Reviewerly/exHarmony huggingface dataset:
Resourceful Program Synthesis from Graded Linear Types

Abstract Linear types provide a way to constrain programs by specifying that some values must be used exactly once. Recent work on graded modal types augments and refines this notion, enabling fine-grained, quantitative specification of data use in programs. The information provided by graded modal types appears to be useful for type-directed program synthesis, where these additional constraints can be used to prune the search space of candidate programs. We explore one of the major implementation challenges of a synthesis algorithm in this setting: how does the synthesis algorithm efficiently ensure that resource constraints are satisfied throughout program generation? We provide two solutions to this resource management problem, adapting Hodas and Miller’s input-output model of linear context management to a graded modal linear type theory. We evaluate the performance of both approaches via their implementation as a program synthesis tool for the programming language Granule, which provides linear and graded modal typing.
Cooking Activity Recognition with Varying Sampling Rates Using Deep Convolutional GRU Framework

Activity recognition is one of the most researched topics in the field of machine learning-based recognition. There are many challenges associated with Human Activity Recognition. One of the most important challenges to overcome is the simultaneous recognition of complex activities as well as smaller activities that are part of such complex activities. The dataset that has been used and the work that has been done in this paper is part of the Cooking Activity Recognition Challenge. The dataset that has been provided in this challenge contains three classes of complex or macro activities and ten classes of smaller or micro activities. The macro activities are mutually exclusive whereas multiple micro activities can occur in a sequence as parts of a particular macro activity. The dataset is very challenging because of the recorded segments having varying sample rates among them for which we have preprocessed the data. In addition to that, the dataset contains several recorded segments with missing data. The task of this challenge has been to classify macro- and micro activities separately from this dataset. We have introduced a deep learning framework combining Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) to extract spatial and temporal features for recognition of macro- and micro activities. The model that we have proposed for this dataset outperforms other conventional and existing deep learning models with classification accuracies of 83.76 and 59.39% for macro- and micro-activity classifications, respectively.
Do stakeholders matter? Stakeholders as moderators in the relationship between formal strategic plan-ning and organizational performance

In this globalized world, every company is struggling to sustain and improve its performance. This study investigated the role of formal strategic planning on organizational performance. Moreover, this relation is explored further with the moderation of stakeholders’ involvement. The empirical data were collected from 220 chief executives or general managers of manufacturing companies using a questionnaire survey. AMOS software was used to analyze the collected data. Structural equation modeling (SEM) was conducted to test the hypothesis, and moderation was investigated using a slop test. The results of the study revealed that formal strategic planning could enhance the performance of the organization. Furthermore, the results highlighted that stakeholder's involvement could strengthen the relationship between formal strategic planning and organizational performance. Previous studies have highlighted the importance of overall strategic management in enhancing an organization's performance. The research also revealed that performance was affected mainly by formal strategic planning and stakeholder involvement. The study used manufacturing companies in Jordan. Moreover, the data was collected from 220 respondents. Therefore, future studies should be conducted from any other country's perspective and use a larger sample size. This study has used moderation of stakeholder's involvement to get more specific results. Future studies should use organizational culture as a moderating variable.
Can a Patent Be Granted for an AI-Generated Invention?

Abstract Three patent offices had to answer the question of whether a patent can be granted for an invention for which an Artificial Intelligence (AI) system called DABUS was named as inventor. All applications were dismissed, but for different reasons. While the European Patent Office focused on formal rules, the UK Intellectual Property Office considered more substantive aspects, and the US Patent and Trademark Office relied on statutory language. From a policy perspective, the decisions find support in the fact that there is no clear consensus for AI to be recognized as an inventor, and that difficult questions would ensue if this were accepted. From a doctrinal perspective, the decisions do not rule out the patentability of AI-assisted inventions in general, as it remains possible to designate a human inventor when AI has merely facilitated the inventive process. This leaves the question of who should own a patent for an AI-generated invention, if patentability for such inventions is considered desirable. A possible solution could be to grant ownership directly to the company operating or owning the AI.
Interactive Robot for Playing Russian Checkers

Human–robot interaction in board games is a rapidly developing field of robotics. This paper presents a robot capable of playing Russian checkers designed for entertaining, training, and research purposes. Its control program is based on a novel unsupervised self-learning algorithm inspired by AlphaZero and represents the first successful attempt of using this approach in the checkers game. The main engineering challenge in mechanics is to develop a board state acquisition system non-sensitive to lighting conditions, which is achieved by rejecting computer vision and utilizing magnetic sensors instead. An original robot face is designed to endow the robot an ability to express its attributed emotional state. Testing the robot at open-air multiday exhibitions shows the robustness of the design to difficult exploitation conditions and the high interest of visitors to the robot.
Craniofacial Reconstruction Method Based on Region Fusion Strategy

Craniofacial reconstruction is to estimate a person’s face model from the skull. It can be applied in many fields such as forensic medicine, archaeology, and face animation. Craniofacial reconstruction is based on the relationship between the skull and the face to reconstruct the facial appearance from the skull. However, the craniofacial structure is very complex and the relationship is not the same in different craniofacial regions. To better represent the shape changes of the skull and face and make better use of the correlation between different local regions, a new craniofacial reconstruction method based on region fusion strategy is proposed in this paper. This method has the flexibility of finding the nonlinear relationship between skull and face variables and is easy to solve. Firstly, the skull and face are divided into five corresponding local regions; secondly, the five regions of skull and face are mapped to low-dimensional latent space using Gaussian process latent variable model (GP-LVM), and the nonlinear features between skull and face are extracted; then, least square support vector regression (LSSVR) model is trained in latent space to establish the mapping relationship between skull region and face region; finally, perform regional fusion to achieve overall reconstruction. For the unknown skull, first divide the region, then project it into the latent space of the skull region, then use the trained LSSVR model to reconstruct the face of the corresponding region, and finally perform regional fusion to realize the face reconstruction of the unknown skull. The experimental results show that the method is effective. Compared with other regression methods, our method is optimal. In addition, we add attributes such as age and body mass index (BMI) to the mappings to achieve face reconstruction with different attributes.
AN ANALYSIS OF CODE MIXING USED IN OPINION RUBRIC OF KOMPAS NEWSPAPER

This study investigated the kinds of code mixing used and its meaning at opinion rubric in kompas newspaper. The research question of this study was what kinds of code mixing used and its meaning at opinion rubric in kompas newspaper. The writer took 15 samples of opinion. Rubric in March 2018. The design of this study is descriptive qualitative. In this study, the writer analyzed the data through the following steps: 1) Morphological analysis: first, the writer identified the English code mixings in word, phrase, clause, idiom and reduplication insertions which consisted of one or more morphemes and then analyzed them based on morphological aspects. Finally, the writer classified the morphological process of the words whether it included inflectional or derivational morphemes. 2) Semantic analysis: the writer interpreted the meaning of English code mixings in word, phrase, clause, idiom and reduplication insertions based on the context of the sentence. The result of this study shows that there are five kinds of code mixings that used in the newspaper, namely code mixing of word insertion that consists of 36 data, phrase insertion consists of 56 data, clause insertion consists of 6 data, idiom or expression insertion consists of 15 data, and reduplication insertion consists of 1 data. The code mixings in phrase insertion are most frequently used in newspaper rather than the other kinds of code mixings, while the most rarely used is code mixing in reduplication insertion.
ABILITY TO FOSTER SCHOOLCHILDREN’S ECOLOGICAL LITERACY AS A RESULT OF PROSPECTIVE BIOLOGY TEACHERS’ PROFESSIONAL TRAINING

An important role in preventing the negative effects of human impact on the environment is played by environmental literacy, which is connected with developing the ability to use natural resources wisely and reduce waste as well as awareness of the role of the environment for safe and healthy living. The scientific studies on formation of students’ ecological literacy were analyzed. It was revealed that the current state of training future Biology teachers for developing students’ environmental literacy is not efficient. Ability of prospective Biology teachers to form students’ ecological literacy is identified by the unity of value, cognitive and procedural components. Theoretical substantiation of the technology of training future Biology teachers for increasing students’ ecological literacy is carried out. The peculiarity of the technological process is the study of the elective courses “Conducting Ecological Pedagogical Activities”, “Technologies of Environmental Education and Upbringing”, “Methods of Teaching Ecology”, use of interactive teaching methods, combination of training sessions, independent work, practical training, and control measures. Criteria for the formation of each component are students' attitude to the environment and its preservation, the level of knowledge acquisition, the level of mastery of the methods of developing school students’ environmental literacy. A three-level approach to evaluating the results was used. The average level of formation of each component dominated. During the experimental training, statistically significant positive changes in the formation of each component took place. This confirms the feasibility of further implementation of the technology. Keywords: ecological literacy, elective courses, environmental upbringing, pre-service biology teachers
A Deep Multiview Active Learning for Large-Scale Image Classification

Multiview active learning (MAL) is a technique which can achieve a large decrease in the size of the version space than traditional active learning and has great potential applications in large-scale data analysis. In this paper, we present a new deep multiview active learning (DMAL) framework which is the first to combine multiview active learning and deep learning for annotation effort reduction. In this framework, our approach advances the existing active learning methods in two aspects. First, we incorporate two different deep convolutional neural networks into active learning which uses multiview complementary information to improve the feature learnings. Second, through the properly designed framework, the feature representation and the classifier can be simultaneously updated with progressively annotated informative samples. The experiments with two challenging image datasets demonstrate that our proposed DMAL algorithm can achieve promising results than several state-of-the-art active learning algorithms.
Trustworthy Artificial Intelligence in Education: Pitfalls and Pathways

Across the world, Artificial Intelligence (AI) applications are entering all domains of our lives, including the educational environment. This development was further spurred by the COVID-19 pandemic, which rendered many educational institutions dependent on (AI-enabled) digital learning tools to continue their activities. While the use of AI systems can lead to numerous benefits, it can also entail ethical risks, which are increasingly appearing on legislators’ agendas. Many of these risks are context-specific and increase in significance when vulnerable individuals are involved, asymmetries of power exist, or human rights and democratic values are at stake more generally. Surprisingly, however, regulators have thus far paid only little attention to the specific risks arising in the context of Artificial Intelligence in education (AIED). This paper hence aims to assess the ethical challenges posed by AIED. Its normative framework consists of the seven requirements for Trustworthy AI, as set out in the Ethics Guidelines of the European Commission’s High-Level Expert Group on AI. After an overview of the broader context in which these requirements took shape (Section 2), the paper examines each requirement in the educational realm, as well as the pitfalls that should be addressed to enable their realisation (Section 3). Particular attention is given to the special role of education in shaping people’s minds, and the manner in which this role can be used both to empower and exploit individuals. The paper notes that AIED’s main strengths – offering education on a wider scale, through more flexible and individualised learning methods, and through the closer monitoring of students’ reception of the materials – are also its main liabilities when left unchecked. Finally, the paper discusses various pathways that policymakers should consider to foster Trustworthy AIED beyond the adoption of guidelines (Section 4), before concluding (Section 5).
Instant Virtual Machine Live Migration

Live migration of virtual machines (VMs) is an important tool for data center operators to achieve maintenance, power management, and load balancing. The relatively high cost of live migration makes it difficult to employ live migration for rapid load balancing or power management operations, leaving much of its promised benefits unused. The advance of fast network interconnects has led to the development of distributed shared memory (DSM) that allows a cluster of nodes to utilize remote memory with relatively low overhead. In this paper, we explore VM live migration over DSM. We present and evaluate a novel live migration algorithm on our own DSM implementation. The evaluation of live migrating various VMs executing real-life workloads shows that live migration over DSM can reduce the total migration time by 70% and the total amount of transferred data by 65% on average. The absolute average total migration time of only 1.1 s demonstrates the potential of live migration over DSM to lead to better load balancing, energy management, and total cost of ownership.
A New Multiscale Residual Learning Network for HSI Inconsistent Noise Removal

Hyperspectral image (HSI) often suffers from various noise disturbances which makes the interpretation difficult. To solve this problem, a lot of HSI denoising algorithms have been proposed and widely used. Although many convolution neural network (CNN) based denoising methods have achieved successful performance in independently and identically distributed (i.i.d) Gaussian denoising, they are still limited to remove inconsistent noise, and even worse than traditional representative algorithms like total variation regularized low-rank matrix factorization (LRTV) and low-rank matrix recovery (LRMR). In this letter, a new multiscale residual learning network (MSRHSID) is proposed for HSIs denoising. In this network, a noise estimation network is used in our proposed method to obtain the image noise prior to achieve the reduction of inconsistent noise. At the same time, an efficient multiscale residual module (MRM) is employed to further improve the denoising effect. Both of the denoising experiments on synthetic and real-data HSIs show that this proposed MSRHSID outperforms the state-of-the-art methods.
University Professional Talents Training on Student Employment and School Quality in the Big Data Era

With the expansion of the scale of higher education in my country, the employment situation of college students is becoming increasingly severe. The basic way to improve the employment situation and the quality of employment is to further improve the quality of personnel training. In the context of the era of big data, my country’s college teaching has also made some reforms in response to the current era of big data. The purpose of this article is to study the impact of professional talent training in colleges and universities on student employment and school quality in the era of big data. Therefore, this article takes the university undergraduate talent training model as the comparative research object, and uses literature research, questionnaire method, comparative analysis, mathematical statistics and other methods to grasp the current situation, similarities and differences of the undergraduate talent training model in ordinary universities under the background of big data. The advantages and disadvantages of the current university teaching model under the background of big data, taking the current situation of university professional education reform as the starting point, through the inevitable discussion that the application of modern education technology is inevitable to deepen the university professional education reform, it is proposed to apply modern education technology to deepen the university Several countermeasures and suggestions for professional education reform. Experimental research results show that, in the context of the rapid development of this big data era, the popularization and improvement of college education also needs to open up new ideas to innovate, and it also requires every teacher and student to actively think and challenge.
Large-Scale KPI Anomaly Detection Based on Ensemble Learning and Clustering

Anomaly detection using KPI (Key Performance Indicator) is critical for Internet-based services to maintain high service availability. However, given the velocity, volume, and diversified nature of monitoring data, it is difficult to obtain enough labelled data to build an accurate anomaly detection model for using supervised machine leaning methods. In this paper, we propose an automatic and generic transfer learning strategy: Detecting anomalies on a new KPI by using pretrained model on existing selected labelled KPI. Our approach, called KADT (KPI Anomaly Detection based on Transfer Learning), integrates KPI clustering and model pretrained techniques. KPI clustering is used to obtain the similarity of different KPI data's distribution, and applied transfer knowledge from source dataset to the target dataset by model pretrained technique. In our evaluation using real-world KPIs from large Internet-based services, the clustering algorithm used to detect various KPI curve pattern achieve the best classification effect and accuracy More importantly, further evaluation on 30 KPIs shows that KADT can significantly reduce the time overhead of the model training with little loss of accuracy.
Parallel Gravitational Search Algorithm in Solving the Inverse Problem of Chemical Kinetics

The article describes a parallel gravitational search algorithm and its application to solving the inverse problem of chemical kinetics. The relevance of the study of metaheuristic algorithms, including the gravitational search algorithm, is given. It is shown that recently, these algorithms are becoming increasingly popular. The optimization problem is formulated on the example of solving the inverse kinetic problem. The process under study is propane pre-reforming over Ni catalyst, which is an industrially important chemical process. The description of the algorithm and its pseudocode are presented, after which the performance of the gravitational search algorithm is compared with other metaheuristic methods. The algorithm demonstrated its competitiveness, as a result of which it was applied to solve a specific industrial problem. Using this algorithm, the direct and inverse problems of chemical kinetics are solved, and the optimal values of the kinetic parameters of the reaction are found. It is proved that the model correctly describes the available experimental data.
Comparative Study of Different Reduced Precision Techniques in Deep Neural Network

There has been rising interest in reduced precision in the training of deep neural networks (DNNs) that is used from the single-precision (FP32) to different precision format (FP16, FP8, bfloat16) due to the rapid increase in model sizes, which require less representational space when stored in lower precision. However, training a DNN in reduced precision format (FP16, FP8, and bfloat16) is challenging, because the data format may be inadequate for representing the gradients during backpropagation. In this research paper, we compare the various novel approaches to train a DNN using the different reduced precision formats and explore the challenges that we get during the training of DNN in reduced precision. Besides, we also examine various layers in the neural network which make a significant reduction in the backpropagation and also observe that when should get sufficient precision.
A Phonology-based Approach for Isolated Sign Production Assessment in Sign Language

Interactive learning platforms are in the top choices to acquire new languages. Such applications or platforms are more easily available for spoken languages, but rarely for sign languages. Assessment of the production of signs is a challenging problem because of the multichannel aspect (e.g., hand shape, hand movement, mouthing, facial expression) inherent in sign languages. In this paper, we propose an automatic sign language production assessment approach which allows assessment of two linguistic aspects: (i) the produced lexeme and (ii) the produced forms. On a linguistically annotated Swiss German Sign Language dataset, SMILE DSGS corpus, we demonstrate that the proposed approach can effectively assess the two linguistic aspects in an integrated manner.