-but-one corruptions. Our protocols both start with an actively secure MPC protocol, which may have linear round complexity in the depth of the circuit, and compile it into a constant-round protocol based on garbled circuits, with very low overhead. In both approaches, the underlying secret-sharing-based protocol is only used for one actively secure $$\mathbb {F}_2$$ multiplication per AND gate. An interesting consequence of this is that, with current techniques, constant-round MPC for binary circuits is not much more expensive than practical, non-constant-round protocols. We demonstrate the practicality of our second protocol with an implementation and perform experiments with up to 9 parties securely computing the AES and SHA-256 circuits. Our running times improve upon the best possible performance with previous protocols in this setting by 60 times.
Teachers’ professional competence: The model of level-based assessment

Модель уровневой оценки профессиональной компетентности учителяС. А. Писарева, М. Ю. Пучков, С. В. Ривкина, А. П. Тряпицына (Санкт-Петербург, Россия
What are We Measuring Anyway?

Research into artificial social agents aims at constructing these agents and at establishing an empirically grounded understanding of them, their interaction with humans, and how they can ultimately deliver certain outcomes in areas such as health, entertainment, and education. Key for establishing such understanding is the community's ability to describe and replicate their observations on how users perceive and interact with their agents. In this paper, we address this ability by examining questionnaires and their constructs used in empirical studies reported in the intelligent virtual agent conference proceedings from 2013 to 2018. The literature survey shows the identification of 189 constructs used in 89 questionnaires that were reported across 81 papers. We found unexpectedly little repeated use of questionnaires as the vast majority of questionnaires (more than 76%) were only reported in a single paper. We expect that this finding will motivate joint effort by the IVA community towards creating a unified measurement instrument.
SecChisel Framework for Security Verification of Secure Processor Architectures

This work presents a design-time security verification framework for secure processor architectures. Our new SecChisel framework is built upon the Chisel hardware construction language and tools, and uses information flow analysis to verify the security properties of an architecture at design-time. To enforce information flow security, the framework supports adding security tags to wires, registers, modules, and other parts of the design description, as well as allows for defining a custom security lattice and custom information flow policies. The framework performs automatic security tag propagation analysis in a new SecChisel parser and information flow checking using the Z3 SMT solver. The same SecChisel codebase is used to design hardware modules as well as to verify the security properties, ensuring that the verified design directly corresponds to the actual design. This framework is evaluated on RISC-V Rocket Chip expanded with AES and SHA modules. The framework was able to capture information leaks in the hardware bugs or Trojans that it was tested with.
SVM-Based Deep Stacking Networks

The deep network model, with the majority built on neural networks, has been proved to be a powerful framework to represent complex data for high performance machine learning. In recent years, more and more studies turn to nonneural network approaches to build diverse deep structures, and the Deep Stacking Network (DSN) model is one of such approaches that uses stacked easy-to-learn blocks to build a parameter-training-parallelizable deep network. In this paper, we propose a novel SVM-based Deep Stacking Network (SVM-DSN), which uses the DSN architecture to organize linear SVM classifiers for deep learning. A BP-like layer tuning scheme is also proposed to ensure holistic and local optimizations of stacked SVMs simultaneously. Some good math properties of SVM, such as the convex optimization, is introduced into the DSN framework by our model. From a global view, SVM-DSN can iteratively extract data representations layer by layer as a deep neural network but with parallelizability, and from a local view, each stacked SVM can converge to its optimal solution and obtain the support vectors, which compared with neural networks could lead to interesting improvements in anti-saturation and interpretability. Experimental results on both image and text data sets demonstrate the excellent performances of SVM-DSN compared with some competitive benchmark models.
Open-Ended Long-Form Video Question Answering via Hierarchical Convolutional Self-Attention Networks

Open-ended video question answering aims to automatically generate the natural-language answer from referenced video contents according to the given question. Currently, most existing approaches focus on short-form video question answering with multi-modal recurrent encoder-decoder networks. Although these works have achieved promising performance, they may still be ineffectively applied to long-form video question answering due to the lack of long-range dependency modeling and the suffering from the heavy computational cost. To tackle these problems, we propose a fast hierarchical convolutional self-attention encoder-decoder network. Concretely, we first develop a hierarchical convolutional self-attention encoder to efficiently model long-form video contents, which builds the hierarchical structure for video sequences and captures question-aware long-range dependencies from video context. We then devise a multi-scale attentive decoder to incorporate multi-layer video representations for answer generation, which avoids the information missing of the top encoder layer. The extensive experiments show the effectiveness and efficiency of our method.
Encoder-decoder with dense dilated spatial pyramid pooling for prostate MR images segmentation

Automatic segmentation of prostate magnetic resonance (MR) images has great significance for the diagnosis and clinical application of prostate diseases. It faces enormous challenges because of the low contrast of the tissue boundary and the small effective area of the prostate MR images. In order to solve these problems, we propose a novel end-to-end professional network which consists of an Encoder-Decoder structure with dense dilated spatial pyramid pooling (DDSPP) for prostate segmentation based on deep learning. First, the DDSPP module is used to extract the multi-scale convolution features in the prostate MR images, and then the decoder is used to capture the clear boundary of prostate. Competitive results are produced over state of the art on 130 MR images which key metrics Dice similarity coefficient (DSC) and Hausdorff distance (HD) are 0.954 and 1.752 mm respectively. Experimental results show that our method has high accuracy and robustness.
Enhancing Case Capture, Quality, and Completeness of Primary Melanoma Pathology Records via Natural Language Processing

PURPOSE Medical records contain a wealth of useful, informative data points valuable for clinical research. Most data points are stored in semistructured or unstructured legacy documents and require manual data abstraction into a structured format to render the information more readily accessible, searchable, and generally analysis ready. The substantial labor needed for this can be cost prohibitive, particularly when dealing with large patient cohorts. METHODS To establish a high-throughput approach to data abstraction, we developed a novel framework using natural language processing (NLP) and a decision-rules algorithm to extract, transform, and load (ETL) melanoma primary pathology features from pathology reports in an institutional legacy electronic medical record system into a structured database. We compared a subset of these data with a manually curated data set comprising the same patients and developed a novel scoring system to assess confidence in records generated by the algorithm, thus obviating manual review of high-confidence records while flagging specific, low-confidence records for review. RESULTS The algorithm generated 368,624 individual melanoma data points comprising 16 primary tumor prognostic factors and metadata from 23,039 patients. From these data points, a subset of 147,872 was compared with an existing, manually abstracted data set, demonstrating an exact or synonymous match between 90.4% of all data points. Additionally, the confidence-scoring algorithm demonstrated an error rate of only 3.7%. CONCLUSION Our NLP platform can identify and abstract melanoma primary prognostic factors with accuracy comparable to that of manual abstraction (&lt; 5% error rate), with vastly greater efficiency. Principles used in the development of this algorithm could be expanded to include other melanoma-specific data points as well as disease-agnostic fields and further enhance capture of essential elements from nonstructured data.
Analysis and Comparison of the Spring Framework and Play Framework Performance, Used to Create Web Applications in Java

In the era of continuous technological development, which is followed by the increase in the speed of computers and the Internet, it is also important to develop techniques and programming languages as well as various mechanisms helping programmers in their work. Therefore, the frameworks were created, or skeleton applications that define the application structure and the general mechanism of its operation, to facilitate writing programs and unify the structure of various applications. The purpose of this article is to analyze and compare the performance of two popular frameworks, used to create a server part of a web application in Java, namely the Spring Framework and Play Framework.
Noise-suppressing channel allocation in dynamic DWDM-QKD networks using LightGBM

Integrating quantum key distribution (QKD) with existing optical networks is highly desired to reduce the deployment costs and achieve efficient resource utilization, and some point-to-point transmitting experiments have verified its feasibility. Nevertheless, there are still many problems in the realistic scenario where QKD coexists with dynamic data traffics. On the one hand, the conventional static channel allocation schemes cannot guarantee the quality of quantum channels in the presence of the time-varying noises. On the other hand, considering the complex noise generation caused by dynamic classical data traffics with variable characters, it is challenging to achieve online high-performance quantum channel assignments. To address these problems, we propose a machine learning based noise-suppressing channel allocation (ML-NSCA) scheme. In this scheme, the LightGBM based ML framework is trained to predict the optimal channel allocations with lowest noise impacts, according to which, the quantum channels are periodically reallocated to guarantee high secure key rate. To improve the accuracy and scalability of the ML framework, we also optimize the method of feature extraction during the training process. The performance evaluation results indicate that the proposed scheme can effectively resist the dynamic noise impacts in the realistic optical networks and obtain higher secure key rate with less operation complexity than the previous schemes.
Research on Automatic Essay Scoring of Composition Based on CNN and OR

Manual scoring may be affected by unconscious factors such as subjective judgment of the reviewer, which may lead to the deviation of scores. Establishing an objective, systematic automatic comment system is of great significance to solve this problem. Most of the existing automatic scoring systems are based on a single neural network or based on feature extraction. The accuracy of the formers need to be improved, and the latter require a lot of time to manually extract features. After exploring and analyzing convolutional neural networks (CNN) and ordinal regression (OR), we propose an automatic essay scoring approach based on a combination of CNN and OR for the characteristics of automatic scoring mechanism. Through the deep learning framework Keras to realize the designing, the experimental results demonstrate that the proposed model has a great improvement on the accuracy and efficiency of the automatic essay scoring than existing methods.
Security and Privacy Issues of Blockchain Technology

Blockchain is a technology that is developed using a combination of various techniques such as mathematics, algorithms, cryptography, economic models, etc. Blockchain is a public ledger of all crypto currency transactions that are digitized and decentralized. All the transactions of cryptocurrencies are stored in chronological order to help users in tracking the transactions without maintaining any central record of the transactions. Application prospects of blockchain are promising and have been delivering the result since its inception. Blockchain technology has evolved from initial cryptocurrency to new-age smart contracts and has been implemented and applied in many fields. Although a lot of studies have been carried out on the security and privacy issues of blockchain, but a systematic examination on the security of blockchain systems is still missing. In this chapter, we will try to demonstrate a systematic illustration on the security threats to blockchain and survey the corresponding real attacks by examining popular blockchain systems. We will discuss the security and the privacy of blockchain along with their impact with regards to different trends and applications in this chapter. The chapter is intended to discuss key security attacks and the enhancements that will help develop better blockchain systems.
Single Image Dehazing and Edge Preservation Based on the Dark Channel Probability-Weighted Moments

The method of single image-based dehazing is addressed in the last two decades due to its extreme variating properties in different environments. Different factors make the image dehazing process cumbersome like unbalanced airlight, contrast, and darkness in hazy images. Many estimating and learning-based techniques are used to dehaze the images to overcome the aforementioned problems that suffer from halo artifacts and weak edges. The proposed technique can preserve better edges and illumination and retain the original color of the image. Dark channel prior (DCP) and probability-weighted moments (PWMs) are applied on each channel of an image to suppress the hazy regions and enhance the true edges. PWM is very effective as it suppresses low variations present in images that are affected by the haze. We have proposed a method in this article that performs well as compared to state-of-the-art image dehazing techniques in various conditions which include illumination changes, contrast variation, and preserving edges without producing halo effects within the image. The qualitative and quantitative analysis carried on standard image databases proves its robustness in terms of the standard performance evaluation metrics.