Reviewerly/exHarmony huggingface dataset:
TempoQR: Temporal Question Reasoning over Knowledge Graphs

Knowledge Graph Question Answering (KGQA) involves retrieving facts from a Knowledge Graph (KG) using natural language queries. A KG is a curated set of facts consisting of entities linked by relations. Certain facts include also temporal information forming a Temporal KG (TKG). Although many natural questions involve explicit or implicit time constraints, question answering (QA) over TKGs has been a relatively unexplored area. Existing solutions are mainly designed for simple temporal questions that can be answered directly by a single TKG fact. This paper puts forth a comprehensive embedding-based framework for answering complex questions over TKGs. Our method termed temporal question reasoning (TempoQR) exploits TKG embeddings to ground the question to the specific entities and time scope it refers to. It does so by augmenting the question embeddings with context, entity and time-aware information by employing three specialized modules. The first computes a textual representation of a given question, the second combines it with the entity embeddings for entities involved in the question, and the third generates question-specific time embeddings. Finally, a transformer-based encoder learns to fuse the generated temporal information with the question representation, which is used for answer predictions. Extensive experiments show that TempoQR improves accuracy by 25--45 percentage points on complex temporal questions over state-of-the-art approaches and it generalizes better to unseen question types.
THE CORRELATION BETWEEN GRAMMAR AND SPEAKING SKILL OF UNDERGRADUATE STUDENTS

This research aims to find out correlation between grammar and speaking skill of the third semester students of English Education Study Program academic year 2019/2020 at IAIN Datokarama Palu. 28 participants of correlational research were asked to describe themselves, daily routines, and unforgettable experience. Then, they were assigned to do grammar test. The data were analyzed using Pearson Product Moment Correlation Coefficient. The result shows that the correlation coefficient between students’ grammar and speaking skill is 0.46. The result is categorized “moderate” (0.40 to 0.59). It indicates that there is positive correlation between the students’ grammar and speaking skill which means that grammar contributes to speaking skilll, but grammar is not the main predictor of speaking skill achievement. There are some other aspects of speaking affecting the students’ speaking skill, i.e accuracy (in terms of vocabulary and pronunciation), fluency, and comprehensibility. The implication of this finding to the teaching of speaking skill is that the lecturers need to integrate the teaching of speaking skill with grammar and other supporting aspects, like vocabulary and pronunciation.
A strong baseline for image and video quality assessment

In this work, we present a simple yet effective unified model for perceptual quality assessment of image and video. In contrast to existing models which usually consist of complex network architecture, or rely on the concatenation of multiple branches of features, our model achieves a comparable performance by applying only one global feature derived from a backbone network (i.e. resnet18 in the presented work). Combined with some training tricks, the proposed model surpasses the current baselines of SOTA models on public and private datasets. Based on the architecture proposed, we release the models well trained for three common real-world scenarios: UGC videos in the wild, PGC videos with compression, Game videos with compression. These three pre-trained models can be directly applied for quality assessment, or be further fine-tuned for more customized usages. All the code, SDK, and the pre-trained weights of the proposed models are publicly available at https://github.com/Tencent/CenseoQoE.
Rating-Aware Self-Organizing Maps

Self-organizing maps (SOM) are one of the prominent paradigms for 2D data visualization. While aiming at preserving topological relations of high-dimensional data, they provide sufficiently organized view of objects and thus improve capability of users to explore displayed information. SOMs were also extensively utilized in visualizing results of multimedia information retrieval systems. However, for this task, SOM lacks the ability to adapt to the relevance scores induced by the underlying retrieval algorithm. Therefore, although exploration capability is enhanced, the capability to exploit the (best) results is severely limited. In order to cope with this problem, we propose a rating-aware modification of SOM algorithm that jointly optimizes for the preservation of both topological as well as relevance-based ordering of results.
Learning Connectivity with Graph Convolutional Networks for  Skeleton-based Action Recognition

Learning graph convolutional networks (GCNs) is an emerging field which aims at generalizing convolutional operations to arbitrary non-regular domains. In particular, GCNs operating on spatial domains show superior performances compared to spectral ones, however their success is highly dependent on how the topology of input graphs is defined. In this paper, we introduce a novel framework for graph convolutional networks that learns the topological properties of graphs. The design principle of our method is based on the optimization of a constrained objective function which learns not only the usual convolutional parameters in GCNs but also a transformation basis that conveys the most relevant topological relationships in these graphs. Experiments conducted on the challenging task of skeleton-based action recognition shows the superiority of the proposed method compared to handcrafted graph design as well as the related work.
FORMATION OF INFORMATION-INTELLECTUAL COMPETENCE OF FUTURE TEACHERS OF COMPUTER SCIENCE THROUGH CRITERIA-BASED ASSESSMENT

This article theoretically substantiates the structure and content of the information-intellectual competence of the future teacher of computer science. The definition of information-intellectual competence is given. Based on the program "Digital Kazakhstan" the design of the architecture of the environment that forms the information-intellectual competence of students in the context of digitalization of education was considered. The information-intellectual competence of students is defined and described in terms of knowledge, skills, and potential criteria. To form the information-intellectual competence of students, education was considered in three stages. The sample questions to determine the information-intellectual competence of students are based on the mobility of knowledge, the method of flexibility, and critical thinking. To perform the situational task, we divided students' level of performance into informational, improvisational, heuristic, considered laboratory work on the information level, independent work of the student with the teacher on the improvisational level, and independent work of the student on the heuristic level. The advantages of teaching technology for the development of information-intellectual competencies of future teachers of computer science were demonstrated and the educational achievements of students on the criteria-based assessment were assessed. Experimental work was carried out on the t-criterion of an independent Student. Irrespective of the results obtained during the study, the effectiveness of the experimental work on the t-test of the Student and the confirmation of the hypothesis was confirmed.
Speech Emotion Recognition System by Quaternion Nonlinear Echo State  Network

The echo state network (ESN) is a powerful and efficient tool for displaying dynamic data. However, many existing ESNs have limitations for properly modeling high-dimensional data. The most important limitation of these networks is the high memory consumption due to their reservoir structure, which has prevented the increase of reservoir units and the maximum use of special capabilities of this type of network. One way to solve this problem is to use quaternion algebra. Because quaternions have four different dimensions, high-dimensional data are easily represented and, using Hamilton multiplication, with fewer parameters than real numbers, make external relations between the multidimensional features easier. In addition to the memory problem in the ESN network, the linear output of the ESN network poses an indescribable limit to its processing capacity, as it cannot effectively utilize higher-order statistics of features provided by the nonlinear dynamics of reservoir neurons. In this research, a new structure based on ESN is presented, in which quaternion algebra is used to compress the network data with the simple split function, and the output linear combiner is replaced by a multidimensional bilinear filter. This filter will be used for nonlinear calculations of the output layer of the ESN. In addition, the two-dimensional principal component analysis technique is used to reduce the number of data transferred to the bilinear filter. In this study, the coefficients and the weights of the quaternion nonlinear ESN (QNESN) are optimized using the genetic algorithm. In order to prove the effectiveness of the proposed model compared to the previous methods, experiments for speech emotion recognition have been performed on EMODB, SAVEE, and IEMOCAP speech emotional datasets. Comparisons show that the proposed QNESN network performs better than the ESN and most currently SER systems.
AVX-512 C++ Programming: Part 1

In the five previous chapters, you learned how to code functions that exploited the computational capabilities of AVX and AVX2 using C++ SIMD intrinsic functions. The chapter you are about to read introduces AVX-512 SIMD programming. It begins with a brief overview of AVX-512 and its various instruction set extensions. This is followed by a section that explains basic packed integer arithmetic using 512-bit wide operands. The chapter concludes with a section that demonstrates a few integer-based image processing techniques using AVX-512 C++ SIMD intrinsic functions.
Residual-Based Adaptive Coefficient and Noise-Immunity ZNN for Perturbed  Time-Dependent Quadratic Minimization

The time-dependent quadratic minimization (TDQM) problem appears in many applications and research projects. It has been reported that the zeroing neural network (ZNN) models can effectively solve the TDQM problem. However, the convergent and robust performance of the existing ZNN models are restricted for lack of a joint-action mechanism of adaptive coefficient and integration enhanced term. Consequently, the residual-based adaption coefficient zeroing neural network (RACZNN) model with integration term is proposed in this paper for solving the TDQM problem. The adaptive coefficient is proposed to improve the performance of convergence and the integration term is embedded to ensure the RACZNN model can maintain reliable robustness while perturbed by variant measurement noises. Compared with the state-of-the-art models, the proposed RACZNN model owns faster convergence and more reliable robustness. Then, theorems are provided to prove the convergence of the RACZNN model. Finally, corresponding quantitative numerical experiments are designed and performed in this paper to verify the performance of the proposed RACZNN model.
Research on Data Access Management Based on Blockchain Engine

With the popularity of cloud computing, cloud storage, as a part of cloud computing services, has also attracted extensive attention because of its flexibility and low-cost economic expenditure. On the basis of searchable access, this paper combines blockchain with searchable access, and proposes a dynamic verifiable ciphertext retrieval scheme based on blockchain. This scheme is mainly applied to the private cloud environment that needs to ensure the absolute security of data, such as the database of confidential institutions. Through the characteristics of Ethereum account, authorized access control in one to many environment can be realized. Relying on the characteristics of Ethereum, it solves the problem of the correctness of the results returned by the malicious server. And aiming at the problem that the ECS does not update data, this scheme innovatively solves the verification of the returned results during data update by introducing the aggregated message authentication code technology. The scheme also supports forward and backward security to ensure that no privacy will be disclosed during data update. At the same time, experimental tests are carried out on the three aspects of index generation, retrieval and verification. The test results show that the scheme has high efficiency.
DPICT: Deep Progressive Image Compression Using Trit-Planes

We propose the deep progressive image compression using trit-planes (DPICT) algorithm, which is the first learning-based codec supporting fine granular scalability (FGS). First, we transform an image into a latent tensor using an analysis network. Then, we represent the latent tensor in ternary digits (trits) and encode it into a compressed bitstream trit-plane by trit-plane in the decreasing order of significance. Moreover, within each trit-plane, we sort the trits according to their rate-distortion priorities and transmit more important information first. Since the compression network is less optimized for the cases of using fewer trit-planes, we develop a postprocessing network for refining reconstructed images at low rates. Experimental results show that DPICT outperforms conventional progressive codecs significantly, while enabling FGS transmission.
Improving Dynamic Regret in Distributed Online Mirror Descent Using  Primal and Dual Information

We consider the problem of distributed online optimization, with a group of learners connected via a dynamic communication graph. The goal of the learners is to track the global minimizer of a sum of time-varying loss functions in a distributed manner. We propose a novel algorithm, termed Distributed Online Mirror Descent with Multiple Averaging Decision and Gradient Consensus (DOMD-MADGC), which is based on mirror descent but incorporates multiple consensus averaging iterations over local gradients as well as local decisions. The key idea is to allow the local learners to collect a sufficient amount of global information, which enables them to more accurately approximation the time-varying global loss, so that they can closely track the dynamic global minimizer over time. We show that the dynamic regret of DOMD-MADGC is upper bounded by the path length, which is defined as the cumulative distance between successive minimizers. The resulting bound improves upon the bounds of existing distributed online algorithms and removes the explicit dependence on $T$.
Learning Generalized Causal Structure in Time-series

The science of causality explains/determines 'cause-effect' relationship between the entities of a system by providing mathematical tools for the purpose. In spite of all the success and widespread applications of machine-learning (ML) algorithms, these algorithms are based on statistical learning alone. Currently, they are nowhere close to 'human-like' intelligence as they fail to answer and learn based on the important "Why?" questions. Hence, researchers are attempting to integrate ML with the science of causality. Among the many causal learning issues encountered by ML, one is that these algorithms are dumb to the temporal order or structure in data. In this work we develop a machine learning pipeline based on a recently proposed 'neurochaos' feature learning technique (ChaosFEX feature extractor), that helps us to learn generalized causal-structure in given time-series data.
Cost-Efficient BAT Algorithm for Task Scheduling in Cloud

Cloud computing is transforming the way small companies and big corporations operate in the coming generations. If the need for cloud computing grows, so does the need for effective resource management in the cloud world to satisfy customer needs. The aim of traditional resource allocation algorithms is to reduce the overall cost and time spent on all tasks. However, in cloud computing systems, computing capability varies depending on the resource (public vs private clouds), and therefore, the expense of resource use varies as well. As a result, it is critical to consider the resource consumption expense. As a result, in this paper, a cost-effective Bat algorithm for job scheduling in cloud computing architecture is proposed. The proposed algorithm is put to the test against current algorithms in terms of execution cost and resource use. As opposed to current algorithms, the proposed algorithm has a lower cost.
Character-level HyperNetworks for Hate Speech Detection

The massive spread of hate speech, hateful content targeted at specific subpopulations, is a problem of critical social importance. Automated methods for hate speech detection typically employ state-of-the-art deep learning (DL)-based text classifiers-very large pre-trained neural language models of over 100 million parameters, adapting these models to the task of hate speech detection using relevant labeled datasets. Unfortunately, there are only numerous labeled datasets of limited size that are available for this purpose. We make several contributions with high potential for advancing this state of affairs. We present HyperNetworks for hate speech detection, a special class of DL networks whose weights are regulated by a small-scale auxiliary network. These architectures operate at character-level, as opposed to word-level, and are several magnitudes of order smaller compared to the popular DL classifiers. We further show that training hate detection classifiers using large amounts of automatically generated examples in a procedure named as it data augmentation is beneficial in general, yet this practice especially boosts the performance of the proposed HyperNetworks. In fact, we achieve performance that is comparable or better than state-of-the-art language models, which are pre-trained and orders of magnitude larger, using this approach, as evaluated using five public hate speech datasets.
Combining Learning from Human Feedback and Knowledge Engineering to  Solve Hierarchical Tasks in Minecraft

Real-world tasks of interest are generally poorly defined by human-readable descriptions and have no pre-defined reward signals unless it is defined by a human designer. Conversely, data-driven algorithms are often designed to solve a specific, narrowly defined, task with performance metrics that drives the agent's learning. In this work, we present the solution that won first place and was awarded the most human-like agent in the 2021 NeurIPS Competition MineRL BASALT Challenge: Learning from Human Feedback in Minecraft, which challenged participants to use human data to solve four tasks defined only by a natural language description and no reward function. Our approach uses the available human demonstration data to train an imitation learning policy for navigation and additional human feedback to train an image classifier. These modules, together with an estimated odometry map, are then combined into a state-machine designed based on human knowledge of the tasks that breaks them down in a natural hierarchy and controls which macro behavior the learning agent should follow at any instant. We compare this hybrid intelligence approach to both end-to-end machine learning and pure engineered solutions, which are then judged by human evaluators. Codebase is available at https://github.com/viniciusguigo/kairos_minerl_basalt.
An efficient heuristic approach combining maximal itemsets and area measure for compressing voluminous table constraints

Constraint Programming is a powerful paradigm to model and solve combinatorial problems. While there are many kinds of constraints, the table constraint is perhaps the most significant-being the most well-studied and has the ability to encode any other constraints defined on finite variables. However, constraints can be very voluminous and their size can grow exponentially with their arity. To reduce space and the time complexity, researchers have focused on various forms of compression. In this paper we propose a new approach based on maximal frequent itemsets technique and area measure for enumerating the maximal frequent itemsets relevant for compressing table constraints. Our experimental results show the effectiveness and efficiency of this approach on compression and on solving compressed table constraints.
Certified Random Number Generation from Quantum Steering

The ultimate random number generators are those certified to be unpredictable -- including to an adversary. The use of simple quantum processes promises to provide numbers that no physical observer could predict but, in practice, unwanted noise and imperfect devices can compromise fundamental randomness and protocol security. Certified randomness protocols have been developed which remove the need for trust in devices by taking advantage of nonlocality. Here, we use a photonic platform to implement our protocol, which operates in the quantum steering scenario where one can certify randomness in a one-sided device independent framework. We demonstrate an approach for a steering-based generator of public or private randomness, and the first generation of certified random bits, with the detection loophole closed, in the steering scenario.
Regulated Multiset Rewriting Systems

Multiset rewriting systems provide a formalism particularly suitable for the description of biological systems. We present an extension of this formalism with additional controls on the derivations as a tool for reducing possible non-deterministic behaviour by providing additional knowledge about the system. We introduce several regulation mechanisms and compare their generative power.
The Mobile Atmospheric Profiling Network (MAPNet): Capabilities and Research Applications

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Other. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]The Mobile Atmospheric Profiling Network (MAPNet): Capabilities and Research ApplicationsAuthorsKevinKnuppiDLawrenceCareyiDRyanWadeDustinPhillipsiDPrestonPangleSee all authors Kevin KnuppiDCorresponding Author• Submitting AuthorUniversity of Alabama in HuntsvilleiDhttps://orcid.org/0000-0002-5325-8449view email addressThe email was not providedcopy email addressLawrence CareyiDThe University of Alabama in HuntsvilleiDhttps://orcid.org/0000-0003-2255-877Xview email addressThe email was not providedcopy email addressRyan WadeUniversity of Alabama in Huntsvilleview email addressThe email was not providedcopy email addressDustin PhillipsiDUniversity of Alabama in HuntsvilleiDhttps://orcid.org/0000-0002-5796-0227view email addressThe email was not providedcopy email addressPreston PangleUniversity of Alabama in Huntsvilleview email addressThe email was not providedcopy email address