Reviewerly/exHarmony huggingface dataset:
Video-Guided Curriculum Learning for Spoken Video Grounding

In this paper, we introduce a new task, spoken video grounding (SVG), which aims to localize the desired video fragments from spoken language descriptions. Compared with using text, employing audio requires the model to directly exploit the useful phonemes and syllables related to the video from raw speech. Moreover, we randomly add environmental noises to this speech audio, further increasing the difficulty of this task and better simulating real applications. To rectify the discriminative phonemes and extract video-related information from noisy audio, we develop a novel video-guided curriculum learning (VGCL) during the audio pre-training process, which can make use of the vital visual perceptions to help understand the spoken language and suppress the external noise. Considering during inference the model can not obtain ground truth video segments, we design a curriculum strategy that gradually shifts the input video from the ground truth to the entire video content during pre-training. Finally, the model can learn how to extract critical visual information from the entire video clip to help understand the spoken language. In addition, we collect the first large-scale spoken video grounding dataset based on ActivityNet, which is named as ActivityNet Speech dataset. Extensive experiments demonstrate our proposed video-guided curriculum learning can facilitate the pre-training process to obtain a mutual audio encoder, significantly promoting the performance of spoken video grounding tasks. Moreover, we prove that in the case of noisy sound, our model outperforms the method that grounding video with ASR transcripts, further demonstrating the effectiveness of our curriculum strategy.
Experimental snapshot verification of non-Markovianity by quantum probing of convex coefficients

We apply the recently proposed quantum probing protocols with an unknown system-probe coupling to probe the convex coefficients in mixtures of commuting states. By using two reference states instead of one as originally suggested, we are able to probe both the lower and upper bounds for the convex coefficient. We perform extensive analysis for the roles of the parameters characterizing the double peaked Gaussian frequency spectrum in the Markovian-to-non-Markovian transition of the polarization dynamics of a single photon. We apply the probing of the convex coefficient to the transition-inducing frequency parameter and show that the non-Markovianity of the polarization dynamics can be confirmed with a single snapshot measurement of the polarization qubit performed at unknown time. Furthermore, we change the ``known'' measurement coupling that corresponds to our dynamics of interest to unknown measurement coupling and show that even for unknown measurement couplings the probing method is precise enough to confirm that using the ``known coupling'' would lead to non-Markovian polarization dynamics. We also show how the protocol can identify Markovian and non-Markovian time intervals in the dynamics. The results are validated with single-photon experiments.
MDITRE: Scalable and Interpretable Machine Learning for Predicting Host Status from Temporal Microbiome Dynamics

The human microbiome, or collection of microbes living on and within us, changes over time. Linking these changes to the status of the human host is crucial to understanding how the microbiome influences a variety of human diseases.
Topic-Grained Text Representation-Based Model for Document Retrieval

Document retrieval enables users to find their required documents accurately and quickly. To satisfy the requirement of retrieval efficiency, prevalent deep neural methods adopt a representation-based matching paradigm, which saves online matching time by pre-storing document representations offline. However, the above paradigm consumes vast local storage space, especially when storing the document as word-grained representations. To tackle this, we present TGTR, a Topic-Grained Text Representation-based Model for document retrieval. Following the representation-based matching paradigm, TGTR stores the document representations offline to ensure retrieval efficiency, whereas it significantly reduces the storage requirements by using novel topic-grained representations rather than traditional word-grained. Experimental results demonstrate that compared to word-grained baselines, TGTR is consistently competitive with them on TREC CAR and MS MARCO in terms of retrieval accuracy, but it requires less than 1/10 of the storage space required by them. Moreover, TGTR overwhelmingly surpasses global-grained baselines in terms of retrieval accuracy.
Digital Technologies and Children

Recent advances in digital technologies and their accessibility with ease have become indispensable for children. In contrast to expectations and many regulations, children use digital technologies effectively in many areas from an early age. Digital technologies and digital media, commonly used in entertainment, learning and everyday activities, cover several user-induced problems and challenges. These uses are user-based, not technology-based, which highlights conscious, controlled and time-limited applications that are suitable for their developmental levels. On the other hand, technology has many positive effects, such as providing children with efficient and rich learning opportunities, providing concrete experiences, and increasing their academic success and interest. Also, digital technologies are frequently preferred in learning environments due to the visual preparation of technology-supported materials, addressing more than one sensory organ at the same time, and the inclusion of interaction components. However, in this process, the selection and use of current technologies and software suitable for children's development levels and the factors that need to be considered in the process of use are important to make the learning process effective.
Abstractive Text Summarization Using Attentive GRU Based Encoder-Decoder

In the current era, a huge volume of information exists everywhere. Therefore, it is very crucial to evaluate that information and extract useful, and often summarized information out of it so that it may be used for relevant purposes. This extraction can be achieved through a powerful technique of artificial intelligence, namely, machine learning. Indeed, automatic text summarization has emerged as an important application of machine learning in text processing. In this paper, an english text summarizer has been built with GRU-based encoder and decoder. Bahdanau attention mechanism has been added to overcome the problem of handling long sequences in the input text. A news-summary dataset has been used to train the model. The output is observed to outperform competitive models in the literature. The generated summary can be used as a newspaper headline.
Calibration error study of binocular stereo camera

Abstract The calibration accuracy of the camera largely affects the accuracy of 3D measurement, so this paper researches on the calibration error of the binocular stereo camera, introduces the imaging model of the camera, conducts the experiment of calibration according to the principle of Zhang Zhengyou's calibration, and responds to the calibration accuracy by the reprojection error, the experimental results show that the calibration meets the requirements and the average error can reach 0.05pixels.
Effects of Kaplan-Shekhtman-Entin-Wohlman-Aharony interactions on the non-markovian dynamics of quantum entanglement and communication

By using the non-Markovian Quantum State Diffusion method, the non-Markovian dynamics of entanglement, quantum dense coding and quantum teleportation in the spin chain system with Kaplan-Shekhtman-Entin-wohlman-Aharony interactions and Dzyaloshinskii-Moriya in- teractions are studied. In particular, the joint effects of Kaplan-Shekhtman-Entin-wohlman-Aharony interactions and different external magnetic fields are discussed under zero and finite temperatures, respectively. The results show that the concurrence, channel capacity of quantum dense coding and average fidelity of quantum teleportation can be effectively increased via the increasing of Kaplan-Shekhtman-Entin-wohlman-Aharony interactions along the z-direction which can be attributed to the constructive role of Kaplan-Shekhtman-Entin-wohlman-Aharony interactions on the O(3) invariance in the spin chain. Non-uniform magnetic fields with the combination of Kaplan-Shekhtman-Entin-wohlman-Aharony interactions are found to be more effective in the increasing of the parameters under discussion.
LaSER: Language-specific event recommendation

While societal events often impact people worldwide, a significant fraction of events has a local focus that primarily affects specific language communities. Examples include national elections, the development of the Coronavirus pandemic in different countries, and local film festivals such as the César Awards in France and the Moscow International Film Festival in Russia. However, existing entity recommendation approaches do not sufficiently address the language context of recommendation. This article introduces the novel task of language-specific event recommendation, which aims to recommend events relevant to the user query in the language-specific context. This task can support essential information retrieval activities, including web navigation and exploratory search, considering the language context of user information needs. We propose LaSER, a novel approach toward language-specific event recommendation. LaSER blends the language-specific latent representations (embeddings) of entities and events and spatio-temporal event features in a learning to rank model. This model is trained on publicly available Wikipedia Clickstream data. The results of our user study demonstrate that LaSER outperforms state-of-the-art recommendation baselines by up to 33 percentage points in MAP@5 concerning the language-specific relevance of recommended events.
PROBLEMS OF HIGHER SCHOOL MODERNIZATION IN THE FORMAT OF COMPETENCE-BASED APPROACH

Evaluation of educational achievements of university students on the basis of rating and competency-based approaches is an urgent problem of pedagogy, psychology and methodology and requires the study of various approaches and the search for effective methods for such an assessment. The main emphasis in the article is made on the study of the existing problems of the formation of the competence model of a university graduate, the formation of key competencies, the development of professional skills and their assessment. This study is devoted to determining the main functions and the importance of their observance when organizing the control of knowledge, skills and abilities, ensuring the availability and quality of the educational process, the result of which should be the formation of a competitive graduate. Object of study: the quality of education at a technical university. Subject of study: assessment of the professional development of students of a technical university as a tool for managing the quality of education. The methodological basis of the study is a systematic approach as a way of researching and designing functional systems that ensure the achievement of educational goals. The theoretical basis of the study is the foundations of a competency-based approach to building the content and technologies of vocational education, which aims at modeling professional activity in the learning process. The result of this article is to identify the components of the competency model, which include: a sign of ownership of an activity (competence) - the ability to achieve a result (receive a product); criteria for ownership of activities: meaning, orienting basis, experience; conditions for mastering competence - solving problems for the design and implementation of the process of creating a product (project-based learning method); the essence of the project method of teaching is the inclusion of students in the construction of a "road map" of the process, intra-team distribution of functions, the translation of knowledge into productive action.
Optimization of FPGA-based CNN accelerators using metaheuristics

In recent years, convolutional neural networks (CNNs) have demonstrated their ability to solve problems in many fields and with accuracy that was not possible before. However, this comes with extensive computational requirements, which made general CPUs unable to deliver the desired real-time performance. At the same time, FPGAs have seen a surge in interest for accelerating CNN inference. This is due to their ability to create custom designs with different levels of parallelism. Furthermore, FPGAs provide better performance per watt compared to GPUs. The current trend in FPGA-based CNN accelerators is to implement multiple convolutional layer processors (CLPs), each of which is tailored for a subset of layers. However, the growing complexity of CNN architectures makes optimizing the resources available on the target FPGA device to deliver optimal performance more challenging. In this paper, we present a CNN accelerator and an accompanying automated design methodology that employs metaheuristics for partitioning available FPGA resources to design a Multi-CLP accelerator. Specifically, the proposed design tool adopts simulated annealing (SA) and tabu search (TS) algorithms to find the number of CLPs required and their respective configurations to achieve optimal performance on a given target FPGA device. Here, the focus is on the key specifications and hardware resources, including digital signal processors, block RAMs, and off-chip memory bandwidth. Experimental results and comparisons using four well-known benchmark CNNs are presented demonstrating that the proposed acceleration framework is both encouraging and promising. The SA-/TS-based Multi-CLP achieves 1.31x - 2.37x higher throughput than the state-of-the-art Single-/Multi-CLP approaches in accelerating AlexNet, SqueezeNet 1.1, VGGNet, and GoogLeNet architectures on the Xilinx VC707 and VC709 FPGA boards.
Towards End-to-End Private Automatic Speaker Recognition

The development of privacy-preserving automatic speaker verification systems has been the focus of a number of studies with the intent of allowing users to authenticate themselves without risking the privacy of their voice. However, current privacy-preserving methods assume that the template voice representations (or speaker embeddings) used for authentication are extracted locally by the user. This poses two important issues: first, knowledge of the speaker embedding extraction model may create security and robustness liabilities for the authentication system, as this knowledge might help attackers in crafting adversarial examples able to mislead the system; second, from the point of view of a service provider the speaker embedding extraction model is arguably one of the most valuable components in the system and, as such, disclosing it would be highly undesirable. In this work, we show how speaker embeddings can be extracted while keeping both the speaker's voice and the service provider's model private, using Secure Multiparty Computation. Further, we show that it is possible to obtain reasonable trade-offs between security and computational cost. This work is complementary to those showing how authentication may be performed privately, and thus can be considered as another step towards fully private automatic speaker recognition.
Integration of Hyperspectral and Magnetic Data for Geological Characterization of the Niaqornarssuit Ultramafic Complex in West-Greenland

The integration of imaging spectroscopy and aeromagnetics provides a cost-effective and promising way to extend the initial analysis of a mineral deposit. While imaging spectroscopy retrieves surface spectral information, magnetic responses are used to determine magnetization at both shallower and greater depths using 2D and 3D modeling. Integration of imaging spectroscopy and magnetics improves upon knowledge concerning lithology with magnetic properties, enhances understanding of the geological origin of magnetic anomalies, and is a promising approach for analyzing a prospective area for minerals having a high iron-bearing content. To combine iron diagnostic information from airborne hyperspectral and magnetic data, we (a) used an iron absorption feature ratio to model pseudo-magnetic responses and compare them with the measured magnetic data and (b) estimated the apparent susceptibility along the surface by some equivalent source modeling, and compared them with iron ratios along the surface. For this analysis, a Modified Iron Feature Depth index was developed and compared to the surface geochemistry of the rock samples in order to validate the spectral information of iron. The comparison revealed a linear increase in iron absorption feature depths with iron content. The analysis was performed by empirically modeling the statistical relationship between the diagnostic absorption features of hyperspectral (HS) image spectra of selected rock samples and their corresponding geochemistry. Our results clearly show a link between the spectral absorption features and the magnetic response from iron-bearing ultra/-mafic rocks. The iron absorption feature ratio of Fe3+/Fe2+ integrated with aeromagnetic data (residual magnetic anomaly) allowed us to distinguish main rock types based on physical properties. This separation matches the lithology of the Niaqornarssuit complex, our study area in West Greenland.
Towards fast machine-learning-assisted Bayesian posterior inference of microseismic event location and source mechanism

SUMMARY Bayesian inference applied to microseismic activity monitoring allows the accurate location of microseismic events from recorded seismograms and the estimation of the associated uncertainties. However, the forward modelling of these microseismic events, which is necessary to perform Bayesian source inversion, can be prohibitively expensive in terms of computational resources. A viable solution is to train a surrogate model based on machine learning techniques to emulate the forward model and thus accelerate Bayesian inference. In this paper, we substantially enhance previous work, which considered only sources with isotropic moment tensors. We train a machine learning algorithm on the power spectrum of the recorded pressure wave and show that the trained emulator allows complete and fast event locations for any source mechanism. Moreover, we show that our approach is computationally inexpensive, as it can be run in less than 1 hr on a commercial laptop, while yielding accurate results using less than 104 training seismograms. We additionally demonstrate how the trained emulators can be used to identify the source mechanism through the estimation of the Bayesian evidence. Finally, we demonstrate that our approach is robust to real noise as measured in field data. This work lays the foundations for efficient, accurate future joint determinations of event location and moment tensor, and associated uncertainties, which are ultimately key for accurately characterizing human-induced and natural earthquakes, and for enhanced quantitative seismic hazard assessments.
Quantum Misuse Attack on Frodo

Research on the security of lattice-based public-key encryption schemes against misuse attacks is an important part of the cryptographic assessment of the National Institute of Standards and Technology (NIST) post-quantum cryptography (PQC) standardization process. In particular, many NIST-PQC cryptosystems follow the same meta-cryptosystem. At EUROCRYPT 2019, Ba˘etu et al. mounted a classical key recovery under plaintext checking attacks (KR-PCA) and a quantum key recovery under chosen ciphertext attacks (KR-CCA). They analyzed the security of the weak version of nine submissions to NIST. In this paper, we focus on learning with error (LWE)-based FrodoPKE, whose IND-CPA security is tightly related to the hardness of plain LWE problems. We first review the meta-cryptosystem and quantum algorithm for solving quantum LWE problems. Then, we consider the case where the noise follows a discrete Gaussian distribution and recompute the success probability for quantum LWE by using Hoeffding bound. Finally, we give a quantum key recovery algorithm based on LWE under CCA attack and analyze the security of Frodo. Compared with the existing work of Ba˘etu et al., our method reduces the number of queries from 22 to 1 with the same success probability.
Sentiment Analysis of Twitter Feeds Using Flask Environment: A Superior Application of Data Analysis

In this challenging world, social media plays a vital role as it is at the pinnacle of data sharing. The advancement in technology has made a huge amount of information available for data analysis and it is on the hotlist nowadays. Opinions of the people are expressed and shared across various social media platforms like Twitter, Facebook, and Instagram. Twitter is a prodigious platform containing an ample amount of data and analyzing the data is of topmost priority. One of the most widely utilized approaches for classifying an individual’s emotions displayed in subjective data is sentiment analysis. Sentiment analysis is done using various algorithms of machine learning like Support Vector Machine, Naive Bayes, Long Short-Term Memory, Decision Tree Classifier, and many more, but this paper aims at the generalized way of performing Twitter sentiment analysis using flask environment. Flask environment provides various inbuilt functionalities to analyze the sentiments of text into three different categories: positive, negative, and neutral. Also, it makes API calls to the Twitter Developer account to fetch the Twitter data. After fetching and analyzing the data, the results get displayed on a webpage containing the percentage of positive, negative, and neutral tweets for a phrase in a pie chart. It displays the language analysis for the same phrase. Furthermore, the webpage calls attention to the tweets done on that phrase and reveals the details of the tweets. Considering the major industry runners of three different sectors namely Enterprises, Sports Apparel Industry, and Multimedia Industry, we have analyzed and compared sentiments of two different Multinational companies from each sector.
Deep Learning BP Neural Network Guided Evaluation of Students’ Innovation and Entrepreneurship Education

With the promotion and development of the “Internet+,” the computer major has become a hot major in innovation and entrepreneurship education. It is more and more necessary to carry out the refined differences between majors. This is a training issue, but not an employment issue. It has become a major measure for the development of international education. The international vocational training system of “three combinations and five drives” has been established and implemented. In February 2020, the State Department of Higher Education issued the key work points of the Department of Higher Education of the Ministry of Education in 2020. The document makes it clear that it must be implemented in the whole process of talent training. BP neural net model will be a brand-new research and development idea. By constructing a scientific and reasonable training assessment index system, the efficiency of computer professional technology training can be improved. Taking the development of computer specialty as the main research objective, this paper firstly establishes the evaluation index system of computer specialty for the first time, then makes a scientific evaluation of computer specialty by using BP neural net model and then carries out an empirical study of innovative employment mode through the evaluation index system and makes an empirical quantitative analysis. It is expected to be an effective basis for the social policy research of developing computer specialties.