Reviewerly/exHarmony huggingface dataset:
Differentiable Logics for Neural Network Training and Verification

Neural network (NN) verification is a problem that has drawn attention of many researchers. The specific nature of neural networks does away with the conventional assumption that a static program is given for verification as in the case of NNs multiple models can be used if one fails a new one can be trained leading to an approach called continuous verification, referring to the loop between training and verification. One tactic for improving the network’s performance is through “constraint-based loss functions” - a method of using differentiable logic (DL) to translate logical constraints into loss functions which can then be used to train the network specifically to satisfy said constraint. In this paper we present a uniform way of defining a translation from logic syntax to a differentiable loss function then examine and compare the existing DLs. We explore mathematical properties desired in such translations and discuss the design space identifying possible directions of future work.
Three-dimensional reachable set for the Dubins car: Foundation of analytical description

The Dubins car is a model of motion, in which the scalar control u determines the instantaneous angular speed of rotation.The paper considers the symmetric variant of the constraints u∈[u 1 ,u 2 ], where u 1 = -u 2 and u 2 = 1.We study the three-dimensional reachable set at a given instant t f > 0. An analytical description of twodimensional sections of the set w.r.t. the angular coordinate ϕ is given.The boundary of each ϕ-section is formed with the help of a certain set of curves obtained using the Pontryagin maximum principle.This set includes circular arcs and circular involutes.The symmetry property of each ϕ-section w.r.t. a certain straight line is established.Classification of possible types of the ϕ-sections is proposed.The greatest difficulty is presented by analysis of the case with non-simply connected ϕ-sections.The range of values ϕ and t f , at which the ϕ-sections are non-simply connected, is indicated.
TOLERANCER: A Fault Tolerance Approach for Cloud Manufacturing Environments

The paper presents an approach to solve the software and hardware related failures in edge-cloud environments, more precisely, in cloud manufacturing environments. The proposed approach, called TOLERANCER, is composed of distributed components that continuously interact in a peer to peer fashion. Such interaction aims to detect stress situations or node failures, and accordingly, TOLERANCER makes decisions to avoid or solve any potential system failures. The efficacy of the proposed approach is validated through a set of experiments, and the performance evaluation shows that it responds effectively to different faults scenarios.
Towards Robust Recommender Systems via Triple Cooperative Defense

Recommender systems are often susceptible to well-crafted fake profiles, leading to biased recommendations. The wide application of recommender systems makes studying the defense against attack necessary. Among existing defense methods, data-processing-based methods inevitably exclude normal samples, while model-based methods struggle to enjoy both generalization and robustness. Considering the above limitations, we suggest integrating data processing and robust model and propose a general framework, Triple Cooperative Defense (TCD), which cooperates to improve model robustness through the co-training of three models. Specifically, in each round of training, we sequentially use the high-confidence prediction ratings (consistent ratings) of any two models as auxiliary training data for the remaining model, and the three models cooperatively improve recommendation robustness. Notably, TCD adds pseudo label data instead of deleting abnormal data, which avoids the cleaning of normal data, and the cooperative training of the three models is also beneficial to model generalization. Through extensive experiments with five poisoning attacks on three real-world datasets, the results show that the robustness improvement of TCD significantly outperforms baselines. It is worth mentioning that TCD is also beneficial for model generalizations.
Stochastic Backpropagation: A Memory Efficient Strategy for Training Video Models

We propose a memory efficient method, named Stochastic Backpropagation (SBP), for training deep neural networks on videos. It is based on the finding that gradients from incomplete execution for backpropagation can still effectively train the models with minimal accuracy loss, which attributes to the high redundancy of video. SBP keeps all forward paths but randomly and independently removes the backward paths for each network layer in each training step. It reduces the GPU memory cost by eliminating the need to cache activation values corresponding to the dropped backward paths, whose amount can be controlled by an adjustable keep-ratio. Experiments show that SBP can be applied to a wide range of models for video tasks, leading to up to 80.0% GPU memory saving and 10% training speedup with less than 1% accuracy drop on action recognition and temporal action detection.
All You Need Is RAW: Defending Against Adversarial Attacks with Camera Image Pipelines

Existing neural networks for computer vision tasks are vulnerable to adversarial attacks: adding imperceptible perturbations to the input images can fool these models into making a false prediction on an image that was correctly predicted without the perturbation. Various defense methods have proposed image-to-image mapping methods, either including these perturbations in the training process or removing them in a preprocessing step. In doing so, existing methods often ignore that the natural RGB images in today’s datasets are not captured but, in fact, recovered from RAW color filter array captures that are subject to various degradations in the capture. In this work, we exploit this RAW data distribution as an empirical prior for adversarial defense. Specifically, we propose a model-agnostic adversarial defensive method, which maps the input RGB images to Bayer RAW space and back to output RGB using a learned camera image signal processing (ISP) pipeline to eliminate potential adversarial patterns. The proposed method acts as an off-the-shelf preprocessing module and, unlike model-specific adversarial training methods, does not require adversarial images to train. As a result, the method generalizes to unseen tasks without additional retraining. Experiments on large-scale datasets, e.g., ImageNet, COCO, for different vision tasks, e.g., classification, semantic segmentation, object detection, validate that the method significantly outperforms existing methods across task domains.
Feature Expansion Using Word2vec for Hate Speech Detection on Indonesian Twitter with Classification Using SVM and Random Forest

Hate speech is one of the most common cases on Twitter. It is limited to 280 characters in uploading tweets, resulting in many word variations and possible vocabulary mismatches. Therefore, this study aims to overcome these problems and build a hate speech detection system on Indonesian Twitter. This study uses 20,571 tweet data and implements the Feature Expansion method using Word2vec to overcome vocabulary mismatches. Other methods applied are Bag of Word (BOW) and Term Frequency-Inverse Document Frequency (TF-IDF) to represent feature values in tweets. This study examines two methods in the classification process, namely Support Vector Machine (SVM) and Random Forest (RF). The final result shows that the Feature Expansion method with TF-IDF weighting in the Random Forest classification gives the best accuracy result, which is 88,37%. The Feature Expansion method with TF-IDF weighting can increase the accuracy value from several tests in detecting hate speech and overcoming vocabulary mismatches.
Auto-regressive Image Synthesis with Integrated Quantization

Deep generative models have achieved conspicuous progress in realistic image synthesis with multifarious conditional inputs, while generating diverse yet high-fidelity images remains a grand challenge in conditional image generation. This paper presents a versatile framework for conditional image generation which incorporates the inductive bias of CNNs and powerful sequence modeling of auto-regression that naturally leads to diverse image generation. Instead of independently quantizing the features of multiple domains as in prior research, we design an integrated quantization scheme with a variational regularizer that mingles the feature discretization in multiple domains, and markedly boosts the auto-regressive modeling performance. Notably, the variational regularizer enables to regularize feature distributions in incomparable latent spaces by penalizing the intra-domain variations of distributions. In addition, we design a Gumbel sampling strategy that allows to incorporate distribution uncertainty into the auto-regressive training procedure. The Gumbel sampling substantially mitigates the exposure bias that often incurs misalignment between the training and inference stages and severely impairs the inference performance. Extensive experiments over multiple conditional image generation tasks show that our method achieves superior diverse image generation performance qualitatively and quantitatively as compared with the state-of-the-art.
Vaccine Supply Optimization and Forecasting using Random Forest and ARIMA Models

In order to tackle the Corona Virus Disease, it took a considerable amount of time for the governments to come up with effective and efficient vaccines. After the vaccines were developed, the next challenge was to supply the vaccines to various designated centers based on demographics, population distribution, and other factors. The whole system for vaccine supply played a vital role during the COVID-19 pandemic. We also saw a lot of haphazard and mismanagement in some places especially when the cases per day surged high, as people weren't prepared for such a situation. Now that we have got enough data, we can use it to optimize the vaccine supply across various Covid Vaccination Centers and be prepared for any such circumstances in the future. In this paper, we have proposed a two-step approach where considering the past supply and wastage data we performed a classification task that indicates whether doses are to get wasted at a given center. If yes, we then perform demand forecasting based on the number of administered doses so that the wastage can be reduced, and supply can be optimized.
FindIt: Generalized Localization with Natural Language Queries

We propose FindIt, a simple and versatile framework that unifies a variety of visual grounding and localization tasks including referring expression comprehension, text-based localization, and object detection. Key to our architecture is an efficient multi-scale fusion module that unifies the disparate localization requirements across the tasks. In addition, we discover that a standard object detector is surprisingly effective in unifying these tasks without a need for task-specific design, losses, or pre-computed detections. Our end-to-end trainable framework responds flexibly and accurately to a wide range of referring expression, localization or detection queries for zero, one, or multiple objects. Jointly trained on these tasks, FindIt outperforms the state of the art on both referring expression and text-based localization, and shows competitive performance on object detection. Finally, FindIt generalizes better to out-of-distribution data and novel categories compared to strong single-task baselines. All of these are accomplished by a single, unified and efficient model. The code will be released at: https://sites.google.com/view/findit-eccv22/home .
Human action recognition based on skeleton features

Based on human bone joints, skeleton information has clear and simple features and is not easily affected by appearance factors. In this paper, an improved feature of Gist, ExGist, is proposed to describe the skeleton information of human bone joints for human action recognition. The joint coordinates are extracted by using OpenPose and the thermodynamic diagram, and ExGist is used for feature extraction. The advantage of ExGist is that it can effectively characterize the local and global features of skeleton information while maintaining the original advantages of Gist feature. Compared with Gist, ExGist achieves better results on different classifiers. Additionally, compared with C3D and APTNet, our model also obtains better results with an accuracy rate of 89.2%.
Asymmetric Relation Consistency Reasoning for Video Relation Grounding

Video relation grounding has attracted growing attention in the fields of video understanding and multimodal learning. While the past years have witnessed remarkable progress in this issue, the difficulties of multi-instance and complex temporal reasoning make it still a challenging task. In this paper, we propose a novel Asymmetric Relation Consistency (ARC) reasoning model to solve the video relation grounding problem. To overcome the multi-instance confusion problem, an asymmetric relation reasoning method and a novel relation consistency loss are proposed to ensure the consistency of the relationships across multiple instances. In order to precisely localize the relation instance in temporal context, a transformer-based relation reasoning module is proposed. Our model is trained in a weakly-supervised manner. The proposed method was tested on the challenging video relation dataset. Experiments manifest that the performance of our method outperforms the state-of-the-art methods by a large margin. Extensive ablation studies also prove the effectiveness and strength of the proposed method.
Linking Followership and Job Satisfaction to Employee Performance: The Mediating Role of OCB

This study aims to investigate the mediating role of OCB on the relationship between followership and job satisfaction on employee performance.The questionnaire was used to collect data from the employees of companies that produce medical devices in Indonesia as many as 254 out of a total of 1615 in total.Data analysis using structural equation modeling using the help of the Smartpls program.The results showed that followership and job satisfaction were directly able to improve employee performance and OCB.Furthermore, OCB has succeeded in mediating the influence of followership on employee performance.Interestingly, this study found that OCB did not succeed in mediating the effect of job satisfaction on employee performance.Theoretically, this study provides a new understanding of the role of followership and job satisfaction in improving employee performance mediated by OCB.Practically, this study suggests that employees who have good job satisfaction cannot improve their performance if it is associated with the presence of OCB, because OCB is an individual right that naturally belongs to employees, while performance is a structured measure by the company.
Rethinking Learning Approaches for Long-Term Action Anticipation

Action anticipation involves predicting future actions having observed the initial portion of a video. Typically, the observed video is processed as a whole to obtain a video-level representation of the ongoing activity in the video, which is then used for future prediction. We introduce Anticipatr which performs long-term action anticipation leveraging segment-level representations learned using individual segments from different activities, in addition to a video-level representation. We propose a two-stage learning approach to train a novel transformer-based model that uses these two types of representations to directly predict a set of future action instances over any given anticipation duration. Results on Breakfast, 50Salads, Epic-Kitchens-55, and EGTEA Gaze+ datasets demonstrate the effectiveness of our approach.
Error-Tolerant Reasoning in the Description Logic $$\mathcal{E}\mathcal{L}$$ Based on Optimal Repairs

Ontologies based on Description Logic (DL) represent general background knowledge in a terminology (TBox) and the actual data in an ABox. Both human-made and machine-learned data sets may contain errors, which are usually detected when the DL reasoner returns unintuitive or obviously incorrect answers to queries. To eliminate such errors, classical repair approaches offer as repairs maximal subsets of the ABox not having the unwanted answers w.r.t. the TBox. It is, however, not always clear which of these classical repairs to use as the new, corrected data set. Error-tolerant semantics instead takes all repairs into account: cautious reasoning returns the answers that follow from all classical repairs whereas brave reasoning returns the answers that follow from some classical repair. It is inspired by inconsistency-tolerant reasoning and has been investigated for the DL $$\mathcal{E}\mathcal{L} $$ , but in a setting where the TBox rather than the ABox is repaired. In a series of papers, we have developed a repair approach for ABoxes that improves on classical repairs in that it preserves a maximal set of consequences (i.e., answers to queries) rather than a maximal set of ABox assertions. The repairs obtained by this approach are called optimal repairs. In the present paper, we investigate error-tolerant reasoning in the DL $$\mathcal{E}\mathcal{L} $$ , but we repair the ABox and use optimal repairs rather than classical repairs as the underlying set of repairs. To be more precise, we consider a static $$\mathcal{E}\mathcal{L} $$ TBox (which is assumed to be correct), represent the data by a quantified ABox (where some individuals may be anonymous), and use $$\mathcal{E}\mathcal{L} $$ concepts as queries (instance queries). We show that brave entailment of instance queries can be decided in polynomial time. Cautious entailment can be decided by a coNP procedure, but is still in P if the TBox is empty.
Category-Independent Articulated Object Tracking with Factor Graphs

Robots deployed in human-centric environments may need to manipulate a diverse range of articulated objects, such as doors, dishwashers, and cabinets. Articulated objects often come with unexpected articulation mechanisms that are inconsistent with categorical priors: for example, a drawer might rotate about a hinge joint instead of sliding open. We propose a category-independent framework for predicting the articulation models of unknown objects from sequences of RGB-D images. The prediction is performed by a two-step process: first, a visual perception module tracks object part poses from raw images, and second, a factor graph takes these poses and infers the articulation model including the current configuration between the parts as a 6D twist. We also propose a manipulation-oriented metric to evaluate predicted joint twists in terms of how well a compliant robot controller would be able to manipulate the articulated object given the predicted twist. We demonstrate that our visual perception and factor graph modules outperform baselines on simulated data and show the applicability of our factor graph on real world data.
RT-KGD: Relation Transition Aware Knowledge-Grounded Dialogue Generation

Grounding dialogue system with external knowledge is a promising way to improve the quality of responses. Most existing works adopt knowledge graphs (KGs) as the external resources, paying attention to the contribution of entities in the last utterance of the dialogue for context understanding and response generation. Nevertheless, the correlations between knowledge implied in the multi-turn context and the transition regularities between relations in KGs are under-explored. To this end, we propose a Relation Transition aware Knowledge-Grounded Dialogue Generation model (RT-KGD). Specifically, inspired by the latent logic of human conversation, our model integrates dialogue-level relation transition regularities with turn-level entity semantic information. In this manner, the interaction between knowledge is considered to produce abundant clues for predicting the appropriate knowledge and generating coherent responses. The experimental results on both automatic evaluation and manual evaluation indicate that our model outperforms state-of-the-art baselines.
Primena tehnologije u pravnog kontekstu: primer legaltech-a

Technology is advancing exponentially, while knowledge of technology is growing linearly. This is the very reason for a disruptive effect technological progress often has on all areas of society including law. The discipline that studies the application of technology to law is called LegalTech. In economic terms, LegalTech connects the technology market with the legal services` providers. Using he degree of influence of technology on the traditional tasks of the legal profession as the criterion, the field of LegalTech is divided into LegalTech 1.0, 2.0. and 3.0. The development of LegalTech includes the following areas: text analysis, information research, automation of legal services and predictive analysis of legal issues. Created as a result of digitalization, LegalTech is here to stay. The efforts of the academic community are crucial for the legally regulated, technologically balanced, and socially controlled development of the LegalTech phenomenon. The application of technology in law does not mean automation at all costs: it should be based on the complementarity of human efforts and the performance technology delivers in the process of providing legal services’ optimal quality.
Data-driven artificial bee colony algorithm based on radial basis function neural network

Search strategies play an essential role in artificial bee colony (ABC) algorithm. Different optimisation problems and search stages may need different search strategies. However, it is difficult to choose an appropriate search strategy. To tackle this issue, this paper proposes a data-driven ABC algorithm based on radial basis function neural network (called RBF-ABC). Firstly, a strategy pool with three distinct search strategies is built. The radial basis function (RBF) network is applied to evaluate offspring generated by the search strategies. The search strategy with the best evaluation value is used to guide the search. Dimension perturbation is employed to update multiple dimensions simultaneously, and it improves the convergence speed and the accuracy of the surrogate model. A set of 22 classical benchmark problems with 30 and 100 dimensions are utilised to verify the performance of RBF-ABC. Results show RBF-ABC can effectively save computational evaluations and outperform six other ABC algorithms.
EvoAttack: An Evolutionary Search-Based Adversarial Attack for Object Detection Models

State-of-the-art deep neural networks in image classification, recognition, and detection tasks are increasingly being used in a range of real-world applications. Applications include those that are safety critical, where the failure of the system may cause serious harm, injuries, or even deaths. Adversarial examples are expected inputs that are maliciously modified such that the machine learning models fail to classify them correctly. While a number of evolutionary search-based approaches have been developed to generate adversarial examples against image classification problems, evolutionary search-based attacks against object detection algorithms remain unexplored. This paper explores how evolutionary search-based techniques can be used as a black-box, model- and data- agnostic approach to attack state-of-the-art object detection algorithms (e.g., RetinaNet and Faster R-CNN). A proof-of-concept implementation is provided to demonstrate how evolutionary search can generate adversarial examples that existing models fail to correctly process. We applied our approach to benchmark datasets, Microsoft COCO and Waymo Open Dataset, applying minor perturbations to generate adversarial examples that prevented correct model detections and classifications on areas of interest.