Reviewerly/exHarmony huggingface dataset:
Rare Correlated Coherent Association Rule Mining With CLS-MMS

Abstract The study of coherent association rules based on propositional logic is an important area of association rule mining. Users may get a large number of itemsets for low minsup and lose valuable itemsets for high minsup. Mining without minsup may cause itemset explosions that contain spurious itemsets with low correlations and take a long time to mine. For mining coherence rules, existing approaches consider only the frequent itemsets, ignoring rare itemsets. Moreover, all items in the database are regarded equally important, which is not practical in real-world applications. By using the confidence-lift specified multiple minimum supports combined with propositional logic, we propose an efficient approach called rare correlated coherent association rule mining that addresses all of the problems stated above. We define and incorporate termination bound of support (${s}_{TB}$) and termination bound of dissociation (${d}_{TB}$) for early pruning of the candidate itemsets. In the proposed approach, support thresholds are automatically applied to the itemsets and coherent association rules are derived from the frequent and rare itemsets with high correlation and confidence. Experimental results obtained from real-life datasets show the effectiveness of the proposed approach in terms of itemsets and rule generation, correlation, confidence, runtime and scalability.
Stochastic Observation Prediction for Efficient Reinforcement Learning in Robotics

Although the recent progress of deep learning has enabled reinforcement learning (RL) algorithms to achieve human-level performance in retro video games within a short training time, the application of real-world robotics remains limited. The conventional RL procedure requires agents to interact with the environment. Meanwhile, the interactions with the physical world can not be easily parallelized or accelerated as in other tasks. Moreover, the gap between the real world and simulation makes it harder to transfer the policy trained in simulators to physical robots. Thus, we propose a model-based method to mitigate the interaction overheads for real-world robotic tasks. In particular, our model incorporates an autoencoder, a recurrent network, and a generative network to make stochastic predictions of observations. We conduct the experiments on a collision avoidance task for disc-like robots and show that the generative model can serve as a virtual RL environment. Our method has the benefit of lower interaction overheads as inference of deep neural networks on GPUs is faster than observing the transitions in the real environment, and it can replace the real RL environment with limited rollout length.
Permutation Invariance of Deep Neural Networks with ReLUs

We look at the problem of verifying permutation invariance in Deep Neural Networks (DNNs) – if certain permutations are applied on the inputs, its effect on the outputs will also be a permutation (possibly identity). These properties surface in many interesting practical applications of DNNs, e.g. consider the aircraft collision avoidance system that guides an aircraft to turn right if the sensory inputs suggest an intruder aircraft coming from the left, and vice-versa. The naive way of verifying such properties – using two copies of the network and a standard DNN verification technique, e.g. Reluplex – is impracticable as the complexity of this task is exponential in the network size. This paper proposes a sound, abstraction-based technique to establish permutation invariance in DNNs with ReLU as the activation function. The technique computes an over-approximation of the reachable states, and an under-approximation of the safe states, and propagates this information across the layers, both forward and backward. The novelty of our approach lies in a useful tie-class analysis, that we introduce for forward propagation, and a scalable 2-polytope under-approximation method that escapes the exponential blow-up in the number of regions during backward propagation. Experiments demonstrate that our method compares favorably with the existing state-of-the-art in DNN verification.
Overmind, A Collaborative Decentralized Machine Learning Framework, the Interpretation of Network Behaviour

This paper is an extension of work originally presented in Overmind: A Collaborative Decentralized Machine Learning Framework, which focused on the presentation of the Overmind framework. Overmind is the conceptual design framework for a decentralized machine learning network containing the multiple agents which are collaboratively performing their tasks and objectives. This network of agents can be dynamically changed when the new features are discovered and introduced to the Overmind with significant changes to a system performance. The network behaviour of Overmind, as loosely described in a previous paper, is now presented with detail and interpretation. In this paper, Overmind has been deployed and applied to the multiple dataset which generates and creates the different outcomes and network pattern, then, its capabilities are tested by adding features to the network. The result shows four possible stages: 1) initial stage, 2) connected stage, 3) fully connected stage, and 3) isolated node. This paper also presents the future works as opportunities for improvement of the Overmind framework.
Forensic Analysis of Memetic Image Propagation: Introducing the <scp>SMOC BRISQUEt</scp> Method

Abstract This paper introduces a mixed‐methods approach for forensically reconstructing the propagation of visual media via networked digital devices. The authors present case studies drawn from political misinformation around the January 6, 2021 riots at the U.S. Capitol. Using interpretive analysis, the authors identify traces of user interfaces that remain in images being shared about the riots. Using computational analysis, the authors evaluate compression levels in digital photographs of the events in question, thus identifying which instances of the image are closer to the source (as well as which images appear to be identical). By combining these two approaches, the authors argue that SMOC BRISQUEt refines our understanding of misinformation's memetic spread—helpful in curbing future abuses as well as in guiding the production of more effective cross‐platform spread when desired.
Integrating generalized domain adaptation and Fisher discriminative learning: A unified framework for face recognition with single sample per person

In this paper, an enhanced discriminative feature learning (EDFL) method is proposed to address single sample per person (SSPP) face recognition. With a separate auxiliary dataset, EDFL integrates Fisher discriminative learning and domain adaptation into a unified framework. The separate auxiliary dataset and the gallery/probe dataset are from two different domains (named source and target domains respectively) and have different data distributions. EDFL is modeled to transfer the discriminative knowledge learned from the source domain to the target domain for classification. Since the gallery set with SSPP contains scarce number of samples, it is hard to accurately represent the data distribution of the target domain, which hinders the adaptation effect. To overcome this problem, the generalized domain adaption (GDA) method is proposed to realize good overall domain adaptation when one domain contains limited samples. GDA considers the both global and local domain adaptation effect at the same time. Further, to guarantee that the learned domain adaptation components are optimal for discriminative learning, the domain adaptation and Fisher discriminant model learning are unified into a single framework and an efficient algorithm is designed to optimize them. The effectiveness of the proposed approach is demonstrated by extensive evaluation and comparison with some state-of-the-art methods.
Advertisement Extraction from Content Marketing Articles via Segment-Aware Sentence Classification

The rapid development of social media has brought the prosperity of online economy. Recently, product promotion in social networks has become an essential way of online marketing. As one of the most common marketing means, Content Marketing (CM) inserts advertisements into regular articles in a roundabout and covert way. However, the values and characteristics of products are often exaggerated to attract users' attention. It could cause severe economic losses to users and influence the creditworthiness of the platforms. In this paper, we model the problem of advertisement extraction from CM articles as a sentence classification task. We propose a topic-enhanced deep neural network to encode the semantic information of a sentence for classification. Motivated by the characteristics of CM articles, we develop a segment-aware optimization method that considers the label transitions of sentences in different segments of an article to improve the performance of the classifier. Experimental results based on real-world datasets demonstrate the superiority of the proposed method over state-of-the-art approaches.
Toward a 3D Velocity Interferometer Testbed: Concept and Algorithm Exploration

We explore a concept retrieving Doppler-shift and 3D location of coherently- illuminated moving objects in a 3D volume from recordings of the reflected light through a velocity interferometer and a multi-plane imager.
Learn to have Color and Detail: An End-to-End Panchromatic Image Enhancement

Due to the limited resolution and chrominance information, panchromatic images can not be widely used in accurate earth observation applications, such as road extraction, vehicle detection, and building segmentation. In this research, we propose a cascaded fully convolutional network (CFCN) to achieve panchromatic image super-resolution and image colorization in an end-to-end manner. Experiments on a multispectral image dataset demonstrate that panchromatic images enhanced by the proposed CFCN can achieve high learned extraction similarity as compared to aerial images.
The ethical shortlisting problem

Hiring is a fundamental, frequent activity for all organizations. Hiring decisions have been reported to be subject to conscious and unconscious biases in the literature. The field of Computational Ethics aims to quantify and maximize the ethicality of decisions. This paper attempts to apply Computational Ethics to the shortlisting process in hiring through the use of Linear Programming. Given a set of applicants for a job with numerical qualification values, the author aims to determine weights for each qualification type to compute scores and resulting rankings for each applicant. To this end, Abstract Moral Theories of Utilitarianism, Maximin/Leximin, Egalitarianism, and Prioritarianism are utilized and applied to a set of randomly generated applicant data. Computational experiments demonstrate that the models are scalable and return interpretable results. The necessity of a quota-based shortlisting system to alleviate disadvantaged candidates is highlighted. The author recommends the use of the Maximin model and iteratively eliminating the applicant with the lowest score.
Mobile Interface Theory: A Conceptual Tool for Identifying Digital Rhetoric in a Mobile Context

This paper seeks to expand the theoretical and methodological practices of technical communicators by expounding on the use of Mobile Interface Theory (MIT) in the usability evaluation of a telemedicine mobile interface. MIT is an effective conceptual tool to use to design usability studies of user interfaces in the mobile context as well as to identify digital rhetoric for which usability serves as a proxy.
Whack-a-Ball: An Exergame Exploring the Use of a Ball Interface for Facilitating Physical Activities

Sedentary lifestyles have posed threats to people’s physical and mental well-being worldwide. Researchers developed exergames to address people’s sedentary lifestyle by encouraging body movement through an entertaining game approach. Our research proposed an innovative ball interface and evaluated the effects of the ball interface on physical activities with and without extra weights. We conducted a user study with 14 participants to evaluate their experiences and physical activities elicited in three interface conditions: Mouse, Ball, and Weighted Ball.We collected participants’ heart rate (HR), Borg Rating of Perceived Exertion (RPE), and Self-Assessment Manikin ratings (SAM), and conducted a semi-structured interview.We found that both the Ball and Weighted Ball conditions facilitated moderate-intensity workouts and more physical activities than the at-rest and the Mouse conditions. However, we didn’t observe any significant improvement of physical activities in the Weighted Ball condition compared to the Ball condition. Our research contributes to the exergames research field and HCI community by bringing more understanding and insights towards the ball interface, including participants’ acceptance and experiences, the ball interface’s effectiveness of fostering physical activities, and design considerations for future research.
A Machine Learning Approach to Flight Control of a VTOL Tailsitter UAV

Unmanned aerial vehicles, commonly known as drones, are used for many different purposes. However, it is still a challenging task to fly a drone, which limits its potential for doing more useful things. The goal of this paper is to explain how to fly a drone using a machine learning approach, which should make its flight more accurate, efficient and stable. In this paper, we propose an artificial-intelligence-based flight control of unmanned aerial vehicles in a realistic simulated environment. In addition, the drone can learn to perform a certain task to elaborate the benefits of this approach.
Fast Swing-Up Trajectory Optimization for a Spherical Pendulum on a 7-DoF Collaborative Robot

In this paper, the experimental swing-up of a spherical pendulum mounted on a collaborative robot is presented. The complete mechanical system consists of nine degrees of freedom (DoFs). The primary focus of this work is the design of a fast trajectory planning for the swing-up by systematically incorporating the kinematic and dynamics constraints. The proposed algorithm consists of two steps: First, an offline trajectory optimization is used to build a database of swing-up trajectories, with an average computing time of 10 s for one trajectory. Second, a fast trajectory replanner based on a constrained quadratic program is described, which computes the swing-up trajectory for an arbitrary initial configuration of the system with an average computing time of 0.2 s. Simulations and experimental results demonstrate the swing-up of the spherical pendulum using a discrete time-variant linear quadratic regulator as a feedback controller.
Sentence Ordering by Context-Enhanced Pairwise Comparison

Sentence ordering is a task arranging the given unordered text into the correct order. A feasible approach is to use neural networks to predict the relative order of all sentence pairs and then organize the sentences into a coherent paragraph with topological sort. However, current methods rarely utilize the context information, which is essential for deciding the relative order of the sentence pair. Based on this observation, we propose an efficient approach context-enhanced pairwise comparison network (CPCN) that leverages both the context and sentence pair information in a post-fusion manner to order a sentence pair. To obtain the paragraph context embedding, CPCN first utilizes BERT to encode all sentences, then aggregates them using a Transformer followed by an average pooling layer. Finally, CPCN predicts the relative order of the sentence pair by the concatenation of the paragraph embedding and the sentence pair embedding. Our experiments on three benchmark datasets, SIND, NIPS and AAN show that our model outperforms all the existing models significantly and achieves a new state-of-the-art performance, which demonstrates the effectiveness of incorporating context information.
Using descriptors for image recognition

Methods for finding singular points and forming their descriptors are considered. The aim is to study the existing search methods and determine the feature point descriptors to select the best match between feature point detectors and their descriptors for different types of images. In this paper, we carry out a comparative analysis of the PCA-SIFT, ORB, BRISK, AKAZE methods, which detect singular points and describe their descriptors in the image. An algorithm has been developed that, based on the work of these methods, groups photographs according to the degree of similarity. A comparative analysis of the methods is carried out on several collections of photographs of different quality at different compression rates and at different values of the descriptor matching parameter.
Supporting Insertion in Encrypted Multi-Maps with Volume Hiding

A new threat in encrypted databases, called volume leakage was reported by Kellaris et al. and Grubbs et al., where the volume of data associated with each key in a multi-map is leaked. An existing method addresses this problem by adding noise entries to the multi-map according to differential privacy so that adversaries cannot infer the volume of data. However, it assumes that the data is static and therefore does not support any update operations, such as insertion, deletion, etc. To this problem, this paper proposes a method that enables data insertion in the encrypted multi-map with volume hiding. The basic idea is to use local differential privacy instead of differential privacy in adding noise entries when performing insertions. Besides, we employ cuckoo hashing to perturb the place of insertion, thereby allowing insertions of new items without leaking the volume of entries.However, our proposed method is not suitable for large databases. We consider the use of cloud database in healthcare as a use case. The cloud database is necessary for healthcare in cases such as when multiple medical institutions want to share medical data or when home healthcare providers want to access medical data from outside of hospitals. There are privacy issues if the number of patients for each disease is known. Furthermore, when inserting data of a new patient, we want to hide which disease the patient is suffering from.We conduct experiments to assess the feasibility of the proposed method, and it presents a reasonable performance.
Mathematical Based Models for Group Decision Support in Telecommunication Network Design and Management—Challenges and Trends

The extremely rapid evolutions of telecommunication network technologies and services and their interactions with complex socio-economic environments, justify the increasing importance in applying, in certain areas of network planning, design and management, group decision approaches. In fact, there is a significant number of decision problems focused on issues of network planning and design, of multiple natures, where more than one decision maker intervenes or where it is possible to develop mathematical formulations of the problems considering multiple DMs, representing entities of the network itself. Moreover, the evolution of these networks and related industries leads to a great variety of multifaceted and complex problems, usually involving multiple dimensions, very frequently of conflicting nature. These factors justify the interest in addressing the applications of mathematical based models for group decision support in telecommunications. Although mathematical based models have been developed in the framework of operations research, systems science, game theory, etc., and they are an essential part of group decision and negotiation support systems, the scope of this study is limited to multicriteria models and game theory models. In fact, we believe these methodologies are adequate to discuss the challenges and trends of formal models for telecommunication applications. An outline of more relevant evolutions of telecommunication network technologies and services will be presented, followed by a brief overview of major concepts concerning multicriteria group decision (MCGD) and game theory (GT) approaches and methods, relevant to these areas. After identifying the major domains of application of MCGD and GT approaches in telecommunications, an overview of representative contributions in these areas, based on MCGD and GT methodologies, will be put forward. Finally, an analysis and outline discussion of current and future research trends and challenges concerning the use of MCGD and GT approaches in this broad area of decision support, also focusing on some relevant methodological issues, will be presented.
P‐8.5: A Visual Display System for Automobile A‐pillars Blind Area Visualization

For safety reasons, the current car's A‐pillars are generally designed to be relatively wide, causing a large visual blind area and resulting in a great safety hazard in driving process[1]. This paper presents a visual display system for the A‐pillar to eliminate the blind area, which combines face detection, pupil detection, gaze calculation and image extraction module, and achieves displaying effect of scene moving with people and seamless splicing. Only the head position is calculated in traditional methods because the head position cannot represent the gaze direction, causing a large spliced error. In this paper, when calculating the spliced image, gaze calculation algorithm is added to the visual system, which greatly improves the calculation accuracy of the spliced image, reaching 1.6°, and improves final splicing performance.
Evaluation of Digital Evidence in Criminal Proceedings in Croatia with a Focus on Preservation Requirements and Role of Standard Operative Procedures

Collection and analysis of digital evidence in criminal proceedings entails risks, such as the contamination of evidence during seizure and/or search of a computer system and the inability to establish its authenticity, which may affect its admissibility and credibility before the courts. For that purpose the requirement on digital evidence preservation is prescribed in the criminal procedure law, which should apply by default to all relevant actors. Analysis of available court decisions and rules of the Criminal Procedure Act confirms that the claims concerning mishandling and/or manipulation of digital evidence do not affect ex lege inadmissibility of such evidence. Such claims would be subject to examination on the credibility (reliability) of evidence before the courts. Any detailed technical procedures and measures to be implemented so as to ensure digital evidence preservation are best suited for regulation by standard operative procedures or perhaps even by sub-legal acts. To that effect, the standard operative procedures discussed in this paper have a proven ability to ensure the common goal of ensuring digital evidence preservation. Adherence to best practices stemming from standardized procedures has shown to be vital for ensuring that investigatory procedures and acquired digital evidence are valid and as such accepted throughout the criminal proceedings.
Parallel-Batched Interpolation Search Tree

A sorted set (or map) is one of the most used data types in computer science. In addition to standard set operations, like Insert, Remove, and Contains, it can provide set-set operations such as Union, Intersection, and Difference. Each of these set-set operations is equivalent to some batched operation: the data structure should be able to execute Insert, Remove, and Contains on a batch of keys. It is obvious that we want these “large” operations to be parallelized. These sets are usually implemented with the trees of logarithmic height, such as 2–3 trees, treaps, AVL trees, red-black trees, etc. Until now, little attention was devoted to parallelizing data structures that work asymptotically better under several restrictions on the stored data. In this work, we parallelize Interpolation Search Tree which is expected to serve requests from a smooth distribution in doubly-logarithmic time. Our data structure of size n performs a batch of m operations in $$O(m \log \log n)$$ work and poly-log span.