Reviewerly/exHarmony huggingface dataset:
Hiding Secret Data in Grayscale Images by Improving the Method of Reduced Difference Expansion

Everyone mostly does technology sophistication in sending data to convey information at this time; so, data security is needed. One of the methods used is data hiding in multi-media files such as image, audio, and video. However, there is a mismatch between image quality and insertion capacity in the embedding process. One of the methods that can meet these requirements is Reduced Difference Expansion (RDE). For the RDE method, research can still be a challenging topic, especially for image quality and similarity between the cover and stego images. In this research, we propose an RDE-based data hiding method for embedding data based on expansion points. We make a reduction in the value of pixels that exists to improve the similarity level. The goal is to have higher image quality. The results of the experiments conducted on five medical images show that the proposed method improves the quality, as shown in its PNSR.
Video Super Resolution with Estimation of Motion Information by Using Higher Resolution Images Obtained by Single Image Super Resolution

Video Super resolution algorithms usually utilize the motion information of each pixel in consecutive frames to interpolate pixel values in a higher resolution and reconstruct frames of a higher resolution video from a lower resolution input video. Recently, architectures based on deep neural networks have gained popularity and can generate higher resolution videos with better visual quality. Also, deep neural networks make single image super resolution possible. In single image super resolution, a higher resolution image is constructed from a given input image by learning the transformation from lower resolution images to the higher resolution images. In this paper we propose to apply the single image super resolution algorithm to each frame in the original video and then use the resulting frames to estimate the movements of each pixel. Since the spatial resolution of the estimated motion of each pixel affects the visual quality of the higher resolution video, it is expected that the proposed approach can improve the visual quality of video super resolution. The proposed approach consistently results in good quality video reconstruction when tested on videos with diverse contents and different motion levels, which outperforms state of the art algorithms and offers competing visual performance on benchmark datasets.
Embeddings and Convolution, Is That the Best You can Do with Sentiment Features?

Rapid growth of digital media motivates research on machine-assisted text analysis. Sentiment analysis, among one of the prevalent applications, has drawn great attention. In addition to the traditional bag-of-words models, embedding methods have become de facto standard for text representation, and various convolutional, recurrent and recursive neural networks are dominating leaderboards. Despite the large number of deep learning models in publication, the performance benchmarks in sentiment analysis are approaching a limit. If language-specific syntactic and semantic knowledge is excluded, is there still room for significant improvements? Over a general neural network that is based on word embedding, 2D convolution and max-pooling, we conduct extensive experiments on its various components, including convolutional kernels, pooling methods, recurrent layers, and attention mechanism. Certain combinations show moderate improvements in classification accuracy which are comparable to more sophisticated networks, but no sign of major breakthrough is in sight. We also extend the scope with potential game changers, covering context-aware representations, linguistic information, and large scale knowledge transfer in natural languages. Reported metrics show their great value in breaking the current performance bottleneck.
Efficient Speech-Recognition Error-Correction Interface for Japanese Text Entry on Smartwatches

We propose an efficient speech-recognition error-correction interface for Japanese text entry on smart-watches. Although the accuracy of automatic speech recognition (ASR) has significantly improved, an interface for text modification is still essential. Considering the strict limitation of a narrow display area and practical demand of text modification, the proposed interface arranges the N-best results of ASR and a list of morphemes consisting of the 1-best result to enable quick access to any word to be modified. Specifically, multiple screens of the N-best results are switched by horizontal flicks, and another extended screen listing a morpheme sequence of the 1-best result is scrolled by vertical flicks. The proposed interface was compared with a software keyboard and a speech-input-enabled input method editor (IME), which was a simple combination of speech input and software keyboard. The proposed interface outperformed the other two interfaces in terms of time required to complete specified sentences, subjective score using system usability scale (SUS), and perceived workload quantified using the NASA Task Load Index (NASA-TLX).
LMLSTM:Extract Event-Oriented Keyphrase From News Stream

Keyphrase extraction, as a basis for many natural language processing and information retrieval tasks, can help people efficiently discover their interested information from vast streams of online documents. Previous methods are mostly proposed in general purpose, where keyphrases that represent the main topics are extracted. However, such keyphrases can hardly distinguish events from massive streams of long text documents that share similar topics and contain highly redundant information. In this paper, we address the task of keyphrase extraction for event-oriented retrieval. We propose a novel Long Short-Term Memory Network Language Model (LMLSTM) to extract event-oriented keyphrases that represent or related to a particular event. We conduct a series of experiments on a real-world dataset. The experimental results demonstrate the better performance of our approach than other state-of-the-art baselines.
P‐12.1: Lighting effects, light distribution matters

The interaction between light and its surroundings (i.e. the geometry and materials) shapes the lighting distribution and determines the appearance of scenes and its visual experiences. In this study, we introduced the concept of light distribution solids by distinguishing luminance distribution solids and illuminance distribution solids. It is helpful to understand the spatial and form‐giving lighting effects on objects' appearance. Furthermore, this study together with our previous study (Xia, Pont et al. 2016, Xia, Pont et al. 2016) provide theoretical basis for the exploration of extracting photometry parameters out of HDR panoramic photographs of scenes.
Fast Identification of Heavy Hitters by Cached and Packed Group Testing

The $$\epsilon $$ -approximate $$\phi $$ -heavy hitters problem is, for any element from some universe $$\mathbb {U}=[0..n)$$ , to maintain its frequency under an arbitrary data stream of form $$(x_i, \varDelta _i)\in \mathbb {U}\times \mathbb {Z}$$ that changes the frequency of $$x_i$$ by $$\varDelta _i$$ , such that one can output every element with frequency more than $$\phi {N}$$ and no element with frequency no more than $$(\phi -\epsilon ){N}$$ for $${N}=\sum _i \varDelta _i$$ and prespecified parameters $$\epsilon , \phi \in \mathbb {R}$$ . To solve this problem in small space, Cormode and Muthukrishnan (ACM TODS, 2005) have proposed an $${O}(\rho \epsilon ^{-1}\lg {n})$$ -space probabilistic data structure with good practical performance, where $$\rho =\lg {(1/(\delta \phi ))}$$ for any failure probability $$\delta \in \mathbb {R}$$ . In this paper, we improve its output time from $${O}(\rho \epsilon ^{-1}(\lg {n}+\rho ))$$ to $${O}(\rho ^2\epsilon ^{-1})$$ for arbitrary updates ( $$\varDelta _i\in \mathbb {Z}$$ ) and its update time from $${O}(\rho \lg {n})$$ to amortized $${O}(\rho )$$ for constant updates ( $$\varDelta _i\in {O}(1)$$ ) with the same space and output guarantee by removing application-specific $$\lg {n}$$ terms that are not tunable, unlike other parameters $$\delta $$ , $$\epsilon $$ , and $$\phi $$ .
12.3: Retinex‐based 8k Contrast Enhancement and Its Real‐time Implementation on FPGA

The contrast enhancement technology is widely used in many display devices and the function is to transform the original frame to a brighter one for better display. With the rapid development of 8k (7680 × 4320) technology, the dense pixels bring fine and smooth display experience but also huge computation burden. This work based on the Retinex theory which first proposed by E. H. Land in [1]‐[3] to achieve 8k 60fps contrast enhancement in real‐time. Compared with the traditional histogram equalization method, Retinex requires less computation and easy to implement on hardware. To avoid extra processing delay and frame buffer, a pipelined processing flow is proposed and optimized with inter‐frame design. The demonstration shows the effectiveness on real‐time 8k system and the hardware report shows the potential for cost sensitive design.
Watermark by Learning Non-saliency

This paper studies the capability of non-salient image regions and pattern selection for advancing the security robustness of region-based watermark. However, considerable unpredictability in the assigning image region for encryption exists, since saliency map itself is changed specifically by particular encryption algorithm and saliency detection techniques. The change may lead to misguided selection of image region for decryption. A new solution for watermarking in non-salient region is described and evaluated to derive improvement of secrecy, integrity, and availability of images. This is achieved by using machine learning approach, which favors the advantageous selection of encryption region based on the variation between the salient maps before and after the encryption process. We validate the method by region selection evaluation and the correctness of decrypted messages. Experimental results show that a range of saliency models are adequate for the proposed watermark solution.
Software Test Planning

Testing is a sub-project of a project and needs to be managed as such, and so good planning and monitoring and control are required. Test planning involves defining the scope of the testing to be performed; defining the test environment; estimating the effort required to define the test cases and to perform the testing; identifying the resources needed (including people, hardware, software, and tools); assigning the resources to the tasks; defining the schedule; and identifying any risks to the testing and managing them.
Meta-Heuristic and Non-Meta-Heuristic Energy-Efficient Load Balancing Algorithms in Cloud Computing

The number of users of cloud computing services is drastically increasing, thereby increasing the size of data centers across the globe. In virtue of it, the consumption of power and energy is a major concern for system designers and developers. Their goal is now to develop power and energy-efficient products at the same time maintaining the quality and cost of products and services. For managing the power and efficiency, several aspects are taken into consideration in cloud computing paradigm. Load balancing, task scheduling, task migration, resource allocation are some of the techniques, which need to be efficiently employed to minimize the energy consumption. This chapter represents the detailed survey of the existing solutions and approaches for energy-efficient load balancing algorithms used in cloud environments. The research challenges as well as future research directions are also discussed in this chapter.
Multi-label learning vector quantization for semi-supervised classification

In the context of expensive and time-consuming acquisition of reliably labeled data, how to utilize the unlabeled instances that can potentially improve the classification accuracy becomes an attractive problem with significant importance in practice. Semi-supervised classification that fills the g ap between supervised learning and unsupervised learning is designed to take advantage of the unlabeled data in regular supervised learning procedure for classification tasks. In this paper we proposed a self-learning framework, that firstly pre-learns a classification model using the labeled data, then makes the prediction of unlabeled instances in the form of soft class labels, and re-learned a model based on the enlarged training data. Two multi-label Learning Vector Quantization Neural Networks (LVQ-NNs) are proposed, namely multi-label online LVQ-NN (mLVQo) and multi-label batch LVQ-NN (mLVQb), to work with the soft labels of training instances. The experiments demonstrate that the semi-supervised models using multi-label LVQ-NN as the base classifier can produce better generalization accuracy than the supervised counterpart.
Learning Syntactic and Dynamic Selective Encoding for Document Summarization

Text summarization aims to generate a headline or a short summary consisting of the major information of the source text. Recent studies employ the sequence-to-sequence framework to encode the input with a neural network and generate abstractive summary. However, most studies feed the encoder with the semantic word embedding but ignore the syntactic information of the text. Further, although previous studies proposed the selective gate to control the information flow from the encoder to the decoder, it is static during the decoding and cannot differentiate the information based on the decoder states. In this paper, we propose a novel neural architecture for document summarization. Our approach has the following contributions: first, we incorporate syntactic information such as constituency parsing trees into the encoding sequence to learn both the semantic and syntactic information from the document, resulting in more accurate summary; second, we propose a dynamic gate network to select the salient information based on the context of the decoder state, which is essential to document summarization. The proposed model has been evaluated on CNN/Daily Mail summarization datasets and the experimental results show that the proposed approach outperforms baseline approaches.
Discovering context specific causal relationships

With the increasing need of personalised decision making, such as personalised medicine and online recommendations, a growing attention has been paid to the discovery of the context and heterogeneity of causal relationships. Most existing methods, however, assume a known cause (e.g. a new drug) and focus on identifying from data the contexts of heterogeneous effects of the cause (e.g. patient groups with different responses to the new drug). There is no approach to efficiently detecting directly from observational data context specific causal relationships, i.e. discovering the causes and their contexts simultaneously. In this paper, by taking the advantages of highly efficient decision tree induction and the well established causal inference framework, we propose the Tree based Context Causal rule discovery (TCC) method, for efficient exploration of context specific causal relationships from data. Experiments with both synthetic and real world data sets show that TCC can effectively discover context specific causal rules from the data.
Targeted Black-Box Adversarial Attack Method for Image Classification Models

Deep neural networks (DNNs) are widely applied to image classification tasks. Due to the fact that these models are usually vulnerable, subtle perturbations of pixels may lead to classification errors, which poses a serious threat to the success of DNN applications. Moreover, perturbations of pixels can also corrupt other pattern recognition models such as Naive Bayes (NB), Decision Tree (DT) and Random Forest (RF). In this paper, a general method is proposed to carry out targeted black-box attacks for image classification models. The proposed method can achieve targeted fool rates (TFRs) of 0.873 and 0.781 on CIFAR-10 dataset with and without the access to the training set of the target model respectively. For cross-model attacks, the proposed method can still achieve a TFR of 0.630 on CIFAR- 10. Furthermore, the proposed method is able to mount attacks for up to 100 classes on CIFAR-100 dataset with a TFR of 0.721, successfully handling 99 cases for each class. In our experiments, the proposed method shows higher performance and higher reliability than other black-box attack methods, with 0.123 greater maximum TFR and 0.602 greater minimum TFR than previous methods UPSET and ANGRI on CIFAR-10 in attacks trained on a single model.
Novel Ceiling Neuron Model and its Applications

In this research paper, a novel neuronal model named "Ceiling Neuron" has been proposed. Learning algorithm and proof of convergence of such a model are discussed. An activation function which mimics the ceiling behavior is proposed for Multi - Layer Neural Networks. The dynamic nature of the proposed activation function is discussed. An associative memory model based on Hopfield network is constructed using the proposed ceiling neuron and dynamics of the network are also studied.
Comparison of Rough Mereology Based Path Planning Algorithms for Intelligent Robotics

The goal of this paper is to compare rough mereology algorithms for path planning of mobile agents in intelligent robotics. The Square Fill Algorithm and CFill algorithm are considered. Moreover, path planning on the created potential fields is tested in various scenarios. Time and general path planning statistics are given for all used rough mereology algorithms. Potential ideas on improvement are given after analyzing test results.
Distributed Construction of a Level Class Description in the Framework of Logic-Predicate Approach to AI Problems

Method of a distributed construction and the use of a level description of goal conditions within the framework of a logic-predicate approach to AI problems is described in the paper. The previously proposed logical-predicate approach to solving AI problems is briefly described. Many of these problems are NP-complete or NP-hard. According to a set of goal conditions, the author has earlier proposed construction of their level (hierarchical) descriptions, the use of which significantly decreases the computational complexity of the problems. The construction of such descriptions is performed only once, and then the resulting descriptions are reused. Both the distributed construction of level descriptions of goal conditions and their distributed use make it possible to decrease the time complexity of solving AI problems.
Teager Energy Operator Based Features with x-vector for Replay Attack Detection

Audio replay attack poses great threat to Automatic Speaker Verification (ASV) systems. In this paper, we propose a set of features based on Teager Energy Operator and a slightly modified version of x-vector system to detect replay attacks. The proposed methods are tested on ASVspoof 2017 corpus. When using GMM with the proposed features, our best system has an EER of 6.13% on dev set and 15.53% on eval set, while the EER for the baseline system (GMM with CQCC) is 30.60% on eval set. When combined with the modified x-vector, the best EER further drops to 5.57% for dev subset and 14.21% for eval subset.
The Study of Web Service Discovery: A Clustering and Differential Evolution Algorithm Approach

With the rapid development of the Internet, the number of web services has a significant increase, providing various types of online services available today. However, finding the target web service in a quick and accurate method to meet the needs of users is becoming the major problem of the service discovery. In this study, we proposed a new method of web service discovery, which is based on the clustering and differential evolution algorithm. To carry out our study, firstly, we clustered the service set and identified the most similar class to narrow down the searching scope by calculating the semantic similarity between each class and the query. Second, the similarities of each service in the target class to the query are calculated by using different measurement methods, including Cosine similarity, Euclidean distance, Manhattan distance, Chebyshev distance and Bray Curtis distance. These results are used as the input of the differential evolution algorithm for our iterative simulation. Our findings show that compared with the traditional methods, our proposed approach that integrates the benefits of the clustering and differential evolution algorithm has a lower error, and is more consistent with the users' requirements. More importantly, relevant web services can be retrieved more efficiently and accurately.
Context Gating with Short Temporal Information for Video Captioning

Video Captioning is a newly emerging task which automatically translates content in a video into a textual description. Similar to image captioning, most existing methods simply utilized extracted visual features to generate sentences. However, in video captioning temporal information is much more important for description. Though the short temporal information (STI) is always ignored. Meanwhile, the context of generated sentence seems not been mined enough. In this paper, we build a context gating mechanism with STI based on encoder-decoder (CG-ED) neural framework for video captioning. In our approach, based on the 2D feature space, we cut and recombine the whole 3D features to extract STI by temporal distribution. To balance the contributions of different context of sentences, context gating is designed. Our proposed model is evaluated on two large-scale datasets: Microsoft Research-Video to Text (MSR-VTT) and Microsoft Research Video Description Corpus(MSVD). Experimental results demonstrate that its precision of caption is higher than most of the state-of-the-art approaches.
Knowledge Mining for Faculty Appraisal Based on Students Feedback Using Classification Techniques in R

Faculty performance assessment systems in any Educational Institute are necessarily regarded as basis for assessing faculty’s’ performance and enhancing organization’s success. Although commercial systems supporting Faculty Assessments have been designed, the focus mainly is limited to keeping a record of the information, thus not providing the kind of effective support for implementation of results obtained from assessments. Faculty’s performance is a major challenge faced by Educational Institutions, since the objectives are not clearly defined, assessing it is a lengthy and time taking subjective process, and there is no known system for defining the goals of appraisal realistically and effectively. Another aspect is that of collecting feedback and designing an effective system for supporting the decision making process. This paper proposes a novel technique for classifying and grading Faculty in M groups based on N data sets collected from students from 25 questions where M is taken as 5 and N is 379. The questions are further reduced to seven attributes which are used for Classification of Faculty for performing Knowledge Mining in R. A comparison of different classification Techniques has been done using 10 fold cross validation and based on accuracy of classification techniques, the best one is chosen for evaluating the results of Test Dataset. The results obtained can be further used for evaluating the overall performance of Faculty and Department to which the faculty is associated with. The proposed framework suggests assessing and grading Faculty performance. The results obtained can be used as a roadmap for assessing the overall performance of Faculty, based on combining the results of Students Feedback with that of Self-Appraisal and grades from the Institutional Management.