Reviewerly/exHarmony huggingface dataset:
Use of artificial intelligence to improve access to initial leukemia diagnosis in low- and middle-income countries.

e14117 Background: Disparities in access to cancer diagnostic methods affect especially low and middle-income countries (LMIC). Honduras, a country with limited resources of laboratory, and trained personnel to diagnose leukemia have an estimated incidence of leukemia of 5.8 per 100,000 person-years and the mortality is 4.8 per 100,000 person-years. This country has a single immunophenotyping center and similar to many other LMICs, the preliminary diagnosis of leukemia is performed by a morphological study of blood. Machine learning (ML) is a form of artificial intelligence (AI) that has the potential to improve medical attention in several medical fields. The purpose of this study is to evaluate the impact of ML use, as a method to reduce the time interval and the access to a diagnosis of leukemia in Honduras, where the current average time to perform an initial diagnosis of leukemia is from 2 weeks to 3 months. Methods: A quantitative correlational study was designed. With the help of local informatic developers, an automated image processing algorithm was designed, which was fed with a total of 1009, digital images of bone marrow aspirates and peripheral blood of patients with proven leukemia through immunophenotyping. The images were captured using a microscope Carl Zeiss lens with a 100x objective, the process included the segmentation of leukemia cells for its analysis. Bivariate outcomes were assessed using the Pearson chi-squared test. Results: From 2016 to 2018, a total of 341 samples of patients with any symptom of leukemia were included. After anonymization of patient identification, the samples were analyzed using our algorithm by comparing the cells with its database. Posteriorly, an expert hematologist performed an analysis of the sample. A total of 20 samples (5.8%), were diagnosed with a preliminary diagnosis of leukemia. Of the 20 samples, a total of 10(50%) were acute myeloid leukemia, 6 samples (30%) lymphoblastic leukemia and the remaining 4 samples (20%) were compatible with chronic myeloid leukemia. The average time to make an initial diagnosis of leukemia was 75% and 24% in 24 and 48 hours respectively. Local hematologists managed to make treatment decisions earlier with benefit to the patients. In 19 of the samples (95%), there was correspondence between the morphology diagnosis obtained by our algorithm and the immunophenotyping diagnosis. Conclusions: This preliminary study demonstrates that the use of artificial intelligence constitutes an important element to improve access and shortening of the time required to obtain a diagnosis of leukemia in LMCIs, and represents a method to reduce disparities in access to diagnosis of hematological cancers.
On the Bias of Reed--Muller Codes over Odd Prime Fields

We study the bias of random bounded-degree polynomials over odd prime fields and show that, with probability exponentially close to 1, $n$-variate polynomials of degree $d$ over $\mathbb{F}_p$ have bias at most $p^{-\Omega(n/d)}$. This also yields an exponential tail bound on the weight distribution of Reed--Muller codes over odd prime fields. These results generalize bounds of Ben-Eliezer, Hod, and Lovett [Comput. Complexity, 21 (2012), pp. 63--81] who proved similar results over $\mathbb{F}_2$. Our bounds are based on an extremal property of the rank of sub-matrices of the generator matrices of Reed--Muller codes over odd prime fields that generalizes a property shown by Keevash and Sudakov [SIAM J. Discrete Math., 18 (2005), pp. 713--727] for the case of $\mathbb{F}_2$. Our tail bounds on the bias can be used to derive exponential lower bounds on the time for space-bounded learning of bounded-degree polynomials from their evaluations over odd prime fields.
MOBILE TECHNOLOGY AFFECTING TEACHING AND LEARNING IN RURAL SCHOOLS

The purpose of this paper is to provide monitoring and evaluation results of how teaching and learning were affected in 24 rural schools in 7 provinces of South Africa after a three-year mobile technology implementation project.Teachers face many challenges in rural schools as access to the Internet, electricity, basic amenities and training to use technology in the classroom are often not attained.Large classes and unsupportive principals can also influence the sustainable use of mobile technology in these schools.Two theories of change (implementation success and improved quality of teaching and learning) were applied through an End-line survey to determine how the Information Communication Technology for Education (ICT4E) project, affected teaching and learning in the selected rural schools.
Symbiotic or Parasitic? Universities, Academic Capitalism and the Global Knowledge Economy

The health of social anthropology as a discipline and practice has long been connected to its position as a university-based subject. However, changes in the political economy of higher education, including cuts in public spending, rising student fees, the privileging of STEM subjects over the arts and humanities, and the proliferation of new regimes of audit and accountability, pose challenges for anthropology as well as the future of the university itself. In countries such as Britain, Australia and New Zealand, academics are being urged to be more entrepreneurial, to focus on ‘impact’, and to engage more proactively with business and commerce in order to create a more commercially oriented ‘innovation ecosystem’. The idea of forging a ‘triple helix’ of university-industry-government relations has become the new common sense that underpins the funding of higher education. But how positive is this supposed symbiosis between public universities and external financial interests? What are the costs of this collaboration? And what are the implications for anthropology? This chapter sets out to address these questions by drawing on a collaborative, multidisciplinary research project on university reform and globalization (URGE) carried out between 2010-2013.
Simulation of a quantum jump in three-level systems using photonic Gaussian modes

Multilevel quantum systems lose coherence due to quantum jumps. One example is the spontaneous decay between the internal levels of an atom. In this work, we propose and implement a way to simulate experimentally a three-level system under a quantum jump using a three-mode photonic system. We simulated three different dynamics of spontaneous decay in a three-level atomic system: cascade decay, $\mathrm{\ensuremath{\Lambda}}$ decay, and $V$ decay. With an attenuated light coherent source at photon level, we have prepared photons in a three-path superposition state, which was encoded in parallel Gaussian modes. By programming a spatial light modulator (SLM) for producing periodical phase modulation, we were able to implement the corresponding dynamical maps for quantum jumps in terms of the Kraus operator decomposition. Our results have verified experimentally the variation of the population levels and the decoherence effects caused by quantum jumps in the three decay configurations of the three-level system.
Detecting Relevant Regions for Watermark Embedding in Video Sequences Based on Deep Learning

In this paper, we propose the original idea for searching the best regions for watermark embedding in the uncompressed and compressed video sequences using a deep neural network. If video sequence is uncompressed, then a huge amount of information can be successfully embedded in the textural regions in each frame or 3D textural volume. The codecs, from MPEG-2 to H.265/HEVC, impose the strict restrictions on a watermarking process due to the standards to transmit any motion in a scene. The basic coding unit is a Group Of Pictures (GOP) including I-frame, P-frame/frames, and B-frame/frames. Among these types of frames, I-frame as a spatial intra-picture prediction from neighboring regions is available for watermarking process. Thus, our goal is to find such frames, which will be I-frames with a high probability, and then detect the textural regions for embedding. The task is complicated by a necessity to detect the scene changes in videos. We use non-end-to-end Siamese LiteFlowNet to detect the frames with low optical flow (non-significant background motion), high optical flow (object motion in a scene), or surveillance failure (scene change).
Auto-annotation for Voice-enabled Entertainment Systems

Voice-activated intelligent entertainment systems are prevalent in modern TVs. These systems require accurate automatic speech recognition (ASR) models to transcribe voice queries for further downstream language understanding tasks. Currently, labeling audio data for training is the main bottleneck in deploying accurate machine learning ASR models, especially when these models require up-to-date training data to adapt to the shifting customer needs. We present an auto-annotation system, which provides high quality training data without any hand-labeled audios by detecting speech recognition errors and providing possible fixes. Through our algorithm, the auto-annotated training data reaches an overall word error rate (WER) of 0.002; furthermore, we obtained a reduction of 0.907 in WER after applying the auto-suggested fixes.
New Descriptors of Textual Records: Getting Help from Frequent Itemsets

The analysis of numerical data, whether structured, semi-structured, or raw, is of paramount importance in many sectors of economic, scientific, or simply social activity. The process of extraction of association rules is based on the lexical quality of the text and on the minimum support set by the user. In this paper, we implemented a platform named “IDETEX” capable of extracting itemsets from textual data and using it for the experimentation in different types of clustering methods, such as [Formula: see text]-Medoids and Hierarchical clustering. The experiments conducted demonstrate the potential of the proposed approach for defining similarity between segments.
Analyzing Speaker Strategy in Referential Communication

We analyze a corpus of referential communication through the lens of quantitative models of speaker reasoning. Different models place different emphases on linguistic reasoning and collaborative reasoning. This leads models to make different assessments of the risks and rewards of using specific utterances in specific contexts. By fitting a latent variable model to the corpus, we can exhibit utterances that give systematic evidence of the diverse kinds of reasoning speakers employ, and build integrated models that recognize not only speaker reference but also speaker reasoning.
Rewriting Approaches for Ontology-Mediated Query Answering

A most promising approach to answering queries in ontology-based data access (OBDA) is through query rewriting. In this paper we present novel rewriting approaches for several extensions of OBDA. The goal is to understand their relative expressiveness and to pave the way for efficient query answering algorithms.
Autonomous Motion Planning in Pedestrian Space Considering Passenger Comfort

In this study, we proposed an autonomous motion planning method for improving passenger comfort while ensuring safety, particularly with respect to mobility scooters used by elderly people. We proposed a trajectory planner for restricting vehicle behaviors with large accelerations and jerks by selecting a safe trajectory from a set of preset trajectories. Then, based on this trajectory planner, we developed an autonomous motion planning method with four different driving modes, and evaluated the effectiveness of the method through a numerical simulation. The simulation results demonstrated that the proposed method increased comfort without compromising on safety.
The Contribution of Oculomotor Functions to Rates of Visual Information Processing in Younger and Older Adults

Abstract Oculomotor functions are established surrogate measures of visual attention shifting and rate of information processing, however, the temporal characteristics of saccades and fixations have seldom been compared in healthy educated samples of younger and older adults. Thus, the current study aimed to compare duration of eye movement components in younger (18–25 years) and older (50–81 years) adults during text reading and during object/alphanumeric Rapid Automatic Naming (RAN) tasks. The current study also aimed to examine the contribution of oculomotor functions to threshold time needed for accurate performance on visually-driven cognitive tasks (Inspection Time [IT] and Change Detection [CD]). Results showed that younger adults fixated on individual stimuli for significantly longer than the older participants, while older adults demonstrated significantly longer saccade durations than the younger group. Results also demonstrated that older adults required longer threshold durations (i.e., performed slower) on the visually-driven cognitive tasks, however, the age-group time difference on the CD task was eradicated when the effects of saccade duration were covaried. Thus, these results suggest that age-related cognitive decline is also related to increased duration of saccades and hence, highlights the need to dissociate the age-related motor constraints on the temporal aspects of oculomotor function from visuo-cognitive speed of processing.
The power of synergy in differential privacy: Combining a small curator with local randomizers

Motivated by the desire to bridge the utility gap between local and trusted curator models of differential privacy for practical applications, we initiate the theoretical study of a hybrid model introduced by Blender [Avent et al., USENIX Security '17], in which differentially private protocols of n agents that work in the local-model are assisted by a differentially private curator that has access to the data of m additional users. We focus on the regime where m ≪ n and study the new capabilities of this (m,n)-hybrid model. We show that, despite the fact that the hybrid model adds no significant new capabilities for the basic task of simple hypothesis-testing, there are many other tasks (under a wide range of parameters) that can be solved in the hybrid model yet cannot be solved either by the curator or by the local-users separately. Moreover, we exhibit additional tasks where at least one round of interaction between the curator and the local-users is necessary - namely, no hybrid model protocol without such interaction can solve these tasks. Taken together, our results show that the combination of the local model with a small curator can become part of a promising toolkit for designing and implementing differential privacy.
Achieving Common Ground in Multi-modal Dialogue

All communication aims at achieving common ground (grounding): interlocutors can work together effectively only with mutual beliefs about what the state of the world is, about what their goals are, and about how they plan to make their goals a reality. Computational dialogue research offers some classic results on grouding, which unfortunately offer scant guidance to the design of grounding modules and behaviors in cutting-edge systems. In this tutorial, we focus on three main topic areas: 1) grounding in human-human communication; 2) grounding in dialogue systems; and 3) grounding in multi-modal interactive systems, including image-oriented conversations and human-robot interactions. We highlight a number of achievements of recent computational research in coordinating complex content, show how these results lead to rich and challenging opportunities for doing grounding in more flexible and powerful ways, and canvass relevant insights from the literature on human–human conversation. We expect that the tutorial will be of interest to researchers in dialogue systems, computational semantics and cognitive modeling, and hope that it will catalyze research and system building that more directly explores the creative, strategic ways conversational agents might be able to seek and offer evidence about their understanding of their interlocutors.
THE RUSSIAN CONSTRUCTICON: A NEW LINGUISTIC RESOURCE, ITS DESIGN AND KEY CHARACTERISTICS

We present a new open-access electronic resource named the Russian Constructicon that offers a searchable database of Russian constructions accompanied by descriptions of their properties and illustrated with corpus examples. The project was carried out over the period 2016–2020 and at present contains an inventory of over 2200 multi-word constructions of Contemporary Standard Russian. We prioritize “partially schematic” constructions that lie between the two extremes of fully compositional syntactic sequences on the one hand and fully idiomatic (phraseological) expressions on the other hand. Constructions of this type are difficult to account for in terms of either lexicon or grammar alone, and are often underrepresented in reference works of Russian. A typical construction in our database contains a fixed part (anchor words) and an open slot that can be filled with a restricted set of lexemes. In this paper we first focus on key characteristics of this resource that make it different from existing constructicons of other languages. Second, we describe how the new interface will be designed and how it will serve the needs of both linguists and L2 learners of Russian. In particular, we discuss various search possibilities relevant for different users and those parameters that are available for specifying the retrieval output. An example of an entry is given to show how the information about each construction is structured and presented. Third, we provide an overview of our multi-level semantic classification of constructions. We argue that our system of semantic and syntactic tags subdivides our items into meaningful classes and smaller groups and eventually facilitates the identification of constructional families and clusters. This methodology works well in turning the initial list of constructions as unrelated units into a structured network and makes it possible to refine and expand the collected inventory of constructions in a systematic way.
Triplet Permutation Method for Deep Learning of Single-Shot Person Re-Identification

Solving Single-Shot Person Re-Identification (Re-Id) by training Deep Convolutional Neural Networks is a daunting challenge, due to the lack of training data, since only two images per person are available. This causes the overfitting of the models, leading to degenerated performance. This paper formulates the Triplet Permutation method to generate multiple training sets, from a certain re-id dataset. This is a novel strategy for feeding triplet networks, which reduces the overfitting of the Single-Shot Re-Id model. The improved performance has been demonstrated over one of the most challenging Re-Id datasets, PRID2011, proving the effectiveness of the method.
High dimensional affine codes whose square has a designed minimum distance

Given a linear code $${\mathcal {C}}$$ , its square code $${\mathcal {C}}^{(2)}$$ is the span of all component-wise products of two elements of $${\mathcal {C}}$$ . Motivated by applications in multi-party computation, our purpose with this work is to answer the following question: which families of affine variety codes have simultaneously high dimension $$k({\mathcal {C}})$$ and high minimum distance of $${\mathcal {C}}^{(2)}$$ , $$d({\mathcal {C}}^{(2)})$$ ? More precisely, given a designed minimum distance d we compute an affine variety code $${\mathcal {C}}$$ such that $$d({\mathcal {C}}^{(2)})\ge d$$ and the dimension of $${\mathcal {C}}$$ is high. The best constructions we propose mostly come from hyperbolic codes. Nevertheless, for small values of d, they come from weighted Reed–Muller codes.
The Dangers of Mystifying Artificial Intelligence and Robotics

From the outset, the field of artificial intelligence (AI) has been steeped in myth and magic, distracting us from the real threat of this technology. Promising miracles, Big Tech has amassed wealth and power by colonizing and privatizing the material world and restructuring labour. From the erosion of democracy, to data theft, to invasive surveillance, to the automation of bias, to tax- evasion, the world run by corporate-owned machines is proving to be anything but “magical.”
AUGMENTED REALITY AS A DISTANCE LEARNING TOOL UNDER QUARANTINE CONDITIONS

The research paper describes methods of performing the laboratory work on studying the electro-measuring devices in the remote mode by using the mobile application Electricity AR.
Earth Observations Informing Cities’ Operations and Planning

Rio de Janeiro, Brazil, and Chicago, Ill., are using NASA Earth observations to map, monitor, and forecast water and air quality, urban heat island effects, landslide risks, and more.
Spatially localized sparse representations for breast lesion characterization

The topic of sparse representation of samples in high dimensional spaces has attracted growing interest during the past decade. In this work, we develop sparse representation-based methods for classification of radiological imaging patterns of breast lesions into benign and malignant states.We propose a spatial block decomposition method to address irregularities of the approximation problem and to build an ensemble of classifiers (CL) that we expect to yield more accurate numerical solutions than conventional whole-region of interest (ROI) sparse analyses. We introduce two classification decision strategies based on maximum a posteriori probability (BBMAP-S), or a log likelihood function (BBLL-S).To evaluate the performance of the proposed approach we used cross-validation techniques on imaging datasets with disease class labels. We utilized the proposed approach for separation of breast lesions into benign and malignant categories in mammograms. The level of difficulty is high in this application and the accuracy may depend on the lesion size. Our results indicate that the proposed integrative sparse analysis addresses the ill-posedness of the approximation problem, producing AUC (area under the receiver operating curve) value of 89.1% for randomized 30-fold cross-validation.Furthermore, our comparative experiments showed that the BBLL-S decision function may yield more accurate classification than BBMAP-S because BBLL-S accounts for possible estimation bias.
Enhancing Graph Neural Networks for Recommender Systems

Recommender systems lie at the heart of many online services such as E-commerce, social media platforms and advertising. To keep users engaged and satisfied with the displayed items, recommender systems usually use the users' historical interactions containing their interests and purchase habits to make personalised recommendations. Recently, Graph Neural Networks (GNNs) have emerged as a technique that can effectively learn representations from structured graph data. By treating the traditional user-item interaction matrix as a bipartite graph, many existing graph-based recommender systems (GBRS) have been shown to achieve state-of-the-art performance when employing GNNs. However, the existing GBRS approaches still have several limitations, which prevent the GNNs from achieving their full potential. In this work, we propose to enhance the performance of the GBRS approaches along several research directions, namely leveraging additional items and users' side information, extending the existing undirected graphs to account for social influence among users, and enhancing their underlying optimisation criterion. In the following, we describe these proposed research directions.
Digital competence of bachelors of pedagogical profile

The article analyzes the actual digital competencies formed by bachelors of pedagogical profile in the educational space of the University. We also consider the possibility of expanding the digital competencies of future teachers to improve the effectiveness of the educational process at school and professional growth of the teacher.