Reviewerly/exHarmony huggingface dataset:
Predicting Document Coverage for Relation Extraction

This paper presents a new task of predicting the coverage of a text document for relation extraction (RE): does the document contain many relational tuples for a given entity? Coverage predictions are useful in selecting the best documents for knowledge base construction with large input corpora. To study this problem, we present a dataset of 31,366 diverse documents for 520 entities. We analyze the correlation of document coverage with features like length, entity mention frequency, Alexa rank, language complexity and information retrieval scores. Each of these features has only moderate predictive power. We employ methods combining features with statistical models like TF-IDF and language models like BERT. The model combining features and BERT, HERB, achieves an F1 score of up to 46%. We demonstrate the utility of coverage predictions on two use cases: KB construction and claim refutation.
Large Scale Ranking Using Stochastic Gradient Descent

A system of linear equations can represent any ranking problem that minimizes a pairwise ranking loss. We utilize a fast version of gradient descent algorithm with a near-optimal learning rate and momentum factor to solve this linear equations system iteratively. Tikhonov regularization is also integrated into this framework to avoid overfitting problems where we have very large and high dimensional but sparse data.
Proceso de subjetivación y uso de TIC en la escuela. Abordajes y perspectivas

Throughout the history of education, the focus of attention has shifted from the establishment of powers, through content to the student as the center of the process. In this scenario, the role of technology in the school has also evolved, starting from the lack of access to equipment and services, a situation that still prevails in many regions, to the achievement of an integration where technology is the best means of learning as it provides recognition and motivation. Thus, the study relates subjectivity, technological mediation and education through a bibliographic review. As a theoretical foundation, the state of the art was reviewed, which included the search for studies on the use of cell phones and their implications in the processes of subjectivation both in Colombia and abroad, as well as the conceptualization and understanding of key categories to guide the existing relationships.
socio-affective stimulation of primary school students

In the post-pandemic context, the growing level of complexity of Cuban social and educational environments, as well as the low perception of risk with minors from home, generated the need for educational practices, although they are based on the principle of the unity of the cognitive with the affective, to ponder the socio-affective content in the formative process of the new generations. Faced with this challenge, the objective of this research was declared: to favor the socio-affective stimulation of primary school students from a sociopedagogical conception. The diagnosis carried out with a mixed approach revealed cognitive and procedural deficiencies in the socio-affective stimulation of primary schoolchildren, which led to the structuring of a sociopedagogical proposal. It was based on the theoretical methods inductive-deductive, analytical-synthetic, historical-logical and systemic-structural-functional. It had a flexible, participatory and contextualized vision, whose feasibility to enter the educational praxis was corroborated by the criterion method of specialists and the user method.
Diagnóstico de Esclerose Múltipla em Adolescentes Segundo os Critérios de McDonald de 2017: Dois Casos Clínicos

Although multiple sclerosis' (MS) diagnosis is usually made in adulthood, clinical presentation before the age of 18 occurs in up to 10% of the individuals.McDonald's criteria applicability in diagnosing MS in adolescents is already established with the 2010 revision.The first clinical case we present is an adolescent with a family history of MS and a right retrobulbar optic neuritis' presentation.Magnetic resonance imaging revealed periventricular and infratentorial lesions white matter lesions.The second case describes an adolescent with a suggestive presentation of acute transverse myelitis, with a sensitive level in D10 and multiple brain and spinal inflammatory lesions.Cerebrospinal fluid oligoclonal bands were present in both patients.Both met the 2017 McDonald criteria for MS diagnosis.MS diagnosis is particularly difficult in children and adolescents.A high degree of suspicion is needed.Differential diagnosis with other cerebral inflammatory disorders must be considered.
Reinforcement Explanation Learning

Deep Learning has become overly complicated and has enjoyed stellar success in solving several classical problems like image classification, object detection, etc. Several methods for explaining these decisions have been proposed. Black-box methods to generate saliency maps are particularly interesting due to the fact that they do not utilize the internals of the model to explain the decision. Most black-box methods perturb the input and observe the changes in the output. We formulate saliency map generation as a sequential search problem and leverage upon Reinforcement Learning (RL) to accumulate evidence from input images that most strongly support decisions made by a classifier. Such a strategy encourages to search intelligently for the perturbations that will lead to high-quality explanations. While successful black box explanation approaches need to rely on heavy computations and suffer from small sample approximation, the deterministic policy learned by our method makes it a lot more efficient during the inference. Experiments on three benchmark datasets demonstrate the superiority of the proposed approach in inference time over state-of-the-arts without hurting the performance. Project Page: https://cvir.github.io/projects/rexl.html
PicArrange -- Visually Sort, Search, and Explore Private Images on a Mac Computer

The native macOS application PicArrange integrates state-of-the-art image sorting and similarity search to enable users to get a better overview of their images. Many file and image management features have been added to make it a tool that addresses a full image management workflow. A modification of the Self Sorting Map algorithm enables a list-like image arrangement without loosing the visual sorting. Efficient calculation and storage of visual features as well as the use of many macOS APIs result in an application that is fluid to use.
CodeLabeller: A Web-based Code Annotation Tool for Java Design Patterns and Summaries

While constructing supervised learning models, we require labelled examples to build a corpus and train a machine learning model. However, most studies have built the labelled dataset manually, which in many occasions is a daunting task. To mitigate this problem, we have built an online tool called CodeLabeller. CodeLabeller is a web-based tool that aims to provide an efficient approach to handling the process of labelling source code files for supervised learning methods at scale by improving the data collection process throughout. CodeLabeller is tested by constructing a corpus of over a thousand source files obtained from a large collection of open source Java projects and labelling each Java source file with their respective design patterns and summaries. Twenty five experts in the field of software engineering participated in a usability evaluation of the tool using the standard User Experience Questionnaire online survey. The survey results demonstrate that the tool achieves the Good standard on hedonic and pragmatic quality standards, is easy to use and meets the needs of the annotating the corpus for supervised classifiers. Apart from assisting researchers in crowdsourcing a labelled dataset, the tool has practical applicability in software engineering education and assists in building expert ratings for software artefacts.
Inpainting Transformer for Anomaly Detection

Anomaly detection in computer vision is the task of identifying images which deviate from a set of normal images. A common approach is to train deep convolutional autoencoders to inpaint covered parts of an image and compare the output with the original image. By training on anomaly-free samples only, the model is assumed to not being able to reconstruct anomalous regions properly. For anomaly detection by inpainting we suggest it to be beneficial to incorporate information from potentially distant regions. In particular we pose anomaly detection as a patch-inpainting problem and propose to solve it with a purely self-attention based approach discarding convolutions. The proposed Inpainting Transformer (InTra) is trained to inpaint covered patches in a large sequence of image patches, thereby integrating information across large regions of the input image. When training from scratch, in comparison to other methods not using extra training data, InTra achieves results on par with the current state-of-the-art on the MVTec AD dataset for detection and surpassing them on segmentation.
Magnitude Adversarial Spectrum Search-based Black-box Attack against Image Classification

Recent development has revealed that deep neural networks used in image classification systems are vulnerable to adversarial attacks. Thus, it is critical to understand the possible adversarial attacks to develop effective defense mechanisms. In this study, we designed an untargeted query-efficient decision-based black-box attack against image classification models that produce imperceptible adversarial examples. The proposed attack method, MASSA, includes two novel components to generate the initial noise and reduce the noise in the frequency domain. The evaluation results show that MASSA requires significantly fewer queries than the state-of-the-art decision-based black-box attack, i.e., HSJA. In addition, MASSA can create adversarial examples with 74,16% lower L2 distance than HSJA after only 250 queries. We also demonstrate that two existing defense mechanisms, namely, JPEG compression and adversarial training, are ineffective in defending against MASSA. Results of the study give new insights into the potential risks of using deep neural networks in critical systems and encourage the community to study improved defense approaches to mitigate the risks.
Cognitive Dissonance Model of Conditional Reasoning based on Truth-making

Conditional reasoning (If A, Then B) is a lasting topic in the psychology of reasoning. The experimental paradigm for conditional reasoning is a card selection task using logical rules. Although the models of conditional reasoning have achieved great success, there are still puzzles about the training effect (logical training doesn’t improve the correct rate for the tasks in people without formal logic background). For example, ordinary people feel confused with the truth table which is the valid formal rule for conditional reasoning after they have been trained for this rule by logicians. Our work contributes these conflicts to the disposition that ordinary people have, that is, truth-making, a process of making every proposition or judgement true or grounded. Most people may perform better in selection tasks in the pre-test (not training) of the card selection task. This paper tries to solve these puzzles with the cognitive dissonance theory by combing them with the truth-maker theory by proposing that conditional reasoning is a continuous truth-seeking process. We then conducted pre-test and post-test and compared the confidence of their own reasoning in two selection tasks, and the purpose is to examine how the truth-makings of people affect conditional reasoning. Experimental results showed that truth-making is an important factor that governs people’s ordinary reasoning processes.
Deep Audio Effects for Snare Drum Recording Transformations

The ability to perceptually modify drum recording parameters in a post-recording process would be of great benefit to engineers limited by time or equipment.In this work, a datadriven approach to post-recording modification of the dampening and microphone positioning parameters commonly associated with snare drum capture is proposed.The system consists of a deep encoder that analyzes audio input and predicts optimal parameters of one or more third-party audio effects, which are then used to process the audio and produce the desired transformed output audio.Furthermore, two novel audio effects are specifically developed to take advantage of the multiple parameter learning abilities of the system.Perceptual quality of transformations is assessed through a subjective listening test, and an object evaluation is used to measure system performance.Results demonstrate a capacity to emulate snare dampening; however, attempts were not successful for emulating microphone position changes.
The Dynamic Grid: Time-Varying Parameters for Musical Instrument Simulations Based on Finite-Difference Time-Domain Schemes

Several well-established approaches to physical modeling synthesis for musical instruments exist.Finite-difference time-domain methods are known for their generality and flexibility in terms of the systems one can model but are less flexible with regard to smooth parameter variations due to their reliance on a static grid.This paper presents the dynamic grid, a method to smoothly change grid configurations of finite-difference time-domain schemes based on subaudio-rate time variation of parameters.This allows for extensions of the behavior of physical models beyond the physically possible, broadening the range of expressive possibilities for the musician.The method is applied to the 1D wave equation, the stiff string, and 2D systems, including the 2D wave equation and thin plate.Results show that the method does not introduce noticeable artefacts when changing between grid configurations for systems, including loss.
Knowledge Graph Completion Method Combined With Adaptive Enhanced Semantic Information

Translation models tend to ignore the rich semantic information in triads in the process of knowledge graph complementation. To remedy this shortcoming, this paper constructs a knowledge graph complementation method that incorporates adaptively enhanced semantic information. The hidden semantic information inherent in the triad is obtained by fine-tuning the BERT model, and the attention feature embedding method is used to calculate the semantic attention scores between relations and entities in positive and negative triads and incorporate them into the structural information to form a soft constraint rule for semantic information. The rule is added to the original translation model to realize the adaptive enhancement of semantic information. In addition, the method takes into account the effect of high-dimensional vectors on the effect, and uses the BERT-whitening method to reduce the dimensionality and generate a more efficient semantic vector representation. After experimental comparison, the proposed method performs better on both FB15K and WIN18 datasets, with a numerical improvement of about 2.6% compared with the original translation model, which verifies the reasonableness and effectiveness of the method.
An improved linear prediction evolution algorithm based on nonlinear least square fitting model for optimization

Abstract The linear prediction evolution algorithm (LPE) is a recent addition in the field of optimization algorithm with few parameters and high exploration capability. LPE has shown excellent results in some real-world problems but still suffers from some issues such as slow convergence speed. To improve the performance of the LPE algorithm, this study presents an improved linear prediction evolution algorithm (ILPE) to enhance its exploration capability. The proposed ILPE algorithm treats the population series of evolutionary algorithms as a time series and uses the non-linear least square fitting model as a reproduction operator to forecast the next generation. The performance of the proposed ILPE is verified using the CEC2014 and CEC2017 benchmark functions. The comparison results show that ILPE outperforms LPE, and it is highly competitive with other state-of-the-art optimization algorithms.
Sistem Pendukung Keputusan Penerima Program Keluarga Harapan (PKH) dengan Metode Simple Additive Weighting (SAW)

The Family Hope Program (PKH) is a program for providing conditional assistance to Beneficiary Families (KPM) who are designated as PKH beneficiary families. This PKH program provides cash assistance to very poor households (RSTM) that meet the requirements and are related to efforts to improve the quality of life in the fields of education and health. However, in the management of PKH data, several problems were found, such as in the distribution aspect. In the distribution aspect, it is often found that the distribution of PKH is not right on target or the distribution of PKH to families who do not deserve this PKH program. Various efforts have been made to overcome these problems, one of which is by making a decision support system using the Simple Additive Weighting (SAW) method. The decision support system with the SAW method can provide recommendations or support to determine the right PKH recipients. The SAW method is a weighted addition method, which is looking for a weighted sum of ratings for each alternative on all attributes/criteria. The result of the total score obtained for an alternative is by adding up all the multiplication results between the ratings compared across attributes and the weight of each attribute. The Decision Support System for determining PKH recipients using the SAW method provides the right results. The alternative is the highest order to have a good score on the criteria that have the highest importance and is very precise with real data so that the distribution of PKH is right on target.
Secure Cryptographic E-Auction System

The evolution of the auction market has been on the upswing throughout the years as technology evolves at an accelerating rate.With the advanced technology nowadays, digital transformation has been applied to the auction markets as a means of transferring goods or services in an online form.Electronic auctions enable sellers to reach a broader range of prospective bidders and participate in the auctions anywhere-anytime.Nevertheless, the security aspects of the eauction system have become the main concerns for the parties involved.Hence, a secure cryptographic electronic auction system is proposed by employing the underlying cryptographic schemes as the building blocks, namely, asymmetric encryption, digital signature scheme, and hash functions.The proposed e-auction system fulfills the security requirements, encompassing anonymity, correctness, confidentiality, privacy, integrity, and fairness.Performance analysis has also been performed on our proposed e-auction system.
Digitisasi Arsip di Warung Arsip Yogyakarta: Analisis Peluang dan Tantangan Menggunakan SWOT

This study aims to describe digitization activities at Warung Arsip Yogyakarta. In addition, this study will also describe the opportunities and challenges of the digitization activity using a SWOT analysis. This study uses descriptive qualitative methods. Collecting data process conducted by observation, interviews, and documentation. The data analysis technique was done several acivities sort of: data reduction, data display, and conclusion drawing. The digitization process at Warung Arsip Yogyakarta uses two methods, namely pemindaian for publications such as magazines; and capturing pictures for publications such as newspapers. The digitization process is experiencing problems due to lack of human resources (HR) and limited digital archive storage. Warung Arsip Yogyakarta are taking advantage of relationship opportunities to reach more information, maximizing creativity in digitizing newspapers, upgrading the digital archive storage system. In an effort to face the challenges, Warung Arsip Yogyakarta needs to carry out promotions to increase public interest in archives, create a special room for archives that have not been digitized, carry out periodic cleaning, make SOPs for archive maintenance, and recruit employees.
Creating and detecting specious randomness

Abstract We present a new test of non-randomness that tests both the lower and the upper critical limit of a χ 2 -statistic. While checking the upper critical value has been employed by other tests, we argue that also the lower critical value should be examined for non-randomness. To this end, we prepare a binary sequence where all possible bit strings of a certain length occurs the same number of times and demonstrate that such sequences pass a well-known suite of tests for non-randomness. We show that such sequences can be compressed, and therefore are somewhat predictable and thus not fully random. The presented test can detect such non-randomness, and its novelty rests on analysing fixed-length bit string frequencies that lie closer to the a priori probabilities than could be expected by chance alone.
Tutorial on particle swarm optimization and its combinations to other evolutionary algorithms

Local optimization with convex function is solved perfectly by traditional mathematical methods such as Newton-Raphson and gradient descent but it is not easy to solve the global optimization with arbitrary function although there are some purely mathematical approaches such as approximation, cutting plane, branch and bound, and interval method which can be impractical because of their complexity and high computation cost. Recently, some evolutional algorithms which are inspired from biological activities are proposed to solve the global optimization by acceptable heuristic level. Among them is particle swarm optimization (PSO) algorithm which is proved as an effective and feasible solution for global optimization in real applications. Although the ideology of PSO is not complicated, it derives many variants, which can make new researchers confused. Therefore, this tutorial focuses on describing, systemizing, and classifying PSO by succinct and straightforward way. Moreover, combinations of PSO and other evolutional algorithms for improving PSO itself or solving other advanced problems are mentioned too.
Feature fusion method for speaker recognition based on embedding mechanism

Speaker recognition is a technology that verifies the identity of a person by his or her voice. Different feature parameters have different potential information in speaker recognition. In order to solve the problem that a single feature parameter cannot fully represent a speaker's identity, this paper proposes a feature fusion approach based on embedding mechanism. The fusion features adopted in our approach are filter bank coefficients (Fbank) and mel frequency cepstrum coefficients (MFCC). Potential and complementary information in two features can be obtained by a neural network model, which takes our embedded features as inputs. The d-vector output of the neural network model is classified using the Softmax loss function and optimized using the generalized end-to-end loss function. Both of the most common models, long short term memory network (LSTM) and bi-directional long short term memory network (BiLSTM), are used as our testbed. Results show that, by using our proposed feature fusion approach, the performance of both models are improved. In particular, the minimum equal error rate is 4.17% under the BiLSTM model, compared with the single MFCC or Fbank feature, which are reduced by 72.2% and 28.4% respectively.