Reviewerly/exHarmony huggingface dataset:
An Indigenous Knowledge System for Promoting Awareness of Water Resource Issues

Water resource issues such as water shortages exert stress on the socioeconomic growth and sustainability of developing countries worldwide. The sharing of complex data, knowledge, and information is crucial for the management of water. The lack of conveying water knowledge to end-users (citizens) in a meaningful manner has been identified as a contributing challenge towards water resource issues. With appropriate tools and technologies such as gamification and social technologies, this challenge can be addressed by developing engaging and motivating systems to promote awareness of water resource issues. This paper systematically reviews the literature to investigate the shortcomings of existing water systems in promoting awareness of water resource issues. Socio-technical systems theory was adopted for understanding and improving citizens’ activities related to the consumption of natural resources such as water, and general environmental sustainability. Knowledge of water resource issues should be relevant to the particular culture of the citizen and incorporate indigenous knowledge. The literature review, theory, and findings of this paper contributed to the design of an Indigenous Knowledge System (IKS) that can promote engagement and awareness of water resource issues.
Direct Product Hardness Amplification

We revisit one of the most fundamental hardness amplification constructions, originally proposed by Yao (FOCS 1982). We present a hardness amplification theorem for the direct product of certain games that is simpler, more general, and stronger than previously known hardness amplification theorems of the same kind. Our focus is two-fold. First, we aim to provide close-to-optimal concrete bounds, as opposed to asymptotic ones. Second, in the spirit of abstraction and reusability, our goal is to capture the essence of direct product hardness amplification as generally as possible. Furthermore, we demonstrate how our amplification theorem can be applied to obtain hardness amplification results for non-trivial interactive cryptographic games such as MAC forgery or signature forgery games.
Sinhala Sign Language Interpreter Optimized for Real – Time Implementation on a Mobile Device

This paper proposes a framework for a vision based Sinhala Sign Language interpreter targeted for implementation on a portable device, optimized for real-time use. The translator is aimed at enabling conversation between a hearing-impaired and a non-signing individual. The scope covers both static and dynamic signs, portrayed using the right hand. Skin segmentation and contour extraction followed by a combination of hand detection and tracking algorithms isolate the signing hand against varied background conditions. A Convolutional Neural Network model was developed to extract and classify the features of the chosen static signs. A standard, expandable dataset of Sinhala static signs was prepared for this task. Dynamic signs were modeled as a tree data structure using a sequence of static signs. The model was optimized using motion based temporal segmentation between consecutive signs, to minimize the processing overhead. The interpreter recorded an average accuracy of 99.5% and 81.2% on the static sign dataset, and combined dataset of static and dynamic signs, respectively. A response time of333 ms was resulted between the occurrence and prediction of a sign, demonstrating the effectiveness of the framework for real-time use.
Analysis of Time Quantity and Motion Quantity Factors in the Meanings of “Changchang (often)”

The word “Changchang (often)” is generally regarded as a time adverb or a frequency adverb. Most dictionaries lay emphasis on its meanings of “large number of actions,” “short unit time” and “short time interval.” That is why the meanings of “Changchang (often)” include both factors of time quantity and motion quantity. Its motion quantity factor lies in the number of actions. Its time quantity factor is reflected in two ways: the length of “unit time” involved in frequency and the length of “time interval” of actions. The findings of this research paper suggest that in sentences that express the frequency of actions, there exists limit of “quantity value” in the meanings of “number of actions,” “no long unit time” and “short time interval” of “Changchang (often).” “Number of actions” is limited by maximum value and minimum value while “unit time” and “time interval” are limited by maximum value. The limit of maximum and minimum values is the logical basis the validity of “Changchang” clauses. Being out of the range of their quantity value would invalidate “Changchang (often).” The limit of quantity value of “Changchang (often)” should be paid special attention to when acquiring and applying “Changchang (often)” sentences.
What Risk Prevention Measures Can I Use?

The question “What risk prevention measures can I use?” describes how to reduce the likelihood of a cyberattack on your organization. The chapter begins with a case study on the SolarWinds hack exemplifying how prevention measures on a specific system, network, or data cannot be effective on their own. The chapter describes why cyber risk management needs to be embedded across all facets of the organization, and how the Embedded Endurance strategy can help readers achieve that. It reviews system security prevention measures that include patch management and antivirus software. It explains network security prevention measures, including intrusion detection and intrusion prevention systems. The chapter also describes data risk prevention measures such as data governance, encryption, and data loss prevention technology, and highlights the importance of physical security for reducing cyber risk. The chapter concludes with Falco’s Embedded Endurance strategy insight on risk prevention gained at his industrial Internet-of-Things security company.
Simulating Unconventional Approach of Post Pandemic Cyber Threats

The Post-Covid has enforced every organization & person to hold new practices like social-distancing & remote working. Public Sectors are reassessing all methods to ensure that their nation remain steady by developing and implementing novel financial plans. Yet, although the world is attentive on the health & monetary threats imposed by post pandemic, hackers around the world unquestionably are take advantage of on this disaster.  This research study's primary focus is to study and analyze latest security threats and trends identified during post pandemic and their motives. The study will analyze several capabilities like: Identifying recent threats, discovering vulnerabilities, evaluating vulnerabilities, prioritizing identified threats, risk assessment and rating, suggesting best practice for security controls.
Reordering 2-d Binary Matrices Without Permutation Generation

Matrix reordering is the arrangement of rows and columns in a given matrix to achieve a representation of the data. This paper presents heuristic methods for reordering rows and columns of 2-dimensional matrices. First, we show that our adopted heuristics can reorder 2-D matrices efficiently without considering permutation generation, an NP complete problem. Second, we evaluate the approach by comparing three heuristic methods to matrix reordering problem: a two-dimensional sort (2-D Sort), barycentre (BC) algorithm and the proposed 2-Dimensional Banding algorithm (2-D Banding). We conduct evaluation using randomly generated datasets and UCI datasets. This paper evaluates the heuristic methods using Average Distance (AD); the distance of nonzero entries from the leading diagonal and the computation time. The results presented shows that the proposed 2-D banding algorithm produced a minimized AD than the 2-D Sort and BC.
Improving Generalization in Meta-RL with Imaginary Tasks from Latent Dynamics Mixture

The generalization ability of most meta-reinforcement learning (meta-RL) methods is largely limited to test tasks that are sampled from the same distribution used to sample training tasks. To overcome the limitation, we propose Latent Dynamics Mixture (LDM) that trains a reinforcement learning agent with imaginary tasks generated from mixtures of learned latent dynamics. By training a policy on mixture tasks along with original training tasks, LDM allows the agent to prepare for unseen test tasks during training and prevents the agent from overfitting the training tasks. LDM significantly outperforms standard meta-RL methods in test returns on the gridworld navigation and MuJoCo tasks where we strictly separate the training task distribution and the test task distribution.
The impact of Administrative Leadership on Job Satisfaction in Chadian Universities: An applied Study on King Faisal University of Chad and Hec-Tchad University Jun2020: تأثير القيادة الإدارية في زيادة أو نقض الرضا الوظيفي في الجامعات التشادية: دراسة تطبيقية على جامعتي الملك فيصل بتشاد وجامعة هيك تشاد، 2020م

The study aims: to clarify the most important administrative leadership forms, identify the impact of leadership on the behavior of Universities’ staffs, demonstrate the extent to which lecturers’ educational performance is affected by the leadership of the university administration and to know the level of relationship between the leaders and lecturers. The study used the descriptive analytical method. The most important findings of the study are: Chadian universities’ leaders don’t take decisions based on the opinion on the majority, universities’ leaders don’t take decision based on the others opinions, universities’ administration don’t take in to account employees suggestions and decisions are not taken clearly and with transparency.
"Herramientas en idioma inglés para el aprendizaje de plataforma AMADEUS, de estudiantes de octavo semestre de la carrera de Turismo Universidad Técnica de Babahoyo Extensión Quevedo"

The use of tools for learning tourist operation platforms is total in the English language. One of the most effective ways to create a transcendental shift from a traditional teaching towards a more meaningful and authentic teaching in the English class is through the implementation of active learning strategies and techniques. This method produced measuring in students who knew about tourism operations, but could not interpret codes and commands from a global platform for flight sales. Then came the audiolingual method, which focused on listening, speaking and pronouncing, but in a passive, monotonous and repetitive way, which made the students simply repeat the dialogues word for word, memorizing them. This method produced students who could only converse in very specific contexts.
Scalable Online Planning via Reinforcement Learning Fine-Tuning

Lookahead search has been a critical component of recent AI successes, such as in the games of chess, go, and poker. However, the search methods used in these games, and in many other settings, are tabular. Tabular search methods do not scale well with the size of the search space, and this problem is exacerbated by stochasticity and partial observability. In this work we replace tabular search with online model-based fine-tuning of a policy neural network via reinforcement learning, and show that this approach outperforms state-of-the-art search algorithms in benchmark settings. In particular, we use our search algorithm to achieve a new state-of-the-art result in self-play Hanabi, and show the generality of our algorithm by also showing that it outperforms tabular search in the Atari game Ms. Pacman.
Multiparty Key Agreement Protocol Using Factorization Search Problem in Five-Dimensional Discrete Heisenberg Group

Cryptography is the art as well as science of secret writing. It enables the communicating entities to share the information securely through an insecure channel. In Cryptography, a Key Agreement Protocol (KAP) is a protocol whereby two or more parties can agree on a key in such a way that both influence the outcome. If properly done, this precludes undesired third parties from forcing a key choice on the agreeing parties. Protocols that are useful in practise also do not reveal to any eavesdropping party what key has been agreed upon. This paper proposes a multiparty key agreement protocol which provides a common key between the communicating entities. The KAP proposed in this paper is based on the intractability of the Factorisation search problem (FSP) in the five-dimensional discrete Heisenberg group. The Factorization Search Problem (FSP) plays a role of one – way function (hard function or trapdoor function)and is chosen from combinatorial group theory. To Implement the FSP a platform group is needed which must be a non – abelian group. The Five – dimensional Discrete Heisenberg group is chosen as the platform group.The computational facts for the platform group are provided with Python coding. As a particular case, a seven party KAP with numerical computations is provided. In this protocol the computations are done with the help of Python code.  In a Multiparty KAP with k communicating entities can arrive a common key in k rounds.
Gridworld Testbed for Intelligent Agent Architecture

The most famous characteristic of the machine in our modern age is autonomy. This autonomous in the conditions of a static and dynamic environment requires building models, adopting statistical hypotheses, and delving a lot into probability. The feature of autonomous in static environments is something that has been overcome, but a lot of talk about dynamic environments, changing and multi-events is the great challenge that presents many solutions. Decision-making strategy and reconsideration of decision using available resources at reasonable cost is a challenge for the intelligent agent, the decision making is an intention according to explanation of Bratman when he produced the BDI agent (belief, desire, and intention) [1], that means the intention is under agent's control to adopt it or to update this intention by replaced it, contrary to belief and desire which are outside the control of the agent.      The project that we present includes a system for empirical evaluation of competing for theoretical and architectural proposals, more precisely we built a Gridworld testbed that simulates a real dynamic environment, and the system has an embedded rational agent.
БИОЛОГИЯНИ УкИТИШЛЛ ДИДАКТИК ТАЪЛИМ ТЕХНОЛОГИЯЛАРИДАН ФОЙДАЛАНИШ

One of the developing directions in the field of education isis the use of modern pedagogical technologies in the learning process and its implementationincrease is an urgent task. It is well known that man has a purpose in xdmma soxdin order to achieve it, it is necessary to use the path, method, means that are acceptable to himactivities. So, if the goal first appears, then accordingly,activity based on available capacity.
Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks

Deep neural networks are powerful machines for visual pattern recognition, but reasoning tasks that are easy for humans may still be difficult for neural models. Humans possess the ability to extrapolate reasoning strategies learned on simple problems to solve harder examples, often by for For example, a person who has learned to solve small mazes can easily extend the very same search techniques to solve much larger mazes by spending more time. In computers, this behavior is often achieved through the use of algorithms, which scale to arbitrarily hard problem instances at the cost of more computation. In contrast, the sequential computing budget of feed-forward neural networks is limited by their depth, and networks trained on simple problems have no way of extending their reasoning to accommodate harder problems. In this work, we show that recurrent networks trained to solve simple problems with few recurrent steps can indeed solve much more complex problems simply by performing additional recurrences during inference. We demonstrate this algorithmic behavior of recurrent networks on prefix sum computation, mazes, and chess. In all three domains, networks trained on simple problem instances are able to extend their reasoning abilities at test time simply by thinking for longer.
H-NeRF: Neural Radiance Fields for Rendering and Temporal Reconstruction  of Humans in Motion

We present neural radiance fields for rendering and temporal (4D) reconstruction of humans in motion (H-NeRF), as captured by a sparse set of cameras or even from a monocular video. Our approach combines ideas from neural scene representation, novel-view synthesis, and implicit statistical geometric human representations, coupled using novel loss functions. Instead of learning a radiance field with a uniform occupancy prior, we constrain it by a structured implicit human body model, represented using signed distance functions. This allows us to robustly fuse information from sparse views and generalize well beyond the poses or views observed in training. Moreover, we apply geometric constraints to co-learn the structure of the observed subject -- including both body and clothing -- and to regularize the radiance field to geometrically plausible solutions. Extensive experiments on multiple datasets demonstrate the robustness and the accuracy of our approach, its generalization capabilities significantly outside a small training set of poses and views, and statistical extrapolation beyond the observed shape.
Code Navigation and Navigation Helpers

When composing a code, nine out of ten times the users refer to already defined information. For example, consider a use case of searching all the usages of a function. Code navigation features such as references, definitions, and declarations are used frequently to navigate between semantic references of language constructs. In this chapter, our focus is to have an understanding of the code navigation features supported by the Language Server Protocol.
Unsupervised Contextualized Document Representation

Several NLP tasks need the effective repre-sentation of text documents.Arora et al.,2017 demonstrate that simple weighted aver-aging of word vectors frequently outperformsneural models. SCDV (Mekala et al., 2017)further extends this from sentences to docu-ments by employing soft and sparse cluster-ing over pre-computed word vectors. How-ever, both techniques ignore the polysemyand contextual character of words.In thispaper, we address this issue by proposingSCDV+BERT(ctxd), a simple and effective un-supervised representation that combines con-textualized BERT (Devlin et al., 2019) basedword embedding for word sense disambigua-tion with SCDV soft clustering approach. Weshow that our embeddings outperform origi-nal SCDV, pre-train BERT, and several otherbaselines on many classification datasets. Wealso demonstrate our embeddings effective-ness on other tasks, such as concept match-ing and sentence similarity.In addition,we show that SCDV+BERT(ctxd) outperformsfine-tune BERT and different embedding ap-proaches in scenarios with limited data andonly few shots examples.
“Wikily” Supervised Neural Translation Tailored to Cross-Lingual Tasks

We present a simple but effective approach for leveraging Wikipedia for neural machine translation as well as cross-lingual tasks of image captioning and dependency parsing without using any direct supervision from external parallel data or supervised models in the target language. We show that first sentences and titles of linked Wikipedia pages, as well as cross-lingual image captions, are strong signals for a seed parallel data to extract bilingual dictionaries and cross-lingual word embeddings for mining parallel text from Wikipedia. Our final model achieves high BLEU scores that are close to or sometimes higher than strong supervised baselines in low-resource languages; e.g. supervised BLEU of 4.0 versus 12.1 from our model in English-to-Kazakh. Moreover, we tailor our wikily translation models to unsupervised image captioning, and cross-lingual dependency parser transfer. In image captioning, we train a multi-tasking machine translation and image captioning pipeline for Arabic and English from which the Arabic training data is a wikily translation of the English captioning data. Our captioning results on Arabic are slightly better than that of its supervised model. In dependency parsing, we translate a large amount of monolingual text, and use it as an artificial training data in an annotation projection framework. We show that our model outperforms recent work on cross-lingual transfer of dependency parsers.
Developer Tools and Language Services

Today, software development has become an area where there are higher expectations when considering the rapid development, go-to market, deployment, distribution, and similar aspects. In this book, we are going to focus on a specific technical perspective related to source code editing or, in other words, writing the software.
Conditional Deep 3D-Convolutional Generative Adversarial Nets for RGB-D Generation

Generation of synthetic data is a challenging task. There are only a few significant works on RGB video generation and no pertinent works on RGB-D data generation. In the present work, we focus our attention on synthesizing RGB-D data which can further be used as dataset for various applications like object tracking, gesture recognition, and action recognition. This paper has put forward a proposal for a novel architecture that uses conditional deep 3D-convolutional generative adversarial networks to synthesize RGB-D data by exploiting 3D spatio-temporal convolutional framework. The proposed architecture can be used to generate virtually unlimited data. In this work, we have presented the architecture to generate RGB-D data conditioned on class labels. In the architecture, two parallel paths were used, one to generate RGB data and the second to synthesize depth map. The output from the two parallel paths is combined to generate RGB-D data. The proposed model is used for video generation at 30 fps (frames per second). The frame referred here is an RGB-D with the spatial resolution of 512 × 512.
EDTC: A Corpus for Discourse-Level Topic Chain Parsing

Discourse analysis has long been known to be fundamental in natural language processing. In this research, we present our insight on discourse-level topic chain (DTC) parsing which aims at discovering new topics and investigating how these topics evolve over time within an article. To address the lack of data, we contribute a new discourse corpus with DTC-style dependency graphs annotated upon news articles. In particular, we ensure the high reliability of the corpus by utilizing a two-step annotation strategy to build the data and filtering out the annotations with low confidence scores. Based on the annotated corpus, we introduce a simple yet robust system for automatic discourse-level topic chain parsing.
Electronic evidence at Trial: Appreciating digital issues and Adjudication process

How to admit electronic evidence to decide cases has become a major conundrum in Indian judicial practice. As a new branch of evidence with high technology, digital evidence has been playing an increasingly important role in solving legal disputes. In the meantime, it has also brought variety of issues and challenges into the legal system regarding evidence, such as how to search, seize and preserve digital evidence, how to analyse, review and digital evidence, and more importantly, how to effectively decide cases based on electronic evidence.Digital or electronic evidence is any probative information stored or transmitted in digital form that a person to a court case may use at trial. Before accepting digital evidence a court will determine if the evidence is relevant, if it is authentic, if it is hearsay and if a copy is acceptable or the original is required.