Reviewerly/exHarmony huggingface dataset:
A Neural Attention Model for Automatic Question Generation Using Dual Encoders

In the field of education, framing right questions is an important way of measuring the knowledge of an individual, and automation of the generation of questions would reduce the strain on educators and thus increase efficiency in learning. Literature survey reveals that while there are several promising methods for testing comprehension generating questions, but none of them can be used reliably for generating meaningful questions all the time, and thus, further research is required. A novel method of generating questions is presented in this paper from input text, by encoding the answers into the model and using attention that learns the dependencies between both input text and answers and between input text and questions, thus generating questions which are relevant to the answer given. The dataset used is the SQuAD dataset, and the model produces fluent questions which have been evaluated with the required metrics.
The Human Dimensions: Co-evolution Between Humans and Digital Technologies

With the evolution of technology, society and human skills evolve accordingly. Indeed, throughout human evolution, our cognitive systems have been altered by the advent of technological inventions such as primitive tools, spoken language, and writing.
Test-optional Policies: Overcoming Strategic Behavior and Informational Gaps

Due to the Covid-19 pandemic, more than 500 US-based colleges and universities went “test-optional” for admissions and promised that they would not penalize applicants for not submitting test scores, part of a longer trend to rethink the role of testing in college admissions. However, it remains unclear how (and whether) a college can simultaneously use test scores for those who submit them, while not penalizing those who do not–and what that promise even means. We formalize these questions, and study how a college can overcome two challenges with optional testing: strategic applicants (when those with low test scores can pretend to not have taken the test), and informational gaps (it has more information on those who submit a test score than those who do not). We find that colleges can indeed do so, if and only if they are able to use information on who has test access and are willing to randomize admissions.
Integration of Rural and Urban Training in Teachers, Students and Older Adults in Computer Security Mediated by Technology

The transmission of knowledge in areas such as Computer Security is a primary factor in the acquisition of skills in the knowledge and use of technologies, allowing to improve Human-Computer Interaction (HCI) in society through a segmented knowledge base for the training of individuals., where technological mediation is a factor to take into account to guarantee learning, acquisition of competences and adaptability to the change that contemporary society requires through Stages in Cognitive Development. This article presents results of the intervention carried out by the Virtual Learning Environment Think Safe App (2020-Urban/Students) and Virtual Learning Objects, Rural App (2021 Rural/Teachers) and SIMPA App (2021 Urban/Older Adults), focused on training in computer security and the development of technological and cognitive skills, necessary for daily life from the professional, educational and individual perspective, prepared for the change that contemporary society in the new virtuality, due to the state of pandemic generated by COVID-19, affecting the normality of intelligent learning and exposing the existing gap in relation to the way in which learning is acquired and accessed.
Obstacle-Centered Trajectory Planning for Autonomous Mobile Robot

Trajectory planning enables Autonomous Mobile Robot (AMR) to have intelligence and avoid a collision in the interaction with obstacles. However, in scenes with multiple obstacles, most of the existing methods cannot minimize the collision risk. It is because that these methods do not distinguish the importance of the obstacles in the scene. Therefore, in this paper, we proposed an Obstacle-Centered Trajectory Planning (OCTP) method to solve the problem. In our method, a novel collision risk evaluation model is constructed, which considers the importance of each obstacle. In addition, a sliding-window-based key points interpolation method is used to smooth the velocity profile obeying constraints of collision risk and curvature. Finally, a comparison with the baseline method is performed. The experimental results show that the proposed method can effectively reduce AMR's collision risk in interacting with obstacles.
Extracting Stances on Pandemic Measures from Social Media Data

Support for national measures against the COVID-19 pandemic can be measured by collecting responses to questionnaires. In this paper we explore a less costly and less time-consuming method: by analyzing social media data. We compare stances regarding anti-pandemic measures extracted from tweets with the questionnaire results provided by the Dutch national health institute RIVM. We find similarities and differences and discuss the results.
Machine Learning Model for Frailty Detectxion using Electric Power Consumption Data from Smart Meter

With the increase of the number of the elderly, healthcare systems to support the daily life and wellbeing of the elderly attracted attention. Especially, frailty syndrome is one of the most significant challenges faced by many countries because of its high association with mortality and hospitalization. Recently, with the progress of ICT (Information and Communication Technology), many frailty detection models which use sensors were proposed. However, many of them require very high costs caused by the installation and management of sensors. Therefore, the objective of this study is to propose a machine learning-based frailty detection model using only electric power consumption data from smart meter, which uses no other devices such as sensors. Also, we examined the feasibility of our model through a case study, in which we have conducted on 24 elderly people. As a result of a cast study, we could detect frailty with 82% accuracy, 77% precision, 84% recall, and 80% f-score for a 2-class classification problem (frailty or non-frailty). The results of our study show that more elderly people can receive frailty diagnoses through smart meters. Moreover, since frailty is a reversible condition that could be restored to a healthy status with early and appropriate intervention, our model has potential to extend the healthy expectancy of the elderly.
AUNet: Adaptive UpdateNet for Dynamic Pedestrian Tracking with Short-Term Occlusion

This paper investigates the pedestrian tracking problem with short-term occlusion and proposes an adaptive UpdateNet (AUNet) framework as the improvement over state-of-the-art. We find that the templates in the original UpdateNet framework are usually &#x2018;corrupted&#x2019; when the tracking target is temporarily occluded, and hence, it is very difficult to recover when the target reappears in the following image frames. We therefore construct an evaluation network, which determines whether the current input image frame is occluded or not, and hence adapt the current image frame&#x0027;s influence in the update process of the object tracking template. In this way, we are able to successfully maintain a good set of templates, and hence improve the network&#x0027;s tracking performance when facing short-term occlusions. Experimental results with the canonical dataset (OTB100) and a real world use case show the superior tracking performance of AUNet over state-of-the-art.
Inner-Product Functional Encryption from Random Linear Codes: Trial and Challenges

Inner-product encryption scheme is an important branch of functional encryption schemes, which has wide applications in modern society. There have existed many inner-product encryption schemes, but as far as we know, no code-based inner-product functional encryption scheme has been proposed. As one of the most popular post-quantum cryptographic techniques, why code-based cryptosystems are seldom applied in these areas raises our concern. In this paper, we build an inner product encryption scheme from random linear codes and prove its security. Unfortunately, our scheme still suffers from the so large parameter size, which indicates that how to build a practical code-based functional encryption scheme remains a challenging problem.
Hardware Implementation of Hierarchical Temporal Memory Algorithm

Hierarchical temporal memory (HTM) is an un-supervised machine learning algorithm that can learn both spatial and temporal information of input. It has been successfully applied to multiple areas. In this paper, we propose a multi-level hierarchical ASIC implementation of HTM, referred to as processor core, to support both spatial and temporal pooling. To improve the unbalanced workload in HTM, the proposed design provides different mapping methods for the spatial and temporal pooling, respectively. In the proposed design, we implement a distributed memory system by assigning one dedicated memory bank to each level of hierarchy to improve the memory bandwidth utilization efficiency. Finally, the hot-spot operations are optimized using a series of customized units. Regarding scalability, we propose a ring-based network consisting of multiple processor cores to support a larger HTM network. To evaluate the performance of our proposed design, we map an HTM network that includes 2,048 columns and 65,536 cells on both the proposed design and NVIDIA Tesla K40c GPU using the KTH database as input. The latency and power of the proposed design is 6.04 ms and 4.1 W using GP 65 nm technology. Compared to the equivalent GPU implementation, the latency and power is improved 12.45× and 57.32×, respectively.
Active Detection Technology of 3-Dimension Artificial Intelligence Robot Based on Visual Positioning

Camera repositioning is a key module in SLAM system, which is of great significance to ensure the robustness of positioning and mapping of mobile robots in complex scenes, and is also one of the core technologies of augmented reality and automatic driving. In order to improve the active detection and spatial positioning ability of 3D artificial intelligence robot with self-adaptive posture, an active detection and spatial positioning method of 3D artificial intelligence robot with self-adaptive posture based on visual positioning and Monte Carlo estimation is proposed. The method comprises the following steps of: constructing a dynamic environment characteristic heat map ray projection recognition model of the posture self-adaptive 3D artificial intelligence robot, collecting scene data in the process of edge characteristic detection of the movement track of the posture self-adaptive 3D artificial intelligence robot by adopting a characteristic heat map ray projection method, and establishing a Monte Carlo parameter estimation model for global positioning detection of the movement track distribution of the posture self-adaptive 3D artificial intelligence robot; Based on visual positioning and Monte Carlo parameter estimation method, the edge feature detection and active detection parameter positioning of 3D artificial intelligence robot with self-adaptive posture adjustment are realized. Through 3D visual positioning and maximum likelihood estimation parameter identification, the motion space planning and active detection of 3D artificial intelligence robot with self-adaptive posture adjustment are realized. Combined with visual SLAM method, the active detection model of 3D artificial intelligence robot is constructed to improve the control stability. Tests show that this method has good adaptability in active detection and spatial positioning of 3D artificial intelligence robot, and has strong obstacle avoidance ability.
Colorizing images with Conditional Adversarial Networks and Transfer Learning

Automatic image colorization is an image-to-image translation problem where a model is able to automatically convert a one-dimensional grayscale image to a three-dimensional color image. Inspiration is taken from the Pix2Pix image-to-image translation model and then a pre-trained MobileNet model is integrated into the generator of the Conditional Generative Adversarial Network (cGAN). The goal is to use transfer learning for the purpose of achieving satisfactory results with less training data while at the same time decreasing training time and improving performance. Moreover, quantitative and qualitative analysis is performed. The results show that by using transfer learning the modified model achieves a PSNR score of 24.21 and a SSIM score of 0.9374 and with that improves upon the baseline.
WordNet construction for under-resourced languages using personalized PageRank

Abstract Wordnet development is an active research area among NLP researchers. Since the manual construction of the English wordnet was very costly both in terms of time and human expertise, automatic approaches have become very popular for wordnet development in languages other than English. Automatic methods usually benefit from an existing wordnet of a high resource language and use it as the backbone of their work. In this article, we present an unsupervised approach for automatic wordnet construction using a combination of Expectation–Maximization and personalized PageRank algorithms. Our method uses some typical and available language resources, so it is applicable to many languages including under-resourced ones. The proposed method needs just a bilingual dictionary and a monolingual corpus for developing a wordnet. In order to evaluate the proposed method, we apply it to the Persian language which is identified as an under-resourced language in NLP tasks. Evaluation results properly indicate the power of the proposed method to construct a high quality and large-scale wordnet for poor-resource languages. According to experiments, we achieve a precision of higher than 93% with a recall of 50%.
Mutual information-based multi-output tree learning algorithm

A tree model with low time complexity can support the application of artificial intelligence to industrial systems. Variable selection based tree learning algorithms are more time efficient than existing Classification and Regression Tree (CART) algorithms. To our best knowledge, there is no attempt to deal with categorical input variable in variable selection based multi-output tree learning. Also, in the case of multi-output regression tree, a conventional variable selection based algorithm is not suitable to large datasets. We propose a mutual information-based multi-output tree learning algorithm that consists of variable selection and split optimization. The proposed method discretizes each variable based on k-means into 2–4 clusters and selects the variable for splitting based on the discretized variables using mutual information. This variable selection component has relatively low time complexity and can be applied regardless of output dimension and types. The proposed split optimization component is more efficient than an exhaustive search. The performance of the proposed tree learning algorithm is similar to or better than that of a multi-output version of CART algorithm on a specific dataset. In addition, with a large dataset, the time complexity of the proposed algorithm is significantly reduced compared to a CART algorithm.
Epilogue: Agile Is Dead—Long Live Agile

While one company after the other is devoting itself to agility, INSEAD professors Phanish Puranam and Julien Clément ask whether Scrum really makes sense outside of software and typical development activities (“search situations”). Under the title, “Why agile may be fragile,” they wonder whether Scrum, when used in situations other than the framework’s core application areas, will not just be a temporary phenomenon. They question whether agile techniques will be able to produce the equality, participation, and autonomy that employees strive for across corporate areas. In their view, there is no “broadly applicable mechanism besides hierarchy for scaling collaboration among a diverse set of interdependent individuals.”
Cyberbullying and Fake Account Detection in Social Media

Enhancement in the technology trend of using social networking is increasing day by day as of now there are more than 50 crores active users are using different social media platforms for the interaction which had affected their life so just like a coin has two face in a similar way misuse of these platforms is going which cause the the rapid rise of cybercrime and exploitation eg harassing someone by sending malicious messages, spreading abusive messages through fake accounts on the social media etc.. In this new era insulting a person physically or emotionally is done by cyberbullying and by using fake accounts, so as a preventive measure to ensure the above things should not happen there is a need of detecting cyberbullying and the fake accounts. In our study to stop cyberbullying and fake accounts we'll use different Machine Learning algorithms for detecting the cybercrime and fake accounts so as to report these issues to the system immediately and to stop the crimes to increase in future and develop a secure online environment.
Design and Implementation of Geological Hazard Data Acquisition System Based on Online Technology

Based on the working process and technical requirements of geological hazard survey in China, a geological hazard field survey data acquisition system was developed based on online technology. The system, capable of presenting remote sensing images or topographic maps with a scale of 1:50,000 or higher accuracy and providing accurate positioning based on GPS and Beidou system, can satisfy the requirements of geological hazard field data collection. The system provides numerous functions, including data download, survey route navigation, online form filling, graph drawing, photo recording, and data synchronism. The development of the proposed system will provide rapid, accurate, and convenient data collection and information search for geological hazard field survey. The proposed system has been widely applied in practice and has significantly improved the working accuracy and efficiency of geological hazard field survey.
A 2-FA for home voice assistants using inaudible acoustic signal

Voice assistants have been shown to be vulnerable to replay attacks, impersonation attacks and inaudible voice commands. Existing defenses do not provide a practical solution as they either rely on external hardware or work under very constrained settings. We introduce a hand gesture-based authentication system for smart home voice assistants called HandLock, which uses built-in microphones and speakers to generate and sense inaudible acoustic signals to detect the presence of a known hand gesture. Our proposed approach can act as a second-factor authentication (2-FA) for performing specific sensitive operations like confirming online purchases through voice assistants. The experiments involving 45 participants show that HandLock can achieve on average 96.51% true-positive-rate at the expense of 0.82% false-acceptance-rate.
Combining CNN with DS<sup>3</sup> for Detecting Bug-prone Modules in Cross-version Projects

The paper focuses on Cross-Version Defect Prediction (CVDP) where the classification model is trained on information of the prior version and then tested to predict defects in the components of the last release. To avoid the distribution differences which could negatively impact the performances of machine learning based model, we consider Dissimilarity-based Sparse Subset Selection (DS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) technique for selecting meaningful representatives to be included in the training set. Furthermore, we employ a Convolutional Neural Network (CNN) to generate structural and semantic features to be merged with the traditional software measures to obtain a more comprehensive list of predictors. To evaluate the usefulness of our proposal for the CVDP scenario, we perform an empirical study on a total of 20 cross-version pairs from 10 different software projects. To build prediction models we consider Logistic Regression (LR) and Random Forest (RF) and we adopt 3 evaluation criteria (i.e., F-measure, G-mean, Balance) to assess the prediction accuracy. Our results show that the use of CNN with both LR and RF models has a significant impact, with an improvement of ∼20% for each evaluation criteria. Differently, we notice that DS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> does not impact significantly in improving prediction accuracy.
LibGuides: Information Technology Help: Internet access

Resources to help Central Penn College students navigate the technology required in their courses.
Technique for Enhancing the Efficiency and Security of Lightweight IoT Devices

Internet of Things (IoT) has become the integral part of our everyday life. IoT devices are delegated for observing our wellbeing, houses, workplaces, industrial facilities, etc., so they need to comply with necessary security requirements. Moreover, such devices should be handled with extra care as they have extraordinary significance and handles vital information that might be identified with one’s life. Their effectiveness matters when these devices are considered as resource constraint devices. Numerous procedures are utilized to make these devices profoundly secure and productive. In the proposed system, we have applied hyper elliptic curve (HEEC) for key generation, considering the fact that such approach is more appropriate for lightweight devices due to its smaller key size. We implemented signcryption approach, where digital signature and encryption should be possible in single logical step. By applying HEEC-based signcryption, we have achieved significant improvement in the security and efficiency for resource constraint devices in IoT .
Predicting Success of a Persuasion through Joint Modeling of Utterance Categorization

Persuasive conversation leverages conversational strategies by the persuader to change the attitude or behavior of a persuadee towards achieving a specific goal. It involves understanding the linguistic and cognitive principles underlying the organization of strategic disclosures and appeals employed in human persuasion. One of the main challenges of such conversation is the inability of a persuader to detect the outcome of their conversation during the interaction. Such prior knowledge can help a persuader to change their conversation strategy and pre-empt possible conversation failures. In this paper, we propose a technique that analyses conversations to predict whether the persuader is going to successfully persuade the persuadee. We propose a joint model of latent utterance categorization to predict the success or the failure of a persuasive conversation. This latent categorization allows the model to identify high-level conversational contexts that influence patterns of language in a persuasive conversation. We evaluate the performance of our model on an openly available dataset. Our preliminary results demonstrate that the proposed model outperforms competitive baselines.