Reviewerly/exHarmony huggingface dataset:
A Novel Approach of Steganalysis to Deal with Steganographic Algorithm Mismatch

Steganalysis is the art to detect the hiding information introduced by the algorithm of steganography. When steganalysis model is trained on the image set with stego images produced by a certain type of steganographic algorithm, it may be hard to accurately detect another type of steganographic algorithm, we call this phenomena as steganographic algorithm mismatch in steganalysis, which greatly prevent the laatory steganalysis model from being put into the real-world applications. To solve this problem, researchers have proposed two widely-used approaches, i.e., Spatial Rich Models (SRM) + Ensemble Classifier (EC), and Convolutional Neural Network (CNN) + Transfer Learning (TL). In this paper, we propose a different approach by constructing the “similar” or close training images that more suitable for fine-tune the steganalysis networks. We use an advanced steganalysis CNN to extract steganographic features, then use the features to match a “similar” image set which may be produced by one or several “similar” steganographic algorithms, and finally use the matched “similar” image set to fine-tune a specific advanced steganalysis networks. Experimental result on three types of spatial steganographic algorithms prove that the proposed method can improve the detection accuracy in the scenario of steganographic algorithm mismatch.
Assessment of Humorous Speech by Automatic Heuristic-based Feature Selection

Following the amount of data and file size, the dimensions of the features can also change, causing heavy usage load on computers by simple multiplication. As technology progressed, we generate clearer sound files, resulting in more High Definition (HD) data with a direct impact on its size. Since many records are critically needed for further analysis, reducing files count and sacrificing clearer sound files is not feasible. In selecting features that best represent humorous speech, we need to implement the Feature Selection (FS) techniques. The FS acts as helpers in computing features with more than ten features/attributes. The purpose of this research is to find the FS technique with the highest accuracy of Random Forest classification, specifically for humorous speech. Unlike the usual FS techniques, we chose to employ the heuristic-based FS techniques, namely, Particle Swarm Optimization, Ant Colony Optimization, Cuckoo Search, and Firefly Algorithm. We applied the FS techniques in WEKA, over their simplification of usage; also jAudio of GUI-based feature extraction for the same reason. Moreover, we used the speech data from the UR-FUNNY dataset, which comprised 10.000 sound clips of both humorous and non-humorous speech by TED Talks speakers.
Standards for Cloud Risk Assessments - What's Missing?

Conducting a risk assessment (RA) for cloud computing platforms presents new challenges in the space of Information Security Management Systems (ISMS). ISO 27001 RA methods have been used for the Alcohol Monitoring Systems (AMS) ISMS across a portfolio of products and services. Scaling these localized techniques to national and international cloud security standards shows that a `one size fits all' risk assessment approach does not exist in the industry today. The context and methods for conducting cloud risk assessment are examined across representative national and international standards and guidelines. Recommendations for standardizing the RA methods for cloud computing are suggested based on industry best practices.
AN ANALYSIS OF SEEKING AGREEMENT STRATEGIES IN THE CRAZY RICH ASIAN MOVIE

Communication is a form of verbal use that people talk about one another. The purpose of communication is to provide information to others. Communication is fundamental, so people need to communicate with others. A well interaction requires the fundamental of politeness to unite communication methods and will provide information to others. Using politeness, can make the interaction avoid ambiguity and will give a good response between the speaker and the listener. But in this research, the researcher will only focus on one of the strategies which is called as Seeking Agreement. The method that the researcher has been using in this research is descriptive qualitative method which is explain by word, sentences or even phrases. The researcher uses observational method and non-participatory as a method in collecting the data. In analyzing the data, the researcher uses pragmatics identity method. The result findings show that there are 15 data found from the character’s utterances that contain an agreement from the speaker and hearer
Person Search via Anchor-Free Detection and Part-Based Group Feature Similarity Estimation

In order to solve the problems of insufficient accuracy of pedestrian bounding boxes in person search and large-scale person matching. A novel person search framework is proposed, which includes: (1) A multi-layer cascade heatmap mechanism (MCHM) is proposed, which aggregates pedestrian features by multi-layer heatmaps cascaded and improves the accuracy of the pedestrian bounding box by optimizating the offset between the center of the bounding box and the center point. (2) A learnable part-based pedestrian feature weight calculation module is proposed, which can learn the weight of the part according to the importance of the part-based feature instead of manually set hyperparameters. (3) A group feature correlation graph convolution network (GFCGCN) is proposed, which can calculate the similarity between group pedestrian features and provide a more accuracy end to end person search work. Some ablation studies and comparative experiments on datasets CUHK-SYSU, PRW show that our model can effectively achieve more accuracy pearch search with accuracy of 88.7% rank-1 and 78.2% mAP.
Effective C

The world runs on code written in C, yet more can be done to help developers learn to write professional, secure, and effective C programs. This article describes why the C programming language has succeeded and what’s next for the language from the perspective of a long term C Standards Committee expert.
Understanding Interactive and Explainable Feedback for Supporting Non- Experts with Data Preparation for Building a Deep Learning Model

It is difficult for non-experts to build machine learning (ML) models at the level that satisfies their needs. Deep learning models are even more challenging because it is unclear how to improve the model, and a trial-and-error approach is not feasible since training these models are time-consuming. To assist these novice users, we examined how interactive and explainable feedback while training a deep learning network can contribute to model performance and users’ satisfaction, focusing on the data preparation process. We conducted a user study with 31 participants without expertise, where they were asked to improve the accuracy of a deep learning model, varying feedback conditions. While no significant performance gain was observed, we identified potential barriers during the process and found that interactive and explainable feedback provide complementary benefits for improving users’ understanding of ML. We conclude with implications for designing an interface for building ML models for novice users.
Identification and Recognition of Printed Distorted Characters Using Proposed DCR Method

Technological advancement has been developed immensely in two areas over the past few years; these are computer and communication networks. This breakthrough has provided an opportunity for people around the world to access literature and important information. If the database of these manuscripts can be developed and make them available on the internet, the people will access those information as desired. The documents can be well-preserved in digital library so that they cannot be degraded. They are trying to improve the recognition rate in order to get the precise meaning of texts. Distorted character recognition is complicated in contrast with simple plain text images. Sometimes distorted character is seemed to be other character and the accuracy rate drops down drastically. As a result significant information is misplaced. In this paper, we are proposing an algorithm which will improve character recognition rate however it is distorted document image. The accuracy rate of our recommended algorithm is 97%.
Visualization of Kernel Function of Convolutional Neural Network

Abstract In recent years, deep learning algorithms have been applied in various fields, and with the continuous development of neural networks, the newly emerging network structures have become more and more complex. However, neural networks are often similar to a black box. The evolution of parameters and the changes of neurons are unknowable. How to understand neural networks is an important research topic. Therefore, for beginners and researchers, a correct way to understand and explain neural networks is needed to improve neural networks. Visualization methods have always been used in the interpretability research of knowledge, which is very helpful to show complex algorithms and networks in the form of specific images, so that users and researchers have significant help in studying the structure of the algorithm and optimizing the performance of the algorithm. The visualization method has been applied to many mining fields. The maximization activation function was been used to realize the visualization of the kernel function in the convolutional neural network (CNN) in this work, and then the network structure and other content are visualized.
Global-Local Mutual Guided Learning for Person Re-identification

Person Re-Identification (Re-ID) plays a significant role in intelligent surveillance systems. Existing popular methods mainly focus on locating regions with specific pre-defined semantics to learn local representations, where pedestrian part-level features are inefficient to fully utilize the global feature information. Besides that, some methods miss out semantic transition information of human body. In this paper, we propose an end-to-end feature learning strategy to get refined feature representations with global-local mutual guided learning. In order to explore global and local information, we design a Global-Local Mutual Guided Network (GLMG-Net). It contains two branches to learn global feature representations, and local feature representations, respectively. For mutual guided module, global features are combined with each local feature by the add-wise operation. In the training process, this module enables branches to guide each other. Comprehensive experiments conducted on the public datasets of Market-1501 and DukeMTMC-ReID indicate that our method outperforms state-of-the-art approaches in several cases. In particular, mean average precision (mAP) scores of our method on those benchmarks are 89.2% and 79.7%, respectively.
Robust Graph Regularized Extreme Learning Machine Auto Encoder and Its Application to Single-Cell Samples Classification

Combined with Auto Encoder (AE), Extreme Learning Machine Auto Encoder (ELM-AE) has attracted the interest of researchers in recent years. Considering the classification tasks of single-cell Ribonucleic Acid sequencing (scRNA-seq) data, in this paper, we propose a novel supervised learning method based on ELM-AE, which is named Robust Graph Regularized Extreme Learning Machine Auto Encoder (RGELMAE). The method introduces L2,1-norm minimization on loss function to improve the robustness, and combines with the manifold regularization framework to explore the internal local structure between data points. Finally, RGELMAE is applied to the classification tasks of scRNA-seq data. The experimental results indicate that our method can effectively extract the key information representing the original data, and improve the classification performance of ELM.
Study on Google Firebase for Real-Time Web Messaging

In today’s fast-paced life real-time communication with everyone is essential as well as appreciated by everyone. This study introduces a real-time database server called the Google Firebase API and it features through a web messaging app. A real-time database is the type of database that stores and fetches the data stored in it very quickly. But firebase’s this feature is just tip of the iceberg, it’s much more than that it provides developers with a variety of features that not just creates a secure system but also develop communication- based applications with ease. This article will cover how firebase helps backend as well as storage purpose of an application and reduces the burden from the technical point of view.
Prototype Smart Home Environment with Biofeedback

In this paper we present a prototype of a smart home system which can actuate different peripherals based on the emotional “arousal” level of an user. The system is comprised of two embedded subsystems named “Wearable” and “Benchtop” which communicates with one another over UPD/IP protocol. The Wearable unit can differentiate the emotional arousal into three distinct classes (Normal, Medium and High) based on physiological data whilst the Benchtop unit can display different colors on a 16 digit NEOPIXEL ring and, play tones to emulate actuation of peripheral devices in the smart home environment. Experiments with three video clips were performed which showed that the system can classify emotional arousal with an average accuracy of 41%. An FSM model of the Benchtop unit was created using Ptolemy II which showed the model to be fully deterministic and robust to communication disruption between the two units. The proposed project will add a new paradigm in smart home and IoT research by incorporating emotional feedback to automatically adjust the indoor environment for greater comfort, ease of living and in-home assisted ambulatory care for the residents.
The Integration of Scheduling, Monitoring, and SLA in Cyber Physical Systems

Cyber-Physical Systems (CPS) is a very complex system where a new management layer must be developed. This chapter presents the benefits and challenges of deploying real-time Scheduling, Monitoring, and End-to-End SLA (SMeSLA) in CPS. We propose an SMeSLA conceptual architecture which allows end-users to submit their service level requirements to an SLA manager; as a result, scheduling and monitoring managers would operate accordingly. The SMeSLA management layer empowers CPS system to meet consumers’ satisfaction and achieve optimal performance. However, in order to successfully deploy SMeSLA in CPS, many technical and general challenges must be addressed.
A Method for Decompensation Prediction in Emergency and Harsh Situations

To save more lives, critically ill patients need to make timely decisions or predictive diagnosis and treatment in emergency and harsh conditions, such as earthquakes, medical emergencies, and hurricanes. However, in such circumstances, medical resources such as medical staff and medical facilities are short supply abnormally. So, we propose a method for decompensation prediction in emergency and harsh conditions. The method includes components such as patient information collection, data selection, data processing, and decompensation prediction. Based on this, this paper demonstrates the method using MIMIC-III data. Firstly, we tried a series of machine learning models to predict physiological decompensation. Secondly, to detect patients whose condition deteriorates rapidly under severe and limited circumstances, we try to reduce the essential physiological variables as much as possible for prediction. The experimental results show that the Bi-LSTM-attention method, combined with eleven essential physiological variables, can be used to predict the decompensation of severe ICUs patients. The AUC-ROC can reach 0.8509. Furthermore, these eleven physiological variables can be easily monitored without the need for complicated manual and massive, costly instruments, which meets the real requirements under emergency and harsh conditions. In summary, our decompensation prediction method can provide intelligent decision support for saving more lives in emergency and harsh conditions.
LSTM Neural Network based Tensile Stress Prediction of Rubber Streching

To explore the effective information contained in mass data and improve the accuracy of stress prediction under low strain rate, a stress prediction method based on a hybrid model of convolutional neural network (CNN) and long short-term memory (LSTM) network is proposed for the temporal characteristics and non-linearity of stress data. Massive historical stress data and strain data are constructed as continuous features according to the time sliding window as input. Firstly, feature vectors are extracted by CNN, constructed in the manner of sequence and used as input data of LSTM network. Then the LSTM network is employed to predict the stress. Stress data obtained in the process of rubber stretching are divided into two parts: training data and test data. The model is trained by training data and test data are used for validation of the proposed model. Experimental results show that the proposed prediction method has higher prediction accuracy than the standard LSTM network.
Enabling Trusted Digital Identities ? From Connected Citizens to Connected Objects

Trust is a critical component of any identity system. Several incidents in the past have demonstrated the existence of possible harm that can arise from misuse of people's personal information. Giving credible and provable reassurances to people is required to build trust and make people feel secure to use the electronic services offered by companies or governments on-line. However, when it comes to privacy, typical identity management systems like PKI fail to provide strong reassurances. For example, in these systems, the so-called "Identity Provider" is able to trace and link all communications and transactions of the users.
Leveraging Explicit Unsupervised Information for Robust Graph Convolutional Neural Network Learning

Most existing graph convolutional networks focus on utilizing supervised information for training semi-supervised graph learning. However, the inherent randomness of supervised information can reduce the robustness of graph convolutional network models in some cases. To cope with this problem, in this paper, we propose a novel semi-supervised graph representation learning method RUGCN by leveraging explicit unsupervised information into training. We first propose a practical training method to ensure unsupervised information measurable by preserving both unsupervised (ranking smoothing) and semi-supervised (Laplacian smoothing) information. And then, we introduce a broadcast cross-entropy function to ensure ranking smoothing run in harmony with Laplacian smoothing. Experiments show that RUGCN achieves competitive results and stronger robustness.
Blockchain: Applications and Challenges in the Industry

Blockchain technology has brought potential security features for the industrial communities. The capability of blockchain can be seen in solving the industrial challenges as in sharing the transaction records with security, interoperability between the devices, transparency in the supply chain and logistics industry, etc. Blockchain provides the distributed, decentralized, and secured transaction ledger. The omitting intermediaries’ property of blockchain reduces the effective cost, while the distributed and decentralized property has shown the application possibility in various cases. This paper has described the potential applications of blockchain and their drawbacks in the various industrial sectors. However, blockchain applications have several challenges which are to be solved.
Weakly Supervised Pedestrian Attribute Recognition with Attention in Latent Space

Pedestrian attribute recognition is a key problem in intelligent surveillance. Relations between attributes and human body structures or relations among attributes are beneficial to attribute recognition, while the annotations are just image-level binary labels. In this work, we propose a novel pedestrian attribute recognition network that takes advantage of latent attribute localizations and local attribute relations to improve the performance of pedestrian attribute recognition. Our method generates latent attribute localization maps by weakly-supervised learning in latent attribute localization (LAL) module. These latent attribute localization maps are fed into the local attribute attention (LAA) module to extract local attributes, and local attributes are interacted with each other with the attention mechanism. Extensive experiments made on the publicly pedestrian attribute datasets of PETA and RAP show that our model outperforms previous methods.
Ensembling Document and Graph Vectors for Improving Topic Modeling of Microblogs

Microblogging is a popular social networking service in which users interact and post messages. Also, the users are provided with facility to frequently engage in various discussions relating to their areas of interests, fields they want to explore, and people they want to connect with. Topic modeling is a mechanism to detect the category to which a group of words belong to. Employing topic modeling in microblogs is a quite productive way of organizing the content. However, topic modeling of microblogs is relatively an unexplored area. A prime challenge is to automatically segregate the data into the abstract topics that they belong to, so that a user need not manually search for posts of his/her delight, but can easily move to the particular topic which provides the sets of related records. This paper proposes a novel topic modeling method by combining graph based document representations and Bag of Words (BoW) representation in order to classify the microblog data into the topic that it belongs to. Our proposed system is validated through experiments, using real-world data. Our method performs well and helps to efficiently designate documents into its topics.
Continuous prefetch for interactive data applications

Interactive data visualization and exploration (DVE) applications are often network-bottlenecked due to bursty request patterns, large response sizes, and heterogeneous deployments over a range of networks and devices. This makes it difficult to ensure consistently low response times (&lt; 100ms). Khameleon is a framework for DVE applications that uses a novel combination of prefetching and response tuning to dynamically trade-off response quality for low latency. Khameleon exploits DVE's approximation tolerance: immediate lower-quality responses are preferable to waiting for complete results. To this end, Khameleon progressively encodes responses, and runs a server-side scheduler that proactively streams portions of responses using available bandwidth to maximize user-perceived interactivity. The scheduler involves a complex optimization based on available resources, predicted user interactions, and response quality levels; yet, decisions must also be made in real-time. To overcome this, Khameleon uses a fast greedy heuristic that closely approximates the optimal approach. Using image exploration and visualization applications with real user interaction traces, we show that across a wide range of network and client resource conditions, Khameleon outperforms existing prefetching approaches that benefit from perfect prediction models: Khameleon always lowers response latencies (typically by 2--3 orders of magnitude) while keeping response quality within 50--80%.
Comparison of Haar and Daubechies wavelet based denoising for speed control of DC motor

This paper presents the comparison between Haar and Daubechies wavelet based denoising for the application of speed control of DC motor in the presence of noise. The multiresolution property of wavelet is utilized to compensate for the effect of noise in the system. In addition, the performance of associated controller is improved in the presence of uncertainty. It has been observed that similarity of the wavelet's wave shape to the signal being denoised has a direct bearing on its denoising performance. Simulation results are obtained with the help of MATLAB@2015a.