Reviewerly/exHarmony huggingface dataset:
Object-fabrication Targeted Attack for Object Detection

Recent researches show that the deep learning based object detection is vulnerable to adversarial examples. Generally, the adversarial attack for object detection contains targeted attack and untargeted attack. According to our detailed investigations, the research on the former is relatively fewer than the latter and all the existing methods for the targeted attack follow the same mode, i.e., the object-mislabeling mode that misleads detectors to mislabel the detected object as a specific wrong label. However, this mode has limited attack success rate, universal and generalization performances. In this paper, we propose a new object-fabrication targeted attack mode which can mislead detectors to `fabricate' extra false objects with specific target labels. Furthermore, we design a dual attention based targeted feature space attack method to implement the proposed targeted attack mode. The attack performances of the proposed mode and method are evaluated on MS COCO and BDD100K datasets using FasterRCNN and YOLOv5. Evaluation results demonstrate that, the proposed object-fabrication targeted attack mode and the corresponding targeted feature space attack method show significant improvements in terms of image-specific attack, universal performance and generalization capability, compared with the previous targeted attack for object detection. Code will be made available.
Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability

Spatio-temporal machine learning is critically needed for a variety of societal applications, such as agricultural monitoring, hydrological forecast, and traffic management. These applications greatly rely on regional features that characterize spatial and temporal differences. However, spatio-temporal data often exhibit complex patterns and significant data variability across different locations. The labels in many real-world applications can also be limited, which makes it difficult to separately train independent models for different locations. Although meta learning has shown promise in model adaptation with small samples, existing meta learning methods remain limited in handling a large number of heterogeneous tasks, e.g., a large number of locations with varying data patterns. To bridge the gap, we propose task-adaptive formulations and a model-agnostic meta-learning framework that ensembles regionally heterogeneous data into location-sensitive meta tasks. We conduct task adaptation following an easy-to-hard task hierarchy in which different meta models are adapted to tasks of different difficulty levels. One major advantage of our proposed method is that it improves the model adaptation to a large number of heterogeneous tasks. It also enhances the model generalization by automatically adapting the meta model of the corresponding difficulty level to any new tasks. We demonstrate the superiority of our proposed framework over a diverse set of baselines and state-of-the-art meta-learning frameworks. Our extensive experiments on real crop yield data show the effectiveness of the proposed method in handling spatial-related heterogeneous tasks in real societal applications.
Cross-Domain Transfer via Semantic Skill Imitation

We propose an approach for semantic imitation, which uses demonstrations from a source domain, e.g. human videos, to accelerate reinforcement learning (RL) in a different target domain, e.g. a robotic manipulator in a simulated kitchen. Instead of imitating low-level actions like joint velocities, our approach imitates the sequence of demonstrated semantic skills like "opening the microwave" or "turning on the stove". This allows us to transfer demonstrations across environments (e.g. real-world to simulated kitchen) and agent embodiments (e.g. bimanual human demonstration to robotic arm). We evaluate on three challenging cross-domain learning problems and match the performance of demonstration-accelerated RL approaches that require in-domain demonstrations. In a simulated kitchen environment, our approach learns long-horizon robot manipulation tasks, using less than 3 minutes of human video demonstrations from a real-world kitchen. This enables scaling robot learning via the reuse of demonstrations, e.g. collected as human videos, for learning in any number of target domains.
A Systematic Review of Sentiment Analysis from Bengali Text using NLP

Sentiment Analysis (SA) is the method of studying a person’s comments and statements through computational means. It is a sub-domain of Natural Language Processing. A sentiment analysis (SA) system is created by training a significant number of positive, negative or neutral sentence datasets. Although there are many research papers on this subject in English, the work of sentiment analysis in Bengali has not become very popular due to the complexity of the Bangla language and the insufficient presence of the Bangla language online. But now the use of Bengali language has increased in online news, social media, and blogs. At the same time, the number of researches on Bengali NLP is also increasing. But what is the current state of sentiment analysis, its limitations and is there still room for improvement in some places, are not being properly reviewed and research is lagging behind. We have conducted a review on sentiment analysis so that future researchers of sentiment analysis can easily find out about the current state of sentiment analysis. In this research, we have tried to survey the current context of sentiment analysis (SA) and at the same time, we have created a sequence of comparatively better research from the existing ones. To create this sequence we followed a method called is TOPSIS. We have also discussed the challenges to overcome for improving the Sentiment analyzer.
Path Planning Based on Ant Colony Algorithm

With the development of electric vehicles, the thermal safety of lithium-ion batteries (LIBs) has received a lot of attention from scholars.The temperature of the battery is an important factor affecting the performance and safety of LIBs, so a battery thermal management (BTM) system is essential.Phase change materials (PCM) can absorb or release a large amount of heat during phase change and keep the temperature largely unchanged, which has a great prospect in battery thermal management.Therefore, in this paper, a PCM-based thermal management system is designed for a 16-cell seriesconnected Li-ion battery pack, and the effects of the thermal parameters (including thermal conductivity, latent heat of phase change and melting point) on the thermal management system are investigated by numerical simulation.The results show that the pure paraffin-based BTM can reduce the maximum battery pack temperature under 3C discharge by 30°C, and the use of composite phase change material (CPCM) can substantially improve the thermal management performance.The thermal management system designed in this paper performs best when the thermal conductivity of CPCM is 2.0 W/(m-K), the melting point is between 36°C and 38°C, and the latent heat of phase change is 210 J/g.
Applications

We started with Lewin’s aphorism, “there is nothing as practical as a good theory”. Vector semantics, the broad theory that was raised from a Firthian slogan to a computational theory by Schütze, 1993, has clearly proven its practicality on a wide range of tasks from Named Entity Recognition (see 8.1) to sentiment analysis. But the farther we move from basic labeling and classification tasks, the more indirect the impact becomes, until we reach a point where some conceptual model needs to be fitted to the text. Perhaps the best known such problem is time extraction and normalization, where our target model is the standard (Gregorian) calendar rather than the simple (naive) model we discussed in 3.2. In 9.1, based almost entirely on the work of Gábor Recski and his co-workers at TU Wien, we outline a system that probes for matches with a far more complex conceptual model, that of building codes and regulations in effect in the city of Vienna.
Trainability and real-world knowledge

Until this point, we concentrated on the lexicon, conceived of as the repository of shared linguistic information. In 8.1 we take on the problem of integrating real-world knowledge, nowadays typically stored in knowledge graphs as billions of RDF triples, and linguistic knowledge, stored in a much smaller dictionary, typically compressible to a few megabytes. We present proper names as point vectors (rather than the polytopes we use for common nouns and most other lexical entries), and introduce the notion of content continuations, algorithms that extend the lexical entries to more detailed hypergraphs that can refer to technical nodes, such as Date, FloatingPointNumber, or Obligation (see 9.1) that are missing from the core lexicon.
Adjectives, gradience, implicature

Adjectives are present in most, though not necessarily all, natural languages. In 7.1 we begin by discussing the major properties of adjectival roots and the vector semantics associated to the base, comparative, and superlative forms. We discuss the logic associated to these, and extend the analysis to intensifiers.
Heuristically Guided Compilation for Multi-Agent Path Finding

Multi-agent path finding (MAPF) is a task of finding non-conflicting paths connecting agents' specified initial and goal positions in a shared environment. We focus on compilation-based solvers in which the MAPF problem is expressed in a different well established formalism such as mixed-integer linear programming (MILP), Boolean satisfiability (SAT), or constraint programming (CP). As the target solvers for these formalisms act as black-boxes it is challenging to integrate MAPF specific heuristics in the MAPF compilation-based solvers. We show in this work how the build a MAPF encoding for the target SAT solver in which domain specific heuristic knowledge is reflected. The heuristic knowledge is transferred to the SAT solver by selecting candidate paths for each agent and by constructing the encoding only for these candidate paths instead of constructing the encoding for all possible paths for an agent. The conducted experiments show that heuristically guided compilation outperforms the vanilla variants of the SAT-based MAPF solver.
Find Someone Who: Visual Commonsense Understanding in Human-Centric Grounding

From a visual scene containing multiple people, human is able to distinguish each individual given the context descriptions about what happened before, their mental/physical states or intentions, etc. Above ability heavily relies on human-centric commonsense knowledge and reasoning. For example, if asked to identify the "person who needs healing" in an image, we need to first know that they usually have injuries or suffering expressions, then find the corresponding visual clues before finally grounding the person. We present a new commonsense task, Human-centric Commonsense Grounding, that tests the models' ability to ground individuals given the context descriptions about what happened before, and their mental/physical states or intentions. We further create a benchmark, HumanCog, a dataset with 130k grounded commonsensical descriptions annotated on 67k images, covering diverse types of commonsense and visual scenes. We set up a context-object-aware method as a strong baseline that outperforms previous pre-trained and non-pretrained models. Further analysis demonstrates that rich visual commonsense and powerful integration of multi-modal commonsense are essential, which sheds light on future works. Data and code will be available https://github.com/Hxyou/HumanCog.
Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving

Multi-object state estimation is a fundamental problem for robotic applications where a robot must interact with other moving objects. Typically, other objects' relevant state features are not directly observable, and must instead be inferred from observations. Particle filtering can perform such inference given approximate transition and observation models. However, these models are often unknown a priori, yielding a difficult parameter estimation problem since observations jointly carry transition and observation noise. In this work, we consider learning maximum-likelihood parameters using particle methods. Recent methods addressing this problem typically differentiate through time in a particle filter, which requires workarounds to the non-differentiable resampling step, that yield biased or high variance gradient estimates. By contrast, we exploit Fisher's identity to obtain a particle-based approximation of the score function (the gradient of the log likelihood) that yields a low variance estimate while only requiring stepwise differentiation through the transition and observation models. We apply our method to real data collected from autonomous vehicles (AVs) and show that it learns better models than existing techniques and is more stable in training, yielding an effective smoother for tracking the trajectories of vehicles around an AV.
Cross-Domain Video Anomaly Detection without Target Domain Adaptation

Most cross-domain unsupervised Video Anomaly Detection (VAD) works assume that at least few task-relevant target domain training data are available for adaptation from the source to the target domain. However, this requires laborious model-tuning by the end-user who may prefer to have a system that works ``out-of-the-box." To address such practical scenarios, we identify a novel target domain (inference-time) VAD task where no target domain training data are available. To this end, we propose a new `Zero-shot Cross-domain Video Anomaly Detection (zxvad)' framework that includes a future-frame prediction generative model setup. Different from prior future-frame prediction models, our model uses a novel Normalcy Classifier module to learn the features of normal event videos by learning how such features are different ``relatively" to features in pseudo-abnormal examples. A novel Untrained Convolutional Neural Network based Anomaly Synthesis module crafts these pseudo-abnormal examples by adding foreign objects in normal video frames with no extra training cost. With our novel relative normalcy feature learning strategy, zxvad generalizes and learns to distinguish between normal and abnormal frames in a new target domain without adaptation during inference. Through evaluations on common datasets, we show that zxvad outperforms the state-of-the-art (SOTA), regardless of whether task-relevant (i.e., VAD) source training data are available or not. Lastly, zxvad also beats the SOTA methods in inference-time efficiency metrics including the model size, total parameters, GPU energy consumption, and GMACs.
QUESTIONNAIRE TO IDENTIFY THE SPECIFIC NEEDS OF STUDENTS INVOLVED IN COUNSELING

: One of the aims of the education system is social and professional integration of young graduates. But in the ever-changing world we live in today, choosing a career becomes a very difficult task. Today's students, subjected to the flow of information from a very young age, enter school with a great deal of knowledge about the world around them. The extracurricular activities in which they participate during the school years open their horizons of thought and many of them may be attracted to several areas, thus being in a very difficult position: what to choose, when I find that I am attracted to more things/professions and I think I can do them well? Therefore, career counseling becomes extremely important in the life of any young person. Counseling refers to a process in which a professional establishes a relationship – based on trust – with a person who needs support. During this process, the young student expresses his/hers ideas and feelings about a problem and receives support in clarifying their meanings, in identifying value patterns based on which solutions will be formulated. The counseling process involves guiding a young person to a profession for which he/she shows interests and skills. At the heart of this process one can find self-knowledge, vocational education, career education, exploration and career planning.
Learning and Predicting Multimodal Vehicle Action Distributions in a Unified Probabilistic Model Without Labels

We present a unified probabilistic model that learns a representative set of discrete vehicle actions and predicts the probability of each action given a particular scenario. Our model also enables us to estimate the distribution over continuous trajectories conditioned on a scenario, representing what each discrete action would look like if executed in that scenario. While our primary objective is to learn representative action sets, these capabilities combine to produce accurate multimodal trajectory predictions as a byproduct. Although our learned action representations closely resemble semantically meaningful categories (e.g., "go straight", "turn left", etc.), our method is entirely self-supervised and does not utilize any manually generated labels or categories. Our method builds upon recent advances in variational inference and deep unsupervised clustering, resulting in full distribution estimates based on deterministic model evaluations.
Prosodic Morphology of Verb and Adjective Syllables in Yi

Prosodic morphology is an important research object of prosodic typology, and also one of the important elements of prosody and morphology. This paper briefly discusses the prosodic form of the Yi language in terms of the number of syllables in verbs and adjectives. Pure verbs in Yi language usually appear in the form of single syllables. When restricted in grammatical activities, they are always flexibly used by adding the number of syllables, the most obvious one being nominalization. There are two types of adjectives in the Yi language, namely property adjective and state adjective. They can be mainly differentiated in terms of the syllable numbers of a word: the property adjectives are often monosyllabic, while the state adjectives are polysyllabic. The category of level is also expressed by adjusting the number of syllables. The more syllables, the greater the category of level change.
SMSMix: Sense-Maintained Sentence Mixup for Word Sense Disambiguation

Word Sense Disambiguation (WSD) is an NLP task aimed at determining the correct sense of a word in a sentence from discrete sense choices. Although current systems have attained unprecedented performances for such tasks, the nonuniform distribution of word senses during training generally results in systems performing poorly on rare senses. To this end, we consider data augmentation to increase the frequency of these least frequent senses (LFS) to reduce the distributional bias of senses during training. We propose Sense-Maintained Sentence Mixup (SMSMix), a novel word-level mixup method that maintains the sense of a target word. SMSMix smoothly blends two sentences using mask prediction while preserving the relevant span determined by saliency scores to maintain a specific word's sense. To the best of our knowledge, this is the first attempt to apply mixup in NLP while preserving the meaning of a specific word. With extensive experiments, we validate that our augmentation method can effectively give more information about rare senses during training with maintained target sense label.
A formal approach to Menger's theorem

Menger's graph theorem equates the minimum size of a separating set for non-adjacent vertices a and b with the maximum number of disjoint paths between a and b. By capturing separating sets as models of an entailment relation, we take a formal approach to Menger's result. Upon showing that inconsistency is characterised by the existence of suficiently many disjoint paths, we recover Menger's theorem by way of completeness.
A Future Primary School Teacher Competence Building Model through Application of Innovative Technologies

Nowadays in the context of innovations in the education system it is necessary to substantiate the organizational and pedagogical conditions for the formation of mathematical competence. These changes necessitated the development of a model for the formation of the mathematical competence of future primary school teachers by means of innovative technologies. The purpose of the article is to get acquainted with the components of primary school teachers’ professional competence, to characterize the content of organizational and pedagogical conditions for implementing the model of mathematical competence of future primary school teachers by means of innovative technologies, to form our own model of mathematical competence. Realization of the purpose of the article will give an opportunity in the educational process of higher pedagogical educational institution to improve the professional training of the future specialist, in particular in the aspect of his mathematical competence formation. The educational and methodological support of the process of building mathematical competence is analyzed, in particular: a set of multimedia presentations in the courses “Mathematics” and “Methods of teaching in the educational field of “Mathematics”. a textbook “Methods of teaching in the educational field of “Mathematics”, a textbook “Fundamentals of work in the Moodle environment” and electronic methodological complexes “Mathematics” and “Methods of teaching in the educational field of “Mathematics”, created in the Mооdle environment.
On the Two Types of Resources for Innovative Primary School Teacher Training

In the article, the authors substantiate the approach to educational innovations as different types of resources - human and technological. Moreover, the concept of “innovation” is understood by the authors in a broad sense: these are objects, phenomena and trends that emerged recently, experienced unexpected development or require urgent changes. The authors discuss the role of human non-technogenic innovation resources and their prospects in personal and organizational-methodological meaning. The purpose of the article is to outline the prospects for the development of an innovative personality for future teachers and demonstrate them some zones of potential occurrence of innovations. The relevance of the study is due to the fact that the educational technologies gradually drive out the teacher from the main role which threatens education, that has always been anthropomorphic, rather than technocentric. Methods. During the study, philosophical metaphysical methods were used, in particular the method of “monologic debate”, reflection on global trends in education. From more specific methods, an analysis of educational processes, generalization and modelling of possible educational consequences were used. This made it possible to outline possible directions of personal innovative growth of the teacher in the context of global changes (presented in Table 1). The result of the article. The authors also investigated Ukrainian and foreign scientific literature on the topic and evaluated it from the point of view of pragmatics and axiology. They also proved a thesis about the new ontological essence of educational and teacher innovation: the change of social and educational environments, the development of electronic technologies force to modify not so much the environment as the professional self-identification of the teacher, the specifics of their resources, professional and ontological “self”. Now the institutional resource support has become less important for the teacher, and the subjectivity comes to the fore, which actively uses innovative resources and is the source thereof. The international significance of the article is explained by the global crisis of the educational values, substitution of the anthropomorphic image of education with hi-tech, which should attract the attention of all civilized humanity.