Reviewerly/exHarmony huggingface dataset:
Quantum Speedup for the Minimum Steiner Tree Problem

A recent breakthrough by Ambainis, Balodis, Iraids, Kokainis, Prūsis and Vihrovs (SODA’19) showed how to construct faster quantum algorithms for the Traveling Salesman Problem and a few other NP-hard problems by combining in a novel way quantum search with classical dynamic programming. In this paper, we show how to apply this approach to the minimum Steiner tree problem, a well-known NP-hard problem, and construct the first quantum algorithm that solves this problem faster than the best known classical algorithms. More precisely, the complexity of our quantum algorithm is $$\mathcal {O}(1.812^k\mathrm {poly}(n))$$ , where n denotes the number of vertices in the graph and k denotes the number of terminals. In comparison, the best known classical algorithm has complexity $$\mathcal {O}(2^k\mathrm {poly}(n))$$ .
A Real-Time Sentiments Analysis System Using Twitter Data

As social media platforms become the go-to for knee-jerk reactions on events by the current populous, it has become extremely important for event managers, celebrities, and organizations to constantly monitor their perceived social image online. This becomes especially difficult during key periods of heightened activity, like events, announcements, etc. As the rate at which the tweets are posted is much higher than what a human can read or comprehend. In this paper, we exploit existing sentiment analysis techniques to develop a real-time sentiment analysis system that provides us with real-time sentiments of the audience on the micro-blogging site, Twitter, toward an event, organization, or person. This system serves to act as a feedback mechanism helping the users to understand, the perceived image of the event/organization. This feedback, if provided in a timely manner, can be used to improve the situation at hand or act as a positive reinforcement for the team. In today’s world, neglecting social media can prove detrimental to the success of an event or organization. We analyze two different events from two separate domains to understand and demonstrate the benefits of our system.
Jaya Optimized Extreme Learning Machine for Breast Cancer Data Classification

As the latest World Health Organization (WHO) statistics show, approximately 15% of all cancer deaths among women, i.e., 627,000 in number in the year 2018 are from breast cancer in the USA. To make a definitive cancer diagnosis, a biopsy is a medical practice for maximum types of cancers, as it provides the most accurate analysis of tissue. To avoid the huge number of undesirable breast biopsies, new diagnosis methods based on microarray data analysis have been proposed in the last decades. The microarray analysis can help the physicians to decide whether to carry out a breast biopsy or not. Gene expression profiling based on microarray data has been evolved as an effective procedure for the classification of cancer along with its diagnosis and treatment. Machine learning techniques like artificial neural network have shown significant potential in cancer classification and clinical diagnosis. In this paper, a classification method based on Jaya optimized extreme learning machine (JELM) has been applied on breast cancer microarray data after relevant genes are selected employing Wilcoxon rank sum test. Jaya is used to pre-train the ELM by selecting the optimal input weights and hidden biases. ELM rectifies the difficulties raised by iterative learning techniques such as local minima, improper learning rate, and overfitting. Finally, a comparative result analysis is presented on the achieved classification accuracy by four well-known classifiers available in the literature, namely SVM, kNN, NB, and C4.5. Accuracy is used as a performance metric to analyze the efficiency of the classifiers. From the obtained results, it is observed that JELM classifier achieves better classification accuracy as compared to other schemes.
Similarity Analysis for Citation Recommendation System using Binary Encoded Data

Citations are a crucial part of an academic dissertation, project or scientific work. The most time-consuming task for any scholar is to find suitable citations for any work. Thus, a convenient citation recommendation system provides completeness and fulfillment for citing the giants' works. Moreover, attaining high quality for any citation recommendation system is challenging as it should not only recommend relevant papers but also should match the context of the paper. An advanced algorithm SABED (Similarity Analysis using Binary Encoded Data) has been proposed that converts text metadata of the article like author name, doi of the paper, keywords, abstract and content of the paper into the binary format and is fired into the database. The binary formatted query fired fetches the accurate matches thereby increasing the accuracy of search probability and similarity analysis. This similarity analysis can be further used to provide recommendations to the users. The proposed system concentrates on the similarity of the content and hence the context of the papers is not taken into consideration.
Investigating Supervised Machine Learning Techniques for Channel Identification in Wireless Sensor Networks

Knowledge of the wireless channel is pivotal for wireless communication links but varies for multiple reasons. The radio spectrum changes due to the number of connected devices, demand, packet size or services in operation, while fading levels, obstacles, path losses, and spurious (non-)malicious interference fluctuate in the physical environment. Typically, these channels are applicable to the time series class of data science problems, as the primary data points are measured over a period. In the case of wireless sensor networks, which regularly provide the device to access point communication links in Internet of Things applications, determining the wireless channel in operation permits channel access. Generally, a clear channel assessment is performed to determine whether a wireless transmission can be executed, which is an approach containing limitations. In this study, received in-phase (I) and quadrature-phase (Q) samples are collected from the wireless channel using a software-defined radio (SDR) based procedure and directly analyzed using python and Matlab. Features are extracted from the probability density function and statistical analysis of the received I/Q samples and used as the training data for the two chosen machine learning methods. Data is collected and produced over wires, to avoid interfering with other networks, using SDRs and Raspberry Pi embedded devices, which utilize available open-source libraries. Data is examined for the signal-free (noise), legitimate signal (ZigBee) and jamming signal (continuous wave) cases in a live laboratory environment. Support vector machine and Random Forest models are each designed and compared as channel identifiers for these signal types.
Cartesian Genetic Programming for Synthesis of Control System for Group of Robots

A control problem for a group of robots is considered. The robots have to move from given initial conditions to terminal ones without collisions between themselves and stationary obstacles. To solve the problem, the optimal synthesized control method is used. According to this method firstly the control system synthesis problem for each robot is solved. As a result, the control system stabilizes the robot relative to some point in the state space. After that positions of these stable equilibrium points in the state space for each robot are found so that all robots can move from point to point till the terminal positions without collisions. For synthesis problem on the first stage the Cartesian genetic programming is used. This method of symbolic regression allows to find a mathematical expression for control function in the form of special code by a special genetic algorithm. It's shown, that using the symbolic regression methods directly doesn't allow to find a synthesized control function in a code space, because this search space does not have numerical measure for distance between two elements of the space. So the Cartesian genetic programming was modified and the principle of small variations of the basic solution was included in it. A computational example of controlling eight robots on the plane with phase constraints is presented.
Taking Multimedia to the Extreme

The role and function of the Multimedia Resources Department has evolved as collections and services have moved online. This article outlines Sprague Library's creative use of space, technology, and programming to reimagine Multimedia Services. Montclair State University's Media Department has expanded the scope of its services and utilized technology to enhance group study spaces to encourage collaborative efforts on group and media projects. This involved working closely with the Library Technical Support Specialist and Information Technology Division in planning and designing the collaborative spaces. In addition, the author will discuss the library's use of exhibits, workshops and programming to highlight media collections and services as well as the use of Zoom in facilitating community and campus engagement through programming. New services such as lending musical instruments and presentation printing will also be discussed.
AutoGesNet: Auto Gesture Recognition Network Based on Neural Architecture Search

The deep-learning-based gesture recognition technologies have developed rapidly in recent years, and more and more convolutional neural network models are proposed. In this paper, we propose a method to automatically generate a convolutional neural network for the gesture recognition task and name the network AutoGesNet, to solve the problem that it is difficult to design a good neural network architecture. To be specific, we firstly fuse and preprocess three gesture recognition data sets. Then we design the overall architecture and the search space of AutoGesNet. And we use reinforcement learning and transfer learning method to automatically generate the detailed architecture of AutoGesNet. Finally, we fine-tune and retrain the searched neural network for two different input sizes. Experiments show that the retrained model achieves above 99% accuracy on NUS Hand Posture Dataset II and our data set, and its parameters and FLOPs are reduced by more than 40% compared with lightweight MobileNet.
Content Based Image Retrieval Using 3D Center Symmetric Local Binary Co-occurrence Pattern

This paper presents a novel algorithm three dimensional center symmetric local binary co-occurrence pattern (3DC-SLBCoP) for retrieval of images. Standard local binary pattern and its forms uses 2D plane of the image. On the other hand, proposed method leads to 3D volume by extracting Gaussian filtered images using multiresolution Gaussian filter banks and computes the relationship between center pixel and its neighbors in five selected directions. Center symmetric local binary pattern (CSLBP) image is formed by encoding the relationship between focus pixel and its center symmetric neighboring pixels. Thus, gray level co-occurrence matrix (GLCM) of the CSLBP map in four directions leads to the formation of feature vector. Experiments are performed and results are analyzed on benchmark datasets. Analyzed retrieval results clearly better than the other well known methods by considering average retrieval precision and average retrieval rate as evaluation measures.
Snitches Get Stitches: On the Difficulty of Whistleblowing

One of the most critical security protocol problems for humans is when you are betraying a trust, perhaps for some higher purpose, and the world can turn against you if you’re caught. In this short paper, we report on efforts to enable whistleblowers to leak sensitive documents to journalists more safely. Following a survey of cases where whistleblowers were discovered due to operational or technological issues, we propose a game-theoretic model capturing the power dynamics involved in whistleblowing. We find that the whistleblower is often at the mercy of motivations and abilities of others. We identify specific areas where technology may be used to mitigate the whistleblower’s risk. However we warn against technical solutionism: the main constraints are often institutional.
Exploiting Sparse Topics Mining for Temporal Event Summarization

Information explosion, both in cyberspace world and real world, nowadays has brought about pressing needs for comprehensive summary of information. The challenge for constructing a quality one lies in filtering out information of low relevance and mining out highly sparse relevant topics in the vast sea of data. It is a typical imbalanced learning task and we need to achieve a precise summary of temporal event via an accurate description and definition of the useful information and redundant information. In response to such challenge, we introduced: (1) a uniform framework of temporal event summarization with minimal residual optimization matrix factorization as its key part; and (2) a novel neighborhood preserving semantic measure (NPS) to capture the sparse candidate topics under that low-rank matrix factorization model. To evaluate the effectiveness of the proposed solution, a series of experiments are conducted on an annotated KBA corpus. The results of these experiments show that the solution proposed in this study can improve the quality of temporal summarization as compared with the established baselines.
Statistical Evaluation of the Solid-Solution State in Ternary Nanoalloys

Quantitative evaluation of the alloying state in nanoalloy systems is key to understanding their functional properties in a diverse range of applications spanning from catalysis and plasmonics to biomedicine and so forth. Here, we develop a method to statistically and visually represent the sub-nanometer local compositional distribution in ternary nanoparticles (NPs) in terms of ternary histograms and kernel density estimation analysis. Further descriptive statistics is performed within the mathematical framework of compositional data analysis to account for the constant-sum constraint and positivity inherent to the nature of compositional data. The approach has been demonstrated on several conceptual particle models and real systems, namely, Pd–Rh–Ru and Ag–Au–Pd NPs, utilizing experimental X-ray energy-dispersive spectroscopy (XEDS) maps acquired from a scanning transmission electron microscope. We regard this as a useful tool for extending to other well-known configurations such as uniformly mixed solid solutions, core–shell, or phase-decomposed clusters often encountered in other nanoalloy systems. Proposed solutions to overcome common problems associated with NPs such as low X-ray counting and XEDS spectral overlapping are also presented and discussed.
Volumetric Analysis of an Solid Object By Supervised Learning based Segmentation of Explicit Homomorphed Images from Multiple Views

The calculation of absolute volume of a regular shaped solid object from image has been experimented and tried using image processing systems in this paper. Volume calculation method consists of image matching, feature extraction and comparison among multiple views of images. From segment based pixel counting approach the volume has been determined by pixel density relation. Finally by multiplying density the volume has been calculated from reconstructed segmented area. This approach has been experimented over several standard images of size 256 X 256 pixel size. The multiple views of a single image have been captured and they are compared to find similarity between various segmented regions. The SIFT based key features has been considered for feature comparisons. Pixel based segmentation method such as watershed segmentation has been tried and later grid based approach has been followed to find ROI (Region of Interest) first and using this segmented areas of the volume of object has been calculated. Using 3D based software the approximate height, width and length has been measured on which approximate volume of object has been calculated and then error margin has been minimized with the proposed segmented approach.
Image Clustering Based on Multi-Scale Deep Maximize Mutual Information and Self-Training Algorithm

Image clustering is a complex procedure that is significantly affected by the choice of the image representation. Generally speaking, image representations are generated by using handcraft features or trained neural networks. When dealing with high dimension data, these two representation methods cause two problems: i) the representation ability of the manually designed features is limited; ii) the non-representative and meaningless feature of a trained deep network may hurt the clustering performance. To overcome these problems, we propose a new clustering method which efficiently builds an image representation and precisely discovers the cluster assignments. Our main tools are an unsupervised representation learning method based on Deep Mutual Information Maximization (DMIM) system, and a clustering method based on self-training algorithm. Specifically speaking, to extract the informative representation of image data, we derive the maximum mutual information theory and propose a system to learn the maximum mutual information between the input images and the latent representations. To discover the clusters and assign each image a clustering label, a self-training mechanism is applied to cluster the learned representations. The superiority and validity of our algorithm are verified in a series of real-world image clustering experiments.
Label-Embedding-Based Multi-core Convolution for Text Categorization

Text categorization is a classical problem in Natural Language Processing (NLP) tasks. There have been a number of studies that applied Convolutional Neural Networks (CNN) to this problem. This paper proposes a novel model to improve the traditional convolution for text classification, which is based on label embedding. It learns the attention on a training set oflabeled samples to ensure that the weight of related words is higher than that of irrelevant words. Meanwhile, the model also encodes the bi-gram features extracted from training data into convolutional filters, instead of randomly initializing them, which can help it focus on learning important semantic features at the beginning of the training. Experimental results on several text datasets indicate that the proposed model is competitive to the state-of-the-art models.
A Node Sorting Method for K2 Algorithm in Bayesian Network Structure Learning

Bayesian network is an important model for reasoning in an uncertain environment. A reliable node rank is required by K2 algorithm to learn Bayesian network structure better. To provide a high-quality node rank tailored for K2 algorithm, we propose a node priority-based sorting algorithm. Given observable data only, our algorithm can be employed to learn a node rank without expert knowledge. Specifically, MCMC algorithm is first utilized to yield some Bayesian network structures that can sufficiently fit the observed data. We then define the priority of each node in these network structures. Node rank is finally obtained through weighted scoring based on the priority. The empirical results show that our sorting algorithm performs significantly better than commonly used methods, e.g., randomly sorting and MCMC algorithm, on an Asia network-learning dataset.
Prototyping and public art

This experience report shares lessons learned from a multi-staged prototyping process, over a five-year period, that involved the creation and iterative development of a mobile platform and dozens of prototype examples of interactive locative-media artifacts, including locative journalism. Thematically linked to a public art collection, the mobile app was designed as a research instrument aimed at an external audience of passersby, actively using smartphones. This paper documents and outlines key decisions made about the platform and content in response to observed experiences. It also identifies emergent areas of research potential intertwined in the undertaking of such a prototyping process.
Breast Tumor Malignancy Classification using Smartphone Compression-induced Sensing System and Deformation Index Ratio

Smartphone-based compression-induced sensing system uses the light diffusion pattern to characterize the early-stage breast tumor noninvasively. The system is built on a smartphone and cloud platform to capture, transfer, and interface with the user. The compressed tissue's deforming pattern creates distinctive tactile images due to the size and hardness of the tumor. From the compression-induced images, we estimate the size of the tumor using projection analysis and the tumor's malignancy using the tissue deformation index ratio. Deformation index ratio is based on the changes of a healthy region over the tumorous region. By using the projection analysis, the human patient tumor size estimation resulted in 52.3% of the average error. For a small number (seven) of the feasibility test, the tumor's malignancy was classified based on the deformation index ratio with 67.0% of sensitivity and 100% specificity.
Instruction Verification of Ethereum Virtual Machine by Formal Method

In recent years, many smart contracts on the ethereum platform are increasingly closer to our life. The existence of smart contract enables us to complete complicated transactions without depending on the third party. It should be not only fast, but also secure guaranteed. When the smart contract is requested to execute, the action is performed by ethereum virtual machine (EVM). If EVM occurs errors in the process of implementation, the contract execution result will also have mistaken. Therefore, ensuring the correctness of the EVM is very important. In this work, we propose a framework to formally verify EVM instruction implementations by model checking to check whether the instruction operation behaviors working on EVM is the same as the expected definitions in the ethereum yellow paper. In addition, we also define a set of interfaces specifically for EVM instruction verification to achieve the reusability of our proposed verification environment. The experimental results indicate that the formal verification method used in this work is more trustworthy than the conventional testing method. It can exactly capture the errors in the design which may be undetected by testing/simulation methods. The defined instructions are divided into 11 categories, and our work has completed to verify 7 categories, including 92 instructions of total 134, in 50 hours.
New Robust LPC-Based Method for Time-resolved Morphology of High-noise Multiple Frequency Signals

This paper introduces a new time-resolved spectral analysis method based on the Linear Prediction Coding (LPC) method that is particularly suited to the study of the dynamics of low Signal-to-noise Ratio (SNR) signals comprising multiple frequency components. One of the challenges of the time-resolved spectral method is that they are limited by the Heisenberg-Gabor uncertainty principle. Consequently, there is a trade-off between the temporal and spectral resolution. Most of the previous studies are time-averaged methods. The proposed method is a parameterisation method which can directly extract the dominant formants. The method is based on a z-plane analysis of the poles of the LPC filter which allows us to identify and to accurately estimate the frequency of the dominant spectral features. We demonstrate how this method can be used to track the temporal variations of the various frequency components in a noisy signal. In particular, the standard LPC method, new proposed LPC method and the Short-time Fourier Transform (STFT) are compared using a noisy Frequency Modulation (FM) signal as a test signal. We show that the proposed method provides the best performance in tracking the frequency changes in real time.