Reviewerly/exHarmony huggingface dataset:
Medication Combination Prediction via Attention Neural Networks with Prior Medical Knowledge

With the adoption of electronic health records (EHR), deep learning technologies have the potential to employ the EHR data to assist experts in better understanding the complex mechanisms underlying the health and disease. Existing studies have made progress on the research of medication combination prediction from the medical data, but few of them take into account the prior medical knowledge. This paper proposes a PKANet model that integrates the prior medical knowledge into the deep learning architecture to predict the medication combination. The prior medical knowledge is calculated from the mapping relation between diagnoses and medications hidden in the EHR data. It can provide the heuristic medications to help the PKANet model learn optimal parameters. In order to predict the possible medication combination, the PKANet model utilizes attention neural networks to obtain the relationship between different elements in the medical sequence data. The experiment results have demonstrated that the proposed PKANet model outperforms the state-of-the-art baselines on evaluation metrics.
A Consideration of Delivering Method for Super-Resolution Video

The satisfaction of users who watch contents from video delivery services is highly dependent on the communication environment between the server and the client. In order to reduce the interruption time while playing the video, several methods have been proposed to change the quality of the video according to the connection. Therefore, many researchers have studied super-resolution processing techniques that convert low-quality video into high-quality video by increasing the resolution of each frame. However, if the client does not have sufficient computing resources, such as CPU and memory, it is difficult to perform super-resolution processing in real time for all the frames that constitute the received video. In this paper, we consider a delivery method for playing super-resolution video in real time.
Link Prediction in Dynamic Heterogeneous Networks via a Novel Embedding Technique

Link prediction via network embedding techniques has been verified to be efficient and reliable in real applications. However, most current studies focus on the link prediction in static homogeneous networks. We aim to predict dynamic links in heterogeneous networks. Most network embedding techniques only have preserved the feature of low or high-order proximity with neighbors. In this paper, we propose a novel embedding technique that combines the proximity feature of node’s neighbors as well as the structural feature of node’s community. Our method is consisted by two levels. The first-level learning is a dynamic embedding algorithm, which enables a node to learn the proximity feature from its neighbors in a continuous snapshot of the network. The second-level makes the node to learn the structural feature from the nodes in the same community. We test our method on a real-world dataset which contains the customer-commodity relations in continuous network snapshots. Experimental result shows that our method outperforms the original method and other state-of-art methods baselines.
Understanding and Mitigating the Impact of Model Compression for Document Image Classification

Compression of neural networks has become a common norm in industrial settings to reduce the cost of inference and deployment. As document classification is a common process in business workflows, there is a dire need of analyzing the potential of compressed models for the task of document image classification. Surprisingly, no such analysis has been done in the past. Furthermore, once a compressed model is obtained using a particular compression algorithm (which achieves similar accuracy to the uncompressed model), the model is directly deployed without much consideration. However, such compression process results in its own set of challenges which have large implications, especially when the uncompressed model is ensured to be fair or unbiased such as a fair model making critical hiring decisions based on the applicant's resume. In this paper, we show that current state-of-the-art compression algorithms can be successfully applied for the task of document image classification. We further analyze the impact of model compression on network outputs and highlight the discrepancy that arises during the compression process. Building on recent findings in this direction, we employ a principled approach based on logit-pairing for minimizing this deviation from the functional outputs of the uncompressed model. Our results on Tobacco-3482 dataset show that we can reduce the number of mismatches between the compressed and the uncompressed model drastically in both structured and unstructured cases (by more than 2x in some cases) by employing label-preservation-aware loss functions. These findings shed light on some important considerations before the compressed models are ready for deployment in the real world.
T-COFFEE Multiple Sequence Aligner on Hadoop Spark Cluster

DNA (Deoxyribose Nucleid Acid) is a series of nucleotide acid proteins that exist in the organism body where DNA will be identical with inheritance.The sequence alignment mechanism is one of the most important methods in finding a match between DNA sequences.This mechanism is also used in the mechanism for making vaccines, where the process takes a large portion of the time.Parallel and distributed systems exist to solve this problem.One of them is using the Hadoop platform, which is currently being used for processing biological data.The making of the COVID-19 vaccine is one example of the implementation of using a distributed and parallel system model, so that the manufacturing process can be done in a fairly short time.In this study, we used MSA (Multiple Sequence Alignment) where one of the algorithms which has a high accuracy value is T-COFFEE (Tree Based Consistency Objective Function for Alignment Evaluation) algorithm.T-COFFEE is an algorithm for multiple sequences which is very suitable for finding similarities in DNA data by focusing on very high accuracy values.Besides having a high accuracy value, T-COFFEE requires a very long time to process.So this research did implementation of T-COFFEE on hadoop parallelization using Spark which has been proven to reduce the execution time.
FPGA Realization of ANFIS Controller Using a Proposed Digital Design

Abstract The design and realization of Adaptive Neuro-Fuzzy Inference System (ANFIS) controller based on Field Programmable Gate Array (FPGA) is presented in this paper. The controller intended to control the temperature of medical oven. A novel design of digital ANFIS is presented here for the implementation process. Different controllers are designed and their results are compared using MATLAB program to show the ANFIS superiority. The designed controllers tested for cell cultures application at 37.5. A reduction is made for the designed digital ANFIS due to the used FPGA limitations. The reduced design minimizes the utilized slices from 366% to 3% and LUTs from 364% to 3%. The reduced design reached an optimum size for this controller to utilize a smallest memory size. A real-time FPGA implementation of the proposed digital ANFIS have been done and verified through Xilinx ISE 14.6 using the VHDL language. The VHDL code for the controller is produced, aggregated and downloaded on the FPGA Spartan 3A/AN FPGA kit. A comparison between the simulation and implementation results is made. The matching between these results proves the effectiveness and robustness of the proposed digital ANFIS and the excellent performance of the FPGA based controller.
Focus on Recruitment Strategies That Work for You

The Volunteer Management ReportVolume 26, Issue 10 p. 2-2 Recruitment Approaches Focus on Recruitment Strategies That Work for You Kim Pawlak, Kim PawlakSearch for more papers by this author Kim Pawlak, Kim PawlakSearch for more papers by this author First published: 06 September 2021 https://doi.org/10.1002/vmr.31746Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume26, Issue10October 2021Pages 2-2 RelatedInformation
Research on Ontology Construction of Scenic Spots and Culture for Smart Tourism Based on the Internet

With the rapid development of the internet, information technology has been deeply applied in various fields of people's daily life. In the field of tourism, the mode of “internet + tourism” can meet the needs of tourists to quickly and accurately obtain valuable information from massive data. In this context, combined with the application and development of big data knowledge acquisition and ontology construction technology, an internet-based ontology construction method for scenic spots and culture is proposed. The ontology of scenic spots and culture constructed according to the method in this paper can represent and manage big data knowledge, which is helpful to extract cultural information of scenic spots from various data sources. It enables tourists to quickly obtain valuable information related to scenic spots culture from massive data and provides a foundation for intelligent application scenarios oriented to smart tourism.
Creativity and uncertainty tolerance as a resource for the psychological well-being of an individual

The objective of the work was to establish the features of creativity and uncertainty tolerance in people with different levels of psychological well-being. An empirical study of the level of manifestation of psychological well-being of the studied group and the relationship between the components of creativity and tolerance for uncertainty was conducted. The study was conducted using a set of psychological techniques aimed at determining the level of psychological well-being, uncertainty tolerance, creativity and their components. Qualitative methods of data processing are used. The study found that people with high levels of psychological well-being have close relationships between the indicators of the components of psychological well-being, creativity and uncertainty tolerance. Instead, these relationships are significantly fewer among people with low levels of psychological well-being.
Improvement of Object Detection from SAR Image Using Speckle Filter

In this paper, a convolutional neural network (CNN) based SAR image classifier is proposed. Synthetic aperture radar (SAR) images are often contaminated by speckle noise. Therefore, in the proposed method, a speckle filter is placed as a pre-process of the classifier. The network was trained after the speckle noise removal from SAR images. This approach helps the classifier to improve the performance. From an experimental evaluation, the significance of the proposed method is verified.
A Software Quality Course: The Breadth Approach

We present a Software Quality course taught in a MSc program in Computer Science and Engineering. The course takes an overview (‘breadth’) approach, reviewing the most important topics that contribute to the quality of software. The course has been taught traditionally as well as online; we discuss the advantages and disadvantages of both styles and point out what should be kept from the online experience. We also discuss the students’ evaluation and feedback.
Checking Network Security Policy Violations via Natural Language Questions

Network security policies provide high-level directives regarding acceptable and unacceptable use of the network. Organizations specify these high-level directives in policy documents written using human-readable natural language. The challenge is to convert these natural language policies to the network configurations/specifications needed to enforce the policy. Network administrators, who are responsible for enforcing the policies, typically translate the policies manually, which is a challenging and error-prone process. As a result, network operators (as well as the policy authors) often want to verify that network policies are being correctly enforced. In this paper, we propose Network Policy Conversation Engine (NPCE), a system designed to help network operators (or policy writers) interact with the network using natural language (similar to the language used in the network policy statements themselves) to understand whether policies are being correctly enforced. The system leverages emerging big data collection and analysis techniques to record flow and packet level activity throughout the network that can be used to answer users policy questions. The system also takes advantage of recent advances in Natural Language Processing (NLP) to translate natural language policy questions into the corresponding network queries. To evaluate our system, we demonstrate a wide range of policy questions – inspired by actual networks policies posted on university websites – that can be asked of the system to determine if a policy violation has occurred.
Learning Ecologies in the Digital Era: A Challenge for Primary Education in Low-Income Contexts

This research focused on the study of learning ecologies utilizing digital technology. The qualitative methodology used has allowed the analysis of the interactions children establish with digital technologies and the manner in which they construct a learning ecology. A total of 46 12-year-olds, their families, and their teachers participated in this study. The children’s schools and homes were in neighborhoods where structural situations of poverty and social and cultural marginality concurred. The children integrated researchers into their digital community, which allowed access to the events that the community was developing through digital technologies for two years. This information was complemented by the development of systematic observations and interviews with each participant. The participants’ multimodal linguistic and literacy practices were analyzed using a social semiotics approach. The results of the research describe and interpret the interactions that took place between participants and digital technologies. The research has identified the processes of recontextualization, transduction, and transcontextualization of the discourses developed in the frame of the participants’ learning ecologies. Digital ethnography has been revealed as an adequate method for studying learning ecologies.
On Some Innovative Technologies and Tools Used by the Forensic Departments of Australia in the Investigation of Crimes

The authors briefly reviewed a number of innovative technologies and technical tools used by forensic experts in the framework of criminal prosecution and combating offenses in Australia. The Australian experience of using the most relevant scientific developments and initiatives in forensics is presented. The social aspect of the activities of the forensic authorities of Australia was analyzed.
A Property-Based Method for Acquiring Commonsense Knowledge

Commonsense knowledge is crucial in a variety of AI applications. However, one kind of commonsense knowledge that has not received attention is that of properties of actions denoted by verbs. To address this limitation, we propose an approach to acquiring commonsense knowledge about action properties. In this paper, we take self-motion actions as an example to present our method. We first identify commonsense properties of actions from their definitions. We then introduce a list of dimensions for acquiring commonsense knowledge based on adjectives. Finally, we extract commonsense knowledge from text by parsing sentences that involve actions. Experiments show that our method allows to obtain high-quality commonsense knowledge.
An adaptive-stylization-based dynamic residual-multi-stage and multi-pooling approach to arbitrary image style transfer

Style transfer means that the characteristic information of the style image is transferred to the content image under a given content and style picture. Meanwhile, the transferred image is faithful to the content image. Currently, the transferred image has many problems such as artifacts and distortion of the spatial structure. To solve these problems, a dynamic residual-multi-level and multi-pooling network combined with our improved style transfer algorithm is proposed in this paper to achieve better effect of arbitrary image style transfer. To elaborate more specifically, First of all, We add a dynamic-residual network layer to the network to speed up the training speed of the network model and improve the robustness of the network. Secondly, we use a multi-level and multi-pooling layer to extract more specific spatial structure, edge texture and other information in the image, and the network layer also has an additional denoising effect. Third, the improved style transfer algorithm aligns the mean and variance of the content and style images from a statistical point of view, and realizes adaptive arbitrary style transfer of the original input image features. Finally, during model training, the convergence speed of the proposed approach is faster than other current advanced methods, and the transferred renderings are better than other network models in quantitative comparisons such as SSIM and Gram. It is worth mentioning that our model has the fastest real-time transfer speed and can realize any image style transfer.
G-Morph: Induced Subgraph Isomorphism Search of Labeled Graphs on a GPU

Induced subgraph isomorphism search finds the occurrences of embedded subgraphs within a single large data graph that are strictly isomorphic to a given query graph. Labeled graphs contain object types and are a primary input source to this core search problem, which applies to systems like graph databases for answering queries. In recent years, researchers have employed GPU parallel solutions to this problem to help accelerate runtimes by utilizing the filtering-and-joining framework, which first filters vertices that cannot be part of the solution then joins partial solutions with candidate edges until full isomorphisms are determined. However, the performance of current GPU-based solutions is hindered by limited filtering effectiveness and presence of extraneous computations. This paper presents G-Morph, a fast GPU-based induced subgraph isomorphism search system for labeled graphs. Our filtering-and-joining system parallelizes both phases by upfront eliminating irrelevant vertices via a novel space-efficient vertex signature hashing strategy and efficiently joining partial solutions through use of a novel sliding window algorithm (slide-join) that provides overflow protection for larger input. Together these techniques greatly reduce extraneous computations by reducing Cartesian products and improving edge verification while supporting large scan operations (split-scan). G-Morph outperforms the state-of-the-art GPU-based GSI and CPU-based VF3 systems on labeled real-world graphs achieving speedups of up to 15.78\(\times \) and 43.56\(\times \) respectively.
Present-80 Encryption Algorithm Implementation on GPRS Arduino Mega-2560 Cyber Physical Tracking System

Cyber Physical System can be characterized as a physical system and an engineering system which can be monitored, controlled, coordinated, and integrated processes with computing and communication. The more development of mobile internet technology, the more vulnerabilities in information security. In the information age, cyberattacks are a challenge. Increasing security on Communication device can map the actual location of the GPS into a different representation. VTS will send vehicle location data to the server or system user periodically. The VTS device consists of the Arduino Mega-2560 and the PRESENT-80 algorithm. The GPS module serves to provide location data, and the sim800L v2 module as a communication module with GPRS services. PRESENT-80 implementation on the Vehicle tracking system was successfully carried out. Location data can be sent in ciphertext form and location data can be decrypted in an Android-based monitoring application.
I’m Not 'Human' After All - Can Artificial Intelligence Survive the Inventorship Requirement?

Artificial intelligence (AI ) has been evolving exponentially, while simultaneously generating excitement about how it has increased prosperity and transformed our lives in multiple ways. The impact of the Industrial and Digital Revolutions has, undoubtedly, been substantial on practically all aspects of society. AI is related to innovation, which in turn is governed by patent laws. This leads to questions regarding the ramifications on patent inventorship in the AI arena. The key question is whether AI (an “artificial” entity) can be considered an “inventor” of a patent application. The United States Patent and Trademark Office (USPTO has provided its answer by clearly rejecting AI as an “inventor,” positing that AI cannot meet certain statutory definitions for an inventor or the jurisprudential tests for determining inventorship. The Patent Act does not expressly limit inventorship rights to humans, but it does suggest that each inventor must have a name and be an “individual.” This paper focuses on AI and patent laws — mainly, on whether AI should be considered an “inventor” under the patent statutes. Unlike other scholars, I argue here for recognizing AI as the inventor. I further argue that allowing AI to be listed as an inventor would incentivize innovation, as AI’s added value would be more clearly recognized. Furthermore, statutory recognition of AI as an inventor would encourage investment in developing inventive AI systems.
Practice Report “Smart Disaster Management” — Combining Smart City Data and Citizen Participation to Increase Disaster Resilience

Abstract Smart cities have a huge potential to increase the everyday efficiency of cities, but also to increase preparation and resilience in case of natural disasters. Especially for disasters which are somewhat predicable like floods, sensor data can be used to provide citizens with up-to-date, personalized and location-specific information (street or even house level resolution). This information allows citizens to better prepare to avert water damage to their property, reduce the needed government support, and — by connecting citizens locally — improve mutual support among neighbors. But how can a smart city application be designed that is both usable and able to function during disaster conditions? Which smart city information can be used? How can the likelihood of mutual, local support be increased? In this practice report, we present the human-centered development process of an app to use Smart City data to better prepare citizens for floods and improve their mutual support during disasters as a case study to answer these questions.
Examination along with Precise Mapping of Radio Frequency Pollution over Environment of Elementary School in Banja Luka

Rapid telecommunications developing during previous decades is followed by novel technology systems as well as enlargement of environmental non-ionized radiation level.Emitted electromagnetic signals from base stations are shifted to the higher frequencies range with limited penetrability, requiring additional strength which implicates increasing risk for human health.The present research shows the stage of electromagnetic pollution from exposed locations in the vicinity of the elementary school in Banja Luka, obtained both by experimental measurements and theoretical estimations.For theoretical prediction, a new upgraded software was developed for visualization in two and three dimensions of real space, where calculations are performed including all fundamental characteristics of the antenna along with environment characteristics.