Reviewerly/exHarmony huggingface dataset:
Representing Experts' Interpretive Trails with Hyperknowledge Specifications

Representing users' creative and interpretive processes may be useful to identify problems and solutions associated with interactive decision-making processes. Generally, these processes are related to the interaction of users with some multimedia content (text, video, audio, images, etc.) and structuring users' tacit and explicit knowledge. Tracking such process should generate a representation of users' trails in a linear construction of time. However, this representation is generally not well structured from a knowledge engineering perspective. Considering highly immersive environments with interaction through multiple modalities, tracking this knowledge becomes even more complex. On the one hand, cognitive agents have been increasingly used to support decision-making practices, which may involve knowledge-intensive activities and critical thinking. On the other hand, these systems may demand an overly complex design and implementation given the lack of knowledge representations capable of describing steps of creative processes, including rich relationships between symbolic and non-symbolic data. Inferring human interpretation and knowledge in such a representation in these processes are fundamental to improve the design of systems that support decision-making, as well as general systems. In this work, we propose applying high-level conceptual components in a knowledge representation to explicitly characterize users' interpretive trails. The solution aims at supporting the representation of the multimedia data and knowledge sources someone interacted with, along with the sequence of steps expressing their interpretation strategies.
Spatial photonic Ising machine with thousands of interacting spins

We propose and experimentally demonstrate the use of spatial light modulation for calculating the ground state of an Ising Hamiltonian. We realize configurations with thousands of interacting spins that settle in a low-temperature ferromagnetic-like phase.
LibGuides: Ithaka S+R Faculty Survey: Stratifications

This guide presents data gathered from the 2018 Ithaka S+R Local Faculty Survey fielded by the A-State Library
Distributive laws for monotone specifications

Turi and Plotkin introduced an elegant approach to structural operational semantics based on universal coalgebra, parametric in the type of syntax and the type of behaviour. Their framework includes abstract GSOS, a categorical generalisation of the classical GSOS rule format, as well as its categorical dual, coGSOS. Both formats are well behaved, in the sense that each specification has a unique model on which behavioural equivalence is a congruence. Unfortunately, the combination of the two formats does not feature these desirable properties. We show that monotone specifications-that disallow negative premises-do induce a canonical distributive law of a monad over a comonad, and therefore a unique, compositional interpretation.
Optimal model for a set of Markov processes

In this paper we develop a model selection procedure for the joint modeling of multiple Markov processes over the same finite alphabet A. The methodology is based on the Bayesian Information Criterion (BIC) by Schwarz [5]. The procedure consists on finding the values on the state space of two or more processes sharing the same transition probabilities. This identification allows us to build a partition of the set of state spaces, such that two states are in the same part if and only if they have the same transition probabilities. Formulating the joint model in this way, the number of parameters is minimized since for each part we need (|A| − 1) parameters. The joint model developed here is a generalization of the partition Markov models by García and González-López [1], [2], [3] and [4].
RETRACTED: A semantic interpretations of citation as assumptions

Abstract Citation is specifically a problem in the world of publication after being used as a benchmark for ranking the writing quality, or indirectly ranking the author. This paper aims to reveal some of the assumptions interpreted from several data sets about citations, especially self-citations. Self-citation becomes a special problem when it becomes a measure for the performance of countries, journals, institutions, and authors. So far, there are links between assumptions about self-citation for the country, journal, and author.
Difference bases in finite Abelian groups

A subset $B$ of a group $G$ is called a difference basis of $G$ if each element $g\in G$ can be written as the difference $g=ab^{-1}$ of some elements $a,b\in B$. The smallest cardinality $|B|$ of a difference basis $B\subset G$ is called the difference size of $G$ and is denoted by $\Delta[G]$. The fraction $\eth[G]:=\frac{\Delta[G]}{\sqrt{|G|}}$ is called the difference characteristic of $G$. Using properies of the Galois rings, we prove recursive upper bounds for the difference sizes and characteristics of finite Abelian groups. In particular, we prove that for a prime number $p\ge 11$, any finite Abelian $p$-group $G$ has difference characteristic $\eth[G]<\frac{\sqrt{p}-1}{\sqrt{p}-3}\cdot\sup_{k\in\mathbb N}\eth[C_{p^k}]<\sqrt{2}\cdot\frac{\sqrt{p}-1}{\sqrt{p}-3}$. Also we calculate the difference sizes of all Abelian groups of cardinality $<96$.
TopSpin: TOPic Discovery via Sparse Principal Component INterference

We propose a novel topic discovery algorithm for unlabeled images based on the bag-of-words (BoW) framework. We first extract a dictionary of visual words and subsequently for each image compute a visual word occurrence histogram. We view these histograms as rows of a large matrix from which we extract sparse principal components (PCs). Each PC identifies a sparse combination of visual words which co-occur frequently in some images but seldom appear in others. Each sparse PC corresponds to a topic, and images whose interference with the PC is high belong to that topic, revealing the common parts possessed by the images. We propose to solve the associated sparse PCA problems using an Alternating Maximization (AM) method, which we modify for the purpose of efficiently extracting multiple PCs in a deflation scheme. Our approach attacks the maximization problem in SPCA directly and is scalable to high-dimensional data. Experiments on automatic topic discovery and category prediction demonstrate encouraging performance of our approach. Our SPCA solver is publicly available.
Theoretical Security Evaluation Against Side-Channel Cube Attack with Key Enumeration

Side-channel cube attack (SCCA) is executed in a situation where an adversary can access some information about the internal states of the cipher. The adversary can obtain a system of linear equations by a set of chosen plaintexts called cube and recover the secret key using the system. Error tolerance is a challenging problem in SCCA. To recover the secret key based on likelihoods under an error-prone environment, we propose SCCA with key enumeration (SCCA-KE). Precise likelihoods are computed to obtain lists for sub-key candidates and an optimal list for the complete key candidate is generated by key enumeration. Then, we propose an evaluation method for SCCA-KE which includes information-theoretic evaluation and experimental evaluation by rank estimation. We apply the proposed evaluation method to PRESENT and show some conditions required to thwart SCCA-KE in realistic assumptions. Using the evaluation method, we can consider countermeasures with a sufficient security margin.
A Survey of Chinese Character Style Transfer

The transfer of Chinese character style is a method of transferring the original character style to other target characters written in different styles and generating the target characters with similar character styles as the original characters. This paper deeply analyzes the related research of character style transfer, summarizes the principle and main methods of character style transfer, and emphatically analyzes the latest progress of the in-depth learning method in the aspect of character style transfer. We finalize the report by the problems to be solved in this field and the future research direction.
Leveraging Domain Context for Question Answering over Knowledge Graph

This paper focuses on the problem of question answering over knowledge graph (KG-QA). With the increasing availability of different knowledge graphs in a variety of domains, KG-QA becomes a prevalent information interaction approach. Current KG-QA methods usually resort to semantic parsing, retrieval or neural matching based models. However, current methods generally ignore the rich domain context, i.e., category and surrounding descriptions within the knowledge graphs. Experiments shows that they can not well tackle the complex questions and information needs. In this work, we propose a new KG-QA approach, leveraging the domain context. The new method designs a neural cross-attention QA framework. We incorporate the new approach with question and answer domain contexts. Specifically, for questions, we enrich them with users' access log, and for the answers, we equip them with meta-paths within the target knowledge graph. Experimental study on real datasets verifies its improvement. The new approach is especially beneficial for domain knowledge graphs.
DFR-net: Learning Dense Features at the Resolution Level

Patch matching is a significant stage in numerous computer vision tasks. This paper proposed a novel network structure, named DFR-net, appropriate to match patches. The proposed network uses a dense connectivity pattern at the resolution level, making the training efficient. This connectivity pattern has been shown improving the accuracy of patch matching. The DFR-net, with a single-tower architecture, focused on the relationship between (non-)corresponding patches, which improved the performance of the traditional Siamese network. The component of DFR-net, named RDCNet block, produces a smaller model size and is demonstrated suiting for patch matching. To ensure the experimental effectiveness, the DFR-net was trained on the public Brown patch dataset and the HPatches dataset.
Customized Visualization of Email Using Sentimental and Impact Analysis in R

In our modern world of social interactions where the analysis of each content on social media is based on the impact of the sentiment it imposes on the forum. The proposed system is used to implement a more personalized and customized report of these impacts. Email is of main focus here where in out of all other social applications, responses to and from the email is the most traditional and ethical way to communicate online. Users share all information, through internet especially emails because of its fast transmission and is considered as the most professional medium. Hence the proposed model focus more on the subjective content of email processed on R libraries created for Natural language processing in a more customized way. Nowadays, crime rate in emails are increasing drastically. Spamming, phishing and email fraudulent are the ways of targeting common people. The sentimental analysis on the impact of the email received is analysed and visualized. The system also proposes a design for establishing a framework that detects the suspicious one by comparing the mail with keywords and also reveals the level of suspiciousness in the particular mail.
The Discourse Structure Recognition Model Based on Text Classification

Discourse structure recognition is an important aspect of the discourse structure rationality research. However, the assessment of discourse structure recognition usually suffer from the problems such as poor theory and lack of professional Chinese discourse structure corpus. We propose a discourse structure recognition model that consisted by the feature extraction method of “part-of-speech ratio & variance & Doc2vec” and three text classification methods that are Conditional Random Field (CRF), Support Vector Machine (SVM) and Naive Bayes for identifying discourse structure composition. We did two experiments to test the precision of the model and the highest precision reached 70.01%. What’s more, we did an experiment to study the effects of five types of paragraph labels on the model and the highest precision reached 82.13%. All experiments uses our own composition corpus of primary and secondary school students. Experimental results on datasets demonstrate that our model can be feasible and effective on research on discourse structure composition.
The Science We Need in Order to Achieve Progress in the Social Development

Abstract The aim of this study is to gather sufficient evidence and arguments in order to prove to society that during the process of change in thinking and especially in the effective practical implementation of such educational models, high levels of efficiency can be achieved at all hierarchical levels in key systems for the society and the state.
Effective model of the market prices forecasting using neural networks

The article highlights the relevance of prices forecasting, as well as the effectiveness of the proposed method “Identification model of high quality”, both at the stage of construction and for the final comparison. This study shows that the proposed method allows to effectively determine the most qualitative model. Also, in the article it is revealed that the model based on neural networks, with the adaptive inclusion of data in the training sample, allows predicting prices in the market more accurately.
DFRWS IoT Forensic Challenge Report 1

In this chapter, we report on the findings of our group’s analysis of the Digital Forensic Research Workshop (DFRWS) IoT Forensic Challenge (2018–2019).
The Cognitive Role in Human Performance Computer-Assisted Control Training and Training Remanence Related Case-Study

In this paper, it is described the inferences on cognitive implications using computer-assisted human performance training. Based on existent bio-feedback-like methodologies used for psycho-neuro-motor control training, there are suggested models to be investigated in order to understand and intentionally control the training mechanism and cognitive processes implied in the improvement of the human performance. Moreover, there is tackled a case study assessing the training remanence aspect on short term.
Illegal Access to a Computer System from the Standpoint of the Current Criminal Code

Abstract One of the forms that cybercrime can take at present is illegal access to a computer system. From the very beginning, the world of computers and of the Internet was based on imperfections, defects, and sometimes on poorly understood processes. We might even call this fact “the original sin” of the Internet. In the end, it is not only computer scientists who have come to exploit such defects, but also criminals. In the real world, there are people who break into homes and take away everything they find valuable. In the virtual world, there are individuals who penetrate into computer systems and steal all valuable data.
Semantic Memory Learning in Image Retrieval Using k Means Approach

To reduce the conceptual gap in content-based image retrieval (CBIR) and small training problem in relevance feedback (RF), this paper attempts to focus on the semantic memory learning in image retrieval using proposed 2-means clustering. In this system, initial retrieval results of CBIR are obtained, and then user’s opinion is given to the system as relevant/irrelevant to the user. With this user feedback, we can easily make the relevant image cluster and the irrelevant image cluster directly instead of random selection. Hence with initial known clusters and number of clusters, computational time is highly reduced for finding cluster center. We have also reduced the burden of clustering by considering only relevant cluster repeatedly for each feedback iteration. We experimented on two different data sets using proposed system. Results are found better compared to the earlier approaches.
Puppeteered rain

Light projection onto falling water produces distinct and impressive experience which is suitable for entertainment and advertising installations in public spaces [Barnum et al. 2010; Eitoku et al. 2006]. One of popular and classical techniques used in illuminating water for such purposes is strobe lighting, which presents optical illusion of levitating --- or slowly falling or rising --- water drops depending on the relation between water dropping and strobe lighting frequencies (e.g. [Pevnick 1981; Rosenthal 1984]).
The characteristics and formation of black shale-hosted Bakyrchik-type gold mineralisation

"The characteristics and formation of black shale-hosted Bakyrchik-type gold mineralisation." Applied Earth Science, 128(2), pp. 61–62
Minerals of oxidation zone of the Elenovskoe copper porphyry deposit, South Urals

Samples of oxidation zone of the Elenovskoe Cu porphyry deposit are studied using optical and scanning electronic microscopy. In addition to Fe hydroxides and abundant malachite, the supergene mineral assemblage includes native Cu and Au, Ag-containing covellite, cuprite, tenorite, plumbogummite, goyazite, iodargyrite, atacamite, and chrysocolla. The oxidized ores contains pyrite relics and high amount of tourmaline (dravite). Some dravite grains contain Cu.
Application of a Selective Desensitization Neural Network to Concept Drift Problems

A selective desensitization neural network (SDNN) has a high function-approximation ability, low hyperparameter dependence, and suitability for online incremental learning. These properties suggest that an SDNN can deal well with temporal changes in the characteristics of data, or concept drift, although this has not been verified. In this study, we conducted experiments on online learning using an artificial dataset generated using a time-varying function and a real-world dataset of a stock prices index, and evaluated the effectiveness of an SDNN to solve concept drift problems. The results show that using the SDNN exhibited superior performance over the existing methods for both datasets, suggesting that an SDNN is highly suitable for certain types of concept drift problems.
Comparing the Performance of Reverse Colussi and Raita in Finding Indonesian Text

Abstract The availability of various string-matching algorithm raises the concern of finding the one with the best performance. With the growth of digital content, especially a text-based content, using an optimal algorithm is crucial to decrease the cost of performing a task. There are numerous studies using a specific content to evaluate the performance of ones algorithm. In this paper, we chose two commonly used string-matching algorithm; Reverse Colussi and Raita and compare the performance of these algorithm in finding text in Indonesian language. We specifically use Indonesian songs lyrics as our samples. To perform the test, we build and text-based song finder Android application. Our test result shows that Raita outperformed Reverse Colussi in most of the cases. This finding is due to the requirement of Reverse Colusi algoritm to have pre-processing step before performing the search.
Research on the Construction of Underwater Platform Combat Deduction System Based on Service-Oriented and Multi-agent Technology

Object-Oriented intelligent modeling, model management, et al., are difficult problems in the designing and development of underwater platform combat deduction system. The command and control description model based on OODA loop depicted the business process of underwater platform combat deduction, using service oriented and Agent modeling technology, established an underwater platforms deduction system architecture based on service oriented and multi-Agent, effectively solve the problem of intelligence, reusing and extensibility in combat deduction modeling, and established program framework. The paper has reference value in the designing and development of underwater platforms deduction system.
Representation Techniques that Best Followed for Semantic Web - Web Mining

Web Mining is a Data Mining Technique used widely in mining billions of information from the World Wide Web (WWW) as faster as possible with the exact match of data. The huge information available in WWW with various formats, like: text format files, images, documents and other forms of data like structured, semi structured and unstructured forms. The amount of this information is increasing day by day. Data mining is the technique used to extract the data available in the internet. Web mining technique is used to determine and mine information from data sources related to web which are documents in web, contents in web, server logs and hyperlinks. The Semantic Web is used to provide information in a defined meaning that enhanced the interoperability between human and machines, which created the space for the machines to handle most of the decisions and tasks. This paper gives a brief idea regarding representation techniques that are best used in semantic web.
An Analysis Of Idiom Translation In “Let It Snow” Novel

This thesis entitled “An Analysis of Idioms Translation in “Let It Snow “ Novel”. The study focused to find the idioms used in the novel and to analyze types of translation strategy used in translating idioms . The object of this research is “Let It Snow” novel written by John Green, Maureen Johnson, and Lauren Myracle and translated into Indonesian by Rosemary Kesauly and Kristi Ardiana. This research used qualitative approach by collecting the idiom found in the novel and compared the source language text to the target language. The collected data to be analyzed based on Baker’s strategy of translating idiom. The result of the study shows that there are four translation strategies used in translation of idiom, which include paraphrase, using idiom of similar meaning and form, using idiom of similar meaning and dissimilar form, and omission of entire idiom. In this research, the most translation strategy used by the translators is paraphrase as it appears 42 times (66, 67%) from the whole data (64 idioms).Keyword: translation, idiom, translation strategy
Impact of Previous Reading Experiences on Effectiveness of e-Textbook

Individual learning experiences have been changed with new technology development. Digital media has become a more favorable format in both reading and learning. This study investigated the reasons behind contradictory results in past e-textbook adoption studies and proposed that previous reading experiences associated with age were related to the effectiveness of e-textbook usage in learning. Results reveal a positive relationship between previous e-textbook reading experience and learning outcomes for current middle school students and older undergraduate students. Implications of changes in reading behaviors were discussed and future research was suggested.
Confluence of CHR Revisited: Invariants and Modulo Equivalence

Abstract simulation of one transition system by another is introduced as a means to simulate a potentially infinite class of similar transition sequences within a single transition sequence. This is useful for proving confluence under invariants of a given system, as it may reduce the number of proof cases to consider from infinity to a finite number. The classical confluence results for Constraint HThe invariant is formalizedandling Rules (CHR) can be explained in this way, using CHR as a simulation of itself. Using an abstract simulation based on a ground representation, we extend these results to include confluence under invariant and modulo equivalence, which have not been done in a satisfactory way before.