Reviewerly/exHarmony huggingface dataset:
Fine-Grained Access Control for Querying Over Encrypted Document-Oriented Database

This paper presents two security models for document-based databases which fulfill three security requirements that are confidentiality, querying over encrypted data, and flexible access control. The first model which we refer to as dynamic is based on a combination of CryptDB [16] and PIRATTE [15] concepts. While CryptDB consists of a proxy between one user and a database for encrypting and decrypting data according to user queries, PIRATTE refers to a proxy wherein encrypted files are shared using a social network between the number of users and the data owner with the files being decrypted using the proxy key on the user side. The second model which we refer to as static is based on CryptDB concepts as well as CP-ABE [6]. CP-ABE is public key encryption which offers fine-grained access control regarding encrypted data and set of attributes that describe the user who is able to decrypt the data provided within the ciphertext. These two models enhance CryptDB security while also helping with data sharing with multi-users using CP-ABE or PIRATTE concept that helps in verifying authentication on the database or application level.
Multichannel CNN with Attention for Text Classification

Recent years, the approaches based on neural networks have shown remarkable potential for sentence modeling. There are two main neural network structures: recurrent neural network (RNN) and convolution neural network (CNN). RNN can capture long term dependencies and store the semantics of the previous information in a fixed-sized vector. However, RNN is a biased model and its ability to extract global semantics is restricted by the fixed-sized vector. Alternatively, CNN is able to capture n-gram features of texts by utilizing convolutional filters. But the width of convolutional filters restricts its performance. In order to combine the strengths of the two kinds of networks and alleviate their shortcomings, this paper proposes Attention-based Multichannel Convolutional Neural Network (AMCNN) for text classification. AMCNN utilizes a bi-directional long short-term memory to encode the history and future information of words into high dimensional representations, so that the information of both the front and back of the sentence can be fully expressed. Then the scalar attention and vectorial attention are applied to obtain multichannel representations. The scalar attention can calculate the word-level importance and the vectorial attention can calculate the feature-level importance. In the classification task, AMCNN uses a CNN structure to cpture word relations on the representations generated by the scalar and vectorial attention mechanism instead of calculating the weighted sums. It can effectively extract the n-gram features of the text. The experimental results on the benchmark datasets demonstrate that AMCNN achieves better performance than state-of-the-art methods. In addition, the visualization results verify the semantic richness of multichannel representations.
Impact to information computer technology: computer competency of Tinajero high school teachers in Philippines

This study aims to describe the computer competency of high school teachers at Tinajero High School, in the Philippines, during the calendar year 2019. Specifically, it sought to identify and describe the teachers’ computer competency, strengths and weaknesses, and problems in MS Word, Excel, and PowerPoint. Most of the conducted studies focused on the computer competency of the students. Few studies addressed the computer competency level of the teachers. In this digital age, teachers are not exempted on learning he Microsoft application. Sooner or later, the world will be embracing a mode of instruction that uses more of computer than of those traditional ways. This study used a descriptive-survey type of study and a validated competency test from Module quizzes from Innovate: A Mentor’s Guide to ICT Foundation Competencies by Microsoft Philippines. Findings showed the teachers are highly competent on MS Word, competent on MS Excel, and MS PowerPoint. Insufficiency of the computer equipment was identified as the main problem. It is therefore recommended to further strengthen the ICT capability training being given to the teachers so that technology will be maximized in making the educative system more impactful to the learners.
A New Approach to Minimize Memory Requirements of Frequent Subgraph Mining Algorithms

Frequent subgraph mining (FSM) is a subsection of graph mining domain which is extensively used for graph classification and graph clustering purposes. Over the past decade, many efficient FSM algorithms have been developed. The improvements generally focus on reducing time complexity by changing the algorithm structure or using parallel programming techniques. FSM algorithms have another problem to solve, which is the high memory consumption. In this study, a new approach called Predictive Dynamic Sized Structure Packing (PDSSP) have been proposed to minimize the memory requirement of FSM algorithms. Proposed approach redesigns the internal data structures of FSM algorithms without any algorithmic modifications. PDSSP has two contributions. The first one is the Dynamic Sized Integer Type (ds_Int) which is a newly designed unsigned integer data type. The second contribution is “Data Structure packaging” component that uses a data structure packing technique which changes the behaviour of the compiler. A number of experiments have been conducted to examine the effectiveness and efficiency of the PDSSP approach by embedding it into two state-of-art algorithms called gSpan and Gaston. Proposed implementation have been compared to the official one. Almost all results show that the proposed implementation consumes less memory on each support level. As a result, PDSSP extensions can save memory and the peak memory usage may decrease up to 38% depending on the dataset.
Development Principles for Electronic Voting System Using Distributed Ledger Technology

the paper reveals the prospects of using electronic trust services, in particular electronic signature services, for building electronic (remote) voting systems. The advantages of such systems are described, as well as problematic issues related to the implementation of remote voting systems in practice. The most well-known existing electronic voting protocols based on classical principles are analyzed. Their shortcomings and difficulties in implementation are highlighted. We offer a new principle for building an electronic voting system. Unlike the analyzed protocols, the new approach is based on a decentralized public key infrastructure. The paper shows how, with the help of DLT technology, it is possible to ensure the fulfillment of the necessary requirements for system security without introducing significant redundancy into the interaction process of all stakeholders. The electronic voting algorithm is a modification of the Nurmi-Salom-Santin protocol which avoids the need to organize special bodies for the formation of voter lists. It also allows maintaining the anonymity of voters without the use of blind signatures.
Dual-Mode NIZKs: Possibility and Impossibility Results for Property Transfer

This paper formulates, and studies, the problem of property transference in dual-mode NIZKs. We say that a property P (such as soundness, ZK or WI) transfers, if, one of the modes having P allows us to prove that the other mode has the computational analogue of P, as a consequence of nothing but the indistinguishability of the CRSs in the two modes. Our most interesting finding is negative; we show by counter-example that the form of soundness that seems most important for applications fails to transfer. On the positive side, we develop a general framework that allows us to show that zero knowledge, witness indistinguishability, extractability and weaker forms of soundness do transfer. Our treatment covers conventional, designated-verifier and designated-prover NIZKs in a unified way.
Evaluation of Side-Channel Key-Recovery Attacks on LoRaWAN End-Device

IoT devices have come into widespread use. The rapid growth of the IoT market is expected in the field of automobiles and transportation, medical and health care, and industry. Data protection and integrity are critical for IoT-based services in order to maintain the security and privacy of them. Low-power wide-area (LPWA) is a wireless communication technology designed for IoT applications and end-devices requiring low cost, long battery life, wide-area coverage, and high system capacity. LoRaWAN is an open standard for LPWA and achieves data protection and integrity by using encryption and message integrity code (MIC). Many studies have pointed out security issues, and attacks against LPWA protocols and have proposed solutions to improve security against such attacks. However, side-channel analysis techniques can directly recover secret information from a device. In this paper, we evaluate the applicability of a side-channel analysis to a real LoRaWAN end-device. Our experiments attempt to recover AES-128 keys to encrypt frame payload and calculate the message integrity code (MIC) for the encrypted payload based on a correlation power analysis, which is a type of side-channel analysis. The 260 electromagnetic(EM)-leakage traces entirely recover the 16-byte key for the frame payload encryption, and the 140 EM-leakage traces recover the 12 bytes of the 16-byte key for MIC generation. Furthermore, we show that our key recovery attack is applicable in real LoRaWAN protocols. Our attack can entirely recover the root key AppKey in LoRaWAN v1.0 and a root key NwkKey in LoRaWAN v1.1.
A Recommender System for Efficient Implementation of Privacy Preserving Machine Learning Primitives Based on FHE

With the increased dependence on cloud computing, there is growing concern for privacy of data that is stored and processed on third party cloud service providers. Of many solutions that achieve privacy preserving computations, fully homomorphic encryption (FHE) is a promising direction. FHE has several applications that can be used to perform computations on encrypted data without decrypting them. In this paper, we focus on realizing privacy preserving machine learning (PPML) using FHE. Our prime motivation behind choosing PPML is the increased use of machine learning algorithms on end-user’s data for predictions or classification, where privacy of end-user’s data is at stake. Given the importance of PPML and FHE, we formulate a recommender system that enables machine learning experts who are new to cryptography to efficiently realize a machine learning application in privacy preserving manner. We formulate the recommender system as a multi objective multi constraints optimization problem along with a simpler single objective multi constraint optimization problem. We solve this optimization using TOPSIS based on experimental analysis performed on three prominent FHE libraries HElib, SEAL and HEAAN from the PPML perspective. We present the observations on the performance parameters such as elapsed time and memory usage for the primitive machine learning algorithms such as linear regression and logistic regression. We also discuss the technical issues in making the FHE schemes practically deployable and give insights into selection of parameters to efficiently implement PPML algorithms. We observe that our estimates for matrix multiplication and linear regression correlate with the experimental analysis when assessed using an optimizer. The proposed recommendation system can be used in FHE compilers to facilitate optimal implementation of PPML applications.
Optimized low-loss integrated photonics silicon-nitride Y-branch splitter

Losses are the main detrimental factor that limit the scale of quantum photonic information processing circuits at which they can implement quantum algorithms effectively. We have designed a compact and low-loss Y-branch integrated photonic element for silicon-nitride waveguide. By finite difference time-domain (FDTD) numerical simulation, we have obtained the optimized geometry of the element with loss level of < 0.1 dB at 808 nm wavelength.
ESPN: Extremely Sparse Pruned Networks

Deep neural networks are often highly overparameterized, prohibiting their use in compute-limited systems. However, a line of recent works has shown that the size of deep networks can be considerably reduced by identifying a subset of neuron indicators (or mask) that correspond to significant weights prior to training. We demonstrate that an simple iterative mask discovery method can achieve state-of-the-art compression of very deep networks. Our algorithm represents a hybrid approach between single shot network pruning methods (such as SNIP) with Lottery-Ticket type approaches. We validate our approach on several datasets and outperform several existing pruning approaches in both test accuracy and compression ratio.
University of Illinois Submission to the SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection

Marc Canby, Aidana Karipbayeva, Bryan Lunt, Sahand Mozaffari, Charlotte Yoder, Julia Hockenmaier. Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology. 2020.
Quantum walk on a graph of spins: Magnetism and entanglement

We introduce a model of a quantum walk on a graph in which a particle jumps between neighboring nodes and interacts with independent spins sitting on the edges. Entanglement propagates with the walker. We apply this model to the case of a one-dimensional lattice to investigate its magnetic and entanglement properties. In the continuum limit, we recover a Landau-Lifshitz equation that describes the precession of spins. A rich dynamics is observed, with regimes of particle propagation and localization, together with spin oscillations and relaxation. Entanglement of the asymptotic states follows a volume law for most parameters (the coin rotation angle and the particle-spin coupling).
Generalizing Multi-Agent Path Finding for Heterogeneous Agents

Multi-Agent Path Finding (MAPF) is the problem of finding non-colliding paths for multiple agents. The classical problem assumes that all agents are homogeneous, with a fixed size and behavior. However, in reality agents are heterogeneous, with different sizes and behaviors. In this paper, we generalize MAPF to G-MAPF for the case of heterogeneous agents. We then show how two previous settings of large agents and k-robust agents are special cases of G-MAPF. Finally, we introduce G-CBS, a variant of the Conflict-Based Search (CBS) algorithm for G-MAPF, which does not cause significant extra overhead.
Multi-Channel Convolutional Neural Networks with Adversarial Training for Few-Shot Relation Classification (Student Abstract)

The distant supervised (DS) method has improved the performance of relation classification (RC) by means of extending the dataset. However, DS also brings the problem of wrong labeling. Contrary to DS, the few-shot method relies on few supervised data to predict the unseen classes. In this paper, we use word embedding and position embedding to construct multi-channel vector representation and use the multi-channel convolutional method to extract features of sentences. Moreover, in order to alleviate few-shot learning to be sensitive to overfitting, we introduce adversarial learning for training a robust model. Experiments on the FewRel dataset show that our model achieves significant and consistent improvements on few-shot RC as compared with baselines.
Hybrid Control of Trajectory Planning for Desired Trajectory and Collision Avoidance Based on Optimization Problem for a Wheeled Mobile Robot

This paper proposes a hybrid control of trajectory planning for a desired trajectory and collision avoidance based on an optimization problem for a wheeled mobile robot. In the proposed method, the priority of trajectory planning for the desired trajectory and collision avoidance is modified using the priority variable α. The priority variable α is designed by solving the optimization problem with constraints. As a constraint condition, the range of the priority variable α where the robot does not collide with multiple obstacles is derived. Under this constraint condition, the priority variable α with the highest priority of trajectory planning for the desired trajectory is selected as the optimal value. As a result, the velocity commands considering trajectory planning for the desired trajectory and collision avoidance are calculated in real time. The effectiveness of the proposed method is confirmed via experimental results.
Perception of Fisheries and Maritime High School Teachers for 'Good Teaching'

The purpose of the study is to find out the perception of teachers at fisheries and maritime high school about "good classes". To that end, a survey was conducted on 100 teachers of fisheries and maritime high schools. A survey of teachers" awareness, in-depth interviews and journal writing was conducted on 25 items comprised of categories of class perspectives, instructional content, instructional methods, instructional environment and atmosphere, and evaluation. The main findings are as follows. In the area of class perspective, "effective delivery of class content" and "building a reliable relationship between teachers and students" were chosen, while in the area of instructional content, "classes that clearly present class goals and students" were recognized as characteristics of good classes. In the instructional method area, ‘Class using class strategies that stimulate students interest and motivation’ was chosen, ‘Class with active student-centered collaborative interaction’ was recognized in the instructional environment and atmosphere area, and ‘Class that creates a reliable evaluation atmosphere for teachers and students’ in the evaluation area was recognized as good classes.
Combining visual analytics and case-based reasoning for rupture risk assessment of intracranial aneurysms

Medical case-based reasoning solves problems by applying experience gained from the outcome of previous treatments of the same kind. Particularly for complex treatment decisions, for example, incidentally found intracranial aneurysms (IAs), it can support the medical expert. IAs bear the risk of rupture and may lead to subarachnoidal hemorrhages. Treatment needs to be considered carefully, since it may entail unnecessary complications for IAs with low rupture risk. With a rupture risk prediction based on previous cases, the treatment decision can be supported.We present an interactive visual exploration tool for the case-based reasoning of IAs. In presence of a new aneurysm of interest, our application provides visual analytics techniques to identify the most similar cases with respect to morphology. The clinical expert can obtain the treatment, including the treatment outcome, for these cases and transfer it to the aneurysm of interest. Our application comprises a heatmap visualization, an adapted scatterplot matrix and fully or partially directed graphs with a circle- or force-directed layout to guide the interactive selection process. To fit the demands of clinical applications, we further integrated an interactive identification of outlier cases as well as an interactive attribute selection for the similarity calculation. A questionnaire evaluation with six trained physicians was used.Our application allows for case-based reasoning of IAs based on a reference data set. Three classifiers summarize the rupture state of the most similar cases. Medical experts positively evaluated the application.Our case-based reasoning application combined with visual analytic techniques allows for representation of similar IAs to support the clinician. The graphical representation was rated very useful and provides visual information of the similarity of the k most similar cases.
A Torus-Chaotic System and Its Pseudorandom Properties

Exploring and investigating new chaotic systems is a popular topic in nonlinear science. Although numerous chaotic systems have been introduced in the literature, few of them focus on torus-chaotic system. The aim of our short work is to widen the current knowledge of torus chaos. In this paper, a new torus-chaotic system is proposed, which has one positive Lyapunov exponent, two zero Lyapunov exponents, and two negative Lyapunov exponents. The dynamic behavior is investigated by Lyapunov exponents, bifurcations, and stability. The analysis shows that this system has an interesting route leading to chaos. Furthermore, the pseudorandom properties of output sequence are well studied and a random number generator algorithm is proposed, which has the potential of being used in several cyber security systems such as the verification code, secure QR code, and some secure communication protocols.
HYBRID CLOUD-ORIENTED LEARNING ENVIRONMENT FOR IT STUDENT PROJECT TEAMWORK

The article proposes a technology for the development and implementation of a hybrid cloud-oriented learning environment for the electronic support of interdisciplinary projects in the educational process of future IT specialists. To organize such a project, a model of hybrid cloud-oriented learning environment was designed. The components of such an environment are as follows: competence, communication and technology. Based on the developed model, a MS Teams-based cloud environment for project teamwork was created. It integrated all the necessary services for the implementation of an interdisciplinary project. The technological component regulates the content, methods and forms of training during the project implementation. According to the content of the interdisciplinary project, methods and forms of training, both traditional and cloud-oriented, were selected. This environment provided interaction between teachers of different disciplines and teams of students. The software component consists of tools for communication, project management and placement of the teaching resources included in MS Teams and complemented by additional tools that integrate with it, for example, professional tools and tools for presenting the results of work. Teachers could additionally integrate necessary e-learning courses and tools. Data analysis of experimental studies demonstrated that MS Teams-based cloud-oriented environment, which ensures execution of tasks on communication, organization of teaching process, project management and makes it possible to integrate additional tools for arranging an interdisciplinary project, namely, professional, educational and the like, – is an effective environment for the development of the digital, professional and personal competences of the future IT specialist. Moreover, interdisciplinary projects contribute to the development of integral competence of future IT specialists. An experimental study conducted on the basis of the National University of Life and Environmental Sciences involved 3rd year students of the specialty 122 – “Computer Science”.
Predicting Gaps in Usage in a Phone-Based Literacy Intervention System

Educational technologies may help support out-of-school learning in contexts where formal schooling fails to reach every child, but children may not persist in using such systems to learn at home. Prior research has developed methods for predicting learner dropout but primarily for adults in formal courses and Massive Open Online Courses (MOOCs), not for children’s voluntary ed tech usage. To support early literacy in rural contexts, our research group developed and deployed a phone-based literacy technology with rural families in Côte d’Ivoire in two longitudinal studies. In this paper, we investigate the feasibility of using time-series classification models trained on system log data to predict gaps in children’s voluntary usage of our system in both studies. We contribute insights around important features associated with sustained system usage, such as children’s patterns of use, performance on the platform, and involvement from other adults in their family. Finally, we contribute design implications for predicting and supporting learners’ voluntary, out-of-school usage of mobile learning applications in rural contexts.