Reviewerly/exHarmony huggingface dataset:
Torii: An aspect‐based sentiment analysis system that can mine conditions

Summary Aspect‐based sentiment analysis systems are a kind of text‐mining systems that specialize in summarizing the sentiment that a collection of reviews convey regarding some aspects of an item. There are many cases in which users write their reviews using conditional sentences; in such cases, mining the conditions so that they can be analyzed is very important not to misinterpret the corresponding sentiment summaries. Unfortunately, current commercial systems or research systems neglect conditions; current frameworks and toolkits do not provide any components to mine them; furthermore, the proposals in the literature are insufficient because they are based on handcrafted patterns that fall short regarding recall or machine learning procedures that are tightly bound with a specific language and require too much configuration. In this article, we present Torii, which is a system that loads a collection of reviews, discovers the aspects on which they report, and summarizes the sentiment that is conveyed on them taking into account the existing conditions, if any. We also describe its architecture, our approach to mine conditions, and our experimental analysis on a large multilingual data set with reviews from multiple categories. To the best of our knowledge, Torii is the first proposal that addresses aspect‐based sentiment analysis taking conditions into account.
Stagewise Knowledge Distillation

Despite the success of Deep Learning (DL), the deployment of modern DL models requiring large computational power poses a significant problem for resource-constrained systems. This necessitates building compact networks that reduce computations while preserving performance. Traditional Knowledge Distillation (KD) methods that transfer knowledge from teacher to student (a) use a single-stage and (b) require the whole data set while distilling the knowledge to the student. In this work, we propose a new method called Stagewise Knowledge Distillation (SKD) which builds on traditional KD methods by progressive stagewise training to leverage the knowledge gained from the teacher, resulting in data-efficient distillation process. We evaluate our method on classification and semantic segmentation tasks. We show, across the tested tasks, significant performance gains even with a fraction of the data used in distillation, without compromising on the metric. We also compare our method with existing KD techniques and show that SKD outperforms them. Moreover, our method can be viewed as a generalized model compression technique that complements other model compression methods such as quantization or pruning.
Analysis review on linguistic steganalysis

&lt;span&gt;Steganography and steganalysis are essential topics for hiding information. Steganography is a technique of conceal secret messages by transmitting data through different domains. Its objective is to avoid discovery of secret messages. Steganalysis, meanwhile, is a method for locating the secret messages contained in the stego text. The objective of steganalysis is to find concealed data and to break the security of its domains. Steganalysis can be categorized into two types: targeted steganalysis and blind steganalysis. Steganography and steganalysis both have domains that are split into natural, also known as linguistic and digital media. There are three kinds of digital media which are picture, video and audio. The aim of this paper is to provide a survey on different linguistic steganalysis techniques used to find secret messages. This paper also highlighted two type of steganalysis method that are used in research and real practice. The discussion include findings on the most recent work on linguistic steganalysis techniques. This review hoped to help future research for improving and enhancing steganalytic capabilities.&lt;/span&gt;
Multi-information Complementarity Neural Networks for Multi-Modal Action Recognition

Multi-modal methods play an important role on action recognition. Each modal can extract different features to analyze the same motion classification. But numbers of researches always separate the one task from the others, which cause the unreasonable utilization of complementary information in the multi-modality data. Skeleton is robust to the variation of illumination, backgrounds and viewpoints, while RGB has better performance in some circumstances when there are other objects that have great effect on recognition of action, such as drinking water and eating snacks. In this paper, we propose a novel Multi-information Complementarity Neural Network (MiCNN) for human action recognition to address this problem. The proposed MiCNN can learn the features from both skeleton and RGB data to ensure the abundance of information. Besides, we design a weighted fusion block to distribute the weights reasonably, which can make each modal draw on their respective strengths. The experiments on NTU RGB-D datasets demonstrate the excellent performance of our scheme, which are superior to other methods that we have ever known.
Customizing Scripted Bots: Sample Efficient Imitation Learning for Human-like Behavior in Minecraft

Although there have been many advances in machine learning and artificial intelligence, video games still define the behavior of characters through careful scripting. These scripted agents follow a fixed set of rules or behavior trees resulting in behavior which is predictable and discernibly different from human gameplay. We demonstrate how to combine imitation learning with scripted agents in order to efficiently train hierarchical policies. Integrating both programmed and learned components, these hierarchical polices improve upon the expressiveness of the original scripted agent, allowing more diverse and human-like behavior to emerge. Additionally, we demonstrate that learned policies also maintain behavior guarantees afforded by the scripted agent, allowing a developer to be confident in the behavior of the learned policy. We demonstrate this interplay between classical AI techniques and statistical machine learning through a case study in Minecraft, and explore open questions about the necessary and sufficient conditions to achieve a fruitful interplay between these two approaches.
Forecasting the Amount of Blood Ordered in the Obstetrics and Gynaecology Ward with the Data Mining Approach

Preoperative blood ordering is frequently used in the obstetrics and gynecology ward of university hospitals in Iran, even for surgeries that rarely require blood transfusions. This routine procedure is an inefficient use of resources and rising costs, wasting time and cause shortage for essential patients. So this study was carried out to propose a new optimal system based on data mining techniques for ordering blood. This cross-sectional study examined the number of units cross-matched and transfused during surgery in the obstetrics and gynecology ward from 2013 to 2015. Data was collected for 1097 patients. Statistical analyzing was applied on data to prove that; the current blood ordering was not optimal. So with use of blood indices, C/T ratio, the new blood ordering variable was introduced. Then decision tree was applied on data with use of Rapid miner. Decision tree evaluation measures were rMSE and accuracy. A total of 1097 patients were examined for which 9747 units of blood were ordered. There was a significant difference between the number of cross-matched and transfused units according to all variables. The new method reduced the cross-matched units about 71.50%. The accuracy of proposed decision tree based on new blood ordering variable (according to C/T index) was 96.10%. The effective variables of blood ordered were type of surgery, blood group and amount of hemoglobin. The recent blood ordering variable prevent blood shortages, reduce costs. Excessive blood ordering is common in the obstetrics and gynecology department. According to proper results of new ordering variable, we suggest to apply this procedure in all hospitals in order to reduce extra costs and the optimal management of blood ordering.
Toward Development Framework for Eliciting and Documenting Knowledge in Requirements Elicitation

Requirements engineering (RE) is the most critical factor in the success or failure of project. A project’s success completely depends on the knowledge and experience of the stakeholders. RE is a team-based process and collaborative task, which involves a huge volume of deliberation and discussion between stakeholders, and comes from their tacit knowledge. During the RE process, the presence of unclear or hidden tacit knowledge causes ambiguity, incomplete, and incorrect requirements. To address these issues, we intended to propose a collaborative knowledge management system framework for identifying, categorizing, eliciting, and managing the requirements tacit knowledge that are required for software development. This work proposes a framework that is based on a combination of rationale-based model and domain ontology. In this paper, a comparison between some of methods that have been used for requirements elicitation and tacit knowledge elicitation with the proposed framework as an evaluation method was presented.
Challenges and Applications for Implementing Machine Learning in Computer Vision

The chapter introduces machine learning and why it is important. Machine learning is generally used to find knowledge from unknown data. There are many approaches and algorithms available for performing machine learning. Different kinds of algorithms are available to find different patterns from the data. This chapter focuses on different approaches with different usage.
On tight sets of hyperbolic quadrics

We prove that the parameter x of a tight set T of a hyperbolic quadric Q+(2n+1,q) of an odd rank n+1 satisfies x2+w(w−x)≡0modq+1, where w is the number of points of T in any generator of Q+(2n+1,q). As this modular equation should have an integer solution in w if such a T exists, this condition rules out roughly at least one half of all possible parameters x. It generalizes a previous result by the author and K. Metsch shown for tight sets of a hyperbolic quadric Q+(5,q) (also known as Cameron–Liebler line classes in PG(3,q)).
Source-free Machine-checked Validation of Native Code in Coq

Picinæ is an infrastructure for machine-proving properties of raw native code programs without sources within the Coq program-proof co-development system. This facilitates formal reasoning about binary code that is inexpressible in source languages or for which no source code exists, such as hand-written assembly code or code resulting from binary transformations (e.g., binary hardening or debloating algorithms). Preliminary results validating some highly optimized, low-level subroutines for Intel and ARM architectures using this new framework are presented and discussed.
Predictive Modeling Through Regression

While classification is the problem of predicting a discrete class label for an example, regression predictive modeling (or just regression) is the problem of learning the strength of association between independent variables (or features) and continuous dependent variables (or outcomes). A continuous output variable is a real value such as an integer or floating point value often quantified as amounts and sizes.
Camera Arrangement in Visual 3D Systems using Iso-disparity Model to Enhance Depth Estimation Accuracy

In this paper we address the problem of automatic arrangement of cameras in a 3D system to enhance the performance of depth acquisition procedure. Lacking ground truth or a priori information, a measure of uncertainty is required to assess the quality of reconstruction. The mathematical model of iso-disparity surfaces provides an efficient way to estimate the depth estimation uncertainty which is believed to be related to the baseline length, focal length, panning angle and the pixel resolution in a stereo vision system. Accordingly, we first present analytical relations for fast estimation of the embedded uncertainty in depth acquisition and then these relations, along with the 3D sampling arrangement are employed to define a cost function. The optimal camera arrangement will be determined by minimizing the cost function with respect to the system parameters and the required constraints. Finally, the proposed algorithm is implemented on some 3D models. The simulation results demonstrate significant improvement (up to 35%) in depth uncertainty in the achieved depth maps compared with the traditional rectified camera setup.
Dynamic Fusion for Multimodal Data.

Effective fusion of data from multiple modalities, such as video, speech, and text, is challenging pertaining to the heterogeneous nature of multimodal data. In this paper, we propose dynamic fusion techniques that model context from different modalities efficiently. Instead of defining a deterministic fusion operation, such as concatenation, for the network, we let the network decide how to combine given multimodal features in the most optimal way. We propose two networks: 1) transfusion network, which learns to compress information from different modalities while preserving the context, and 2) a GAN-based network, which regularizes the learned latent space given context from complimenting modalities. A quantitative evaluation on the tasks of machine translation, and emotion recognition suggest that such adaptive networks are able to model context better than all existing methods.
Non-Linear Polynomial Filters for Contrast Enhancement of Mammograms

It is known that the characteristic equation of Non-linear Polynomial Filters (NPF) can be conceptualized as a combination of linear and quadratic terms.
Location Based Authentication and Information Security in Cloud Environment: LBAIS

In cloud computing users are able to access the information and resources. Data and network security are lot of security challenges are facing by this technology on the time of accessing information and resources from the cloud. The secure communication of digital information is very necessary. The intruders can perform various types of network attacks during information transmission. A Location Based Authentication and an Information Security (LBAIS) framework is proposed in this paper, which authenticate the cloud users using Global Positioning System (GPS) and protect the information using symmetric cryptography algorithm.
NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution

Negation is an important characteristic of language, and a major component of information extraction from text. This subtask is of considerable importance to the biomedical domain. Over the years, multiple approaches have been explored to address this problem: Rule-based systems, Machine Learning classifiers, Conditional Random Field Models, CNNs and more recently BiLSTMs. In this paper, we look at applying Transfer Learning to this problem. First, we extensively review previous literature addressing Negation Detection and Scope Resolution across the 3 datasets that have gained popularity over the years: the BioScope Corpus, the Sherlock dataset, and the SFU Review Corpus. We then explore the decision choices involved with using BERT, a popular transfer learning model, for this task, and report state-of-the-art results for scope resolution across all 3 datasets. Our model, referred to as NegBERT, achieves a token level F1 score on scope resolution of 92.36 on the Sherlock dataset, 95.68 on the BioScope Abstracts subcorpus, 91.24 on the BioScope Full Papers subcorpus, 90.95 on the SFU Review Corpus, outperforming the previous state-of-the-art systems by a significant margin. We also analyze the model's generalizability to datasets on which it is not trained.
Design of PID Type Local Controller Network with Fuzzy Supervision

This paper proposes a feasible fuzzy strategy, network of local PID controllers type, applicable to the improvement of the dynamic stability and robustness of a well-known electromechanical system. Experimental tests confirm the superiority of the proposed method over classical control methods to treat nonlinearities and parametric variations of the plant.
Storage and Update of Knowledge

Experience in developing large knowledge-based AI projects suggests a progressive approach: the system needs maintenance to keep pace with demands and accumulation of commonsense knowledge to prevent having to start all over again. Financial asset management is no exception. The balance between leveraging the current knowledge base and adding to it is analogous to the learning and thought relation described by Confucius. In the previous chapter, sentic computingSentic computing is actively “thinking” with the knowledge base, however, not learning anything. An example also shows the problem of unable to retrieve domain-specific concepts from the knowledge base. In this chapter, discussions on the forms of storing semantic and sentiment knowledge are presented. A special effort on adding and updating polarity scores of words with high-level supervision is made. The same idea can be extended to other application domains as well as the curation of concepts or events.
How to Find It Better?

WeChat Mini Program is a lightweight app relying on the WeChat client, which can be accessed directly from the search list without downloading and installing. Retrieval and ranking for the Mini Programs differ from traditional web search in two sides. On the one hand, as the search queries are often short and most Mini Programs contain few useful textual information, it is hard to retrieve when the user input is inaccurate. On the other hand, without user scoring and rating system like App Store and Google Play, it is hard to rank relatively better results in a more advanced position. In this paper, we propose a Cross-Learning strategy to improve the search experience, where the semantics of queries and Mini Programs are represented not by itself, but by each other. We treat the search task as an extreme multi-label classification problem where the queries are inputs and the Mini Programs are labels. We propose a N-Gram self-attention query encoder to capture the search intention behind these short queries, and carefully design the label selection strategy based on user behavior to rank higher quality Mini Programs in higher positions. Our model outperforms some state-of-the-art baselines in the offline environment, and brought improvement to our actual business in the online A/B Test, which proves the practical significance of our work.
Fashion brands’ use of emojis on Twitter: An exploratory study

The aim of this study is to explore the use of emojis by fashion brands on their Twitter accounts. We conducted a content analysis on tweets created by the top ten fashion brands for the month of December 2016. These top fashion brands consisted of five mass fashion brands (Nike, Adidas, Victoria’s Secret, Zara and H&amp;M) and five luxury brands (Chanel, Dior, Gucci, Louis Vuitton and Prada). After training, two coders analysed predetermined categories from a coding scheme in each brand’s tweet, including (1) the presence of an emoji in the tweet, (2) the type of emoji used, (3) the content and context of the tweet, (4) the number of replies, (5) the number and content of the replies that contained emojis, (6) the number of retweets and (7) the number of likes given to the tweet. The use of emojis by fashion brands was minimal and tended to be limited to simple objects as opposed to conveying emotion or brand personality. None of the luxury brands in our sample used emojis in their tweets. In examining the effectiveness of emojis on Twitter, we found that the use of emojis was positively related to the number of replies, retweets and likes. The use of emojis did not differ significantly based on the types of messages. This study contributes knowledge to the development and implementation of effective Twitter strategies for fashion brands.
Dilemma-thinking as a means to enhance criticality in design for wellbeing

This chapter focuses on work in which three ideation strategies were proposed that could be used to trigger emotional dilemmas as a means to design for provocation. Design for wellbeing is intricately linked to handling complex societal challenges through design. Wellbeing-oriented design approaches, such as social design, persuasive technologies or positive design, are often applied in domains such as social exclusion, obesity, mental ill-health, online privacy or the refugee crisis. Critical design, a term originally coined by Dunne and Raby, emphasises a distinction between ‘affirmative’ design practices that reinforce the status quo defined by global capitalism and ‘critical’ design practices that subvert this status quo. The challenges indicate that much is left to designers’ intuition, empathic capacity and worldview when striving to adopt a viable approach to contribute to societal wellbeing. A good starting point to foster critical reflection is to make it a routine exercise to structurally analyse existing designs.
Big Data Environment for Realtime Earthquake Data Acquisition and Visualization

Indonesia is a country that has the highest level of earthquake risk in the world. In the past 10 years, there have been Â± 90,000 earthquake events recorded and always increasing along with the explosion of earthquake data occurs at any time. The process of collecting and analyzing earthquake data requires more effort and takes a long computational time. In this paper, we propose a new system to acquire, store, manage and process earthquake data in Indonesia in real-time, fast and dynamic by utilizing features in the Big Data Environment. This system improves computational performance in the process of managing and analyzing earthquake data in Indonesia by combining and integrating earthquake data from several providers to form a complete unity of earthquake data. An additional function is the existence of an API (Application Programming Interface) embedded in this system to provide access to the results of earthquake data analysis such as density, probability density function and seismic data association between provinces in Indonesia. The process in this system has been carried out in parallel and improved computing performance. This is evidenced by the computational time in the preprocessing process on a single-core master node, which requires 55.6 minutes, but a distributed computing process using 15 cores can speeds up with only 4.82 minutes.
Using instructional media based on technology in west sumatera: “what are the problems faced by teachers in secondary high school?”

Abstract Instructional media is a tool used by teachers to deliver learning material to all students. This tool plays an important role in efforts to improve learning outcomes, as a whole. The development of instructional media in the last few years is not only focused on printed media, but also media based on technology, internet and information. However, based on our observations, it is known that the use of instructional media that suits the need of students and the development of the times is still very low. Meanwhile, teachers are demanded by the government to improve the quality of education at the elementary to high level. This study presents fact about teachers in several high schools in West Sumatera, who have problems to create, to use and to empowerment of instructional media. The research method are interview and survey. The purpose of the study was to describe qualitatively the main problem of the teacher which was a barrier for them. The teacher who is the object of research works in schools in West Sumatera Province. The selection of schools was carried out by purposive sampling technique. The conclusion of the study is about the dominant factor that is become a problem for the teacher.