Reviewerly/exHarmony huggingface dataset:
Exploring Various Aspects of Gabor Filter in Classifying Facial Expression

Facial expression detection is a well-studied domain in which facial features are extracted and then classified into six common expressions. One of the most common techniques used for extracting features is the Gabor filter. In literature, for extracting the features, the combined magnitude and phase values of the Gabor filter are used. This paper is exploring the performance of methods using the combined filtering method, using magnitude alone and using phase alone in the domain of facial expression detection. It is observed that considering phase values with the support vector machine classifier yielded an additional 8% accuracy when compared to combined methods.
Shared Task on Evaluating Accuracy in Natural Language Generation

We propose a shared task on methodologies and algorithms for evaluating the accuracy of generated texts. Participants will measure the accuracy of basketball game summaries produced by NLG systems from basketball box score data.
Duality-based approximation algorithms for depth queries and maximum depth

We design an efficient data structure for computing a suitably defined approximate depth of any query point in the arrangement $\mathcal{A}(S)$ of a collection $S$ of $n$ halfplanes or triangles in the plane or of halfspaces or simplices in higher dimensions. We then use this structure to find a point of an approximate maximum depth in $\mathcal{A}(S)$. Specifically, given an error parameter $ε&gt;0$, we compute, for any query point $q$, an underestimate $d^-(q)$ of the depth of $q$, that counts only objects containing $q$, but is allowed to exclude objects when $q$ is $ε$-close to their boundary. Similarly, we compute an overestimate $d^+(q)$ that counts all objects containing $q$ but may also count objects that do not contain $q$ but $q$ is $ε$-close to their boundary. Our algorithms for halfplanes and halfspaces are linear in the number of input objects and in the number of queries, and the dependence of their running time on $ε$ is considerably better than that of earlier techniques. Our improvements are particularly substantial for triangles and in higher dimensions.
Digital Contracts (June 1, 2018 - May 31, 2019)

This Article provides an update of cases involving non-negotiated digital contracts, also referred to as “wrap contracts,” during the period of approximately June 1, 2018 to May 31, 2019. Cases decided during the survey period continue to reflect confusion regarding the meaning of the standard of notice-and-manifestation of assent. In particular, they reflect a lack of clarity regarding whether notice refers to the terms of service generally or whether the offeree must have notice of, and assent to, specific terms. Recent cases manifest a developing trend that imposes greater responsibility on drafting parties. Courts are examining successive screenshots, referred to as “webflows” or “flows,” to understand the path a user takes to the terms of service and whether the process of engaging with the website would have alerted the user that a contract was being formed by certain actions. Consequently, more courts are placing an evidentiary burden on the drafting party to prove what the webflow was like at the time the user allegedly viewed the notice, and to prove that the user accessed the website during the relevant period.
Generalized Quantum Deutsch-Jozsa Algorithm

Quantum computing aims to provide algorithms and hardware that allows for solving computational problems asymptotically faster than on classical computers. Yet, design of new, fast quantum algorithms is not straightforward, and the field faces high barriers of entry for traditional computer scientists. One of the main didactic examples used to introduce speedup resulting from quantum computing is the Deutsch-Jozsa algorithm for discriminating between constant and balanced functions. Here, we show a generalization of the Deutsch-Jozsa algorithm beyond balanced functions that can be used to further illustrate the design choices underpinning quantum algorithms.
A Constructive, Type-Theoretic Approach to Regression via Global Optimisation

We examine the connections between deterministic, complete, and general global optimisation of continuous functions and a general concept of regression from the perspective of constructive type theory via the concept of 'searchability'. We see how the property of convergence of global optimisation is a straightforward consequence of searchability. The abstract setting allows us to generalise searchability and continuity to higher-order functions, so that we can formulate novel convergence criteria for regression, derived from the convergence of global optimisation. All the theory and the motivating examples are fully formalised in the proof assistant Agda.
Paying more attention to snapshots of Iterative Pruning: Improving Model Compression via Ensemble Distillation

Network pruning is one of the most dominant methods for reducing the heavy inference cost of deep neural networks. Existing methods often iteratively prune networks to attain high compression ratio without incurring significant loss in performance. However, we argue that conventional methods for retraining pruned networks (i.e., using small, fixed learning rate) are inadequate as they completely ignore the benefits from snapshots of iterative pruning. In this work, we show that strong ensembles can be constructed from snapshots of iterative pruning, which achieve competitive performance and vary in network structure. Furthermore, we present simple, general and effective pipeline that generates strong ensembles of networks during pruning with large learning rate restarting, and utilizes knowledge distillation with those ensembles to improve the predictive power of compact models. In standard image classification benchmarks such as CIFAR and Tiny-Imagenet, we advance state-of-the-art pruning ratio of structured pruning by integrating simple l1-norm filters pruning into our pipeline. Specifically, we reduce 75-80% of total parameters and 65-70% MACs of numerous variants of ResNet architectures while having comparable or better performance than that of original networks. Code associate with this paper is made publicly available at https://github.com/lehduong/kesi.
Unifying Few- and Zero-Shot Egocentric Action Recognition

Although there has been significant research in egocentric action recognition, most methods and tasks, including EPIC-KITCHENS, suppose a fixed set of action classes. Fixed-set classification is useful for benchmarking methods, but is often unrealistic in practical settings due to the compositionality of actions, resulting in a functionally infinite-cardinality label set. In this work, we explore generalization with an open set of classes by unifying two popular approaches: few- and zero-shot generalization (the latter which we reframe as cross-modal few-shot generalization). We propose a new set of splits derived from the EPIC-KITCHENS dataset that allow evaluation of open-set classification, and use these splits to show that adding a metric-learning loss to the conventional direct-alignment baseline can improve zero-shot classification by as much as 10%, while not sacrificing few-shot performance.
Expert System Using the Weighting Method for Eye Disease Symptoms

The expert system is a branch of artificial intelligence that was developed to emulate an expert's ability to solve problems. Expert systems have been widely developed by many researchers in various fields, including the health field. Various methods have been applied to make a decision in the form of an early diagnose that is obtained accurately. This study begins with simple idea to develop an expert system which more accurate than previous research. This study applies a backward chaining method to trace the disease coupled with giving weight value to the symptoms for each eye disease. The backward chaining method works by selecting one of the diseases to explore the rules. After the disease is determined, the supporting symptoms with the highest weight of the disease will be displayed by the system to be answered by the user. Symptoms and eye diseases in this study are sourced from the eye disease reference book. The symptom weight value for each disease was obtained from giving questionnaires and direct interviews to the ophthalmologist. Giving weights value is done in order to get an early diagnosis more accurate. The early diagnosis that is obtained accurately will support the decision making for the next action that must be done. The results indicate how much the percentage of early diagnosis of eye disease that the patient may suffer based on the symptoms that are answered. The early diagnosis produced by the system is not a final decision, but rather will be used as a decision support to take further action.
Depth Map Estimation with Consistent Normals from Stereo Images

The total variation regularization of non-convex data terms in continuous variational models can be convexified by the so called functional lifting, which may be considered as a continuous counterpart of Ishikawa’s method for multi-label discrete variational problems. We solve the resulting convex continuous variational problem by the augmented Lagrangian method. Application of this method to the dense depth map estimation allows us to obtain a consistent normal field to the depth surface as a byproduct. We illustrate the method with numerical examples of the depth map estimation for rectified stereo image pairs.
Image Recommendation for Wikipedia Articles

Multimodal learning, which is simultaneous learning from different data sources such as audio, text, images; is a rapidly emerging ﬁeld of Machine Learning. It is also considered to be learning on the next level of abstraction, which will allow us to tackle more complicated problems such as creating cartoons from a plot or speech recognition based on lips movement. In this paper, we will introduce a basic model to recommend the most relevant images for a Wikipedia article based on state-of-the-art multimodal techniques. We will also introduce the Wikipedia multimodal dataset, containing more than 36,000 high-quality articles.
Cascaded Regression Tracking: Towards Online Hard Distractor Discrimination

Visual tracking can be easily disturbed by similar surrounding objects. Such objects as hard distractors, even though being the minority among negative samples, increase the risk of target drift and model corruption, which deserve additional attention in online tracking and model update. To enhance the tracking robustness, in this paper, we propose a cascaded regression tracker with two sequential stages. In the first stage, we filter out abundant easily-identified negative candidates via an efficient convolutional regression. In the second stage, a discrete sampling based ridge regression is designed to double-check the remaining ambiguous hard samples, which serves as an alternative of fully-connected layers and benefits from the closed-form solver for efficient learning. Extensive experiments are conducted on 11 challenging tracking benchmarks including OTB-2013, OTB-2015, VOT2018, VOT2019, UAV123, Temple-Color, NfS, TrackingNet, LaSOT, UAV20L, and OxUvA. The proposed method achieves state-of-the-art performance on prevalent benchmarks, while running in a real-time speed.
Effect of the Technology-Supported Learning on the Academic Performance of Secondary School Students

A Posttest only Controlled Group Experiment research was used to explore the effect of teaching through the technology- supported environment on learners� performance. Students of Biology group Grade IX, (n = 177), from public schools of Karachi, participated in the experimental research for 20 weeks in the year 2016-17. The technologysupported learning was intervened in the experiment group whereas, a conventional teaching was intervened in the controlled group. The annual examination�s 2017 result was examined and scores of both experimental and controlled participants were compared by the independent sample t-test. The data analysis showed a noteworthy variance in the scores of controlled and experiment batch. The students studying in a technology-supported
Uncertainty Principle based optimization; new metaheuristics framework.

To more flexibly balance between exploration and exploitation, a new meta-heuristic method based on Uncertainty Principle concepts is proposed in this paper. UP is is proved effective in multiple branches of science. In the branch of quantum mechanics, canonically conjugate observables such as position and momentum cannot both be distinctly determined in any quantum state. In the same manner, the branch of Spectral filtering design implies that a nonzero function and its Fourier transform cannot both be sharply localized. After delving into such concepts on Uncertainty Principle and their variations in quantum physics, Fourier analysis, and wavelet design, the proposed framework is described in terms of algorithm and flowchart. Our proposed optimizer's idea is based on an inherent uncertainty in performing local search versus global solution search. A set of compatible metrics for each part of the framework is proposed to derive preferred form of algorithm. Evaluations and comparisons at the end of paper show competency and distinct capability of the algorithm over some of the well-known and recently proposed metaheuristics.
How to Extract and Explore Big Data for Fraud Detection in the Healthcare Sector: The EOPYY Case Study.

Big Data technologies can contribute to medical fraud detection. The aim of this paper is to present the methodological approach of the Hellenic National Organization for the Provision of Health Services (EOPYY) in data analysis to detect financial or medical fraud. To analyze the data for fraud detection, a selection of prescription data from the year 2018 were examined. The Local Correlation Integral algorithm was applied to detect any outliers on the dataset. The results revealed that 7 out of 879 cases could be characterized as outliers. These outliers must be further investigated to determine if they have been associated with fraud. According to the results of this study, this outliers' detection approach can support and help the fraud detection process conducted by the auditing services in Healthcare sector.
FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search

Deep neural networks achieve remarkable performance in many computer vision tasks. Most state-of-the-art (SOTA) semantic segmentation and object detection approaches reuse neural network architectures designed for image classification as the backbone, commonly pre-trained on ImageNet. However, performance gains can be achieved by designing network architectures specifically for detection and segmentation, as shown by recent neural architecture search (NAS) research for detection and segmentation. One major challenge though is that ImageNet pre-training of the search space representation (a.k.a. super network) or the searched networks incurs huge computational cost. In this paper, we propose a Fast Network Adaptation (FNA++) method, which can adapt both the architecture and parameters of a seed network (e.g. an ImageNet pre-trained network) to become a network with different depths, widths, or kernel sizes via a parameter remapping technique, making it possible to use NAS for segmentation and detection tasks a lot more efficiently. In our experiments, we apply FNA++ on MobileNetV2 to obtain new networks for semantic segmentation, object detection, and human pose estimation that clearly outperform existing networks designed both manually and by NAS. We also implement FNA++ on ResNets and NAS networks, which demonstrates a great generalization ability. The total computation cost of FNA++ is significantly less than SOTA segmentation and detection NAS approaches: 1737x less than DPC, 6.8x less than Auto-DeepLab, and 8.0x less than DetNAS. A series of ablation studies are performed to demonstrate the effectiveness, and detailed analysis is provided for more insights into the working mechanism. Codes are available at https://github.com/JaminFong/FNA.
Computer-Based Facial Expression Recognition

Face recognition and its classification is a rapidly growing field in the area of image processing and machine learning. Object recognition is a key feature of image classification and has its own numerous commercial implications. Facial gesture plays important role in many aspect, such as presence system, security and access system, and identification system. Model trained on diverse dataset to generalize better. Using deep region of interest (ROI) convolutional neural networks (CNNs) along with transfer learning are exploited. The Haar-Cascade is trained by superimposing the positive image over a set of negative images which is used to detect countenance.
DEVELOPMENT OF AN AUTOMATED RESEARCH SYSTEM FOR STUDYING THE PROCESS OF ENVIRONMENTAL POLLUTION

An example of the development of an automated research system for studying the process of environmental pollution, an integral part of which is the mathematical calculation of the concentration of pollutants.
Author Correction: GPU-Accelerated GLRLM Algorithm for Feature Extraction of MRI

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Improving Reading Habits of Students Through Geulis Trees in 7th Ciamis State Primary School

In the 4.0 industrial revolution, reading is part of basic literacy.Since 2016, 7th Ciamis State Primary School has launched a school literacy movements program.In improving reading habits, the School has a media called the Geulis Tree.The purpose of this study was to find out how the application of the Geulis Tree to improve the reading habits of 7 th Ciamis State Primary School students.The method of research is descriptive qualitative.The subjects of this study were students in grades 4, 5, and 6 of the 7 th Ciamis State Primary School.Data collected through observation and interviews.The results showed that the Geulis Tree effective in improving reading habits in students.As many as 80% of students in grades 4, 5, and 6 successfully achieved the target of reading 24 books in 10 months.Geulis Tree, as a medium, enhances the habit of students gaining support from several habituation models.Consists of programmed and unprogrammed habituation activities.The programmed activities are carried out through spontaneous and exemplary activities.All teachers and the school community play as role models for students.Besides, students' parents supervise students reading at home.The school environment is vital in improving students' reading habits.
Extraction and Evaluation of Formulaic Expressions Used in Scholarly Papers

Formulaic expressions, such as 'in this paper we propose', are helpful for authors of scholarly papers because they convey communicative functions; in the above, it is showing the aim of this paper'. Thus, resources of formulaic expressions, such as a dictionary, that could be looked up easily would be useful. However, forms of formulaic expressions can often vary to a great extent. For example, 'in this paper we propose', 'in this study we propose' and 'in this paper we propose a new method to' are all regarded as formulaic expressions. Such a diversity of spans and forms causes problems in both extraction and evaluation of formulaic expressions. In this paper, we propose a new approach that is robust to variation of spans and forms of formulaic expressions. Our approach regards a sentence as consisting of a formulaic part and non-formulaic part. Then, instead of trying to extract formulaic expressions from a whole corpus, by extracting them from each sentence, different forms can be dealt with at once. Based on this formulation, to avoid the diversity problem, we propose evaluating extraction methods by how much they convey specific communicative functions rather than by comparing extracted expressions to an existing lexicon. We also propose a new extraction method that utilises named entities and dependency structures to remove the non-formulaic part from a sentence. Experimental results show that the proposed extraction method achieved the best performance compared to other existing methods.
Pengembangan Multimedia berbasis Libreoffice Impress pada Tema Prasasti – Prasasti Sriwijaya

This research entitled "Development Based Multimedia LibreOffice Impress On the Subject of History in Class X at Senior High School number 4 Palembang" contains data on the Role of Inscription-inscription Sriwijaya In Explaining Political Life, Social Religious, and Economic kingdom of Srivijaya, this research has been carried out and implemented in the classroom X Science 1 at Senior High School number 4 Palembang. This research is the development of research development model Hanafin &amp; Peck. As for the formulation of the problem in this research is how to develop Based Multimedia Libreoffice Impress On the Subject of History in Class X at Senior High School number 4 Palembang valid, and how to develop Multimedia Based Libreoffice Impress On the Subject of History in Class X at Senior High School number 4 Palembang has an impact effectiveness. Furthermore, the purpose of this research is to develop a valid instructional media and have an impact effectiveness. Influence Based Multimedia LibreOffice Impress Impress On History Lesson in Class X at Senior High School number 4 Palembang visible on improving learning outcomes of students before and after using instructional media at the stage of field trials of 36.57% with Ngain of 0.7. This indicates Based Multimedia LibreOffice Impress Impress a valid value and effectiveness.
Hypergraph Grammar-Based Model of Adaptive Bitmap Compression

JPEG algorithm defines a sequence of steps (essential and optional) executed in order to compress an image. The first step is an optional conversion of the image color space from RBG (red-blue-green) to YCbCr (luminance and two chroma components). This step allows to discard part of chrominance information, a useful gain due to the fact, that the chrominance resolution of the human eye is much lower than the luminance resolution. In the next step, the image is divided into 8 $$\times $$ 8 blocks, called MCUs (Minimum Coded Units). In this paper we present a new adaptive bitmap compression algorithm, and we compare it to the state-of-the-art of JPEG algorithms. Our algorithm utilizes hypergraph grammar model, partitioning the bitmap into a set of adaptively selected rectangles. Each rectangle approximates a bitmap using MCUs with the size selected according to the entire rectangular element. The hypergraph grammar model allows to describe the whole compression algorithm by a set of five productions. They are executed during the compression stage, and they partition the actual rectangles into smaller ones, until the required compression rate is obtained. We show that our method allows to compress bitmaps with large uniform areas in a better way than traditional JPEG algorithms do.