Reviewerly/exHarmony huggingface dataset:
Fake Product Identification with the Help Of Block Chain Technology

Blockchain has gotten a lot of publicity recently, and there are a lot of applications that have come out of it. The cryptocurrency, Bitcoin is a well-known Blockchain technology which effectively solves the double-spending issue, can validate the validity of transactional history without depending on a centralised framework to do the same. As a result, the application which uses Blockchain technology as its foundation guarantees that the data is tamper-free. The paper employs a decentralised Blockchain based proposal to verify that customers do not solely depend on retailers to decide whether or not goods are original. We define a decentralised Blockchain framework without fake goods, so that producers can use it to produce legitimate products without directly handling stores, lowering product quality assurance costs significantly.
Extraction of rock and alteration geons by FODPSO segmentation and GP regression on the HyMap imagery: A case study of SW Birjand, Eastern Iran

In lithological and mineralogical mapping geons are generated by transforming the solid spatial/spectral information of imagery objects into individual rocks and minerals through complex algorithms including interpolation, segmentation, generalization, and regionalization. In geological applications, the foundation of most remote sensing image interpretation is based on mapping and classification. The most common classification techniques are established for multispectral data analysis and are pixel-based. As a consequence, such analysis show disadvantages for classification of the hyperspectral images that contain abundant detailed spatial, spectral, and textural information. The major objective of this research was to extract the mineral and rock geons of HyMap mosaicked imagery scenes from Birjand, eastern Iran. The approach combines object based segmentation and machine learning classification algorithms. The utilized imagery was segmented by FODPSO algorithm that creates higher between-class variances while the output image was classified by Gaussian process (GP) algorithm using 272 and 127 geons as training and testing areas. The key alteration minerals including alunite, jarosite, kaolinite, muscovite, and montmorillonite, and the dominant rock types consisting of granodiorite, tuff breccia, tuff, ignimbrite, dacite, and andesite were mapped accordingly. All of the geons were verified through field observations and the final output map showed an overall accuracy of 89.66 %. This investigation showed that integration of FODPSO and GP algorithms could be introduced as a new approach to create geons of rocks and minerals, and for lithological and mineralogical mapping in various geological settings.
One Step Further: Investigating Problematic Files of Architecture Anti-patterns

Architecture anti-patterns violate design principles and negatively impact software internal quality. Both academia and industry have designed methods and tools to detect anti-patterns. However, these tools tend to report a large number of defects, hindering developers from prioritizing true debts. In this work, we take one step further to explore the most problematic files (we define them as root files) in the architecture anti-patterns, which are potential causes leading to the difficulty of software maintenance. Using 45 Python projects as subjects, we investigate root files' maintainability, evolution (i.e., birth, living, and death), and their interactions in different architecture anti-patterns. Our results reveal that, compared with other files in anti-patterns, these root files take only a small proportion but incur heavy maintenance costs. Our study of their evolution and interactions can help developers identify potential causes of anti-patterns. We believe our findings will benefit the practice of design problem fixing.
The Quantum Communications and Networking Project at the Information Technology Laboratory of NIST

Research in the Quantum Communications and Networking Project at the National Institute of Standards and Technology’s (NIST) Information Technology Laboratory (ITL) focuses on developing quantum devices and studying them for use in quantum communications and quantum networking applications. In this paper, we review parts of our research on quantum communications devices and introduce the case for the development of metrology tools and testbeds for quantum network systems.
Contextual-Semantic-Aware Linkable Knowledge Prediction in Stack Overflow via Self-Attention

In Stack Overflow, a question and its answers are defined as a knowledge unit. These knowledge units can be linked together for different purposes, which typically subdivided into four classes: Duplicate, Directly linkable, Indirectly linkable, and Isolated. Developers usually use these linkable knowledge units to search for more targeted information. Prior studies have found that deep learning or SVM technique can effectively predict the class of linkable knowledge units. However, they focus on short-distance semantic relationship but fail to capture global information (semantic relationship between a word and all the words in the same knowledge unit) and ignore joint semantics (semantic relationship between a word with all the words in different knowledge units). To address the issues, we propose a Self-Attention-based contextual semantic aware Linkable Knowledge prediction model (SALKU). SALKU leverages self-attention to pay attention to all the words in a knowledge unit and fully capture the global information needed for each word, then utilizes a variant of self-attention to extract joint semantics between two knowledge units. Experiment results on an existing dataset show that SALKU out-performs the state-of-the-art approaches CNN, Tuning SVM, and Soft-cos SVM in terms of three metrics, respectively. Additionally, SALKU is faster than the three baseline approaches.
A constant-size lattice-based partially-dynamic group signature scheme in quantum random oracle model

A group signature allows a group member who signs the message on behalf of the other group members. Any recipient also verifies the signature without knowing the signer. However, it is possible to reveal the signer’s identity since the group signatures have the tracing mechanism. In general, the group signature size increases the number of group members. However, the schemes are also proposed with the constant signature size. On the other hand, the security of some schemes are based on the discrete logarithm and factorization problems while some schemes which are secure against quantum attacks are proposed. To the best of our knowledge, the signature scheme in the group concept has not been proposed on lattices in quantum oracles. In this paper, we propose a new constant-size and partially-dynamic group signature scheme on lattice assumptions. We combine the Dilithium signature scheme, a Stern-like zero-knowledge scheme and Regev’s encryption scheme. We give a zero-knowledge argument system for our proposed scheme and security analysis in the quantum random oracle model. According to the comparison results, the proposed scheme has better size complexity in asymptotic concept.
Low-level feature extraction (including edge detection)

We shall define low-level features to be those basic features that can be extracted automatically from an image without any shape information (information about spatial relationships). As such, thresholding is actually a form of low-level feature extraction performed as a point operation. Naturally, all of these approaches can be used in high-level feature extraction, where we find shapes in images. There are very basic techniques and more advanced ones and we shall look at some of the most popular approaches. The first order detectors are equivalent to first order differentiation and, naturally, the second order edge detection operators are equivalent to a one-higher level of differentiation. An alternative form of edge detection is called phase congruency and we shall again see the frequency domain used to aid analysis, this time for low-level feature extraction. We shall also consider corner detection which can be thought of as detecting those points where lines bend very sharply with high curvature, and saliency which are important points. These are another low-level features that again can be extracted automatically from the image. Finally, we shall investigate techniques that describes motion, called optical flow.
Comparative Study of Ensemble Learning Techniques for Text Classification

The task of labelling textual data from a set of thematic labels has become increasingly important nowadays. This is not unconnected to the large volumes of such data generated in recent times and the need to search with effectiveness meanings from these vast data with flexible queries. Therefore, text classification is a key focus in machine learning applications and research as a way of doing away with manual labelling of text data which is not only time consuming but abysmally tedious. This study aims to comprehensively compare the performances of several ensemble learning techniques with base supervised machine learning techniques for text classification. For the experiments, we utilized two encoding paradigms for feature engineering; Bag-of-Words technique and Term Frequency Inverse Document Frequency or TF-IDF. The effective feature vectors obtained were passed as input to the supervised learning algorithms (ensemble learning methods and others). Hence, each of the algorithms were trained on YouTube Spam Collection Dataset based on 5-fold and 10-fold cross-validation. The results show that Adaboost and LightGBM outperform other approaches on both evaluations. This indicate that some of the Ensemble learning methods often yield better text classification performance compared with base techniques examined.
Crafting Tangible Interfaces for Human Digestion: Unpacking the Research through Design Prototyping Journey

This pictorial unpacks the prototyping journey of Digestive Tumble - a tangible system that demonstrates the processes involved in the human digestive system. The system aims to support awareness of our digestive processes and help users to make informed decisions about their dietary choices. Digestive Tumble consists of nine modules, where the modules visualise a unique stage of the digestion. The system represents the digestion of five major food groups: grains, meat, vegetables, fruits and dairy through colourful tokens. Users interact with the system by inserting tokens representing their meal. These tokens transform into colourful beads, which then traverse through different modules to show the digestion of the given meal. We reflect upon the steps involved in visualising an internal bodily process through a tangible system. We hope that our design reflection will inspire future systems on visualising other internal bodily processes to support body learning through everyday reflection.
A Comparative Study of Various Machine Learning Methods on Ovarian Tumor

Ovarian tumor is a kind of cancer which is commonly encountered in females. It is critical to accurately anticipate the development of Benign Ovarian Tumors (BOT) cancers. This research focuses on Machine Learning Methods (MLTs), which are utilized to battle important common diseases such as malignancies, hepatitis, and heart disease among others. Artificial Neural Networks, K-Nearest Neighbors, Decision Trees, image processing methods and Associative Classification are some of the topics discussed in this study. As part of a comparative research, this work looks at the current management of illness, which includes the use of MLT on ovarian tumors. Deep learning, networking, clustering, feature extraction and transformation, as well as feature facilitation approaches, are used to analyze all of the survey data collected. On the basis of this study, we provide an overview and a comparative analysis of the key categories of tumor detection while emphasizing the relationships between them.
Text-Based Emotion Recognition in Indonesian Tweet using BERT

Human uses social media platform such as Twitter to express feelings and opinions through text about the surrounding issues. Understanding emotions at the subtle level of expressed feelings are essential for better human and computer interactions. The previous emotion recognition approach required many training data and lexical databases. Unfortunately, the availability of very little labeled training data is a limitation and challenge to achieving high model performance. Therefore, we investigate the BERT language model for emotion recognition in Indonesian-language Tweets in this study. We choose to use fine-tuning instead of pre-training, which requires extensive data and resources. Two pre-trained models were used to determine the effectiveness and performance of the proposed model. Experiments show that the proposed model outperforms all existing baseline models, with the highest accuracy is 77&#x0025;. Another advantage that we analyze is that BERT requires a relatively short computation time. In addition, BERT has a better context representation.
Implementation of Fast Huffman Encoding Based on FPGA

Abstract In order to improve the efficiency of JPEG encoding architecture, some imagine compression systems use pipeline technology. Different modules will spend different cycles in the image compression, so the pipeline is prone to blockage. This paper proposes uses doublebyte splicing output Huffman code architecture and double Huffman encoding units on FPGA, so that Huffman encoding process spent less clock cycles in JPEG encoding, and pipelined JPEG encoding algorithm can be encoded in real time without blocking. According to the FPGA verification, although the number of logic units used will increase, Huffman encoding takes less clock cycles.
Digital Education

Abstract Education plays an essential role in transferring social norms and building human capital. There is widespread enthusiasm for the adoption and integration of digital technologies in education. This digitalisation of education has become a pillar of education policy worldwide, driven by growing optimism that such a policy approach can bestow a wide range of potential benefits to economies and society as a whole. Unfortunately, despite this optimism, digital inequalities remain in education—with these inequalities impacting the most vulnerable in society, including those who are socio-economically disadvantaged and/or residing in rural areas. Robust measurement of digital technologies in education is critical for informing policy and action, as well as for monitoring progress. This chapter defines digital education and discusses the rationales, benefits and challenges in integrating digital technologies in education. It concludes with an overview of existing international indicators for measuring digital technology in education.
Spherical perspective on learning with normalization layers

Normalization Layers (NLs) are widely used in modern deep-learning architectures. Despite their apparent simplicity, their effect on optimization is not yet fully understood. This paper introduces a spherical framework to study the optimization of neural networks with NLs from a geometric perspective. Concretely, the radial invariance of groups of parameters, such as filters for convolutional neural networks, allows to translate the optimization steps on the L2 unit hypersphere. This formulation and the associated geometric interpretation shed new light on the training dynamics. Firstly, the first effective learning rate expression of Adam is derived. Then the demonstration that, in the presence of NLs, performing Stochastic Gradient Descent (SGD) alone is actually equivalent to a variant of Adam constrained to the unit hypersphere, stems from the framework. Finally, this analysis outlines phenomena that previous variants of Adam act on and their importance in the optimization process are experimentally validated.
Real-World Quantum-Resistant IPsec

The recent advances in the development of quantum computers pose a threat to the cryptography used today. While symmetric algorithms can adapt to those challenges, no prevalent key exchange method can protect against quantum computer based attacks. There already exist several new, quantum-resistant approaches. Yet, all of them either lack in confidence regarding their security or require large data to be transmitted. Due to the lack of confidence in these algorithms, researchers tend towards hybrid key exchange methods which combine at least two different algorithms. As long as one of them prevails, the shared secret remains secure. Yet, these hybrid key exchanges together with algorithms which transmit large payloads require significant changes to the design of security protocols. IKEv2 is willing to introduce radical changes in order to support as many different algorithms as possible. This was expected to impact the protocol's operational feasibility, but remained to be tested due to the lack of a working reference implementation of the newly proposed internet drafts. To address this issue, we implemented these drafts using a modified version of the OpenIKED as basis that already comprised the first steps towards the concept's integration. During the implementation we found that several design choices severely hinder a clean and maintainable structure. With the resulting implementation, we tested the adjusted protocol under real-world conditions. The results show that the result generally works but struggles to cope with slow, lossy networks. Based on these insights, we drafted an improved protocol design which also supports the full range of quantum-resistant key exchange methods. In contrast to the current drafts, it promises a cheaper and more maintainable implementation.
Convolutional Neural Network Training System For Eye Location On Infrared Driver Recordings Using Automatically Generated Ground Truth Data

One of the most important factors in being able to train neural networks with good accuracy is the quality of training data. This is the reason why in every project with such purpose a very big part of the effort is to label data, double check and clean the labeled data and finally select the datasets which will be used for training of the neural networks. This paper presents promising results of an automatic system which selects and uses the data from a ground truth data generator for eye location on infrared driver recordings to train neural networks. The quality of the training data is based on the quality of the generator and it is not altered in any way by any human factor. All the selection of the training dataset is done automatically and the system outputs different neural networks, some of them with very good accuracy of eye detection. This paper presents an automatic system that reduces the human effort for labeling and selection of datasets and outputs neural networks with good accuracy for eye location on infrared driver recordings.
Developing Event Routing Service to Support Context-Aware Service Integration

In advanced smart systems, heterogeneous distributed services are integrated dynamically, based on various contexts in cyber/physical worlds. Currently, the logic of such service integration is implemented specifically in each application. Thus, as the way of integration becomes sophisticated, the complexity and development effort of the application become quite expensive. In this research, we propose a service, called Uni-messe (Unified Rule-based Message Delivery Service), which provides context-aware service integration in an application-neutral manner. Based on ECR (Event-Condition-Routing) rules, Uni-messe routes an event message from an application to a designated application based on a specified condition. We first present the architecture of Uni-messe with publish/subscribe messaging platform. We then propose the data model of the ECR rules based on 6W1H (Who/Whom/When/Where/What/Why/How) event model. Finally, we define the condition evaluation and routing behaviors. Using the implementation of Uni-messe, we demonstrate automatic curtain control and daily routine automation in a smart home. Since Uni-messe de-couples the rule-based service integration from individual applications, it allows developers and users to implement and reuse flexible integration efficiently.
TiNNA: A Tiny Accelerator for Neural Networks With Efficient DSP Optimization

Accelerators for convolutional neural networks (CNNs) based on field-programmable gate arrays (FPGAs) have drawn much attention in recent years. Two INT-8 multiplications are often implemented on a single digital signal processor (DSP) to improve DSP efficiency and performance. However, most of these works only use DSPs to implement multiplications and do not involve accumulations, which requires a lot of look-up tables (LUTs) and consequently leads to greater energy consumption. Furthermore, these works do not detail how to obtain two signed INT-8 multiplications with a DSP. This brief thus proposes a tiny accelerator for CNNs and a DSP-based processing element (PE) that is designed to implement two signed INT-8 multiply-and-accumulate (MAC) operations to reduce the overhead of LUTs. To allow the DSP to support two signed INT-8 MAC operations and avoid the large loss of accuracy caused by data overflow of partial sums (psum), we apply an efficient DSP optimization approach and a dynamic-precision quantization method. The accelerator is implemented on a Xilinx ZC706 FPGA device and achieves a DSP utilization efficiency <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.7\times $ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.3\times $ </tex-math></inline-formula> greater than state-of-the-art accelerators implemented on the same device. Furthermore, our accelerator offers <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.56\times $ </tex-math></inline-formula> greater energy efficiency than other FPGA-based accelerators.
Design of an IoT-based Body Mass Index (BMI) Prediction Model

Overweight and obesity have become a major health concern associated with diseases such as cardiac arrest, type 2 diabetes, stroke, high blood pressure, and other non-communicable diseases (NCD) and are the leading risks for deaths globally, killing more people than underweight. Body Mass Index (BMI) is a measure that uses weight and height to work out a person's nutrition status. Research throughout to calculate BMI is based on traditional manual methods which are time consuming, error prone and they are not cloud-based. Very few systems have incorporated machine learning yet with low accuracy. This research presents the design and development of a IoT based body mass index prediction model. This system consists of a NodeMCU microcontroller for computations with an inbuilt ESP8266 WiFi module, human load cell sensor for body weight measurement, a HX711 load cell amplifier module and HC-SR04 ultrasonic sensor for height measurement. Values are displayed on a 16x2 LCD and send to ThingSpeak for storage and analysis. ThingSpeak is integrated with MATLAB Machine Learning to make the prediction based on height and weight sensory data. This research uses Supervised Exponential Gaussian Process Regression algorithm to predict whether a person is underweighted, normal weight, overweight or obese. The designed IoT Based BMI computation system achieves an accuracy of 99.18% with a time reduction of 1.1 % per person while the ML model achieves an accuracy of 98%.