Reviewerly/exHarmony huggingface dataset:
A Plug-and-Play Method for Controlled Text Generation

Large pre-trained language models have repeatedly shown their ability to produce fluent text. Yet even when starting from a prompt, generation can continue in many plausible directions. Current decoding methods with the goal of controlling generation, e.g., to ensure specific words are included, either require additional models or fine-tuning, or work poorly when the task at hand is semantically unconstrained, e.g., story generation. In this work, we present a plug-and-play decoding method for controlled language generation that is so simple and intuitive, it can be described in a single sentence: given a topic or keyword, we add a shift to the probability distribution over our vocabulary towards semantically similar words. We show how annealing this distribution can be used to impose hard constraints on language generation, something no other plug-and-play method is currently able to do with SOTA language generators. Despite the simplicity of this approach, we see it works incredibly well in practice: decoding from GPT-2 leads to diverse and fluent sentences while guaranteeing the appearance of given guide words. We perform two user studies, revealing that (1) our method outperforms competing methods in human evaluations; and (2) forcing the guide words to appear in the generated text has no impact on the fluency of the generated text.
Face Super-Resolution Reconstruction Method Fusing Reference Image

While low-resolution face images are reconstructed via deep learning based super-resolution reconstruction method,some problems emerge,such as blurred reconstructed images and obvious difference between reconstructed images and real images.Aiming at these problems,a face super-resolution reconstruction method fusing reference image is proposed to reconstruct low-resolution human face images effectively.The multi-scale features of reference image are extracted by reference image feature extraction subnet to retain the detail information of key parts and remove the redundant information,such as facial contour and facial expression.Based on the multi-scale features of reference image,the step-by-step super-resolution main network fills the features to low-resolution face image step by step.Finally,the high-resolution face image is generated.Experiments on datasets indicate that the proposed method reconstructs low-resolution face images effectively with good robustness.
Controlled Neural Sentence-Level Reframing of News Articles

Framing a news article means to portray the reported event from a specific perspective, e.g., from an economic or a health perspective. Reframing means to change this perspective. Depending on the audience or the submessage, reframing can become necessary to achieve the desired effect on the readers. Reframing is related to adapting style and sentiment, which can be tackled with neural text generation techniques. However, it is more challenging since changing a frame requires rewriting entire sentences rather than single phrases. In this paper, we study how to computationally reframe sentences in news articles while maintaining their coherence to the context. We treat reframing as a sentence-level fill-in-the-blank task for which we train neural models on an existing media frame corpus. To guide the training, we propose three strategies: framed-language pretraining, named-entity preservation, and adversarial learning. We evaluate respective models automatically and manually for topic consistency, coherence, and successful reframing. Our results indicate that generating properly-framed text works well but with tradeoffs.
Visually Grounded Concept Composition

We investigate ways to compose complex concepts in texts from primitive ones while grounding them in images. We propose Concept and Relation Graph (CRG), which builds on top of constituency analysis and consists of recursively combined concepts with predicate functions. Meanwhile, we propose a concept composition neural network called Composer to leverage the CRG for visually grounded concept learning. Specifically, we learn the grounding of both primitive and all composed concepts by aligning them to images and show that learning to compose leads to more robust grounding results, measured in text-to-image matching accuracy. Notably, our model can model grounded concepts forming at both the finer-grained sentence level and the coarser-grained intermediate level (or word-level). Composer leads to pronounced improvement in matching accuracy when the evaluation data has significant compound divergence from the training data.
3D Noise Adaptive Wiener Filtering of Images

In this paper a new technique is presented for 3D noise adaptive filtering of images containing additive white Gaussian noise on the basis of the Wiener filter. It uses preliminary estimation of the noise variance from unknown set of input images which relies on statistical analysis of the relatively homogenous areas in them. Its application may turn useful for industrial applications involving multispectral, CT, MRI and other types of images.
Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding

Large-scale, pre-trained language models (LMs) have achieved human-level performance on a breadth of language understanding tasks. However, evaluations only based on end task performance shed little light on machines' true ability in language understanding and reasoning. In this paper, we highlight the importance of evaluating the underlying reasoning process in addition to end performance. Toward this goal, we introduce Tiered Reasoning for Intuitive Physics (TRIP), a novel commonsense reasoning dataset with dense annotations that enable multi-tiered evaluation of machines' reasoning process. Our empirical results show that while large LMs can achieve high end performance, they struggle to support their predictions with valid supporting evidence. The TRIP dataset and our baseline results will motivate verifiable evaluation of commonsense reasoning and facilitate future research toward developing better language understanding and reasoning models.
Syntactic Specificity of Internet Discourse Language

Although internet communication is a relatively new field of social relations in this article, it can cover the whole of humanity in a short time, which raises a number of issues such as communication system on the Internet, user interaction, formation of virtual community, how a person expresses himself, speech influence thought about.
The Critical Review to Practical Inquiries of Action Research Framework (Action Research Paradigms)

Action research is identical to the educational field, but on the other hand, the methodology has widespread practical values to others field of education. Moreover, to the extent of the use and the impact of action research in widespread, researcher has conceptualized the critical review of action research practical inquiries' framework cross its research paradigm, that in-depth has been stimulated and improved human resources, steak-holder problem solving, the policy of the government, and also education field. This paper has proposed the recommendation to all system in science and also a government which intend to construct research and practitioner skill, and also synthesized the nature of research and research bases to mutual development in all field of science.
Earth Observation Techniques for Spatial Disaggregation of Exposure Data&amp;#160;

&amp;lt;p&amp;gt;Exposure describes elements which are imperiled by natural hazards and susceptible to damage. The affiliated vulnerability characterizes the likelihood to experience damage regarding a given level of hazard intensity. Frequently, the compilation of exposure information is the costliest component (in terms of time and labor) in risk assessment. Existing data sets and models often describe exposure in an aggregated manner, e.g., by relying on statistical/census data for given administrative entities. Nowadays, earth observation techniques allow to collect spatially continuous information for large geographic areas while enabling a high geometric and temporal resolution. In parallel, modern data interpretation tools based on Artificial Intelligence concepts enable the extraction of thematic information from such data with a high accuracy and detail. Consequently, we exploit measurements from the earth observation missions TanDEM-X and Sentinel-2, which collect data on a global scale, to characterize the built environment in terms of fundamental morphologic properties, namely built-up density and height. Subsequently, we use this information to constrain existing exposure data in a spatial disaggregation approach. Thereby, we compare different methods for disaggregation and evaluate how different resolution properties of the earth observation data affect the risk assessment result. Results are presented for the city of Santiago de Chile, Chile, which is prone to natural hazards such as earthquakes. We present loss estimations and corresponding sensivity with respect to the resolution properties of the exposure data used in the model. Thereby, it can be noted how loss estimations vary substantially and that aggregated exposure information underestimates losses in our scenarios. As such, this study underlines the benefits of deploying modern earth observation technologies for refined exposure estimation and related loss estimation.&amp;lt;/p&amp;gt;
Individual learning: the role of mental models in the learning of software developers

This study aims to understand the role of individual mental models in the learning of software developers. Qualitative research of a multiple case study was conducted in four software development organisations (SDOs) applying semi-structured interviews. The subjects interviewed were involved in the software development process. The analyses were performed by the qualitative data analysis software Weft_QDA, which allowed the identification and analysis of speeches related to the theoretical concept of this study. It was identified that the previous knowledge of software experts, the complexity of projects, the rotation of tasks, and the information and communication technologies employed in the work environment enhance individual mental models from their internal images-worldviews, leading to an efficient process of software development in SDOs. The results show that individual mental models manage the acquisition, retention, use, and elimination of information and experiences during the learning process of developers.
Coderelictions for Free Exponential Modalities

In a categorical model of the multiplicative and exponential fragments of intuitionistic linear logic (MELL), the exponential modality is interpreted as a comonad ! such that each cofree !-coalgebra !A comes equipped with a natural cocommutative comonoid structure. An important case is when ! is a free exponential modality so that !A is the cofree cocommutative comonoid over A. A categorical model of MELL with a free exponential modality is called a Lafont category. A categorical model of differential linear logic is called a differential category, where the differential structure can equivalently be described by a deriving transformation !A⊗A →{𝖽_A} !A or a codereliction A →{η_A} !A. Blute, Lucyshyn-Wright, and O'Neill showed that every Lafont category with finite biproducts is a differential category. However, from a differential linear logic perspective, Blute, Lucyshyn-Wright, and O'Neill’s approach is not the usual one since the result was stated in the dual setting and the proof is in terms of the deriving transformation 𝖽. In differential linear logic, it is often the codereliction η that is preferred and that plays a more prominent role. In this paper, we provide an alternative proof that every Lafont category (with finite biproducts) is a differential category, where we construct the codereliction η using the couniversal property of the cofree cocommtuative comonoid !A and show that η is unique. To achieve this, we introduce the notion of an infinitesimal augmentation k⊕A →{𝖧_A} !(k ⊕ A), which in particular is a !-coalgebra and a comonoid morphism, and show that infinitesimal augmentations are in bijective correspondence to coderelictions (and deriving transformations). As such, infinitesimal augmentations provide a new equivalent axiomatization for differential categories in terms of more commonly known concepts. For a free exponential modality, its infinitesimal augmentation is easy to construct and allows one to clearly see the differential structure of a Lafont category, regardless of the construction of !A.
Competency Problems: On Finding and Removing Artifacts in Language Data

Much recent work in NLP has documented dataset artifacts, bias, and spurious correlations between input features and output labels. However, how to tell which features have “spurious” instead of legitimate correlations is typically left unspecified. In this work we argue that for complex language understanding tasks, all simple feature correlations are spurious, and we formalize this notion into a class of problems which we call competency problems. For example, the word “amazing” on its own should not give information about a sentiment label independent of the context in which it appears, which could include negation, metaphor, sarcasm, etc. We theoretically analyze the difficulty of creating data for competency problems when human bias is taken into account, showing that realistic datasets will increasingly deviate from competency problems as dataset size increases. This analysis gives us a simple statistical test for dataset artifacts, which we use to show more subtle biases than were described in prior work, including demonstrating that models are inappropriately affected by these less extreme biases. Our theoretical treatment of this problem also allows us to analyze proposed solutions, such as making local edits to dataset instances, and to give recommendations for future data collection and model design efforts that target competency problems.
Sifat Kepahlawanan Tokoh Bujang dalam Novel "Pulang" Karya Tere Liye

This study aims to provide an overview of heroic traits shown by the character Bujang in Tere Liye's novel Pulang. The method used in analyzing the data is qualitative method because the data collected and analyzed are in the form of narratives and dialogues in the text related to the heroic traits possessed by Bujang as the main character in the novel. Furthermore, this study uses descriptive techniques to define the heroic traits found in Bujang character. From the results of the analysis, it was found that eight heroic traits possessed by Bujang in the novel Pulang. The traits include caring, charismatic, inspiring, reliable, resilient, selfless, smart, and strong. This finding shows that heroic traits can be found in characters who are involved in the world of crime and also in literary works that use the setting and themes of the criminal world. By having these eight heroic traits, Bujang is a representation of a hero even though he is a character who has a role in the criminal world he lives in the novel.
Average-Reward Learning and Planning with Options

We extend the options framework for temporal abstraction in reinforcement learning from discounted Markov decision processes (MDPs) to average-reward MDPs. Our contributions include general convergent off-policy inter-option learning algorithms, intra-option algorithms for learning values and models, as well as sample-based planning variants of our learning algorithms. Our algorithms and convergence proofs extend those recently developed by Wan, Naik, and Sutton. We also extend the notion of option-interrupting behavior from the discounted to the average-reward formulation. We show the efficacy of the proposed algorithms with experiments on a continuing version of the Four-Room domain.
Professional growth of teachers: we increase motivation

The article is devoted to the problem of motivation of professional growth of teachers.Issues of activation of internal motivation, awareness of teachers of current goals and setting priorities are considered. Recommendations for the development of motivational training are provided.The author offers a basic version of motivational training, which can be changed and supplemented with the proposed exercises depending on the needs of a particular group
Task allocation for decentralized training in heterogeneous environment

The demand for large-scale deep learning is increasing, and distributed training is the current mainstream solution. Ring AllReduce is widely used as a data parallel decentralized algorithm. However, in a heterogeneous environment, each worker calculates the same amount of data, so that there is a lot of waiting time loss among different workers, which makes the algorithm unable to adapt well to heterogeneous clusters. Resources are not used as they should be. In this paper, we design an implementation of static allocation algorithm. The dataset is artificially allocated to each worker, and samples are drawn proportionally for training, thereby speeding up the training speed of the network in a heterogeneous environment. We verify the convergence and influence on training speed of the network model under this algorithm on one machine with multi-card and multi-machine with multi-card. On this basis of feasibility, we propose a self-adaptive allocation algorithm that allows each machine to find the data it needs to adapt to the current environment. The self-adaptive allocation algorithm can reduce the training time by nearly one-third to half compared to the same proportional allocation.In order to better show the applicability of the algorithm in heterogeneous clusters, We replace a poorly performing worker with a good performing worker or add a poorly performing worker to the heterogeneous cluster. Experimental results show that training time will decrease as the overall performance improves. Therefore, it means that resources are fully used. Further, this algorithm is not only suitable for straggler problems, but also for most heterogeneous situations. It can be used as a plug-in for AllReduce and its variant algorithms.
Distinguishing of different tissue types using K-Means clustering of color segmentation

Millions of lives might be saved if stained tissues could be detected quickly. Image classification algorithms may be used to detect the shape of cancerous cells, which is crucial in determining the severity of the disease. With the rapid advancement of digital technology, digital images now play a critical role in the current day, with rapid applications in the medical and visualization fields. Tissue segmentation in whole-slide photographs is a crucial task in digital pathology, as it is necessary for fast and accurate computer-aided diagnoses. When a tissue picture is stained with eosin and hematoxylin, precise tissue segmentation is especially important for a successful diagnosis. This kind of staining aids pathologists in distinguishing between different tissue types. This work offers a clustering-based color segmentation approach for medical images that can successfully find the core points of clusters through penetrating the red-green-blue (RGB) pairings without previous information. Here, the number of RGB pairs functions as a clusters’ number to increase the accuracy of current algorithms by establishing the automated initialization settings for conventional K-Means clustering algorithms. On a picture of tissue stained with eosin and hematoxylin, the developed K-Means clustering technique is used in this study (H&amp;E). The blue items are found in Cluster 3. There are things in both light and dark blue. The results showed that the proposed technique can differentiate light blue from dark blue employing the 'L*' layer in L*a*b* Color Space (L*a*b* CS). The work recognized the cells' nuclei with a dark blue color successfully. As a result, this approach may aid in precisely diagnosing the stage of tumor invasion and guiding clinical therapies
Co-training for Commit Classification.

Commits in version control systems (e.g. Git) track changes in a software project. Commits comprise noisy user-generated natural language and code patches. Automatic commit classification (CC) has been used to determine the type of code maintenance activities performed, as well as to detect bug fixes in code repositories. Much prior work occurs in the fully-supervised setting – a setting that can be a stretch in resource-scarce situations presenting difficulties in labeling commits. In this paper, we apply co-training, a semi-supervised learning method, to take advantage of the two views available – the commit message (natural language) and the code changes (programming language) – to improve commit classification.
Research on Vocational Practical Physical Education Courses for Automobile Majors (Armored Vehicles)——Taking Hunan Defense Industry Polytechnic as an example

All students in Hunan Defense Industry Polytechnic take physical education classes, with the purpose of teaching and educating people, implement the health first idea, comprehensively promote quality education, cultivate students' awareness of and scientific fitness capabilities, enhance students' physical and mental health, and stimulate Students’ interest in actively participating in sports activities, constantly optimizing the existing curriculum system and faculty, and this emphasizes the sense of responsibility, mission, patriotism and dedication of college students, which encourages every automotive student to contribute to society. Noncommissioned officers can strengthen their belief in lifelong national defense and stimulate their morale in order to achieve an ideal the effect of professional sports learning and training.
Improving Dialogue State Tracking by Joint Slot Modeling

Dialogue state tracking models play an important role in a task-oriented dialogue system. However, most of them model the slot types conditionally independently given the input. We discover that it may cause the model to be confused by slot types that share the same data type. To mitigate this issue, we propose TripPy-MRF and TripPy-LSTM that models the slots jointly. Our results show that they are able to alleviate the confusion mentioned above, and they push the state-of-the-art on dataset MultiWoz 2.1 from 58.7 to 61.3.
Neural Dubber: Dubbing for Videos According to Scripts.

Dubbing is a post-production process of re-recording actors' dialogues, which is extensively used in filmmaking and video production. It is usually performed manually by professional voice actors who read lines with proper prosody, and in synchronization with the pre-recorded videos. In this work, we propose Neural Dubber, the first neural network model to solve a novel automatic video dubbing (AVD) task: synthesizing human speech synchronized with the given video from the text. Neural Dubber is a multi-modal text-to-speech (TTS) model that utilizes the lip movement in the video to control the prosody of the generated speech. Furthermore, an image-based speaker embedding (ISE) module is developed for the multi-speaker setting, which enables Neural Dubber to generate speech with a reasonable timbre according to the speaker's face. Experiments on the chemistry lecture single-speaker dataset and LRS2 multi-speaker dataset show that Neural Dubber can generate speech audios on par with state-of-the-art TTS models in terms of speech quality. Most importantly, both qualitative and quantitative evaluations show that Neural Dubber can control the prosody of synthesized speech by the video, and generate high-fidelity speech temporally synchronized with the video.