Reviewerly/exHarmony huggingface dataset:
Optimal Release Planning Using Machine Learning and Linear Integer Programming for Ideas in a Crowdsourcing Platform

Abstract Obtaining and analyzing customer and product information from various sources has become a top priority for major competitive companies who are striving to keep up with the digital and technological progress. Therefore, the need for creating a crowdsourcing platform to collect ideas from different stakeholders has become a major component of a company’s digital transformation strategy. However, these platforms suffer from problems that are related to the voluminous and vast amount of data. Different large sets of data are being spurred in these platforms as time goes by that render them unbeneficial. The aim of this paper is to propose a solution on how to discover the most promising ideas to match them to the strategic decisions of a business regarding resource allocation and product development (PD) roadmap. The paper introduces a 2-stage filtering process that includes a prediction model using a Random Forest Classifier that predicts ideas most likely to be implemented and a resource allocation optimization model based on Integer Linear Programming that produces an optimal release plan for the predicted ideas. The model was tested using real data on an idea crowdsourcing platform that remains unnamed in the paper due to confidentiality. Our prediction model has proved to be 92% accurate in predicting promising ideas and our release planning optimization problem results were found out to be 85% accurate in producing an optimal release plan for ideas.
Research on UAV Path Planning with Mountain Stratification Model Based on Ant Colony Algorithm

With the widespread use of drones in the military field, future battlefields will use more and more drones, so the collaborative planning of drones is becoming more and more important. A multi-UAV flight path planning model was established and an effective model solving algorithm was studied. Aiming at the detection of mountainous areas, this paper proposes a mountain layered model optimization method based on ant colony algorithm. The search conditions of the ant colony algorithm are optimized, and the time and space complexity of the ant colony algorithm is reduced.
Conditional probing: measuring usable information beyond a baseline

Probing experiments investigate the extent to which neural representations make properties—like part-of-speech—predictable. One suggests that a representation encodes a property if probing that representation produces higher accuracy than probing a baseline representation like non-contextual word embeddings. Instead of using baselines as a point of comparison, we’re interested in measuring information that is contained in the representation but not in the baseline. For example, current methods can detect when a representation is more useful than the word identity (a baseline) for predicting part-of-speech; however, they cannot detect when the representation is predictive of just the aspects of part-of-speech not explainable by the word identity. In this work, we extend a theory of usable information called V-information and propose conditional probing, which explicitly conditions on the information in the baseline. In a case study, we find that after conditioning on non-contextual word embeddings, properties like part-of-speech are accessible at deeper layers of a network than previously thought.
Transferability of Adversarial Attacks: Main Influencing Factors

The growth in computation power and storage strongly reduced the time required for a machine learning model to be trained and allowed the development of more complex models that can address more sophisticated tasks. These improvements led in the last years to the introduction of AI systems into a growing number of applications. Some of them are also used in contexts that could lead to safety issue, where a wrong behaviour, in some cases, could even endanger the life of a person. In the thesis we tried to deepen the understanding of malicious attacks that can be carried out against machine learning based systems. The most common type of this attack is known as adversarial examples. It allows to fool a network into a wrong behaviour by providing slightly ad hoc modified inputs. The relevance of this attack is due to the fact a human observer cannot notice the difference from a normal input. In detail, we analyzed the property of transferability of an attack by focusing on the knowledge the attacker has with respect to the target ML system. In this context, we analyse how knowing the training set and the network model affects the transferability of an adversarial example. The research analyzes both image and face recognition challenges, with the application of more complex netowrk models to address the second problem. The results highlight that the knowledge of the training set is relevant only in the case where also the network model is known. Otherwise, knowing the dataset only affects the result of few percentage points. What happen to be significant in the generation of the adversarial example is the quantity of noise we introduce. Moreover, comparing the scenario of image recognition with the one of face recognition, we notice a remarkable drop in the number of transferable case and in the influence of the quantity of noise. We attribute this difference to the higher complexity of the network used in the second challenge.
Counterfactual Interventions Reveal the Causal Effect of Relative Clause Representations on Agreement Prediction

When language models process syntactically complex sentences, do they use their representations of syntax in a manner that is consistent with the grammar of the language? We propose AlterRep, an intervention-based method to address this question. For any linguistic feature of a given sentence, AlterRep generates counterfactual representations by altering how the feature is encoded, while leaving in- tact all other aspects of the original representation. By measuring the change in a model’s word prediction behavior when these counterfactual representations are substituted for the original ones, we can draw conclusions about the causal effect of the linguistic feature in question on the model’s behavior. We apply this method to study how BERT models of different sizes process relative clauses (RCs). We find that BERT variants use RC boundary information during word prediction in a manner that is consistent with the rules of English grammar; this RC boundary information generalizes to a considerable extent across different RC types, suggesting that BERT represents RCs as an abstract linguistic category.
Psychological Features of the Formation of the Personality of the Teacher of the Karakapak School

For the effective implementation of the educational process in higher education, there should be a continuous interaction between students and teachers. This influence differs from the relationship between a secondary school teacher and a student, or between a lyceum and college teacher and a student, which, on the one hand, depends on the age characteristics between them, depending on their socio-legal status, in the classroom in the form of teacher-teacher and student-listener, outside the classroom, friendly, brotherly, and even parent-child It is expedient. However, to date, the psychology of higher education, the psychological environment between students and teachers in it has not been thoroughly analyzed.
Object Recognition in a Cluttered Scene

Object Recognition is an important and challenging computer vision task, which plays a major role in various applications such as robotics, medical imaging, recognition and verification systems, information retrieval systems and so on. Within the past few years, many single object tracking systems have come into picture. However, in the presence of many objects, object recognition task becomes difficult, especially when parts of the object are fully or partially occluded. In this paper, an object recognition system based on deep learning techniques is proposed. RetinaNet Model has been used for object detection and identification. RetinaNet model has demonstrated to work well with both small scale as well as dense objects.
Classification of Documents using Clustering Algorithm with term frequency-inverse documents frequency

Abstract:-In order to overcome the limitations, this paper proposes a research paper classification system that can cluster research papers into the meaningful class in which papers are very likely to have similar subjects. Numerous research papers have been published online as well as offline with the increasing advance of computer and information technologies, which makes it difficult for users to search and categorize their interesting research papers for a specific subject Clustering analysis method is one of the main analytical methods in data mining,the method of clustering algorithm will influence the clustering results directly. The K-means clustering algorithm is computationally faster than the other clustering algorithms. However, it produces different clustering results for different number of clusters. So, it is required to determine the number of clusters (i.e., K value) in advance before clustering.
A Note On Auriferous Mineralization In Parasi Area, Ranchi District, Jharkhand, India

The Auriferous mineralization occurs both in primary and placer/secondary forms with diverse litho stratigraphic and tectonic set up in Jharkhand. The Ranchi district, three-forth area is underlain by Chhotanagpur granite gneissic complex of pre-Cambrian age in the northern part and southern part of the area is covered by some part of North Singhbhum Mobile Belt (NSMB) of Palaeo to Meso-Proterozoic (1.0- 2.4 Ga). The gold mineralization is reported from different parts of NSMB along shear/ fracture zone within the meta-sedimentary and volcano-sedimentary rock sequence. The Palaeo-Mesoproterozoic gold occurrence is found in Parasi area, Ranchi district, Jharkhand. Gold occurs in the form of disseminations, specks, streaks, fracture, fillings etc. The phyllite and quartzite are host rock of the area. The Parasi gold mineralization is structurally controlled and shear zone has played a significant role and mostly in associated with sulfides such as arsenopyrite, pyrite and rarely pyrrhotite etc. The paper presents a note on auriferous mineralization of the Parasi area of NSMB.
NOPE: A Corpus of Naturally-Occurring Presuppositions in English

Understanding language requires grasping not only the overtly stated content, but also making inferences about things that were left unsaid. These inferences include presuppositions, a phenomenon by which a listener learns about new information through reasoning about what a speaker takes as given. Presuppositions require complex understanding of the lexical and syntactic properties that trigger them as well as the broader conversational context. In this work, we introduce the Naturally-Occurring Presuppositions in English (NOPE) Corpus to investigate the context-sensitivity of 10 different types of presupposition triggers and to evaluate machine learning models’ ability to predict human inferences. We find that most of the triggers we investigate exhibit moderate variability. We further find that transformer-based models draw correct inferences in simple cases involving presuppositions, but they fail to capture the minority of exceptional cases in which human judgments reveal complex interactions between context and triggers.
Do pretrained transformers infer telicity like humans

Pretrained transformer-based language models achieve state-of-the-art performance in many NLP tasks, but it is an open question whether the knowledge acquired by the models during pretraining resembles the linguistic knowledge of humans. We present both humans and pretrained transformers with descriptions of events, and measure their preference for telic interpretations (the event has a natural endpoint) or atelic interpretations (the event does not have a natural endpoint). To measure these preferences and determine what factors influence them, we design an English test and a novel-word test that include a variety of linguistic cues (noun phrase quantity, resultative structure, contextual information, temporal units) that bias toward certain interpretations. We find that humans’ choice of telicity interpretation is reliably influenced by theoretically-motivated cues, transformer models (BERT and RoBERTa) are influenced by some (though not all) of the cues, and transformer models often rely more heavily on temporal units than humans do.
Efficient long-range distribution of multi-photon entanglement

We demonstrate a protocol to perform long-distance quantum communications. It is robust even to extremely high transmission losses. The entanglement can be verified by a loophole-free Bell test using the CHSH inequality. It is the perfect scheme to establish an Earth-to-space quantum channel.
Autonomous Reinforcement Learning via Subgoal Curricula

Reinforcement learning (RL) promises to enable autonomous acquisition of complex behaviors for diverse agents. However, the success of current reinforcement learning algorithms is predicated on an often under-emphasised requirement -- each trial needs to start from a fixed initial state distribution. Unfortunately, resetting the environment to its initial state after each trial requires substantial amount of human supervision and extensive instrumentation of the environment which defeats the goal of autonomous acquisition of complex behaviors. In this work, we propose Value-accelerated Persistent Reinforcement Learning (VaPRL), which generates a curriculum of initial states such that the agent can bootstrap on the success of easier tasks to efficiently learn harder tasks. The agent also learns to reach the initial states proposed by the curriculum, minimizing the reliance on human interventions into the learning. We observe that VaPRL reduces the interventions required by three orders of magnitude compared to episodic RL while outperforming prior state-of-the art methods for reset-free RL both in terms of sample efficiency and asymptotic performance on a variety of simulated robotics problems.
Bridging the Imitation Gap by Adaptive Insubordination

When expert supervision is available, practitioners often use imitation learning with varying degrees of success. We show that when an expert has access to privileged information that is unavailable to the student, this information is marginalized in the student policy during imitation learning resulting in an ''imitation gap'' and, potentially, poor results. Prior work bridges this gap via a progression from imitation learning to reinforcement learning. While often successful, gradual progression fails for tasks that require frequent switches between exploration and memorization skills. To better address these tasks and alleviate the imitation gap we propose 'Adaptive Insubordination' (ADVISOR), which dynamically weights imitation and reward-based reinforcement learning losses during training, enabling switching between imitation and exploration. On a suite of challenging didactic and MiniGrid tasks, we show that ADVISOR outperforms pure imitation, pure reinforcement learning, as well as their sequential and parallel combinations.
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation

We present two sample-efficient differentially private mean estimators for $d$-dimensional (sub)Gaussian distributions with unknown covariance. Informally, given $n \gtrsim d/\alpha^2$ samples from such a distribution with mean $\mu$ and covariance $\Sigma$, our estimators output $\tilde\mu$ such that $\| \tilde\mu - \mu \|_{\Sigma} \leq \alpha$, where $\| \cdot \|_{\Sigma}$ is the Mahalanobis distance. All previous estimators with the same guarantee either require strong a priori bounds on the covariance matrix or require $\Omega(d^{3/2})$ samples. Each of our estimators is based on a simple, general approach to designing differentially private mechanisms, but with novel technical steps to make the estimator private and sample-efficient. Our first estimator samples a point with approximately maximum Tukey depth using the exponential mechanism, but restricted to the set of points of large Tukey depth. Proving that this mechanism is private requires a novel analysis. Our second estimator perturbs the empirical mean of the data set with noise calibrated to the empirical covariance, without releasing the covariance itself. Its sample complexity guarantees hold more generally for subgaussian distributions, albeit with a slightly worse dependence on the privacy parameter. For both estimators, careful preprocessing of the data is required to satisfy differential privacy.
Learning Optimal Predictive Checklists

Checklists are simple decision aids that are often used to promote safety and reliability in clinical applications. In this paper, we present a method to learn checklists for clinical decision support. We represent predictive checklists as discrete linear classifiers with binary features and unit weights. We then learn globally optimal predictive checklists from data by solving an integer programming problem. Our method allows users to customize checklists to obey complex constraints, including constraints to enforce group fairness and to binarize real-valued features at training time. In addition, it pairs models with an optimality gap that can inform model development and determine the feasibility of learning sufficiently accurate checklists on a given dataset. We pair our method with specialized techniques that speed up its ability to train a predictive checklist that performs well and has a small optimality gap. We benchmark the performance of our method on seven clinical classification problems, and demonstrate its practical benefits by training a short-form checklist for PTSD screening. Our results show that our method can fit simple predictive checklists that perform well and that can easily be customized to obey a rich class of custom constraints.
Summary Explorer: Visualizing the State of the Art in Text Summarization

This paper introduces Summary Explorer, a new tool to support the manual inspection of text summarization systems by compiling the outputs of 55 state-of-the-art single document summarization approaches on three benchmark datasets, and visually exploring them during a qualitative assessment. The underlying design of the tool considers three well-known summary quality criteria (coverage, faithfulness, and position bias), encapsulated in a guided assessment based on tailored visualizations. The tool complements existing approaches for locally debugging summarization models and improves upon them. The tool is available at https://tldr.webis.de/
METHODS OF REFLECTIVE HEURISM IN THE CREATIVE SOLUTION OF THE TASKS OF ART EDUCATION

The article presents the methods of reflective heuristics. The classical methods of reflexive heuristics are the Socrates method and the Descartes method. The contemporary ones are: the method of G. Poly and the systemic approach as well as the vision of A. Goralski. The main task of the Socratic method is to assist man in the search for truth. The basic means of achieving the truth is self-reflection, leading to rational action, and the method – dialogue.Descartes' method is based on the dualization of knowledge, namely that certain knowledge is justified knowledge, and that certain knowledge is available – it only needs to be obtained methodically. For Descartes, truth means certainty; true cognition is certain cognition, that is, clear and distinct. Descartes was guided by the principle of reducing all that is complex to simple and elementary.G. Polya considered the basic goal of all teaching to teach to think. Thinking is the process by which present facts suggest other facts or truths in such a way that from those facts an opinion of others is formed. However, the most specific feature of thinking is that it enables the production of information new to the subject, therefore thinking should be treated primarily as a creative process that leads to the acquisition of new knowledge that allows you to go beyond the given information. The act of thinking consists not only in producing information, but also in selecting it.According to A. Goralski, creativity belongs to two areas: the area of Nature and the area of Culture. These areas, although appearing in different and different forms, are identical. The qualification of creativity to these areas emphasizes the holistic nature of the world, and at the same time reveals the possibility of complements, revealing the specificity of these complements – the world of Nature, the world of Culture.
CIDEr-R: Robust Consensus-based Image Description Evaluation.

This paper shows that CIDEr-D, a traditional evaluation metric for image description, does not work properly on datasets where the number of words in the sentence is significantly greater than those in the MS COCO Captions dataset. We also show that CIDEr-D has performance hampered by the lack of multiple reference sentences and high variance of sentence length. To bypass this problem, we introduce CIDEr-R, which improves CIDEr-D, making it more flexible in dealing with datasets with high sentence length variance. We demonstrate that CIDEr-R is more accurate and closer to human judgment than CIDEr-D; CIDEr-R is more robust regarding the number of available references. Our results reveal that using Self-Critical Sequence Training to optimize CIDEr-R generates descriptive captions. In contrast, when CIDEr-D is optimized, the generated captions’ length tends to be similar to the reference length. However, the models also repeat several times the same word to increase the sentence length.
Grouped-Attention for Content-Selection and Content-Plan Generation

Recent neural data-to-text generation models employ Pointer Networks to explicitly learn content-plan given a set of attributes as input.They use LSTM to encode the input, which assumes a sequential relationship in the input.This may be sub-optimal to encode a set of attributes, where the attributes have a composite structure: the attributes are disordered while each attribute value is an ordered list of tokens.We handle this problem by proposing a neural content-planner that can capture both local and global contexts of such a structure.Specifically, we propose a novel attention mechanism called GSC-attention.A key component of the GSCattention is grouped-attention, which is tokenlevel attention constrained within each input attribute that enables our proposed model captures both local and global context.Moreover, our content-planner explicitly learns contentselection, which is integrated into the contentplanner to select the most important data to be included in the generated text via an attention masking procedure.Experimental results show that our model outperforms the competitors by 4.92%, 4.70%, and 16.56% in terms of Damerau-Levenshtein Distance scores on the WIKIALL, WIKIBIO, and ROTOWIRE datasets, respectively.