Reviewerly/exHarmony huggingface dataset:
Behavior Gated Language Models

Most current language modeling techniques only exploit co-occurrence, semantic and syntactic information from the sequence of words. However, a range of information such as the state of the speaker and dynamics of the interaction might be useful. In this work we derive motivation from psycholinguistics and propose the addition of behavioral information into the context of language modeling. We propose the augmentation of language models with an additional module which analyzes the behavioral state of the current context. This behavioral information is used to gate the outputs of the language model before the final word prediction output. We show that the addition of behavioral context in language models achieves lower perplexities on behavior-rich datasets. We also confirm the validity of the proposed models on a variety of model architectures and improve on previous state-of-the-art models with generic domain Penn Treebank Corpus.
THE EFFECT OF WORD WALL MEDIA ON WRITING ACHIEVEMENT OF THE SEVENTH GRADE STUDENTS OF SMP NEGERI 1 SERIRIT IN ACADEMIC YEAR 2018/2019

This study aimed at investigating the effect of using word wall media on writing achievement of the seventh grade students of SMP N 1 Seririt. The research design was Quasi Experimental of Post-Test Only Control Group design taking the seventh grade students of SMP N 1 Seririt. On the other hand, VII D as the control group was treated by using conventional media without word wall. The result of data analysis shows that there is a significant effect on students’ writing achievement between students from the experimental group and those from the control group (t= 8.826, df= 62, p &lt; 0.05). Moreover, the result of effect size in this study was 0.56 which indicates that word wall media give a strong influence on students’ writing achievement. Referring to those results, it can be concluded that writing activities by using word wall media can give significant effect on students’ writing achievement.
Mapping Agricultural Tillage Practices Using Extreme Learning Machine

In this paper, an efficient classifier based on extreme learning machine (ELM) is proposed to use for mapping agricultural tillage practices from hyperspectral remote sensing imagery. The kernel version, called kernel ELM (KELM), is implemented due to its powerfulness. To utilize spatial information of an image, a spatial convolution filter is adopted to generate spatial-spectral features of a hyperspectral pixel by incorporating its surrounding pixels, which are the actual inputs to the KELM. Experimental results using airborne hyperspectral images demonstrate that the KELM can outperform other classic methods, such as support vector machine and random forest.
Combining Spans into Entities: A Neural Two-Stage Approach for Recognizing Discontiguous Entities

In medical documents, it is possible that an entity of interest not only contains a discontiguous sequence of words but also overlaps with another entity. Entities of such structures are intrinsically hard to recognize due to the large space of possible entity combinations. In this work, we propose a neural two-stage approach to recognize discontiguous and overlapping entities by decomposing this problem into two subtasks: 1) it first detects all the overlapping spans that either form entities on their own or present as segments of discontiguous entities, based on the representation of segmental hypergraph, 2) next it learns to combine these segments into discontiguous entities with a classifier, which filters out other incorrect combinations of segments. Two neural components are designed for these subtasks respectively and they are learned jointly using a shared encoder for text. Our model achieves the state-of-the-art performance in a standard dataset, even in the absence of external features that previous methods used.
Quantum imaging for enhanced microscopy and light modulation

In this work we do a brief analysis of some widespread technologies for imaging science and security, while we also propose new techniques to exceed their standard classical limits by using quantum illumination and quantum detection, and exploiting special quantum modulation phenomena.
Rancang Bangun Knowledge Management System Politeknik Statistika STIS

STIS Polytechnic of Statistics (PS-STIS) is an institutional unit with high work complexity and density. In carrying out its personnel activities, there is no support system that can be used by employees to store and process knowledge related to their work. This may indirectly lead to ineffective and inefficient work in term of costs, time, and other resources. Job-related knowledge is stored in accordance with the decisions of each employee. When needed, knowledge acquisition becomes slow and knowledge is also prone to be scattered or even lost. In this study, we will discuss about design and development of knowledge management system within the scope of PS-STIS. The focus of this research is related to the integration of the system functions to be able to perform knowledge storage, collaboration, and retrieval. The web-based system built applies the basic principles of Knowledge Management System (KMS). it has pages and permissions in accordance with the interests and responsibilities of users. In this research, we used SDLC development method to build the system and evaluated it using blackbox testing method and Survey Usability Scale (SUS). Result of blackbox testing showed that all function performed like expected and SUS score of 74 showed that system built was acceptable to users.
CMU GetGoing: An Understandable and Memorable Dialog System for Seniors.

Voice-based technologies are typically developed for the average user, and thus generally not tailored to the specific needs of any subgroup of the population, like seniors. This paper presents CMU GetGoing, an accessible trip planning dialog system designed for senior users. The GetGoing system design is described in detail, with particular attention to the senior-tailored features. A user study is presented, demonstrating that the senior-tailored features significantly improve comprehension and retention of information.
QuaRTz: An Open-Domain Dataset of Qualitative Relationship Questions

We introduce the first open-domain dataset, called QuaRTz, for reasoning about textual qualitative relationships. QuaRTz contains general qualitative statements, e.g., A sunscreen with a higher SPF protects the skin longer., twinned with 3864 crowdsourced situated questions, e.g., Billy is wearing sunscreen with a lower SPF than Lucy. Who will be best protected from the sun?, plus annotations of the properties being compared. Unlike previous datasets, the general knowledge is textual and not tied to a fixed set of relationships, and tests a system's ability to comprehend and apply textual qualitative knowledge in a novel setting. We find state-of-the-art results are substantially (20%) below human performance, presenting an open challenge to the NLP community.
A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding

Intent detection and slot filling are two main tasks for building a spoken language understanding (SLU) system. The two tasks are closely tied and the slots often highly depend on the intent. In this paper, we propose a novel framework for SLU to better incorporate the intent information, which further guides the slot filling. In our framework, we adopt a joint model with Stack-Propagation which can directly use the intent information as input for slot filling, thus to capture the intent semantic knowledge. In addition, to further alleviate the error propagation, we perform the token-level intent detection for the Stack-Propagation framework. Experiments on two publicly datasets show that our model achieves the state-of-the-art performance and outperforms other previous methods by a large margin. Finally, we use the Bidirectional Encoder Representation from Transformer (BERT) model in our framework, which further boost our performance in SLU task.
Memory Efficient Deployment of an Optical Flow Algorithm on GPU Using OpenMP

In this paper, we consider the recent set of OpenMP directives related to GPU deployment and seek an evaluation through the case of an optical flow algorithm. We start by investigating various agnostic transformations that attempt to improve memory efficiency. Our case study is the so-called Lucas-Kanade algorithm, which is typically composed of a series of convolution masks (approximation of the derivatives) followed by $$2\times 2$$ linear systems for the optical flow vectors. Since, we are dealing with a stencil computation for each stage of the algorithm, the overhead of memory accesses together with the impact on parallel scalability are expected to be noticeable, especially with the complexity of the GPU memory system. We compare our OpenMP implementation with an OpenACC one from our previous work, both on a Quadro P5000.
Bandwidth Embeddings for Mixed-bandwidth Speech Recognition

In this paper, we tackle the problem of handling narrowband and wideband speech by building a single acoustic model (AM), also called mixed bandwidth AM. In the proposed approach, an auxiliary input feature is used to provide the bandwidth information to the model, and bandwidth embeddings are jointly learned as part of acoustic model training. Experimental evaluations show that using bandwidth embeddings helps the model to handle the variability of the narrow and wideband speech, and makes it possible to train a mixed-bandwidth AM. Furthermore, we propose to use parallel convolutional layers to handle the mismatch between the narrow and wideband speech better, where separate convolution layers are used for each type of input speech signal. Our best system achieves 13% relative improvement on narrowband speech, while not degrading on wideband speech.
Generating Personalized Recipes from Historical User Preferences

Existing approaches to recipe generation are unable to create recipes for users with culinary preferences but incomplete knowledge of ingredients in specific dishes. We propose a new task of personalized recipe generation to help these users: expanding a name and incomplete ingredient details into complete natural-text instructions aligned with the user's historical preferences. We attend on technique- and recipe-level representations of a user's previously consumed recipes, fusing these 'user-aware' representations in an attention fusion layer to control recipe text generation. Experiments on a new dataset of 180K recipes and 700K interactions show our model's ability to generate plausible and personalized recipes compared to non-personalized baselines.
Study of Community Capacity Enhancement in Fire Disaster Mitigation in Kuta Alam Sub-District, Banda Aceh

Kuta Alam Subdistrict is one of the subdistricts in the Banda Aceh city that has a risk of fire disasters. This is because this area is a residential area with a moderate category. Semi-permanent buildings and temporary constructions are still at high risk of fire often found in the area. Based on the results of interviews with the Banda Aceh Fire and Rescue Service, there were 27 fire incidents from 2014 to 2017, of which 12 houses were burned down, 2 schools, 3 office units, and 10 other public facilities affected by the fire. This research is needed to improve the ability of people to prevent and protect themselves, especially in pre-disaster conditions so that they can reduce the fire incidence. The community capabilities in fire disaster mitigation are the methods used in this study. Subdistrict consist of optimizing land use in city development, minimizing street parking, increasing green open space, increasing the ability of water sources and wet towel / clothes as a method of fire outage if there is a small fire at home, access information about procedures for firing and providing early warning, and must be able to evacuate to the designated location.
KONTRIBUSI KETERAMPILAN MEMBACA PEMAHAMAN TEKS DESKRIPSI TERHADAP KETERAMPILAN MENULIS TEKS DESKRIPSI SISWA KELAS VII SMP NEGERI 1 BAYANG UTARA

ABSTRACT The porpose of this research there are three. First, to describes the skills of reading comprehension text description grade VII SMP Negeri 1 Bayang Utara. Second, to describes a text description of the writing skills of students grade VII SMP Negeri 1 Bayang Utara. Third, describing the contribution of the intermediate reading comprehension text descriptions against writing skills text description grade VII SMP Negeri 1 Bayang Utara. This research type is quantitative descriptive method. The design used in this research is the design contribution. This research population is grade VII SMP Negeri 1 Bayang Utara that are listed in the 2017/2018, i.e., 268 students. The sample in this research are determined by proportional random sampling of 15% of the population, i.e. 40 students. Research data is a score test results intermediate reading comprehension text description and score the writing skills test result text description. Research instrument is an objective test to measure the skills of reading comprehension text description and performance tests to measure the skills of writing a text description. The results of the research there are three. First, a text description of the writing skills of students of grade VII SMP Negeri 1 Bayang Utara lies on the qualifications of either (B). Second, the skills of reading comprehension text description grade VII SMP Negeri 1 Bayang Utara are on Good qualifications (B). Third, intermediate reading comprehension text descriptions account for 47.20% against writing skills text description grade VII SMP Negeri 1 Bayang Utara. Kata kunci: Kontribusi, Membaca Pemahaman, Menulis, Teks Deskripsi
Intrinsic Dynamic Shape Prior for Fast, Sequential and Dense Non-Rigid Structure from Motion with Detection of Temporally-Disjoint Rigidity

While dense non-rigid structure from motion (NRSfM) has been extensively studied from the perspective of the reconstructability problem over the recent years, almost no attempts have been undertaken to bring it into the practical realm. The reasons for the slow dissemination are the severe ill-posedness, high sensitivity to motion and deformation cues and the difficulty to obtain reliable point tracks in the vast majority of practical scenarios. To fill this gap, we propose a hybrid approach that extracts prior shape knowledge from an input sequence with NRSfM and uses it as a dynamic shape prior for sequential surface recovery in scenarios with recurrence. Our Dynamic Shape Prior Reconstruction (DSPR) method can be combined with existing dense NRSfM techniques while its energy functional is optimised with stochastic gradient descent at real-time rates for new incoming point tracks. The proposed versatile framework with a new core NRSfM approach outperforms several other methods in the ability to handle inaccurate and noisy point tracks, provided we have access to a representative (in terms of the deformation variety) image sequence. Comprehensive experiments highlight convergence properties and the accuracy of DSPR under different disturbing effects. We also perform a joint study of tracking and reconstruction and show applications to shape compression and heart reconstruction under occlusions. We achieve state-of-the-art metrics (accuracy and compression ratios) in different scenarios.
Constant Temperature Control System for Indoor Environment of Buildings Based on Internet of Things

In order to improve the stability of constant temperature control for indoor environment of buildings, a design method of constant temperature optimisation control system for buildings' indoor environment based on internet of things (IoT) is proposed. The system design is divided into two parts – the control algorithm design and the hardware structure design of the control system – to conduct the overall design framework analysis and functional component analysis of indoor environment constant temperature control system. The time-delay feedback error compensation method is adopted to optimise the design for temperature control law for indoor environment of buildings. The test results show that the minimum root-mean-square error of the designed constant temperature control system is 0.021, which significantly reduces the temperature error of the constant temperature control system, indicating that the system has good temperature output stability, strong adaptive adjustment ability and effectively improves the constant temperature control ability of indoor environment.
Learning Distributions Generated by One-Layer ReLU Networks

We consider the problem of estimating the parameters of a $d$-dimensional rectified Gaussian distribution from i.i.d. samples. A rectified Gaussian distribution is defined by passing a standard Gaussian distribution through a one-layer ReLU neural network. We give a simple algorithm to estimate the parameters (i.e., the weight matrix and bias vector of the ReLU neural network) up to an error $ε||W||_F$ using $\tilde{O}(1/ε^2)$ samples and $\tilde{O}(d^2/ε^2)$ time (log factors are ignored for simplicity). This implies that we can estimate the distribution up to $ε$ in total variation distance using $\tilde{O}(κ^2d^2/ε^2)$ samples, where $κ$ is the condition number of the covariance matrix. Our only assumption is that the bias vector is non-negative. Without this non-negativity assumption, we show that estimating the bias vector within any error requires the number of samples at least exponential in the infinity norm of the bias vector. Our algorithm is based on the key observation that vector norms and pairwise angles can be estimated separately. We use a recent result on learning from truncated samples. We also prove two sample complexity lower bounds: $Ω(1/ε^2)$ samples are required to estimate the parameters up to error $ε$, while $Ω(d/ε^2)$ samples are necessary to estimate the distribution up to $ε$ in total variation distance. The first lower bound implies that our algorithm is optimal for parameter estimation. Finally, we show an interesting connection between learning a two-layer generative model and non-negative matrix factorization. Experimental results are provided to support our analysis.
TEASPN: Framework and Protocol for Integrated Writing Assistance Environments

Language technologies play a key role in assisting people with their writing. Although there has been steady progress in e.g., grammatical error correction (GEC), human writers are yet to benefit from this progress due to the high development cost of integrating with writing software. We propose TEASPN, a protocol and an open-source framework for achieving integrated writing assistance environments. The protocol standardizes the way writing software communicates with servers that implement such technologies, allowing developers and researchers to integrate the latest developments in natural language processing (NLP) with low cost. As a result, users can enjoy the integrated experience in their favorite writing software. The results from experiments with human participants show that users use a wide range of technologies and rate their writing experience favorably, allowing them to write more fluent text.
KORELASI ANTARA KEMAMPUAN MEMBACA KRITIS DENGAN KEMAMPUAN MENULIS ARGUMENTASI SISWA KELAS VII A SMP NEGERI 9 KOTA BENGKULU

This research is a quantitative research that uses a correlational method. The population and sample in this study were all students of class VII A SMP Negeri 9 Kota Bengkulu in the 2016/2017 school year, which amounted to 30 students. The results showed that the existence of critical reading skills in the ability to write arguments is classified as very strong (0,852), meaning that critical reading skills are very supportive of the ability to write arguments. Where the variable critical reading ability contributes to the variable ability to write arguments at 72,59%, and also shows that tcount is greater than t table or 8,623 &gt; 1,701, meaning that there is a significant relationship between critical reading ability to the ability to write arguments.Penelitian ini merupakan penelitian kuantitatif yang menggunakan metode korelasional. Populasi dan sampel dalam penelitian ini adalah seluruh siswa kelas VII A SMP Negeri 9 Kota Bengkulu pada tahun pelajaran 2016/2017, yang berjumlah 30 orang siswa. Hasil penelitian menunjukkan bahwa keberadaan kemampuan membaca kritis terhadap kemampuan menulis argumentasi tergolong adalah sangat kuat (0,852), artinya kemampuan membaca kritis sangat mendukung terhadap keberadaan kemampuan menulis argumentasi. Di mana variabel kemampuan membaca kritis memberikan kontribusi terhadap variabel kemampuan menulis argumentasi sebesar 72,59%, dan juga terlihat bahwa t hitung lebih besar dari t tabel atau 8,623 &gt; 1,701, artinya ada hubungan yang signifikan antara kemampuan membaca kritis terhadap kemampuan menulis argumentasi..
A proposal to eliminate the skew ray error in corneal topography using Placido disks images

Corneal topographers based in Placido disks are widely used for their simplicity and easy implementation, however, this technique has an inherent error known as the skew ray error, which is generated by the problem that exists during the integration to relate a single point on the Placido screen with its correspondent point in the captured image. In this research work, we present a proposal of integration to recover the corneal topography from Placido disks images, which could eliminate the skew ray error. This process is made in a post-processing step, right after a meridional integration. The proposed integration is based under the hypothesis that the meridional integration really has information about the topography changes in the angle direction. The effect of the proposed technique over a topography is presented.
Testing Deep Learning Models for Image Analysis Using Object-Relevant Metamorphic Relations

Deep learning models are widely used for image analysis. While they offer high performance in terms of accuracy, people are concerned about if these models inappropriately make inferences using irrelevant features that are not encoded from the target object in a given image. To address the concern, we propose a metamorphic testing approach that assesses if a given inference is made based on irrelevant features. Specifically, we propose two novel metamorphic relations to detect such inappropriate inferences. We applied our approach to 10 image classification models and 10 object detection models, with three large datasets, i.e., ImageNet, COCO, and Pascal VOC. Over 5.3% of the top-5 correct predictions made by the image classification models are subject to inappropriate inferences using irrelevant features. The corresponding rate for the object detection models is over 8.5%. Based on the findings, we further designed a new image generation strategy that can effectively attack existing models. Comparing with a baseline approach, our strategy can double the success rate of attacks.
KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning

Commonsense reasoning aims to empower machines with the human ability to make presumptions about ordinary situations in our daily life. In this paper, we propose a textual inference framework for answering commonsense questions, which effectively utilizes external, structured commonsense knowledge graphs to perform explainable inferences. The framework first grounds a question-answer pair from the semantic space to the knowledge-based symbolic space as a schema graph, a related sub-graph of external knowledge graphs. It represents schema graphs with a novel knowledge-aware graph network module named KagNet, and finally scores answers with graph representations. Our model is based on graph convolutional networks and LSTMs, with a hierarchical path-based attention mechanism. The intermediate attention scores make it transparent and interpretable, which thus produce trustworthy inferences. Using ConceptNet as the only external resource for Bert-based models, we achieved state-of-the-art performance on the CommonsenseQA, a large-scale dataset for commonsense reasoning.