Reviewerly/exHarmony huggingface dataset:
Detection of One Dimensional Anomalies Using a Vector-Based Convolutional Autoencoder

Anomaly detection is important to significant real life entities such as network intrusion and credit card fraud. Existing anomaly detection methods were partially learned the features, which is not appropriate for accurate detection of anomalies. In this study we proposed vector-based convolutional autoencoder (V-CAE) for one dimensional anomaly detection. The core of our model is a linear autoencoder, which is used to construct a low-dimensional manifold of feature vectors for normal data. At the same time, we used vector-based convolutional neural network (V-CNN) to extract the features from vector data before and after the linear autoencoder that makes the model learned deep features for efficient anomaly detection. This unsupervised learning method used only normal data in the training phase. We used the combined abnormal score calculated from two reconstruction errors: (i) error between the input and output of the whole architecture and (ii) error between the input and output of the linear encoder. Compared with the nine state-of-the-arts methods, our proposed V-CAE shows effective and stable results of AUC with 0.996 in estimating anomalies based on several benchmark datasets.
Predicting Transaction Latency with Deep Learning in Proof-of-Work Blockchains

Proof-of-work based cryptocurrencies, like Bitcoin, have a fee market where transactions are included in the blockchain according to a first-price auction for block space. Many attempts have been made to adjust and predict the fee volatility, but even well-formed transactions sometimes experience delays and evictions unless an enormous fee is paid. In this paper, we present a novel machine-learning model, solving a binary classification problem, that can predict transaction fee volatility in the Bitcoin network so that users can optimize their fees expenses and the approval time for their transactions. The model's output will give a confidence score whether a new incoming transaction will be included in the next mined block. The model is trained on data from a longitudinal study of the Bitcoin blockchain, containing more than 10 million transactions. New features that we generate include information on how many bytes were already occupied by other transactions in the mempool, assuming they are ordered by fee density in each mining pool. The collected dataset allows to generate a model for transaction inclusion pattern prediction in the Bitcoin network, hence telling whether a transaction is well formed or not, according to the previous transactions analyzed. With this, we obtain a prediction score for up to 86%.
Generation of Image Captions Using VGG and ResNet CNN Models Cascaded with RNN Approach

Recent advancements in technology have made available a variety of image capturing devices, ranging from handheld mobiles to space-grade rovers. This has generated a tremendous visual data, which has made a necessity to organize and understand this visual data. Thus, there is a need to caption thousands of such images. This has resulted in tremendous research in computer vision and deep learning. Inspired by such recent works, we present an image caption generating system that uses convolutional neural network (CNN) for extracting the feature embedding of an image and feed that as an input to long short-term memory cells that generates a caption. We are using two pre-trained CNN models on ImageNet, VGG16 and ResNet-101. Both the models were tested and compared on Flickr8K dataset. Experimental results on this dataset demonstrate that the proposed architecture is as efficient as the state-of-the-art multi-label classification models. Experimental results on public benchmark dataset demonstrate that the proposed architecture performs as efficiently as the state-of-the-art image captioning model.
Incremental Clustering Algorithms

This chapter presents the general scheme for incremental clustering algorithms. These algorithms start with the calculation of the center of the whole data set and add one cluster center at each iteration. A procedure—that utilizes the incremental essence of clustering algorithms—for finding starting cluster centers is given. Furthermore, the multi-start incremental clustering algorithm that uses this procedure is discussed. Finally, the incremental k-medians algorithm is described, and the discussion on the decrease of its computational complexity is provided. The detailed descriptions of all algorithms as well as their flowcharts are given.
Efficient load Balancing algorithm analysis in Cloud Computing

Cloud Computing (CC) emerges as the torrid research topic in recent years. Cloud computing has integrated the distributing computing, web services, internet, software, and virtualization together. Most of the companies are using cloud computing on large scale. Cloud Computing (CC) services are demanding in the market. Cloud computing has been growing very fast since last few years. Over the past few years, cloud computing has the most powerful technology. A researcher has also shown his interest in cloud computing in past few years. We have also discussed about response time, turnaround time, and throughput. We have comprehensively analyzed and compared results of the above mentioned LBA. In CC, role of virtual machine is to utilize processor speed, which is discussed in this paper. Cloud Computing models, VM allocation, role of load balancing, simulation, cloudsim have also been explored in this paper.
Web service composition on IoT reliability test based on cross entropy

Abstract Web service has developed the managed IoT application to let connected devices easily and securely interact with cloud applications and other devices. As an important factor for web service, the reliability of web services refers to the probability of web service running success. For modeling web service composition, we should abstract the process of web service composition. Due to the diversity and complexity of web service composition, it is unlikely to do exhaustive testing. In order to improve the quality of web service composition test cases and find out which path leads to the greatest probability of service combination failure, heuristic test case generation method is adopted to obtain the optimal test path. First, the web service composition test is abstracted into the MDP model. The QoS of the web service composition is taken as the software test optimization goal, and the cross‐entropy strategy is used to optimize the test case. The experimental results show that the test profile given by the cross‐strategy is better than the random test strategy. Detect and exclude the same number of software defects. Cross‐entropy strategy can significantly reduce the number of test cases, reduce test costs, and improve defect detection efficiency.
Center-Symmetric Local Binary Pattern-Based Image Authentication Using Local and Global Features Vector

Center-symmetric local binary pattern (CS-LBP), an important tool for feature extraction, which is widely used for object recognition. In this research work, a reversible watermarking technique has been introduced for image authentication making use of local and global feature vector generated by exploiting CS-LBP, discrete cosine transform (DCT) and discrete wavelet transform (DWT). In the embedding procedure, authentication code (AC) is generated by employing CS-LBP operator on the colored cover image using a shared secret key and local feature vector and global feature vector generated by exploiting DCT and DWT which has been stored in an INFO matrix as well as embedded within the host image. In the detection procedure, authentication code (AC) is extracted with the help of a shared secret key and verified its originality by comparing it with a shared INFO matrix. Here, the symmetric property of LBP provides a minimum change in the watermarked image which helps to enhance the quality and authenticate the watermarked image.
AD<sup>2</sup>: Improving Quality of IoT Data through Compressive Anomaly Detection

With recent technological advances in sensor nodes, IoT enabled applications have great potential in many domains. However, sensing data may be inaccurate due to not only faults or failures in the sensor and network but also the limited resources and transmission capability available in sensor nodes. In this paper, we first model streams of IoT data as a handful of sampled data in the transformed domain while assuming the information attained by those sampled data reveal different sparsity profiles between normal and abnormal. We then present a novel approach called AD2 (Anomaly Detection using Approximated Data) that applies a transformation on the original data, samples top k-dominant components, and detects data anomalies based on the disparity in k values. To demonstrate the effectiveness of AD2, we use IoT datasets (temperature, humidity, and CO) collected from real-world wireless sensor nodes. Our experimental evaluation demonstrates that AD2 can approximate and successfully detect 64%-94% of anomalies using only 1.9% of the original data and minimize false positive rates, which would otherwise require the entire dataset to achieve the same level of accuracy.
Information measure based tone mapping of outdoor LDR image for maximum scale‐invariant feature transform extraction

A novel tone mapping method referred to here as information measure-based tone mapping is proposed for extracting the maximum number of scale-invariant feature transform (SIFT) feature points from outdoor low dynamic range (LDR) images. To this end, first, an image is transformed to YUV colour space in which every pixel is assigned two information scores: forward and inverse scores. The forward and inverse information scores represent the number of neighbour pixels that differ in their intensities from the pixel under consideration by a certain threshold for the respective YUV and inverse YUV images. The proposed tone mapping is then carried out in such a way as to emphasise the pixels of high information scores defined by a weighted sum of forward and inverse information scores. Experimental results show that the proposed method offers superior performance to state-of-the-art methods in terms of the number of features extracted and matched.
CC2020 -- Visualization Tool Preview and Review

The CC2020 project was charged with two main objectives, to produce a comprehensive report and a visualization tool to provide global guidance in an evolving computing environment as it affects computing baccalaureate degree programs worldwide. The report is now in draft form and has undergone four rounds of review. The other goal of the CC2020 project is to develop a set of visualization-based tools that will help users to explore questions they may have concerning curricular guidelines, as well as local computing curricula. A visualization tool has been scoped and a prototype built. This special session will unveil the prototype for the SIGCSE community who will have the opportunity to view and review the tool.
A Deep Learning Model for Extracting Live Streaming Video Highlights using Audience Messages

Live streaming has become a ubiquitous channel for people to learn new happenings. Although live streaming videos generally attract a large audience of watchers, their contents are long and contain relatively unexciting stretches of knowledge transmission. This observation has prompted artificial intelligence researchers to establish advanced models that automatically extract highlights from live streaming videos. Most streaming highlight extraction research has been based on visual analysis of video frames, and seldom have studies considered the messages posted by the audiences. In this paper, we propose a deep learning model that examines the messages posted by streaming audiences. The video segments whose messages reveal audience excitement are extracted to compose the highlights of a streaming video. We evaluate our model in terms of multiple Twitch streaming channels. The precision of our highlight extraction model is 51.3% and is superior to several baseline methods.
Markov Recurrent Neural Network Language Model

Recurrent neural network (RNN) has achieved a great success in language modeling where the temporal information based on deterministic state is continuously extracted and evolved through time. Such a simple deterministic transition function using input-to-hidden and hidden-to-hidden weights is usually insufficient to reflect the diversities and variations of latent variable structure behind the heterogeneous natural language. This paper presents a new stochastic Markov RNN (MRNN) to strengthen the learning capability in language model where the trajectory of word sequences is driven by a neural Markov process with Markov state transitions based on a K-state long short-term memory model. A latent state machine is constructed to characterize the complicated semantics in the structured lexical patterns. Gumbel-softmax is introduced to implement the stochastic backpropatation algorithm with discrete states. The parallel computation for rapid realization of MRNN is presented. The variational Bayesian learning procedure is implemented. Experiments demonstrate the merits of stochastic and diverse representation using MRNN language model where the overhead of parameters and computations is limited.
One-Shot Learning-Based Handwritten Word Recognition

One-Shot and Few-shot Learning algorithms have emerged as techniques that can imitate a humans ability to learn from very few examples. This is an advantage over traditional deep networks which require a lot of training samples and lack of robustness due to their excessive domain specific discriminators. In this paper, we explore a one-shot learning approach to recognizing handwritten words using Siamese networks to classify the handwritten images at the word level. The Siamese network’s ability to compute similarities between two images is learned using a supervised metric but the fully trained Siamese network can be used to classify new data that has previously not been used to train the network. The model learns to discriminate inputs from a small labelled support set. By using a convolutional architecture we were able to achieve robust results. We also expect that training the system over a larger distributions of data will result in improved general handwritten word classification. Accuracy as high as 92.4% was obtained while performing 5-way one-shot word recognition on a publicly available dataset which is quite high in comparison to the state-of-the-art methods.
Model-based exception mining for object-relational data

This paper develops model-based exception mining and outlier detection for the case of object-relational data. Object-relational data represent a complex heterogeneous network, which comprises objects of different types, links among these objects, also of different types, and attributes of these links. We follow the well-established exceptional model mining (EMM) framework, which has been previously applied for subgroup discovery in propositional data; our novel contribution is to develop EMM for relational data. EMM leverages machine learning models for exception mining: An object is exceptional to the extent that a model learned for the object data differs from a model learned for the general population. In relational data, EMM can therefore be used for detecting single outlier or exceptional objects. We combine EMM with state-of-the-art statistical-relational model discovery methods for constructing a graphical model (Bayesian network), that compactly represents probabilistic associations in the data. We investigate several outlierness metrics, based on the learned object-relational model, that quantify the extent to which the association pattern of a potential outlier object deviates from that of the whole population. Our method is validated on synthetic data sets and on real-world data sets about soccer and hockey matches, IMDb movies and mutagenic compounds. Compared to baseline methods, the EMM approach achieved the best detection accuracy when combined with a novel outlinerness metric. An empirical evaluation on soccer and movie data shows a strong correlation between our novel outlierness metric and success metrics: Individuals that our metric marks out as unusual tend to have unusual success.
A diagonal-based algorithm for the longest common increasing subsequence problem

The longest common increasing subsequences (LCIS) problem is to find out a common increasing subsequence with the maximal length of two given sequences A and B. In this paper, we propose an algorithm for solving the LCIS problem with O((n+L(m−L))log⁡log⁡|Σ|) time and O(n) space, where m and n denote the lengths of A and B, respectively, m≤n, L denotes the LCIS length, and Σ denotes the alphabet set. The main idea of our algorithm is to extend the answer from some previously feasible solutions, in which the domination function is invoked. To accomplish the extension and domination functions, the data structure of the van Emde Boas tree is utilized. From the time complexity, it is obvious that our algorithm is extremely efficient when L is very small or L is close to m. Some experiments are performed to demonstrate the efficiency of our algorithm.
Computing Ranges for Temporal Parameters of Composed Web Services

Temporal Service Level Agreements (SLAs) are a distinguishing characteristics for offered (composed) services: the weaker the restrictions, the broader the ranges of input parameters, the wider the applicability of an offered service. We propose a technique for the providers of composed services with temporal parameters to compute the broadest ranges for the temporal input parameters such that all service invocations where the parameters are within these ranges are dynamically controllable, i.e. that the adherence to temporal constraints can be guaranteed.
Image Recognition based SMART AID FOR VISUALLY CHALLENGED PEOPLE

This project tries to transform the visual world into the audio world with the potential to inform visually challenged people the spatial locations of the objects in their vicinity. Our paper presents the development of a real-time system based on detection, classification, and position estimation of objects in an outdoor environment to provide the visually impaired individuals with a voice output-based scene perception. The system is low-cost, light weight, simple, and easily wearable. The module is integrated into the stick, and the pi-camera is used to take the picture, and a controller is provided to move the camera in the required direction. The valuable insights gained from the feedback are then used to modify the system to best suit the requirements of the user. The object detection and classification framework exploit a multi-modal fusion-based mask RCNN using motion, sharpening and blurring filters for efficient feature representation. The image recognition classifies the detected objects along with the positions of the objects. Experimental results carried out in the outdoor environment are demonstrated. These data obtained is then converted to voice, and blind people will get the visual of what the environment is around them.
Non-locally Enhanced Feature Fusion Network for Aircraft Recognition in Remote Sensing Images

Aircraft recognition has great application value, but aircraft in remote sensing images have some problems such as low resolution, poor contrasts, poor sharpness, and lack of details caused by the vertical view, which make the aircraft recognition very difficult. Especially when there are many kinds of aircraft and the differences between aircraft are subtle, the fine-grained recognition of aircraft is more challenging. In this paper, we propose a non-locally enhanced feature fusion network(NLFFNet) and attempt to make full use of the features from discriminative parts of aircraft. First, according to the long-distance self-correlation in aircraft images, we adopt non-locally enhanced operation and guide the network to pay more attention to the discriminating areas and enhance the features beneficial to classification. Second, we propose a part-level feature fusion mechanism(PFF), which crops 5 parts of the aircraft on the shared feature maps, then extracts the subtle features inside the parts through the part full connection layer(PFC) and fuses the features of these parts together through the combined full connection layer(CFC). In addition, by adopting the improved loss function, we can enhance the weight of hard examples in the loss function meanwhile reducing the weight of excessively hard examples, which improves the overall recognition ability of the network. The dataset includes 47 categories of aircraft, including many aircraft of the same family with slight differences in appearance, and our method can achieve 89.12% accuracy on the test dataset, which proves the effectiveness of our method.
User Interface Using Arm Motion for Consumer Device

This paper describes a new user interface for consumer devices. The authors propose a user interface for consumer devices which detects the movement of an acceleration sensor attached to an arm. In experiments, gestures of the arm were detected, and a consumer device was operated following these movements. The experimental results verified that the proposed method is effective.
Assessing The Effectiveness Of Test-Driven Development and Behavior-Driven Development in an Industry Setting

Agile software development deviates from the traditional and plan-based approaches to software engineering as its iterative cycles embrace changes in software requirements. Test-Driven Development (TDD) and Behavior-Driven Development (BDD) are techniques that have been recently adopted by the software industry and have evolved from agile practices. These techniques aim at improving both the quality of the delivered software and the developers' productivity. They are several studies that have been conducted on TDD in both academic and industry settings with however only a few on BDD. While TDD and BDD have both become popular, many organizations and developers still do not understand the differences between these concepts or where they overlap. The aim of this paper is to assess the effectiveness of these two approaches in terms of external code quality, internal code quality and developers' productivity. To understand the weaknesses and strengths of each of these methods, a literature review was first performed. An experiment was then carried out in an industry setting to observe the effectiveness of TDD and BDD on a number of subjects. Our results showed that both techniques indeed increased the external quality of the delivered product. However, a decrease in productivity and internal quality were noted when BDD was used compared to TDD which might be due to the additional steps involved in BDD.