Reviewerly/exHarmony huggingface dataset:
Comparative Study of Single-task and Multi-task Learning on Research Protocol Document Classification

Research protocol is an important document to be scrutinized by the ethical committee. As the research proposal is growing, the necessity for quick and concise protocol review is rising. This study undergoes a comparative study of multi-task learning (MTL) and single-task learning (STL) to classify research protocol documents. We try to carry out the classification process from the summary of health research. We represent research documents as multi-label classification problems and develop a deep learning model based on MTL and STL strategies. In our evaluation, multi-task learning achieved a better result with 0.125 loss and 0.785 Jaccard score than 0.182 and 0.720 in single-task learning. In consequence, MTL has a 27% slower computation time than STL.
Hierarchical Integrated Signature and Encryption

In this work, we introduce the notion of hierarchical integrated signature and encryption (HISE), wherein a single public key is used for both signature and encryption, and one can derive a secret key used only for decryption from the signing key, which enables secure delegation of decryption capability. HISE enjoys the benefit of key reuse, and admits individual key escrow. We present two generic constructions of HISE. One is from (constrained) identity-based encryption. The other is from uniform one-way function, public-key encryption, and general-purpose public-coin zero-knowledge proof of knowledge. To further attain global key escrow, we take a little detour to revisit global escrow PKE, an object both of independent interest and with many applications. We formalize the syntax and security model of global escrow PKE, and provide two generic constructions. The first embodies a generic approach to compile any PKE into one with global escrow property. The second establishes a connection between three-party non-interactive key exchange and global escrow PKE. Combining the results developed above, we obtain HISE schemes that support both individual and global key escrow.
Developments on Support Vector Machines for Multiple-Expert Learning

In supervised learning scenarios, some applications require solve a classification problem wherein labels are not given as a single ground truth. Instead, the criteria of a set of experts is used to provide labels aimed at compensating for the erroneous influence with respect to a single labeler as well as the error bias (excellent or lousy) due to the level of perception and experience of each expert. This paper aims to briefly outline mathematical developments on support vector machines (SVM), and overview SVM-based approaches for multiple expert learning (MEL). Such MEL approaches are posed by modifying the formulation of a least-squares SVM, which enables to obtain a set of reliable, objective labels while penalizing the evaluation quality of each expert. Particularly, this work studies both two-class (binary) MEL classifier (BMLC) and its extension to multiclass through one-against all (OaA-MLC) including penalization of each expert's influence. Formal mathematical developments are stated, as well as remarkable discussion on key aspects about the least-squares SVM formulation and penalty factors are provided.
Research on reliability test of hybrid information intelligent retrieval in Mobile English Teaching

In the new digital environment, digital literacy has gradually become the core skills of human survival. Since English teaching includes many modules, mobile English teaching platform is required to have the ability to deal with mixed information. At present, the existing mobile English teaching platform cannot deal with the mixed information of multiple modules, which leads to the problems of low precision and long time consuming. Based on the analysis of the problems existing in the construction of mobile English teaching information retrieval course and the requirements of the concept and ability of digital literacy, this paper constructs a multi-dimensional information retrieval teaching system from the perspective of digital literacy training from four aspects of teaching environment, teaching object, course content and teaching information. Experimental results show that the proposed system has ideal accuracy and running time.
Few-shot Classification of Radar Equipment Fault Based on TF-IDF Feature Date Augmentation and BERT

In radar equipment fault text classification tasks, the existing fault data labeling sample size is small, and the cost of labeling a large number of unlabeled data is higher. Aiming at this practical problem, this paper proposes a data augmentation method based on TF-IDF features. First, new samples are synthesized by TF-IDF features of the corresponding fault category. Secondly, the new samples are expanded to the original samples and the fault text is vector-encoded using the BERT pretraining language model. Finally, the classification of radar equipment faults is achieved through the Softmax regression model. The experiment shows that, TEDA can be well combined with the big data training model, and the macro F1 value of the model has increased by 7 percentage points.
Extending Transformer Decoder with Working Memory for Sequence to Sequence Tasks

The paper introduces methods of memory augmentation in the Transformer decoder for sequence-to-sequence task. Transformers recently showed state-of-the-art performance on a wide range of NLP tasks. Transformer encodes existing elements of an input sequence but lacks storage for information associated with a context but not presented explicitly in the text. Learnable memory incorporated in Transformer decoder provides additional space to keep information necessary for better performance. Writing tokens from the vocabulary to the memory works like memorizing concepts related to the sequence. Such knowledge can be further employed during sequence processing.We showed that augmenting the Transformer decoder with learnable working memory results in a performance boost for machine translation task. In the experiments, we tested different working memory training setups and methods for decoding memory elements. We found that training of Transformer with working memory augmentation for fifteen epochs after memory-less pretraining for five epochs gives better results than training Transformer with working memory augmentation from scratch for twenty epochs.
A Prototype System for Monitoring Emotion and Sentiment Trends Towards Nuclear Energy on Twitter Using Deep Learning

Nuclear energy is one of controversial topics that affects people's lives, and it is important for policy makers to analyze what people feel towards the subject. But manual analysis of related user-generated contents on social media platforms is a daunting task, and automatic data analysis and visualization come to help. So, in this research, we firstly developed a model for classifying the emotion of nuclear energy related tweets and another model for the aspect-based sentiment analysis of nuclear energy tweets using the BERT (Bidirectional Encoder Representations from Transformers). After that, we developed a prototype system for visualization of the analyzed results stored in the database. The user interface dashboards of the system allow users to monitor emotion and sentiment trends towards nuclear energy by analyzing recent nuclear energy tweets crawled weekly.
Systematic Analysis of Hateful Text Detection Using Machine Learning Classifiers

In today's internet-based world, social media is one of the most popular platforms through which users can outburst their different types of feelings, emotions, frustration, anger, happiness etc. without having concern about distinguishes between moral and social values. These kinds of abusive or offensive texts cause social disturbances, crimes, and many unethical deeds. So, there is a huge necessity to distinguish these kinds of abusive texts/posts and remove them from social media. Different researchers have distinguished different text detection processes in their related work. In our proposed work, three classifiers have been used: Naïve Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM) for detecting hateful text. Bag of Words (BoW) and TF-IDF feature extraction methods have been used to compare these three classifiers for both unigram and bigrams words. To balance hateful and clean content, the Twitter dataset has been under-sampled. Text preprocessing is essential for NLP to produce better and more accurate results which have been carried out in this work. In our result, Naive Bayes has provided the highest accuracy (89%) using the TF-IDF feature extraction model, whereas Random Forest has provided the most accuracy (88%) using Bag of words (BoW) in the case of unigram word. Overall, we got much better performance using unigram than using bigrams word. Finally, we made a number of principle contributions.
Gaussoids are two-antecedental approximations of Gaussian conditional independence structures

The gaussoid axioms are conditional independence inference rules which characterize regular Gaussian CI structures over a three-element ground set. It is known that no finite set of inference rules completely describes regular Gaussian CI as the ground set grows. In this article we show that the gaussoid axioms logically imply every inference rule of at most two antecedents which is valid for regular Gaussians over any ground set. The proof is accomplished by exhibiting for each inclusion-minimal gaussoid extension of at most two CI statements a regular Gaussian realization. Moreover we prove that all those gaussoids have rational positive-definite realizations inside every $\varepsilon$-ball around the identity matrix. For the proof we introduce the concept of algebraic Gaussians over arbitrary fields and of positive Gaussians over ordered fields and obtain the same two-antecedental completeness of the gaussoid axioms for algebraic and positive Gaussians over all fields of characteristic zero as a byproduct.
Intelligent dual-axis solar tracking system in the Peruvian highlands: Implementation and performance

Maximizing electricity production in photovoltaic systems is very important, even more with solar tracking systems, to reduce greenhouse gas emissions and promote a sustainable energy system. The objective of this research was to implement and evaluate the performance of an intelligent dual-axis solar tracking system in the Peruvian highlands. The solar tracker was implemented with double axis (two degrees of freedom), the movement of both axes was driven by two linear actuators, so that, this system was controlled by a programmable logic controller (PLC), following the trajectory of the sun during the day. The results showed that on completely sunny days the solar tracker has a photovoltaic performance 29.48% higher than a fixed system in capturing solar irradiation, on the contrary, on partially cloudy days the solar tracker has a similar performance to a fixed system with no significant increase in photovoltaic performance. Therefore, the Peruvian highlands have a high potential for solar photovoltaic energy on sunny days, which can be better exploited with solar tracking systems, however, the capture of solar irradiance is affected by transient cloud blockage.
Integrated Bidirectional LSTM – CNN Model for Customers Reviews Classification

The tremendous increase of Internet users and various social media platforms provide a massive amount of data. Companies are seeking an automated method to assess their customers' satisfaction with their products. Collecting and analyzing opinions and customers' feedback from social media rely on what so called sentiment classification. Several types of research are carried out to investigate opinions in English. As the Arabic language analysis faces many numerous challenges and problems. In our current research, two powerful hybrid deep learning models (CNN-LSTM) and (CNN- BILSTM) are represented. Bidirectional LSTMs are an expansion of conventional LSTMs that can make substantial improvements in sequence classification tasks and identify the most valuable features, CNN is applied. Various data preparation processes are performed, and two regular deep learning models (CNN, LSTM) are implemented to conduct a series of experiments. Experimental results show that the two proposed models have superior performance compared to baselines deep learning models (CNN, LSTM). Furthermore, the (CNN-BI-LSTM) model exceeds the hybrid (CNN-LSTM) model in terms of achieving highest efficiency.
User-Centred Design in Educational Applications: A systematic literature review

User-centred Design (UCD) is an approach used to develop an efficient, effective and satisfying user needs application by putting the user in the centre of the design. The primary purpose of this article is to comprehensively analyse the previous research around education application developed through user-centred design methods and draw a map for the best practice for the UCD process. The findings allow education application user interface designers to improve student engagement, motivation, and performance of their application. Adding to the knowledge base of user-centred design applications across a wide range of learning contexts will establish if UDC design improvements made are universally applicable and effective.
Analyzing SAFe Practices with Respect to Quality Requirements: Findings from a Qualitative Study

Quality Requirements (QRs) pose challenges in many agile large-scale distributed projects. Often, project organizations counter these challenges by borrowing some heavyweight practices, e.g. adding more documentation. At the same time, agile methodologists proposed a few scaled agile frameworks to specifically serve agile organizations working on large and distributed systems. Little is known about the extent to which these proposals address QRs and the specific ways in which this happens. Moreover, evidence regarding the practical implementation of these frameworks with respect to QRs is scarce. Our paper makes a step towards narrowing this gap of knowledge. Using an exploratory research process, we analyze one well-documented framework, namely the Scaled Agile Framework (SAFe). We first analyzed the elements of SAFe as they were described in the methodological book of SAFe to identify the possible remedies to the QRs challenges reported in previous work. We then conducted a qualitative interview-based study to understand the practices that SAFe practitioners actually use to mitigate those QRs challenges. Our documentary analysis of SAFe resulted in identifying 25 SAFe elements that could (at least partially) mitigate one or more of the reported QRs challenges. Nine of those SAFe elements were reported in our interview-based study by SAFe practitioners as remedy for some of the reported QRs challenges. While practitioners attempted to use the recommended SAFe strategies for QRs, they often changed them in their own ways, or altogether resorted to heavyweight practices that the case study organizations knew from previously done non-SAFe projects.
Identification of Characters (Digits) Through Customized Convolutional Neural Network

Digitalization is showing a greater impact across the fields. The improved digitalization has brought various technicalities to object detection. In this aspect, the proposed framework deals with the identification of characters. This paper highlights the issue of identification of characters or digits in the CHARS74K data from handwriting, printed, natural image samples. This framework also illustrates the study performed on handwritten character recognition that describes the various manuscripts of various languages such as Urdu, English, Devanagari, Arabic. A customized CNN model has been proposed for identifying the various forms of digits in the dataset and the precision, the recall has been mentioned for individual digit forms.
How to Build a Trapdoor Function from an Encryption Scheme

In this work we ask the following question: Can we transform any encryption scheme into a trapdoor function (TDF)? Alternatively stated, can we make any encryption scheme randomness recoverable? We propose a generic compiler that takes as input any encryption scheme with pseudorandom ciphertexts and adds a trapdoor to invert the encryption, recovering also the random coins. This universal TDFier only assumes in addition the existence of a hinting pseudorandom generator (PRG). Despite the simplicity, our transformation is quite general and we establish a series of new feasibility results: The first identity-based TDF [Bellare et al. EUROCRYPT 2012] from the CDH assumption in pairing-free groups (or from factoring), thus matching the state of the art for identity-based encryption schemes. Prior works required pairings or LWE. The first collusion-resistant attribute-based TDF (AB-TDF) for all (\(NC^1\), resp.) circuits from LWE (bilinear maps, resp.). Moreover, the first single-key AB-TDF from CDH. To the best of our knowledge, no AB-TDF was known in the literature (not even for a single key) from any assumption. We obtain the same results for predicate encryption. As an additional contribution, we define and construct a trapdoor garbling scheme: A simulation secure garbling scheme with a hidden “trigger” that allows the evaluator to fully recover the randomness used by the garbling algorithm. We show how to construct trapdoor garbling from the DDH or LWE assumption with an interplay of key-dependent message (KDM) and randomness-dependent message (RDM) techniques.Trapdoor garbling allows us to obtain alternative constructions of (single-key) AB-TDFs with additional desirable properties, such as adaptive security (in the choice of the attribute) and projective keys. We expect trapdoor garbling to be useful in other contexts, e.g. in case where, upon successful execution, the evaluator needs to immediately verify that the garbled circuit was well-formed.
Employing Analog Circuits by Neural-Network based Multi-Layer-Perceptron

This manuscript presents an artificial-neural- network based programmable-neuron for implementation of analog circuits with multi-layer-perceptron. The proposed programmable-neuron can estimate linear, hyperbolic, tangent and sigmoid functions which are used to activate the analog circuits. With this, a neural-network-designer can utilize maximum number of controller-bits to select an activation- function kind with no actual change. For this neuron, 0.18-µm CMOS-technology is used for simulations and demonstrates a good estimation in peak error with ideal sigmoid and hyperbolic tangent function by 7.3% and 29.34% respectively. To assess the usefulness of the neuron, a Multi-Layer-Perceptron-neural- network (MLP-NN) is used. The MLP-NN is trained to carry out XOR-logic gate for handling signals in frequency-range from 3mHz to 60MHz. The correctness of the proposed-neuron is over 99.9%. These results shows that there is a decrease of 49% in power consumption with related to previous works.
Experience Paper: Danaus

Containers are a mainstream virtualization technique commonly used to run stateful workloads over persistent storage. In multi-tenant hosts with high utilization, resource contention at the system kernel often leads to inefficient handling of the container I/O. Assuming a distributed storage architecture for scalability, resource sharing is particularly problematic at the client hosts serving the applications of competing tenants. Although increasing the scalability of a system kernel can improve resource efficiency, it is highly challenging to refactor the kernel for fair access to system services. As a realistic alternative, we isolate the storage I/O paths of different tenants by serving them with distinct clients running at user level. We introduce the Danaus client architecture to let each tenant access the container root and application filesystems over a private host path. We developed a Danaus prototype that integrates a union filesystem with a Ceph distributed filesystem client and a configurable shared cache. Across different host configurations, workloads and systems, Danaus achieves improved performance stability because it handles I/O with reserved per-tenant resources and avoids intensive kernel locking. Danaus offers up to 14.4x higher throughput than a popular kernel-based client under conditions of I/O contention. In comparison to a FUSE-based user-level client, Danaus also reduces by 14.2x the time to start 256 high-performance webservers. Based on our extensive experience from building and evaluating Danaus, we share several valuable lessons that we learned about resource contention, file management, service separation and performance stability.
Vote and KNN outlier detection in Wireless Sensor Networks

The target of a Wireless Sensor Network (WSN) designer is to improve the robustness of the network while taking into account constraints such as verifying the detection of anomalies. Preventing anomalies can be assured by data analysis via outliers identification as it strongly affects decision-making in the case of smart-home, e-health and other high-risk applications. In this paper, we study some families of outliers and we propose to detect data anomalies using K Nearest Neighbours (KNN) and Voting techniques by a benchmark suitable to be a simulation for a smart-house. In order to evaluate the dependability, some performance metrics are analyzed such as data rate detection, precision, specificity and accuracy. Performance results are in the order of 100% detection rate with reduced false alarm and fast response time compared to some other existing ones.
Adaptive Skin Color Model for Clothing Genre Recognition via Particle Swarm Optimization

Clothing genre recognition has shown its capabilities in many intelligent fashion scenarios. Given an unannotated consumer photo of a full-body person, the proposed study addresses the problem of recognizing the upperwear genres presented in that photo. Although the topic continues to show progress, most of the existing studies suffered from weaknesses related to skin identification. Therefore, to achieve this goal, we exploit the visual style elements of clothes to capture the discriminative attributes of each clothing genre and utilize an adaptive skin color model based on hill-climbing segmentation and Particle Swarm Optimization (PSO) to identify the skin color. The experimental results show that integrating these two approaches into a clothing recognition framework can lead to significant improvements over baselines, achieving new state-of-the-art results. Importantly, our method achieves these satisfactory results with a compact representation that does not require a large amount of training data to generate.
Recurrent Vision Transformer for Solving Visual Reasoning Problems

Although convolutional neural networks (CNNs) showed remarkable results in many vision tasks, they are still strained by simple yet challenging visual reasoning problems. Inspired by the recent success of the Transformer network in computer vision, in this paper, we introduce the Recurrent Vision Transformer (RViT) model. Thanks to the impact of recurrent connections and spatial attention in reasoning tasks, this network achieves competitive results on the same-different visual reasoning problems from the SVRT dataset. The weight-sharing both in spatial and depth dimensions regularizes the model, allowing it to learn using far fewer free parameters, using only 28k training samples. A comprehensive ablation study confirms the importance of a hybrid CNN + Transformer architecture and the role of the feedback connections, which iteratively refine the internal representation until a stable prediction is obtained. In the end, this study can lay the basis for a deeper understanding of the role of attention and recurrent connections for solving visual abstract reasoning tasks. The code for reproducing our results is publicly available here: https://tinyurl.com/recvit .