Reviewerly/exHarmony huggingface dataset:
Physical Layer Detection of Malicious Relays in LTE-A Network Using Unsupervised Learning

For Long Term Evolution Advanced (LTE-A) network, although there exist many studies that focus on improving the performance with relays, security issues are often neglected. Due to the broadcast nature of wireless channels, relay nodes in LTE-A network may act maliciously, affect communication, reduce quality, and cause delays. Recently, physical (PHY) layer security has attracted researchers to provide secure communication and data privacy. In this study, we propose using unsupervised learning approach at the destination node to detect malicious relay attacks in cooperative LTE-A network based on received source signal in the PHY layer. Outlier detection algorithms such as one class support vector machine (OCSVM), local outlier factor (LOF) and isolation forest (i Forest) are applied to detect various malicious relay behaviors such as garbling, regenerative, and false data injection type attacks. As input to these algorithms, feature vectors are constructed by using amplitude, phase, and relative phase information of modulated baseband symbols. The performance of the outlier detectors are evaluated with respect to precision, accuracy, and under the area curve (AUC) measures under changing signal-to-noise ratio (SNR) levels, different modulation types, allocated number of resource blocks (RBs), and varying data size. The results demonstrate the effectiveness of our proposed outlier detection approach for detecting malicious relays in the LTE-A network. Accuracy and precision of the algorithms are observed to be above 90% for 10 dB and larger SNR levels for the relay attack scenarios considered here. AUC values for all algorithms for all SNR levels is also above 0.9 for the attack detection cases, and the performance of the LOF algorithm with 0.95 and above AUC values is superior to other algorithms. The results verify the contribution of this study, which is the demonstration of the effectiveness of one class outlier detection approaches for detecting malicious relays in the LTE-A network.
IMPROVING STUDENTS READING COMPREHENSION THROUGH “SURVEY, QUESTION, READING, RECITE, REVIEW (SQ3R)” STRATEGY

Abstract. The aims of this study was to find out whether or not of teaching of narrative text through SQ3R Strategy has improve students reading comprehension of X MIPA 1 class of SMA Negeri 4 Sidrap, South Sulawesi, Indonesia. This study used a pre-experimental method that consists pre-test and post-test. The total population is 90 students by taking one class as a sample using the quota sampling technique, namely class X MIPA 1 as a sample. The results of this study indicate the mean score of students’ post-test (76.25) is greater than the pre-test (53.2). This shows a significant difference between students taught by using the SQ3R strategy in teaching reading comprehension of narrative texts. The results in pre-test and post-test that p-value was lower α (0,000 &lt;0.05). This means that H1 is accepted and H0 is rejected. The concluded that teaching reading comprehension by implementation of SQ3R Strategy has influence to improve the students of X MIPA 1 class in SMA Negeri 4 Sidrap, South Sulawesi, Indonesia.
THE STUDENTS’ DIFFICULTIES FACTORS IN SPEAKING

Speaking is a subject matter that must be learned by the students because it is the most important factor in the development of English language skills. However, it is more difficult than another subject because there are some difficulties experienced by students in speaking, such as low vocabulary mastery, the difficulties in pronouncing, confused in arranging words, afraid of making mistake and many another factors that cause of speaking difficulties such as teaching strategy, the curriculum, and the environment. The research purpose is to know the factors of the students' speaking difficulty. The design of the research is descriptive qualitative. The number of population as many as 157 students of eleventh-grade students of SMA Negeri 2 Tembilahan that consist of 6 classes. In taking the sample, the researcher uses a simple random sampling technique. The number of samples is 40 students or 25% of the population. The researcher uses a questionnaire as an instrument as many as 15 items and also uses an interview technique. After analyzing the data by using the descriptive qualitative method, it is known that the four factors of speaking difficulty have a positive value or more than 50%. The values of these four factors were 62.5% for personal factors, 95% for teaching strategies factor, 90% for curriculum factor and 57.5% for the environmental factor. Therefore, it can be inferred that the dominant factor which causes students’ difficulties in speaking is teaching strategies.
How to Calibrate your Neural Network Classifier

Research in Machine Learning (ML) for classification tasks has been primarily guided by metrics that derive from a confusion matrix (e.g. accuracy, precision and recall). Several works have highlighted that this has lead to training practices that produce over-confident models and void the assumption that the model learns a probability distribution over the classification targets; this is referred to as miscalibration. Consequently, modern ML architectures struggle to perform in applications where a probabilistic forecaster is needed. Research efforts on calibration techniques have explored the possibility of recovering probability distributions from traditional architectures. This tutorial covers the key concepts required to understand the motivations behind calibration and aims at providing participants with the tools that they require assess the calibration of ML models and calibrate them when required.
Gender Detection Using Random Forest

Today's machine learning is widely used in diverse areas. For example, fraudulent systems, recommender systems, exploited prediction, and many other applications. One of these applications, is being exploited in this search. This paper presents an approach to detecting a person's gender through the front face image by using extraction features and classification techniques. Gender prediction can be a very useful method in HCI (Human-Computer Interaction) systems. As a very powerful method of extracting data, the classification is used here to collect class data and to classify the gender as either male or female. To extract data features, Local Binary Pattern (LBP) is used, whereas the Random Forest (RF) algorithm of classification is used to gauge the maximum accuracy. Various database models were used in this search: ORL database, FEI database, Jaffe database, and CUHK database where Jaffe database gave a very high level of accuracy which is 99.89% in contrast CUHK database which gave a lower level of accuracy 76.18% with relative stability. Details of the prediction model and results model are reported in this paper.
Scholarly Communications Training: Professional Development for the Next Generation of Scholars

Many campuses are seeing increased needs for support in scholarly communications areas such as open access, research identity management, scholarship metrics, and related topics. This article discusses a professional development program that addresses scholarly communications needs for online and in-person graduate students and faculty at UNC Greensboro (UNCG), through a collaborative, interdepartmental effort that brings together librarians from the departments of Research, Outreach, and Instruction (ROI) and Technical Services. The authors provide a brief overview of the literature related to scholarly communications needs and training in academic libraries and discuss the UNCG program’s inception, modules, format, assessment, and future directions.
On the depth overhead incurred when running quantum algorithms on near-term quantum computers with limited qubit connectivity

This paper addresses the problem of finding the depth overhead that will be incurred when running quantum circuits on near-term quantum computers. Specifically, it is envisaged that near-term quantum computers will have low qubit connectivity: each qubit will only be able to interact with a subset of the other qubits, a reality typically represented by a qubit interaction graph in which a vertex represents a qubit and an edge represents a possible direct 2-qubit interaction (gate). Thus the depth overhead is unavoidably incurred by introducing swap gates into the quantum circuit to enable general qubit interactions. This paper proves that there exist quantum circuits where a depth overhead in Omega(\log n) must necessarily be incurred when running quantum circuits with n qubits on quantum computers whose qubit interaction graph has finite degree, but that such a logarithmic depth overhead is achievable. The latter is shown by the construction of a 4-regular qubit interaction graph and associated compilation algorithm that can execute any quantum circuit with only a logarithmic depth overhead.
Creating Dialogue Between a Tutee Agent and a Tutor in a Lecture Video Improves Students’ Attention

One of the challenges found with learning using a lecture video is the short attention span of students due to high cognitive load when they are watching the video. Several methods have been proposed to increase students’ attention, such as segmenting learning content into smaller pieces where each piece has short video duration. In this study we proposed an enhanced monologue lecture video with a tutee agent to mimic the dialogue between a tutor and a tutee to increase student attention to the lecture video. Based on lab evaluation including eye fixations data from an eye tracker, the videos enhanced with the tutee agent make the students’ attention more frequent to the learning material presented in the lecture video and at the same time lowering their attention span to it.
Prediction of POS Tagging for Unknown Words for Specific Hindi and Marathi Language

Part of Speech (POS) tagging for Indian languages like Hindi and Marathi is generally not an investigated territory. Some of the best taggers accessible for Indian dialects utilize crossbreeds of machine learning or stochastic techniques and phonetic information. Available corpuses for Hindi and Marathi are limited. Hence, when Natural Language Processing (NLP) is applied to Hindi and Marathi sentences, desired results are not achieved. Current POS tagging techniques give UNKNOWN (UNK) POS tag for words which are not present in the corpus. This paper proposes how Hidden Markov Model (HMM)-based approach for POS tagging can be extended using Naïve Bayes theorem for prediction of UNK POS tag.
Learning Non-Monotonic Automatic Post-Editing of Translations from Human Orderings

Recent research in neural machine translation has explored flexible generation orders, as an alternative to left-to-right generation. However, training non-monotonic models brings a new complication: how to search for a good ordering when there is a combinatorial explosion of orderings arriving at the same final result? Also, how do these automatic orderings compare with the actual behaviour of human translators? Current models rely on manually built biases or are left to explore all possibilities on their own. In this paper, we analyze the orderings produced by human post-editors and use them to train an automatic post-editing system. We compare the resulting system with those trained with left-to-right and random post-editing orderings. We observe that humans tend to follow a nearly left-to-right order, but with interesting deviations, such as preferring to start by correcting punctuation or verbs.
Smart Contract Development from the Perspective of Developers: Topics and Issues Discussed on Social Media

Blockchain-based platforms are emerging as a transformative technology that can provide reliability, integrity, and auditability without trusted entities. One of the key features of these platforms is the trustworthy decentralized execution of general-purpose computation in the form of smart contracts, which are envisioned to have a wide range of applications. As a result, a rapidly growing and active community of smart-contract developers has emerged in recent years. A number of research efforts have investigated the technological challenges that these developers face, introducing a variety of tools, languages, and frameworks for smart-contract development, focusing on security. However, relatively little is known about the community itself, about the developers, and about the issues that they face and discuss. To address this gap, we study smart-contract developers and their discussions on two social media sites, Stack Exchange and Medium. We provide insight into the trends and key topics of these discussions, into the developers’ interest in various security issues and security tools, and into the developers’ technological background.
Intervenções de Terapia Ocupacional com pessoas com esclerose múltipla: revisão integrativa da literatura

Introduction: Multiple sclerosis is a more common autoimmune, demyelinating disease of the central nervous system, which affects mainly adults and causes significant impacts on occupational performance and social participation.Objective: To analyze and systematize the knowledge produced by Occupational Therapy related to interventions in people with multiple sclerosis, between 2012 and 2017.
Research on path planning of robot based on deep reinforcement learning

In this paper, to avoid the problem of local optimization and slow convergence in complex environment, a reinforcement learning algorithm is proposed to solve the problem. A robot path planning model is built and its feasibility is verified by simulation. In addition, this paper proposes a deep environment to neural network for robot camera to establish a deep reinforcement learning path planning model, and establishes a deep recursive Q-network (DRQN) and Deep Dueling Q-network(DDQN) respectively. In the comparison of the final simulation results, DRQN needs to consume more computation time, but can achieve better results with higher accuracy.
Accurate Regression-Based 3D Gaze Estimation Using Multiple Mapping Surfaces

Accurate 3D gaze estimation using a simple setup remains a challenging issue for head-mounted eye tracking. Current regression-based gaze direction estimation methods implicitly assume that all gaze directions intersect at one point called the eyeball pseudo-center. The effect of this implicit assumption on gaze estimation is unknown. In this paper, we find that this assumption is approximate based on a simulation of all intersections of gaze directions, and it is conditional based on a sensitivity analysis of the assumption in gaze estimation. Hence, we propose a gaze direction estimation method with one mapping surface that satisfies conditions of the assumption by configuring one mapping surface and achieving a high-quality calibration of the eyeball pseudo-center. This method only adds two additional calibration points outside the mapping surface. Furthermore, replacing the eyeball pseudo-center with an additional calibrated surface, we propose a gaze direction estimation method with two mapping surfaces that further improves the accuracy of gaze estimation. This method improves accuracy on the state-of-the-art method by 20 percent (from a mean error of 1.84 degrees to 1.48 degrees) on a public dataset with a usage range of 1 meter and by 17 percent (from a mean error of 2.22 degrees to 1.85 degrees) on a public dataset with a usage range of 2 meters.
DeepKAF: A Heterogeneous CBR &amp; Deep Learning Approach for NLP Prototyping

With widespread modernization, digitization and transformations of most of industries, Artificial Intelligence (AI) has become the key enabler in that modernization journey. AI offers substantial capabilities to solve new problems and optimise existing solutions specialising on specific problems and learning from different domains. AI solutions can be either explainable or black box ones with the latter being urged to improve since they cannot trust. Case-based Reasoning (CBR) is an explainable AI approach where solutions are provided along with relevant explanations in terms of why a solution was selected. However, CBR, like most other explainable approaches, has several limitations in terms of scalability, large data volumes, domain complexity, that reduce its ability to scale any CBR system in industrial applications. In this paper, we provide a heterogeneous CBR framework - DeepKAF where we combine CBR paradigm with Deep Learning architectures to solve complicated Natural Language Processing (NLP) problems (eg. mixed language and grammatically incorrect text).DeepKAF is built based on continuous research in the area of Deep Learning and CBR. DeepKAF has been implemented and used across different domains, test use cases and research models as an ensemble deep learning and CBR Architecture.
Characterizing and Mitigating the Impact of Data Imbalance for Stakeholders in Recommender Systems

Recommender systems are key tools to push the consumption of items. Imbalances in the data distribution might affect the exposure given to providers, thus affecting their business and experience in the platform. In my work, I study the impact of data imbalances for the stakeholders, according to how recommendations are generated.
Deep Hash Assisted Network for Object Detection in Remote Sensing Images

Remote Sensing Images (RSIs) often have extremely wide width and abundant terrain. In order to achieve rapid object detection in large RSIs, in this paper, a Deep Hash Assisted Network (DHAN) is constructed by introducing a hashing encoding of images in a two-stage deep neural network. Different with the available detection networks, DHAN first locates candidate object regions and then transfers the learned features to another Region Proposal Network (RPN) for detection. On the one hand, it can avoid the calculations on the background irrelevant to objects. On the other hand, the built hash encoding layer in DHAN can accelerate the detection via binary hash features. Moreover, a self attention layer is designed and combined with the convolution layer, to distinguish relatively small objects regions from a very large scene. The proposed method is tested on several public data sets, and the comparison results show that DHAN can remarkably improve the detection efficiency on large RSIs and simultaneously achieve high detection accuracy.
Multi-Channel Interactive Reinforcement Learning for Sequential Tasks.

The ability to learn new tasks by sequencing already known skills is an important requirement for future robots. Reinforcement learning is a powerful tool for this as it allows for a robot to learn and improve on how to combine skills for sequential tasks. However, in real robotic applications, the cost of sample collection and exploration prevent the application of reinforcement learning for a variety of tasks. To overcome these limitations, human input during reinforcement can be beneficial to speed up learning, guide the exploration and prevent the choice of disastrous actions. Nevertheless, there is a lack of experimental evaluations of multi-channel interactive reinforcement learning systems solving robotic tasks with input from inexperienced human users, in particular for cases where human input might be partially wrong. Therefore, in this paper, we present an approach that incorporates multiple human input channels for interactive reinforcement learning in a unified framework and evaluate it on two robotic tasks with 20 inexperienced human subjects. To enable the robot to also handle potentially incorrect human input we incorporate a novel concept for self-confidence, which allows the robot to question human input after an initial learning phase. The second robotic task is specifically designed to investigate if this self-confidence can enable the robot to achieve learning progress even if the human input is partially incorrect. Further, we evaluate how humans react to suggestions of the robot, once the robot notices human input might be wrong. Our experimental evaluations show that our approach can successfully incorporate human input to accelerate the learning process in both robotic tasks even if it is partially wrong. However, not all humans were willing to accept the robot's suggestions or its questioning of their input, particularly if they do not understand the learning process and the reasons behind the robot's suggestions. We believe that the findings from this experimental evaluation can be beneficial for the future design of algorithms and interfaces of interactive reinforcement learning systems used by inexperienced users.
A robot that keeps it simple: Hello robot wants to reinvent how autonomous machines perform tasks at home

Ten years ago, a now-defunct Silicon Valley startup caused a sensation when it introduced the Personal Robot 2, or PR2. Researchers, in particular, loved the PR2 because it was versatile and easier to program than other robots. It allowed them to focus on their research rather than building a new system from scratch. The PR2 helped advance robotic sensing, navigation, and manipulation, but it wasn't perfect. It was big, heavy, and expensive: Each cost US $400,000.
Analyzing ELMo and DistilBERT on Socio-political News Classification

This study evaluates the robustness of two state-of-the-art deep contextual language representations, ELMo and DistilBERT, on supervised learning of binary protest news classification (PC) and sentiment analysis (SA) of product reviews. A ”cross-context” setting is enabled using test sets that are distinct from the training data. The models are fine-tuned and fed into a Feed-Forward Neural Network (FFNN) and a Bidirectional Long Short Term Memory network (BiLSTM). Multinomial Naive Bayes (MNB) and Linear Support Vector Machine (LSVM) are used as traditional baselines. The results suggest that DistilBERT can transfer generic semantic knowledge to other domains better than ELMo. DistilBERT is also 30% smaller and 83% faster than ELMo, which suggests superiority for smaller computational training budgets. When generalization is not the utmost preference and test domain is similar to the training domain, the traditional machine learning (ML) algorithms can still be considered as more economic alternatives to deep language representations.
Addressing the False Positives in Pedestrian Detection

Pedestrian detection is a subfield of object detection that is necessary for several applications such as person tracking, intelligent surveillance system, abnormal scene detection, and intelligent cars. We prepared a dataset for addressing the false positives that occur during the person detection process. Some objects have very similar features to those of a person. If a model is trained using a dataset containing only persons, it leads to several false positives since it cannot differentiate such objects from that of a person. Our dataset includes person and person-like objects (PnPLO). Person-like objects that we introduce in our dataset are statues, mannequins, scarecrows, and robots. We used the SSD model to show that, on training a model using our dataset, we can significantly reduce the false positives during detection when compared to models trained on standard person datasets, thereby improving the precision. The dataset consists of 944 training images, 160 validation images, and 235 images for testing, with a total of 1626 person and 1368 nonhuman labelling.
Detecting &amp;#x201C;DeepFakes&amp;#x201D; in H.264 Video Data Using Compression Ghost Artifacts

In recent years, the number of forged videos circulating on the Internet has immensely increased. Software and services to create such forgeries have become more and more accessible to the public. In this regard, the risk of malicious use of forged videos has risen. This work proposes an approach based on the Ghost effect knwon from image forensics for detecting forgeries in videos that can replace faces in video sequences or change the mimic of a face. The experimental results show that the proposed approach is able to identify forgery in high-quality encoded video content.