Reviewerly/exHarmony huggingface dataset:
Strong Stubborn Set Pruning for Star-Topology Decoupled State Space Search

Analyzing reachability in large discrete transition systems is an important sub-problem in several areas of AI, and of CS in general. State space search is a basic method for conducting such an analysis. A wealth of techniques have been proposed to reduce the search space without affecting the existence of (optimal) solution paths. In particular, strong stubborn set (SSS) pruning is a prominent such method, analyzing action dependencies to prune commutative parts of the search space. We herein show how to apply this idea to star-topology decoupled state space search, a recent search reformulation method invented in the context of classical AI planning.&#x0D; Star-topology decoupled state space search, short decoupled search, addresses planning tasks where a single center component interacts with several leaf components. The search exploits a form of conditional independence arising in this setting: given a fixed path p of transitions by the center, the possible leaf moves compliant with p are independent across the leaves. Decoupled search thus searches over center paths only, maintaining the compliant paths for each leaf separately. This avoids the enumeration of combined states across leaves.&#x0D; Just like standard search, decoupled search is adversely affected by commutative parts of its search space. The adaptation of strong stubborn set pruning is challenging due to the more complex structure of the search space, and the resulting ways in which action dependencies may affect the search. We spell out how to address this challenge, designing optimality-preserving decoupled strong stubborn set (DSSS) pruning methods. We introduce a design for star topologies in full generality, as well as simpler design variants for the practically relevant fork and inverted fork special cases. We show that there are cases where DSSS pruning is exponentially more effective than both, decoupled search and SSS pruning, exhibiting true synergy where the whole is more than the sum of its parts. Empirically, DSSS pruning reliably inherits the best of its components, and sometimes outperforms both.
A Novel Model for Arbitration Between Planning and Habitual Control Systems

It is well established that humans decision making and instrumental control uses multiple systems, some which use habitual action selection and some which require deliberate planning. Deliberate planning systems use predictions of action-outcomes using an internal model of the agent's environment, while habitual action selection systems learn to automate by repeating previously rewarded actions. Habitual control is computationally efficient but are not very flexible in changing environments. Conversely, deliberate planning may be computationally expensive, but flexible in dynamic environments. This paper proposes a general architecture comprising both control paradigms by introducing an arbitrator that controls which subsystem is used at any time. This system is implemented for a target-reaching task with a simulated two-joint robotic arm that comprises a supervised internal model and deep reinforcement learning. Through permutation of target-reaching conditions, we demonstrate that the proposed is capable of rapidly learning kinematics of the system without a priori knowledge, and is robust to (A) changing environmental reward and kinematics, and (B) occluded vision. The arbitrator model is compared to exclusive deliberate planning with the internal model and exclusive habitual control instances of the model. The results show how such a model can harness the benefits of both systems, using fast decisions in reliable circumstances while optimizing performance in changing environments. In addition, the proposed model learns very fast. Finally, the system which includes internal models is able to reach the target under the visual occlusion, while the pure habitual system is unable to operate sufficiently under such conditions.
Geology and mineral resources of Khudi-Bahundanda area of west-central Nepal along Marshyangdi Valley

Geological study was carried out along the Khudi-Bahundanda area of the Marshyangdi Valley in the west central Nepal. The area lies partly in the Main Central Thrust (MCT) zone and partly in the Higher Himalayan Crystalline Zone. The aim of the study was to prepare a detail geological map and cross section in the scale of 1:25,000 to work out on stratigraphy, metamorphism and mineral resource potential of the area. The rocks of the Higher Himalaya have been mapped under a single unit as Formation I. This unit consists of kyanite-garnet para-gneiss. The lithological units of the MCT zone are mapped into three units as the Benighat Slate, the Malekhu Formation and the Robang Formation from the bottom to the top, respectively. The Benighat Slate consists of dark grey to black schist with some carbonate beds as members. The Malekhu Formation consists of creamy white siliceous dolomite marble with parting of schist. The Robang Formation comprises of light grey psammitic schist with garnet and white micaceous quartzite in various proportion.&#x0D; Many secondary structures are observed in the study area, but primary structures are missing due to extreme metamorphism. The large-scale structures are the MCT, which separates the Lesser Himalayan rocks to the south from the Higher Himalaya to the north, and the Bahundanda Thrust (BT). Numerous outcrop-scale structures like meso-scale folds, quartz veins, boudinage and ptygmatic folds are abundant. Folds in the MCT zone are mostly E-W trending, and rocks have experienced multiple metamorphism and dynamic crystallization of minerals. The Lesser Himalayan rocks resemble the garnet zone while the Higher Himalayan rocks resemble to the kyanite grade of metamorphism. As in the other sections of the Himalaya, the present section also clearly shows the inverted metamorphism in the MCT zone. The MCT zone is considered as the potential site for precious and semi-precious stones, of which the most potential ones are the garnet and kyanite.
Geological setting of the Chandragiri-Chitlang Range, southwest of Kathmandu Valley, central Nepal

Geological mapping was carried out in the scale of 1:25000 in the Chandragiri-Chitlang Range of central Nepal, SW of the Kathmandu Valley. In the area, lower four formations of the Phulchoki Group (Paleozoic age) from older to younger sequence, i.e. the Tistung Formation, the Sopyang Formation, the Chandragiri Limestone and the Chitlang Formation along with the Alluvial Fan Deposits and the Kathmandu valley sediments were mapped. Low-grade metamorphic rocks like met sandstone, phyllite, argillaceous limestone, ortho-quartzite and slate dominate the study area. The whole succession of the area is calcareous. The succession consists of a biotitezone in the lower part of the Tistung Formation, whereas the overlying younger succession belongs to a chlorite zone. The Chitlang Formation lies at the core of the Mahabharata Synclinorium and other local-scale structures in the area are the Chitlang Syncline, the Kirtipur Anticline and the Chandragiri Thrust (CT). The repetition of the older Sopyang Formation around the southeastern area and the Tistung Formation around northwestern area over the younger Chandragiri Limestone justifies the presence of the Chandragiri Thrust (CT).
Cascaded Kalman filter for target tracking in automotive radar

In automotive anti-collision radar system, the accuracy of velocity is one of the key indicators to measure radar performance. For frequency modulation continuous wave radar with chirp sequence, which is commonly used in automotive radar, there are two problems that are velocity ambiguity and measurement error. In order to address these problems, a cascaded Kalman filter algorithm is proposed in this article, which is able to overcome velocity ambiguity and improve the accuracy of measurements. In addition, the problem of velocity ambiguity can be solved just in the data processing and it avoids transmitting staggered or multiple pulse repetition frequency signal. The simulation results show that this algorithm can effectively improve the tracking performance.
TINJAUAN PUSTAKA SISTEMATIS PADA BASIS DATA PUSTAKA DIGITAL: TREN RISET, METODOLOGI, DAN COVERAGE FIELDS

The characterization of digital databases is needed to make it easier for academics to identify scientific literature properly and efficiently. This literature review intends to provide characterizations and descriptions related to research trends, methods and coverage fields studied in research related to the scientific database of scientific literature from around 2007 to the present (January 2019). By applying the specified inclusion and exclusion criteria, 54 relevant studies were chosen to be studied further. The systematic literature review method was applied in this study to analyze and identify previous studies related to this topic. Based on the selected primary literature there is an increasing trend of studies related to the scientific database of scientific literature. In addition, we can see that there are four of the most influential and influential publication journals related to this topic, namely the Journal of Informetrics, Journal of Cleaner Production, Asian Social Science and Journal of Academic Librarianship which are characterized by high levels of productivity issues related to the topics studied and SJR values rank is in the range Q1. Most of the studies were conducted on Scopus digital database (41%), Web of Sciences (WoS) 38% and Google Scholar (GS) 13% and the rest spread in other publication journals. The results of this study also identified that Scopus is a scientific database which has the most varied coverage fields compared to other digital database scientific literature. WoS is a digital database of scientific literature that has proven to have a paper with a higher impact factor than others. GS has the predicate digital database with the largest collection level.
Context capturing of multi-information resources for the data exchange in collaborative project environments

This paper introduces a framework for context capturing of multi information resources and is based on the three major approaches which have been developed over the last decade.Having similar goals in mind they have come up with partially different objectives, methods and representation technologies.The three are, respectively, the Multimodel (MM) approach of the Mefisto project in Germany, the COINS approach in the Netherlands of linked documents and models and the Linked Building Data (LBD) approach of the Linked Data Group in Europe.They were developed in recent years to tackle the central problems of management and exchange of various types of data in a single package expressing a certain context and goal in an overall digital AEC environment.The differences and the commonalities of the first two approaches are addressed and the more future oriented LBD approach is compared to them.The related upcoming standard ISO 21597, pursuing a merging of the first two approaches in its Part 1, is discussed, whereas the third approach, targeting reasoning, will be a subject of Part 2 that is still under discussion.
The problem of detecting when two implicit plane algebraic curves are similar

We make use of the complex implicit representation in order to provide a deterministic algorithm for checking whether or not two implicit algebraic curves are related by a similarity. The algorithm has been implemented in the computer algebra system Maple 2016. The implementation can be freely downloaded from the webpage of one of the authors. Examples and evidence of the good practical performance of the algorithm are given.
Automatic recall of software lessons learned for software project managers

Lessons learned (LL) records constitute the software organization memory of successes and failures. LL are recorded within the organization repository for future reference to optimize planning, gain experience, and elevate market competitiveness. However, manually searching this repository is a daunting task, so it is often disregarded. This can lead to the repetition of previous mistakes or even missing potential opportunities. This, in turn, can negatively affect the organization's profitability and competitiveness. We aim to present a novel solution that provides an automatic process to recall relevant LL and to push those LL to project managers. This will dramatically save the time and effort of manually searching the unstructured LL repositories and thus encourage the LL exploitation. We exploit existing project artifacts to build the LL search queries on-the-fly in order to bypass the tedious manual searching. An empirical case study is conducted to build the automatic LL recall solution and evaluate its effectiveness. The study employs three of the most popular information retrieval models to construct the solution. Furthermore, a real-world dataset of 212 LL records from 30 different software projects is used for validation. Top-k and MAP well-known accuracy metrics are used as well. Our case study results confirm the effectiveness of the automatic LL recall solution. Also, the results prove the success of using existing project artifacts to dynamically build the search query string. This is supported by a discerning accuracy of about 70% achieved in the case of top-k. The automatic LL recall solution is valid with high accuracy. It will eliminate the effort needed to manually search the LL repository. Therefore, this will positively encourage project managers to reuse the available LL knowledge – which will avoid old pitfalls and unleash hidden business opportunities.
PREDICTING CLASS-IMBALANCED BUSINESS RISK USING RESAMPLING, REGULARIZATION, AND MODEL EMSEMBLING ALGORITHMS

We aim at developing and improving the imbalanced business risk modeling via jointly using proper evaluation criteria, resampling, cross-validation, classifier regularization, and ensembling techniques. Area Under the Receiver Operating Characteristic Curve (AUC of ROC) is used for model comparison based on 10-fold cross validation. Two undersampling strategies including random undersampling (RUS) and cluster centroid undersampling (CCUS), as well as two oversampling methods including random oversampling (ROS) and Synthetic Minority Oversampling Technique (SMOTE), are applied. Three highly interpretable classifiers, including logistic regression without regularization (LR), L1-regularized LR (L1LR), and decision tree (DT) are implemented. Two ensembling techniques, including Bagging andBoosting, are applied on the DT classifier for further model improvement.The results show that, Boosting on DT by using the oversampled data containing 50% positives via SMOTE is the optimal model and it can achieve AUC, recall, and F1 score valued 0.8633, 0.9260, and 0.8907, respectively.
Novel Approaches for Designing Reversible Counters

Reversible logic offers an alternative computation for future low-power computing devices. In this paper, an efficient and potent universal 33 and 44 reversible gates are considered to implement 4-bit counter. Performance of the proposed 33 gate is verified using thirteen standard three variables Boolean functions, which demonstrate from 17.8 to 45.2% superiority in term of gate counts obtained with other reversible gates. New structures for T flip-flop and D flip-flop, which utilize two efficient reversible gates are presented. These flip-flops and some existing gates are utilized to implement the Mod-16 counter and 4-bit Up/down counter. The reported architectures are modeled using VHDL and functional simulations are done using ISIM.
Cutset Bayesian Networks: A New Representation for Learning Rao-Blackwellised Graphical Models

Recently there has been growing interest in learning probabilistic models that admit poly-time inference called tractable probabilistic models from data. Although they generalize poorly as compared to intractable models, they often yield more accurate estimates at prediction time. In this paper, we seek to further explore this trade-off between generalization performance and inference accuracy by proposing a novel, partially tractable representation called cutset Bayesian networks (CBNs). The main idea in CBNs is to partition the variables into two subsets X and Y, learn a (intractable) Bayesian network that represents P(X) and a tractable conditional model that represents P(Y|X). The hope is that the intractable model will help improve generalization while the tractable model, by leveraging Rao-Blackwellised sampling which combines exact inference and sampling, will help improve the prediction accuracy. To compactly model P(Y|X), we introduce a novel tractable representation called conditional cutset networks (CCNs) in which all conditional probability distributions are represented using calibrated classifiers—classifiers which typically yield higher quality probability estimates than conventional classifiers. We show via a rigorous experimental evaluation that CBNs and CCNs yield more accurate posterior estimates than their tractable as well as intractable counterparts.
Learning Image-Specific Attributes by Hyperbolic Neighborhood Graph Propagation

As a kind of semantic representation of visual object descriptions, attributes are widely used in various computer vision tasks. In most of existing attribute-based research, class-specific attributes (CSA), which are class-level annotations, are usually adopted due to its low annotation cost for each class instead of each individual image. However, class-specific attributes are usually noisy because of annotation errors and diversity of individual images. Therefore, it is desirable to obtain image-specific attributes (ISA), which are image-level annotations, from the original class-specific attributes. In this paper, we propose to learn image-specific attributes by graph-based attribute propagation. Considering the intrinsic property of hyperbolic geometry that its distance expands exponentially, hyperbolic neighborhood graph (HNG) is constructed to characterize the relationship between samples. Based on HNG, we define neighborhood consistency for each sample to identify inconsistent samples. Subsequently, inconsistent samples are refined based on their neighbors in HNG. Extensive experiments on five benchmark datasets demonstrate the significant superiority of the learned image-specific attributes over the original class-specific attributes in the zero-shot object classification task.
Data Driven Learning and teaching of prepositions in ESL: A study of Arab learners

This paper explores the effectiveness of data driven learning approach in teaching prepositions to Arab learners. English prepositions have been reported to be a typical feature to learn by the Arab ESL learners. Moreover, intermediate or advance level learners have also reported their difficulties in the usage of English prepositions; reasons might be perceptual and cultural differences bet the two language communities. Teacher trained materials prepared from online resources along with TPP corpus, the aim of the present study was to compare DDL and traditional prescriptive rule based method of grammar teaching. Two distinct classes; experimental (n = 41) and control (n = 19) groups were formed who were study English major. Standardized test was conducted as a pre-test to ensure participants equal level of proficiency in using prepositions. During the study duration for a semester, experimental group was exposed to teacher-prepared materials along with TPP to see prepositions in context. Moreover they were asked to explore further through the prepared materials to reinforce their learning from the data. A post-test was administered at the end of the course. The results showed that the experimental group outperformed the control group. It was found that learners’ active role to direct their own learning process caused this encouraging outcome and better learning experience for the learners.&#x0D;
A Nonparametric Bayesian Approach to the Rare Type Match Problem

The "rare type match problem" is the situation in which, in a criminal case, the suspect's DNA profile, matching the DNA profile of the crime stain, is not in the database of reference. Ideally, the evaluation of this observed match in the light of the two competing hypotheses (the crime stain has been left by the suspect or by another person) should be based on the calculation of the likelihood ratio and depends on the population proportions of the DNA profiles that are unknown. We propose a Bayesian nonparametric method that uses a two-parameter Poisson Dirichlet distribution as a prior over the ranked population proportions and discards the information about the names of the different DNA profiles. This model is validated using data coming from European Y-STR DNA profiles, and the calculation of the likelihood ratio becomes quite simple thanks to an Empirical Bayes approach for which we provided a motivation.
ANALISIS KEBIJAKAN SISTEM ZONASI TERHADAP PERILAKU SISWA SMP DI YOGYAKARTA

Zoning-based new student admission (PPDB) is applied in Yogyakarta for the first time in 2018/2019, this system provides 75% opportunities for prospective students in the school zone, the remaining 25% is taken based on grades and 5% for special pathways such as taking assignments parents as state officials. This study uses a qualitative approach conducted at the SMP in Yogyakarta with a psychological approach, while the data collection uses interviews, documents and observations. In the implementation of PPDB zoning there are 2 positive impacts - diverse environments will stimulate students, teachers remain competent in teaching, save rates and time more efficiently - and the negative impact of various elements - children cannot enter Public Middle School even though the grades are good, the best students cannot going to a public junior high school because it is in a blank spot area, teachers must spend more time teaching low-achieving students, less discipline, negative student behavior that will easily spread to other students -. So that it can be an illustration of the zoning policy in addition to the positive impact it also has an impact on the behavior of students in Yogyakarta Junior High School - lack of discipline - which ultimately results in low academic achievement
Autonomy in use for space situation awareness

Advancements in artificial intelligence, information communication, and systems design are potential for autonomous systems emerging for space situation awareness (SSA) architectures. Examples of architecture designs are autonomy in motion (AIM) for dynamic data assessment systems (e.g., robotics) and autonomy at rest (AAR) for static data collection systems (e.g., surveillance). However, there is a need for data architectures which are tailored to the SSA missions, which necessitates autonomy in use (AIU). AIU requires pragmatic use of message passing and data flow architectures, contextual and theoretic modeling, and user and information fusion. Information fusion provides methods for data aggregation, correlation, and temporal assessment and awareness. Together, AIU accesses the dynamic data for autonomy in change (AIC), information fusion from AAR in order to make AIM real-time decisions. The paper discusses issues for space situation awareness directions focusing on autonomy in use.