Reviewerly/exHarmony huggingface dataset:
A Mathematical Foundation for the Numberlink Game

Numberlink is a puzzle game in which players are given a grid with nodes marked with a natural number, $n$, and asked to create $n$ connections with neighboring nodes. Connections can only be made with top, bottom, left and right neighbors, and one cannot have more than two connections between any neighboring nodes. In this paper, we give a mathematical formulation of the puzzles via graphs and give some immediate consequences of this formulation. The main result of this work is an algorithm which provides insight into characteristics of these puzzles and their solutions. Finally, we give a few open questions and further directions.
VLSNR:Vision-Linguistics Coordination Time Sequence-aware News Recommendation

News representation and user-oriented modeling are both essential for news recommendation. Most existing methods are based on textual information but ignore the visual information and users' dynamic interests. However, compared to textual only content, multimodal semantics is beneficial for enhancing the comprehension of users' temporal and long-lasting interests. In our work, we propose a vision-linguistics coordinate time sequence news recommendation. Firstly, a pretrained multimodal encoder is applied to embed images and texts into the same feature space. Then the self-attention network is used to learn the chronological sequence. Additionally, an attentional GRU network is proposed to model user preference in terms of time adequately. Finally, the click history and user representation are embedded to calculate the ranking scores for candidate news. Furthermore, we also construct a large scale multimodal news recommendation dataset V-MIND. Experimental results show that our model outperforms baselines and achieves SOTA on our independently constructed dataset.
Data-Driven Meets Navigation: Concepts, Models, and Experimental Validation

The purpose of navigation is to determine the position, velocity, and orientation of manned and autonomous platforms, humans, and animals. Obtaining accurate navigation commonly requires fusion between several sensors, such as inertial sensors and global navigation satellite systems, in a model-based, nonlinear estimation framework. Recently, data-driven approaches applied in various fields show state-of-the-art performance, compared to model-based methods. In this paper we review multidisciplinary, data-driven based navigation algorithms developed and experimentally proven at the Autonomous Navigation and Sensor Fusion Lab (ANSFL) including algorithms suitable for human and animal applications, varied autonomous platforms, and multi-purpose navigation and fusion approaches
MIXCODE: Enhancing Code Classification by Mixup-Based Data Augmentation

Inspired by the great success of Deep Neural Networks (DNNs) in natural language processing (NLP), DNNs have been increasingly applied in source code analysis and attracted significant attention from the software engineering community. Due to its data-driven nature, a DNN model requires massive and high-quality labeled training data to achieve expert-level performance. Collecting such data is often not hard, but the labeling process is notoriously laborious. The task of DNN-based code analysis even worsens the situation because source code labeling also demands sophisticated expertise. Data augmentation has been a popular approach to supplement training data in domains such as computer vision and NLP. However, existing data augmentation approaches in code analysis adopt simple methods, such as data transformation and adversarial example generation, thus bringing limited performance superiority. In this paper, we propose a data augmentation approach MIXCODE that aims to effectively supplement valid training data, inspired by the recent advance named Mixup in computer vision. Specifically, we first utilize multiple code refactoring methods to generate transformed code that holds consistent labels with the original data. Then, we adapt the Mixup technique to mix the original code with the transformed code to augment the training data. We evaluate MIXCODE on two programming languages (Java and Python), two code tasks (problem classification and bug detection), four benchmark datasets (JAVA250, Python800, CodRep1, and Refactory), and seven model architectures (including two pre-trained models CodeBERT and GraphCodeBERT). Experimental results demonstrate that MIXCODE outperforms the baseline data augmentation approach by up to 6.24% in accuracy and 26.06% in robustness.
Is it Truly Microteching Subject can Push Teaching Skill Students?

The research aims to find out the perception of English Education students toward the effectiveness of microteaching subject to support their teaching skills in terms of planning for the lesson plan and classroom management. The subject of this research was the English Language Education students 2016 of Sulawesi Barat University which consist of 5 participants who were selected by using purposive sampling method. This study used qualitative method in collecting data. The researcher conducted interview by using telephone and recorded it directly due to the government’s appeal to stay at home due to the soaring of covid-19 outbreak in Indonesia. Data collection was conducted on 16th May 2020 and 18th May 2020. This study revealed that microteaching subject is effective to support the teaching skills of English Education students of Sulawesi Barat University, both in preparing learning materials and also classroom management. The findings revealed that Students of English Education of Sulawesi Barat University gained knowledge from microteaching subject in TEFL II course and they believe that microteaching subject is a great importance to prepare them to face the actual teaching process situation. The result of this research is expected to give positive contribution for education field. The findings of this research are expected to provide useful information to educators as well as students. The results can be used to enlighten parties concerned on the impact of microteaching on teaching skills.
Learning Strategies in Speaking Classroom: A Teaching Acceleration for Second Language

The objective of the research is to get information of learning strategies which are commonly used by the eighth grade Students of the SMP Negeri 2 in speaking in the classroom and to know the strategies which are most effective to improve the speaking skill according to the eighth grade Students of the SMP Negeri 2 Parepare. This study apply a descriptive method. To get the good result, this study applies two questionnaires about students’ learning strategies and which learning strategies are most affective the students’ learning. The population of this research is the third semester students of the eighth grade Students of the SMP Negeri 2 Parepare in academic year 2020/2021. The sample of this study was 35 students. The result of this study found that the students 1) using English in classroom, 2) asking by using English, 3) actively imitating their teacher, 4) translating difficult sentences, 5) making quiz acively, and 6) correcting the structure mistake making by their friends, 7) paying attention to vocabulary, tenses, pronunciation, etc.
The Utilization of E-Learning Model in Writing Class for University Context: Voices of the Lecturer and the Students

This study aims to find out how is the students’ and lecturer’s voices on the utilization of E-learning in teaching writing at Universitas Muhammadiyah Parepare. E-learning can be a solution to various educational problems. In particular, the use of technology and information is actually believed to be able to improve the quality of learning, develop student skills, expand access to education and much more. The researchers intended to know how the utilization of e-learning specially in writing subject on pandemic era. It applied qualitative approach as a research design. The participants in this research are students of English education study program at Universitas Muhammadiyah Parepare. The selected samples were interviewed to get deep information about implementation of e-Learning in teaching writing. The result showed there was negative and positive responses of the students and lecturer about the implementation of e-learning specially in writing subject. The researchers concluded that the implementation of e-learning in learning writing should be considered and adapt with the lecturer, students and the environment. The lecturers have to master and has a quality before conducting an e-learning for the students specially for the content and delivering the materials so, it would make the students would not faced a difficulties thing when they learn by e-learning. Other side is the big problem of the students was bad network.
Discourse Analysis of Grammatical Cohesion Devices in Student Explanation Texts

This study aims to find out the use of cohesive grammatical devices in twelfth-grade students’ explanation texts. In addition, this study also aims to explore the types of grammatical cohesion specifications in their essays. In this study, the researcher wanted to find out how good the quality of their student’s writing explanation texts was. To achieve this goal, the researcher used the conceptual framework of Halliday and Hasan (1976) regarding grammatical cohesion. This study applied to the twelfth grade of the Senior High School Level. The research uses descriptive and analytic studies by assigning students to write the texts. The result from the students’ explanation texts as the data in this research, there are 146 cohesive grammatical items found in students’ explanation texts. The reference occurs 68 times which has 46.5 of percentages. Conjunction occurs 53 times with 36.3% percent. Substitution occurs 23 with 15.7% percent. Ellipsis occurs two times with the lowest rate of 1.3%. The result shows that all four types of grammatical cohesion appear in students’ explanatory texts, which are the primary data in this study. However, there are subtypes of cohesive devices that don’t exist in students’ explanation texts from all of those devices. The most dominant was the reference and conjunction; on the other hand, ellipsis was the smallest presentation among grammatical cohesive, and only a few students used them. The result also indicated that the lack of grammatical cohesion devices used in terms of the generical structure of explanation text, knowledge, and ability in writing leads the college students to use inappropriate grammatical cohesion devices to be applied to this type of text. Thus, this study comes up with feedback to teachers that the discussion on the structural writing of texts and the use of coherence and cohesion should be more intensive.
Individual Risk Profiling for Portable Devices Using a Neural Network to Process the Recording of 30 Successive Pairs of Cognitive Reaction and Emotional Response to a Multivariate Situational Risk Assessment

In this paper, we are presenting a novel method and system for neuropsychological performance testing that can establish a link between cognition and emotion. It comprises a portable device used to interact with a cloud service which stores user information under username and is logged into by the user through the portable device; the user information is directly captured through the device and is processed by artificial neural network; and this tridimensional information comprises user cognitive reactions, user emotional responses and user chronometrics. The multivariate situational risk assessment is used to evaluate the performance of the subject by capturing the 3 dimensions of each reaction to a series of 30 dichotomous questions describing various situations of daily life and challenging the user's knowledge, values, ethics, and principles. In industrial application, the timing of this assessment will depend on the user's need to obtain a service from a provider such as opening a bank account, getting a mortgage or an insurance policy, authenticating clearance at work or securing online payments.
Sorting Short Integers

We build boolean circuits of size $O(nm^2)$ and depth $O(\log(n) + m \log(m))$ for sorting $n$ integers each of $m$-bits. We build also circuits that sort $n$ integers each of $m$-bits according to their first $k$ bits that are of size $O(nmk(1 + \log^*(n) - \log^*(m)))$ and depth $O(\log^{3}(n))$. This improves on the result of Asharov et al. arXiv:2010.09884 and resolves some of their open questions.
Multi-Attention Generative Adversarial Network for Remote Sensing Image Super-Resolution

Image super-resolution (SR) methods can generate remote sensing images with high spatial resolution without increasing the cost, thereby providing a feasible way to acquire high-resolution remote sensing images, which are difficult to obtain due to the high cost of acquisition equipment and complex weather. Clearly, image super-resolution is a severe ill-posed problem. Fortunately, with the development of deep learning, the powerful fitting ability of deep neural networks has solved this problem to some extent. In this paper, we propose a network based on the generative adversarial network (GAN) to generate high resolution remote sensing images, named the multi-attention generative adversarial network (MA-GAN). We first designed a GAN-based framework for the image SR task. The core to accomplishing the SR task is the image generator with post-upsampling that we designed. The main body of the generator contains two blocks; one is the pyramidal convolution in the residual-dense block (PCRDB), and the other is the attention-based upsample (AUP) block. The attentioned pyramidal convolution (AttPConv) in the PCRDB block is a module that combines multi-scale convolution and channel attention to automatically learn and adjust the scaling of the residuals for better results. The AUP block is a module that combines pixel attention (PA) to perform arbitrary multiples of upsampling. These two blocks work together to help generate better quality images. For the loss function, we design a loss function based on pixel loss and introduce both adversarial loss and feature loss to guide the generator learning. We have compared our method with several state-of-the-art methods on a remote sensing scene image dataset, and the experimental results consistently demonstrate the effectiveness of the proposed MA-GAN.
Constant-round Blind Classical Verification of Quantum Sampling

In a recent breakthrough, Mahadev constructed a classical verification of quantum computation (CVQC) protocol for a classical client to delegate decision problems in BQP to an untrusted quantum prover under computational assumptions. In this work, we explore further the feasibility of CVQC with the more general sampling problems in BQP and with the desirable blindness property. We contribute affirmative solutions to both as follows. (1) Motivated by the sampling nature of many quantum applications (e.g., quantum algorithms for machine learning and quantum supremacy tasks), we initiate the study of CVQC for quantum sampling problems (denoted by SampBQP). More precisely, in a CVQC protocol for a SampBQP problem, the prover and the verifier are given an input $x\in \{0,1\}^n$ and a quantum circuit $C$, and the goal of the classical client is to learn a sample from the output $z \leftarrow C(x)$ up to a small error, from its interaction with an untrusted prover. We demonstrate its feasibility by constructing a four-message CVQC protocol for SampBQP based on the quantum Learning With Error assumption. (2) The blindness of CVQC protocols refers to a property of the protocol where the prover learns nothing, and hence is blind, about the client's input. It is a highly desirable property that has been intensively studied for the delegation of quantum computation. We provide a simple yet powerful generic compiler that transforms any CVQC protocol to a blind one while preserving its completeness and soundness errors as well as the number of rounds. Applying our compiler to (a parallel repetition of) Mahadev's CVQC protocol for BQP and our CVQC protocol for SampBQP yields the first constant-round blind CVQC protocol for BQP and SampBQP respectively, with negligible and inverse polynomial soundness errors respectively, and negligible completeness errors.
Generalized Lagrange Coded Computing: A Flexible Computation-Communication Tradeoff for Resilient, Secure, and Private Computation

We consider the problem of evaluating arbitrary multivariate polynomials over a massive dataset containing multiple inputs, on a distributed computing system with a master node and multiple worker nodes. Generalized Lagrange Coded Computing (GLCC) codes are proposed to simultaneously provide resiliency against stragglers who do not return computation results in time, security against adversarial workers who deliberately modify results for their benefit, and information-theoretic privacy of the dataset amidst possible collusion of workers. GLCC codes are constructed by first partitioning the dataset into multiple groups, then encoding the dataset using carefully designed interpolation polynomials, and sharing multiple encoded data points to each worker, such that interference computation results across groups can be eliminated at the master. Particularly, GLCC codes include the state-of-the-art Lagrange Coded Computing (LCC) codes as a special case, and exhibit a more flexible tradeoff between communication and computation overheads in optimizing system efficiency. Furthermore, we apply GLCC to distributed training of machine learning models, and demonstrate that GLCC codes achieve a speedup of up to $2.5\text{--}3.9\times$ over LCC codes in training time, across experiments for training image classifiers on different datasets, model architectures, and straggler patterns.
Fast construction of optimal composite likelihoods

A composite likelihood is a combination of low-dimensional likelihood objects useful in applications where the data have complex structure. Although composite likelihood construction is a crucial aspect influencing both computing and statistical properties of the resulting estimator, currently there does not seem to exist a universal rule to combine low-dimensional likelihood objects that is statistically justified and fast in execution. This paper develops a methodology to select and combine the most informative low-dimensional likelihoods from a large set of candidates while carrying out parameter estimation. The new procedure minimizes the distance between composite likelihood and full likelihood scores subject to a constraint representing the afforded computing cost. The selected composite likelihood is sparse in the sense that it contains a relatively small number of informative sub-likelihoods while the noisy terms are dropped. The resulting estimator is found to have asymptotic variance close to that of the minimum-variance estimator constructed using all the low-dimensional likelihoods.
The Role of Product Life Cycle Balancing in Reducing Costs: Iraqi's experience

The current research aims to present a cognitive discussion of one of the modern strategic techniques in the field of cost and management accounting, which is represented by the product life cycle balancing technique, and to indicate the role it plays in terms of reducing the costs of laboratory products, the research sample, mainly if time is used as a cost vector in its application, with Maintaining its quality, as well as assisting the administration in making various decisions by calculating the cost more accurately than traditional methods. The General Company for Electrical and Electronic Industries in Baghdad was chosen to achieve this goal. One of its laboratories was represented by the air-cooled engine production plant in Al-Waziriya as a sample. For the research, the researcher relied when applying this technique on data and information obtained from the laboratory records of the research sample, as well as visits and field coexistence of researchers in the above laboratory, interviewing officials and individuals working in it, with a view of cost reports, accounting records, and time cards related to The lab, to apply the above technique. The research concluded that the air-cooled engine production plant suffers from the absence of any features of the application of modern strategic techniques that are concerned with reducing costs, the most important of which is the product life cycle technology and preparing the budget in its shadow and by using time as a cost vector through which the costs of the laboratory products can be reduced, the research sample, as well as On the lack of a clear role for the activity of the research and development department, to follow up the changes and developments witnessed by the contemporary business environment, in which the laboratory has become directed by the customer, in addition to the fact that the use of modern technologies in the field of cost and management accounting would contribute to keeping pace with progress, development and growth in the industrial and commercial reality. The country's economy,
Quantum Fault Trees

Fault tree analysis is a technique widely used in risk and reliability analysis of complex engineering systems given its deductive nature and relatively simple interpretation. In a fault tree, events are usually represented by a binary variable that indicates whether an event occurs or not, traditionally associated with the values 1 and 0, respectively. Different events are linked together using logical gates, modelling the dependencies that a subsystem or system may have over its basic components. In this study, quantum computing is leveraged to propose a novel approach to encode a traditional fault tree into a quantum algorithm. This quantum fault tree method uses quantum bits to represent basic events, effectively encoding the original fault tree into a quantum circuit. The execution of the resulting quantum circuit represents a full simulation of the fault tree, and multiple executions can be utilized to compute the failure probability of the whole system. The proposed approach is tested on a case study portraying a dynamic positioning system. Results verify that the quantum-based proposed approach is able to effectively obtain the dynamic positioning failure probability through simulation, opening promising opportunities for future investigations in the area.
The Technical Design Foundations for Linear Formations in the Kiswah of the Kaaba: An Analytical Study

The linear formations in the covering of the Kaaba are characterized by a number of design principles that have effectively contributed to giving the covering of the Holy Kaaba the aesthetic appearance that it possesses. Therefore, this study aims to reveal the technical design foundations of the covering of the Kaaba, according to a methodological and analytical perspective. The method used in this study is the descriptive analytical method. This study is classified under the applied field studies. The study concluded that the linear formations present in the covering of the Holy Kaaba enjoy a number of design foundations of an artistic and aesthetic nature. These foundations are balance, supremacy, rhythm, unity, contrast, coordination, proportionality, symmetry, and sequence. The study also concluded that the principles and principles of artistic design have been applied to the highest degree to achieve the aesthetic goal in designing the linear formations in the cladding and to enhance the artistic dimensions through a functional and aesthetic rhythm. KEY WORDS: Artistic design, design principles, calligraphy formations, Islamic art, the covering of the Kaaba.