Reviewerly/exHarmony huggingface dataset:
Nirjas: An open source framework for extracting metadata from the source code

Metadata/Comments are the critical element of any software development process. In this paper, we went on to explain how the metadata/comments in the source code can play an essential role in comprehending the software. We introduced a python based open-source framework &#x201C;Nirjas&#x201D; that helps us in extracting the metadata in a structured manner. There are various syntax, types and widely accepted conventions for adding a comment in the source file of different programming languages. Various edge cases can create noise in our extraction, for which we used Regex to accurately retrieve these metadata. The non-Regex method can give us the result but misses out on accuracy and noise separation. Nirjas can also separate different types of comments, source code and provide us with the details about those comments in terms of the line number, file name, language used, total SLOC, etc. Nirjas is a standalone python framework/library and can be easily and quickly installed using the source installation or using pip (package installer for Python). Nirjas was first created and started out as one of the projects during the Google Summer of Code program and is currently developed and maintained under the FOSSology organization
ADS-B-Based Spatiotemporal Alignment Network for Airport Video Object Segmentation

Video object segmentation (VOS) is the fundamental problem of vision-based intelligent transportation, and many VOS algorithms relying on inference from reference masks have been proposed. Due to the inherent defects of the inference strategy and the complex changes of targets, VOS methods that perform well on public datasets are usually ineffective in airport scenarios. We propose a spatiotemporal alignment network (STA-Net) that makes use of Automatic Dependent Surveillance-Broadcast (ADS-B) data as prior information to guide the long-term segmentation of aircraft. ADS-B is an airport-specific signal, which indicates the location of aircraft in real time. Based on ADS-B, we continuously generate new reference masks instead of using previous masks for inference, which greatly reduces the accumulation of inference errors. To achieve this, previous masks of each aircraft are aligned on the temporal domain based on the position information in ADS-B. All temporally-aligned masks are compared, and the one most similar to the current instant is reserved. This mask is both temporally and spatially aligned; hence it is a better reference mask for inference. Aligned masks are updated every time new ADS-B data arrive, so that they can support long-term inference. With the selected mask as a reference, aircraft of interest are segmented within a unified encoder-decoder framework over the long term. Experiments on a benchmark dataset and in a real airport scenario verify the effectiveness of the presented method.
XQM: Search-Oriented vs. Classifier-Oriented Relevance Feedback on Mobile Phones

In today’s media-rich and mobile-dominated society, an important research direction in multimedia retrieval concerns scaling multimedia interfaces down to mobile phones. We present XQM, an interactive learning app for images on Android mobile phones, with two different interface variants: (a) a search-oriented interface, which emphasises finding a particular image rapidly; and (b) a classifier-oriented interface, which emphasises helping users to build the interactive classifier.
Steganalysis for LSB inserts in low stego-payload artificial color images

Abstract In this study a new method of steganalysis is presented for detecting and locating a hidden message embedded using LSB replacement as a steganography algorithm. The method is applied for analysing artificial color images of a gradient fill and shows high detection and locating accuracy in low stego-payload of 25 to 10 percentage. The method is based on analysing pixel combinations of an LSB map to classify unique and non-unique pixel combinations and finding the largest rectangle algorithm for, respectively, detecting and locating purposes. To exclude noisy data from further consideration, a pre-processing filter is developed and presented.
Approach to equilibrium and the ordered state of DMMs

Abstract This Chapter describes how DMMs approach equilibrium, solution of the problem they are attempting to solve. It also demonstrates that DMMs go through a critical branching process that persists until a solution is found. It also argues about the topological order of their state, and demonstrates the reasons behind their robustness against numerical and physical noise. It finally concludes by enumerating the features these machines share with the animal brain.
DABA-Net: Deep Acceleration-Based AutoEncoder Network for Violence Detection in Surveillance Cameras

Violent crime is one of the main reasons for death and mental disorder among adults worldwide. It increases the emotional distress of families and communities, such as depression, anxiety, and post-traumatic stress disorder. Automatic violence detection in surveillance cameras is an important research area to prevent physical and mental harm. Previous human behavior classifiers are based on learning both normal and violent patterns to categorize new unknown samples. There are few large datasets with various violent actions, so they could not provide sufficient generality in unseen situations. This paper introduces a novel unsupervised network based on motion acceleration patterns to derive and abstract discriminative features from input samples. This network is constructed from an AutoEncoder architecture, and it is required only to use normal samples in the training phase. The classification has been performed using a one-class classifier to specify violent and normal actions. Obtained results on Hockey and Movie datasets showed that the proposed network achieved outstanding accuracy and generality compared to the state-of-the-art violence detection methods.
Evaluation and Optimization of Tracking Modes of Solar Photovoltaic System Based on a Novel Weather Type Classification

Abstract —Evaluation and optimization of tracking modes is vital for photovoltaic (PV) power system to enhance the ability of receiving solar radiation. This manuscript proposes a method for evaluation and optimization of tracking modes of solar photovoltaic system based on a novel weather type classification. First , a simplified weather type classification indicator named sky condition factor (SCF) is proposed using measured radiation and meteorological data. The SCF combines three parameters of the modified clearness index, total cloud cover, direct radiation ratio with different weights. Second, four classical in-plane radiation models are evaluated based on SCF. In addition, performances of different PV tracking systems are evaluated. The results show that the radiation benefits of different tracking systems are different under different weather types. Finally, a strategy is presented to determine the optimal operation mode of dual-axis tracking system in different weather types. This strategy is conducive to increasing radiation benefits, reducing costs and enhancing system stability.
Face Recognition utilizing Novel Face Descriptor &amp; Algorithm of Feature Extraction

Face recognition is generally considered exploration issue since from most recent twenty years due its significance in biometric confirmation applications. Accordingly, with propels in face catching gadgets face recognition frameworks should be versatile to precise play out the face recognition. For productive face recognition, there various conditions ought to be in contemplations like low goal face pictures, facial feelings, diverse enlightenment conditions like shades. This paper proposed novel way to deal with play out the powerful and effective face recognition paying little heed to face picture quality and enlightenment conditions. We planned the novel face descriptor strategy dependent on half and half neighbourhood heading number (HLDN) to address the issue of various picture brightening conditions and bad quality pictures. The HLDN descriptor is made out of face picture pre-handling utilizing versatile Gaussian sifting utilizing distinction of Gaussian (DoG) and face descriptor age utilizing Kirsch compass veils to produce the directional code for each face picture. The Kirsch administrator deteriorate face picture into 8 distinct bearings and create the exceptional face descriptor. After the face descriptor age, we separated and combined the 2D-DWT and histogram based highlights. We separated both single and staggered histograms to improve the recognition exactness. We applied the Principal part investigation (PCA) to create last picture highlight vector. PCA assists with advancing the recognition execution. The proposed strategy is assessed with various face datasets utilizing ANN classifier. The exhibition of proposed strategy is improved when contrasted with against condition of-craftsmanship strategies.
The Acceptance of Primary Health Centre Information System Among Health Staff: An extended TAM Model

Abstract Ngaglik I Public Health Center in Sleman is one of the health centers that have already applied SIMPUS (an application of management information system for public health center). The implementation of clinical information systems could potentially improve the performance of health care facilities, save operational costs, and increase patient satisfaction. In fact, the clinical information systems applied by public health center vary. One of them is SIMPUS. However until present, there have not been any assessments of the acceptance of the clinical information system, especially SIMPUS, at Ngaglik I Public Health Center. To describe the use of SIMPUS at Ngaglik I Public Health Center and evaluate user understanding in accepting SIMPUS application based on the Technology Acceptance Model (TAM). This was a quantitative study which used a survey method and a cross-sectional design. The research sample consisted of 50 respondents who were selected using a simple random sampling method. The data analysis technique was the SEM-PLS analysis with the TAM model and SmartPLS software. Statistically, the relationship between the Job Relevance and Perceived Ease of Use variables was weak (p-value = 0.818). There was a weak relationship between Output Quality and Perceived Ease of Use (p-value = 0.208). There was a strong relationship between Result Demonstrability and Perceived Ease of Use (0.000). There was a strong relationship between Result Demonstrability and Intention to Use (p-value = 0.011). There was a weak relationship between Screen Design and Intention to Use (p-value = 0.870). There was a weak relationship between Perceived Ease of Use and intention to use. Users’ job relevance to SIMPUS did not have any effect on perceived ease of use. Output quality did not have any effect on perceived ease of use. The screen design of SIMPUS display had no effect on intention to use. Intention to use did not affect system use. Intention to use did not affect system use. Facilitating condition did not have any effect on system use. System use was influenced by perceived ease of use and intention to use. Perceived ease of use and intention to use were influenced by result demonstrability.
SHTVS: Shot-level based Hierarchical Transformer for Video Summarization

In this paper, a Shot-level based Hierarchical Transformer for Video Summarization (SHTVS) is proposed for supervised video summarization. Different from most existing methods that employ bidirectional long short-term memory or use self-attention to replace certain components while keeping their overall structure in place, our methods show that a pure Transformer with video feature sequences as its input can achieve competitive performance in video summarization. In addition, to make better use of the multi-shot characteristic in a video, each video feature sequence is firstly split into shot-level feature sequences with kernel temporal segmentation, and then fed into shot-level Transformer encoder to learn shot-level representations. Finally, shot-level representations and original video feature sequence are integrated for the frame-level Transformer encoder to predict frame-level importance scores. Extensive experimental results on two benchmark datasets (SumMe and TVSum) prove the effectiveness of our methods.
Development of COMOS Architecture Based on a Story-Centric View of the Mind

A general aim in studies on cognitive architectures or artificial cognitive systems is a systematic framework and computational principles for achieving comprehensive, human–analogous, artificial intelligence. We have taken a story-centered approach to this issue during the past few years. The essential idea of this approach is to construct the foundation of an artificial cognitive system as the internal movement of stories. The internal movement of stories is considered to be the mid-level cognition between a lower level associated with neural/bodily processes and a higher-level associated with symbolic/manipulative processes. From this standpoint, we present the architecture of cognitive monogatari (narrative/story) system (COMOS). Parallel distributed processing is adopted as the central principle of the system in dealing with the complex process at the mid-level. The development of COMOS is a continuous long-term project, and the present implementation remains at an early stage. However, the work presented in this paper mainly includes the following two contributions: First, generic concepts for constructing COMOS are systematized. Second, the implementation of the COMOS architecture is presented along with primitive process scenarios.
GAs with escape from stagnation of search for multiset iteration permutations problem – consecutive meals planning

Abstract In this paper, consecutive meals planning is formulated as a multiset iteration permutation problem that determines the optimal meals plan on a period consisting of consecutive days. In this problem, a meal is characterized by some characteristics such as food style, ingredient, cooking method, and so on. The evaluation function is defined by use of information entropy for measuring the appearance order of meal's characteristics on the meals plan. For optimizing this problem, Genetic Algorithms (GAs) with escape from stagnation of search are proposed. It is empirically shown that the proposed GAs with escape operation work better than the conventional permutation GA without escape operation for small and large size optimization problems.
A decision framework for software startups to succeed in COVID-19 environment

The world is witnessing a pandemic of a severe infectious nature known as COVID-19. Many countries have used partial or complete lockdown to slow the spread of the virus, which has devastated the economies all over the world, and made hundreds of millions of people unemployed overnight. The lockdown measures and work from home practices as a response to the spread of the new coronavirus threaten the existence of many innovative startups. In fact, many software startups are either with no projects on hand and only a few of them are able to attract customers for even maintenance projects. In this paper, we provide a decision framework to help software startups choose projects which will increase their chance of success given their historically low success rate and diminished level of IT investment.
Boost Smart Recruiting and Portfolio Building Platform with AI Bot

This is the era of the internet and everything has now turned online, from ordering food to ordering daily services people now cannot live without the web. Due to the pandemic the recruiting companies were also forced to conduct all of their business online. This paper discusses about the importance of web portfolios, and a one site stop for building your own portfolio from scratch along with numerous artificial intelligence features. It also discusses about chatbots and its functions. How a chatbot is made using RASA framework is also explained here.
Investigating the COVID19 Characteristics of the Countries Based on Time Series Clustering

The objective of this study is to reveal the COVID19 characteristics of the countries by using time series clustering. Up to now, various studies have been conducted for similar objectives. But, it has been observed that these studies belong to early time of pandemic and are involved limited number of countries. To analyze the characteristic of COVID19 more, this study has considered 111 countries and time period between the 4th of April 2020 and the 1st of January 2021. Fuzzy K-Medoid (FKM) is preferred as clustering method due to its three abilities: i) FKM enables to determine the similarities and differences between the countries in more detail by utilizing the membership degrees, ii) In FKM, cluster centers are selected among from objects in the data set. Thus, it has the ability of detecting the countries which represent the behavior of all countries, iii) FKM is a robust method against to outliers. Thanks to this ability, FKM prevents that the countries exhibiting abnormal behavior negatively affect to the clustering results. At the results of the analyses, it is observed that 111 countries have three different behaviors in terms of confirmed cases and five different behaviors in terms of deaths.
Operational Technology Driven Smart Navigator

People who are blind or partially sighted rely significantly on their other senses, it being the sense of touch or auditory senses, to comprehend their surroundings. It is extremely difficult for a blind person to determine the objects that might come in their path without coming in contact with it. When travelling from one location to another, a blind person finds it difficult to notice the presence of any obstacles in their path. Physical touch between a person and an object can be hazardous, if not deadly. This research aims to convert the visual world into an aural world capable of informing blind individuals about items and their spatial placements. Objects recognized in the scene are identified and then transformed to speech. On the client side, video is recorded with a camera device and sent to the server for real-time image identification. We will be using different algorithms like YOLO, Darkflow (YOLO and TensorFlow), Darknet.
Efficient joint noise removal and multi exposure fusion

Multi-exposure fusion (MEF) is a technique that combines different snapshots of the same scene, captured with different exposure times, into a single image. This combination process (also known as fusion) is performed in such a way that the parts with better exposure of each input image have a stronger influence. Therefore, in the result image all areas are well exposed. In this paper, we propose a new method that performs MEF and noise removal. Rather than denoising each input image individually and then fusing the obtained results, the proposed strategy jointly performs fusion and denoising in the Discrete Cosinus Transform (DCT) domain, which leads to a very efficient algorithm. The method takes advantage of spatio-temporal patch selection and collaborative 3D thresholding. Several experiments show that the obtained results are significantly superior to the existing state of the art.
Design of Clustering Enabled Intrusion Detection with Blockchain Technology

Recent advancements in hardware and networking technologies have resulted in a large growth in the number of Internet of Things (IoT) devices connected to the Internet, which is likely to continue growing in the coming years.Traditional security solutions are insufficiently suited to the IoT context due to the restrictions and diversity of the resources available to objects.Security techniques such as intrusion detection and authentication are considered to be effective.Additionally, the decentralised and distributed nature of Blockchain technology makes it an excellent solution for overcoming the security issue.This paper proposes a chaotic bird swarm algorithm (CBSA)-based clustering technique based on an optimum deep belief network (ODBN) and Blockchain technology for secure authentication in an IoT setting.The CBSA-ODBN technique creates a clustering algorithm that utilises CBSA to pick cluster heads (CHs).The ODBN model is then utilised to identify the network, and the learning rate of the Deep Belief Network (DBN) model is optimally adjusted using the flower pollination technique (FPA).The suggested concept creates a layered security network paradigm for the Internet of Things using blockchain technology.Numerous simulations are run, with the outcomes analysed using a variety of measures, including detection rate, packet delivery ratio, energy usage, end-to-end latency, and processing cost.A careful comparison of the suggested model's performance to recently developed methodologies demonstrated the proposed model's superior performance.
Image segmentation based on super-pixel with neighborhood constrained and multi-level FCM clustering

In order to solve the problem of noise sensitivity and low precision in image segmentation, a new image segmentation method based on super-pixel with neighborhood constrained and multi-level FCM clustering is proposed. Firstly, an improved SLIC algorithm of FCM is designed based on the local pixel region of the image to get the refined super-pixel. Then, the improved FCM clustering algorithm is combined with the spatial relationship of neighborhood super-pixel to mark the super-pixel categories. In order to verify the effectiveness of the algorithm, the artificial synthetic image with noise and natural image without noise are used for comparison experiments. The experimental result show the algorithm proposed in this paper can not only suppress the noise, but also improve the processing accuracy.
Selective quantum state tomography for continuous-variable systems

We present a protocol that allows the estimation of any density matrix element for continuous-variable quantum states, without resorting to the complete reconstruction of the full density matrix. The algorithm adaptatively discretizes the state and then, by resorting to controlled squeezing and translation operations, which are the main requirements for this algorithm, measures the density matrix element value. Furthermore, we show how this method can be used to achieve full quantum state tomography for continuous-variable quantum systems, alongside numerical simulations.
Premium Rate Services Fraud Detection

Premium Rate Services (PRS) destination fraud occurs when telephone calls are made to high-cost (premium) destinations through fraudulent means. Early detection and termination of such calls can significantly reduce the cost they incur. We investigate various features of such calls to determine which can be used to quickly predict and terminate them. This is achieved through feature selection and clustering analyses followed by fraud detection using a Classification and Regression Tree (CART) model. Additionally, we propose the use of costs/benefits to evaluate the model’s performance through the consideration of an adjusted decision tree cost function at the training level. This approach is compared to the traditional decision tree to highlight its advantages.
Introduction by Judea Pearl

chapter Share on Introduction by Judea PearlProbabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages 393–398https://doi.org/10.1145/3501714.3501737Online:04 March 2022Publication History 0citation3DownloadsMetricsTotal Citations0Total Downloads3Last 12 Months3Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access