Reviewerly/exHarmony huggingface dataset:
Learning Evaluation Effectiveness Through Dashboard of Knowledge Using Dimensions of Multi-Criteria of WELS

A Discussion Forum is one component of the learning media in a Learning Management System. There is much information and knowledge that are discussed among a lecturer and students in forums as a part of the learning process. The motivation of this study is to evaluate the learning process in a discussion forum through a dashboard, namely, the Dashboard of Knowledge (DoK). The DoK is formed using an Information Retrieval Architecture and Entity Relationship Diagram that is designed using four category interfaces. Furthermore, the effectiveness of the learning evaluation through the DoK is measured by a survey. The survey is conducted using three out of four dimensions of multi-criteria of the Web-based E-Learning System (WELS), namely, the Learner Interface, the System Context, and Personalization. The respondents of the survey are the heads of department that are DoK users. The results show that most respondents strongly agree that the DoK is useful for evaluating the learning in a discussion forum. This is exhibited by the survey results that show an average score of 3.53 out of 4.00 and 84.32% satisfaction. These results reveal that the DoK can effectively assist heads of department in monitoring the effectiveness of the learning process in a discussion forum.
A Comparative Study on the Robustness of Chaos-Based Random Number Generators

This paper presents a comparative study on continous-time chaos-based random number generation methods regarding their robustness against changes in chaos controlling parameters and external interference. Chaotic systems suggest enabling high throughput random data without need for postprocessing and with less complex hardware. However, due to effects of aging or fabrication process variations, the chaos controlling parameters of the random number generator may change. Furthermore, external interference can be applied on the chaotic oscillator to manipulate its output. Therefore, in a chaotic RNG, the bit generation method should be immune to parameter variation and external interference. In this study, two widely used methods for random number generation have been compared: 1) Regular sampling of chaotic waveform (RSCW), and 2) Chaotic sampling of regular waveform (CSRW). A double-scroll chaotic system is chosen as the chaotic oscillator and it is numerically simulated in normalized time domain to generate random bit sequences using both methods. Applying the concepts of autocorrelation and approximate entropy to the output bitstreams, the robustness of the two bit generation methods against parameter variation and external interference have been compared. It is demonstrated that chaotic sampling of regular waveform method provides more robustness against parameter changes and external interference compared to regular sampling of chaotic waveform method.
Eventually Consistent Distributed Ledger Relying on Degraded Atomic Broadcast

The great increase of the Bitcoin crypto-currency value these last months has brought blockchain and distributed ledger technologies back on the front stage. However, it also gives incentive to malicious users who would like to break the system or take advantage of it (steal money, hide some information stored in the ledger, isolate a particular node from the rest of the network, etc.). Thus, our research focuses on overcoming potential attacks to distributed ledgers. In this paper, we focus on attacks that damage underlying networks of distributed ledgers. Underlying networks offer useful communication primitives such as an atomic broadcast, however such attacks may degrade the property of the primitives and make distributed ledgers relying on the primitives no longer work. Hence we should design algorithms to make the distributed ledgers still work even when some attacks degrade the primitives. As the first study of this problem, we consider distributed ledgers that rely on an atomic broadcast to maintain consistency. We define different cases of attacks affecting the uniform agreement property of the atomic broadcast: We first assume that a single server can fail to deliver a particular message and then we assume that several servers can fail to deliver the message. Afterwards, we present an algorithm to recover the states of the servers that fail to deliver some messages, and consider the situation where the maximum of servers which keep a wrong sequence is known and bounded.
PIVO: Probabilistic Inverse Velocity Obstacle for Navigation under Uncertainty

In this paper, we present an algorithmic framework which computes the collision-free velocities for the robot in a human shared dynamic and uncertain environment. We extend the concept of Inverse Velocity Obstacle (IVO) to a probabilistic variant to handle the state estimation and motion uncertainties that arise due to the other participants of the environment. These uncertainties are modeled as non-parametric probability distributions. In our PIVO: Probabilistic Inverse Velocity Obstacle, we propose the collision-free navigation as an optimization problem by reformulating the velocity conditions of IVO as chance constraints that takes the uncertainty into account. The space of collision-free velocities that result from the presented optimization scheme are associated to a confidence measure as a specified probability. We demonstrate the efficacy of our PIVO through numerical simulations and demonstrating its ability to generate safe trajectories under highly uncertain environments.
Construction of consistency judgment system of diploma policy and curriculum policy using character‐level CNN

Abstract Higher education institutions or universities secure the quality of their education in order to build the confidence of stakeholders. In the mechanism of quality assurance, it is necessary to handle an enormous amount of text data related to their education activities. National Institution for Academic Degrees and Quality Enhancement of Higher Education conducts a research for quality assurance to analyze the text data of their admission, curriculum, and diploma policies. An effective support system for text information processing by using information technology is desired. In this paper, we will consider the mechanism of preliminary system using character‐level convolutional neural networks to judge the consistency between diploma policy and curriculum policy.
Faster initial splitting for small characteristic composite extension degree fields

Let p be a small prime and n=n1n2>1 be a composite integer. For the function field sieve algorithm applied to Fpn, Guillevic (2019) had proposed an algorithm for initial splitting of the target in the individual logarithm phase. This algorithm generates polynomials and tests them for B-smoothness for some appropriate value of B. The amortised cost of generating each polynomial is O(n22) multiplications over Fpn1. In this work, we propose a new algorithm for performing the initial splitting which also generates and tests polynomials for B-smoothness. The advantage over Guillevic splitting is that in the new algorithm, the cost of generating a polynomial is O(nlogp⁡(1/π)) multiplications in Fp, where π is the relevant smoothness probability.
A Design Archival Approach to Knowledge Production in Design Research and Practice

In this paper, we develop the notion of design archives to understand how different forms of knowledge are systematically accumulated and shared in and across design processes. Drawing on philosophy and media theory, we present a working definition of design archives as more than documentation. Through an interview study, we investigate how various archives systematically inform design work and govern the way design processes are represented and reflected upon. The study provides insights into an abundance of tools used to access, record, store and share information. We highlight the difference between personal, shared, and public archives, different archival barriers for sharing, how prototypes act as (an)archival conduits of design potentials, and how information (and people) tend to get lost in the archives. Finally, we discuss how a design archival approach might help identify power relations in design while also facilitating a move from 'dead' to 'living' archives in design work.
Metaoptimization on a Distributed System for Deep Reinforcement Learning

Training intelligent agents through reinforcement learning (RL) is a notoriously unstable procedure. Massive parallelization on GPUs and distributed systems has been exploited to generate a large amount of training experiences and consequently reduce instabilities, but the success of training remains strongly influenced by the choice of the hyperparameters. To overcome this issue, we introduce HyperTrick, a new metaoptimization algorithm, and show its effective application to tune hyperparameters in the case of deep RL, while learning to play different Atari games on a distributed system. Our analysis provides evidence of the interaction between the identification of the optimal hyperparameters and the learned policy, that is peculiar of the case of metaoptimization for deep RL. When compared with state-of-the-art metaoptimization algorithms, HyperTrick is characterized by a simpler implementation and it allows learning similar policies, while making a more effective use of the computational resources in a distributed system.
A Revision of the Buechner–Tavani Model of Digital Trust and a Philosophical Problem It Raises for Social Robotics

In this paper the Buechner–Tavani model of digital trust is revised—new conditions for self-trust are incorporated into the model. These new conditions raise several philosophical problems concerning the idea of a substantial self for social robotics, which are closely examined. I conclude that reductionism about the self is incompatible with, while the idea of a substantial self is compatible with, trust relations between human agents, between human agents and artificial agents, and between artificial agents.
Associative Classification using Common Instances among Conflicting Discriminative Patterns

This paper proposes a white-box, associative classifier that uses discriminative patterns mined from a dataset including numeric values. In the proposed model, when there exist conflicting patterns for a test instance, we take into account the training instances covered in common by them as the “neighbors” of the test instance. By this design, we can accurately capture the space around the test instance, and as a result, it is observed in our experiments that the predictive performance is improved from some simpler methods. We also show another advantage of the proposed classifier by inspecting its interpretability/explanability.
Comparison of Algorithm Selection to Analyze Elderly Activity Recognition Based on Sensor Data Using R Program

This paper presents a comparison of algorithms used to classify human activity of elderly by sensor data from UCI Machine Learning Repository. We compare three popular algorithms used to classify activities as Artificial Neural Networks, Support Vector Machine and C4.5 Decision Tree to find the most efficient algorithm for classifying human activities. This research used data set “Activity recognition with healthy older people using a batteryless wearable sensor Data Set” of 6000 records. The result is that Artificial Neural Networks has the highest classification accuracy of 96.5%, followed by the Support Vector Machine 96.41% and the C4.5 Decision Tree 95.75%. These experimental data can be applied to the system of detecting or monitoring the activities of the elderly.
Credit Card Fraud Detection Using Correlation-Based Feature Extraction and Ensemble of Learners

There has been a rapid improvement in the detection of frauds in the domain of banking due to advancements in technology. This paper proposes a collaborative model for fraud identification integrating correlation-based feature extraction and an ensemble layer of classifiers. CFS (Correlation-based Feature Selection) implements a fusion of correlation measure and exploratory search approach to rank the highest correlated attributes in the dataset. The correlation measure extracts features that have a high correlation with the class label but uncorrelated inter se. The optimal attribute set retrieved from CFS is used by three supervised classifiers, namely, Naive Bayes, KNN (K-Nearest Neighbor) and Random Forest. The final class label is predicted using the weighted majority voting technique. Experiments on real-time credit card fraud datasets reveal the elimination of irrelevant attributes by CFS and the increase in the accuracy to 99.99% through the use of the ensemble layer of classifiers. Thus, the proposed approach shows the production of superior results in comparison with the existing credit card fraud detection techniques.
Redefine and Organize, 4th Authentication Factor, Behavior

While the use of intelligent devices such as smartphones is spreading rapidly, they continue to rely on old IDs/passwords, and modernization of authentication technology is recognized as a challenge for society. Recent Challenge of authentication technology that has come to our notice is the authentication technology that uses IT to identify people based on information about their habits. In this technology, we utilize information from smartphones and wearable devices and individuals are authenticated without the user carrying out any authentication operation. We re-define and organize 4th personal authentication factor, Behavior. The technology is a recent trend for smartphone's society. Also, we describe the technical issues for realizing behaviour authentication and the measures for it.
On Improving Bounding Box Regression Towards Accurate Object Detection and Tracking

In this paper, an improved bounding box regression technique is studied for CNN-based object detection and tracking. The proposed approach uses a CNN-based network to identify and localise objects which are boxed by a region of interest regressor. In many cases, a generated bounding box does not fit tightly to the detected object which leads to inaccurate results for spatial analysis such as geometric warping. In this paper, a technique is designed to improve this situation by means of background modelling, motion analysis, template correlation and Kalman filters. The experimental results suggest that the proposed method outperforms single frame object detector-based tracking techniques with an improvement of over 80% in the context of surveillance camera-based pedestrian analysis.
Unfavorable structural properties of the set of neural networks with fixed architecture

In this note, we present a variety of results from the recent paper [1] in which the structural properties of the set of functions that can be implemented by neural networks with a fixed architecture have been studied. As it turns out, this set has many unfavorable properties: It is highly non-convex, except possibly for a few uncommon activation functions. Additionally, the set is not closed with respect to L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> -norms, 0 <; p <; ∞, for all frequently used activation functions, and also not closed with respect to the L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sup> -norm for all practically-used activation functions except for the (parametric) ReLU. Finally, the function that maps a family of parameters to the function computed by the associated network is not inverse stable for every practically used activation function. Overall, our findings identify potential causes for issues in the optimization of neural networks such as no guaranteed or very slow convergence and the explosion of parameters.
Real-time Detection of Facial Expression Based on Improved Residual Convolutional Neural Network

An improved residual convolutional neural network is designed for real-time expression detection algorithms. Instead of the standard convolutional layer, the depth-wise separable convolutional layer is used. And the global average pooling, rather than the fully connected layer, is used. And the residual block is used to solve the phenomenon that the over-fitting and the gradient disappear due to the excessive number of layers when the number of network layers is increased. This paper uses the FER-2013 face dataset to train the network model. Compared with the standard convolutional neural network, the parameters and the size of the model in the network are greatly reduced. Experiments illustrate that the rate of facial expression recognition presented in this paper is greatly improved.
Accessing Sensor Data via Hybrid Virtual Private Network Using Multiple Layer Encryption Method in Big Data

Enormous data generation takes place with the increase in the number of systems and enhanced technology. This data generation occurs exponentially with time. Sensor networks are one of the main generators of big data. The data/information received from the sensor network is transferred to the other end user/s via hybrid virtual private network (HVPN) using multiple layer encryption through Internet of things (IoT). The communication between sensors and end user/s takes place through the server with the help of inter-networking procedure called Internet of things (IoT). With the help of IoT, the objects can be traced and controlled from large distances over the network for a nonstop assimilation of the world into computer-based systems resulting in superior efficiency, accuracy, and reduced human intervention. The HVPN using multiple layer encryption strategy will permit the association between the initiator and responder to remain unidentifiable. We have used multiple layer encryption technique to scramble the information to secure IoT. Furthermore, we experimented the contrast of the speed for information transmission and multiple layer encryption and without multiple layer encryption with IoT.
Collective Teaching of Mathematics and Informal Knowledge

The aim of this research is to improve the quality of teaching mathematics. In the process of collective learning, we mobilize informal knowledge. The best results are obtained when the whole system “students - teachers - studied disciplines -delivery modes” is optimal. Formal knowledge is presented through cognitive methods and supplemented by a reflective commentary. In addition to the well-known methods, the authors' components of the methodology for organizing the learning process are presented. They include the “4F” formalization method, the “4C” approach (collective, cognitive, configurative, convergent), cognitive pictography, an analysis of “Chernoff faces” modifications, a reflective theater of a situation center, the use of V. Lefebvre reflective analysis, cognitive infrastructure, a service team of a situational center, “cognitive pharmacy”, an algorithm for designing educational projects, medical monitoring of the health of participants. An experiment was conducted based on the “formula of human” of V. Lefebvre to assess the degree of guilt of participants in a series of fictional situations. The concept of “cognitive immunity” is introduced, to overcome which a network interaction format using masks and virtual personalities is proposed. To the classical ternary “task, performer, program” the authors added the thesis “basis adequate to the task in relation to the performer”.
A New Watermarking Scheme Based on SIFT Feature Points with Reversible Rotation

This paper proposes a new watermarking technique based on scale invariant feature transform (SIFT) integer rotation to protect the confidentiality of medical data. SIFT is used to extract the positions of the original data, and then the positions are rotated by a reversible integer rotation algorithm by multiplying triangular matrix to generate the appropriate positions for embedding an encrypted and divided person data of patients. Histogram of scale and distortion is estimated to generate the stego data. Different positions according histogram of scale are used for embedding after applying integer rotation with angles as an alternative parameter. The experimental results show the proposed watermarking method imperceptible with an average peak signal-to-noise ratio (PSNR [dB]) 50.595 and 5.216 as the average entropy.
Speech Tracking Algorithm for an Automated Reading Tutor in Filipino

Speech tracking stimulates continuity of reading, improves miscue detection and provides immediate feedback. Automated reading tutors (ART) need this feature to emulate coaching of human teachers. A previously developed ART for Filipino (Tanglaw) uses an automatic speech recognizer (ASR) to implement a miscue detection feature, but it does not track the current reading position. In this research, experiments on implementing speech tracking for Filipino ART to indicate the word to be read, in order to mimic guided reading. The next word will be highlighted when the previous word has been uttered correctly. Results show that using a phoneme-level ASR with online decoding, dynamic programming with revised cost function, and speaker's reading rate using words-correct-per-minute (WCPM) as parameters yield the highest timing accuracy of tracking (TAT) of 41.18% compared to 29.03% and 37.11 % with Levenshtein distance at most one and half word recognized thresholds, respectively.
SPrinter: A Static Checker for Finding Smart Pointer Errors in C++ Programs

Smart pointers are widely used to prevent memory errors in modern C++ code. However, improper usage of smart pointers may also lead to common memory errors, which makes the code not as safe as expected. To avoid smart pointer errors as early as possible, we present a coding style checker to detect possible bad smart pointer usages during compile time, and notify programmers about bug-prone behaviors. The evaluation indicates that the currently available state-of-the-art static code checkers can only detect 25 out of 116 manually inserted errors, while our tool can detect all these errors. And we also found 521 bugs among 8 open source projects with only 4 false positives.
Discovering Spatial Weighted Frequent Itemsets in Spatiotemporal Databases

Weighted Frequent Itemset (WFI) mining is an important model in data mining. It aims to discover all itemsets whose weighted sum in a transactional database is no less than the user-specified threshold value. Most previous works focused on finding WFIs in a transactional database and did not recognize the spatiotemporal characteristics of an item within the data. This paper proposes a more flexible model of Spatial Weighted Frequent Itemset (SWFI) that may exist in a spatiotemporal database. The recommended patterns may be found very useful in many real-world applications. For instance, an SWFI generated from an air pollution database indicates a geographical region where people have been exposed to high levels of an air pollutant, say PM2.5. The generated SWFIs do not satisfy the anti-monotonic property. Two new measures have been presented to effectively reduce the search space and the computational cost of finding the desired patterns. A pattern-growth algorithm, called Spatial Weighted Frequent Pattern-growth, has also been presented to find all SWFIs in a spatiotemporal database. Experimental results demonstrate that the proposed algorithm is efficient. We also describe a case study in which our model has been used to find useful information in air pollution database.