Reviewerly/exHarmony huggingface dataset:
End-to-End Multi-Modal Behavioral Context Recognition in a Real-Life Setting

Smart devices of everyday use (such as smartphones and wearables) are increasingly integrated with sensors that provide immense amounts of information about a person&#x0027;s daily life. The automatic and unobtrusive sensing of human behavioral context can help develop solutions for assisted living, fitness tracking, sleep monitoring, and several other fields. Towards addressing this issue, we raise the question: can a machine learn to recognize a diverse set of contexts and activities in a real-life through jointly learning from raw multi-modal signals (e.g., accelerometer, gyroscope and audio)? In this paper, we propose a multi-stream network comprising of temporal convolution and fully-connected layers to address the problem of multi-label behavioral context recognition. A four-stream network architecture handles learning from each modality with a contextualization module which incorporates extracted representations to infer a user&#x0027;s context. Our empirical evaluation suggests that a deep convolutional network trained end-to-end achieves comparable performance to manual feature engineering with minimal effort. Furthermore, the presented architecture can be extended to include similar sensors for performance improvements and handles missing modalities through multi-task learning on a highly imbalanced and sparsely labeled dataset.
SMDICFBA: Software Model for Distributed Incremental Closeness Factor Based Algorithms

The number of users utilizing internet services per day is in billions today. Also with the invent of “Internet of Everything (IoE)” and “Internet of People (IoP)”, the gigantic data is getting generated in real time every moment. To effectually handle, control, guide and utilize such vast amount of data in real time, it is essential to have distributed systems at place. Such distributed system for data management needs to be iterative in nature and parameter-free, so as to achieve quality decision making with prediction and or forecasting. “Distributed Incremental Closeness Factor Based Algorithm (DICFBA)” is primarily designed to accommodate ever growing data in numeric as well as text form. Assorted versions of CFBA are developed as per the need of the analysis till date. The primary purpose of all these CFBA driven incremental clustering models was to learn incrementally about embedded patterns from the given raw datasets. This research covers transformed CFBA models, its real-time varied domain applications, and future extensions for incremental classification point of view. Software development life cycle (SDLC) and software model for DICFBA (SMDICFBA) variants are also discussed in this chapter.
Text Summarization Model of Combining Global Gated Unit and Copy Mechanism

Text summarization is a common task in NLP. Automatic text summarization aims to transform lengthy documents into shortened versions. Recently, the neural networks based on seq2seq with attention are good at generating summarization. However, the accuracy of the summarization too difficult are to guarantee. In addition, the Out-of-Vocabulary (OOV) problem is also an important factor affecting the quality of the generated summary. To solve these problems, we hybrid the advantages of the extractive and abstractive summarization systems to propose text summarization model of combining global gated unit and copy mechanism (GGUC). The experiment results demonstrate that the performance of the model is better than the other text summary system on LCSTS datasets.
Workload and SLA Violation Prediction in Cloud Computing

Recently, cloud computing has come up as an evolving technology in various real-time computing field and widely adopted in several real-time computing systems. Due to its advantages of pay-as-you go services where users need not consider other factors such as hardware and platforms. Due to increasing demand of these applications, providing efficient resource and managing these resources is a crucial task because wastages of resources can lead towards the economic loss to the users and service providers. In order to deal with these issues, we focus on the workload prediction and SLA violation prediction approach for improving the overall performance. According to the proposed model, we develop a workload prediction model using clustering approach where similar workload patterns are grouped together to reduce the overhead and later SLA violation scheme is applied which is used for reducing the violations using Naïve Bayes classification approach. Finally, comparative experimental study is carried out which shows that the proposed approach archives better performance when compared with the state-of-art techniques.
Consideration of a Selecting Frame of Finger-Spelled Words from Backhand View

To understand finger alphabet from backhand sign video, there are many redundant video frames between consecutive alphabets and among video frames of an alphabet. These redundant video frames cause loss in finger alphabet understanding, and should be considered to delete. This paper proposes a method to select significant video frames of sign for finger-spelled words of each letter to make more information from backhand view. In this method, finger-spelled words video is divided into frames, and each frame is converted to a binary image by an automatic threshold, and a binary image change to contour frames. Then, we apply the located centroid as the center of the contour image frame to calculate the distance to all boundaries of image frames. After that, all distances of each frame are presented as signature signals that identify each frame, and these values are used with the selected frame equation to select a significant frame. Finally, 1D Signature signal as their feature is extracted from selected frames. For evaluation of our proposed method, 6 samples of finger-spelled words of the American Sign Language (ASL) are used to select a significant frame, and Hidden Markov Models (HMM) is used to classify the words. The accuracy of the proposed method is evaluated 97.5% approximately.
Pain modulation by illusory body rotation: A new way to disclose the interaction between the vestibular system and pain processing

Abstract Background Clinical and experimental evidence advocates a structural and functional link between the vestibular and other sensory systems. For instance, visuo‐vestibular and vestibular–somatosensory interactions have been widely reported. However, whether visual inputs carrying vestibular information can modulate pain is not yet clear. Recent evidence using natural vestibular stimulation or moving visual stimuli, points at an unspecific effect of distraction. Methods By using immersive virtual reality (VR), we created a new way to prompt the vestibular system through the vision of static visual cues, studying the possible interaction with pain. Twenty‐four healthy participants were visually immersed in a virtual room which could appear with five different degrees of rotation in the sagittal axis, either towards the right, left or with no rotation. Participants' heat pain thresholds and subjective reports of perceived body rotation, sense of presence and attention were measured. Results ‘Being’ in a tilted room induced the sensation of body rotation in our participants, even though they were always in an upright position. We also found that rotating the visual scenario can modulate the participants' pain thresholds, determining a significant increase when a left tilt is displayed. In addition, a positive correlation between the perceived body midline rotation and pain threshold was found when the virtual room was titled 15 degrees toward the left. Importantly, all VR conditions were found to be equally distractive. Conclusions Vestibular information present in static visual cues can modulate experimentally‐induced acute pain according to a side‐dependent manner and bypassing supramodal attentional mechanisms. These findings may help refining pain management approaches based on multimodal stimulation. Significance This study explored how the visualization of static environments in immersive virtual reality can lead to pain threshold modulation through the activation of the vestibular system. Immersion into rotated virtual environments led to the illusory sensation of body rotation, and this sensation was found to be related with a modulation of pain perception. Possible analgesic effects due to distraction could be ruled out. These results expand our current knowledge about how the visual, vestibular and somatosensory (pain) systems interact. These findings may influence future pain treatment strategies based on multisensory stimulation.
Three Dimensional Adaptive Path Planning Simulation Based On Ant Colony Optimization Algorithm

Ant Colony Optimization (ACO) is a metaheuristic widely used to solve different problems. This work proposes a three-dimensional simulation of adaptive path planning. New features were added to the basic ACO algorithm. First, the Random Walk based on visibility for initializing the pheromone matrix. The visibility of a node is the distance from the current node to the target node (d <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">it</sub> ) over the distance from the possible node to the target node (d <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">jt</sub> ). The second feature is the inclusion of Killer Nodes for adaptive behavior. These nodes remove an ant and execute a decay function that removes the contributions over a wrong path. Finally, several experiments were performed to evaluate the solution accuracy, convergence time, and computational complexity. These results showed that the feasible ACO solution is near to the optimal solution with accuracy over 95% for most cases. It demonstrates that the algorithm provides promising results and finds a route after the addition of dynamic obstacles.
Parametric synthesis of a robust controller on a base of D-partition and method of dominant poles

The paper is dedicated to a problem of parametric synthesis of a controller for a control system having interval parameters and affine uncertainty of characteristic polynomial coefficients. Such polynomial can be represented as a sum of productions of interval parameters and polynomials including zero and real poles. The paper proposes a method of synthesizing a linear robust controller placing system poles allocation areas according to desired control quality. The method is based on dividing parameters of a controller in two groups: restricted ones and unrestricted ones. Restricted parameters of a controller provide desired allocation of a dominant pole; unrestricted parameters determine allocation of all other poles of a system. In this case, the right border of an allocation area of a dominant pole defines minimal stability degree of a system synthesized. Values of controller parameters are proposed to be found via D-partition in vertices of a interval parameters' polytope. An example of the proposed method application is provided.
Polynomial Criterion for Abelian Difference Sets

Difference sets are subsets of a group satisfying certain combinatorial property with respect to the group operation. They can be characterized using an equality in the group ring of the corresponding group. In this paper, we exploit the special structure of the group ring of an Abelian group to establish a one-to one correspondence of the class of difference sets with specific parameters in that group with the set of all complex solutions of a specified system of polynomial equations. The correspondence also develops some tests for a Boolean function to be a bent function.
FSKYMINE: A Faster Algorithm For Mining Skyline Frequent Utility Itemsets

Skyline frequent utility itemsets mining is a challenging task in frequent itemsets mining and plays an important role in many data mining applications. Previous studies presented two algorithms, namely SKYMINE and SKYMINE2, to mine Skyline Frequent Utility Itemsets (SFUIs). In which, the SKYMINE2 based on utility-list data structure is a state-of-the-art algorithm. However, the SKYMINE2 remains computationally expensive because the algorithm generates numerous utility lists, join operations, and potential SFUIs. In this paper, we propose a more effective algorithm to mine the SFUIs based on extent utility list data structure and using a new strategy to prune the potential skyline frequent utility itemsets. Notably, experimental results with four datasets show the proposed algorithm reduces the number of utility lists, join operations, potential SFUIs effectively and outperforms the SKYMINE2 in terms of runtime.
Brain CT Image Segmentation Based on Improved Watershed Algorithm

For the traditional watershed algorithm has the problem of over-segmentation and under-segmentation, an improved watershed algorithm is used to segment brain CT images in this paper. By the segmentation, the segmentation results of the improved watershed algorithm show that noise and over-segmentation can be effectively suppressed, and the hemorrhagic areas on brain CT images can also be marked out.
Automatic Pronunciation Generator for Indonesian Speech Recognition System Based on Sequence-to-Sequence Model

Pronunciation dictionary plays an important role in a speech recognition system. Expert knowledge is required to obtain an accurate dictionary by manually giving pronunciation for each word. On account of the continually increasing vocabulary size, especially for Indonesian language, it is impractical to manually give the pronunciation for each word. Indonesian spelling-to-pronunciation rules are relatively regular; thus, it is plausible to produce pronunciation for a word by using the predefined rules. Nevertheless, the rules still contain a few irregularities for some spellings and they still cannot handle the presence of code-mixed words and abbreviations. In this paper, we employ a sequence-to-sequence (seq2seq) approach to generate pronunciation for each word in an Indonesian dictionary. It is demonstrated that by using this approach, we can obtain a similar speech-recognition error-rate while requiring only a fractional amount of resource. Our cross-validation experiment for validating the resulting phonetic sequences achieves 4.15-6.24% phone error rate (PER). When an automatically produced dictionary is applied in a speech recognition system, the word accuracy only degrades 2.22 percentage point compared to the one produced manually. Therefore, creating a new large pronunciation dictionary using the proposed model is more efficient without degrading the recognition accuracy significantly.
Integrating Security into Computer Science Curriculum

This work uses a concrete example to show how security-related content can be added to various courses in a Computer Science curriculum. The course contents are mapped to the most prevalent software security flaws that are identified in the National Vulnerability Database of the National Institute of Standards and Technology. This work demonstrates that security-related content can be added to the curriculum in a comprehensive manner. These course content should not require significant re-tooling of existing faculty to gain specialized knowledge in software security.
A Variation in the Working of Playfair Cipher

Cryptography has decidedly been in the field of research for decades with the motif of enhancing the security of information exchange. This paper exhibits a variation in the implementation of a classical cipher technique, the Playfair cipher. The motive is to make the ciphertext produced less vulnerable to attacks; we have tested the same with a common attack, the brute-force attack. The proposed model is also statistically analyzed for vulnerability against the performance of the classical encryption technique.
A Surrogate Data-Based Approach for Validating Deep Learning Model Used in Healthcare

IoT-based communication between medical devices has encouraged the healthcare industry to use automated systems which provide effective insight from the massive amount of gathered data. AI and machine learning have played a major role in the design of such systems. Accuracy and validation are considered, since copious training data is required in a neural network (NN)-based deep learning model. This is hardly feasible in medical research, because the size of data sets is constrained by complexity and high cost experiments. The availability of limited sample data validation of NN remains a concern. The prediction of outcomes on a NN trained on a smaller data set cannot guarantee performance and exhibits unstable behaviors. Surrogate data-based validation of NN can be viewed as a solution. In the current chapter, the classification of breast tissue data by a NN model has been detailed. In the absence of a huge data set, a surrogate data-based validation approach has been applied. The discussed study can be applied for predictive modelling for applications described by small data sets.
Enhanced Network Representation Learning With Community Aware and Relational Attention

Network representation learning is proposed to make it easier to perform complex inference processes on large-scale networks. It aims to represent each node in the network as a low-dimensional potential representation while preserving the structure and inherent features of the network. Most existing learning methods do not consider the comprehensive structure or the rich semantics of between nodes, which lead to incomplete embeddings. We attempt to find a way to learn network representation while keeping the network structure and relations between nodes to address this issue. In this paper, we propose a Community Aware and Relational Attention (CARA) method to enhance network representation learning. In CARA, community aware random walks capture both the community and local context from a global perspective to enhance the structure embedding. And we design a two-layer attention mechanism to model the semantic relationships between nodes, which can learn different text embedding for different neighbors. Then, these two embeddings are combined together to obtain the node representation. Experiments on four real-world networks show that CARA has better performance than other methods in link prediction and node classification.
An Analysis of Log Management Practices to reduce IT Operational Costs Using Big Data Analytics

The security operations center (SOC) is a team that is related to many people, processes, and all the technologies. They must monitor the situation through detection, quarantine, and remediation of IT threats. They must consider the potential events in IT operations, whether it is a malicious threat or how it may otherwise affect the business. They need to have adequate staff to ensure that all relevant screens are monitored and tracked. Consequently, IT operational costs have increased according to the number of people employed, as well as the technology tools invested in building SOC teams. This paper proposes a log management architecture using Big Data Analytics to make a new generation of SOC. We have integrated all necessary notification tools from `many' to `one screen.' All monitoring modules have been combined for increasing automation, including replacing new hires in routine operational tasks. Furthermore, based on the results of this analysis, it is found that this form of work is a useful solution that can cover all the necessary tasks better than the original work model. Moreover, the cost analysis in operational reporting has shown that IT expenses decreased by 60% while the return on investment (ROI) shows that the solution can be cost-effective within six months. Consequently, a satisfaction survey shows this solution has a good satisfaction score (4.5 / 5) because employees have more work flexibility and therefore feel less stressed and overburdened.
A New Hybrid (MD5 and RC4) Cryptography Algorithm Using Multi-Logic States

In cryptography, the MD5 and RC4 algorithms are used in many applications to provide security. Both depend on two-state operations, such as XOR, for manipulating bits, and these operations are considered a weakness that can be vulnerable to attack. This paper presents a new hybrid of MD5 and RC4 based on a multi-logic states operation, called (E#), instead of XOR to increase the security level. This new (E#) uses three keys instead of one for controlling the variable block bits sizes of (1, 2, 4, and 8) bits and for determining the table numbers, which are formed from the addition in the Galois Field GF (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ) based on the block bit size for increasing the complexity of the proposed algorithm. This new hybrid algorithm leverages (E#) in both MD5 and RC4 and takes the output of the modified MD5 for use as the input key to RC4 increasing the security of RC4. The results are evaluated based on complexity, National Institute of Standards and Technology (NIST), histogram analysis, number of pixels of change rate (NPCR) and unified average changing intensity (UACI) and demonstrate that our modifications to both algorithms increase their complexity and security.
Electronic Opinion Analysis System for Library (E-OASL)

This paper demonstrates a new algorithm for Electronic Opinion Analysis System (E-OASL) for university library where it can analyze the user opinion on library and categorized it in different classification such as positive, negative, neutral and suggestions. The system also shows the percentage of each classification as system output. Our proposed algorithm is based on hybrid approach of sentiment analysis where machine learning's rule-based classifier and lexicon approach's corpus datastore are utilized. We needed to collect around 1200 raw data from the different types of user who are using university library to build our algorithm. The system integrates MySQL database for faster and better data processing. The paper also shows the evaluation of the E-OASL which showed satisfactory result of effectiveness and efficiency of EOASL compare to manual opinion analysis approach.
A Study of Distnict Image Encryption Techniques

Image Encryption is one of the most underrated encryption types available, about which no exposure was given. While Steganography is hiding plain text in an image, image encryption is encrypting the image itself with help of pixels present. Image encryption can be done in many ways like image to base64, image to binary (basic levels) but the real encryption is done using public and private keys. Few of the Encryption techniques like “Chaotic Map Sequence”, “Image Encryption using Matlab”, “Image Encryption using Python” involve the use of keys like public and private. Photo authentication methods are attempting to transform the unique picture to a picture that is hard to retrieve. Security is a significant issue in the correspondence and handling of pictures with the ever growing multimedia applications. Various methods are utilized in every now and then to scramble pictures so as to make them more secure. In this paper, image encryption based on python and a low-complexity cryptographic algorithm known as Secure Force with Affine Transformation was suggested along with “Chaotic Map Sequence” for future references. The encryption segment can be executed by utilizing straightforward design by comprising just of fundamental numerical tasks (AND, OR, XOR, XNOR, SHIFTING, SWAPPING). This can decrease the weight on the encoder, as the more confounded key extension technique is just performed on the decoder. The plan of this paper is to play out the proposed calculation's security investigation and execution assessment. Results of the proposed algorithm was checked on different types of images and comparative analysis was provided with other techniques.