Reviewerly/exHarmony huggingface dataset:
Two-Stage Encoder for Pointer-Generator Network with Pretrained Embeddings

Abstractive summarization has become the mainstream of automatic text summarization because of its unique flexibility. Pointer-Generator Network(PGN) has already been the de facto standard for the text summarization model. However, this model still has two main shortcomings: first, since the word embeddings obtained from the word embedding matrix lack of contextual semantic information, the model tends to ignore the global information of the input text when generating the summary. Second, the embedding matrix is only learned on a single corpus from scratch, which decreases the convergence speed of the PGN model and affects the generalization ability of the word embeddings. In this paper, we propose a new text summarization framework, Two-Stage Encoder for pointer-generator network with Pretrained Embeddings, which aims to solve the problems mentioned above. We introduce an encoder based on the multi-head self-attention for the first-stage encoding. This encoder module can improve the word representation by solving the polysemy problem in the PGN model and alleviate the problem of ignoring global information. Besides, we apply the pretrained word embeddings GloVe to initialize the word embedding matrix and then fine-tune it. A range of experiments are conducted on the CNN/Daily Mail dataset. For the CNN/Daily mail dataset, comparing with the baseline model, the proposed model enhances the ROUGE metrics by 0.75, 0.74 and 0.77 respectively.
COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression

Training wide and deep neural networks (DNNs) require large amounts of storage resources such as memory because the intermediate activation data must be saved in the memory during forward propagation and then restored for backward propagation. However, state-of-the-art accelerators such as GPUs are only equipped with very limited memory capacities due to hardware design constraints, which significantly limits the maximum batch size and hence performance speedup when training large-scale DNNs. Traditional memory saving techniques either suffer from performance overhead or are constrained by limited interconnect bandwidth or specific interconnect technology. In this paper, we propose a novel memory-efficient CNN training framework (called COMET) that leverages error-bounded lossy compression to significantly reduce the memory requirement for training, to allow training larger models or to accelerate training. Different from the state-of-the-art solutions that adopt image-based lossy compressors (such as JPEG) to compress the activation data, our framework purposely adopts error-bounded lossy compression with a strict error-controlling mechanism. Specifically, we perform a theoretical analysis on the compression error propagation from the altered activation data to the gradients, and empirically investigate the impact of altered gradients over the training process. Based on these analyses, we optimize the error-bounded lossy compression and propose an adaptive error-bound control scheme for activation data compression. We evaluate our design against state-of-the-art solutions with five widely-adopted CNNs and ImageNet dataset. Experiments demonstrate that our proposed framework can significantly reduce the training memory consumption by up to 13.5X over the baseline training and 1.8X over another state-of-the-art compression-based framework, respectively, with little or no accuracy loss.
The Effect of HOTS-Oriented Blended Learning Student Worksheets on Students' Cognitive Learning Outcomes on Work and Energy Materials

This study aims to determine the effect of HOTS-oriented blended learning student worksheets on students' cognitive learning outcomes on work and energy materials for class X MIPA at SMAN 1 Gadingrejo in the 2020/2021 academic year. The research design used is a quasi-experimental form of nonequivalent control group design. The sample in this study was class X MIPA 5 (experimental class) and class X MIPA 4 (control class). The experimental class was treated using HOTS-oriented blended learning worksheets, while the control class was treated using the HOTS-oriented student worksheets. The test instrument used is in the form of a description of 5 questions. Based on the research results that have been done, the average N-Gain in the experimental class is 0.46 in the medium category. In the control class, the average N-Gain is 0.34 in the medium category. The results of the independent sample t-test showed that the significant value of 0.05 was 0.002. It shows an effect of using HOTS-oriented blended learning student worksheets on students' cognitive learning outcomes on work and energy materials.
Бездротові мережеві технології в навчальному процесі

International scientific conference «Modern European psychological and pedagogical education. The development of a creative learning environment» : conference proceedings, October 8–9, 2021. Łódź, the Republic of Poland : “Baltija Publishing”, 2021. 162 pages.
SECURE COMMUNICATION USING THE SYNCHRONIZATION OF TWO FRACTIONAL-ORDER CHAOTIC SYSTEMS WITH ORDER CHANGES USING THE FINITE-TIME OPTIMAL CONTROL APPROACH

In this paper synchronization problem for two different fractional-order chaotic systems has been investigated. Based on fractional calculus, optimality conditions for this synchronization have been achieved. Synchronization Time and control signals are two factors that are optimized. After that, the synchronization method is applied in secure communication. Finally using the simulation example, the performance of the proposed method for synchronization and chaotic masking is shown.
Few-Shot Novel Concept Learning for Semantic Parsing.

Humans are capable of learning novel concepts from very few examples; in contrast, state-of-the-art machine learning algorithms typically need thousands of examples to do so. In this paper, we propose an algorithm for learning novel concepts by representing them as programs over existing concepts. This way the concept learning problem is naturally a program synthesis problem and our algorithm learns from a few examples to synthesize a program representing the novel concept. In addition, we perform a theoretical analysis of our approach for the case where the program defining the novel concept over existing ones is context-free. We show that given a learned grammar-based parser and a novel production rule, we can augment the parser with the production rule in a way that provably generalizes. We evaluate our approach by learning concepts in the semantic parsing domain extended to the few-shot novel concept learning setting, showing that our approach significantly outperforms end-to-end neural semantic parsers.
RANCANG BAGUN SISTEM INFORMASI AGENDA KERJA UNTUK ANGGOTA KEPOLISIAN BERBASIS KINERJA MENGGUNAKAN METODE BERORIENTASI OBJEK

Within the body of the National Police, the Polsekta / Polsek have an important role in protecting, nurturing, serving and enforcing the law in the Tegowanu Police area community. Therefore, the Tegowanu Police are required to be able to serve the community where one of the main tasks of the Republic of Indonesia Police is as a public servant. As one of the law enforcement officers, the National&#x0D; The old work agenda system still uses manual methods and takes a lot of time and with this system will slow down the performance of Polsek members. Documentation of activities carried out by the Tegowanu Police Officer cannot be seen by the police chief and members, because photo documentation is only stored on the officer's computer. Another problem is that there is no data storage for community activity data and activity schedules, because there is no storage in the database so that data processing has not been well integrated.&#x0D; By designing a Performance-based Activity information system using the Object Oriented Method at the Tegowanu Police, Resort Grobogan is expected to help data processing so that it is more well integrated and the reporting process and data retrieval are faster if data is needed at any time and create an integrated system with the database. The information that will be built can speed up the process of processing and sending information and activities of the Tegowanu Police to the head of the Sector Police.
Improve Agents without Retraining: Parallel Tree Search with Off-Policy  Correction

Tree Search (TS) is crucial to some of the most influential successes in reinforcement learning. Here, we tackle two major challenges with TS that limit its usability: \textit{distribution shift} and \textit{scalability}. We first discover and analyze a counter-intuitive phenomenon: action selection through TS and a pre-trained value function often leads to lower performance compared to the original pre-trained agent, even when having access to the exact state and reward in future steps. We show this is due to a distribution shift to areas where value estimates are highly inaccurate and analyze this effect using Extreme Value theory. To overcome this problem, we introduce a novel off-policy correction term that accounts for the mismatch between the pre-trained value and its corresponding TS policy by penalizing under-sampled trajectories. We prove that our correction eliminates the above mismatch and bound the probability of sub-optimal action selection. Our correction significantly improves pre-trained Rainbow agents without any further training, often more than doubling their scores on Atari games. Next, we address the scalability issue given by the computational complexity of exhaustive TS that scales exponentially with the tree depth. We introduce Batch-BFS: a GPU breadth-first search that advances all nodes in each depth of the tree simultaneously. Batch-BFS reduces runtime by two orders of magnitude and, beyond inference, enables also training with TS of depths that were not feasible before. We train DQN agents from scratch using TS and show improvement in several Atari games compared to both the original DQN and the more advanced Rainbow.
Saath: Speeding up CoFlows by Exploiting the Spatial Dimension

Coflow scheduling improves data-intensive application performance by improving their networking performance. State-of-the-art Coflow schedulers in essence approximate the classic online Shortest-Job-First (SJF) scheduling, designed for a single CPU, in a distributed setting, with no coordination among how the flows of a Coflow at individual ports are scheduled, and as a result suffer two performance drawbacks: (1) The flows of a Coflow may suffer the out-of-sync problem -- they may be scheduled at different times and become drifting apart, negatively affecting the Coflow completion time (CCT); (2) FIFO scheduling of flows at each port bears no notion of SJF, leading to suboptimal CCT. We propose SAATH, an online Coflow scheduler that overcomes the above drawbacks by explicitly exploiting the spatial dimension of Coflows. In SAATH, the global scheduler schedules the flows of a Coflow using an all-or-none policy which mitigates the out-of-sync problem. To order the Coflows within each queue, SAATH resorts to a Least-Contention-First (LCoF) policy which we show extends the gist of SJF to the spatial dimension, complemented with starvation freedom. Our evaluation using an Azure testbed and simulations of two production cluster traces show that compared to Aalo, SAATH reduces the CCT in median (P90) cases by 1.53x (4.5x) and 1.42x (37x), respectively.
Cross-Platform Relational Data Extraction Utilizing SQL Server (X-PRESS)

The use of data is vital to the advantage and success of every company and businesses.However, querying, extracting and analyzing data from cross-platform relational databases in just a single run without the need for programming is very challenging.While this process can be done manually, it requires more resources, very time consuming and prone to error.This also involves the use of different tools and lots of consolidation.Data are coming from multi-platform applications, from open source or proprietary software, from different operating systems, from different relational database management systems, from different servers and from any location within the organization.Data are complex that requires it to be processed, stored and managed in several ways.There is a need for a solution that can analyze through the company's different data sources and allowing users to perform queries.Analyzing data is best performed with the use of tools that facilitates efficient data exploration and better querying.This study finds its way to the development of a single tool that is capable of exploring, analyzing and transforming data into information regardless of its environment and configuration and makes it available to everyone.This all-in-one database querying tool provides the facility for accessing data from any cross-platform database across the network via SQL Server linked server connection through distributed and heterogeneous query execution.This tool commonly known as X-PRESS is built on top of a framework based on dynamic creation of entities and attributes and utilizes the power of Structured Query Language.Connection to data is more secure and took advantage of server processing power thus making this tool faster and more suitable for data analysis.X-PRESS is the acronym of Cross-Platform Relational Data Extraction Utilizing SQL Server."X-P" derives from Cross-Platform while "RESS" is the initial of the keywords Relational Data, Extraction, SQL and Server, respectively.
Penerapan Rocchio Classification dalam Klasifikasi Testimoni Pelanggan Taksi

Testimonials are expressions of users of products or services in the form of satisfaction or laughter. When testimonials contain satisfaction, it can be the attraction of a product or service in getting consumers. Conversely, if the testimonial is in the form of disappointment then it becomes an evaluation material and a big task for the owner or management of the company to improve the services that have been provided. Testimonials we can get from social media such as twitter, instagram, etc. Testimonial classification by reading and classifying manually is not effective if testimonials in large numbers. Therefore, in terms of assisting the evaluation of services that have been provided, testimonial classification is carried out by applying the Rocchio Classification algorithm. Testimonials used in this study are testimonials of taxi customers obtained from twitter. The data is processed in the preprocessing stage, tf-idf, classification process. The results showed that Rocchio has a fairly high accuracy of 80%, an average precession of 83.40%, an average recall of 63.28%, an average f-measure of 65.16%.
Informaatiovuorovaikutus historian tutkimuksessa

In information science, the user-oriented research tradition focuses on information seeking in the context of human behavior, while the system-oriented tradition focuses more narrowly on the information retrieval. These traditions still form largely disparate research fields. As an attempt to bridge these traditions, we present a metasynthesis of studies on information interactions in the historical domain, which is related to an ongoing research project. We approach our research task by focusing on both the cognitive space of human actors with task-specific information needs requiring interpretative close-reading and reasoning, and the document space containing potentially relevant pieces of information for the task at hand. We first study information needs and interactions in real work tasks of historians to understand the desired conceptual access points into relevant information (cognitive space). Then we study historical sources from the point of view of task-specific needs at the level of text (document space). We utilize the task-based information interaction (TBII) model to conceptualize the different types of activities during interaction which are important to consider. We believe that this approach is required to learn to support complex task-based information needs extending beyond topicality.
SStaGCN: Simplified stacking based graph convolutional networks

Graph convolutional network (GCN) is a powerful model studied broadly in various graph structural data learning tasks. However, to mitigate the over-smoothing phenomenon, and deal with heterogeneous graph structural data, the design of GCN model remains a crucial issue to be investigated. In this paper, we propose a novel GCN called SStaGCN (Simplified stacking based GCN) by utilizing the ideas of stacking and aggregation, which is an adaptive general framework for tackling heterogeneous graph data. Specifically, we first use the base models of stacking to extract the node features of a graph. Subsequently, aggregation methods such as mean, attention and voting techniques are employed to further enhance the ability of node features extraction. Thereafter, the node features are considered as inputs and fed into vanilla GCN model. Furthermore, theoretical generalization bound analysis of the proposed model is explicitly given. Extensive experiments on $3$ public citation networks and another $3$ heterogeneous tabular data demonstrate the effectiveness and efficiency of the proposed approach over state-of-the-art GCNs. Notably, the proposed SStaGCN can efficiently mitigate the over-smoothing problem of GCN.
High-probability Bounds for Non-Convex Stochastic Optimization with Heavy Tails

We consider non-convex stochastic optimization using first-order algorithms for which the gradient estimates may have heavy tails. We show that a combination of gradient clipping, momentum, and normalized gradient descent yields convergence to critical points in high-probability with best-known rates for smooth losses when the gradients only have bounded $\mathfrak{p}$th moments for some $\mathfrak{p}\in(1,2]$. We then consider the case of second-order smooth losses, which to our knowledge have not been studied in this setting, and again obtain high-probability bounds for any $\mathfrak{p}$. Moreover, our results hold for arbitrary smooth norms, in contrast to the typical SGD analysis which requires a Hilbert space norm. Further, we show that after a suitable burn-in period, the objective value will monotonically decrease for every iteration until a critical point is identified, which provides intuition behind the popular practice of learning rate warm-up and also yields a last-iterate guarantee.
Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems

As the labeling cost for different modules in task-oriented dialog (ToD) systems is expensive, a major challenge is to train different modules with the least amount of labeled data. Recently, large-scale pre-trained language models, have shown promising results for few-shot learning in ToD. In this paper, we devise a self-training approach to utilize the abundant unlabeled dialog data to further improve state-of-the-art pre-trained models in few-shot learning scenarios for ToD systems. Specifically, we propose a self-training approach that iteratively labels the most confident unlabeled data to train a stronger Student model. Moreover, a new text augmentation technique (GradAug) is proposed to better train the Student by replacing non-crucial tokens using a masked language model. We conduct extensive experiments and present analyses on four downstream tasks in ToD, including intent classification, dialog state tracking, dialog act prediction, and response selection. Empirical results demonstrate that the proposed self-training approach consistently improves state-of-the-art pre-trained models (BERT, ToD-BERT) when only a small number of labeled data are available.
A SATISFAÇÃO COM O TAMANHO DAS MORADIAS DURANTE A PANDEMIA DE COVID-19

This article assesses how the Covid-19 pandemic affected individuals perception of satisfaction with the size of their house.It also examines whether the increase in hours in the home is a factor capable of affecting this perception.For this, a Survey was carried out, in which 128 questionnaires were distributed to residents of the metropolitan region of Curitiba.The collected data were treated by means of multivariate analysis and descriptive analysis.The results showed that the size, in isolation, does not seem to be a factor of great impact in the loss of satisfaction with the size of the environment.However, the increase in the length of stay of individuals in their homes after the onset of the pandemic proved to be a significant factor.Therefore, this study contributes to the understanding of the new perceptions generated by the pandemic, placing the time that the individual needs to remain at home as an important aspect to be considered in the new housing projects.
Thai Political Opinion Classification on Facebook Comments

The 2019 Thai general election was held in Thailand in 2019. Social network websites, especially facebook, shared various political opinions. These opinions reflected the writers’ opinions toward parties and be useful for the parties. The conceptual idea for this study was to Thai political opinion classification for positive, negative, and neutral polarity. The methodology consisted of (i) data collection from news pages in facebook, (ii) data pre-processing which were data cleaning removing all question sentences, numbers and special characters; word segmentation by using LongLexTo and LEXiTRON dictionary by NECTEC; and feature extraction using Bag of Words (BOW) technique, (iii) classification compared between Naive Bayes and K-nearest neighbors (KNN), and (iv) performance measurement. The experimental results found that Naive Bayes gave precision mean, recall mean, F-measure and accuracy, which are 65.46%, 61.00%, 63.15% and 70.43% on data A and 55.87%, 48.61%, 51.99% and 71.33% on data B while KNN gave 65.45%, 52.77%, 58.43%, and 68.45% on data A and 54.67%, 42.77%, 47.99% and 68.81% on data B, respectively. Hence, Naive Bayes provided the better performance than KNN approach.
Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization

Skill chaining is a promising approach for synthesizing complex behaviors by sequentially combining previously learned skills. Yet, a naive composition of skills fails when a policy encounters a starting state never seen during its training. For successful skill chaining, prior approaches attempt to widen the policy's starting state distribution. However, these approaches require larger state distributions to be covered as more policies are sequenced, and thus are limited to short skill sequences. In this paper, we propose to chain multiple policies without excessively large initial state distributions by regularizing the terminal state distributions in an adversarial learning framework. We evaluate our approach on two complex long-horizon manipulation tasks of furniture assembly. Our results have shown that our method establishes the first model-free reinforcement learning algorithm to solve these tasks; whereas prior skill chaining approaches fail. The code and videos are available at https://clvrai.com/skill-chaining