Reviewerly/exHarmony huggingface dataset:
Treewidth and Counting Projected Answer Sets

In this paper, we introduce novel algorithms to solve projected answer set counting . #PAs asks to count the number of answer sets with respect to a given set of projection atoms, where multiple answer sets that are identical when restricted to the projection atoms count as only one projected answer set. Our algorithms exploit small treewidth of the primal graph of the input instance by dynamic programming (DP). We establish a new algorithm for head-cycle-free (HCF) programs and lift very recent results from projected model counting to #PAs when the input is restricted to HCF programs. Further, we show how established DP algorithms for tight, normal, and disjunctive answer set programs can be extended to solve #PAs. Our algorithms run in polynomial time while requiring double exponential time in the treewidth for tight, normal, and HCF programs, and triple exponential time for disjunctive programs. Finally, we take the exponential time hypothesis (ETH) into account and establish lower bounds of bounded treewidth algorithms for #PAs. Under ETH, one cannot significantly improve our obtained worst-case runtimes.
ICT-Pedagogy Integration in Elementary Classrooms: Unpacking the Pre-service Teachers’ TPACK

This study aimed to investigate the Technological Pedagogical Content Knowledge (TPACK) self-efficacy and ICT integration skills of elementary pre-service teachers in elementary classrooms. The respondents were fifty-two (52) elementary pre-service teachers enrolled in the student teaching program at the Central Luzon State University. Results revealed that most of respondents perceived themselves to be highly proficient in all domains of TPACK framework: Technology Knowledge (TK), Content Knowledge (CK), Pedagogical Knowledge (PK), Pedagogical Content Knowledge (PCK), Technological Content Knowledge (TCK), Technological Pedagogical Knowledge (TPK) and Technological Pedagogical Content Knowledge (TPCK). Most of them were found to be good in integrating ICT in classroom instruction, particularly in terms of planning and implementation.Respondents’ GPA in Educational Technology and ICT-related courses was found to have negative significant relationship with their planning and implementation of ICT-Integrated instruction. Their TPACK self-efficacy had a highly significant relationship with their planning and implementation of ICT-integrated instruction.
Tibetan Multi-Dialect Speech Recognition Using Latent Regression Bayesian Network and End-To-End Mode

We proposed a method using latent regression Bayesian network (LRBN) to extract the shared speech feature for the input of end-to-end speech recognition model. The structure of LRBN is compact and its parameter learning is fast. Compared with Convolutional Neural Network, it has a simpler and understood structure and less parameters to learn. Experimental results show that the advantage of hybrid LRBN/Bidirectional Long Short-Term Memory-Connectionist Temporal Classification architecture for Tibetan multi-dialect speech recognition, and demonstrate the LRBN is helpful to differentiate among multiple language speech sets.
Visual Instance Retrieval with Deep Convolutional Networks

This paper provides an extensive study on the availability of image representations based on convolutional networks (ConvNets) for the task of visual instance retrieval. Besides the choice of convo ...
One-Step, Three-Factor Passthought Authentication With Custom-Fit, In-Ear EEG

In-ear EEG offers a promising path toward usable, discreet brain-computer interfaces (BCIs) for both healthy individuals and persons with disabilities. To test the promise of this modality, we produced a brain-based authentication system using custom-fit EEG earpieces. In a sample of N = 7 participants, we demonstrated that our system has high accuracy, higher than prior work using non-custom earpieces. We demonstrated that both inherence and knowledge factors contribute to authentication accuracy, and performed a simulated attack to show our system's robustness against impersonation. From an authentication standpoint, our system provides three factors of authentication in a single step. From a usability standpoint, our system does not require a cumbersome, head-worn device.
Unified Aspect Search Algorithm

Man does the studying of systems by modelling it. Examining parameters which are describing the operation of the system is important when creating the model. The parameters required for the test are retained and the irrelevant parameters are discarded. The remainder of the model determines the aspect system of the examination. The system can be tested according to many of the disciplines. These aspect systems are not uniform. For this reason, the product of the examinations by different disciplines is difficult to compare. Placing examination on uniform basis could provide common foundation for a single modelling process. In this article an aspect search algorithm is created which is based on the philosophical topics. All disciplines can be derived from philosophy. Because of these examinations based on philosophy can be uniform. The results of the tests are comparable. The requirements for the examination can be standardized. In addition, this method makes easier to adapt the operation. For example, the operating principles of energetic, mechanical, IT and social organizations can be adapted to each other.
Image Hashing Using DWT-CSLBP

Center Symmetric Local Binary Pattern (CSLBP) is widely used in texture and object detection, but its utilization in image hashing is still limited.Image hashing is a powerful technique to identify whether image content is changed or preserved.Two main issues in image hashing are its compact length and its robust discrimination power.To deal with these issues, a robust image hashing method is proposed by combining features of CSLBP and Discrete Wavelet Transform(DWT).CSLBP is applied on LL band of DWT for compact length hash.Discrimination power of hashing is enhanced by weigh factor which comprises information from all sub bands of 1-Level DWT.All sub-band images of 1-Level DWT is combined to form secondary image.Local statistical features like Standard Deviation and Magnitude Average are calculated to obtain local weight factor MASD (Magnitude Average and Standard Deviation).Weighted LL-CSLBP histogram is constructed using this weight factor.Experimental results are demonstrated with Receiver Operating Characteristics (ROC) and Normalized Hamming Distance (NHD).The results shows that, the proposed scheme is robust against content preserving operations and sensitive to malicious operations.
A Deep Learning-Based Named Entity Recognition in Biomedical Domain

In the biomedical field, huge amounts of data have been produced day by day. These data drives the development of the biomedical area researches in so many ways. This paper mainly focusing on biomedical named entity recognition (NER) with the aim to enhance the performance through deep learning. Impressive results in natural language processing are made possible by deep learning techniques. Deep learning enables us to use them for NLP tasks and producing huge differences in accuracy compared to traditional methods. NER is a crucial initial step in information extraction in the biomedical domain. Here we use RNN, LSTM, and GRU on GENIA version 3.02 corpus and achieves an F score of 90%, which is better than the most state-of-the-art systems.
Complex Human–Object Interactions Analyzer Using a DCNN and SVM Hybrid Approach

Nowadays, with the emergence of sophisticated electronic devices, human daily activities are becoming more and more complex. On the other hand, research has begun on the use of reliable, cost-effective sensors, patient monitoring systems, and other systems that make daily life more comfortable for the elderly. Moreover, in the field of computer vision, human action recognition (HAR) has drawn much attention as a subject of research because of its potential for numerous cost-effective applications. Although much research has investigated the use of HAR, most has dealt with simple basic actions in a simplified environment; not much work has been done in more complex, real-world environments. Therefore, a need exists for a system that can recognize complex daily activities in a variety of realistic environments. In this paper, we propose a system for recognizing such activities, in which humans interact with various objects, taking into consideration object-oriented activity information, the use of deep convolutional neural networks, and a multi-class support vector machine (multi-class SVM). The experiments are performed on a publicly available cornell activity dataset: CAD-120 which is a dataset of human–object interactions featuring ten high-level daily activities. The outcome results show that the proposed system achieves an accuracy of 93.33%, which is higher than other state-of-the-art methods, and has great potential for applications recognizing complex daily activities.
Computing Minimal Projection Modules for $$\mathcal{ELH}^{r}$$ -Terminologies

For the development of large-scale representations of knowledge, the application of methodologies and design principles becomes relevant. The knowledge may be organized in ontologies in a modular and hierarchical fashion. An upper-level (reference) ontology typically provides specifications of requirements, functions, design or standards that are to be complied with by domain ontologies for a specific task on a lower level (task ontology) in the hierarchy. Verifying whether and how specifications have been implemented by a task ontology becomes a challenge when relevant axioms of the domain ontology need to be inspected. We consider specifications to be defined using entailments of certain queries over a given vocabulary. For selecting the relevant axioms from task ontologies, we propose a novel module notion called projection module that entails the queries that follow from a reference ontology. We develop algorithms for computing minimal projection modules of Description Logic terminologies for subsumption, instance and conjunctive queries.
CLOUD COMPUTING AND LOAD BALANCING

ABTRACTCloud Computing is utility software that has the ability to change the software company and making the software more attractive.Cloud Computing has altered the way IT world used to design software and hardware.The rise in web traffic and different services are increasing day by day making load balancing a big research topic.Cloud computing is technology which uses virtual machine as an alternative of physical machine to host, store and network the different components of computing.Load balancers are used for distributing loads to different virtual machines as an alternative of real machines in such a way that none of the machines gets burdened heavily or lightly.The load balancing needs to be done appropriately because disappointment in any one of the node can lead to inaccessibility of data.
The Challenges of Connecting Smart Home Health Sensors to Cloud Analytics

There is great potential to assess the well-being of older adults in their homes, using sensors. The data derived from these sensors can be used to create solutions that can improve the lives of the users and their family by providing knowledge of health and enable independence. The system architecture commonly proposed is based on sensors deployed in the residence to collect ambient information or information through interactive use. These sensors are then connected through the Internet to cloud services for archiving and processing that is typically based on data analytics and artificial intelligence. This paper specifically focuses on modeling these flows and presents the challenges associated with the demands they place on Internet connectivity services. The paper presents an architecture for the resulting implementation models required for these solutions to be able to be scaled in communities.
Constraint Programming for Association Rules

Discovering association rules among items in a dataset is one of the fundamental problems in data mining. It has recently been shown that constraint programming is a flexible way to tackle data mining tasks. In this paper we propose a declarative model based on constraint programming to capture association rules. Our model also allows us to specify any additional property and/or user's constraints on the kind of rules the user is looking for. To implement our model, we introduce a new global constraint, Confident, for ensuring the confidence of rules. We prove that completely propagating Confident is NP-hard. We thus provide a decomposition of Confident. In addition to user's constraints on the items composing body and head of the rules, we show that we can capture the popular minimal non-redundant property of association rules. An experimental analysis shows the practical effectiveness of our approach compared to existing approaches.
Framework for Web Application Vulnerability Discovery and Mitigation by Customizing Rules Through ModSecurity

In the current era, Digitization has taken day-to-day utilities starting from a cab to grossary on the internet. All the service providers heavily leverage IT and IT Services. Web Application plays a significant role in providing these services. While Digital opens infinite opportunities to increase business and enhance delivery, it also exposes business to an unseen world of cyber-attacks. To prevent the business from digital dysfunctioning, organizations pro-actively and continuously perform Vulnerability Assessments & Penetration Tests on their IT Assets (i.e. Web Applications, Network Devices, Servers, Security Devices, etc.). However, quite often such scans become time consuming and generate unreliable results and none of the tools can find out the effective vulnerability. However, with a set of combined tools in one framework, it is possible to increase the coverage of vulnerability issues. The aim of the research is to present two approaches: (1) Web application vulnerability discovery through a set of web application vulnerability scanners and its integration on the same framework (python as scripting engine) using multi-threading technique (to reduce the scan time). (2) Mitigation of the detected web application vulnerabilities by customizing configuration rules through web application firewall ModSecurity.
Machine Translation on a Parallel Code-Switched Corpus

Code-switching (CS) is the phenomenon that occurs when a speaker alternates between two or more languages within an utterance or discourse. In this work, we investigate the existence of code-switching in formal text, namely proceedings of multilingual institutions. Our study is carried out on the Arabic-English code-mixing in a parallel corpus extracted from official documents of United Nations. We build a parallel code-switched corpus with two reference translations one in pure Arabic and the other in pure English. We also carry out a human evaluation of this resource in the aim to use it to evaluate the translation of code-switched documents. To the best of our knowledge, this kind of corpora does not exist. The one we propose is unique. This paper examines several methods to translate code-switched corpus: conventional statistical machine translation, the end-to-end neural machine translation and multitask-learning.
Light Field Image Compression with Random Access

In light field compression, besides coding efficiency, providing random access to individual views is also a very significant factor. Highly efficient compression methods usually lack random access. Similarly, random access methods usually reduce the compression efficiency. To address this trade-off, a light field image encoding method is proposed in this paper which favors random access. In the proposed scheme 15×15 view images are divided into 25 independent 3×3 view images which are called Macro View Image (MVI). To encode MVIs, the central view image is used to compress its immediate neighboring view images using a hierarchical reference structure. To encode the central view of each MVI, the most central view image, along with the center of at most three MVIs, are used as the reference images for the disparity estimation. In addition, the proposed method enables the use of parallel computation to improve encoding/decoding time complexity. To reduce memory footprint in case a Region of Interest (ROI) is required, HEVC tile partitioning is used.
Band-limited Training and Inference for Convolutional Neural Networks

The convolutional layers are core building blocks of neural network architectures. In general, a convolutional filter applies to the entire frequency spectrum of the input data. We explore artificially constraining the frequency spectra of these filters and data, called band-limiting, during training. The frequency domain constraints apply to both the feed-forward and back-propagation steps. Experimentally, we observe that Convolutional Neural Networks (CNNs) are resilient to this compression scheme and results suggest that CNNs learn to leverage lower-frequency components. In particular, we found: (1) band-limited training can effectively control the resource usage (GPU and memory); (2) models trained with band-limited layers retain high prediction accuracy; and (3) requires no modification to existing training algorithms or neural network architectures to use unlike other compression schemes.
Combining parametric and nonparametric models for off-policy evaluation

We consider a model-based approach to perform batch off-policy evaluation in reinforcement learning. Our method takes a mixture-of-experts approach to combine parametric and non-parametric models of the environment such that the final value estimate has the least expected error. We do so by first estimating the local accuracy of each model and then using a planner to select which model to use at every time step as to minimize the return error estimate along entire trajectories. Across a variety of domains, our mixture-based approach outperforms the individual models alone as well as state-of-the-art importance sampling-based estimators.
Blockchain for Power Grids

Sharing information is an important part of regulating and maintaining efficient and safe power grids. This project's goal is to develop a way of using blockchain technology to share transaction information among different power grids in a secure, controlled, monitored, and efficient manner. The biggest concern regarding the data is integrity. By leveraging blockchain technology, the data will be reliable and resilient to attacks, such as man-in-the-middle and data spoofing attacks. The Hyperledger Fabric implementation provides a permissioned network in which power grids will act as nodes that maintain ledger information. By using a distributed ledger to validate transactions though the process of consensus, the system can share information in a manner that is more secure and transparent than traditional information sharing systems in which data is less secure and takes longer to validate. The additional layers of security and speed that Hyperledger technology provide help to prevent issues, such as power grid failures, that could stem from the latency or integrity issues involved with traditional methods of validating, processing, and reacting to shared data.
Cognition and the future of information security

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Cognitive science is the interdisciplinary, scientific study of the mind and its processes. It examines the nature, the tasks, and the functions of cognition (in a broad sense). Cognitive scientists study intelligence and behavior, with a focus on how nervous systems represent, process, and transform information. The goal of cognitive science is to understand the principles of intelligence with the hope that this will lead to better comprehension of the mind and of learning and to develop intelligent devices. The cognitive sciences began as an intellectual movement in the 1950s often referred to as the cognitive revolution. The information system security is critical issue for the organizations since it leads to big financial losses. The understanding of cyber security threats is not only an innovative requirement but also it is a conservative task. The rapid changes in technologies and services are major driving and leading concerns to the cyber security, requiring reassessment and renewal of standardized policies for counter measures to the resistant vulnerabilities. The main aim of this talk is to improve the understanding and perception of latest security threats, security counter measures, and the future trends of Information security based on using the cognitive science in many aspects related to the Cryptology discipline.
Supporting Software Engineering Research and Education by Annotating Public Videos of Developers Programming

Software engineering has long studied how software developers work, building a body of work which forms the foundation of many software engineering best practices, tools, and theories. Recently, some developers have begun recording videos of themselves engaged in programming tasks contributing to open source projects, enabling them to share knowledge and socialize with other developers. We believe that these videos offer an important opportunity for both software engineering research and education. In this paper, we discuss the potential use of these videos as well as open questions for how to best enable this envisioned use. We propose creating a central repository of programming videos, enabling analyzing and annotating videos to illustrate specific behaviors of interest such as asking and answering questions, employing strategies, and software engineering theories. Such a repository would offer an important new way in which both software engineering researchers and students can understand how software developers work.
Gradient nuclear norm minimization-based image filter

Designing an efficient filtering technique is an ill-posed problem especially for image affected from high density of noise. The majority of existing techniques suffer from edge degradation and texture distortion issues. Therefore, in this paper, an efficient weighted nuclear norm minimization (NNM)-based filtering technique to preserve the edges and texture information of filtered images is proposed. The proposed technique significantly improves the quantitative improvements on the low rank approximation of nonlocal self-similarity matrices to deal with the overshrink problem. Extensive experiments reveal that the proposed technique preserves edges and texture details of filtered image with lesser number of visual artifacts on visual quality. The proposed technique outperforms the existing techniques over the competitive filtering techniques in terms of structural similarity index metric (SSIM), peak signal-to-noise ratio (PSNR) and edge preservation index (EPI).