Reviewerly/exHarmony huggingface dataset:
Dynamically-biased Fixed-point LSTM for Time Series Processing in AIoT Edge Device

In this paper, a Dynamically-Biased Long Short-Term Memory (DB-LSTM) neural network architecture is proposed for artificial intelligence internet of things (AIoT) applications. Different from the conventional LSTM which uses static bias, DB-LSTM adjusts the cell bias dynamically based on the previous status. Hence, a DB-LSTM cell contains information of both the previous output and the current cell state. With more information, the DB-LSTM is able to achieve faster training convergence and better accuracy. Furthermore, weight quantization is performed to reduce the weights to either 1-bit or 2-bit, so that the algorithm can be implemented in portable edge device. With the same 100 epochs training setup, more than 70% loss reduction are achieved for floating 32-bit, 1-bit and 2-bit weights, respectively. The loss degradation due to weight quantization is also negligible. The performance of the proposed model is also validated with the classical air passenger forecasting problem. 0.075 loss and 94.96% accuracy are achieved with 2-bit weight when compared to the ground truth, which is comparable to full-length 32-bit weight.
Industry Trends in Computer Software

In the modern age of technological innovation, organizations are evolving rapidly due to the abundance of opportunities. This evolution has provided organizations with the ability to leverage technology for their benefit; however, cybersecurity risks have also developed due to the emerging trends. As the software industry faces a surge of emerging trends, it is critical to address the impeding threats to our cybersecurity infrastructure. The computer software industry is classified under the North American Industry Classification System (NAICS) code 511210 for software publishers. The software development market has experienced an exponential growth as a shift to the cloud has rendered a greater need for cutting edge technology, and this trend is projected to continue.
Interpreting digital licensing contracts between a metaphorical and functional direction: A comparative analytical study

This study seeks to clarify the importance of explaining the type of digital license contracts to see if it can continue subjecting them to the metaphorical direction that adopts the direction of applying the traditional rules to this types of contract or must it be subject to the functional direction that adopts the necessity of establishing independent legal rules in the theory of contracts in Egypt. The problem of the user not reading the contract terms is still there. Although consumers do not read the terms of digital licensing contracts, some jurisprudence in the US often insist that it must be the exchange of consent must take place that the offeree must see the terms and conditions before assenting in some sort of this contracts, this differs from the nature of digital licensing contracts and the way they are contracted. It is right that the consumers discover in some the types of contracts do not express the consent by the traditional way in the contract but are subject to specific instructions set by the site, due to the inability to read and understand the terms of the contract, but rather that in some types of digital licensing contracts the consumers are not aware to be a party to a contract according to the traditional concept of contract theory. Legal recognition is important by the Egyptian and Arab legislations in the role of technical and digital in regulations next to the contract and the law (functional direction) which can contribute to help the user to read the terms of use, we must make use of digital technology to fulfill the function of the contract, which aims to create obligations on both parties, businesses and the user. The following questions were analyzed: Is it possible an individual can enter into a contract without realizing it on the internet? Is the individual obligated to contractual terms that he did not read and understand? How can the provisions of the traditional contract be applied to digital licensing contracts? Then I concluded the necessity of enacting new legal rules that regulate digital licensing contracts within Egyptian legislation.
A Unified Framework for Saliency Segmentation

Saliency segmentation is an extendable saliency detection that can detect and segment the most interested object(s) of an image. During the past decade, many saliency detection methods have been proposed. Those methods can correctly identify salient locations, but they provide the detected saliency maps with a diversity of intensity values, highlighting only high contrast edges, unevenly salient regions, and ill-defined salient boundaries, thus making them unextendible to saliency segmentation. Therefore, this paper proposes a unified framework for saliency segmentation. This framework consists of three main processes: (i) saliency feature extraction, implemented with a set of i-Hola filters, our recently proposed method, (ii) saliency map selection, using a multi-solution technique in selecting the optimal saliency map, and (iii) saliency segmentation, implemented with an iterative approach. Based on a challenging dataset divided into seven categories with different characteristics, the experimental results showed that our proposed method outperformed the baselines in almost all categories in terms of IOU performance.
Key Elements of Successful Virtual Teamwork in Projects Management during Pandemic Periods; a Case of COVID-19

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Cyberbullying and Hate Speech Detection on Kazakh-Language Social Networks

Currently, there is a growing number of online bullying, attacks, and propaganda of prohibited materials over the Internet. The main means of exchanging information, selecting and promoting personnel for such structures is the Internet, namely web resources, social networks and e-mail. In this regard, the task of identifying, identifying communication topics, connections, as well as monitoring behavior and predicting threats emanating from individual users, groups and network communities that generate and distribute information of a bullying network on the Internet. The article is devoted to the research and data mining related to the topic of detecting bullying on the Internet. The study examines the development of the data set aimed to train the machine learning algorithms in the Kazakh language and marking them as con-sist of bullying phrasesor not.
Conveying value via categories

A sender sells an object of unknown quality to a receiver who pays his expected value for it. Sender and receiver might hold different priors over quality. The sender commits to a monotone categorization of quality. We characterize the sender's optimal monotone categorization, the optimality of full pooling or full separation, and make precise a sense in which pooling is dominant relative to separation. As an application, we study the design of a grading scheme by an educational institution that seeks to signal student qualities and simultaneously incentivize students to learn. We show how these incentive constraints are embedded as a distortion of the school's prior over student qualities, generating a monotone categorization problem with distinct sender and receiver priors.
An Under-Sampling Algorithm Based on SVM

Tradition classification algorithms often get poor performance in imbalanced datasets because they are proposed under the assumption that the datasets are nearly balanced. Random under-sampling(RUS) algorithm is a popular algorithm to solve imbalance problem through removing some majority class samples randomly. However, RUS algorithm may neglect some key information of datasets. A new under-sampling algorithm based on SVM is proposed in this paper. The proposed algorithm aims to reserve samples distribution information in undersampling process. The simulation results show that the proposed algorithm could achieve satisfying performance.
Detect Noisy Label Based on Ensemble Learning

The success of machine learning relies on high-quality labeled training data. If there are incorrectly labeled data in the training data, the performance of the best classifier will be greatly reduced in a wide range of classification problems, and noisy tags are also often more harmful than noisy attributes. Unfortunately, large datasets almost contain incorrect or inaccurate labels. This paper proposes a simple and effective method that can identify noisy data in text classification datasets to a certain extent. We analyze the characteristics of the noisy data, and design the new method based on the idea of ensemble learning. The method combines with the majority voting and iterative methods to select the noisy data hidden in the dataset. Under the same conditions, our method can select more noisy data, and perform corresponding evaluations on the recall and precision of noise. The experimental results show that this method is better than some previous methods.
Computer Application for Assessing Subjective Answers using AI

Finding the similarity and relatability between two pieces of text is often used in multiple places today. Search Engines, recommendations, web indexes, etc., make use of the similarity between two pieces of data to come to various different conclusions. However, no reliable system has been developed so far that takes into account the similarity between a student’s answer and the teacher’s model answer to give the most accurate and precise marks to the student. The major reason lies in the fact that such a system must be built on multiple parameters and hence the complexity of the system increases. However, on close examination, it could be found that there are various methods that work towards helping in the formulation of the final scores secured by the student based on the similarity between the words of the student answer and model answer as well as the similarity between the meaning of then sentences formed by the student and the model answers. These methods have been reviewed and the most efficiently working ones have been put forward in this proposed system.
The Cryptographic Technology of Bitcoin Will Eventually Be Broken

Blockchains depend on technologies from the field of cryptography, such as digital signatures, encryption, and hash functions; thus, using secure cryptographic technology is essential.
Electrical Appliance Usage Timeline Data Visualization

In a storytelling context, the visualization of the timelines for appliance usage could convey a great amount of information to audiences about the patterns or habits of households when using appliances in smart home. However, today's research on smart home visualization is more focused on time series data, while research on timeline does not focus much on expressiveness in a storytelling context. If we simply visualize the time series or use traditional visualization methods to visualize the timeline, the intention and effectiveness of storytelling may be reduced. In this paper, we analyze the above issues in detail and propose a new visualization design based on machinima and a dynamic circle.
Dialog prediction in Institute Admission: A Deep Learning Way

Chatbot, an application capable to conduct online chat conversation via test, text to speech or speech to text, giving an experience of providing direct contact to a human agent. A chatbot is designed to simulate the way a human talks / behaves during a conversation and require lots of tuning and testing to pass the turing test. Chatbots are used for various purposes like customer service, routing a request, information gathering, complaint registration, e-commerce, health, educations and many more. Chatbot domains can be categorized in two types, Open and Closed. To make a closed domain chatbot precise information, questions and their answers are needed for training. While making closed domain chatbots the amount of information is less compared to open domain. Chatbots can also be further divided into two types - retrieval based chatbots, which use predefined input patterns and predefined responses, and generative based which use seq2seq neural networks and generate a answer based on large amount of data. The required data for this chatbot is collected by a survey done with the help of Google forms and is cleaned using Python and Natural Language Tool-Kit. Classification of classes is done using various machine learning techniques such as Stochastic gradient, Sequential model, Rectified Linear activation function (ReLu), Softmax. 3 Layers of Neural Networks are used to create the model. The above mentioned models and functions give accuracy of outputs more than 90%.
Technical Perspective

Visualization enables effective data exploration by harnessing the higher bandwidth interactivity of the human visual cortex. But the space of possible visualizations is immense, such that general abstractions for creating (i.e., finding) the right visualization are elusive, despite recent progressin systems like vega [2] and Draco [1].
N-Clustering of Text Documents Using Graph Mining Techniques

The chapter is about the clustering of text documents based on the input of the n-number of words on the m-number of text documents using graph mining techniques. The author has proposed an algorithm for clustering of text documents by inputting n-number of words on m-number of text documents. First of all the proposed algorithm starts the selection of documents with extension name “.txt” from m-numbers of documents having various types of extension names. The n-number of words are input on the selected “.txt” documents, the algorithm starts n-clustering of text documents based on an n-input word. This is possible by way of creation of a document-word frequency matrix in the memory. Then the frequency-word table is converted into the un-oriented document-word incidence matrix by replacing all non-zeros with 1s. Using the un-oriented document-word incidence matrix, the algorithm starts the creation of n-number of clusters of text documents having the presence of words ranging from 1 to n respectively. Finally, these n-clusters based on word-wise as well as 1 to n word-wise.
The Representation of Large-Scale Graph Based on Semi-Supercised Learning

Abstract With the development of artificial intelligence, the technology of graph representation has been widely used in graph structure data mining. The traditional graph representation mostly uses adjacency matrix. The representation of adjacency matrix is faced with two major challenges. One reason is extremely inefficient in the storage and computing of large graphs with the massive growth of data; the other is that the characterized data is difficult to retain the original structural information of graphs. Aiming at the above two points, an efficient graph representation method is proposed in this paper. The first-order similarity and second-order similarity of the graph are used as constraints to train the original graph structure data, and the nodes in the graph are mapped to a low-dimensional space, which greatly reduces the cost of storage space and computing while retaining the graph structure information. Experimental results show that the proposed method is significantly better than the Deepwalk and spectral clustering methods in different proportion of label data. Meanwhile, the proposed method can be applied to the semi-supervised learning task with a small number of labels, which provides convenience for the subsequent data analysis.
Developing Writing Assessment Rubric For Explanation Text Of Grade XI Students At SMKN 4 Medan

Writing is one of skills in English and it’s important for students, also it is not an easy skill to master. As stated by Richards and Renandya (2002: 303), writing is the most difficult skill to master for second or foreign language students. Writing skills are placed in the last section after three skills namely listening, speaking, and reading. It can be said that students must apply everything they have in the previous three stages. This study aims to develop a writing assessment rubric for explanation text for eleventh grade, to assess student writing related to the availability of the 2013 Curriculum for English subjects in the school. This research is about how and why, which is the process of explanation text itself and at the same time can improve their writing skills. This research method uses research and development methods (R&D). It is hoped that by using reliable rubrics, teachers can easily assess the writing performance of their students who meet the requirements of the writing aspects. The expert gives some corrections and suggestions on the development writing assessment rubric. Based on the result of the writing assessment rubric, the average was 3,7 and the percentage was 93,7% that categorized in “Very Good” category. Keywords : Assessment Rubric, Students’ Explanation Text, Writing
Host-Graph-Sensitive RETE Nets for Incremental Graph Pattern Matching

Efficient querying of large graph structures is a problem at the heart of several application domains such as social networks and model driven engineering. In particular in the context of model driven engineering, where the same query is executed frequently over an evolving graph structure, incremental techniques based on discrimination networks such as RETE nets are a popular solution. However, the construction of adequate RETE nets for a specific problem instance is a challenge in and of itself. In this paper, we propose an approach to RETE net construction for queries in the form of simple graph patterns that considers not only the structure of the query, but also the structure of the graph the query is being executed over in order to improve the net’s performance with respect to execution time and memory consumption. Furthermore, we suggest a technique for adapting the net structure to changing characteristics of the underlying graph. We evaluate the presented concepts empirically based on queries and data from two independent benchmarks.
A Model Hybrid Recommendation Approach Based on Knowledge Graph Convolution Networks

In previous works, researchers proposed the knowledge graph (KG) and knowledge graph convolution networks (KGCN) for mitigating sparsity and cold start complications present in collaborative filtering recommender systems. It is noted that KGCN is mainly used to capture the correlations between items; they generally ignore the correlations between users, thus resulting in insufficient accuracy of recommendation outcomes. In this study, we developed a model hybrid recommendation approach based on knowledge graph convolution network collaborative filtering (KGCN-CF). This approach adds user-based collaborative filtering based on KGCN to capture the correlations between users, which counterbalances the defects of KGCN, while addressing the sparsity and cold start complications of collaborative filtering to a certain extent. The proposed approach was applied to three datasets provided by Dianping-Food, Book-Crossing, and Last.FM. The experimental results demonstrated that this approach can effectively improve the area under the curve (AUC) and F1-measure of the recommendation.
A Recommendation Algorithm using Adaptive Aggregation of Binary Ratings

In this paper, we describe a novel application area of recommendation systems; helping people with disabilities find accessible Point-of-Interest (POI) using binary ratings on various accessibility criteria based on crowd-sourced data. We discuss an adaptive aggregation technique based on time fading aggregation for binary rating stream to predict the current state or confidence of each accessibility criteria for POIs. The confidence along with the user profile is used to calculate personalized accessibility score for the POI. The proposed method can be used with other POI recommendation criteria to recommend accessible places to users. We evaluate our model using synthesized datasets of different size that simulate the change of accessibility confidence over time. On comparison of the results with widely used adaptive, as well as non-adaptive aggregation techniques, we found that the proposed technique significantly improves the accuracy.
A Data Security module based on Crypto and Steganography techniques

Data security is one of such concern which cannot be avoided in today's world. Protecting the sensitive data and personal information from unauthorised access and preventing it from being misused is important and can be done using various password protected software or systems, but is also possible to overcome these protecting software and systems through hacking and various other attacks. It is possible to store the sensitive data in an encrypted as well as secretly hidden way which will double the security level as first of all the attacker would have no idea where the secret data is hidden, even if in some way the attacker may find the secret data the it will be in an encrypted format which will be of no use to them. Therefore, it is necessary to introduces more than one level of security for data security. Hence in this paper one such approach has been proposed to use more than one technique for data security simultaneously namely cryptography and steganography, both of them are very popular techniques. The former is used to encrypt the data and the later is used to hide the data, together using both of them could help in increasing the security in a better way. This paper intends to improvise the technique of data security with a combination of the above two mentioned techniques and for a more secure approach, the data will be first encrypted using a key, and this key will be generated using the Diffie-Hellman key exchange algorithm and the key will be modified by key expansion, reduction and rotation techniques to increase the randomness for each round of encryption. Later this encrypted data will hidden into another image file, using the Least Significant bit steganography method by embedding encrypted data into lower order bits of the carrier image. In this paper the data hiding capacity is improved by increasing more than one least significant bits of the image and hence hence it will also reduce the number of clock cycles used for embedding and retrieving process. And the quality of the obtained output image will be benchmarked by calculating its PSNR (Peak signal to noise ratio) and MSE (Mean Square Error). The tools used for implementation are XILINX VIVADO, using verilog code for the proposed Architecture and MATLAB for image characterization and key generation.
The house that is here but is not that: On the Lonesome Skyscraper by Ivana Ognjanović

This paper interprets the piece Lonesome Skyscraper for orchestra and electronics (2012) by Ivana Ognjanović, relying on the theoretical essay about the composition given by the author herself. Inspired by the fate of the tallest residential building in Pécs, which was evacuated because of a construction error and left abandoned and isolated for almost 30 years, I. Ognjanović creates an organic sound unity by connecting various ambient (field recordings) and concrete sounds in the electronic part with responses coming from the orchestral part. Using the time stretching technique, suspending the melodic component, and avoiding the formal and motivic development, the author builds her own version of the skyscraper, an acoustic space through which the memory of the sonic environment is howling.