Reviewerly/exHarmony huggingface dataset:
NEW TRENDS IN LESS-RESOURCED LANGUAGE PROCESSING: CASE OF AMAZIGH LANGUAGE

The coronavirus (COVID-19) pandemic has dramatically changed lifestyles in much of the world. It forced people to profoundly review their relationships and interactions with digital technologies. Nevertheless, people prefer using these technologies in their favorite languages. Unfortunately, most languages are considered even as low or less-resourced, and they do not have the potential to keep up with the new needs. Therefore, this study explores how this kind of languages, mainly the Amazigh, will behave in the wholly digital environment, and what to expect for new trends. Contrary to last decades, the research gap of low and less-resourced languages is continually reducing. Nonetheless, the literature review exploration unveils the need for innovative research to review their informatization roadmap, while rethinking, in a valuable way, people’s behaviors in this increasingly changing environment. Through this work, we will try first to introduce the technology access challenges, and explain how natural language processing contributes to their overcoming. Then, we will give an overview of existing studies and research related to under and less-resourced languages’ informatization, with an emphasis on the Amazigh language. After, based on these studies and the agile revolution, a new roadmap will be presented.
Interactive Book Design for Speech Delay Child Therapy

The ability of speech is influenced by intrinsic and extrinsic factors. If the two factors do not support the development of speech in the child, then the speech ability will be late, which is called speech delay. If speech delay is not addressed immediately, then the impact will continue until the child is an adult. Therefore, interactive book design has a purpose as a media of stimulation that can be used by children and parents. This research will raise topics about the creation and application of interactive visuals in books. The results of this study are expected to be played by parents with children on a regular basis so that the features of this interactive book can be used effectively until the child is interested in learning to recognize and pronounce words or sentences and at the same time helps improve fine motor skills in the child.&#x0D; Keywords : speech delay; interactive; book; stimulation
Hand Sign Detection System for Deaf and Dumb People

Hand gestures are a type of non-verbal communication that can be utilized in many contexts, such as deafmute communication.The automatic interpretation of sign language is a research area that has not gotten much attention, despite the fact that it is essential for hearing impaired and silent persons to live independent lives as sign language is their primary mode of communication.Numerous methods and algorithms have been created in this field with the help of artificial intelligence and image processing.To recognize the signs and translate them into the necessary patterns, any system that understands sign language has undergone considerable training.This will help deaf people to communicate with the outside world easily.This proposed technique helps vocally disabled people to communicate.
The Needs of English Culinary Booklet to Support Domestic and Foreign Promotion at the South Sumatra Culture and Tourism Office

Abstract. This research aimed to determine the Domestic and Foreign Promotion at the South Sumatra Culture and Tourism Office needs in booklet development based on South Sumatra culinary. The data were obtained through the use of a questionnaire and semi-structured interviews. The sample of this study was stakeholders’ and staff at South Sumatera Culture and Tourism office. The data were analyzed both quantitatively and qualitatively. The needs analysis performed included TSA, deficiency analysis, PSA, and strategy analysis. The result of the need analysis showed that: 1) booklet material was important to support the region’s promotion. 2) developed English culinary booklet is important to help South Sumatra promote well because most of its employees already have an intermediate level of English. 3) Booklets that are remade only once a year with almost the same topic and only updated. 4) Integrating a culinary booklet was needed since there was only a booklet related to South Sumatera in general. 5) Integrating the Traditional Culinary Booklet of South Sumatra could encourage the awareness to learn traditional culinary of South Sumatra. 6) Most readers needed to learn the South Sumatera culinary booklet related to the main course, Dessert and Appetizer. 7) The readers prefer it will be using easy-to-understand language, and there is an explanation for difficult terms. Due to the needs, it was necessary to develop an English booklet by integrating South Sumatra culinary, which can support domestic and foreign promotion at the South Sumatra Culture and Tourism Office.Keywords: English Culinary Booklet, Tourism, Needs Analysis
Shortest Path Clustering Dalam Menyaring Tingkat Kepadatan Arus Lalu Lintas

This study explores the application of graph clustering in identifying and analyzing the shortest traffic-related routes. Graph clustering groups points (vertices) based on road attributes. The DBSCAN method and ant algorithm are applied to classify vertices based on traffic intensity and find the optimal shortest path. This case study focuses on the Tangerang Selatan region, resulting in three clusters and identifying seven noises. Two of the three clusters are selected to calculate the shortest distance, resulting in the sequence [7, 6, 0, 1, 2, 3, 4, 8, 5] and a distance of 0.2526. This research provides insights into how to graph clustering can be used to optimize traffic routes and is expected to serve as a foundation for further exploration
Enhancing Network Forensic and Deep Learning Mechanism for Internet of Things Networks

The integration of intelligence into everyday products has been possible due to the ongoing shrinking of hardware and a rise in power efficiency. The Internet of Things (IoT) area arose from the tendency to add computational capabilities to so-called non-intelligent daily items. IoT systems are attractive targets for cyber-attacks because they have many applications. Adversaries use a variety of Advanced Persistent Threat (APT) strategies and trace the source of cyber-attack events to safeguard IoT networks. The Particle Deep Framework (PDF), which is proposed in this study, is a novel Network Forensics (NF) that encompasses the digital investigative phases for spotting &amp; tracing attack activity in IoT networks. The suggested framework contains three novel functionalities for dealing with encrypted networks, such as collecting network data flows &amp; confirming their integrity, using a PSO algorithm, "Bot-IoT "&amp; "UNSW NB15" datasets. The suggested PDF is related to several deep-learning methods. Experimental&nbsp;outcomes show that the proposed framework is very good at discovering &amp; tracing cyber-attack occurrences when compared to existing approaches. The proposed design is implemented using neural network technology. The proposed design has 10% accuracy when compared with the existing structure.&nbsp;This paper is expected to offer a quick reference for researchers interested in understanding the use of network forensics and IOT.
Klasterisasi Data Penerima Bantuan Langsung Tunai Menggunakan Algoritma K-Means

Increasing population and unequal distribution of population even with conditions of varying poverty levels need to be the center of attention and proper handling. In Pelangsian Village, there were 202 residents who received BLTD in 2021. The existence of a quota of beneficiaries and the number of recipients' conditions that were not suitable often became an obstacle in determining beneficiaries. So that from the data obtained in this study it is necessary to do clustering. Clustering results can be used to find out if the population receiving BLTD meets predetermined criteria. so that it can further assist the government in seeing the categories of people who are really entitled to get this assistance. Data clustering can be done using algorithms in data mining. The algorithm used in the data clustering of Pelangsian villagers in this study is the K-Means algorithm. The research methodology was carried out in several stages, such as problem selection, data collection, data preprocessing, data mining algorithm selection, results evaluation, and results interpretation. Clustering is done by forming 2 data clusters. Before the data is clustered, 202 records need to be preprocessed so that it is found that there are 196 valid data records that can be processed according to research needs. The results of data processing are done by clustering the data into 2 groups. Clustering uses the K-Means algorithm by determining the value of K = 2 so that it is obtained that cluster0 has 115 residents and cluster1 has 81 residents. Algorithm performance testing shows that the K-Means Algorithm obtains a Devies-Bouldin value of -0.794. With a Davies-Bouldin-0.794 value, it can be said that the performance of the clustering algorithm is quite good.
Higher Education Student Loans and Enrolment in India, Ghana, Kenya, Tanzania and Uganda

The study uses generalised least squares (GLS), descriptive, phenomenological and mixed methods to examine the effects of higher education (HE) loans on tertiary education enrolment in India, Ghana, Kenya, Tanzania and Uganda. The data sets employed in the empirical analyses were obtained from the United Nations Educational Scientific and Cultural Organisation (UNESCO), India Educational Statistics, the Ghana Ministry of Education, the Kenya Higher Education Loan Board, the Student Loan Financing Board of Tanzania and the Uganda Ministry of Education and Sports. The paper examines the HE student loan schemes introduced and implemented in Ghana, India, Kenya, Tanzania and Uganda through commercial banks, public banks or governments along with their respective effects on higher education enrolment. Empirical results show that boosting higher education is a student loan phenomenon. Meanwhile, in the last two decades, student loan schemes performed better in India, Ghana, Kenya, and Tanzania than in Uganda. To become as successful as India in implementing the HE student loan scheme and improve HE enrolment, the paper suggests that the Government of Uganda must formulate a more comprehensive education loan scheme in consultation with the Bank of Uganda and other banks in Uganda.
SteamBR: a dataset for game reviews and evaluation of a state-of-the-art method for helpfulness prediction

The digital revolution has led to exponential growth in user-generated content, including ratings and reviews, across numerous online platforms. One such platform is Steam, a multifaceted digital distribution network primarily for video games, that also functions as an active social network. Like many e-commerce, travel, and restaurant platforms, Steam users rely heavily on reviews to inform their purchasing decisions. However, the vast amount of data and varying quality of reviews may hinder the utility of such reviews. Furthermore, there is a significant challenge in assessing the helpfulness of recent or less-voted reviews. This study proposes a method for automating review helpfulness evaluation, focusing particularly on Brazilian Portuguese game reviews. The research involved the collection of a large dataset, including 2,789,893 reviews from over 12,000 games, creating a novel dataset for game reviews. Using feature extraction techniques, we were able to capture the metadata, semantic elements, and distributional characteristics present in the reviews. Subsequently, Machine Learning algorithms were employed to perform classification and regression tasks, with the objective of discerning helpful from unhelpful reviews. The achieved results demonstrated that the method was highly effective in predicting review helpfulness.
Efficient and Multiply Robust Risk Estimation under General Forms of Dataset Shift

Statistical machine learning methods often face the challenge of limited data available from the population of interest. One remedy is to leverage data from auxiliary source populations, which share some conditional distributions or are linked in other ways with the target domain. Techniques leveraging such \emph{dataset shift} conditions are known as \emph{domain adaptation} or \emph{transfer learning}. Despite extensive literature on dataset shift, limited works address how to efficiently use the auxiliary populations to improve the accuracy of risk evaluation for a given machine learning task in the target population. In this paper, we study the general problem of efficiently estimating target population risk under various dataset shift conditions, leveraging semiparametric efficiency theory. We consider a general class of dataset shift conditions, which includes three popular conditions -- covariate, label and concept shift -- as special cases. We allow for partially non-overlapping support between the source and target populations. We develop efficient and multiply robust estimators along with a straightforward specification test of these dataset shift conditions. We also derive efficiency bounds for two other dataset shift conditions, posterior drift and location-scale shift. Simulation studies support the efficiency gains due to leveraging plausible dataset shift conditions.
FedADT: An Adaptive Method Based on Derivative Term for Federated Learning

Federated learning is served as a novel distributed training framework that enables multiple clients of the internet of things to collaboratively train a global model while the data remains local. However, the implement of federated learning faces many problems in practice, such as the large number of training for convergence due to the size of model and the lack of adaptivity by the stochastic gradient-based update at the client side. Meanwhile, it is sensitive to noise during the optimization process that can affect the performance of the final model. For these reasons, we propose Federated Adaptive learning based on Derivative Term, called FedADT in this paper, which incorporates adaptive step size and difference of gradient in the update of local model. To further reduce the influence of noise on the derivative term that is estimated by difference of gradient, we use moving average decay on the derivative term. Moreover, we analyze the convergence performance of the proposed algorithm for non-convex objective function, i.e., the convergence rate of 1/nT can be achieved by choosing appropriate hyper-parameters, where n is the number of clients and T is the number of iterations, respectively. Finally, various experiments for the image classification task are conducted by training widely used convolutional neural network on MNIST and Fashion MNIST datasets to verify the effectiveness of FedADT. In addition, the receiver operating characteristic curve is used to display the result of the proposed algorithm by predicting the categories of clothing on the Fashion MNIST dataset.
Automated Planning to Prioritise Digital Forensics Investigation Cases Containing Indecent Images of Children

Law enforcement agencies (LEAs) globally are facing high demand to view, process, and analyse digital evidence. Arrests for Indecent Images of Children (IIOC) have risen by a factor of 25 over the previous decade. A case typically requires the use of computing resources for between 2-4 weeks. The lengthy time is due to the sequential ordering of acquiring a forensically sound copy of all data, systematically extracting all images, before finally analysing each to automatically identify instances of known IIOC images (second-generation) or manually identifying new images (first-generation). It is therefore normal practice that an understanding of the image content is only obtained right at the end of the investigative process. A reduction in processing time would have a transformative impact, by enabling timely identification of victims, swift intervention with perpetrators to prevent re-offending, and reducing the traumatic psychological effects of any ongoing investigation for the accused and their families. In this paper, a new approach to the digital forensic processes containing suspected IIOC content is presented, whereby in-process metrics are used to prioritise case handling, ensuring cases with a high probability of containing IIOC content are prioritised. The use of automated planning (AP) enables a systematic approach to case priorisation. In this paper, a planning approach is presented where AP is used to generate investigative actions in 60-minute segments, before re-planning to account for discoveries made during the execution of planned actions. A case study is provided consisting of 5 benchmark cases, demonstrating on average a reduction of 36% in processing time and a 26% reduction in time required to discover IIOC content.
Planning for Manipulation among Movable Objects: Deciding Which Objects Go Where, in What Order, and How

We are interested in pick-and-place style robot manipulation tasks in cluttered and confined 3D workspaces among movable objects that may be rearranged by the robot and may slide, tilt, lean or topple. A recently proposed algorithm, M4M, determines which objects need to be moved and where by solving a Multi-Agent Pathfinding (MAPF) abstraction of this problem. It then utilises a nonprehensile push planner to compute actions for how the robot might realise these rearrangements and a rigid body physics simulator to check whether the actions satisfy physics constraints encoded in the problem. However, M4M greedily commits to valid pushes found during planning, and does not reason about orderings over pushes if multiple objects need to be rearranged. Furthermore, M4M does not reason about other possible MAPF solutions that lead to different rearrangements and pushes. In this paper, we extend M4M and present Enhanced-M4M (E-M4M) -- a systematic graph search-based solver that searches over orderings of pushes for movable objects that need to be rearranged and different possible rearrangements of the scene. We introduce several algorithmic optimisations to circumvent the increased computational complexity, discuss the space of problems solvable by E-M4M and show that experimentally, both on the real robot and in simulation, it significantly outperforms the original M4M algorithm, as well as other state-of-the-art alternatives when dealing with complex scenes.
Planning with Multi-Agent Belief Using Justified Perspectives

Epistemic planning plays an important role in multi-agent and human-agent interaction domains. Most existing works solve multi-agent epistemic planning problems by either pre-compiling them into classical planning problems; or, using explicit actions and their effects to encode Kripke-based semantics. A recent approach called Planning with Perspectives (PWP) delegates epistemic reasoning in planning to external functions using F-STRIPS, keeping the search within the planning algorithm and lazily evaluating epistemic formulae. Although PWP is expressive and efficient, it models S5 epistemic logic and does not support belief, including false belief. In this paper, we extend the PWP model to handle multi-agent belief by following the intuition that agents believe something they have seen until they see otherwise. We call this justified perspectives. We formalise this notion of multi-agent belief based on the definition of knowledge in PWP. Using experiments on existing epistemic and doxastic planning benchmarks, we show that our belief planner can solve benchmarks more efficiently than the state-of-the-art baseline, and can model some problems that are infeasible to model using propositional-based approaches.
Generalizing Action Justification and Causal Links to Policies

We revisit two concepts popularly used within the context of classical planning, namely action justification and causal links. While these concepts have come to underpin some of the most popular notions of explanations in classical planning, these notions are restricted to sequential plans. To address this shortcoming, we propose a generalization of these concepts that is applicable to state-action policies. We introduce algorithms that can identify justified actions and causal links contributed by such actions for policies generated for Fully Observable Non-Deterministic (FOND) planning problems. We also present an empirical evaluation that demonstrates the computational characteristics of these algorithms on standard FOND benchmarks.
On K* Search for Top-K Planning

Finding multiple high-quality plans is essential in many planning applications, and top-k planning asks for finding the k best plans, naturally extending cost-optimal classical planning. Several attempts have been made to formulate top-k classical planning as a k-shortest paths finding problem and apply K* search, which alternates between A* and Eppstein's algorithm. However, earlier work had shortcomings, among which were failing to handle inconsistent heuristics and degraded performance in Eppstein's algorithm implementations. As a result, existing evaluation results severely underrate the performance of the K* based approach to top-k planning. In this paper, we present a new top-k planner based on a novel variant of K* search. We address the following three aspects. First, we show an alternative implementation of Eppstein's algorithm for classical planning, which resolves a major bottleneck in earlier attempts. Second, we present a new strategy for alternating A* and Eppstein's algorithm, that improves the performance of K* on the classical planning benchmarks. Last, we introduce a simple mitigation of the limitation of K* to tasks with a single goal state, allowing us to preserve heuristic informativeness in face of imposed task reformulation. Empirical evaluation results show that the proposed approach achieves the state-of-the-art performance on the classical planning benchmarks. The code is available at https://github.com/IBM/kstar.
A-ePA*SE: Anytime Edge-Based Parallel A* for Slow Evaluations

Anytime search algorithms are useful for planning problems where a solution is desired under a limited time budget. Anytime algorithms first aim to provide a feasible solution quickly and then attempt to improve it until the time budget expires. On the other hand, parallel search algorithms utilize the multithreading capability of modern processors to speed up the search. One such algorithm, ePA*SE (Edge-Based Parallel A* for Slow Evaluations), parallelizes edge evaluations to achieve faster planning and is especially useful in domains with expensive-to-compute edges. In this work, we propose an extension that brings the anytime property to ePA*SE, resulting in A-ePA*SE. We evaluate A-ePA*SE experimentally and show that it is significantly more efficient than other anytime search methods. The open-source code for A-ePA*SE, along with the baselines, is available here: https://github.com/shohinm/parallel_search
Act-Then-Measure: Reinforcement Learning for Partially Observable Environments with Active Measuring

We study Markov decision processes (MDPs), where agents control when and how they gather information, as formalized by action-contingent noiselessly observable MDPs (ACNO-MPDs). In these models, actions have two components: a control action that influences how the environment changes and a measurement action that affects the agent's observation. To solve ACNO-MDPs, we introduce the act-then-measure (ATM) heuristic, which assumes that we can ignore future state uncertainty when choosing control actions. To decide whether or not to measure, we introduce the concept of measuring value. We show how following this heuristic may lead to shorter policy computation times and prove a bound on the performance loss it incurs. We develop a reinforcement learning algorithm based on the ATM heuristic, using a Dyna-Q variant adapted for partially observable domains, and showcase its superior performance compared to prior methods on a number of partially-observable environments.