Reviewerly/exHarmony huggingface dataset:
Conv-inheritance: A hardware-efficient method to compress convolutional neural networks for edge applications

Convolutional Neural Networks (CNNs) have won tremendous success in various applications, such as image recognition and natural language processing. Due to the computing-intensive and memory-intensive features of CNN models, it is challenging to deploy them on devices with limited resources and tight power budgets. To address this limitation, pruning, quantization, weights sharing, and other methods have been proposed as efficient solutions to compress CNN models. However, these works usually do not optimize the hardware design when compressing the CNNs. In this paper, we introduce Conv-Inheritance, a hardware-efficient compression method for CNNs by reducing the number of convolution operations to simultaneously reduce the inference time and hardware computing resources on chip of CNN edge devices. We also develop an extended version, called adaptive Conv-Inheritance, to further improve the inference performance by considering the similarity between pixels. We applied our Conv-Inheritance method to compressing various CNN models and performed extensive experiments on different image datasets. Experimental results demonstrate that Conv-Inheritance is both hardware-efficient and time-efficient on edge devices. Besides, Conv-Inheritance is a plug-and-play compression method for CNNs, and we can run Conv-Inheritance on top of other compression methods without any conflicts.
Virtualization Technology to Support Green Computing Among IT Personnel in the Public Sector

This paper presents virtualization technology that supports green computing among IT personnel in the public sector. The selection of effective technology in virtualization is crucial to reduce the negative impact of IT usage in the public sector by leveraging resources and maximizing the use of IT devices. Virtualization can provide important processes related to green computing implementation in the organization. However, the organization still faces problems in identifying suitable technology used by IT personnel in the organization. Thus, there is a need to understand the process to select the suitable technology based on the role of people, process, technology, and IT organization structure. The methodology comprises that the work process exploration, end-user segmentation, and technology evaluation process, which is validated by interviewing targeting IT personnel in government-based institutions in Malaysia that have in-house green data centers. The qualitative approach was adopted to understand the work activities of IT personnel and IT devices used to perform the task. A case study approach was used where the data was collected from open-ended interviews, observation, and report documents. The data was analyzed using a thematic analysis approach. Then, technology evaluation was conducted to select appropriate virtualization technologies to be used to support green computing in the IT work process by integrating four dimensions that consist of the people, process, technology, and IT organization structure. The results show a list of virtualization technology to support green computing based on the technology evaluation process.
Exploring the Runtime Performance of Knowledge Graph Embedding Methods

In recent years, Knowledge Graphs (KGs) have become ubiquitous, powering recommendation systems, natural language processing, and query answering, among others. Moreover, representation learning on graphs has reached unprecedentedly effective graph mining. In particular, Knowledge Graph Embedding (KGE) methods have gained increasing attention due to their effectiveness in representing real-world structured information while preserving relevant properties. Current research mainly focuses on improving and comparing the effectiveness of new KGE models on different predictive tasks. However, the application of KGE techniques in the industrial scenario sets a series of requirements on the runtime performance of the employed models. For this reason, this work aims to enable an effortless characterization of the runtime performance of KGE methods in terms of memory footprint and execution time. To this extent, we propose KGE-Perf, a framework for evaluating available state-of-the-art implementations of KGE models against graphs with different properties, focusing on the efficacy of the adopted optimization strategies. Experimental evaluation of three representative KGE algorithms on open-access KGs shows that multi-threading on CPU is effective, but its benefits decrease as the number of threads grows. The usage of vectorized instruction shows encouraging results in speeding up the training of KGE models, but GPU proves, hands down, to be the best architecture for the given task. Moreover, experimental results show how the RAM usage strongly depends on the input KG, with only slight variations between different models or hardware configurations.
Secure Collaborative Learning for Predictive Maintenance in Optical Networks

Building a reliable and accurate machine learning (ML) model is challenging in optical networks when training datasets are business-sensitive. We propose a framework of secure collaborative ML learning for predictive maintenance on cross-vendor datasets. Our framework is based on federated learning and multi-party computation technologies. Each vendor builds a local ML model based on its own private data. A server builds a global ML model by aggregating multiple local ML models in a private-preserving way. The server computes only the sum of the local models but cannot see any local model individually by the multi-party computation technique. The vendor-confidential dataset is never exposed to the server or other vendors. Moreover, after the global ML model is deployed in optical networks, the measured data compared to the prediction are privately distributed to the local model owners, which is beneficial to vendors. We applied our framework to the remaining useful life (RUL) prediction of laser device. Our experiments show that an accurate ML model can be built using sensitive datasets in a federated learning setting.
A Classification Algorithm: Data Mining and Mathematical Model

Abstract In this paper, we propose a classification algorithm based on Recency-Frequency-Monetary (RFM) model and K-means data mining method. In addition, the designed algorithm is verified by the experiments on the member data in a large shopping mall. The experiments results show that the proposed algorithm can provide an accurate classification of the members. Finally, some marketing strategies for different classes of members are given according to the classification results.
A Secure Color Image Encryption Scheme Based on Chaos

With the current explosion of the Internet, the transmission of sensitive personal data also increased significantly. Large volumes of color images and multimedia data transferred over the insecure channel are prone to unintentional disclosure. The privacy and integrity of such data are especially important and can be assured by encrypting them. In this paper, we propose a scheme based on permutation and diffusion to encrypt color images. We use a chaotic Duffin map to generate pseudo-random numbers. The encrypted image is obtained by performing permutation as well as diffusion of the image pixels using these numbers. The proposed scheme demonstrates potential cryptographic strength in the test results that includes correlation coefficient, entropy, PSNR, UACI, and NPCR. This scheme is scalable to very large images.
DC-YOLOv3: A novel efficient object detection algorithm

Abstract Feature pyramids have become an essential component in most modern object detectors, such as Mask RCNN, YOLOv3, RetinaNet. In these detectors, the pyramidal feature representations are commonly used which represent an image with multi-scale feature layers. However, the detectors can’t be used in many real world applications which require real time performance under a computationally limited circumstance. In the paper, we study network architecture in YOLOv3 and modify the classical backbone--darknet53 of YOLOv3 by using a group of convolutions and dilated convolutions (DC). Then, a novel one-stage object detection network framework called DC-YOLOv3 is proposed. A lot of experiments on the Pascal 2017 benchmark prove the effectiveness of our framework. The results illustrate that DC-YOLOv3 achieves comparable results with YOLOv3 while being about 1.32× faster in training time and 1.38× faster in inference time.
Whatsapp Chatbot Implementation Using Node JS for a Da'wah Media Digitalization

Da'wah is an activity teaching people about Islamic values to be practiced in everyday life. Besides, Da'wah can be interpreted as providing lessons about Islamic values to the society. Along with the development of technologies in 4.0 era, an innovation in da'wah activities is needed to penetrate geographical and social boundaries. The widespreading use of social media can be utilized as a medium of da'wah digitalization. Therefore, for increasing the efficiency of information dissemination, we created a WhatsApp Chatbot. The implementation of it did automatic reply service that able to simulate conversations with users. We used the utilizing WhatsApp platform and node JS module as the framework. Node JS has a library that provides a high-level API for controlling WhatsApp accounts. The result of our task was this chatbot can be a new alternative for da'wah and simplify public to get the information about Islamic studies easier and faster.
Myrio Based Mobile Robot for Rescue Systems

Recent natural and man-made devastations have prompted researchers to look into rescue robot systems. A novel shape-shifting remotely controlled mobile robot system rescue framework with live video telecasting is presented in this paper. Currently, the rescue system is under-trained and under-equipped. Since the majority of rescue operations are conducted there, it takes time and is often difficult to reach the accident site with such a large device. As a result, we present a paper in which these considerations are the primary focus. This paper describes a robotic device that is powered by a microcontroller and a motor driver, all of which are processed by the Arduino IDE, and which can be controlled from a remote location.
Stable Isotopes and Halogen Geochemistry of the Huayuan Carbonate‐hosted Pb‐Zn Ore District, South China: Implications for the Salt Source of Ore‐forming Fluids

Abstract The Huayuan Pb‐Zn ore district in China, located in western Hunan Province, is a giant carbonate‐hosted Pb‐Zn ore district. The source of ore‐forming brines in this ore district remains poorly constrained. Whether the highly saline brines are derived from evaporated seawater or dissolved evaporates continues to be intensely debated. Carbonate minerals associated with Pb‐Zn mineralization have δ 13 C V‐PDB and δ 18 O V‐SMOW values ranging from −5.55‰ to +1.35‰ (mean value of −0.69‰; n = 14) and +16.28‰ to +25.05‰ (mean value of +20.22‰; n = 14), respectively. This indicates that carbonate minerals are dominantly formed from dissolved ore‐hosted carbonate rocks. The δ 34 S values of sulfides range from +20.2‰ to +36.8‰, with an average value of +30.0‰ ( n = 27). These results suggest that sulfur is predominantly derived from the thermochemical sulfate reduction of marine sulfate. The crush‐leach analyzed solute data of fluid inclusions in sphalerite show the ore‐forming fluids have Cl/Br molar ratios range from 118 to 384, and Na/Br molar ratios from 39 to 160 ( n = 8). These Cl/Br ratios of hydrothermal fluid are much lower than those of seawater (657 to 564), but are consistent with bittern brines through early halite precipitation. We propose that ore‐forming fluids are mainly derived from evaporitic basin brines, which leached base metals from the basement and/or country rocks. The brine then migrated to the basin margins through clastic rocks of basement and then precipitated sulfides by thermochemical sulfate reduction.
Clams: A Cloud Application Modeling Solution

A wide range of new modeling languages with a specific focus on cloud computing, also known as cloud modeling languages (CMLs), have been introduced to help developers describe, evaluate, and deploy cloud applications. In general, CMLs define applications as interconnected cloud components within an architectural topology. However, in agile software development, developers describe system-level functionalities using user stories or epics to define end-user scenarios. So far, a CML bridging the gap between formal architectural descriptions and the informal scenario descriptions from agile development is missing. We present Clams (Cloud application modeling solution), a scenario-based CML. Clams uses cloud computing patterns as architectural placeholders in combination with message sequence charts. We introduce standard tooling to handle Clams models and show how one can refine patterns to concrete service offerings. Additionally, we also provide a development framework to support the creation of custom tools to evaluate, analyze, or translate Clams models efficiently.
Attribute Network Alignment Based on Network Embedding

Nodes with similar network structure and attribute features probably distribute across different networks. For instance, people tend to have accounts across various social networks. In recent years, network alignment to identify potential correspondences between nodes across networks has been research focus on social computing. In this paper, we propose an attribute network alignment method ANANE based on network embedding, which uses the network structure and node attributes together. Different from the previous embedding method based only on network structure and the existing iterative process based on structure and attributes, the proposed ANANE integrates heterogeneous network structure and attribute features into a unified embedding for node similarity measurement. We solve both the attribute network embedding and the network alignment simultaneously under a unified framework. In particular, we use neighbor approximation to generate the structure embedding and an auto-coder to obtain the attribute embedding. Then the attention mechanism is used to get the unified embedding for alignment. Empirically, we evaluate our proposed model ANANE over several real-world datasets, and it demonstrates effectiveness compared with several state-of-the-art methods on network alignment tasks.
Breast Tumour Classification using MobileNetV2

Breast Cancer is the most common cancer in Indian women. While a cure for cancer has not been discovered yet, it has been observed that the survival rate can reach 100% if it is detected early. Thus, our project aims to provide a model that gives an accurate and faster approach of classifying the tumor as benign or malignant present in the dataset of histopathology images which is a 3GB file. This model has used MobileNetV2 as the convolutional neural network which has fewer number of operations as compared to other architectures making the model training faster. MobileNetV2 is also ideal for mobile devices because of its compact size, fast computational and competitive performance in comparison to other models thus helping us achieve our aim better. The model is further automated by building an app using Flask to further improve the efficiency of cancer detection. The overall accuracy for the model is 89% and the sensitivity is 89.92%, specificity is 86.84% and AUC score is 88.38%.
A new framework for identifying absolute maximum nonlinear functions

Highly nonlinear functions (perfect nonlinear, maximum nonlinear, etc.) on finite fields and finite (abelian or nonabelian) groups have been studied in numerous papers. Among them are absolute maximum nonlinear functions on finite nonabelian groups introduced by Poinsot and Pott [15] in 2011. Recently, properties and constructions of absolute maximum nonlinear functions were studied in [19]. In this paper we study the characterizations of absolute maximum nonlinear functions on arbitrary finite groups. Then as an application of these characterizations, we discuss the existence of absolute maximum nonlinear functions on dihedral groups. We will prove that for a dihedral group D2n of order 2n, where n≥3, if there is an absolute maximum nonlinear function on D2n, then n∈{3,12,15,18,30,33,42,66,138}. In particular, if there exists an absolute maximum nonlinear function from D2n to Z2, where Z2 is the group of order 2, then we show that n=12. All absolute maximum nonlinear functions from D24 to Z2 will be determined.
Ethical Risks of Digital Transformation of Higher Education

The aim of the study is to identify the ethical risks of digital transformation of higher education. The article examines the main aspects of higher education digitalization basing on the materials of modern research; the ethical problems of using digital technologies in conditions of forced distance learning are concretized; the possible ethical consequences of digital technologies introduction into the higher education system are highlighted and analysed. The scientific originality of the research is determined by the fact that, on the basis of ethical problems that revealed themselves in the course of online education, the ethical risks of introducing and using digital technologies in the educational process of a university in the context of training personnel for the digital economy and the transition to a digital society are identified and determined; recommendations on the organization and implementation of educational activity of universities in the context of digitalization are given. As a result, it was proved that the digital transformation of higher education, on the one hand, is a necessary condition for the development of modern society, and on the other, it is a challenge for the classical education system and university ethics, the possible consequences of which are the following: dehumanization and instrumentalization of the learning process, loss of the true “spirit” of the university, disintegration of the moral identity of an individual’s personality.
Eco-feller: Minimizing the Energy Consumption of Random Forest Algorithm by an Eco-pruning Strategy over MLC NVRAM

Random forest has been widely used to classifying objects recently because of its efficiency and accuracy. On the other hand, nonvolatile memory has been regarded as a promising candidate to be a part of a hybrid memory architecture. For achieving the higher accuracy, random forest tends to construct lots of decision trees, and then conducts some post-pruning methods to fell low contribution trees for increasing the model accuracy and space utilization. However, the cost of writing operations is always very high on non-volatile memory. Therefore, writing the to-be-pruned trees into non-volatile memory will significantly waste both energy and time. This work proposed a framework to ease such hurt of training a random forest model. The main spirit of this work is to evaluate the importance of trees before constructing it, and then adopts different writing modes to write the trees to the non-volatile memory space. The experimental results show the proposed framework can significantly mitigate the waste of energy with high accuracy.
A Comparison of Distributed Ledger Technologies in IoT: IOTA versus Ethereum

There is an increasing interest in adopting distributed ledger technologies (DLTs) to IoT applications that enable secured interaction between IoT devices without third-party involvement. Among recent advanced DLTs, Ethereum blockchain and IOTA Tangle are emerging as promising candidates for various IoT use cases. Both offer various IoT-friendly features such as lightweight crypto, open-source implementations, energy-efficient operations, etc. So far, the two technologies have only been qualitatively evaluated together in the literature. There has not yet been a performance comparison between IOTA and Ethereum in an IoT application. To address the issue, we build an IoT environment that can run both DLTs and compare their performance. We thoroughly investigate the DLTs' three layers (i.e., Consensus, Network, and Storage layers). In the first layer, we evaluate the CPU utilization and number of transaction; in the second one, the DLTs' network performance is compared. Meanwhile, disk and memory usage are derived in the third layer. The results show that IOTA performs better with most performance metrics.
Towards an ABAC Break-Glass to access EMRs in case of emergency based on Blockchain

Blockchain technology is a fast-evolving sector that has proposed value in different domains. A distributed health care system for managing electronic medical records has various significant advantages in comparison to centralized health care systems. In a distributed system without a central authority, many threats such as data leakage by human mistake or a single point of failure are no longer feasible. A small number of recent research have proposed health care systems that have used IPFS and Blockchain as part of their security and storage components. Both have a transparent process and clear logic as a distributed system. In emergency care, access to medical records is an indisputable need to make efficient decisions fast. Current regulatory and bureaucratic processes make it near impossible to serve the data in a timely manner. This research designs a model of a break-glass mechanism for EMR management systems to provide access to healthcare professionals just in case of emergency. This conceptual model provides access to patients' records regarding patient privacy and data security, which they set previously by themselves.
Event classification from the Urdu language text on social media

The real-time availability of the Internet has engaged millions of users around the world. The usage of regional languages is being preferred for effective and ease of communication that is causing multilingual data on social networks and news channels. People share ideas, opinions, and events that are happening globally i.e., sports, inflation, protest, explosion, and sexual assault, etc. in regional (local) languages on social media. Extraction and classification of events from multilingual data have become bottlenecks because of resource lacking. In this research paper, we presented the event classification task for the Urdu language text existing on social media and the news channels by using machine learning classifiers. The dataset contains more than 0.1 million (102,962) labeled instances of twelve (12) different types of events. The title, its length, and the last four words of a sentence are used as features to classify the events. The Term Frequency-Inverse Document Frequency (tf-idf) showed the best results as a feature vector to evaluate the performance of the six popular machine learning classifiers. Random Forest (RF) and K-Nearest Neighbor (KNN) are among the classifiers that out-performed among other classifiers by achieving 98.00% and 99.00% accuracy, respectively. The novelty lies in the fact that the features aforementioned are not applied, up to the best of our knowledge, in the event extraction of the text written in the Urdu language.