Reviewerly/exHarmony huggingface dataset:
Extractive Text Summarization Using LSTM-Based Encoder-Decoder Classification

Nowadays, text summarization is one of the important areas to be focused on. As the World Wide Web is growing, a huge amount of text articles (especially blogs, scientific articles) are also generated on the internet. Automatic text summarization is one of the important techniques to shorten the original text in such a way that shorten or summarized text covers incisive and meaningful sentences of original huge text. Extractive summarization extracts important sentences from original documents and then aggregates all these sentences to generate the summary. We have proposed a novel LSTM based encoder-decoder, which plays a vital role in the extractive text summarization process. CNN news article dataset is utilized for training our model. Our model is evaluated on standard metrics like Gold Standard, Recall Oriented Understudy for Gisting Evaluation (ROUGHE)-1, and ROUGHE-2. After evaluation, our model achieved an average F1-Score of 0.8353. Our model also outperformed other models available in the literature.
Collaborative Sketching with Tangibles: Let’s Stop Soulless Meetings

During meetings, one person is often the owner of the whiteboard or PowerPoint to sketch the problem or idea. This person commonly “owns” the meeting leading to passive meeting moments for others. In this course, we will bring the whiteboard to the table to start with collaborative sketching. By also using tangibles, a topic can be discussed in a more interactive and efficient way. We will also learn how to apply the techniques in micro-communications such as coffee machine talks. Participants will leave the course with own hands-on material to use back home. Let’s stop having daily soulless meetings.
Anonymization of Quasi-Sensitive Attribute Sets in Aggregated Dataset

The widespread use of Internet of Things (IoT) and Data Fusion technologies make privacy protection an urgent problem to be solved. The aggregated datasets generated in these two scenarios face extra privacy disclosure. We define attribute sets with different sources in an aggregated dataset as quasi-sensitive attribute sets (QS sets). The QS set itself is not sensitive, but internal linking attacks may occur when two QS sets in an aggregated dataset are linked. In this paper, we propose a new privacy model, namely, the QS k-anonymity model. The QS k-anonymity model is effective in preventing internal linking attacks. We provide two algorithms for the QS k-anonymity model, the Greedy QS k-anonymity algorithm and the Efficient QS k-anonymity algorithm. We evaluate our algorithms on real datasets. The experimental results show that the Greedy QS k-anonymity algorithm has good data utility, the Efficient QS k-anonymity algorithm shows better efficiency, and both algorithms are well scalable.
Role of Internet of Things (IoT) Increasing Quality Implementation in Oman Hospitals During COVID-19

Internet of Things (IoT) has had an influence on the health information industry since it has grown so quickly. This goal may be achieved through increasing the effectiveness, speed, and affordability of health care delivery. A framework for health-related IoT success developed by Mclean and DeLone Information Systems (IS) has yet to be proven. Its aim is thus to demonstrate the value of information systems (IS) in connection to client happiness, IoT Net benefits, and user intent in five Oman healthcare providers. This research will also look at the infrastructure and technologies that are necessary to improve the efficiency of social healthcare procedures for residents. A hospital's management should be encouraged by this conclusion to concentrate on critical components of IoT adoption in health systems. There were 750 questionnaires sent out, and 430 of them had been returned to Oman's five healthcare facilities. The response rate to the survey was 57%. For the purposes of this study, we want to examine the impact of infrastructure and technology on the well-being of citizens and users of the Internet of Things (IoT). It was also used to test assumptions pertaining to technology, infrastructure, system quality, and information quality. Oman's healthcare facilities may be hindered by a lack of quality of service, system quality, and information quality, according to a recent research. This research will enhance McLean and Delone's model by looking at the impact of infrastructure and technology in Arab countries.
Stochastic Path Optimization to Improve Navigation Safety in Urban Environment

This letter proposes a post-processing method to improve the smoothness and safety of the path for an autonomous vehicle navigating in an urban environment. The proposed method transforms the initial path given by local path planning algorithms using a stochastic approach to improve its smoothness and safety. Using the proposed method, the initial path is efficiently transformed by iteratively updating the position of each waypoint within it. The proposed method also guarantees the feasibility of the transformed path. Experimental results verify that the proposed method can improve the smoothness and safety of the initial path and ensure the feasibility of the transformed path.
Geochemistry of the Middle Eocene Gercus Sandstone, Shaqlawa Area, Northern Iraq: Implications for Provenance, Tectonic Setting and Paleoweathering

The study of the geochemical characterization of sandstone rocks contributes to understand the nature of the source rocks, their tectonic setting and the effects of paleoclimate and paleo-weathering. Accordingly, twenty sandstone samples from the red sandstone units of the Gercus Formation were collected from the northeastern limb of Safin Anticline at Shaqlawa Area – Northern Iraq. The studied sandstone samples show depletion in major, trace elements contents, except for CaO, MgO and Ni, Co and Cr respectively, and general depletion in the rare earth elements (REE) contents. Major element geochemistry indicates that the red bed sandstones of the Gercus Formation are of an oceanic island arc (OIA) tectonic setting. On the other hand, the results revealed that the paleo-weathering of the source area of the Gercus Formation experienced intensive chemical weathering under humid and/or warm climate conditions. The maturity and paleoclimate conclusions of the studied samples suggest a low feldspar content and low values of K2O / Al2O3 ratio, which in turn resulted from the dominance of low grade metamorphic and sedimentary rock types in the source area.
Establishment of an Engaged and Active Learning Community in the Biology Classroom and Lab with Discord

Discord is an instant messaging platform that facilitates voice and video calls and media exchanges. Although Discord was initially developed for video gaming, it is currently used by other communities where voice chatting is frequent. Here, Discord is described as a useful tool in the science classroom. We implemented Discord in several biology labs and classes and gained experience using it as a professor (AW) and as both a teaching assistant and a student (SS). In this Discord-enhanced learning space, students seemed to be more engaged with the subject material, their peers, and their instructors in comparison with a more traditional physical classroom and an entirely online classroom. In addition, real-time communication was augmented in a physical lab or classroom by Discord's interconnectivity. Discord facilitated real-time peer and instructor discussion and provided opportunities for group interaction outside conventional science teaching spaces. These increased connections also extended to the lab setting, both in and out of scheduled class time, providing a space to build community within and between groups and between students and instructors. Introducing Discord to a physical classroom or using it for an online class provided us with opportunities for a more engaged and active student, and they found it may lead to greater productivity and learning.
Low-Light Image Enhancement via Feature Restoration

Besides poor visibility, under-exposed images often suffer from severe noise and color distortion. Most existing Retinex-based methods deal with the noise and color distortion via some careful designs to denoising and/or color correction. In this paper, we propose a simple yet effective network from the perspective of feature map restoration to mitigate such issues without constructing any explicit modules. More concretely, we build an encoder-decoder network to reconstruct images, while a feature restoration subnet is introduced to transform the features of low-light images to those of corresponding clear ones. The enhanced images are consequently acquired through assembling the restored features by the decoder, in which, the noise and possible color distortion can be greatly remedied. Extensive experiments on widely-used datasets are conducted to validate the superiority of our design over other state-of-the-art alternatives both quantitatively and qualitatively. Our code is available at https://github.com/YaN9-Y/FRLIE.
Speakometer: English Pronunciation Coach

Stimulating learners’ engagement in a large class is one of the most important issues for language educators (Lamb, 2017). The present review aims to offer a review of Mentimeter for creating interactive presentations in large language classrooms. Driven by the continuous development of mobile technologies, language educators and teachers apply ‘Bring Your Own Device’ (BYOD) approach in using technology in classes. BYOD allows teachers and students to use their personally owned device and invite them to actively participate in various forms of in-class activities (Stowell, 2015). Mentimeter is an engaging and interactive Learner Response System (LRS). It allows real-time interaction between language educators and learners via mobile devices. At present, Mentimeter has over 200 million users, and 90,000 customers across more than 220 countries and territories (Mentimeter, 2021), and the application of this tool in language education deserves a deep and critical review
Parameter-Efficient Sparsity for Large Language Models Fine-Tuning

With the dramatically increased number of parameters in language models, sparsity methods have received ever-increasing research focus to compress and accelerate the models. While most research focuses on how to accurately retain appropriate weights while maintaining the performance of the compressed model, there are challenges in the computational overhead and memory footprint of sparse training when compressing large-scale language models. To address this problem, we propose a Parameter-efficient Sparse Training (PST) method to reduce the number of trainable parameters during sparse-aware training in downstream tasks. Specifically, we first combine the data-free and data-driven criteria to efficiently and accurately measure the importance of weights. Then we investigate the intrinsic redundancy of data-driven weight importance and derive two obvious characteristics i.e. low-rankness and structuredness. Based on that, two groups of small matrices are introduced to compute the data-driven importance of weights, instead of using the original large importance score matrix, which therefore makes the sparse training resource-efficient and parameter-efficient. Experiments with diverse networks (i.e. BERT, RoBERTa and GPT-2) on dozens of datasets demonstrate PST performs on par or better than previous sparsity methods, despite only training a small number of parameters. For instance, compared with previous sparsity methods, our PST only requires 1.5% trainable parameters to achieve comparable performance on BERT.
Hand Gesture Recognition Using Leap Motion Controller, Infrared Information, and Deep Learning Framework

Hand gesture recognition (HGR) systems are the current topic, attracting interest in many fields. This broad interest is because people use hand movements to communicate and interact with the physical world. HGR systems are overgrowing, and the reason is that they have applications for different fields of study. Fields can be human-computer interaction (HIC), augmented and virtual reality, robotics, medicine, and video games. Recognizing the frames to correspond to the hand gesture from a frames sequence is essential to developing HIC systems. Thus, this paper presents algorithms to detect the images corresponding to a hand gesture from a frame sequence acquired by the Leap Motion Controller. The frames sequence contains non-gestures images because the movement follows a video pattern in which the initial and final images correspond to the transition of the gesture. Therefore, this paper develops an automatic (AID) and manual (MID) images discriminator. Every algorithm returns a dataset with images corresponding to the hand gesture. To validate the algorithms, we present an HGR model with every algorithm. The models take as input the new dataset and feed an architecture based on convolutional neural networks (CNN). Our models recognize five static gestures: open hand, fist, wave in, wave out and pinch. The results show a classification accuracy of 92.31% with MID and 94.70% with AID.
MusIAC: An Extensible Generative Framework for Music Infilling Applications with Multi-level Control

We present a novel music generation framework for music infilling, with a user friendly interface. Infilling refers to the task of generating musical sections given the surrounding multi-track music. The proposed transformer-based framework is extensible for new control tokens as the added music control tokens such as tonal tension per bar and track polyphony level in this work. We explore the effects of including several musically meaningful control tokens, and evaluate the results using objective metrics related to pitch and rhythm. Our results demonstrate that adding additional control tokens helps to generate music with stronger stylistic similarities to the original music. It also provides the user with more control to change properties like the music texture and tonal tension in each bar compared to previous research which only provided control for track density. We present the model in a Google Colab notebook to enable interactive generation.
Path embedded hybrid reasoning model for relation prediction in knowledge graphs

To conquer incompleteness of knowledge graphs (KGs), we focus on a relation prediction task that completes KGs by ranking all relationships between entities and selecting the top one. Most existing methods for relation prediction are representation-based models, which learn the structural information of KGs to obtain the embeddings of entities and relationships but fail to effectively use relation paths to predict potential relationships between two entities. The traditional path-based logical models consider the path characteristics but ignore the global semantics of entities in KGs, such as surrounding entities and relationships. In this paper, we propose path embedded hybrid reasoning model (PE-HRM), a novel reasoning mode based on logical reasoning, representation learning and neural network. PE-HRM considers structural characteristics of entities and path features between them and effectively fuses these features. Finally, we evaluate PE-HRM on datasets FB15K237 and NELL995. Experimental results show that PE-HRM significantly outperforms baseline models.
EqualNet: A Secure and Practical Defense for Long-term Network Topology Obfuscation

Path tracing tools, such as traceroute, are simple yet fundamental network debugging tools for network operators to detect and fix network failures.Unfortunately, adversaries can also use such tools to retrieve previously unknown network topology information which is key to realizing sophisticated Denialof-Service attacks, such as Link Flooding Attacks (LFAs), more efficiently.Over the last few years, several network obfuscation defenses have been proposed to proactively mitigate LFAs by exposing virtual (fake) topologies that conceal potential bottleneck network links from adversaries.However, to date there has been no comprehensive and systematic analysis of the level of security and utility their virtual topologies offer.A critical analysis is thus a necessary step towards better understanding their limitations and building stronger and more practical defenses against LFAs.In this paper, we first conduct a security analysis of the three state-of-the-art network obfuscation defenses.Our analysis reveals four important, common limitations that can significantly decrease the security and utility of their virtual topologies.Motivated by our findings, we present EqualNet, a secure and practical proactive defense for long-term network topology obfuscation that alleviates LFAs within a network domain.EqualNet aims to equalize tracing flow distributions over nodes and links so that adversaries are unable to distinguish which of them are the most important ones, thus significantly increasing the cost of performing LFAs.Meanwhile, EqualNet preserves subnet information, helping network operators who use path tracing tools to debug their networks.To demonstrate its feasibility, we implement a full prototype of it using Software-Defined Networking (SDN) and perform extensive evaluations both in software and hardware.Our results show that EqualNet is effective at equalizing the tracing flow distributions of small, medium and large networks even when only a small number of routers within the network support SDN.Finally, we analyze the security of EqualNet against a wide variety of attacks.
Formation of Educational Independence of Primary Schoolchildren by Means of Project Tasks

The article reveals the essence of the concept of "educational independence", which became a subject of study back in the 19th century. and remains one of the most pressing problems of modern education. The main criteria for its formation have been determined. Approaches to the formation of educational independence in younger students are considered. The idea is substantiated that the project task is an effective means of developing the educational independence of students. Specific examples of assignments for solving design problems with the aim of developing educational independence of primary schoolchildren are given.
Open source deep learning inference libraries for autonomous driving systems

Automated driving functions, like highway driving and parking assist, are increasingly getting deployed in high-end cars with the goal of realizing self-driving car using Deep learning (DL) techniques like convolution neural network (CNN), Transformers etc. Traditionally custom software provided by silicon vendors are used to deploy these DL algorithms on devices.This custom software is optimal for given hardware, but supports limited features, resulting in-flexible for evaluating various deep learning model architectures tradeoffs by means of rapid prototyping.This paper proposes usage of various open-source deep learning inference frameworks to quickly deploy any model architecture without any performance/latency impact.The proposed solution consists of automatic Graph Partitioning, Post-Training Quantization and Optimal Tensor Management.The proposed solution has been implemented with three open-source inference frameworks namely Tensorflow-Lite, TVM/Neo-AI-DLR and ONNX Runtime running on Linux OS.The proposed solution using opensource frameworks provides ease of use and improved coverage for network types due to fall back for unsupported features from custom software provided by silicon venders.
Vehicle Road Privacy Recommendation System Equipped with Markov Chain

Due to the development of in-vehicle network, location-based service has brought many conveniences to users. However, the user’s behavior of constantly updating the location of the service provider will cause the private information to be exposed to the attacker, thus threatening the user’s information security. Most of the current schemes ignore the differential protection of different road environments, which may lead to the abuse of privacy budget or be excluded by attackers according to actual environment analysis. We propose a method to protect the road privacy and meet the individual needs of users, and carry out the differentiated privacy protection based on road environment. First, we calculate the length of each road section, create a normalized matrix by a Markov model to describe the congestion degree of road sections, and filter the best route according to users’ privacy preferences. Then, according to the congestion degree of the road section, the sensitive circle range is defined for each query position of the recommended route, and the acceptable deviation range of the user virtual position is superimposed to protect the differential privacy. Experimental results show that, compared with the current methods, the proposed scheme can reasonably protect users’ privacy and obtain better service quality while satisfying users’ preferences and considering the surrounding road environment.
Differentially Private Synthesis and Sharing of Network Data Via Bayesian Exponential Random Graph Models

Abstract Network data often contain sensitive relational information. One approach to protecting sensitive information while offering flexibility for network analysis is to share synthesized networks based on the information in originally observed networks. We employ differential privacy (DP) and exponential random graph models (ERGMs) and propose the DP-ERGM method to synthesize network data. We apply DP-ERGM to two real-world networks. We then compare the utility of synthesized networks generated by DP-ERGM, the DyadWise Randomized Response (DWRR) approach, and the Synthesis through Conditional distribution of Edge given nodal Attribute (SCEA) approach. In general, the results suggest that DP-ERGM preserves the original information significantly better than two other approaches in network structural statistics and inference for ERGMs and latent space models. Furthermore, DP-ERGM satisfies node DP through modeling the global network structure with ERGM, a stronger notion of privacy than the edge DP under which DWRR and SCEA operate.
Breast Cancer Detection Using Machine Learning Algorithms

Cancer detection at an early stage is important so that the patient can be ready to deal with it. There are many machine learning architectures that help in predicting cancer. Gene expressions are the most prominent thing in classification of breast cancer (Fan et al in Recursive SVM biomarker selection for early detection of breast cancer in peripheral blood [1]). The gene expression dataset currently available includes fewer records and a huge number of features (very high dimensionality). We are going to implement three different approaches on different datasets to find out the best performing approach. In the first approach, we have used a recursive feature elimination (RFE) and random forest classifier (RBF) on a gene expression dataset. In the second approach, we have used the ResNet-34 model over histopathology image dataset. And in the third approach, we are proposing a CNN architecture which has a small number of convolutional layers for the classification of cancerous genes from healthy ones using gene expression data available at Pan-Cancer Atlas. Due to high dimensionality of gene expression data, that too with small count of records and high noise, it brings more challenges (Elbashir et al in IEEE Access J Mag 7, 2019 [9]).