Reviewerly/exHarmony huggingface dataset:
A Comparative Analysis of Various Local Feature Descriptors in Content-Based Image Retrieval System

Abstract Image acquisitions are increasing day by day due to progress in social networking and digital technologies. Nowadays, with the evolution of various image capturing devices, an enormous quantity of complex images is being produced.content-primarily based image Retrieval (CBIR) is the answer to access images without difficulty wherein proper indexing and association are required. It makes CBIR a distinguished field in computer vision research. There are several uses of CBIR systems in day to day life for example medical, internet, scientific research and various other communication media. In the CBIR system, the user gives a query to obtain images from large datasets having a large number of images. In information transfer via electronic media using particular formats of data, images play an essential role. The extraction of information from communicated images is necessary with extra processing. In this paper, a comparative survey has been carried out on different content-based image retrieval implementation. These methods are determined by several authors for the feature extraction process of images and classification. This will help to plan the strategy for optimizing the CBIR system.
Loss, post-processing and standard architecture improvements of liver deep learning segmentation from Computed Tomography and magnetic resonance

As deep learning is increasingly applied to segmentation of organs from Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) sequences, we should understand the importance of certain operations that can improve the quality of results. For segmentation of the liver from those sequences, we quantify the improvement achieved with segmentation network, loss function and post-processing steps. Our results on a publicly available dataset show an improvement of 11% points (pp) by using DeepLabV3 instead of UNet or FCN, 4 pp by applying post-processing operations and 2pp using the top-performing loss function. The conclusions of this work help researchers and practitioners choosing the network and loss function and implementing effective post-processing operations.
A Neural Model for Automatic Bidding of Contract Bridge

Contract bridge is a typical representative of imperfect information game, which consists of two phases: bidding and playing. One of the most challenging tasks in the contract bridge is bidding. Prior studies mainly focus on bidding without competition. However, it is not the case in reality, which inevitably leads to information loss. This work introduces hand understanding to model the real bidding process and restore the multi-task nature of the work. Therefore, Automatic Bidding Model(ABM), a novel neural architecture, is designed to solve this multi-task learning problem. A layered LSTM is provided to players to model real data, as well as an understanding and a bidding component. ABM proves its advantage through comprehensive experiments on real data set. Lastly, this study summarizes the advantage of ABM.
The Subarachnoid Hemorrhage–Weather Myth: A Long-Term Big Data and Deep Learning Analysis

Objective: The frequency of aneurysmal subarachnoid hemorrhage (aSAH) presents complex fluctuations that have been attributed to weather and climate changes in the past. In the present long-term big data and deep learning analysis, we have addressed this long-held myth. Methods: Bleeding dates and basic demographic data for all consecutive patients ( n = 1,271) admitted to our vascular center for treatment of aSAH between January 2003 and May 2020 (6,334 days) were collected from our continuously maintained database. The meteorological data of the local weather station, including 13 different weather and climate parameters, were retrieved from Germany's National Meteorological Service for the same period. Six different deep learning models were programmed using the Keras framework and were trained for aSAH event prediction with meteorological data from January 2003 to June 2017, with 10% of this dataset applied for data validation and model improvement. The dataset from July 2017 to May 2020 was tested for aSAH event prediction accuracy for all six models using the area under the receiver operating characteristic curve (AUROC) as the metric. Results: The study group comprised of 422 (33.2%) male and 849 (66.8%) female patients with an average age of 55 ± 14 years. None of the models showed an AUROC larger than 60.2. From the presented data, the influence of weather and climate on the occurrence of aSAH events is extremely unlikely. Conclusion: The myth of special weather conditions influencing the frequency of aSAH is disenchanted by this long-term big data and deep learning analysis.
Design of Orthogonal Wavelet Filters with Minimum RMS Bandwidth Using A Symbolic Approach

Filterbanks have many applications. The orthogonal wavelet filter is widely used in filterbank design because of its advantage in energy preservation. In this work, the orthogonal wavelet filter with a given length and a prescribed number of Vanishing Moment (VM) is designed to achieve minimum Root- Mean-Square (RMS) bandwidth by using a symbolic approach.
An Adaptive Immune Ant Colony Optimization for Reducing Energy Consumption of Automatic Inspection Path Planning in Industrial Wireless Sensor Networks

Industrial wireless sensor networks (IWSNs) are usually fixedly deployed in industrial environments, and various sensor nodes cooperate with each other to complete industrial production tasks. The efficient work of each sensor node of IWSNs will improve the efficiency of the entire network. Automated robots need to perform timely inspection and maintenance of IWSNs in an industrial environment. Excessive inspection distance will increase inspection costs and increase energy consumption. Therefore, shortening the inspection distance can reduce production energy consumption, which is very important for the efficient operation of the entire system. However, the optimal detection path planning of IWSNs is an N-P problem, which can usually only be solved by heuristic mathematical methods. This paper proposes a new adaptive immune ant colony optimization (AIACO) for optimizing automated inspection path planning. Moreover, novel adaptive operator and immune operator are designed to prevent the algorithm from falling into the local optimum and increase the optimization ability. In order to verify the performance of the algorithm, the algorithm is compared with genetic algorithm (GA) and immune clone algorithm (ICA). The simulation results show that the inspection distance of IWSNs using AIACO is lower than that of GA and ICA. In addition, the convergence speed of AIACO is faster than that of GA and ICA. Therefore, the AIACO proposed in this paper can effectively reduce the inspection energy consumption of the entire IWSN system.
Multi-Label Bengali article classification using ML-KNN algorithm and Neural Network

Multi-label classification is a very complex and critical task to solve in Natural Language Processing and Text Mining domain. Moreover, Bengali has limited resources to work with. The goal of this research is to overcome these constraints and provide a sophisticated and standard solution that will solve this problem for Bengali text. This research output can be utilized by any Bengali newspaper portals to improve their recommendation system as well as reduce manual labor of document tagging. In this work, we have utilized a large dataset that contains 4,16,289 news articles and 4,302 unique labels. These news articles are collected from one of the most popular Bengali newspapers of Bangladesh named Prothom Alo. The news articles span over seven years (2013 to 2019). These news articles are categorized into six categories named Sports, Technology, Economy, Entertainment, International, and State. This huge dataset helps us to build a supervised model using the ML-KNN algorithm and Neural Network. Furthermore, for the word embedding feature, we have utilized Count Vectorizer. We will also briefly discuss how different parameters like words per document, labels per category impact the result.
The Paper of Record Meets an Ephemeral Web: An Examination of Linkrot and Content Drift within The New York Times

Hyperlinks are a powerful tool for journalists and their readers. Diving deep into the context of an article is just a click away. But hyperlinks are a double-edged sword; for all of the internet’s boundlessness, what’s found on the web can also be modified, moved, or entirely disappeared. This often-irreversible decay of web content is commonly known as linkrot. It comes with a similar problem of content drift, or the often-unannounced changes––retractions, additions, replacement––to the content at a particular URL. Our team of researchers at Harvard Law School has undertaken a project to gain insight into the extent and characteristics of journalistic linkrot and content drift. We examined hyperlinks in New York Times articles starting with the launch of the Times website in 1996 up through mid-2019, developed on the basis of a dataset provided to us by the Times. We focus on the Times not because it is an influential publication whose archives are often used to help form a historical record. Rather, the substantial linkrot and content drift we find here across the New York Times corpus accurately reflects the inherent difficulties of long-term linking to pieces of a volatile web. Results show a near linear increase of linkrot over time, with interesting patterns emerging within certain sections of the paper or across top level domains. Over half of articles containing at least one URL also contained a dead link. Additionally, of the ostensibly “healthy” links existing in articles, a hand review revealed additional erosion to citations via content drift.
Sequence-Level Reference Frames in Video Coding

The proliferation of low-cost DRAM chipsets now begins to allow for the consideration of substantially-increased decoded picture buffers in advanced video coding standards such as HEVC, VVC, and Google VP9. At the same time, the increasing demand for rapid scene changes and multiple scene repetitions in entertainment or broadcast content indicates that extending the frame referencing interval to tens of minutes or even the entire video sequence may offer coding gains, as long as one is able to identify frame similarity in a computationally- and memory-efficient manner. Motivated by these observations, we propose a “stitching” method that defines a reference buffer and a reference frame selection algorithm. Our proposal extends the referencing interval of inter-frame video coding to the entire length of video sequences. Our reference frame selection algorithm uses well-established feature descriptor methods that describe frame structural elements in a compact and semantically-rich manner. We propose to combine such compact descriptors with a similarity scoring mechanism in order to select the frames to be “stitched” to reference picture buffers of advanced inter-frame encoders like HEVC, VVC, and VP9 without breaking standard compliance. Our evaluation on synthetic and real-world video sequences with the HEVC and VVC reference encoders shows that our method offers significant rate gains, with complexity and memory requirements that remain manageable for practical encoders and decoders.
Clinical Decision Support System Using Fuzzy Logic Programming and Data Analysis

Clinical decision support systems (CDSS) are intelligent systems, developed to assist physicians for taking crucial decisions. This work proposes a hybrid architecture for a CDSS using a rule base and a data-driven module. The rule base uses a variant of interval-valued fuzzy answer set programming to represent a physician's knowledge. The data-driven module refines these rules using available patient data. This hybrid structure has reasoning capability of a rule-based module and reliability for using real-world data.
Joint Demodulation and Decoding with Multi-Label Classification Using Deep Neural Networks

In this paper, we leverage the power of artificial intelligence in the receiver design for joint baseband demodulation and channel decoding. We consider a point-to-point communication system and develop a deep neural network (DNN) based joint demodulator and decoder (DeModCoder) that accomplishes the tasks of demodulation and decoding in a single operational block. We incorporate a multi-label classification (MLC) scheme for the considered DNN framework, which is trained offline over a wide-range of signal-to-noise ratios (SNRs) in a supervised learning manner and deployed online in real-time applications. Simulation results demonstrate that our developed DeModCoder outperforms the conventional block-based sequential demodulation and decoding schemes. We also observe that the MLC DeModCoder shows better performance than conventional multiple output classifier in high SNR region while incurring lower computational complexity.
"We Even Borrowed Money From Our Neighbor"

Mobile-based scams are on the rise in emerging markets. However, the awareness about these scams and ways to avoid them remains limited among mobile users. We present a qualitative analysis of the dynamics of mobile-based fraud (specifically, SMS and call-based fraud) in Pakistan. We interviewed 96 participants, including different stakeholders in the mobile financial ecosystem: 71 victims of mobile-based scams, seven non-victims, 15 mobile money agents, and three officials from regulatory agencies that investigate mobile-based fraud. Leveraging the perspectives from these stakeholders and analyzing mobile-based fraud with a four-step social-engineering attack framework, we make four concrete contributions: First, we identify the nuances as well as specific tactics, methods, and resources that fraudsters use to scam mobile users. Second, we look at other actors, beyond the victim and the adversary, involved or affected by fraud and their roles at each step of the fraud process. Third, we discuss victims' understanding of mobile fraud, their behavior post-realization, and their attitudes toward reporting fraud. Finally, we discuss possible points of intervention and offer design recommendations to thwart mobile fraud, including addressing the vulnerabilities discovered in the ecosystem, utilizing existing actors to mitigate the consequences of these attacks, and realigning the design of fraud reporting mechanisms with the sociocultural practices.
Research on Automatic Recognition Algorithm of Pointer Meter Based on Machine Vision

Pointer meters can directly reflect the changing trend of measured values, so they are widely used in industry field measurements. The digital reading of pointer meters is conductive to real-time data monitoring and management. The paper firstly describes the structural characteristics of the pointer meter, and briefly describes the preprocessing algorithm of the meter picture, and then uses the algorithm to complete the position of the dial and the pointer location relative to the dial. The pointer positioning is achieved by monitoring the edges on both sides of the pointer. Finally, the pointer reading is determined according to the position of the pointer scale relative to the minimum scale. The algorithm is not affected by the tilt of the dial, and the final recognition accuracy is one-fifth of the smallest scale.
Sentiment classification based on weak tagging information and imbalanced data

Sentiment classification aims to solve the problem of automatic judgment of sentiment polarity. In the sentiment classification task of text data, such as online reviews, traditional deep learning models are dedicated to algorithm optimization but ignore the characteristics of imbalanced distribution of the number of classified samples and the inclusion of weak tagging information such as ratings and tags. Based on the traditional deep learning model, the method of random oversampling and cost sensitivity is used to increase the contribution of a minority of samples to the model loss function and avoid the model biasing to the majority of samples. The model training is divided into two stages. In the first stage, a large amount of weak tagging data is used to train the model, therefore a model that captures the sentiment semantics of the data is obtained. After that, the model parameters trained in the first stage are used as the initial parameters of the second stage model training, and only a small amount of tagging data is used to continue training the model to reduce the impact of noise, thus reducing the use of manual tagging samples. The experimental results show that the method is considerably better than traditional deep learning models in the sentiment classification task of hotel review data.
How do we react to cluttered displays? Evidence from the first seconds of visual search in websites

Display clutter is known to degrade search performance and lead to differences in eye movement measures in different contexts. The goal of this study was to determine whether these differences in eye movements could be detected in the first few seconds of a search task using a realistic display, both with or without time pressure. Participants were asked to search for image or word targets in 40 website screenshots. Time pressure was introduced for half the trials. Clutter algorithms were used to classify the websites as low- or high-clutter. Performance, subjective, and eye-tracking metrics were collected. Results showed that people's attention allocation within the first 3 s of search is different when viewing high-clutter websites. In particular, people's spread of attention was larger in high-clutter websites. The results can be used to detect whether a person is struggling with clutter early on after they view a display. Practitioner summary: Eye-tracking metrics showed that people react differently to a cluttered website in a variety of conditions. These differences were evident within the first 3 s of the search. The eye-tracking metrics identified can be used to detect people struggling with clutter as soon as they look at a website.
SPS: A Subjective Perception Score for Text-to-Image Synthesis

A fundamental problem of text-to-image synthesis is the lack of quality assessment for a single generated image. Quantitative indicators of this work (such as Inception Score and Fréchet Inception Distance) only affect plenty of images' feature distribution. It causes monotonous evaluation and plenty of poor-quality image results. This paper proposes a new evaluation criterion for text-to-image synthesis by the blind image quality assessment(BIQA) method. To train the model, a Multi-Metrics Quality Assessment Dataset for generated birds' images(MMQA) is proposed. Besides, the Multi-hyper model is proposed to fit our dataset better. Experiments show that our method evaluates text- to-image tasks more comprehensively and optimize their results.
Movie Genre Classification with Deep Neural Network using Poster Images

The utility of the deep learning model on poster images to classify their corresponding movie genre is an incredible challenge in computer vision. The objective of this study is to make a computerized framework that works with pictures, both low-level and high-level highlights (such as object detection and form) to classify movie genres. Categorizing motion pictures makes it easier for the watcher to choose a movie they would like to watch. The method of categorizing movies is known as a genre. Many people started to work with low-level features (colour, edge) utilizing deep neural networks for movie genre classification. In this work, a convolutional neural network has been applied to classify the movie genre using their corresponding poster images. At first, this study extracted features from the movie's poster images and then classified the movie genres. A multilayered convolutional neural network is outlined which is trained over a large number of poster images. This study reached an accuracy of 91.15%, f1 score of 0.22, precision 0.67, hamming loss 0.1 and zero one loss 0.75 for the classification of the movie genre.
Improvements to Sanov and PAC Sublevel-set Bounds for Discrete Random Variables

We derive an improvement for probably approximately correct (PAC) sublevel-set bounds for the multinomial distributed discrete random variables. Previous bounds (including Sanov's Theorem) show that the Kullback Leibler (KL) divergence between the empirical probability mass function (pmf) and the true PMF converges with rate O(log(N)/N), where N is the number of independent and identically distributed (i.i.d.) samples used to compute the empirical pmf. We interpret the KL divergence as bounding the probability that a multinomial distributed random variable (RV) deviates into a halfspace and construct improved uniform PAC sublevel-set bounds that converge with rates O(log (log (N)) / N). These results bound the worst case performance for a number of machine learning algorithms. Finally, the `halfspace bound' methodology suggests further improvements are possible for non-uniform bounds. In this paper, we derive an improvement (on the convergence rate) for various Probably Approximately Correct (PAC) bounds (including Sanov's Theorem) for multinomially distributed discrete random variables.
Supporting Collaborative Sequencing of Small Groups through Visual Awareness

Collaborative Sequencing (CoSeq) is the process by which a group collaboratively constructs a sequence. CoSeq is ubiquitous, occurring across diverse situations like trip planning, course scheduling, or book writing. Building a consensus on a sequence is desirable to groups. However, accomplishing this requires groups to dedicate significant effort to comprehensively discuss preferences and resolve conflicts. Furthermore, as numerous decisions must be assessed to construct a sequence, this challenge can be exacerbated in CoSeq. However, little research has aimed to effectively support consensus building in CoSeq. As a first step to systematically understand and support consensus building in CoSeq, we conducted a formative study to gain insights into how visual awareness may facilitate the holistic recognition of preferences and the resolution of conflicts within a group. From the study, we identified design requirements to support consensus building and designed a novel visual awareness technique for CoSeq. We instantiated this design in a collaborative travel itinerary planning system, Twine, and conducted a summative study to evaluate its effects. We found that visual awareness could decrease the effort of communicating preferences by 21%, and participants' comments suggest that it also encouraged group members to behave more cooperatively when building a consensus. We discuss future research directions to further explore the needs and challenges in this unique context and to advance the development of support for CoSeq tasks.
FSDS: A practical and fully secure document similarity search over encrypted data with lightweight client

In this paper, we propose a highly accurate, fully secure document similarity search (FSDS) scheme that makes use of both a novel variant of the secure K-NN algorithm and somewhat homomorphic encryption (SWHE). The scheme provides data, query and search pattern privacy and is amenable to access pattern privacy. We provide formal security analyzes of both the original and the new secure K-NN algorithms and show the latter is IND-CPA secure. We also rely on IND-CPA security of the SWHE scheme to meet the strong privacy claims. The scheme provides a speedup of about two orders of magnitude over the schemes using only SWHE while its overall performance is comparable (and faster for certain cases) to other schemes in the literature with weaker forms of privacy claims. We present implementation results including those from the literature pertaining to response times, storage and bandwidth requirements and show that the proposed scheme facilitates a lightweight client implementation.