Reviewerly/exHarmony huggingface dataset:
Optimal Pose and Shape Estimation for Category-level 3D Object Perception

We consider a category-level perception problem, where one is given 3D sensor data picturing an object of a given category (e.g. a car), and has to reconstruct the pose and shape of the object despite intra-class variability (i.e. different car models have different shapes). We consider an active shape model, where -- for an object category -- we are given a library of potential CAD models describing objects in that category, and we adopt a standard formulation where pose and shape estimation are formulated as a non-convex optimization. Our first contribution is to provide the first certifiably optimal solver for pose and shape estimation. In particular, we show that rotation estimation can be decoupled from the estimation of the object translation and shape, and we demonstrate that (i) the optimal object rotation can be computed via a tight (small-size) semidefinite relaxation, and (ii) the translation and shape parameters can be computed in closed-form given the rotation. Our second contribution is to add an outlier rejection layer to our solver, hence making it robust to a large number of misdetections. Towards this goal, we wrap our optimal solver in a robust estimation scheme based on graduated non-convexity. To further enhance robustness to outliers, we also develop the first graph-theoretic formulation to prune outliers in category-level perception, which removes outliers via convex hull and maximum clique computations; the resulting approach is robust to 70%-90% outliers. Our third contribution is an extensive experimental evaluation. Besides providing an ablation study on a simulated dataset and on the PASCAL3D+ dataset, we combine our solver with a deep-learned keypoint detector, and show that the resulting approach improves over the state of the art in vehicle pose estimation in the ApolloScape datasets.
AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization

Redundancy-aware extractive summarization systems score the redundancy of the sentences to be included in a summary either jointly with their salience information or separately as an additional sentence scoring step.Previous work shows the efficacy of jointly scoring and selecting sentences with neural sequence generation models.It is, however, not well-understood if the gain is due to better encoding techniques or better redundancy reduction approaches.Similarly, the contribution of salience versus diversity components on the created summary is not studied well.Building on the state-of-the-art encoding methods for summarization, we present two adaptive learning models: AREDSUM-SEQ that jointly considers salience and novelty during sentence selection; and a two-step AREDSUM-CTX that scores salience first, then learns to balance salience and redundancy, enabling the measurement of the impact of each aspect.Empirical results on CNN/DailyMail and NYT50 datasets show that by modeling diversity explicitly in a separate step, AREDSUM-CTX achieves significantly better performance than AREDSUM-SEQ as well as state-of-the-art extractive summarization baselines.
Energy Blockchain for Public Energy Communities

This paper suggests an application of blockchain as an energy open data ledger, designed to save and track data regarding the energy footprint of public buildings and public energy communities. The developed platform permits writing energy production and consumption of public buildings using blockchain-enabled smart meters. Once authenticated on the blockchain, this data can be made available to the public domain for techno-economic analyses for either research studies and internal or third parties audits, increasing, in this way, the perceived transparency of the public institutions. A further feature of the platform, starting on the previously disclosed raw data, allows calculating, validating, and sharing sustainability indicators of public buildings and facilities, allowing the tracking of their improvements in sustainability goals. The paper also provides the preliminary results of a field-test experimentation of the proposed platform on a group of public buildings, highlighting the possible benefits of its widespread exploitation.
Disambiguation of author entities in ADS using supervised learning and graph theory methods

Abstract Disambiguation of authors in digital libraries is essential for many tasks, including efficient bibliographical searches and scientometric analyses to the level of individuals. The question of how to link documents written by the same person has been given much attention by academic publishers and information retrieval researchers alike. Usual approaches rely on publications’ metadata such as affiliations, email addresses, co-authors, or scholarly topics. Lack of homogeneity in the structure of bibliographic collections and discipline-specific dissimilarities between them make the creation of general-purpose disambiguators arduous. We present an algorithm to disambiguate authorships in the Astrophysics Data System (ADS) following an established semi-supervised approach of training a classifier on authorship pairs and clustering the resulting graphs. Due to the lack of high-signal features such as email addresses and citations, we engineer additional content- and location-based features via text embeddings and named-entity recognition. We train various nonlinear tree-based classifiers and detect communities from the resulting weighted graphs through label propagation, a fast yet efficient algorithm that requires no tuning. The resulting procedure reaches reasonable complexity and offers possibilities for interpretation. We apply our method to the creation of author entities in a recent ADS snapshot. The algorithm is evaluated on 39 manually-labeled author blocks comprising 9545 authorships from 562 author profiles. Our best approach utilizes the Random Forest classifier and yields a micro- and macro-averaged BCubed $$\mathrm {F}_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> score of 0.95 and 0.87, respectively. We release our code and labeled data publicly to foster the development of further disambiguation procedures for ADS.
Gold deposit type and implication for exploration in the Abiete-Toko Gold District, South Cameroon: constraint from morphology and microchemistry of alluvial gold grains

<h2>Abstract</h2> The morphology and quantitative chemical analyses of fifty alluvial gold grains from fourteen studied sites were used to constraint gold deposit type and its implications to exploration in the Abiete-Toko Gold District in South Cameroon. The main results revealed that the gold grains show a core-rim zonation marked by Ag depleted rims as a result of leaching during transportation. The fineness of grains ranges from 826 to 1000 and their composition is almost binary gold-silver. Gold fineness refers to the relative amounts of Ag and Au present, given as a number out of 1000 and defined by Au/(Ag + Au)∗1000. The chemical composition of the gold grains are range as thus: 83.40–100 wt.% Au, 0.07–17.45 wt.% Ag, 0–0.96 wt.% Cu, 0–0.01 wt.% As, 0–0.02 wt.% Ni, 0–0.02 wt.% Co, 0–0.01 wt.% Se, 0–0.08 wt.% Hg and 0.003–0.03 wt.% S. The high sulphur concentrations of the gold grains probably imply primary deposition of gold by sulphidation; The ranges of Ni and Co concentrations suggest an interaction with greenstones. Such findings indicate that the Abiete-Toko area hosts a mesothermal-orogenic gold deposit. Exploration operations must target fractures and contact zones between ironstones, felsic gneiss and greenstones. Soil sampling is not recommended in this orogenic gold district due to the very thick and transported nature of the weathering mantle. Detailed mapping coupled to geophysical surveys are the recommended key exploration methods prior to drilling program.
Enhancement algorithm for high visibility of underwater images

Underwater image enhancement has been attracting researchers in present day scenario for exploring the marine life. But underwater images suffer from various glitches like haziness, low contrast and faded colours due to absorption and scattering properties of light in water. To overcome these issues, the present paper proposes an enhancement method for underwater images. The proposed enhancement method comprises of three steps, i.e. automatic white balancing, dehazing and Rayleigh stretching in spatial domain. Automatic white balancing technique removes the colour cast from underwater images. Haze removal algorithm efficiently overcomes the haziness but it also reduces the local contrast and making the image appear dull. For improving the dehazing result and the visual quality simultaneously, the proposed method process the image by histogram stretching technique based on Rayleigh distribution. The proposed method corrects the colour cast and improves the contrast along with removing the haze from underwater images. Subjective and objective analysis on U 45 dataset validates the efficiency of proposed method and the enhanced images achieve better visual quality as compared to the existing methods. High values of EMEE, EME, UIQM and UCIQE for different types of underwater images further proves the potential and efficacy of the proposed method.
A Workload-Adaptive Streaming Partitioner for Distributed Graph Stores

Abstract Streaming graph partitioning methods have recently gained attention due to their ability to scale to very large graphs with limited resources. However, many such methods do not consider workload and graph characteristics. This may degrade the performance of queries by increasing inter-node communication and computational load imbalance. Moreover, existing workload-aware methods cannot consistently provide good performance as they do not consider dynamic workloads that keep emerging in graph applications. We address these issues by proposing a novel workload-adaptive streaming partitioner named WASP, that aims to achieve low-latency and high-throughput online graph queries. As each workload typically contains frequent query patterns, WASP exploits the existing workload to capture active vertices and edges which are frequently visited and traversed, respectively. This information is used to heuristically improve the quality of partitions either by avoiding the concentration of active vertices in a few partitions proportional to their visit frequencies or by reducing the probability of the cut of active edges proportional to their traversal frequencies. In order to assess the impact of WASP on a graph store and to show how easily the approach can be plugged on top of the system, we exploit it in a distributed graph-based RDF store. Our experiments over three synthetic and real-world graph datasets and the corresponding static and dynamic query workloads show that WASP achieves a better query performance against state-of-the-art graph partitioners, especially in dynamic query workloads.
Becoming Human? Ableism and Control in Detroit: Become Human and the Implications for Human-Machine Communication

In human-machine communication (HMC), machines are communicative subjects in the creation of meaning. The Computers are Social Actors and constructivist approaches to HMC postulate that humans communicate with machines as if they were people. From this perspective, communication is understood as heavily scripted where humans mindlessly apply human-to-human scripts in HMC. We argue that a critical approach to communication scripts reveals how humans may rely on ableism as a means of sense-making in their relationships with machines. Using the choose-your-own-adventure game Detroit: Become Human as a case study, we demonstrate (a) how ableist communication scripts render machines as both less-than-human and superhuman and (b) how such scripts manifest in control and cyborg anxiety. We conclude with theoretical and design implications for rescripting ableist communication scripts.
Particle-Swarm-Optimization-Enhanced Radial-Basis-Function-Kernel-Based Adaptive Filtering Applied to Maritime Data

The real-life signals captured by different measurement systems (such as modern maritime transport characterized by challenging and varying operating conditions) are often subject to various types of noise and other external factors in the data collection and transmission processes. Therefore, the filtering algorithms are required to reduce the noise level in measured signals, thus enabling more efficient extraction of useful information. This paper proposes a locally-adaptive filtering algorithm based on the radial basis function (RBF) kernel smoother with variable width. The kernel width is calculated using the asymmetrical combined-window relative intersection of confidence intervals (RICI) algorithm, whose parameters are adjusted by applying the particle swarm optimization (PSO) based procedure. The proposed RBF-RICI algorithm’s filtering performances are analyzed on several simulated, synthetic noisy signals, showing its efficiency in noise suppression and filtering error reduction. Moreover, compared to the competing filtering algorithms, the proposed algorithm provides better or competitive filtering performance in most considered test cases. Finally, the proposed algorithm is applied to the noisy measured maritime data, proving to be a possible solution for a successful practical application in data filtering in maritime transport and other sectors.
Importance of Internet of Things (IoT) in Marketing Research and Its Ethical and Data Privacy Challenges

This paper focuses on examining IoT applications in marketing research and provides information on how organizations utilize IoT devices and other new technologies such as artificial intelligence (AI), machine learning (ML), and social media to promote various products and services. Moreover, this study specifies how the consistent use of IoT technologies results in substantial organizational competitiveness. The paper also explains how IoT can be referred to as a new component of business analytics and digital marketing and discusses the challenges related to introducing IoT solutions. The most pressing issues that need to be addressed in the IoT context are described as ethical and data privacy issues. It can be explained by the fact that a substantial amount of data is generated during the development of IoT solutions, which raises the question of whether the respective information could be properly managed. It necessitates implementing a transparent, ethical framework, which should be supported by an adequately regulatory process. The extensive exploration of IoT solutions allows academics and marketing practitioners to generate a wide range of data that can be used to improve products and services provided to diverse consumer groups. Therefore, this paper offers a framework for the collaboration process between academics and practitioners. This collaboration in developing IoT tools is marked by adopting a holistic approach to facilitating the progress of such innovation. Academic researchers’ expertise can be utilized to generate creative and innovative ideas on the IoT sector. The combination of important theoretical assumptions and practical experience is vital to achieving the strategic goals of the collaboration between academics and practitioners.
Meta-evaluation of Conversational Search Evaluation Metrics

Conversational search systems, such as Google assistant and Microsoft Cortana, enable users to interact with search systems in multiple rounds through natural language dialogues. Evaluating such systems is very challenging, given that any natural language responses could be generated, and users commonly interact for multiple semantically coherent rounds to accomplish a search task. Although prior studies proposed many evaluation metrics, the extent of how those measures effectively capture user preference remain to be investigated. In this article, we systematically meta-evaluate a variety of conversational search metrics. We specifically study three perspectives on those metrics: (1) reliability : the ability to detect “actual” performance differences as opposed to those observed by chance; (2) fidelity : the ability to agree with ultimate user preference; and (3) intuitiveness : the ability to capture any property deemed important: adequacy, informativeness, and fluency in the context of conversational search. By conducting experiments on two test collections, we find that the performance of different metrics vary significantly across different scenarios, whereas consistent with prior studies, existing metrics only achieve weak correlation with ultimate user preference and satisfaction. METEOR is, comparatively speaking, the best existing single-turn metric considering all three perspectives. We also demonstrate that adapted session-based evaluation metrics can be used to measure multi-turn conversational search, achieving moderate concordance with user satisfaction. To our knowledge, our work establishes the most comprehensive meta-evaluation for conversational search to date.
On the Hardness of Module-LWE with Binary Secret

We prove that the Module Learning With Errors (\(\mathrm {M\text {-}LWE}\)) problem with binary secrets and rank d is at least as hard as the standard version of \(\mathrm {M\text {-}LWE}\) with uniform secret and rank k, where the rank increases from k to \(d \ge (k+1)\log _2 q + \omega (\log _2 n)\), and the Gaussian noise from \(\alpha \) to \(\beta = \alpha \cdot \varTheta (n^2\sqrt{d})\), where n is the ring degree and q the modulus. Our work improves on the recent work by Boudgoust et al. in 2020 by a factor of \(\sqrt{md}\) in the Gaussian noise, where m is the number of given \(\mathrm {M\text {-}LWE}\) samples, when q fulfills some number-theoretic requirements. We use a different approach than Boudgoust et al. to achieve this hardness result by adapting the previous work from Brakerski et al. in 2013 for the Learning With Errors problem to the module setting. The proof applies to cyclotomic fields, but most results hold for a larger class of number fields, and may be of independent interest.
SPPL: probabilistic programming with fast exact symbolic inference

We present the Sum-Product Probabilistic Language (SPPL), a new probabilistic programming language that automatically delivers exact solutions to a broad range of probabilistic inference queries. SPPL translates probabilistic programs into sum-product expressions, a new symbolic representation and associated semantic domain that extends standard sum-product networks to support mixed-type distributions, numeric transformations, logical formulas, and pointwise and set-valued constraints. We formalize SPPL via a novel translation strategy from probabilistic programs to sum-product expressions and give sound exact algorithms for conditioning on and computing probabilities of events. SPPL imposes a collection of restrictions on probabilistic programs to ensure they can be translated into sum-product expressions, which allow the system to leverage new techniques for improving the scalability of translation and inference by automatically exploiting probabilistic structure. We implement a prototype of SPPL with a modular architecture and evaluate it on benchmarks the system targets, showing that it obtains up to 3500x speedups over state-of-the-art symbolic systems on tasks such as verifying the fairness of decision tree classifiers, smoothing hidden Markov models, conditioning transformed random variables, and computing rare event probabilities.
Quantum algorithms for computing general discrete logarithms and orders with tradeoffs

Abstract We generalize our earlier works on computing short discrete logarithms with tradeoffs, and bridge them with Seifert's work on computing orders with tradeoffs, and with Shor's groundbreaking works on computing orders and general discrete logarithms. In particular, we enable tradeoffs when computing general discrete logarithms. Compared to Shor's algorithm, this yields a reduction by up to a factor of two in the number of group operations evaluated quantumly in each run, at the expense of having to perform multiple runs. Unlike Shor's algorithm, our algorithm does not require the group order to be known. It simultaneously computes both the order and the logarithm. We analyze the probability distributions induced by our algorithm, and by Shor's and Seifert's order-finding algorithms, describe how these algorithms may be simulated when the solution is known, and estimate the number of runs required for a given minimum success probability when making different tradeoffs.
CNN-based and DTW features for human activity recognition on depth maps

Abstract In this work, we present a new algorithm for human action recognition on raw depth maps. At the beginning, for each class we train a separate one-against-all convolutional neural network (CNN) to extract class-specific features representing person shape. Each class-specific, multivariate time-series is processed by a Siamese multichannel 1D CNN or a multichannel 1D CNN to determine features representing actions. Afterwards, for the nonzero pixels representing the person shape in each depth map we calculate statistical features. On multivariate time-series of such features we determine Dynamic Time Warping (DTW) features. They are determined on the basis of DTW distances between all training time-series. Finally, each class-specific feature vector is concatenated with the DTW feature vector. For each action category we train a multiclass classifier, which predicts probability distribution of class labels. From pool of such classifiers we select a number of classifiers such that an ensemble built on them achieves the best classification accuracy. Action recognition is performed by a soft voting ensemble that averages distributions calculated by such classifiers with the largest discriminative power. We demonstrate experimentally that on MSR-Action3D and UTD-MHAD datasets the proposed algorithm attains promising results and outperforms several state-of-the-art depth-based algorithms.
Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization

Text summarization is one of the most critical Natural Language Processing (NLP) tasks. More and more researches are conducted in this field every day. Pre-trained transformer-based encoder-decoder models have begun to gain popularity for these tasks. This paper proposes two methods to address this task and introduces a novel dataset named pn-summary for Persian abstractive text summarization. The models employed in this paper are mT5 and an encoder-decoder version of the ParsBERT model (i.e., a monolingual BERT model for Persian). These models are fine-tuned on the pn-summary dataset. The current work is the first of its kind and, by achieving promising results, can serve as a baseline for any future work.
Globally normalized neural model for joint entity and event extraction

Extracting events from texts using neural networks has gained increasing research focus in recent years. However, existing methods prepare candidate arguments in a separate classifier suffering from the error propagation problem and fail to model correlations between entity mentions and event structures. To improve the performance of both entity recognition and event extraction, we propose a transition-based joint neural model for the tasks by converting graph structures to a set of transition actions. In particular, we design ten types of novel actions and introduce a global normalization strategy to alleviate the label bias issue. We conduct experiments based on the widely used Automatic Content Extraction (ACE) corpora and the results show that our model achieves 88.7% F1-score on entities and 75.3% F1-score on event triggers, outperforming the baseline neural networks by a large margin. Further in-depth analysis shows the effectiveness of our model in capturing structural dependencies in long sentences. The proposed model can be used for facilitating a range of downstream tasks.