Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating e-commerce demand for last-mile delivery optimization through parcel locker

The widespread diffusion of the online channel in the retail marketplace is impacting considerably modern society in recent years. Given the growing demand, Business to Consumer (B2C) e-commerce entails a much higher complexity of the delivery process due to significant fragmentation of parcel shipments in the “last-mile”, especially in urban areas, where traffic and congestion problems are arising together with environmental issues. All these aspects are of high interest not only for companies - that want to maintain a high target service level for their customers - but also for public administrations, that aim to foresee the implications of this phenomenon and how to manage it properly. In this context, the purpose of the study is to investigate the potential of an alternative solution to the traditional home delivery, a self-collection service called "parcel locker". The research study is based on data gathered from an online survey submitted to a sample of residents living in the metropolitan area of Turin, Italy. An estimation of the willingness to buy online among different population segments will be provided by taking into account the main drivers arising from an extensive literature review. Then, e-commerce demand will be expressed in terms of quantity and spatial distribution. To conclude, the parcel locker’s potential to capture the actual demand will be assessed to determine the feasibility of the delivery solution under consideration.
Designing for human rights in AI

In the age of Big Data, companies and governments are increasingly using algorithms to inform hiring decisions, employee management, policing, credit scoring, insurance pricing, and many more aspects of our lives. Artificial intelligence (AI) systems can help us make evidence-driven, efficient decisions, but can also confront us with unjustified, discriminatory decisions wrongly assumed to be accurate because they are made automatically and quantitatively. It is becoming evident that these technological developments are consequential to people’s fundamental human rights. Despite increasing attention to these urgent challenges in recent years, technical solutions to these complex socio-ethical problems are often developed without empirical study of societal context and the critical input of societal stakeholders who are impacted by the technology. On the other hand, calls for more ethically and socially aware AI often fail to provide answers for how to proceed beyond stressing the importance of transparency, explainability, and fairness. Bridging these socio-technical gaps and the deep divide between abstract value language and design requirements is essential to facilitate nuanced, context-dependent design choices that will support moral and social values. In this paper, we bridge this divide through the framework of Design for Values, drawing on methodologies of Value Sensitive Design and Participatory Design to present a roadmap for proactively engaging societal stakeholders to translate fundamental human rights into context-dependent design requirements through a structured, inclusive, and transparent process.
Improving The Performance Of The K-means Algorithm

The Incremental K-means (IKM), an improved version of K-means (KM), was introduced to improve the clustering quality of KM significantly. However, the speed of IKM is slower than KM. My thesis proposes two algorithms to speed up IKM while remaining the quality of its clustering result approximately. The first algorithm, called Divisive K-means, improves the speed of IKM by speeding up its splitting process of clusters. Testing with UCI Machine Learning data sets, the new algorithm achieves the empirically global optimum as IKM and has lower complexity, $O(k*log_{2}k*n)$, than IKM, $O(k^{2}n)$. The second algorithm, called Parallel Two-Phase K-means (Par2PK-means), parallelizes IKM by employing the model of Two-Phase K-means. Testing with large data sets, this algorithm attains a good speedup ratio, closing to the linearly speed-up ratio.
Is the primary CoRoT target HD 43587 under a Maunder minimum phase?

Context. One of the most enigmatic phenomena related to solar activity is the so-called Maunder minimum phase. It consists in the lowest sunspot count ever registered for the Sun and has not been confirmed for other stars to date. Since the spectroscopic observations of stellar activity at the Mount Wilson Observatory, the solar analog HD 43587 has shown a very low and apparently invariant activity level, which makes it a Maunder minimum candidate. Aims. We aim to analyze the chromospheric activity evolution of HD 43587 and its evolutive status, with the intention of unraveling the reasons for this low and flat activity. Methods. We used an activity measurements dataset available in the literature, and computed the S-index from HARPS and NARVAL spectra to infer a cycle period. Additionally, we analyzed the CoRoT light curve of HD 43587, and applied gyrochronology and activity calibrations to determine its rotation period. Finally, based on an evolutionary model and the inferred rotation period, we used the EULAG-MHD code to perform global MHD simulations of HD 43587 to get some insight into its dynamo process. Results. We confirm the almost flat activity profile, with a cycle period Pcyc = 10.44 ± 3.03 yr deduced from the S-index time series, and a long-term trend that might be a period of more than 50 yr. It was impossible to define a rotation period from the light curve, however gyrochronology and activity calibrations allow us to infer an indirect estimate of ¯Prot = 22.6 ± 1.9 d. Furthermore, the MHD simulations confirm an oscillatory dynamo with a cycle period in good agreement with the observations and a low level of surface magnetic activity. Conclusions. We conclude that this object might be experiencing a “natural” decrease in magnetic activity as a consequence of its age. Nevertheless, the possibility that HD 43587 is in a Maunder minimum phase cannot be ruled out.
Why some clades have low bootstrap frequencies and high Bayesian posterior probabilities

Bayesian posterior probabilities are wrongly considered by many systematists as indicative of character support, and equivalent to non-parametric bootstrap frequencies. Here I argue against this view. Non-parametric bootstrap is indicative of the amount of evidence in a data matrix supporting each clade in the tree, while Bayesian posterior probabilities are not intended to represent that property. Clades with high posterior probability may not have a large amount of characters favouring them, and their frequencies are the result of the particular sampling procedure of the Bayesian Markov chain Monte Carlo method, which tends to sample very similar topologies according to their posterior probabilities. Both metrics may relate to the notion of confidence, but depict different properties.
Reinforcement Learning based Beamforming for Massive MIMO Radar Multi-target Detection

This paper considers the problem of multi-target detection for massive multiple input multiple output (MMIMO) cognitive radar (CR). The concept of CR is based on the perception-action cycle that senses and intelligently adapts to the dynamic environment in order to optimally satisfy a specific mission. However, this usually requires a priori knowledge of the environmental model, which is not available in most cases. We propose a reinforcement learning (RL) based algorithm for cognitive beamforming in the presence of unknown disturbance statistics. The radar acts as an agent which continuously senses the unknown environment (i.e., targets and disturbance). Consequently, it optimizes the beamformers through tailoring the beampattern based on the acquired information. Furthermore, we propose a solution to the beamforming optimization problem with less complexity than the existing methods. Numerical simulations are performed to assess the performance of the proposed RL-based algorithm in both stationary and dynamic environments. The RL based beamforming is compared to the conventional omnidirectional approach with equal power allocation. As highlighted by the proposed numerical results, our RL-based beamformer greatly outperforms the omnidirectional one in terms of target detection performance. The performance improvement is even more remarkable under environmentally harsh conditions such as low SNR, heavy-tailed disturbance and rapidly changing scenarios.
Intelligent Tutoring Systems for Generation Z's Addiction

As generation Z's big data is flooding the Internet through social nets, neural network based data processing is turning an important cornerstone, showing significant potential for fast extraction of data patterns. Online course delivery and associated tutoring are transforming into customizable, on-demand services driven by the learner. Besides automated grading, strong potential exists for the development and deployment of next generation intelligent tutoring software agents. Self-adaptive, online tutoring agents exhibiting "intelligent-like" behavior, being capable "to learn" from the learner, will become the next educational superstars. Over the past decade, computer-based tutoring agents were deployed in a variety of extended reality environments, from patient rehabilitation to psychological trauma healing. Most of these agents are driven by a set of conditional control statements and a large answers/questions pairs dataset. This article provides a brief introduction on Generation Z's addiction to digital information, highlights important efforts for the development of intelligent dialogue systems, and explains the main components and important design decisions for Intelligent Tutoring System.
Understanding Dynamic Scenes using Graph Convolution Networks

We present a novel Multi-Relational Graph Convolutional Network (MRGCN) based framework to model on-road vehicle behaviors from a sequence of temporally ordered frames as grabbed by a moving monocular camera. The input to MRGCN is a multi-relational graph where the graph’s nodes represent the active and passive agents/objects in the scene, and the bidirectional edges that connect every pair of nodes are encodings of their Spatio-temporal relations.We show that this proposed explicit encoding and usage of an intermediate spatio-temporal interaction graph to be well suited for our tasks over learning end-end directly on a set of temporally ordered spatial relations. We also propose an attention mechanism for MRGCNs that conditioned on the scene dynamically scores the importance of information from different interaction types.The proposed framework achieves significant performance gain over prior methods on vehicle-behavior classification tasks on four datasets. We also show a seamless transfer of learning to multiple datasets without resorting to fine-tuning. Such behavior prediction methods find immediate relevance in a variety of navigation tasks such as behavior planning, state estimation, and applications relating to the detection of traffic violations over videos.
Joint Uplink-Downlink Resource Allocation for OFDMA-URLLC MEC Systems

In this paper, we study resource allocation algorithm design for multiuser orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) in mobile edge computing (MEC) systems. To achieve the stringent end-to-end delay and reliability requirements of URLLC MEC systems, we propose joint uplink-downlink resource allocation and finite blocklength transmission. Furthermore, we propose a partial time overlap between the uplink and downlink frames to minimize the end-to-end delay, which introduces new time causality constraints. Then, the proposed resource allocation algorithm is formulated as an optimization problem for minimization of the total weighted transmit power of the network under constraints on the minimum quality-of-service regarding the number of computed URLLC user bits within the maximum allowable computing time, i.e., the end-to-end delay of a computation task. Due to the non-convexity of the optimization problem, finding the globally optimal solution entails a high computational complexity which is not tolerable for real-time applications. Therefore, a low-complexity algorithm based on successive convex approximation is proposed to find a high-quality sub-optimal solution. Our simulation results show that the proposed resource allocation algorithm design facilitates the application of URLLC in MEC systems, and yields significant power savings compared to a benchmark scheme.
Towards Robustifying NLI Models Against Lexical Dataset Biases

While deep learning models are making fast progress on the task of Natural Language Inference, recent studies have also shown that these models achieve high accuracy by exploiting several dataset biases, and without deep understanding of the language semantics. Using contradiction-word bias and word-overlapping bias as our two bias examples, this paper explores both data-level and model-level debiasing methods to robustify models against lexical dataset biases. First, we debias the dataset through data augmentation and enhancement, but show that the model bias cannot be fully removed via this method. Next, we also compare two ways of directly debiasing the model without knowing what the dataset biases are in advance. The first approach aims to remove the label bias at the embedding level. The second approach employs a bag-of-words sub-model to capture the features that are likely to exploit the bias and prevents the original model from learning these biased features by forcing orthogonality between these two sub-models. We performed evaluations on new balanced datasets extracted from the original MNLI dataset as well as the NLI stress tests, and show that the orthogonality approach is better at debiasing the model while maintaining competitive overall accuracy.
Scope Head for Accurate Localization in Object Detection

Existing anchor-based and anchor-free object detectors in multi-stage or one-stage pipelines have achieved very promising detection performance. However, they still encounter the design difficulty in hand-crafted 2D anchor definition and the learning complexity in 1D direct location regression. To tackle these issues, in this paper, we propose a novel detector coined as ScopeNet, which models anchors of each location as a mutually dependent relationship. This approach quantizes the prediction space and employs a coarse-to-fine strategy for localization. It achieves superior flexibility as in the regression based anchor-free methods, while produces more precise prediction. Besides, an inherit anchor selection score is learned to indicate the localization quality of the detection result, and we propose to better represent the confidence of a detection box by combining the category-classification score and the anchor-selection score. With our concise and effective design, the proposed ScopeNet achieves state-of-the-art results on COCO
Using Computer Vision to enhance Safety of Workforce in Manufacturing in a Post COVID World

The COVID-19 pandemic forced governments across the world to impose lockdowns to prevent virus transmissions. This resulted in the shutdown of all economic activity and accordingly the production at manufacturing plants across most sectors was halted. While there is an urgency to resume production, there is an even greater need to ensure the safety of the workforce at the plant site. Reports indicate that maintaining social distancing and wearing face masks while at work clearly reduces the risk of transmission. We decided to use computer vision on CCTV feeds to monitor worker activity and detect violations which trigger real time voice alerts on the shop floor. This paper describes an efficient and economic approach of using AI to create a safe environment in a manufacturing setup. We demonstrate our approach to build a robust social distancing measurement algorithm using a mix of modern-day deep learning and classic projective geometry techniques. We have deployed our solution at manufacturing plants across the Aditya Birla Group (ABG). We have also described our face mask detection approach which provides a high accuracy across a range of customized masks.
Assessing species boundaries and evolutionary relationships in a group of south‐western European species of Isotomurus (Collembola, Isotomidae) using allozyme data

In this study allozyme data were used to examine evolutionary relationships in the cosmopolitan collembolan genus Isotomurus. Fifty‐six south‐western European populations from 13 species were compared, with the aim of delimiting species boundaries and testing the robustness of the most frequently used morphological characters for species identification. Observed allozyme variability suggests that genetic differentiation is more pronounced than the amount of morphological variability among and within species. Convergence in models of pigmentation pattern may lead to erroneous taxonomic identification when this is the only feature used for species diagnosis. In this respect, this study confirms that diagnosis is greatly improved when assisted by biochemical or molecular analyses. Allozyme data have also been used to reconstruct evolutionary hypotheses for relationships at the species and population level. The monophyly of Isotomurus maculatus, I. unifasciatus, I. fucicolus, I. nebulosus and I. pseudopalustris was confirmed on molecular grounds. Conversely, the monophyly of I. ghibellinus and I. palustris was rejected, suggesting the presence of cryptic species.
3-D Diffusive Molecular Communication With Two Fully-Absorbing Receivers: Hitting Probability and Performance Analysis

Exact analytical channel models for molecular communication via diffusion (MCvD) systems involving multiple fully absorbing receivers (FARs) in a three-dimensional (3-D) medium are hard to obtain due to the mathematical intractability of corresponding diffusion equations. Therefore, this work considers an MCvD system with two spherical FARs in a 3-D diffusion-limited medium and develop several insights using an approximate analytical expression for the hitting probability of information molecule (IM). Further, based on the hitting probability, a novel approximate closed-form analytical expression for the area under the receiver operating characteristic curve (AUC) is derived to analyze the detection performance at each FAR in the presence of other FAR. Finally, simulation results are presented to validate the analytical results using the particle-based and Monte-Carlo simulations and to yield important insights into the MCvD system performance with two FARs.
Cytotoxic Triterpenes and Sterol from the Fruit of Rabbiteye Blueberry (Vaccinium ashei)

Seven triterpenes, α-amyrin (1), uvaol (2), ursolic acid (3), β-amyrin (4), erythrodiol (5), lupeol (6) and betulin (7), two sterols, β-sitosterol (8) and β-sitosterol 3-O-β-glucopyranoside (9), and one flavonoid, syringetin 3-O-α-rhamnopyranoside (10) were isolated from the methanol extract of the fruit of rabbiteye blueberry (Vaccinium ashei). Their chemical structures were determined on the basis of spectroscopic data. Among them, 3, 4 and 9 exhibited moderate growth inhibitory activity against human lung cancer cells (PC-12) and human colon cancer cells (HCT116) using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay.
TREC-COVID

TREC-COVID is a community evaluation designed to build a test collection that captures the information needs of biomedical researchers using the scientific literature during a pandemic. One of the key characteristics of pandemic search is the accelerated rate of change: the topics of interest evolve as the pandemic progresses and the scientific literature in the area explodes. The COVID-19 pandemic provides an opportunity to capture this progression as it happens. TREC-COVID, in creating a test collection around COVID-19 literature, is building infrastructure to support new research and technologies in pandemic search.
The leaf size/number trade‐off in trees

1 Using a sample of 24 common deciduous angiosperm trees of the Eastern Deciduous Forest region of North America, we tested the hypothesis that leaf size variation across species can be interpreted in terms of a trade‐off between individual leaf mass and the number of leaves produced. 2 The true nature of a resource allocation trade‐off is detectable only if variation in the total amount of growth is accounted for. We controlled for this effect by measuring all of the components of annual growth associated with leaf production at the individual terminal shoot level. Hence, number of leaves produced was expressed as ‘leafing intensity’, i.e. the number of leaves produced by newly emerged (current year's) shoots, divided by the total volume of these shoots. 3 Ninety per cent (r2 = 0.90) of the variation in mean individual leaf mass across species, spanning two orders of magnitude, could be accounted for by proportional variation in mean leafing intensity, i.e. representing an isometric trade‐off, with a slope for log‐transformed data that did not deviate significantly from −1.0. 4 We suggest that this isometric relationship may represent a generalized trade‐off strategy for leaf deployment at the shoot level within temperate deciduous woody species. Following traditional interpretations, adaptation here may involve a fitness benefit associated with a particular leaf size. The present results also suggest an alternative, i.e. selection may instead favour high leafing intensity, with small leaf mass resulting not as a direct adaptation, but simply as a trade‐off. 5 According to this ‘leafing intensity premium’ hypothesis, the fitness benefits of higher leafing intensity are associated primarily with the fitness benefits of a larger pool of lateral meristems, because each leaf is usually associated with an axillary bud. This may in turn provide greater facility for wide phenotypic plasticity in the allocation of these meristems to vegetative vs. reproductive functions. This represents a plausible hypothesis, we suggest, in accounting for why most woody deciduous angiosperms, even some of the largest/tallest ones, have relatively small leaves.
Transformer Based Language Models for Similar Text Retrieval and Ranking

Most approaches for similar text retrieval and ranking with long natural language queries rely at some level on queries and responses having words in common with each other. Recent applications of transformer-based neural language models to text retrieval and ranking problems have been very promising, but still involve a two-step process in which result candidates are first obtained through bag-of-words-based approaches, and then reranked by a neural transformer. In this paper, we introduce novel approaches for effectively applying neural transformer models to similar text retrieval and ranking without an initial bag-of-words-based step. By eliminating the bag-of-words-based step, our approach is able to accurately retrieve and rank results even when they have no non-stopwords in common with the query. We accomplish this by using bidirectional encoder representations from transformers (BERT) to create vectorized representations of sentence-length texts, along with a vector nearest neighbor search index. We demonstrate both supervised and unsupervised means of using BERT to accomplish this task.