Reviewerly/exHarmony huggingface dataset:
Uncertainty of Skill Estimates in Operational Deep Knowledge Tracing

This paper reviews challenges and opportunities of using deep knowledge tracing (DKT) in a production hybrid instructional and assessment system (HIAS) for measuring learner skills. An empirical data analysis using K-12 Mathematics practice data from an operational HIAS showed that the DKT estimates in the training set were well calibrated, although the estimates in a holdout set overestimated student skill mastery at lower ability levels and underestimated mastery at higher ability levels. Examination of expected vs. observed plots across ability levels provided a means for evaluating the generalizability of the predictions to new students and suggested a stratified sampling approach may be needed for creating training and validation sets that better represented the full practice data. Estimation accuracy was further examined in a parameter recovery study, with response data simulated from patterns in the empirical data. The study showed that in general, the DKT procedure had high estimation accuracy, with an absolute mean difference of approximately 0.03 units on the [0, 1] probability scale and a Pearson correlation of +0.89 between true and estimated values. To examine uncertainty in the estimates, 80% and 95% confidence intervals were constructed around the mean estimate for each true ability level. As expected, results showed less uncertainty at ability levels where more response data were generated, indicating the DKT approach is sensitive to ability level information in the training data. Finally, the DKT approach was able to detect learning across attempts within a skill, suggesting this approach may be preferred over traditional measurement approaches in scenarios where ability levels change over time.
Evaluation on Data Operations Management using CMMI and DMBOK: BDPJN Case Study

National Institute of Aeronautics and Space of Indonesia is obliged to organize remote sensing data storage and distribution through National Remote Sensing Data Bank (BDPJN). BDPJN is a node of the remote sensing data network in a spatial national data network system. BDPJN has the primary function of collecting, storing, distributing metadata and remote sensing data for Indonesia territory. Evaluation of data operation management is needed to guarantee the continuity of the remote sensing data service at BDPJN. It has to ensure that: the catalog can be accessed by users, the catalog is continuously updated with data stored in the BDPJN storage system, and the data is available when needed. This research is purposed to evaluate data operation management on BDPJN. It is implemented by measuring the maturity level of data operations management activities using the CMMI and DMBOK frameworks. The results indicated that BDPJN has ten activities that have met an expected level of maturity. However, five of fifteen activities have below maturity level 3. Three activities at maturity level 1 are Monitor and Tune Database Performance, Plan for Data Retention, Archive, Retain, and Purge Data. The two others are Define the Data Technology Architecture and Evaluate Data Technology, which is at maturity level 2. Recommendations have been arranged based on references from DMBOK for these five activities.
Centralised and Decentralised Control of Video Game Agents

In this paper, the game of partially observable Ms. Pacman is used as a sandbox to evaluate Artificial Neural Networks (ANNs) that control multiple opponents (i.e. the ghosts). Comparisons between one central ANN that controls all ghosts, and multiple distinct ANNs, each controlling one ghost, are made. The NEAT algorithm is employed to evolve the ANNs. We find that chasing Ms. Pacman and exploring the map are both harder behaviours to learn for a centralised controller than for decentralised control. Further, both centralised and decentralised approaches produce vastly different behaviours for exploring the map. Novel techniques for comparing networks are also explored.
Forking Belief in Cryptocurrency

When the community of believers in a cryptocurrency splits into two, the currency may experience a “hard fork” and split into two independent currencies. Indeed, like fiat currencies, cryptocurrencies only have value if people believe they have value and are willing to use them in transactions. Hard forks do not create new value unless they inspire new belief; otherwise, they merely split the value of the original currency between the two new currencies. The Internal Revenue Service is wrong to conclude in Revenue Ruling 2019–24 that the value of the new currency resulting from a hard fork constitutes gross income inthe hands of coin owners. A hard fork is properly understood as a division of each coin of the original currency into two resulting coins and is no more a taxable event than when a property owner subdivides a larger parcel of land into two smaller lots. The appropriate question is not whether there is income, but how the owner’s basis in the original property should be split between the two resulting parts. After delving into the nature of hard forks and exploring the governing law, the author suggests an approach for basis allocation.
Sequent Calculi for Orthologic with Strict Implication

In this study, new sequent calculi for a minimal quantum logic (\(\bf MQL\)) are discussed that involve an implication. The sequent calculus \(\bf GO\) for \(\bf MQL\) was established by Nishimura, and it is complete with respect to ortho-models (O-models). As \(\bf GO\) does not contain implications, this study adopts the strict implication and constructs two new sequent calculi \(\mathbf{GOI}_1\) and \(\mathbf{GOI}_2\) as the expansions of \(\bf GO\). Both \(\mathbf{GOI}_1\) and \(\mathbf{GOI}_2\) are complete with respect to the O-models. In this study, the completeness and decidability theorems for these new systems are proven. Furthermore, some details pertaining to new rules and the strict implication are discussed.
A Comparative Study of Collider Types &amp; Input Methods for Interaction with Nested Holograms

Mixed reality-based medical holography is expected to have powerful applications in many surgical fields through easy and intuitive interaction with anatomic holograms using gestures. However, interaction with nested holograms using default gesture interaction, as is the case in visualized complex anatomic structures, is found to be challenging. This work develops, implements, and compares two modes of interaction with nested holograms, using native gestures and 3D SpaceMouse. Each of this mode uses ray casting and either Box Collider (BC) or Mesh Collider (MC) techniques to select target hologram. The use of SpaceMouse is found to have superior control over the selection of nested holograms irrespective of the mode of interaction, while a combination of native gestures and BC mode seems to most improve the efficiency of interaction of a selected nested hologram.
Application of Intelligent Recommendation for Agricultural Information: A Systematic Literature Review

With the ever-growing volume of online information, recommender system has been an effective strategy to overcome such information overload. Recommender systems are widely used in many web applications, such as e-commerce, news, agriculture and other fields. Agricultural informatization is an important driving force for the development of agricultural modernization. With the further improvement of agricultural informatization infrastructure construction, the use of modern information technology to achieve personalized agricultural information resource recommendation services and providing users with timely and effective information has become an effective solution. This article aims to provide a comprehensive review of recent research efforts on application of agricultural information based on intelligent recommender systems. Firstly, the method of content analysis used in this article to sort out the papers is introduced. Secondly, the background concepts of recommender systems and the key technologies are presented. Thirdly, the applications of recommender systems/technologies for agricultural information are described in detail. Finally, a summary and outlook on the application of recommender systems for agricultural information are provided.
A Novel Encryption/Decryption Framework for Ultrasonic Secure Video Transmission

Ultrasonic communication is proven to be reliable for transmitting data, images, and video streams through solid channels to overcome certain physical barriers which prevent conventional wired or wireless communication networks. In this study, we propose a novel encryption framework for securing the video stream transmission while taking into consideration the characteristics and constraints associated with ultrasonic communication through solid channels. In this framework, a Chaotic encryption algorithm employing the 2-D Around Cat Map and 1-D Logistic Map is used to confuse and diffuse the video stream. A benchmark system was designed to examine the performance of the Chaotic encryption algorithm applied to ultrasonic video transmission. The horizontally adjacent pixels in a video frame and the same pixel in two consecutive frames are randomly selected to evaluate the frame-to-frame pixel correlation before and after encryption. The scatter plots and the correlation coefficient reveal that the Chaotic encryption fully scrambles and diffuses the original video stream.
Towards Portuguese Sign Language Identification Using Deep Learning

In Portugal there are above 80,000 people with hearing impairment with the need to communicate through the sign language. Equal opportunities and social inclusion are the major concerns of the current society. It is aim of this research to create and evaluate a Deep Learning model that using a dataset with images of characters in Portuguese sign language can identify the gesture of a user, recognizing it. For model training, 5826 representative samples of the characters 'C', 'I', 'L', 'U' and 'Y' in Portuguese sign language. The Deep Learning model is based on a convolutional neural network. The model evaluated using the sample allowed for an accuracy of 98.5%, which is considered as a satisfactory result. However, there are two gaps: the existence of datasets with the totality of the alphabet in the Portuguese sign language and with the various representations of movement that each word has at the layout of letters. Using the proposed model with more complete datasets would allow to develop more inclusive user interfaces and equal opportunities for users with auditory difficulties.
Wrong Answers for Wrong Reasons: The Risks of Ad Hoc Instruments

To evaluate novel pedagogies, approaches, and tools, Computer Science Education researchers often conduct experiments to look for differences among groups treated with different interventions. The methodological rigor of such experiments affects the soundness of the conclusions the researchers can draw. In this paper we focus on a central aspect of such experimental research: the instruments used to assess participants’ knowledge. Specifically, we study the use of ad hoc instruments and the risks due to their insufficient validation. We present a literature survey that highlights how, even though standardized instruments exist, the majority of published experiments in the last five years at major Computer Science Education conferences carries out pre/post-tests using ad hoc instruments, often with multiple-choice as question type. We demonstrate the risks of such commonly used but insufficiently validated multiple-choice instruments. We propose a richer way to analyze and assess the correctness of answers to multiple-choice questions, requiring participants to add brief explanation texts as a justification of each answer. We run an experiment and analyze the collected answers using the two approaches, with and without explanations, to show that the risk of drawing opposite conclusions from the statistical analysis is real.
Parallel Dot Products Using Silicon Photonics

This paper proposes a parallel photonic architecture for computing dense dot products, such as those found during deep neural network (DNN) inference, and quantifies the architecture's computation error induced by crosstalk and noise.
Adaptive Low-Pass Filtering using Sliding Window Gaussian Processes

When signals are measured through physical sensors, they are perturbed by noise. To reduce noise, low-pass filters are commonly employed in order to attenuate high frequency components in the incoming signal, regardless if they come from noise or the actual signal. Therefore, low-pass filters must be carefully tuned in order to avoid significant deterioration of the signal. This tuning requires prior knowledge about the signal, which is often not available in real-world applications. In order to overcome this limitation, we propose an adaptive low-pass filter based on Gaussian process regression. By considering a constant window of previous observations, updates and predictions fast enough for real-world filtering applications can be realized. Moreover, the online optimization of hyperparameters leads to an adaptation of the low-pass behavior, such that no prior tuning is necessary. We show that the estimation error of the proposed method is uniformly bounded, and demonstrate the flexibility and efficiency of the approach in several simulations.
Dynamic Graph Convolutional Network: A Topology Optimization Perspective

Recently, graph convolutional networks(GCNs) have drawn increasing attention in many domains, e.g., social networks, recommendation systems. It's known that, in the task of graph node classification, inter-class edges connecting nodes from different categories often degrade the GCN model performance. On the other hand, a stronger intra-class connection in terms of the edge number and edge weights is always beneficial to node classification. Most existing GCN models assume that the topology and edge weights of the underlying graph are both fixed. However, real-world networks are often noisy and incomplete. To take into account such uncertainty in graph topology, we propose in this paper a dynamic graph convolution network (DyGCN), where edge weights are treated as learnable parameters. A novel adaptive edge dropping (AdaDrop) strategy is developed for DyGCN, such that even graph topology can be optimized. DyGCN is also a flexible architecture that can be readily combined with other deep GCN models to cope with the oversmoothness encountered when the network goes very deep. Experimental results demonstrate that the proposed DyGCN and its deep variants can achieve competitive classification accuracy in many datasets.
Research on the Mechanism of Blockchain Technology to Equity Management Innovation

Aimed at the problems existing in domestic equity management, such as the difficulty of equity issuance, the complexity of equity incentive process, the low efficiency of equity transaction and the defects of internal equity management, this paper explores the mechanism of the application of blockchain technology such as decentralization, traceability, non-tamperable, distributed ledger, token and smart contract on equity management innovation, and put forward corresponding safeguard measures to promote the role of blockchain technology in equity management.
On the virtual reality technologies application to support decision-making

The article discusses the using of modern information technologies augmented and virtual reality issues to provide support for decision-making. The work relevance is determined by the fact that in dynamic spatially distributed man-machine systems control conditions, existing control methods and technologies do not always provide the required efficiency. At the same time, effective management is the key to any system success. In the article, based on a typical control cycle and errors analysis arising during its implementation, proposals for the models use formed by virtual reality means are synthesized to provide an algorithm for converting the controlled system current state with the required one, planned to achieve the goal. The solution to the formulated problem will provide a general increase in management efficiency both by increasing the managers' information awareness and by simplifying the formation and control actions adjustment by calculations and modelling means.
Grounding Psychological Shape Space in Convolutional Neural Networks

Shape information is crucial for human perception and cognition, and should therefore also play a role in cognitive AI systems. We employ the interdisciplinary framework of conceptual spaces, which proposes a geometric representation of conceptual knowledge through low-dimensional interpretable similarity spaces. These similarity spaces are often based on psychological dissimilarity ratings for a small set of stimuli, which are then transformed into a spatial representation by a technique called multidimensional scaling. Unfortunately, this approach is incapable of generalizing to novel stimuli. In this paper, we use convolutional neural networks to learn a generalizable mapping between perceptual inputs (pixels of grayscale line drawings) and a recently proposed psychological similarity space for the shape domain. We investigate different network architectures (classification network vs. autoencoder) and different training regimes (transfer learning vs. multi-task learning). Our results indicate that a classification-based multi-task learning scenario yields the best results, but that its performance is relatively sensitive to the dimensionality of the similarity space.
A Subtle Effect of Inducing Positive Words by Playing Web-Based Word Chain Game

A variety of chatbots were developed to elicit people’s emotional responses and manage the emotions of people. These chatbots often use text-based emotional sentences. However, there are few studies that chatbots use extremely simplified communications to induce some emotional responses. We investigated the effect of mood contagion and tried to induce positive or negative words from people on our web-based ”word chain game” system as the most simplified conversational style. We also defined the system’s emotion as making the system use words that have polarity, which means each word’s strength of positive or negative emotion. The result shows that users of the system using positive words were slightly influenced to use more positive words than the neutral or negative system.
A method for the comparison of general sequences via type-token ratio

This article proposes a new method for analyzing and comparing general linear sequences with the minimum prior knowledge on the sequences needed. Sequence analysis is a broad problem studied by various fields from sociology and computer security to linguistics or biology. The method presented here applies the simplest quantitative linguistic tools in order to achieve methods transparency and easily interpretable results. The results form a vector describing the sequence and allow their clustering, machine learning and simple visualizations by line charts or multidimensional methods as MDS or tSNE. For completeness, artifacts and several formal models are derived to describe methods behavior in both common and extreme cases.
The Models for Knowledge Acquisition in PMI Specific Requirements Engineering

Knowledge acquisition is an important part of any requirements engineering activity, as it builds the foundation for all decisions and actions performed. In PMI activities knowledge acquisition is equally important and more complicated as in requirements engineering in general. This leads us to the conclusion that PMI initiative should have an additional focus on the knowledge acquisition. This paper explores requirements elicitation for PMI as a knowledge acquisition process and proposes models for its accomplishment. It covers the following research steps - required characteristics of an effective PMI requirements elicitation are identified, corresponding existing research is analyzed, possible adjustments to the previously presented requirements elicitation model are identified, enhanced knowledge acquisition model is proposed, and the proposed model is illustrated with a real application example. As a result of this research the adjusted and detailed PMI specific knowledge acquisition model for PMI specific requirements engineering has been created that consists of two parts – the knowledge acquisition process model and the knowledge acquisition data model.
Comprehensive geophysical methods and core digitization: a case study of a polymetallic mining area in Northeast China

In the whole process of geological prospecting, based on the principles of fine design, fine construction, and fine interpretation and the technical route of “expanding on the surface, constructing on points, combining points and surfaces, and focusing on the breakthrough”, more and more geophysical prospecting methods are comprehensively applied in the work of geological prospecting, because a single geophysical method is very limited for following interpretation. For example, the geology, geochemical exploration, anti-interference electrical method, gravity, magnetic method are carried out to optimize multiple potential ore-forming favourable ore sites. By summarizing the collected geological data and a large amount of basic geological work and physical and chemical exploration work, the survey results can be obtained by using a comprehensive geophysical method. In this paper, an application of the comprehensive geophysical method on geological survey by a case study in a China mining area.
Fuzzy Control of Electric Wheelchair with Intuitive User Interface for Soil Cultivation

In recent years, many Robot Technology (RT) and Information and Communication Technology (ICT) have been introduced into the agricultural field. Many of them are aiming to automate tasks such as harvesting strawberries. However, there are many challenges in fully automating such tasks with robots. For example, simplifying the operation of the robot is challenging, and the high initial cost for introducing RT and ICT technologies becomes a barrier for farmers to install them. In the case of soil cultivation, a worker has to bend forward at the waist and move sideways. A wheelchair to reduce the burden of this work on the body is commercially available. However, this chair has only passive wheels, so the worker must move it on their legs. In this study, we developed an electric wheelchair with an intuitive user interface. The chair is equipped with a pressure sensor on the seat and wheels with a motor. Using the pressure sensor, we calculate the direction in which the passenger wants to move from the weight shift of the passenger, and then the chair assists the passenger in the calculated direction. We used Fuzzy control to assist the movement of the passenger to suit the passenger preference of the assistance. To show the effectiveness of the electric wheelchair, we compared the difference of the surface EMG on the lateral great muscle of both legs during the movement on a flat ground using between the proposed electric wheelchair and a passive wheelchair.