Reviewerly/exHarmony huggingface dataset:
Web Service Recommendation Technology Based on Knowledge Graph Representation Learning

Abstract This paper proposed a recommendation algorithm based on knowledge graph representation learning (RABKGRL).The algorithm embeds the entities and relationships of the knowledge graph into the low-dimensional vector space. The relationship information of services is incorporated into the recommendation algorithm by calculating the distance between the service entities. The association between services that is not considered when using the collaborative filtering algorithm can be supplemented, and the recommendation effect is enhanced. The experimental results show that this algorithm can not only effectively improve the accuracy rate, recall rate and coverage rate of recommendation, but also solve the cold start problem to some extent.
A Comprehensive Framework for Understanding Security Culture in Organizations

Organizational security is exposed to internal and external threats, with a greater level of vulnerabilities coming from the former. Drawing upon findings from prior works as a foundation, this study aims to highlight the significant factors that influence the security culture within organizations. Phase one of the study reports upon an interview-based investigation undertaken with thirteen experienced, knowledgeable security specialists from seven organizations. The main findings confirmed the importance of the identified factors from the previous work. The focus to emerge from the interviews concludes that continuously subjecting employees to targeted training and awareness development improves security culture. Indeed, there was a clear lack of awareness and compliance regarding the implementation and clarity of security policies in organizations. Also, the inefficient training program and limit to specific employees in organizations leads to a lack of awareness and compliance.
Implementation of Cosine Similarity in an automatic classifier for comments

Classification of text with a large amount is needed to extract the information contained in it. Student comments containing suggestions and criticisms about the lecturer and the lecture process on the learning evaluation system are not well classified, resulting in a difficult assessment process. So from that, we need a classification model that can classify comments automatically into classification categories. The method used is the Cosine Similarity method, which is a method for calculating similarities between two objects expressed in two vectors. The data used in this study were 1,630 comment data with several different categories. The test in this study uses k-fold cross-validation with k = 10. The results showed that the percentage accuracy of the classification model was 80.87%.
ASSESSING THE SELECTED HEAVY METALS CONCENTRATION ON PALM OIL SOIL AND FRUIT FARM SOIL

This study was conducted at Felda Jengka 8, Pahang in Malaysia to find out the contents of heavy metals in palm oil soil and fruit farm soil adjacent to estimate the pollution level. Four types of selected heavy metals namely Cu, Zn, Pb and Ni in different soil sampling point were collected in triplicates and analysed by Inductively Coupled Plasma – Optical Emission Spectrometer (ICP-OES) after wet digestion. Contamination factor (CF) and geo-accumulation index (Igeo) were used to estimate the degree of contamination heavy metal in soil. Heavy metal concentration range were as follow; 0.910 – 2.000 mg/kg for Cu, 0.460 – 2.060 mg/kg for Zn, 0.080 – 0.220 mg/kg for Pb and 0.020 – 0.100 mg/kg for Ni. Cu and Zn concentration was found to be higher than other metal concentrations. The continuous application of chemical fertilizers possibly related to accumulation of heavy metals in soil. Results showed CF values were between moderate to considerable contaminant especially in palm oil soil. While, for Igeo values were indicated uncontaminated to fairly contaminated for the study sites. This recommends the utilization of chemical fertilizer is still in the monitoring. However, more physicochemical analyses are needed for metal analysis and determination of other heavy metal elements such as Cd, As, Se and Cr for further research.&#x0D; Keywords: Chemical Fertilizer, Contamination Factor, Geo-accumulation Index, Heavy Metal
A Tamper Proofing Text Watermarking Shift Algorithm for Copyright Protection

Watermarking has proved its great reliability and flexibility for property rights protection and tamper detection of diverse forms of online distributed data. Among the different mediums in which watermarking technology is used, text watermarking represents the most challenging issue due to the distinctive nature of its various formats. Plain text is widely used for transferring information on the internet. Despite this, there is a lack of used plain text watermarking techniques, so obviously it’s necessary to focus on that point and pay it more attention. In this paper a blind text watermarking approach is proposed for copyright protection and tamper detection of plain text by inter-word spacing (depending on a secret binary number and a key word) and text fingerprint through using MD5 hash function. Simulation results proved the success of our approach in detecting any tiny content tampering and due to the secret key and the fingerprint of watermarked text copyright protection are accomplished efficiently. This approach is a combination of several steps which work cooperatively to reach the desired goals.
A Fast Image Denoising Algorithm Based on TLS model and Sparse Representation

The sparse representation algorithms (SRs) plays important role to remove the noise from noisy images. The Quantum Particle Swarm Optimization based on the matching pursuit algorithm (QPSO-MP) denoising algorithm that takes advantage of the SRs and the meta-heuristic algorithms has shown hopeful results with respect to noise reduction. Despite effective combination between the meta-heuristic and SRs, the learning dictionary and the fixed populations make these algorithms computational exacting, which largely locates its applicability in many applications. To solve this problem, the fast version of the QPSO-MP denoising algorithm, called FQPSO-MP denoising algorithm, based on a pre learned dictionary and Dynamic-Multi-Swarm (DMS), were proposed in this paper. The pre-learned dictionary produces the best average performance of the denoised images. So, the learned dictionary is no longer required to provide the best average performance of the denoising images. While the denoising approach is applied, the learned dictionary is obtained. So, the execution time of the pre-learned dictionary is shorter than the learned dictionary. The proposed algorithm is compared to the original QPSO-MP and the state of the art algorithms after modifications. These comparisons proof that the PSNR results and the execution time of the proposed algorithm are better than other algorithms.
Hour-ahead Forecasting of Photovoltaic Power Output based on Hidden Markov Model and Genetic Algorithm

It is well known that the variability in PV power output is primarily owing to fluctuations in radiation received by the solar panels. With short-term forecasting, intermittency problem associated with PV based power production as well as power quality issues can be addressed. Forecasting in the short-term horizon particularly is very crucial to power quality and power schedules such as load drop or gain, and power dispatch planning. This study details an innovative method based on ordinary model (Hidden Markov Model, HMM) and HMM optimized with Genetic Algorithm (GA) for hour-ahead forecasting of the power output ( P o ) of a 1.2 kW PV system. Solar irradiance ( G s ), module temperature ( T m ) acquired by mathematical modelling and wind speed ( w ) were used as initial forecast data. The forecast data was preprocessed and split into two quotas. The first was used in training the model developed based on HMM. To improve the performance of the forecasting model, GA was integrated. Secondly, the data was deployed to test and validate the proposed scheme. The model testing and validation was built on the computation of normalized Root Mean Square Error (nRMSE). As the results, GA-optimized HMM (HMM+GA) is able to forecast P o an hour-ahead with low nRMSE than HMM under clear sky day (CSD) condition. However, the abnormalities of the forecasting model resulting from instantaneous fluctuations in solar irradiance under cloudy day (CD) condition were decreased with correction factor (x). It was deduced that if the average change in the absolute value of solar irradiance is more than 128% and 90% in the morning and evening times respectively, the GA-optimized forecasting model with or without x presents average nRMSE of 2.33%. Therefore, HMM+GA gives more accurate P o forecast for CSDs whereas HMM+GA+x presents the best P o for CDs, supporting the consideration of the proposed forecast model as a good technique for hour-ahead power output forecasting of PV system.
Computing the Antiperiod(s) of a String

A string S[1, n] is a power (or repetition or tandem repeat) of order k and period n/k, if it can be decomposed into k consecutive identical blocks each n/k letters in length. Powers and periods are fundamental structures in the study of strings and algorithms to compute them efficiently have been widely studied. Recently, Fici et al. (Proc. ICALP 2016) introduced an antipower of order k to be a string composed of k distinct blocks of the same length, n/k, called the antiperiod. Antipowers are a natural converse to powers, and are objects of combinatorial interest in their own right. In this paper, we describe efficient algorithm for computing the smallest antiperiod t of a string S of length n in O(n log∗ t) time. We also describe an algorithm to compute all the antiperiods of S that runs in O(n log n) time.
The Use of Educational Data Mining Technique in Technology Assimilation Evaluation: A Response Assessment

This study predicts the accuracy of the students' responses in the actual evaluation conducted in Surigao State College of Technology (SSCT) to be used for decision making and technology assimilation in classroom learning.In this paper, a data mining technique like prediction using the Naïve Bayes algorithm was used to test the accurateness of the responses as perceived by the 53 computer science and information system student-respondents of SSSCT.Simulation result showed that the algorithm depicted a high 88.75% accuracy on the used dataset performed in 10-folds cross validation using WEKA software.It is suggested that the output of this study may be used as input and avenue for future researches or as data proliferation that will influence managerial decisions for the betterment of educational practices.Future researchers may utilize other data mining techniques with notable reputations in determining the accuracy of the same dataset.It is hoped that the study will contribute on the two major literatures; in data mining and in higher education mining.
KEARIFAN EKOLOGIS DALAM LEKSIKON BAHASA RIMBA DI HUTAN BUKIT DUABELAS JAMBI: KAJIAN EKOLINGUISTIK

This study aims to explain the forms of ecological wisdom in the lexicon of jungle language regarding forest ecosystems, subsistence fulfillment, and naming and interpreting flora and fauna. This research was a qualitative research. The research data were in the form of the jungle lexicon related to forest ecosystems, subsistence, flora and fauna. Data were obtained from the Jungle people gathered in Ketemenggungan Makekal Hulu. Data were collected using ethnographic interviewing techniques, participant observation, and the application of ecolinguistic knowledge in praxis. The data analysis technique used in this study refered to the principle of the ethnographic method proposed by Spradley (2006), namely the research data that had been obtained through interviews and participant observation was coded so that the data source can still be traced. Then the data inside contained some local terms in the language of the jungle lexicon being transliterated. Furthermore, data were classified and analyzed based on ecolinguistic theory. After that, the data were presented in the form of in-depth descriptions and conclusions based on the results of the study. The discussions of the results of this study outlines three problems based on the formulation of the problem, namely (1) forms of ecological wisdom in the Jungle language lexicon relating to forest ecosystems including: changes in weather and disasters, about types of rain, time according to jungle people, rivers and regulation, and the classification and treatment of forest areas; (2) forms of ecological wisdom in the jungle lexicon that are related to subsistence fulfillment, namely behuma ‘farming’, beburu ‘hunting’, gathering (collecting forest product); and (3) the forms of ecological wisdom in the lexicon of the jungle language, which relates to the naming and meaning of flora and fauna, out of the fifteen flora described the origin of their names and meanings, seven of which have no scientific names yet, as well as the fifteen outlined, eleven of them have also not been identified.
A Root-to-Leaf Algorithm Computing the Tree of Shapes of an Image

We propose an algorithm computing the tree of shapes of an image, a unified variation of the component trees, proceeding from the root to the leaf shapes in a recursive fashion. It proceeds differently from existing algorithms that start from leaves, which are regional extrema of intensity, and build the intermediate shapes up to the root, which is the whole image. The advantage of the proposed method is a simpler, clearer, and more concise implementation, together with a more favorable running time on natural images. For integer-valued images, the complexity is proportional to the total variation, which is the memory size of the output tree, which makes the algorithm optimal.
AI-Josyu: Thinking Support System in Class by Real-time Speech Recognition and Keyword Extraction

In this paper, we present a thinking support system, AI-Josyu. This system also operates as a class support system which helps to teachers for lightening their work. AI-Josyu is implemented based on media-driven real-time content management framework. The system links real world media and legacy media contents together. In resent years, it is easier to collect a large amount of various kinds of data which are created with sensors in the real world. The system realizes interconnection and utilization of legacy media contents. The legacy media contents are generated and scattered on the Internet. The framework has four modules, which are called â€œacquisition,â€ â€œextraction,â€ â€œselection,â€ and â€œretrieval.â€ The real world media and the legacy media contents are interconnected by these modules. This interconnection includes semantic components. This system records teacher's voice of its lecture in real time and presents retrieved legacy media contents corresponding to subject of the lecture. By this presentation, preparing of the legacy contents is not required. This system automatically retrieves and shows the legacy media contents. This system helps students to understand contents of the lecture. In addition, the system attends to expansion of ideas.Â We constructed the system and conducted the demonstration in class. It shows that the system is helpful to teacher and students for expansion of thinking.
Miniature 3D Microscope and Reflectometer for Space Exploration

Characterization of objects in all dimensions at a microscopic level is important in numerous applications including surface analysis on planetary bodies. Existing microscopes fit for this task are large bench-top devices unsuitable for in-situ use, particularly in resource-constrained remote robotic exploration. Computational imaging techniques present a powerful means to overcome physical limitations in fielded sensors, but have seen especially little use in space applications. We present a miniature (150 gram) 3D microscopic imager without moving parts capable of providing 1-megapixel images at approximately 1 micron horizontal and 5 micron vertical resolution. This device combines light-field imaging and photometric stereo to provide both 3D reconstruction and reflectance characterization of individual soil grains. We thoroughly evaluate its performance by designing and nanofabricating a 3D-fiducial and further demonstrate its operation on a library of planetary soil simulants. This system opens vast opportunities for extension, demonstrating the potential of computational imaging to amplify sensing capabilities in space.
An Efficient Convolutional Neural Network Computation using AVX-512 Instructions

Recently, Convolutional Neural Networks (CNNs) are widely used for image processing. Since the computation cost is high, it is necessary to accelerate the computation. Therefore, in this paper, we propose an efficient implementation using Intel AVX-512 instructions on the multicore CPUs. AVX-512 instructions suppose 512-bit vector operations, in which 16 32-bit floating point number operations can be executed simultaneously. In this implementation, to reduce the computation, we use an idea of the fused filter that combines a convolutional layer and its following pooling layer. As a result, we achieve a speed-up factor of 1.62 over an existing library implementation using Intel Math Kernel Library for Deep Neural Networks.
The pairwise quantum correlations for teleported state via a symmetric multi-qubit system

We study the pairwise quantum correlations for teleported state via a symmetric multi-qubitsystem. In the other words, the proposed model is considered as a quantum channel. Using thequantum discord, super quantum discord and concurrence to quantify quantum correlations forteleported state, some analytical and numerical results are presented. Moreover, we compare thedynamical evolutions of quantum correlations and fidelity versus measurement strength and thenumber of qubit channel for teleported state via symmetric multi-qubit model. Our main goalnow is to study how we can increase the quantum correlations and the fidelity of the teleportedstate in the presence of decoherence. The results show that, measurement strength and thenumber of qubit can control the quantum information obtained through the quantum channel.Therefore, measurement strength can be a good option for measuring exchanged information inthe teleportation process. In addition to, quantum correlations can provide an effective role inquantum teleportation
ProblemSolver at SemEval-2019 Task 10: Sequence-to-Sequence Learning and Expression Trees

This paper describes our participation in SemEval-2019 shared task "Math Question Answering", where the aim is to create a program that could solve the Math SAT questions automatically as accurately as possible.We went with a dual-pronged approach, building a Sequence-to-Sequence Neural Network pre-trained with augmented data that could answer all categories of questions and a Tree system, which can only answer a certain type of questions.The systems did not perform well on the entire test data given in the task, but did decently on the questions they were actually capable of answering.The Sequence-to-Sequence Neural Network model managed to get slightly better than our baseline of guessing "A" for every question, while the Tree system additionally improved the results.
Hardware-based Real-time Workload Forensics via Frame-level TLB Profiling

We propose a hardware-based solution for performing real-time workload forensics that enables identification of a process while it is being executed. More specifically, we divide execution flow of a process into consecutive frames and we extract descriptive features related to the Translation Lookaside Buffer (TLB) utilization profile for each such frame. These features are then processed through trained machine learning models to analyze program behavior and identify workload at the granularity of a process. Unlike previous research on workload forensics that performs ex post facto analysis based on the complete process execution profile, this method continuously analyzes the segmented workload execution flow; thus, it does not require knowledge of process creation, switch, and termination timestamps. Furthermore, as compared with software-based workload forensics solutions, whose data logging mechanism may be compromised by software attacks, the proposed hardware-based logging mechanism does not rely on services from the operating system (OS) or high-level applications and is, therefore, inherently immune to software tampering. The proposed workload forensics method was evaluated using a Linux OS loaded on Spike, an open-source RISC-V simulator. Experimental results using the Mibench benchmark suite indicate an overall identification accuracy of 98.9% with practicable logging overhead.
Observations: What for?

Observations are to test hypotheses and without hypotheses there can be no usable observations. Geology students should be taught to be independent, bold, imaginative thinkers before they become assiduous observers.
Pengenalan Aksara Batak Dengan Metode Perceptron

Water is a good solvent, so that the water in nature is never pure. If the content of various substances and microorganisms contained in water exceeds the allowable threshold, the water quality will be compromised. Impaired water quality is said to be contaminated water. With so keep in mind how the level of contamination found in the water. With the construction of an artificial neural network using the Perceptron network is expected to be built capable of providing the level of water pollution prediction results.
Approximating Approximate Pattern Matching

Given a text $T$ of length $n$ and a pattern $P$ of length $m$, the approximate pattern matching problem asks for computation of a particular \emph{distance} function between $P$ and every $m$-substring of $T$. We consider a $(1\pm\varepsilon)$ multiplicative approximation variant of this problem, for $\ell_p$ distance function. In this paper, we describe two $(1+\varepsilon)$-approximate algorithms with a runtime of $\widetilde{O}(\frac{n}{\varepsilon})$ for all (constant) non-negative values of $p$. For constant $p \ge 1$ we show a deterministic $(1+\varepsilon)$-approximation algorithm. Previously, such run time was known only for the case of $\ell_1$ distance, by Gawrychowski and Uznanski [ICALP 2018] and only with a randomized algorithm. For constant $0 \le p \le 1$ we show a randomized algorithm for the $\ell_p$, thereby providing a smooth tradeoff between algorithms of Kopelowitz and Porat [FOCS~2015, SOSA~2018] for Hamming distance (case of $p=0$) and of Gawrychowski and Uznanski for $\ell_1$ distance.
The role of ambient noise in the evolution of robust mental representations in cognitive systems

Natural environments are full of ambient noise; nevertheless, natural cognitive systems deal greatly with uncertainty but also have ways to suppress or ignore noise unrelated to the task at hand. F...