Reviewerly/exHarmony huggingface dataset:
Dynamic Graph Based Encryption Scheme for Cloud Based Services and Storage

Cloud security includes the strategies which works together to guard data and infrastructure with a set of policies, procedures, controls and technologies. These security events are arranged to protect cloud data, support supervisory obedience and protect customers' privacy as well as setting endorsement rules for individual users and devices. The partition-based handling and encryption mechanism which provide fine-grained admittance control and protected data sharing to the data users in cloud computing. Graph partition problems fall under the category of NP-hard problems. Resolutions to these problems are generally imitative using heuristics and approximation algorithms. Partition problems strategy is used in bi-criteria approximation or resource augmentation approaches with a common extension of hyper graphs, which can address the storage hierarchy.
Application of Mutation Operators on Grey Wolf Optimizer

Nature-inspired computing has been widely used for solving different optimization problems. Grey wolf optimization (GWO) is one of the recent addition in the category of swarm-based techniques. The swarm-based algorithms try to find a balance between exploitation and exploration through different steps/processes. In this work, mutation operation is performed on the GWO algorithm to create exploration and avoid local stagnation. A comparative analysis has been performed to evaluate the effect of various mutation operators on GWO. The algorithms are tested on ten well-known unimodal and multimodal benchmark functions. Unimodal functions evaluate the exploitation property, whereas multimodal functions evaluate the exploration property of the algorithms. The practical performance of these algorithms is shown on three different CEC2011 real-world problems. The results show that the mutation operator generate improved or comparable results in different optimization problems.
Natural continual learning: success is a journey, not (just) a destination

Biological agents are known to learn many different tasks over the course of their lives, and to be able to revisit previous tasks and behaviors with little to no loss in performance. In contrast, artificial agents are prone to 'catastrophic forgetting' whereby performance on previous tasks deteriorates rapidly as new ones are acquired. This shortcoming has recently been addressed using methods that encourage parameters to stay close to those used for previous tasks. This can be done by (i) using specific parameter regularizers that map out suitable destinations in parameter space, or (ii) guiding the optimization journey by projecting gradients into subspaces that do not interfere with previous tasks. However, these methods often exhibit subpar performance in both feedforward and recurrent neural networks, with recurrent networks being of interest to the study of neural dynamics supporting biological continual learning. In this work, we propose Natural Continual Learning (NCL), a new method that unifies weight regularization and projected gradient descent. NCL uses Bayesian weight regularization to encourage good performance on all tasks at convergence and combines this with gradient projection using the prior precision, which prevents catastrophic forgetting during optimization. Our method outperforms both standard weight regularization techniques and projection based approaches when applied to continual learning problems in feedforward and recurrent networks. Finally, the trained networks evolve task-specific dynamics that are strongly preserved as new tasks are learned, similar to experimental findings in biological circuits.
SyzScope: Revealing High-Risk Security Impacts of Fuzzer-Exposed Bugs in Linux kernel

Fuzzing has become one of the most effective bug finding approach for software. In recent years, 24*7 continuous fuzzing platforms have emerged to test critical pieces of software, e.g., Linux kernel. Though capable of discovering many bugs and providing reproducers (e.g., proof-of-concepts), a major problem is that they neglect a critical function that should have been built-in, i.e., evaluation of a bug's security impact. It is well-known that the lack of understanding of security impact can lead to delayed bug fixes as well as patch propagation. In this paper, we develop SyzScope, a system that can automatically uncover new "high-risk" impacts given a bug with seemingly "low-risk" impacts. From analyzing over a thousand low-risk bugs on syzbot, SyzScope successfully determined that 183 low-risk bugs (more than 15%) in fact contain high-risk impacts, e.g., control flow hijack and arbitrary memory write, some of which still do not have patches available yet.
Q-Learning for MDPs with General Spaces: Convergence and Near Optimality via Quantization under Weak Continuity.

Reinforcement learning algorithms often require finiteness of state and action spaces in Markov decision processes (MDPs) and various efforts have been made in the literature towards the applicability of such algorithms for continuous state and action spaces. In this paper, we show that under very mild regularity conditions (in particular, involving only weak continuity of the transition kernel of an MDP), Q-learning for standard Borel MDPs via quantization of states and actions converge to a limit, and furthermore this limit satisfies an optimality equation which leads to near optimality with either explicit performance bounds or which are guaranteed to be asymptotically optimal. Our approach builds on (i) viewing quantization as a measurement kernel and thus a quantized MDP as a POMDP, (ii) utilizing near optimality and convergence results of Q-learning for POMDPs, and (iii) finally, near-optimality of finite state model approximations for MDPs with weakly continuous kernels which we show to correspond to the fixed point of the constructed POMDP. Thus, our paper presents a very general convergence and approximation result for the applicability of Q-learning for continuous MDPs.
Transformation and simulation for a generalised queuing problem using a G/G/n/G/+ queuing model

We examine a generalised queuing model which we call the G/G/n/G/+ model, which encompasses the G/G/n and G/G/n/s models as special cases. Our model accommodates useful generalisations in user behaviour and limitations on the facilities for the queuing process. Give a set of inputs for the users and facilities, we develop a recursive algorithm that computes all aspects of the queuing process, including the waiting-times, use-times and unserved-times for each user. We also show how the queue can be represented graphically in a "queuing plot". We use our algorithm to undertake simulation analysis to determine the distribution of queuing outputs given specified distributions for the inputs, and we show how this can be used to optimise the number of facilities. We conduct some simple simulations to illustrate the method using standard queuing models. Our method is implemented in various queuing functions in the utilities package in R.
USING GUIDING QUESTION STRATEGY TO TEACH WRITING SKILL IN WRITING SKILL IN RITING RECOUNT TEXT AT THE FIRST GRADE STUDENTS OF MA DARUSSALAM PATALASSANG

This research is based on social studies research results of students was still low, it is necessary to update in improving social studies research result of students and improving creativity of teachers of managing the learning process. The aim of this research was to examine the extent guiding question strategy improve achievement in writing recount text at the first grade students of MA Darussalam Patalassang in academic years 2017/2018. The approach of this study was quantitative. The researcher did the treatment in teaching writing recount text to the students experimental class by using guiding question strategy while, in controlled class was expository strategy. Writing tests were used as the instruments of eliciting the data. The population of this research was 38 students. They were all from two classes of the first grade students of MA Darussalam Patalassang. The comparison value of the t-test and t-table was used to test the hypothesis. The result showed that the t-test for both classes in the post-test is 2.381 compared to 2.258 for the t-table with 0.05 and degree of freedom (df)=18. Since the score of the t-table, the alternatives (H1) was accepted and the null hypothesis (H0) was rejected. This indicates that guiding question increased students writing achievement. This suggests that guiding question strategy can be used as alternative media in teaching writing recount text.
WebGIS Technology and Architectures

WebGIS allows everybody to reach geo-spatial data. It creates a rapid and advanced environment without place, time and the limitations of high processing power and high client computer. WebGIS, reshapes and makes use of all computer functions including gathering attribute data, storing, acquiring, analyzing and visualizing. WebGIS was employing the internet based maps and client-server architecture in order to provide these facilities to all. This architecture, however, has been switched to service oriented architecture, not being able to meet cope with, increasing data volume and number of access requests. Service oriented architecture, provides a service system, dynamic, elastic and re-contractible where meeting with different users.  Also this improves the decision making progresses by spreading spatial information. This article analyzes WebGIS architectures and Technologies by emphasizing architecture.
Debiasing Methods in Natural Language Understanding Make Bias More Accessible

Model robustness to bias is often determined by the generalization on carefully designed out-of-distribution datasets. Recent debiasing methods in natural language understanding (NLU) improve performance on such datasets by pressuring models into making unbiased predictions. An underlying assumption behind such methods is that this also leads to the discovery of more robust features in the model’s inner representations. We propose a general probing-based framework that allows for post-hoc interpretation of biases in language models, and use an information-theoretic approach to measure the extractability of certain biases from the model’s representations. We experiment with several NLU datasets and known biases, and show that, counter-intuitively, the more a language model is pushed towards a debiased regime, the more bias is actually encoded in its inner representations.
Developing Speaking Skill through Group Discussion

This research aims at investigating effectiveness of group discussion in developing speaking skill of grade VIII students at SMPN 9 Palu. This is a quasi-experimental research design. Its samples were 48 students of VIII C and VIII D which were selected purposively. Its data were collected through pretest and posttest and analyzed statistically. The pretest was conducted to find out the students’ speaking skill before treatments. Mean scores of the pretest are 36.98 for the experimental class and 56.25 for the control class. The posttest was administered to measure their speaking skill after the treatments. Mean scores of the posttest are 75.52 for the experimental class and 72.4 for the control class. By applying degree of freedom (df) 46 and 0.08 of significance level, results of this research indicate that its t-counted value is 5.9 (high) and its t-table value is 2.0129 (low), so that the research hypothesis is accepted. In other words, using group discussion can develop students’ speaking skill. Keywords: Speaking Skill, Group Discussion Abstrak Penelitian ini bertujuan untuk menyelidiki efektivitas kelompok diskusi dalam mengembangkan keterampilan berbicara siswa kelas VIII pada SMPN 9 Palu. Ini adalah rancangan penelitian eksperimental semu. Sampel nya adalah 48 siswa kelas VIII C dan VIII D yang dipilih secara purposi f . Data dikumpulkan melalui pretest dan posttest dan dianalisis secara statistik.. Pretest dilakukan untuk men emukan keterampilan berbicaranya sebelum perlakuan . Nilai rata-rata pretest adalah 3 6 .9 8 untuk kel as eksperimen tal dan 56 ,25 untuk kel as kontrol. Posttest diberikan untuk mengukur ke terampilan berbicara mereka setelah perlakuan . Nilai rata-rata posttest itu adalah 75,52 untuk kel as eksperimen tal dan 72 ,4 untuk kel as kontrol. Dengan menerapkan derajat kebebasan (df) 46 dan 0,08 dari tingkat signifikansi, hasil penelitian ini menunjukkan bahwa nilai t-counted -nya adalah 5 , 9 (tinggi) dan nilai t-tabel -nya adalah 2,0129 (rendah) , sehingga hipotesis penelitian itu diterima. Dengan kata lain, menggunakan diskusi kelompok dapat mengembangkan keterampilan berbicara siswa . Katakunci : Keterampilan Berbicara, Kelompok Diskusi
Exploiting Reasoning Chains for Multi-hop Science Question Answering

We propose a novel Chain Guided Retriever-reader ({\tt CGR}) framework to model the reasoning chain for multi-hop Science Question Answering. Our framework is capable of performing explainable reasoning without the need of any corpus-specific annotations, such as the ground-truth reasoning chain, or human-annotated entity mentions. Specifically, we first generate reasoning chains from a semantic graph constructed by Abstract Meaning Representation of retrieved evidence facts. A \textit{Chain-aware loss}, concerning both local and global chain information, is also designed to enable the generated chains to serve as distant supervision signals for training the retriever, where reinforcement learning is also adopted to maximize the utility of the reasoning chains. Our framework allows the retriever to capture step-by-step clues of the entire reasoning process, which is not only shown to be effective on two challenging multi-hop Science QA tasks, namely OpenBookQA and ARC-Challenge, but also favors explainability.
Squeezing Italian dictionaries in search of citrus juice and fruit

Abstract Southern Mediterranean regions significantly differ from the northern countries not only for their climate, but also and foremost for the influence that the latter has on the landscape and the products that are related to it. Dictionaries should relate the lexical variation used to describe the variety of landscapes and their characteristics, thus reflecting the peculiarities of a given territory. This paper deals with the variety of citrus fruits that can be found in the Italian landscapes, with particular attention to the lexicographic treatment that they receive in both monolingual and bilingual dictionaries, where a precise meaning is not always registered and the treatment of the superordinate agrume (pl. agrumi ) is controversial: its taxonomic status seems not to be recognized and fully exploited in dictionary articles.
Learning Formulas in Finite Variable Logics

We consider grammar-restricted exact learning of formulas and terms in finite variable logics. We propose a novel and versatile automata-theoretic technique for solving such problems. We first show results for learning formulas that classify a set of positively- and negatively-labeled structures. We give algorithms for realizability and synthesis of such formulas along with upper and lower bounds. We also establish positive results using our technique for other logics and variants of the learning problem, including first-order logic with least fixed point definitions, higher-order logics, and synthesis of queries and terms with recursively-defined functions.
Combinatorial Pure Exploration with Bottleneck Reward Function

In this paper, we study the Combinatorial Pure Exploration problem with the Bottleneck reward function (CPE-B) under the fixed-confidence (FC) and fixed-budget (FB) settings. In CPE-B, given a set of base arms and a collection of subsets of base arms (super arms) following a certain combinatorial constraint, a learner sequentially plays a base arm and observes its random reward, with the objective of finding the optimal super arm with the maximum bottleneck value, defined as the minimum expected reward of the base arms contained in the super arm. CPE-B captures a variety of practical scenarios such as network routing in communication networks, and its \emph{unique challenges} fall on how to utilize the bottleneck property to save samples and achieve the statistical optimality. None of the existing CPE studies (most of them assume linear rewards) can be adapted to solve such challenges, and thus we develop brand-new techniques to handle them. For the FC setting, we propose novel algorithms with optimal sample complexity for a broad family of instances and establish a matching lower bound to demonstrate the optimality (within a logarithmic factor). For the FB setting, we design an algorithm which achieves the state-of-the-art error probability guarantee and is the first to run efficiently on fixed-budget path instances, compared to existing CPE algorithms. Our experimental results on the top-$k$, path and matching instances validate the empirical superiority of the proposed algorithms over their baselines.
Analysis of Semiotic Found in Football Shoes Advertisements

Aims in this study, first is to find out verbal and non-verbal signs found in football shoes advertisements, and second is to analyze the meaning of verbal and non-verbal signs found in football shoes advertisements. The data sources from this study were taken from the Official Account of Nike, Puma, and Adidas. This study used the descriptive qualitative method. The data were analyzed based on the theory proposed by Saussure (1983) about sign elements, the theory from Barthes (1964) about the meaning of signs, and the supporting theory from Cerrato (2012) to analyze the color terms. The result of this study showed the advertisements used the written texts as the verbal signs and the color choice and picture of advertisements as non-verbal signs. These signs also contained denotative and connotative meanings to make a good combination to attract the attention of readers.
Authenticated Web Based Voting System

Authenticated e voting system (AEVS) is developed taking into consideration the security. The AEVS system is designed for the welfare of the nation to provide an opportunity to the residents who are outside the nation want to choose their representative. Traditional way of electing the representatives is either through paper ballot or an automated ballot. There has been a huge transition from a traditional ballet to an automated but still there are few limitations which are to be addressed in the current system too. This paper aims at proposing AEVS is a highly secure system which is designed and developed to replace the traditional systems and provides much accurate and immediate results. AVES provides a high success rate unlike the traditional systems as it prevents the user from multiple registrations and proxy voting system. The system aims at enhancing the authentication by considering the fingerprint and the unique identification number provided by the government. AVES even considers and provide solution to eligibility of the voter and decree hiccups. Voters are given a leverage of voting at their leisure and use the devices with an internet connection using the unique identification number, for additional authentication security question is to be answered during the initial step of registration. Further a token would be automatically generated as a reference for every individual who has registered for the casting their votes. This token prevents the duplication of the voters from reregistering using the same details. Every token is automatically disseminated to the mail id provided by the user and voting is now done ubiquitously; it is an auto expiring token which loses its validity after a certain period.
Tera-OP/s Neuromorphic Processing with Kerr Microcombs

We report optical neuromorphic processors based on Kerr micro-combs that are programmable, scalable and ultra-high speed. We demonstrate a perceptron at 11.9Giga-OPS at 8 bits, or 95.2 Gbps, and a convolutional accelerator at 11TeraOPs/s.
Re-entry Prediction for Online Conversations via Self-Supervised Learning

In recent years, world business in online discussions and opinion sharing on social media is booming. Re-entry prediction task is thus proposed to help people keep track of the discussions which they wish to continue. Nevertheless, existing works only focus on exploiting chatting history and context information, and ignore the potential useful learning signals underlying conversation data, such as conversation thread patterns and repeated engagement of target users, which help better understand the behavior of target users in conversations. In this paper, we propose three interesting and well-founded auxiliary tasks, namely, Spread Pattern, Repeated Target user, and Turn Authorship, as the self-supervised signals for re-entry prediction. These auxiliary tasks are trained together with the main task in a multi-task manner. Experimental results on two datasets newly collected from Twitter and Reddit show that our method outperforms the previous state-of-the-arts with fewer parameters and faster convergence. Extensive experiments and analysis show the effectiveness of our proposed models and also point out some key ideas in designing self-supervised tasks.
AIT* and EIT*: Asymmetric bidirectional sampling-based path planning

Optimal path planning is the problem of finding a valid sequence of states between a start and goal that optimizes an objective. Informed path planning algorithms order their search with problem-specific knowledge expressed as heuristics and can be orders of magnitude more efficient than uninformed algorithms. Heuristics are most effective when they are both accurate and computationally inexpensive to evaluate, but these are often conflicting characteristics. This makes the selection of appropriate heuristics difficult for many problems. This paper presents two almost-surely asymptotically optimal sampling-based path planning algorithms to address this challenge, Adaptively Informed Trees (AIT*) and Effort Informed Trees (EIT*). These algorithms use an asymmetric bidirectional search in which both searches continuously inform each other. This allows AIT* and EIT* to improve planning performance by simultaneously estimating and exploiting increasingly accurate, problem-specific heuristics. The benefits of AIT* and EIT* relative to other sampling-based algorithms are demonstrated on twelve problems in abstract, robotic, and biomedical domains optimizing path length and obstacle clearance. The experiments show that AIT* and EIT* outperform other algorithms on problems optimizing obstacle clearance, where a priori cost heuristics are often ineffective, and still perform well on problems minimizing path length, where such heuristics are often effective.
RESEARCH AND COMMUNITY SERVICE MANAGEMENT INFORMATION SYSTEM AT P3-M UNIVERSITY OF YAPIS PAPUA

Improving the quality of lecturers include in Tri Darma of University, they are: teaching, research and community service. These activities are fully managed by research and community service units or institutions in each university. University of Yapis Papua has a work unit that manages these activities, it called Pusat Penelitian dan Pengabdian Masyarakat (P3-M), where the process of managing research and community service activities is not yet fully integrated and well-documented. This study aims to create information system of activity management from P3-M unit at University of Yapis Papua. This information system will be used, the management of activities research and community service includes administrative data for submitting activity proposals, and reporting results. It hopes can be coordinated well. The methods were used waterfall as a system development method, Unifield Modeling Language (UML) for system design, and pieces method for problem analysis. The result of this study is produce a web-based information system with several menus that could help P3-M University of Yapis Papua.