Reviewerly/exHarmony huggingface dataset:
Handling partially labeled network data: A semi-supervised approach using stacked sparse autoencoder

Network traffic analytics has become a crucial task in order to better understand and manage network resources, especially in the network softwarization era where the implementation of this concept can be done easily with network function virtualization. Currently, many approaches have been proposed to improve the performance of traffic classification. However, as new types of traffic emerge every day (and they are generally not labeled), this opens a new challenge to be handled. Moreover, the question of how to accurately classify the traffic using a limited amount of labeled data or partially labeled data is also another important concern. In fact, labeling data is often difficult and time-consuming. In order to solve the previously described issues, we reformulate traffic classification into a semi-supervised learning where both supervised learning (using labeled data) and unsupervised learning (no label data) are combined. To do so, this paper presents a stacked sparse autoencoder (SSAE) based semi-supervised deep-learning model for traffic classification. The main motivations of this approach are: (i) unlabeled data is often abundant and easily available; (ii) classification performance of the whole model can be greatly improved when a large amount of unlabeled traffic is included in the training process; (iii) there is a limit to how much human effort can be thrown at the labeling problem. To investigate the performance of our approach, an empirical study has been conducted on a real dataset and results indicate that using a large amount of unlabeled data in the SSAE pre-trained phase can improve significantly the classification performance of the whole model. Furthermore, the proposed approach is compared against other representative machine-learning and deep-learning models, which are Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Multi-Layer Perceptron (MLP), eXtreme Gradient Boosting (XGBoost), and Autoencoder.
Rings of Multisets and Integer Multinumbers

In the paper, we consider a ring structure on the Cartesian product of two sets of integer multisets. In this way, we introduce a ring of integer multinumbers as a quotient of the Cartesian product with respect to a natural equivalence. We examine the properties of this ring and construct some isomorphisms to subrings of polynomials and Dirichlet series with integer coefficients. In addition, we introduce finite rings of multinumbers “modulo (p,q)” and propose an algorithm for construction of invertible elements in these rings that may be applicable in Public-key Cryptography. An analog of the Little Fermat Theorem for integer multinumbers is proved.
An efficient and secure aggregation encryption scheme in edge computing

Edge computing is a highly virtualized paradigm that can services the Internet of Things(IoT) devices more efficiently. It is a non-trivial extension of cloud computing, which can not only meet the big data processing requirements of cloud computing, but also collect and analyze distributed data. However, it inherits many security and privacy challenges of cloud computing, such as: authentication and access control. To address these problem, we proposed a new efficient privacy-preserving aggregation scheme for edge computing. Our scheme consists of two steps. First, we divided the data of the end users with the Simulated Annealing Module Partition (SAMP) algorithm. And then, the end sensors and edge nodes performed respectively differential aggregation mechanism with the Differential Aggregation Encryption (DAE) algorithm which can make noise interference and encryption algorithm with trusted authority (TA). Experiment results show that the DAE can preserve user privacy, and has significantly less computation and communication overhead than existing approaches.
Information systems project management success

This paper aims to provide new insights into information systems (IS) project management success. Even though many studies found in the literature show results of software development projects, few studies address the success of IS (socio-technical) projects. Responses to an international survey, regarding 472 projects in total, showed that IS project management is achieving high levels of success; yet, only a minority of projects end without changes in scope, schedule or cost. Furthermore, the results show that changes in scope, schedule or cost are frequent in this kind of project and do not significantly affect the perception of success. These results provide researchers and practitioners with a better understanding of IS project management success evaluation.
Interview Bot Development with Natural Language Processing and Machine Learning

Interview for competency assessment takes essential roles in Human Resource Management practices. However, the traditional competency interview process needs considerable time and costs and often requires face-to-face meetings that may endanger both interviewers and interviewees during a pandemic. This study aims to present the development of an interview bot for identifying competency based on the Behavioural Event Interview method by using artificial intelligence technology. It is an automation of the interview process to explore a person’s competencies levels based on past behavioural experiences. The development of the interview bot involved two main activities. The first is the data training process to develop learning models to determine competency levels based on provided valid participant’s responses. The second is the testing and evaluation model for assessment to determine competency levels. We found that our method can predict a person's competence levels based on their responses. Our approach can make predictions with acceptable accuracy. The interview bot is a valuable and reliable tool to conduct online interviews and support the assessment centre process, especially with conditions of physical and social distancing constraints. It provides flexibility in terms of time and place for participants, and its process is delivered in Indonesia's Language. The interview bot is more cost efficient than traditional interviews with the same behavioural event interview methods, and it would also be preferable for millennials.
Two-Layer Bus-Independent Instruction Set Architecture for Securing Long Protocol Data Units in Automotive Open System Architecture-Based Automotive Electronic Control Units

In this paper, we propose a bus-independent hardware (HW)-based approach to secure long protocol data units (PDUs) in Automotive Open System Architecture (AUTOSAR)-based automotive electronic control units (ECUs). Our approach is based on extending previous works that implemented two AUTOSAR communication (COM) application-specific instruction set processors (ASIPs). COM ASIP V1 introduced two instructions to handle the transmission and reception of PDUs no larger than 8 bytes and signals no larger than 32 bits individually through send signal and receive signal instructions. COM ASIP V2 introduced two extra instructions to handle long signals and PDUs of arbitrary lengths. We extended the instruction set architecture (ISA) of our previous ASIPs by introducing six new instructions, in COM ASIP V3, to hash PDUs that contain these signals to authenticate transmission and reception of such PDUs. The experimental results show that COM ASIP V3 can handle (i.e., transmit, receive, calculate hash, or verify hash) a 64-byte controller area network flexible data-rate (CAN FD) frame in 1.575 μs and a 254-byte FlexRay frame in 6.301 μs. These measurements indicate that the throughput of our new COM ASIP is much higher, 42× to 75×, than the throughput required by these communication buses.
Unsupervised Multi-Modal Medical Image Registration via Discriminator-Free Image-to-Image Translation

In clinical practice, well-aligned multi-modal images, such as Magnetic Resonance (MR) and Computed Tomography (CT), together can provide complementary information for image-guided therapies. Multi-modal image registration is essential for the accurate alignment of these multi-modal images. However, it remains a very challenging task due to complicated and unknown spatial correspondence between different modalities. In this paper, we propose a novel translation-based unsupervised deformable image registration approach to convert the multi-modal registration problem to a mono-modal one. Specifically, our approach incorporates a discriminator-free translation network to facilitate the training of the registration network and a patchwise contrastive loss to encourage the translation network to preserve object shapes. Furthermore, we propose to replace an adversarial loss, that is widely used in previous multi-modal image registration methods, with a pixel loss in order to integrate the output of translation into the target modality. This leads to an unsupervised method requiring no ground-truth deformation or pairs of aligned images for training. We evaluate four variants of our approach on the public Learn2Reg 2021 datasets. The experimental results demonstrate that the proposed architecture achieves state-of-the-art performance. Our code is available at https://github.com/heyblackC/DFMIR.
Using Interactive Feedback to Improve the Accuracy and Explainability of Question Answering Systems Post-Deployment

Most research on question answering focuses on the pre-deployment stage; i.e., building an accurate model for deployment.In this paper, we ask the question: Can we improve QA systems further post-deployment based on user interactions?We focus on two kinds of improvements: 1) improving the QA system's performance itself, and 2) providing the model with the ability to explain the correctness or incorrectness of an answer.We collect a retrievalbased QA dataset, FEEDBACKQA, which contains interactive feedback from users.We collect this dataset by deploying a base QA system to crowdworkers who then engage with the system and provide feedback on the quality of its answers.The feedback contains both structured ratings and unstructured natural language explanations.We train a neural model with this feedback data that can generate explanations and re-score answer candidates.We show that feedback data not only improves the accuracy of the deployed QA system but also other stronger non-deployed systems.The generated explanations also help users make informed decisions about the correctness of answers. 1
A Robust and Accurate Landing Methodology for Drones on Moving Targets

This paper presents a framework for performing autonomous precise landing of unmanned aerial vehicles (UAVs) on dynamic targets. The main goal of this work is to design the methodology and the controlling algorithms that will allow multi-rotor drones to perform a robust and efficient landing in dynamic conditions of changing wind, dynamic obstacles, and moving targets. Unlike existing GNSS-based vertical landing solutions, the suggested framework does not rely on global positioning and uses adaptive diagonal approaching angle visual landing. The framework was designed to work on existing camera-drone platforms, without any need for additional sensors, and it was implemented using DJI’s API on Android devices. The presented concept of visual sliding landing (VSL) was tested on a wide range of commercial drones, performing hundreds of precise and robust autonomous landings on dynamic targets, including boats, cars, RC-boats, and RC-rovers.
The Utility of Receiver Operating Characteristic Curve in Educational Assessment: Performance Prediction

When examinees are classified into groups based on scores from educational assessment, two indices are widely used to gauge the psychometric quality of the classifications: accuracy and consistency. The two indices take correct classifications into consideration while overlooking incorrect ones, where unbalanced class distribution threatens the validity of results from the accuracy and consistency indices. The single values produced from the two indices also fail to address the inconsistent accuracy of the classifier across different cut score locations. The current study proposed the concept of classification quality, which utilizes the receiver operating characteristics (ROC) graph to comprehensively evaluate the performance of classifiers. The ROC graph illustrates the tradeoff between benefits (true positive rate) and costs (false positive rate) in classification. In this article, a simulation study was conducted to demonstrate how to generate and interpret ROC graphs in educational assessment and the benefits of using ROC graphs to interpret classification quality. The results show that ROC graphs provide an efficient approach to (a) visualize the fluctuating performance of scoring classifiers, (b) address the unbalanced class distribution issue inherent in the accuracy and consistency indices, and (c) produce more accurate estimation of the classification results.
Design Discourses of Transformation

The aim of this article is to develop knowledge and understanding that supports critical conceptual interventions in design innovation theory and practice. Existing discourses of design are dissonant and paradoxical, for example positioning design as at once value-free and virtuous. We explain various instances of dissonance by establishing relationships between modes of design, design discourses, and knowledge systems. We map and interpret four design discourses, revealing the plural, dynamic, and mutable nature they share. Our understanding of design in the context of social transformation varies according to how we relate to knowledge systems, how these are produced through discourses, and how the two inform distinct modes of design. We argue that dominant discourses and entrenched knowledge systems must be consciously and actively upended. For this, we present a framework for transformative action to foster encounters across discourses and engender new critical expressions of and interventions in design theory and practice.
Aggregate Queries on Knowledge Graphs: Fast Approximation with Semantic-aware Sampling

A knowledge graph (KG) manages large-scale and real-world facts as a big graph in a schema-flexible manner. Aggregate query is a fundamental query over KGs, e.g., “what is the average price of cars produced in Germany?”. Despite its importance, answering aggregate queries on KGs has received little attention in the literature. Aggregate queries can be supported based on factoid queries, e.g., “find all cars produced in Germany”, by applying an additional aggregate operation on factoid queries' answers. However, this straightforward method is challenging because both the accuracy and efficiency of factoid query processing will seriously impact the performance of aggregate queries. In this paper, we propose a “sampling-estimation” model to answer aggregate queries over KGs, which is the first work to provide an approximate aggregate result with an effective accuracy guarantee, and without relying on factoid queries. Specifically, we first present a semantic-aware sampling to collect a high-quality random sample through a random walk based on knowledge graph embedding. Then, we propose unbiased estimators for COUNT, SUM, and a consistent estimator for AVG to compute the approximate aggregate results based on the random sample, with an accuracy guarantee in the form of confidence interval. We extend our approach to support iterative improvement of accuracy, and more complex queries with filter, GROUP-BY, and different graph shapes, e.g., chain, cycle, star, flower. Extensive experiments over real-world KGs demonstrate the effectiveness and efficiency of our approach.
Efficient Experimental Verification of Quantum Gates with Local Operations

Verifying the correct functioning of quantum gates is a crucial step toward reliable quantum information processing, but it becomes an overwhelming challenge as the system size grows due to the dimensionality curse. Recent theoretical breakthroughs show that it is possible to verify various important quantum gates with the optimal sample complexity of O(1/ε) using local operations only, where ε is the estimation precision. In this Letter, we propose a variant of quantum gate verification (QGV) that is robust to practical gate imperfections and experimentally realize efficient QGV on a 2-qubit controlled-not gate and a 3-qubit Toffoli gate using only local state preparations and measurements. The experimental results show that, by using only 1600 and 2600 measurements on average, we can verify with 95% confidence level that the implemented controlled-not gate and Toffoli gate have fidelities of at least 99% and 97%, respectively. Demonstrating the superior low sample complexity and experimental feasibility of QGV, our work promises a solution to the dimensionality curse in verifying large quantum devices in the quantum era.
Intelligent Intuitionistic Fuzzy with Elephant Swarm Behaviour Based Rule Pruning for Early Detection of Alzheimer in Heterogeneous Multidomain Datasets

In this Digital age, a rapid advancement in rise of both communication and information technologies provide many services by incorporating intelligent system of automated health assessment of resident welfare. This is achieved by tracking elderly persons activities using smart home technologies and with this activity-based learning, it helps to discover individuals suffering from early stage of Alzheimer can be predicted without distributing their living style. The main purpose of this paper is to use two different domains of datasets for predicting the alzheimer’s in elderly person during its initial stage. Thus, this work uses ubiquitous computing technologies like smart home dataset which collects the daily activities of individuals and as the clinical dataset for prediction of alzheimer’s. The objective of this proposed work is to handle the hesitancy of uncertainty by introducing intelligent intuitionistic fuzzy classifier, which inhibits irrelevant rule generation by acquiring the knowledge of elephant swarm behavior (IIF-ESB). Using elephant swarm search behavior, the rules generated by intuitionistic fuzzy are finetuned to ovoid overfitting problem and thus it eliminates the irrelevant rules. The selected potential rules highly influence the accuracy rate of the prediction model in presence of uncertainty. Performance result of the proposed model (IIF-ESB) proved that with the ability to handle the impreciseness in prediction of alzheimer’s, the usage of degree of hesitancy and intelligent of elephant swarm searching behaviour increases the accurate prediction rate and decrease the misclassification rate considerably while compared with existing prediction models.
Gesture To Speech Conversion using Flex sensors, MPU6050 and Python

Communicating through hand gestures is one of the most common forms of non-verbal and visual communication adopted by speech impaired population all around the world. The problem existing at the moment is that most of the people are not able to comprehend hand gestures or convert them to the spoken language quick enough for the listener to understand. A large fraction of India’s population is speech impaired. In addition to this communication to sign language is not a very easy task. This problem demands a better solution which can assist speech impaired population conversation without any difficulties. As a result, reducing the communication gap for the speech impaired. This paper proposes an idea which will assist in removing or at least reducing this gap between the speech impaired and normal people. The research going on this area mostly focuses on image processing approaches. However, a cheaper and user-friendly approach has been used in this paper. The idea is to make a glove that can be worn by the speech impaired people which will further be used to convert the sign language into speech and text. Our prototype involves Arduino Uno as a microcontroller which is interfaced with flex sensors and accelerometer, gyroscopic sensor for reading the hand gestures. Furthermore, to perform better execution, we have incorporated an algorithm for better interpretation of data and therefore producing more accurate result. Thereafter, we use python to interface Arduino Uno with a microprocessor and finally converting into speech. The prototype has been calibrated in accordance with the ASL (American Sign Language).
Feature Engineering and Selection

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.
Performance identification in large-scale class data from advanced facets of computational intelligence and soft computing techniques

Enormous hemodialysis (HD) treatments have caused concern regarding negative information as the world's highest prevalence of end-stage renal disease in Taiwan. This topic is the motivation to identify an adequate HD remedy. Although previous researchers have devised various models to address HD adequacy, the following five deficiencies form obstacles: 1) lack of consideration for imbalanced class problems (ICPs) with medical data; 2) lack of methods to establish mathematical distributions for the given data; 3) lack of explanatory ability on the given data; 4) lack of effective methods to identify the determinants of HD adequacy; 5) lack of appropriate classifiers to define HD adequacy. This study proposes hybrid models to integrate expert knowledge, imbalanced resampling methods, decision tree and random forests-based feature-selection methods, LEM2 algorithm, rough set theory, and rule-filtering techniques to process medical practice with ICPs. These models have better performance than the listed methods from the empirical results.
Cofense: Malware Trends Q4 2019

Although the title of this report uses the word ‘trends’, one is almost tempted to replace it with ‘fashions’. Malware families and attack vectors wax and wane in popularity – presumably on the basis of whatever works best at any given time. And, as this report highlights, the use of malware is also subject to seasonal effects because, as the summary states, “even criminals take a holiday”.
Contract Interpretation

It is quite likely that smart contracts, which represent a further step in formalizing contracts, will diminish the courts’ ability to intervene through interpretation. In a smart contract context, it is not obvious that the smart contract can go beyond the borders of the program to find relevant rules (off-chain elements and occurrences), limiting therefore the capacity to use blockchain technology in highly contextualized types of contracting. Courts generally come into play when complexity, doubts, and litigation around them arise. In order to assure the smooth development of automated contracts, the courts must develop the expertise to interpret them and smart contracts need to provide for parties to control real world consequences and be able to terminate the contracts if necessary during and after performance.