Reviewerly/exHarmony huggingface dataset:
Dynamic Resource Discovery Scheme for Vehicular Cloud Networks

To discover computing resources available for any application before they are allocated to requests dynamically on demand, developing effective mechanism for resource discovery in Vehicular Cloud Networks (VCN) is very important.Providing the services to the requested vehicle in time is a major concern in the VCN environment.Dynamic and intelligent resource discovery schemes are essential in VCN environment so that services are provided to the vehicles in time.Resource discovery is key characteristic of VCN.VCN requires intelligent algorithms for resource discovery.Creating a mechanism for resource management and search resources is the largest challenge in VCN.There is a need to consider for dynamic way to discover the resources in the VCN.The lack of intelligence in resource handling, less flexible for dynamic simultaneous requests, and low scalability are issues to be addressed for the resource discovery in VCN.In this paper we proposed dynamic resource discovery scheme in VCN.Proposed resource discovery scheme uses Honey Bee Optimization (HBO) technique integrated with static and mobile agents.Mobile agent collects the vehicular cloud information and static agent intelligently identifies the required resources by the vehicle.Dynamic discovery model will take into account different parameters influencing the task execution time to optimize subsequent schedule.To test the performance effectiveness of the scheme, proposed dynamic resource discovery scheme is compared with fixed time scheduling algorithm.The objective of the proposed scheme is to search the resources in VCN with a minimum delay.The simulation results of the proposed scheme is better than the existing scheme.
Pengembangan Literasi Anak Melalui Permainan Dadu Literasi Di TK AL-AQSHO Konawe Selatan

&lt;p class="DAbstract"&gt;Abstract: DEVELOPMENT OF CHILDREN LITERATION THROUGH LITERATING DADU GAMES IN TK AL-AQSHO KONAWE SELATAN. Literacy is an aspect of language development in early childhood education. Literacy is often associated with the words reading and writing, although the terminology of literacy is broader than reading and writing. The aim of this article is to describe the development of children's literacy through the literacy dice game. The researcher used a qualitative approach with a descriptive analysis method, with the subject of the study being the children of group A TK Al-Aqsa South Konawe determined by the researcher by purposive sampling. Techniques of data analysis using an interactive model with the steps of data reduction, data presentation, conclusions drawn and verification. The results showed that children's literacy on child development indicators can recognize symbols for reading preparation, children can recognize the initial letters of the names of objects, write and pronounce letters a-z and children can write their own names. All indicators of children's literacy develop very well through the use of literacy dice games.&lt;/p&gt;&lt;p class="EKw12345"&gt;Keywords:&lt;strong&gt; &lt;/strong&gt;Literacy, Children, Games&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p class="FAbstrakIndonesia"&gt;&lt;strong&gt;Abstrak: &lt;/strong&gt;Literasi merupakan aspek perkembangan bahasa dalam pendidikan anak usia dini. Literasi sering dilekatkan pada istilah membaca dan menulis, walaupun terminologi literasi lebih luas dari baca-tulis. Tujuan penulisan artikel ini untuk mendeskripsikan pengembangan literasi anak melalui permainan dadu literasi. Peneliti menggunakan pendekatan kualitatif dengan metode analisis deskriptif, dengan subjek penelitian yaitu anak kelompok A TK Al-Aqsho Konawe Selatan yang ditetapkan peneliti secara &lt;em&gt;purposive&lt;/em&gt; &lt;em&gt;sampling.&lt;/em&gt; Teknik analisis data menggunakan interaktif model dengan langkah-langkah yaitu reduksi data, penyajian data, penarikan kesimpulan dan verifikasi. Hasil penelitian menunjukkan bahwa literasi anak pada indikator perkembangan anak dapat mengenal simbol untuk persiapan membaca, anak dapat mengenal huruf awal dari nama benda, menuliskan dan mengucapkan huruf a-z dan anak dapat menuliskan nama sendiri. Seluruh indikator literasi anak tersebut berkembang dengan sangat baik melalui penggunaan permainan dadu literasi.&lt;/p&gt;&lt;strong&gt;Kata kunci: &lt;/strong&gt;Literasi, Anak, Permainan
A Polymorphic Vampire

We have modified the Vampire theorem prover to support rank-1 polymorphism. In this paper we discuss the changes required to enable this and compare the performance of polymorphic Vampire against other polymorphic provers. We also compare its performance on monomorphic problems against standard Vampire. Finally, we discuss how polymorphism can be used to support theory reasoning and present results related to this.
Using Thinkalouds to Understand Rule Learning and Cognitive Control Mechanisms Within an Intelligent Tutoring System

Cognitive control and rule learning are two important mechanisms that explain how goals influence behavior and how knowledge is acquired. These mechanisms are studied heavily in cognitive science literature within highly controlled tasks to understand human cognition. Although they are closely linked to the student behaviors that are often studied within intelligent tutoring systems (ITS), their direct effects on learning have not been explored. Understanding these underlying cognitive mechanisms of beneficial and harmful student behaviors can provide deeper insight into detecting such behaviors and improve predictive models of student learning. In this paper, we present a thinkaloud study where we asked students to narrate their thought processes while solving probability problems in ASSISTments. Students are randomly assigned to one of two conditions that are designed to induce the two modes of cognitive control based on the Dual Mechanisms of Control framework. We also observe how the students go through the phases of rule learning as defined in a rule learning paradigm. We discuss the effects of these different mechanisms on learning, and how the information they provide can be used in student modeling.
Clause Size Reduction with all-UIP Learning

Almost all CDCL SAT solvers use the 1-UIP clause learning scheme for learning new clauses from conflicts, and our current understanding of SAT solving provides good reasons for using that scheme. In particular, the 1-UIP scheme yields asserting clauses, and these asserting clauses have minimum LBD among all possible asserting clauses. As a result of these advantages, other clause learning schemes, like i-UIP and all-UIP, that were proposed in early work are not used in modern solvers. In this paper, we propose a new technique for exploiting the all-UIP clause learning scheme. Our technique is to employ all-UIP learning under the constraint that the learnt clause’s LBD does not increase (over the minimum established by the 1-UIP clause). Our method can learn clauses that are significantly smaller than the 1-UIP clause while preserving the minimum LBD. Unlike previous clause minimization methods, our technique is not limited to learning a sub-clause of the 1-UIP clause. We show empirically that our method can improve the performance of state of the art solvers.
Beyond Notations: Hygienic Macro Expansion for Theorem Proving Languages

In interactive theorem provers (ITPs), extensible syntax is not only crucial to lower the cognitive burden of manipulating complex mathematical objects, but plays a critical role in developing reusable abstractions in libraries. Most ITPs support such extensions in the form of restrictive “syntax sugar” substitutions and other ad hoc mechanisms, which are too rudimentary to support many desirable abstractions. As a result, libraries are littered with unnecessary redundancy. Tactic languages in these systems are plagued by a seemingly unrelated issue: accidental name capture, which often produces unexpected and counterintuitive behavior. We take ideas from the Scheme family of programming languages and solve these two problems simultaneously by proposing a novel hygienic macro system custom-built for ITPs. We further describe how our approach can be extended to cover type-directed macro expansion resulting in a single, uniform system offering multiple abstraction levels that range from supporting simplest syntax sugars to elaboration of formerly baked-in syntax. We have implemented our new macro system and integrated it into the upcoming version (v4) of the Lean theorem prover. Despite its expressivity, the macro system is simple enough that it can easily be integrated into other systems.
Optimalisasi Penggunaan E-learning dengan Model Delone dan McClean

The University of Padang (UNP) is one of the educational institutions in implementing e-learning. This research used quantitative method. The researcher evaluated e-learning by using the information success system DeLone &amp; McClean model. This research was expected to be able to find out the overview of UNP e-learning. Based on data analysis, it showed that respondents’ level of achievement (TCR) reached 88.07% was categorized into good. The quality of information showed that the TCR reached 87.36% was categorized into good. Service quality of TCR reached 87.76% was categorized into good. The use that TCR reached 88.24% was categorized into good. Respondents’ satisfaction showed that TCR reached 88.73% was categorized into good. The benefit of TCR reached 84.10% was categorized into good. So, e-learning of Padang University had "Good" measurement on all variables, of course it would affect the use of e-learning itself, especially managed by educators and students' use.
Post-human Interaction Design, Yes, but Cautiously

Post-human design runs the risk of obscuring the fact that AI technology actually imports a Cartesian humanist logic, which subsequently influences how we design and conceive of so-called 'smart' or 'intelligent' objects. This leads to unwanted metaphorical attributions of human qualities to 'smart objects'. Instead, starting from an 'embodied sensemaking' perspective, designers should demand of engineers to radically transform the very structure of AI technology, in order to truly support critical posthuman values of collectivity, relationality and community building.
A Study on Encodings for Neural Architecture Search

Neural architecture search (NAS) has been extensively studied in the past few years. A popular approach is to represent each neural architecture in the search space as a directed acyclic graph (DAG), and then search over all DAGs by encoding the adjacency matrix and list of operations as a set of hyperparameters. Recent work has demonstrated that even small changes to the way each architecture is encoded can have a significant effect on the performance of NAS algorithms. In this work, we present the first formal study on the effect of architecture encodings for NAS, including a theoretical grounding and an empirical study. First we formally define architecture encodings and give a theoretical characterization on the scalability of the encodings we study Then we identify the main encoding-dependent subroutines which NAS algorithms employ, running experiments to show which encodings work best with each subroutine for many popular algorithms. The experiments act as an ablation study for prior work, disentangling the algorithmic and encoding-based contributions, as well as a guideline for future work. Our results demonstrate that NAS encodings are an important design decision which can have a significant impact on overall performance. Our code is available at this https URL.
It Never Rains but It Pours: Analyzing and Detecting Fake Removal Information Advertisement Sites

Fake antivirus (AV) software is a serious threat on the Internet to make users install malware and expose their personal information. Fake removal information advertisement (FRAD) sites, which introduce fake removal information for cyber threats, have emerged as platforms for distributing fake AV software. Although FRAD sites seriously threaten users who have been suffering from cyber threats and need information for removing them, little attention has been given to revealing these sites. In this paper, we propose a system to automatically crawl the web and identify FRAD sites. To shed light on the pervasiveness of this type of attack, we performed a comprehensive analysis of both passively and actively collected data. Our system collected 2,913 FRAD sites in 31 languages, which have 73.5 million visits per month in total. We show that FRAD sites occupy search results when users search for cyber threats, thus preventing the users from obtaining the correct information.
Heisenberg picture of photodetection

We construct a class of Hamiltonians that describe the photodetection process from beginning to end. Our Hamiltonians describe the creation of a photon, how the photon travels to an absorber (such as a molecule), how the molecule absorbs the photon, and how the molecule after irreversibly changing its configuration triggers an amplification process---at a wavelength that may be very different from the photon's wavelength---thus producing a macroscopic signal. We use a simple prototype Hamiltonian to describe the single-photon detection process analytically in the Heisenberg picture, which neatly separates desirable from undesirable effects. Extensions to more complicated Hamiltonians are pointed out.
Rule Covering for Interpretation and Boosting

We propose two algorithms for interpretation and boosting of tree-based ensemble methods. Both algorithms make use of mathematical programming models that are constructed with a set of rules extracted from an ensemble of decision trees. The objective is to obtain the minimum total impurity with the least number of rules that cover all the samples. The first algorithm uses the collection of decision trees obtained from a trained random forest model. Our numerical results show that the proposed rule covering approach selects only a few rules that could be used for interpreting the random forest model. Moreover, the resulting set of rules closely matches the accuracy level of the random forest model. Inspired by the column generation algorithm in linear programming, our second algorithm uses a rule generation scheme for boosting decision trees. We use the dual optimal solutions of the linear programming models as sample weights to obtain only those rules that would improve the accuracy. With a computational study, we observe that our second algorithm performs competitively with the other well-known boosting methods. Our implementations also demonstrate that both algorithms can be trivially coupled with the existing random forest and decision tree packages.
Lip Print Analysis in Malaysian Chinese Population (Klang Valley): LipstickCellophane Tape Technique

Cheiloscopy is recognised as a tool for personal identification due to its strength in criminalistics. In this study, the lip print of Malaysian Chinese population in Klang Valley was used for sex determination using lipstick-cellophane technique. 412 subjects (203 males and 209 females) were selected conveniently. Lipstick was first applied on the lips and the lip print was lifted using cellophane tape prior to pasting them onto a plain A4 paper and analysis using magnifying glass. Six topography areas of lip prints were classified using Suzuki and Tsuchihashi’s classification, including upper left, upper middle, upper right, lower right, lower middle and lower left. Chi-square test showed that all lip sections were significantly different from each other ( p &lt; 0.05). Type II was the dominant pattern in four lip sections for both sexes: Upper left (male 81.3%, female 57.4%), upper right (male 71.9%, female 42.9%), lower left (male 98.0%, female 90.0%) and lower right (male 96.1%, female 86.1%). Type IV was the dominant pattern for both middle part of the lips; upper middle (male 56.2%, female 50.7%) and lower middle (male 62.6%, female 50.2%). This finding suggested that cheiloscopy could be used in aiding personal identification for forensic investigation. DOI: https://doi.org/10.17576/jskm-2020-1802-04
Colour Quantisation by Human Mental Search

Colour quantisation is a common image processing technique to reduce the number of distinct colours in an image which are then represented by a colour palette. The selection of appropriate entries in this palette is a challenging issue while the quality of the quantised image is directly related to the colour palette. In this paper, we propose a novel colour quantisation algorithm based on the human mental search (HMS) algorithm. HMS is a recent population-based metaheuristic algorithm with three main operators: mental search to explore the vicinity of candidate solutions based on Levy flight, grouping to determine a promising region based on a clustering algorithm, and movement towards the best strategy. The performance of our proposed algorithm is evaluated on a set of benchmark images and in comparison to four conventional algorithms and seven soft computing-based colour quantisation algorithms. The obtained experimental results convincingly show that our proposed algorithm is capable of outperforming these approaches.
An evaluative study of criminalistics: A case of South African counterfeiting and knock-off menace

Counterfeit goods are a health hazard to human life, environment and business.More so as the goods destroy the viability of legitimate businesses across the world.Counterfeit goods are characterized as a threat to the financial viability of legitimate businesses and has a dire impact on the tax revenue of countries, necessitating its effective and efficient policing for the financial wellbeing of countries.If not well managed, counterfeit goods could lead to unemployment, disinvestment and capital flight in countries.The available evidence reveals that even though counterfeit goods has dire consequences for the economic wellbeing of countries, its penalties in most African countries are less severe in comparison to penalties for other crimes.The sight of counterfeit goods in many places, such as on busy street corners and transportation terminals in the Southern parts of Africa, attests to its prevalence in the continent.Especially in South Africa as the country has become a transit point for contrabands.Post 1994, South Africa opened its borders to the world of branded and protected goods and this enabled rogue people to trade in counterfeit goods.Effective policing is hindered by the use of sophisticated skills and expertise that leads to counterfeit goods resembling genuine goods.Moreover, in South Africa, the problem persists despite the promulgation of the Counterfeit Goods Act 37 of 1997, thus questioning the effectiveness of the policing strategies in use to quell the problem.
Effect of Debiasing on Information Retrieval

Word embeddings provide a common basis for modern natural language processing tasks, however, they have also been a source of discussion regarding their possible biases. This has led to a number of publications regarding algorithms for removing this bias from word embeddings. Debiasing should make the embeddings fairer in their use, avoiding potential negative effects downstream. For example: word embeddings with a gender bias that are used in a classification task in a hiring process. In this research, we compare regular and debiased word embeddings in an Information Retrieval task. We show that the two methods produce different results, however, this difference is not substantial.
Automated Journalism as a Source of and a Diagnostic Device for Bias in Reporting

In this article we consider automated journalism from the perspective of bias in news text. We describe how systems for automated journalism could be biased in terms of both the information content and the lexical choices in the text, and what mechanisms allow human biases to affect automated journalism even if the data the system operates on is considered neutral. Hence, we sketch out three distinct scenarios differentiated by the technical transparency of the systems and the level of cooperation of the system operator, affecting the choice of methods for investigating bias. We identify methods for diagnostics in each of the scenarios and note that one of the scenarios is largely identical to investigating bias in non-automatically produced texts. As a solution to this last scenario, we suggest the construction of a simple news generation system, which could enable a type of analysis-by-proxy. Instead of analyzing the system, to which the access is limited, one would generate an approximation of the system which can be accessed and analyzed freely. If successful, this method could also be applied to analysis of human-written texts. This would make automated journalism not only a target of bias diagnostics, but also a diagnostic device for identifying bias in human-written news.
Hybrid Krill Herd Algorithm with Particle Swarm Optimization for Image Enhancement

Image enhancement, aimed at improving the image contrast and information quality, is one of the most critical steps in image processing. Due to insufficient enhancement and the mean shift problem of conventional image enhancement techniques, new artificial intelligence-based image enhancement approaches have become an inevitable need in image processing. This paper employs the krill herd algorithm (KHA) and particle swarm optimization (PSO) to suggest a novel hybrid approach, called (PSOKHA) for image enhancement. The suggested PSOKHA method is used in search of optimum transfer function parameters to increase the quality of the images. For comparative evaluation, the performance of the PSOKHA is compared with six latest successful enhancement methods: PSO, KHA, screened Poisson equation (SPE), histogram equalization (HE), brightness preserving dynamic fuzzy HE (BPDFHE), and adaptive gamma correction weighted distribution (AGCWD). Experiments results in testing images include a medical image, a satellite image, and a handwritten image, demonstrate that the suggested strategy can produce better enhanced images in terms of several measurement criteria such as contrast, PSNR, entropy, and structure similarity index (SSIM).
PCA Based Kernel Initialization for Convolutional Neural Networks

The initialization of Convolutional Neural Networks (CNNs) is about providing reasonable initial values for the convolution kernels and the fully connected layers. In this paper, we proposed a convolution kernel initialization method based on the two-dimensional principal component analysis (2DPCA), in which a parametric equalization normalization method is used to adjust the scale between each neuron weight. After that the weight initial value can be adaptively adjusted according to different data samples. This method enables each neuron to fully back-propagate errors and accelerate network model training. Finally, a network model was built and experiments were performed using Tanh and ReLU activation functions. The experimental results verify the effectiveness of the proposed method through the distribution of histograms and the curve comparison diagrams of model training.