Reviewerly/exHarmony huggingface dataset:
A Broad-Coverage Deep Semantic Lexicon for Verbs

Progress on deep language understanding is inhibited by the lack of a broad coverage lexicon that connects linguistic behavior to ontological concepts and axioms. We have developed COLLIE-V, a deep lexical resource for verbs, with the coverage of WordNet and syntactic and semantic details that meet or exceed existing resources. Bootstrapping from a hand-built lexicon and ontology, new ontological concepts and lexical entries, together with semantic role preferences and entailment axioms, are automatically derived by combining multiple constraints from parsing dictionary definitions and examples. We evaluated the accuracy of the technique along a number of different dimensions and were able to obtain high accuracy in deriving new concepts and lexical entries. COLLIE-V is publicly available.
Discourse Analysis on the Critiques of Grade-10 World Literature Students: Approaches to Literary Criticism in Focus

This qualitative corpora-based study examined the content of the critiques of Grade 10 world literature students in one of the Division of Davao del Norte schools as they analyzed a story reflected in the Self-Learning Module.This study aimed to identify the approaches to literary criticism used by the students as their literary frameworks in interpreting the story.These approaches were based on the Literary Theory and Schools of Criticism proposed by Dobie (2011).It also investigated how the approaches to literary criticism may benefit students in writing critiques.The results of the study showed that reader-response criticism, Marxist criticism, formalist criticism, feminist criticism, psychological criticism, and deconstructive criticism were present in the critiques of the students.On the other hand, literary criticisms may benefit students through understanding meaning, establishing philosophy, discovering history, and developing their writing skills.Literary criticisms, in general, foster critical thinking, and it is advised that literary criticism must be intensified in the teaching-learning process in order to produce citizens who are critical thinkers and to contribute to UNESCO's Sustainable Development Goal 4, which is the quality education, and in support to DepEd Basic Education Development Plan 2030 (BEDP 2030) which aimed to embed critical thinking as one of the 21 st -century skills, and address the significant component of the reskilling/upskilling of teachers in Sulong Edukalidad which were formulated to address the poor students' performances in PISA 2018.
A Phenomenological Study on the Experiences of Public Elementary School Teachers in Crafting Self-Learning Modules (SLM) in the New Normal

This study aimed to explore and understand the lived experiences of SLM writers in crafting Self-Learning Modules (SLM) in the New Normal in Tagum City Division.It used qualitative research design employing phenomenological approach.It comprised of fourteen beginning public elementary teachers: seven teachers for online in-depth interview (IDI) and seven teachers for online focus group discussion (FGD).This study was limited to describing and understanding the lived experiences of the participants and the data were taken from the results of IDI and FGD.For data analysis, this study employed coding and thematic analysis.For experiences, findings revealed that they lack training and knowledge on the assigned subjects; experienced time constraints; had hesitations, stress, and anxiety; and encountered dilemma on copyright issues and social contents.Participans coped with their challenges by being resourceful in doing research; asking supports from experts and colleagues; using appropriate and relatable contents; avoiding sensitive topics; and paraphrasing and citing sources to avoid plagiarism.They also cited insights which revolved around the following themes: becoming a writer as an opportunity for professional growth; provision of support for teacher-writers; DepEd to select expert teachers; give enough time for writing modules; teacher-writers to consider learner's capacity; and grant of rewards and promotion.The results implied that writers should have enough trainings and seminar-workshops about SLM crafting.Also, the study would give awareness and suggestions that could be helpful for teachers, students, and DepEd authorities as well.
Securing Digital Image using Modified Chaotic Logistic Key

Digital image is an important data type due to the wide applications requiring this data type. Some applications require image securing to protect the image from being hacked. Increasing the complicity of the private key will enhance the security level of the image by increasing the kay space size. In this paper research a chaotic logistic map model will be used to generate the required key for data cryptography. A modification in the CLMM will be added to generate a 3D chaotic key, this key will be converted to integer key, then the obtained key will be rotated left using another information imbedded in the PK, the rotated key will be resized to match the color channel size using simple and efficient method of image resizing. Each color channel will be encrypted-decrypted separately using its own generated key. The proposed method will be implemented using various images, the obtained results will be analyzed using various methods of data analysis. The quality analysis will be use the calculated values of the quality parameters MSE, PSNR, CC and NSCR to prove the quality requirements in the encryption and decryption phase. Efficiency analysis of ET/DT and TP will be used to show the enhancements provided by the proposed method and to measure the speedup of the proposed method comparing with other existing methods of data cryptography.
Exploratory Study to the Common Architecture of University Computerized Systems and the Solution to its Architectural Problems

This research has been focused on exploring the common architecture of university computerized systems (UCSs) to discover the common gaps and problems available in the architecture of most universities worldwide. Three universities have been selected to study and explore the common architecture of their systems; in addition to the literature of other universities. As a result, there are several problems in this architecture; these problems are direct results of tight-coupling, stand-alone applications, integration gap and interoperability gap. Therefore, this research has presented Service Oriented Architecture (SOA) as a solution to the mentioned problems. The solution has been described and a prototype has been designed, implemented and tested successfully as a proof to concept.
Neural Databases

In recent years, neural networks have shown impressive performance gains on long-standing AI problems, and in particular, answering queries from natural language text. These advances raise the question of whether they can be extended to a point where we can relax the fundamental assumption of database management, namely, that our data is represented as fields of a pre-defined schema. This paper presents a first step in answering that question. We describe NeuralDB, a database system with no pre-defined schema, in which updates and queries are given in natural language. We develop query processing techniques that build on the primitives offered by the state of the art Natural Language Processing methods. We begin by demonstrating that at the core, recent NLP transformers, powered by pre-trained language models, can answer select-project-join queries if they are given the exact set of relevant facts. However, they cannot scale to non-trivial databases and cannot perform aggregation queries. Based on these findings, we describe a NeuralDB architecture that runs multiple Neural SPJ operators in parallel, each with a set of database sentences that can produce one of the answers to the query. The result of these operators is fed to an aggregation operator if needed. We describe an algorithm that learns how to create the appropriate sets of facts to be fed into each of the Neural SPJ operators. Importantly, this algorithm can be trained by the Neural SPJ operator itself. We experimentally validate the accuracy of NeuralDB and its components, showing that we can answer queries over thousands of sentences with very high accuracy.
A walk on max-plus algebra

Max-plus algebra is a kind of idempotent semiring over $\mathbb{R}_{\max}:=\mathbb{R}\cup\{-\infty\}$ with two operations $\oplus := \max$ and $\otimes := +$.In this paper, we introduce a new model of a walk on one dimensional lattice on $\mathbb{Z}$, as an analogue of the quantum walk, over the max-plus algebra and we call it max-plus walk. In the conventional quantum walk, the summation of the $\ell^2$-norm of the states over all the positions is a conserved quantity. In contrast, the summation of eigenvalues of state decision matrices is a conserved quantity in the max-plus walk.Moreover, spectral analysis on the total time evolution operator is also given.
The concept of life skills employed in the epic Ramayana: A theoretical perspectives

Every human being in the world is unique creation of this universe and one is born with specific qualities and characteristics. If we are not able to use the hereditary and acquired qualities to increase our efficiency, then these qualities have no meaning in the life. In this context, the importance of life skills in the field of education is being explained today. Life skills education provides the person with the skills to face the conflicts that come in life. This creates the foundation of a cultured society that is prudent. Even scholars in modern times admit that although the current education system is familiar with these life skills principles, most of these have been developed by Western countries. While there is a comprehensive description of life skills pervading in ancient Indian scriptures. The theoretical perspective of those aspects are compiled and presented here with examples related to life skills described in the Ramcharitmanas. The current attempts may serve as a guide for moral, spiritual and social advancement in education needed in current learning system. Theoretical presentation may be contributory to the current battery of knowledge in the areas of life skill exploration in the contexts of ancient scriptures.
Performance measurement with high performance computer of HW-GA anomaly detection algorithms for streaming data

Anomaly detection is very important in every sector as health, education, business, etc. Knowing what is going wrong with data/digital system help peoples from every sector to take decision. Detection anomalies in real time Big Data is nowadays very crucial. Dealing with real time data requires speed, for this reason the aim of this paper is to measure the performance of our previously proposed HW-GA algorithm compared with other anomaly detection algorithms. Many factors will be analyzed which may affect the performance of HW-GA as visualization of result, amount of data and performance of computers. Algorithm execution time and CPU usage are the parameters which will be measured to evaluate the performance of HW-GA algorithm. Also, another aim of this paper is to test the HW-GA algorithm with large amount of data to verify if it will find the possible anomalies and the result to compare with other algorithms. The experiments will be done in R with different datasets as real data Covid-19 and e-dnevnik data and three benchmarks from Numenta datasets. The real data have not known anomalies but in the benchmark data the anomalies are known this is in order to evaluate how the algorithms work in both situations. The novelty of this paper is that the performance will be tested in three different computers which one of them is high performance computer.
Cyber Security for Internet of Things

Abstract: The Internet of things has gained intense popularity in the past few years. It has become one of the most important technologies. However along with gaining popularity, threats related to cyber security have increased. As the name suggests, Internet of Things, it is completely based on the internet which means a hacker can find a weaklink in the network and exfiltrate data to the cloud and threaten to keep, delete or make the data public. Thisincludes sensitive data, personally identifiable information (PII), protected health information (PHI), personal information, intellectual property, data, and governmentaland industry information systems. Cybersecurity is a set ofprocesses, tools and frameworks to protect networks, devices, programs and data from cyber attacks.This paper highlights the different types of Cyber attacks possible on IoT and suggests few mitigations towards it
Handmade Eco-Friendly Filter Using Rice Husk for Water Purification

Abstract: Rice husk is a biomass of polymers with abrasive hardness and higher adsorbing capacities. This work investigates the use of rice husks in the purification of Water. A filter is constructed using carbonized rice husk, and Water is filtered. Results show that this method is efficient for removing hardness, turbidity, toxic metals etc. Also, the filter constructed offers an efficiency of 54 mL per second. The efficiency of adsorbing material shows that this method is economically feasible and ecofriendly. Rice husk can replace the synthetic adsorbing materials used in modern filters, which causes many health issues.
Notions of Tensor Rank

Tensors, or multi-linear forms, are important objects in a variety of areas from analytics, to combinatorics, to computational complexity theory. Notions of tensor rank aim to quantify the "complexity" of these forms, and are thus also important. While there is one single definition of rank that completely captures the complexity of matrices (and thus linear transformations), there is no definitive analog for tensors. Rather, many notions of tensor rank have been defined over the years, each with their own set of uses. In this paper we survey the popular notions of tensor rank. We give a brief history of their introduction, motivating their existence, and discuss some of their applications in computer science. We also give proof sketches of recent results by Lovett, and Cohen and Moshkovitz, which prove asymptotic equivalence between three key notions of tensor rank over finite fields with at least three elements.
Unbounded Gradients in Federated Learning with Buffered Asynchronous Aggregation

Synchronous updates may compromise the efficiency of cross-device federated learning once the number of active clients increases. The \textit{FedBuff} algorithm (Nguyen et al., 2022) alleviates this problem by allowing asynchronous updates (staleness), which enhances the scalability of training while preserving privacy via secure aggregation. We revisit the \textit{FedBuff} algorithm for asynchronous federated learning and extend the existing analysis by removing the boundedness assumptions from the gradient norm. This paper presents a theoretical analysis of the convergence rate of this algorithm when heterogeneity in data, batch size, and delay are considered.
Aggregator Reuse and Extension for Richer Web Archive Interaction

Memento aggregators enable users to query multiple web archives for captures of a URI in time through a single HTTP endpoint. While this one-to-many access point is useful for researchers and end-users, aggregators are in a position to provide additional functionality to end-users beyond black box style aggregation. This paper identifies the state-of-the-art of Memento aggregation, abstracts its processes, highlights shortcomings, and offers systematic enhancements.
Electron-Photon Vertex and Dynamical Chiral Symmetry Breaking in Reduced QED: An Advanced Study of Gauge Invariance

We study the effect of a refined electron-photon vertex on the dynamical breaking of chiral symmetry in reduced quantum electrodynamics. We construct an educated {\em ansatz} for this vertex which satisfies the required discrete symmetries under parity, time reversal and charge conjugation operations. Furthermore, it reproduces its asymptotic perturbative limit in the weak coupling regime and ensures the massless electron propagator is multiplicatively renormalizable in its leading logarithmic expansion. Employing this vertex {\em ansatz}, we solve the gap equation to compute dynamically generated electron mass whose dependence on the electromagnetic coupling is found to satisfy Miransky scaling law. We also investigate the gauge dependence of this dynamical mass as well as that of the critical coupling above which chiral symmetry is dynamically broken. As a litmus test of our vertex construction, both these quantities are rendered virtually gauge independent within a certain interval of values considered for the covariant gauge parameter.
Structural Estimation of Markov Decision Processes in High-Dimensional State Space with Finite-Time Guarantees

We consider the task of estimating a structural model of dynamic decisions by a human agent based upon the observable history of implemented actions and visited states. This problem has an inherent nested structure: in the inner problem, an optimal policy for a given reward function is identified while in the outer problem, a measure of fit is maximized. Several approaches have been proposed to alleviate the computational burden of this nested-loop structure, but these methods still suffer from high complexity when the state space is either discrete with large cardinality or continuous in high dimensions. Other approaches in the inverse reinforcement learning (IRL) literature emphasize policy estimation at the expense of reduced reward estimation accuracy. In this paper we propose a single-loop estimation algorithm with finite time guarantees that is equipped to deal with high-dimensional state spaces without compromising reward estimation accuracy. In the proposed algorithm, each policy improvement step is followed by a stochastic gradient step for likelihood maximization. We show that the proposed algorithm converges to a stationary solution with a finite-time guarantee. Further, if the reward is parameterized linearly, we show that the algorithm approximates the maximum likelihood estimator sublinearly. Finally, by using robotics control problems in MuJoCo and their transfer settings, we show that the proposed algorithm achieves superior performance compared with other IRL and imitation learning benchmarks.
Distance Based Image Classification: A solution to generative classification's conundrum?

Most classifiers rely on discriminative boundaries that separate instances of each class from everything else. We argue that discriminative boundaries are counter-intuitive as they define semantics by what-they-are-not; and should be replaced by generative classifiers which define semantics by what-they-are. Unfortunately, generative classifiers are significantly less accurate. This may be caused by the tendency of generative models to focus on easy to model semantic generative factors and ignore non-semantic factors that are important but difficult to model. We propose a new generative model in which semantic factors are accommodated by shell theory's hierarchical generative process and non-semantic factors by an instance specific noise term. We use the model to develop a classification scheme which suppresses the impact of noise while preserving semantic cues. The result is a surprisingly accurate generative classifier, that takes the form of a modified nearest-neighbor algorithm; we term it distance classification. Unlike discriminative classifiers, a distance classifier: defines semantics by what-they-are; is amenable to incremental updates; and scales well with the number of classes.
ASAP: Accurate semantic segmentation for real time performance

Feature fusion modules from encoder and self-attention module have been adopted in semantic segmentation. However, the computation of these modules is costly and has operational limitations in real-time environments. In addition, segmentation performance is limited in autonomous driving environments with a lot of contextual information perpendicular to the road surface, such as people, buildings, and general objects. In this paper, we propose an efficient feature fusion method, Feature Fusion with Different Norms (FFDN) that utilizes rich global context of multi-level scale and vertical pooling module before self-attention that preserves most contextual information while reducing the complexity of global context encoding in the vertical direction. By doing this, we could handle the properties of representation in global space and reduce additional computational cost. In addition, we analyze low performance in challenging cases including small and vertically featured objects. We achieve the mean Interaction of-union(mIoU) of 73.1 and the Frame Per Second(FPS) of 191, which are comparable results with state-of-the-arts on Cityscapes test datasets.
Learning to Collocate Visual-Linguistic Neural Modules for Image Captioning

Humans tend to decompose a sentence into different parts like \textsc{sth do sth at someplace} and then fill each part with certain content. Inspired by this, we follow the \textit{principle of modular design} to propose a novel image captioner: learning to Collocate Visual-Linguistic Neural Modules (CVLNM). Unlike the \re{widely used} neural module networks in VQA, where the language (\ie, question) is fully observable, \re{the task of collocating visual-linguistic modules is more challenging.} This is because the language is only partially observable, for which we need to dynamically collocate the modules during the process of image captioning. To sum up, we make the following technical contributions to design and train our CVLNM: 1) \textit{distinguishable module design} -- \re{four modules in the encoder} including one linguistic module for function words and three visual modules for different content words (\ie, noun, adjective, and verb) and another linguistic one in the decoder for commonsense reasoning, 2) a self-attention based \textit{module controller} for robustifying the visual reasoning, 3) a part-of-speech based \textit{syntax loss} imposed on the module controller for further regularizing the training of our CVLNM. Extensive experiments on the MS-COCO dataset show that our CVLNM is more effective, \eg, achieving a new state-of-the-art 129.5 CIDEr-D, and more robust, \eg, being less likely to overfit to dataset bias and suffering less when fewer training samples are available. Codes are available at \url{https://github.com/GCYZSL/CVLMN}
Generalizing Bayesian Optimization with Decision-theoretic Entropies

Bayesian optimization (BO) is a popular method for efficiently inferring optima of an expensive black-box function via a sequence of queries. Existing information-theoretic BO procedures aim to make queries that most reduce the uncertainty about optima, where the uncertainty is captured by Shannon entropy. However, an optimal measure of uncertainty would, ideally, factor in how we intend to use the inferred quantity in some downstream procedure. In this paper, we instead consider a generalization of Shannon entropy from work in statistical decision theory (DeGroot 1962, Rao 1984), which contains a broad class of uncertainty measures parameterized by a problem-specific loss function corresponding to a downstream task. We first show that special cases of this entropy lead to popular acquisition functions used in BO procedures such as knowledge gradient, expected improvement, and entropy search. We then show how alternative choices for the loss yield a flexible family of acquisition functions that can be customized for use in novel optimization settings. Additionally, we develop gradient-based methods to efficiently optimize our proposed family of acquisition functions, and demonstrate strong empirical performance on a diverse set of sequential decision making tasks, including variants of top-$k$ optimization, multi-level set estimation, and sequence search.