Reviewerly/exHarmony huggingface dataset:
Multimodal Emotion Recognition Using the Symmetric S-ELM-LUPI Paradigm

Multimodal emotion recognition has become one of the new research fields of human-machine interaction. This paper focuses on feature extraction and data fusion in audio-visual emotion recognition, aiming at improving recognition effect and saving storage space. A semi-serial fusion symmetric method is proposed to fuse the audio and visual patterns of emotional recognition, and a method of Symmetric S-ELM-LUPI is adopted (Symmetric Sparse Extreme Learning Machine-Learning Using Privileged Information). The method inherits the generalized high speed of the Extreme Learning Machine, and combines this with the acceleration in the recognition process by the Learning Using Privileged Information and the memory saving of the Sparse Extreme Learning Machine. It is a learning method, which improves the traditional learning methods of examples and targets only. It introduces the role of a teacher in providing additional information to enhance the recognition (test) without complicating the learning process. The proposed method is tested on publicly available datasets and yields promising results. This method regards one pattern as the standard information source, while the other pattern as the privileged information source. Each mode can be treated as privileged information for another mode. The results show that this method is appropriate for multi-modal emotion recognition. For hundreds of samples, the execution time is less than one percent seconds. The sparsity of the proposed method has the advantage of storing memory economy. Compared with other machine learning methods, this method is more accurate and stable.
Prediction of Writing True Scores in Automated Scoring of Essays by Best Linear Predictors and Penalized Best Linear Predictors

Abstract Many assessments of writing proficiency that aid in making high‐stakes decisions consist of several essay tasks evaluated by a combination of human holistic scores and computer‐generated scores for essay features such as the rate of grammatical errors per word. Under typical conditions, a summary writing score is provided by a linear combination of the holistic scores and the feature scores. The best linear predictor (BLP) is used to approximate the true composite writing score by a linear combination of holistic scores and scores of essay features. However, because the relationship between computer‐generated feature score and human scores may depend on subgroup membership and the same scoring rules must normally be applied to all test takers, Yao, Haberman, and Zhang proposed a modified methodology of the penalized best linear predictor (PBLP) by incorporating a quadratic penalty function into the conventional BLP method. This research report contains full accounts of the BLP results as well as supplementary PBLP results to Yao et al. for three assessments of writing that aid in making high‐stakes decisions: the TOEFL iBT ® Writing test, the GRE ® General Analytical Writing subject test, and the Praxis ® Core Academic Skills for Educators: Writing assessment. Results obtained indicate the added value in using machine features for prediction of composite true scores of essay writings and effectiveness of the penalty function in suppressing the lack of population invariance.
Conundrum-Pass: A New Graphical Password Approach

Graphical passwords are most widely used as a mechanism for authentication in today's mobile computing environment. This methodology was introduced to enhance security element and overcome the vulnerabilities of textual passwords, pins, or other trivial password methodologies which were difficult to remember and prone to external attacks. There are many graphical password schemes that are proposed over time, however, most of them suffer from shoulder surfing and could be easily guessed which is quite a big problem. The proposed technique in this paper allows the user to keep the ease-to-use property of the pattern lock while minimizing the risk of shoulder surfing and password guessing. The proposed technique allows the user to divide a picture into multiple chunks and while unlocking, selecting the previously defined chunks results successfully in unlocking the device. This technique can effectively resist the shoulder surfing and smudge attacks, also it is resilient to password guessing or dictionary attacks. The proposed methodology can significantly improve the security of the graphical password system with no cost increase in terms of unlocking time.
Towards Locally Consistent Object Counting with Constrained Multi-stage Convolutional Neural Networks

High-density object counting in surveillance scenes is challenging mainly due to the drastic variation of object scales. The prevalence of deep learning has largely boosted the object counting accuracy on several benchmark datasets. However, does the global counts really count? Armed with this question we dive into the predicted density map whose summation over the whole regions reports the global counts for more in-depth analysis. We observe that the object density map generated by most existing methods usually lacks of local consistency, i.e., counting errors in local regions exist unexpectedly even though the global count seems to well match with the ground-truth. Towards this problem, in this paper we propose a constrained multi-stage Convolutional Neural Networks (CNNs) to jointly pursue locally consistent density map from two aspects. Different from most existing methods that mainly rely on the multi-column architectures of plain CNNs, we exploit a stacking formulation of plain CNNs. Benefited from the internal multi-stage learning process, the feature map could be repeatedly refined, allowing the density map to approach the ground-truth density distribution. For further refinement of the density map, we also propose a grid loss function. With finer local-region-based supervisions, the underlying model is constrained to generate locally consistent density values to minimize the training errors considering both the global and local counts accuracy. Experiments on two widely-tested object counting benchmarks with overall significant results compared with state-of-the-art methods demonstrate the effectiveness of our approach.
Hierarchical clustering of mixed variable panel data based on new distance

One of the important aspects of panel data is the poolability of different units in the data set. However, considering that regression parameters (coefficients) are homogeneous across different units, it is normal for the units to be pooled. Clustering may occur in the panel data to solve the problem. In this study, we suggest a new distance for a mixed variable panel data set containing invariant time binary variable, without performing variable conversion to avoid information loss. In this approach, the mixed variable panel data set is divided into pure categorical data and pure numerical data sets. Then, distance measures are calculated using simple matching for the categorical data set and using distance that normalizes all variables in the numerical data set. After distance measures of each data set are combined, the new distance measure is integrated into the agglomerative hierarchical clustering algorithms. The experimental analysis was exemplified by the real data groups using STATA and R software package. The performance of proposed distance is compared with the Gower and K-prototype distances by cluster validation methods. The experimental results demonstrated that the new approach we suggest here provides better clustering results than the Gower and K-prototype approaches.
Optimal Filtering for Time-Varying Stochastic System With Delay and Multiplicative Noise

This paper pays attention to the problem of optimal filtering for linear continuous time-varying It ô stochastic system with multiple delayed measurements and multiplicative noise. The stochastic analysis and calculus of stochastic variables are employed to analyze and design the optimal filtering. For the It ô stochastic continuous-time system with multiple delayed measurements and multiplicative noise, a delay is first transferred from the state to integral term with the Brownian motion in measurements by solving the stochastic equation of multiplicative noise. Then, based on the delay-free state in measurements and the independent increment characteristics of the Brownian motion the optimal filter is derived through the calculation of the conditional expectation. It should be stressed that the optimal filter follows directly from the manipulation of the performance. Finally, a price-volatility feedback rate model in mathematical finance is chosen to demonstrate the design of the optimal filter via the proposed approach in this paper.
A Study on Path Planning Algorithms of UAV Collision Avoidance

The unmanned aerial vehicle (UAV) has been a research hotspot worldwide. The UAV system is developing to be more and more intelligent and autonomous. UAV path planning is an important part of UAV autonomous control and the important guarantee of UAV's safety. For the purpose of improving the collision avoidance and path planning algorithms, the artificial potential field, fuzzy logic algorithm and ant colony algorithm are simulated respectively in the static obstacle and dynamic obstacle environments, and compared based on the minimum avoidance distance and range ratio. Meanwhile, an improved algorithm of artificial potential field is proposed, and the improvement helps the UAV escape the local minimum by introducing the vertical guidance repulsion. The simulation results are rigorous and reliable, which lay a foundation for the further fusion of multiple algorithms and improving the path planning algorithms.
Financial development and economic growth nexus: A rejoinder to Tsionas

Abstract Tsionas has offered a more revisionist approach to the empirics of the nexus between financial development and economic growth following our paper entitled ‘Financial development and economic growth nexus: A revisionist approach’. In the current note, we engage Tsionas’ approach in tandem with our original finance–growth nexus review. We concur with Tsionas’ approach and reiterate that the debate on the relationship between financial development and economic growth remains a complex terrain both on the theoretical and empirical fronts.
Weighted finite-state transducers for normalization of historical texts

Abstract This paper presents a study about methods for normalization of historical texts. The aim of these methods is learning relations between historical and contemporary word forms. We have compiled training and test corpora for different languages and scenarios, and we have tried to read the results related to the features of the corpora and languages. Our proposed method, based on weighted finite-state transducers, is compared to previously published ones. Our method learns to map phonological changes using a noisy channel model; it is a simple solution that can use a limited amount of supervision in order to achieve adequate performance. The compiled corpora are ready to be used for other researchers in order to compare results. Concerning the amount of supervision for the task, we investigate how the size of training corpus affects the results and identify some interesting factors to anticipate the difficulty of the task.
Robust Web Data Extraction Based on Unsupervised Visual Validation

Visual validation is the process of validating sets of extracted entities by means of visual information. The main advantage of visual validation is to make use of visual information for web information extraction without impacting on the robustness of extractors. In this paper, we show that unsupervised visual validation can be used to create robust web data extractors. More precisely, we evaluate the performance of visual validation on a corpus of visually heterogeneous documents. The selected extraction task consists in extracting the price, name, description, and SKU of unspecified products from unseen documents. Our corpus contains 1000 various products from 100 different sources, which we render public. Results also show that visual validation improves web data extraction even when the extractor is trained with visual features.
A Quality-Retaining Power-Saving Framework for Video Applications on Mobile OLED Displays

As people increase their dependency on mobile applications and services, saving power consumption of mobile devices becomes an important challenge for supporting video streaming applications. This paper investigates how to minimize the power consumption of an organic light-emitting diode (OLED) display when displaying a video under consideration for the user's visual experience. We first model the minimization problem as an OLED video scaling optimization problem. We then propose an algorithm to solve the optimization problem and prove that the algorithm is optimal in terms of power savings. We next introduce an energy-saving cloud service based on the algorithm. Finally, the results of the extensive experiments conducted on a commercial mobile smartphone with four real-world videos to evaluate the performance of the proposed algorithm are very encouraging.
An Assessment of Reference Levels in HDR Content

The movie and broadcast industries are gaining experience with high-dynamic-range (HDR) video technologies and are starting to produce HDR content at scale. This is accompanied by a learning process, particularly involving questions regarding the use of the extra dynamic range afforded by these technologies. To create further insight into the aspects of HDR content, in this paper, a variety of different types of content is analyzed. Manual annotation processes are used to determine the range of luminance values that, for example, diffuse white objects or white overlay graphics objects have in this content. We found that, for each type of object analyzed, the mean luminance value is reasonably constant across different types of content, but the spread of luminance values is remarkably large and strongly content dependent. Our results may form a basis for understanding HDR content and contribute toward forming opinions on defining reference levels in reports and standards, including International Telecommunications Union - Radiocommunication Rec. BT.2408.
Digital Restoration for Damaged Thangka Image

The “Thangka” is a special artistic presentation from Tibetan, which is also widely regarded as one of the Intangible Cultural Heritage. Because of a long history and its ingredient (paper and silk) being hard to conserve, Thangka has been damaged by different degrees. In this paper, the technology of damaged Thangka image inpainting is introduced, which included damaged region segmentation, image inpainting and image inpainted quality assessment. The technology not only provides a technical route for the developer to repair the image of the Thangka, but also supply a theoretical reference for the Thangka researchers in other fields, and finally we promote some protection in the Thangka intangible cultural heritage.
On the search behaviour of users in the context of interactive social book search

Understanding the search behaviour of online users is among the long-tail practices of Interactive Information Retrieval that helps identify the user information needs. The Interactive Social Book Search (SBS), under the umbrella of Interactive Information Retrieval (IIR), aims to understand the user interactions with book collections and the associated professionally-curated and socially-constructed metadata on the baseline and multistage user interfaces (UIs). This paper reports on the book search behaviour of users by reviewing research publications related to the Interactive SBS published during the last two decades. It presents a holistic view of the overall progress of Interactive SBS by summarising and visualising the experimental structure, search systems, datasets, demographics of participants, and findings to identify the research trends and possible future directions. Based on the collected evidence, it attempts to answer how the search system, user interface (UI), and the nature of tasks affect the book search behaviour of users. The article is the first of its kind that attempts to understand the book search behaviour of users in the context of Social Book Search with implications for usability experts and others working in UI design, web search engines, book search engines, digital libraries, collaborative social cataloguing websites, and e-Commerce applications.
Enhancing Robot Learning with Human Social Cues

Imagine a learning scenario between two humans: a teacher demonstrating how to play a new musical instrument or a craftsman teaching a new skill like pottery or knitting to a novice. Even though learning a skill has a learning curve to get the nuances of the technique right, some basic social principles are followed between the teacher and the student to make the learning process eventually succeed. There are several assumptions or social priors in this communication for teaching: mutual eye contact to draw attention to instructions, following the gaze of the teacher to understand the skill, the teacher following the student's gaze during imitation to give feedback, the teacher demonstrating by pointing towards something she is going to approach or manipulate and verbal interruptions or corrections during the learning process [1], [2]. In prior research, verbal and non-verbal social cues such as eye gaze and gestures have been shown to make human-human interactions seamless and augment verbal, collaborative behavior [3], [4]. They serve as an indicator of engagement, interest and attention when people interact face-to-face with one another [5], [6].
SART - Similarity, Analogies, and Relatedness for Tatar Language: New Benchmark Datasets for Word Embeddings Evaluation

There is a huge imbalance between languages currently spoken and corresponding resources to study them. Most of the attention naturally goes to the "big" languages—those which have the largest presence in terms of media and number of speakers. Other less represented languages sometimes do not even have a good quality corpus to study them. In this paper, we tackle this imbalance by presenting a new set of evaluation resources for Tatar, a language of the Turkic language family which is mainly spoken in Tatarstan Republic, Russia. We present three datasets: Similarity and Relatedness datasets that consist of human scored word pairs and can be used to evaluate semantic models; and Analogies dataset that comprises analogy questions and allows to explore semantic, syntactic, and morphological aspects of language modeling. All three datasets build upon existing datasets for the English language and follow the same structure. However, they are not mere translations. They take into account specifics of the Tatar language and expand beyond the original datasets. We evaluate state-of-the-art word embedding models for two languages using our proposed datasets for Tatar and the original datasets for English and report our findings on performance comparison. The datasets are available at https://github.com/tat-nlp/SART.
Personalized augmented reality via fog-based imitation learning

Augmented reality (AR) technologies are rapidly gaining momentum in society and are expected to play a critical role in the future of cities and transportation. In such dynamic settings with a heterogeneous population of AR users, it is important for holograms to be placed in the surrounding environment with regard to the users' preferences. However, the area of AR personalization remains largely unexplored. This paper proposes to use behavioral cloning, an algorithm for imitation learning, as a means of automatically generating policies that capture user preferences of hologram positioning. We argue in favor of employing the fog computing paradigm to minimize the volume of data sent to the cloud, and thereby preserve user privacy and increase both communication efficiency and learning efficiency. Through preliminary results obtained with a custom, Unity-based AR simulator, we demonstrate that user-specific policies can be learned quickly and accurately.
Ensemble of Predictions from Augmented Input as Adversarial Defense for Face Verification System

Face identification is employed in security system. Recently, it is gaining reliable result thanks to the deep learning method. However, the deep learning-based methods are prone against adversarial attack that leads to wrong prediction in presence of simple alteration on the pixel values. Thus the reliability of such system is compromised. This paper proposed a simple defense strategy to improve the reliability of a system in the presence of adversarial attack. By combining the prediction from few samples of altered input image, the effect of adversarial attack can be reduced effectively. The proposed method has been tested using public face dataset in the presence of strong attacks. Experiment results shows that the proposed method is reliable to suppress the adversarial attacks.
Hypotheses and their dynamics in legal argumentation

We investigate some legal interpretation techniques from the viewpoint of the Argentinian jurisprudence. This allows the proposal of a logical framework –from a computer science perspective– for modeling such specific reasoning techniques towards an appropriate construction of legal arguments. Afterwards, we study the usage of assumptions towards construction of hypotheses. This is proposed in the dynamic context of legal procedures, where the referred argumentation framework evolves as part of the investigation instance prior to the trial. We propose belief revision operators to handle such dynamics, preserving a coherent behavior with regards to the legal interpretation used. Abduction is finally proposed to construct systematic hypothesization, with the objective to bring semi-automatic recommendations to push forward the investigation of a legal case.
Comparative Performance Evaluation of Keyword and Semantic Search Engines using Different Query Set Categories

Background: Keyword search engines are unable to understand the intention of user as a result they produce enormous results for user to distinguish between relevant and non relevant answers of user queries. This has led to rise in requirement to study search capabilities of different search engines. In this research work, experimental evaluation is done based on different metrics to distinguish different search engines on the basis of type of query that can be handled by them. Methods: To check the semantics handling performance, four types of query sets consisting of 20 queries of agriculture domain are chosen. Different query set are single term queries, two term queries, three term queries and NLP queries. Queries from different query set were submitted to Google, DuckDuckGo and Bing search engines. Effectiveness of different search engines for different nature of queries is experimented and evaluated in this research using Grade relevance measures like Cumulative Gain, Discounted Cumulative Gain, Ideal Discounted Cumulative Gain, and Normalized Discounted Cumulative Gain in addition to the precision metric. Results: Our experimental results demonstrate that for single term query, Google retrieves more relevant documents and performs better and DuckDuckGo retrieves more relevant documents for NLP queries. Conclusion: Analysis done in this research shows that DuckDuckGo understand human intention and retrieve more relevant result, through NLP queries as compared to other search engines.
Teaching Data Visualization as a Skill

This paper explores the meaning of the term "skill" in the context of information (data) visualization and its place in the labor market. It examines the visualization skills and software competencies that are in high demand in industry today, and the ramifications for teaching Data Visualization for professional students in higher education.