Reviewerly/exHarmony huggingface dataset:
Expected complexity analysis of stochastic direct-search

This work presents the convergence rate analysis of stochastic variants of the broad class of direct-search methods of directional type. It introduces an algorithm designed to optimize differentiable objective functions f whose values can only be computed through a stochastically noisy blackbox. The proposed stochastic directional direct-search (SDDS) algorithm accepts new iterates by imposing a sufficient decrease condition on so called probabilistic estimates of the corresponding unavailable objective function values. The accuracy of such estimates is required to hold with a sufficiently large but fixed probability $$\beta$$ . The analysis of this method utilizes an existing supermartingale-based framework proposed for the convergence rates analysis of stochastic optimization methods that use adaptive step sizes. It aims to show that the expected number of iterations required to drive the norm of the gradient of f below a given threshold $$\epsilon$$ is bounded in $${\mathcal {O}}\left( \epsilon ^{\frac{-p}{\min (p-1,1)}}/(2\beta -1)\right)$$ with $$p>1$$ . Unlike prior analysis using the same aforementioned framework such as those of stochastic trust-region methods and stochastic line search methods, SDDS does not use any gradient information to find descent directions. However, its convergence rate is similar to those of both latter methods with a dependence on $$\epsilon$$ that also matches that of the broad class of deterministic directional direct-search methods which accept new iterates by imposing a sufficient decrease condition.
Semi-supervised Training of Acoustic Models Leveraging Knowledge Transferred from Out-of-Domain Data

More recently, a novel objective function of discriminative acoustic model training, namely lattice-free MMI (LF-MMI), has been proposed and achieved the new state-of-the-art in automatic speech recognition (ASR). Although LF-MMI shows excellent performance in a wide array of ASR tasks with supervised training settings, there is a dearth of work on investigating its effectiveness in the scenario of unsupervised or semi-supervised training. On the other hand, semi-supervised (or self-training) of acoustic model suffers from the problem that it is hard to estimate a good model when only a limited amount of correctly transcribed data is made available. It is also generally acknowledged that the performance of discriminative training is vulnerable to correctness of speech transcripts employed for training. In view of the above, this paper explores two novel extensions to LF-MMI. The first one is to distill knowledge (acoustic training statistics) from a large amount of out-of-domain data to better estimate the seed models for use in semi-supervised training. The second one is to make effective selection of the untranscribed target domain data for semi-supervised training. A series of experiments conducted on the AMI benchmark corpus demonstrate the gains from these two extensions are pronounced and additive, which also reveals their effectiveness and viability.
AGRITECHNO: A Development of a Revolutionized Farmer Assisted Agricultural Product Forecasting Mobile App System

The agricultural sector in the Philippines is important in economic development. It helps supply crop products to the community and provides major employment in rural areas. But lately, the farmers are facing problems due to the low in price value of crops produced locally because of high importation from the neighboring Asian countries. Most farmers belong to the marginal sector of the community and sometimes given less importance in government support. Thus, the demand for food products escalates as the population increases. Another is the lifestyle and awareness of people which affect the food demand the consumer eats every day. The local market is mostly located in trading post areas and primarily serve as places where farmers can meet with traders to sell their products. This develops a potential market for domestic consumption to have a better quality of farm products produced delivered to them.The development of Agricultural Technology of Negros Occidental (AgriTechNo), an agricultural product forecasting system application helps the farmers in deciding where to market their product and assess the price value available in the market that is monitored in the trading post.
Comparing performance of metaheuristic algorithms for finding the optimum structure of CNN for face recognition

Local and global based methods are two main trends for face recognition. Local approaches extract salient features by processing different parts of the image whereas global approaches find a general template for face of each person. Unfortunately, most global approaches work under controlled environments and they are sensitive to changes in the illumination. On the other hand, local approaches are more robust but finding their optimal parameters is a challenging task. This work proposes a new local-based approach that automatically tunes its parameters. The proposed method incorporates different techniques. In the first step, convolutional neural network (CNN) is employed as a trainable feature extraction procedure. In the second step, different metaheuristic methods are merged with CNN so that its best structure is found automatically. Finally, in the last step the decision is made by employing proper multi-class support vector machine (SVM). In this fashion a fully automated system is developed that is self-tuned and do not need manual adjustments. Simulation results demonstrate efficacy of the proposed method.
Multimodal Strategies in Teaching Children with Autism: A Discourse Analysis

The process of teaching and learning involving children with autism requires teachers to be creative in designing special and effective strategies to ensure students’ understanding. This is due to the fact that these children have problems in communicating with others, thus need special attention from the teachers. The present research hopes to further understand the needs of these children by examining strategies undertaken by teachers at the Schoolsfor Exceptional Children and Autism Centers in Solo citywhich assist children with autism. Five classes were observed and recorded audio-visually. Data representing the aspects of verbal behaviors, non verbal behaviors, and teaching aids exploitation were collected. Results show that the condition of autism children is a primary influence on the strategy in exploiting multimodal aspects. Thus, teachers must design different approaches in transferring knowledge to these children. In general, teachers always perform verbal exploitation in collaboration with the nonverbal aspects such asfacial gestures, body movements, standing positions, and paralinguistic resources such as intonation and loudness. The study also found thatout the five classes being studied, two classes were supported by an effective use of teaching aids. The findings contributed towards a better understanding for teachers and parents of the needs of autism children during the teaching and learning process.
Supervised Structural Sparse Subspace Learning Based on Hierarchical Locality Preservation for Multimodal and Mixmodal Data

We study the multimodal and mixmodal data-driven supervised structural sparse subspace learning problem in this paper, and present the α-structural regularization based hierarchical locality analysis (α-SRHLA) model. Unlike most existing sparse subspace learning models that merely constrain the cardinalities of the subspace basis vectors, the present α-SRHLA model takes into account the structural correlations of the original data variables and generates “variable groups”for sparse subspace learning. As a result, the sparsity is induced in the scale of the variable group instead of the single variable, i.e., “structural sparsity”. In addition, the α-SRHLA considers the “hierarchical locality”of multimodal and mixmodal data, and derives the weighted local affinity correlations in both data-level and class-level. This helps to reveal the intrinsic distribution characteristics of the considered multimodal and mixmodal manifold structures. A series of experiments on normal and multimodal data classification, multimodal and mixmodal digit as well as face recognition verify the effectiveness of the present α-SRHLA model in dealing with both multimodal and mixmodal data.
Quantum key distribution solution over indoor visible light communication networks

In this paper, we study an application of quantum key distribution (QKD) for indoor visible light communication (VLC) networks. Continuous variable QKD (CV-QKD) is used due to the fact that it is less expensive, working at room temperature, and much easier to implement. We design and analyze the security performance of the CV-QKD protocol based on sub-carrier intensity modulation (SIM) over indoor VLC systems taking into account the effects of VLC channel and other physical layer impairments. The mathematical expressions for quantum bit-error rate (QBER) and secret-key rate are derived. Based on the mathematical expressions, various systems’ metrics, including the modulation depth and the dual-threshold scale coefficient, can be determined so as to QBER and secret-key rate meet the design criteria.
Multimodal Smartphone: Millennial Student Learning Style

Purpose: This study aims to describe multimodal smartphones as millennial student learning styles, including the functions and how multimodal smartphones as millennial student learning styles. This research was conducted at STKIP PGRI Pasuruan, East Java, Indonesia. Research Methods: This research uses a qualitative approach with a case study method. Data in the form of verbal and written speech. Data sources for this research are questionnaire and interview. Data collected through questionnaires and interviews. Data analysis was carried out through four stages, namely data organization, data synthesis, data description, and conclusions of data analysis results. Findings: The findings of this study indicate that (1) multimodal smartphones as learning styles aim for academics that lead to an academic focus. This is indicated by the data of 27% or 8 students choosing academic goals with indicators of communication with friends regarding studies, finding information related to studies, and communication with lecturers regarding studies, (2) multimodal smartphones as learning styles aimed at information that is aimed at the ability in combining analysis and complexity of semiotics. This is indicated by the data of 47% or 14 students choosing academic goals with indicators of reading emails, reading daily news, and writing emails that lead to information processing, (3) multimodal smartphones as learning styles aimed at socially that lead to lifelong learning experiences. This is indicated by the data of 13% or 4 students choosing academic goals with indicators writing short messages, reading social media comments, and writing comments on social media (Facebook, Instagram, and WhatsApp), and (4) multimodal smartphones as learning styles aims for entertainment that is aimed at providing new experiences in learning. This is shown through 13% or 4 students choosing entertainment destinations with video clip indicators, watching long films, and playing games. Implications for Research and Practice: Based on the results of the study, it can be concluded that multimodal smartphones as learning styles aim for academic, information, social and entertainment. The findings of this study contribute to increasing the capability of students in tertiary institutions and increasing student competence in understanding cultural semiotics. Students as adult students lead to the achievement of self-identity consisting of, (a) self-concept, (b) the role of experience, (c) readiness for learning, and (d) learning orientation. This is trained to students as a learning style in the age of knowledge (knowledge age) with the acceleration of an extraordinary increase in knowledge. However, the development of smartphone multimodal competencies as learning styles needs to be directed especially in the fields of technology, media and information, learning and innovation skills as well as life and career skills. Keywords: Multimodal Smartphone, Learning Style, Millennial Students
Efficiency of Small Groups Method in Teaching Philosophy

The processes of globalization and universal digitalization have a transforming effect on the education system. To succeed in the profession, a modern specialist needs to have not only special skills, but also be able to navigate in a situation of uncertainty, be prepared to change his occupation, and constantly improve his educational level. The importance of philosophy as an academic discipline in the system of university education is considered. The relevance and argumentativeness of the question of the methodology of teaching philosophy are shown, taking into account the special tasks of the discipline, the focus on the formation of the philosophical and methodological culture of man. The focus is on teaching the student philosophizing, the development of critical thinking skills. It substantiates the refusal to teach in the form of translation of historical and philosophical knowledge and the productivity of the “Socratic dialogue”, which requires the student to search intellectually and creatively. The method of small groups is analyzed as an interactive teaching method, which involves changing the nature of the interaction between the student and the teacher, shifting the emphasis towards student activity and turning the learning task into a search-oriented, research-oriented one. The process of organizing a seminar on philosophy in accordance with the methodology of small groups on the example of the topic «Consciousness» is presented. The results obtained during the implementation of this method in seminars are studied. The conclusion is drawn, as a result of which the small group method seems productive and effective in organizing philosophical work, since not only is the student assimilating other people’s philosophical constructions (connecting to the philosophy of teachings and systems, according to M.K. Mamardashvili), but also building a version of his personal philosophy taking into account their attitudes, programs and values (real philosophy).
Patentability of AI Generated Drugs

Pharmaceutical companies are increasingly involving Artificial Intelligence (AI) in those processes that may be defined as ‘inventive’ as they lead to inventions. As a result, these inventive processes are changing and, thus, also altering the contributions of human inventors working with AI. The author, therefore, questions whether those inventions generated by AI might face obstacles in attracting patent protection from a legal perspective. This article explores two particular aspects of the patent system. Namely, the patentability requirement of inventive step and the requirement to name an inventor in patent applications. Indeed, these are considered to present theoretical and practical incompatibilities in relation to inventions generated by AI. Furthermore, the author discusses the potential consequences of the patentability of AI generated inventions from a competition law perspective. This brings into focus the crucial role of data ownership in relation to AI driven innovation, and consequently the possibility of applying the essential facilities doctrine to data. The article concludes that both the assessment of inventive step and the requirement to name an inventor might require adaptation in relation to AI generated inventions. Moreover, it might be desirable to engage in further sectorial research on data management in the pharmaceutical industry.
VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering

CC is funded by DREAM CDT and was supported by Mila during the timein Montreal. EB is funded by IVADO. We also thank the University of Cambridge ResearchComputing Services for providing HPC cluster resources.
A Modern Writing Instrument Used as a Weapon for Committing Bank Cheque Fraud

In the recent era, investigation of dye, pigments and powder-based ink examination are big challenges.Literature and survey reveal that, some erasable ink pens were consistently used to commit economic frauds in bank instruments like cheques, withdrawal slips, demand drafts, fix deposit receipts, counterfeit currency, and other essential documents.Some manufacturers provide magic erasable writing instruments.Here, we deciphered this type of ink on different documents with the help of VSC-6000/HS.The main aim of this research work is to develop simple, rapid, sensitive, eco-friendly to retrieve original writings of erasable ink.Total of 640 test samples were prepared under the same conditions with different ink pens on different papers/cheques, where each sample was written with the ReWrite-erasable ink pen, thermal ink pen, roller ball high tech ink pen, normal ballpoint pen and gel pens for the comparison purpose.The test samples were examined under different types of scientific instruments, physical examination before and after exposing to different environmental conditions, sunlight and at room temperature (25 o C) for the period of one hundred and eightyone (181) days.This technique to retrieve the original disappeared writings done with a ReWrite-erasable ink pen is very easy, simple, rapid, non-destructive, eco-friendly, convenient and sensitive to detect manipulations done in forgery and in suspect documents.Detection of ghost strokes under VSC-6000/HS with different functions, like fluorescence spotlight and specific (300 to 365 nm) UV range will provide a new avenue by, which the forensic document
Unsupervised learning by a "softened" correlation game: duality and convergence

Neural networks with Hebbian excitation and anti-Hebbian inhibition form an interesting class of biologically plausible unsupervised learning algorithms. It has recently been shown that such networks can be regarded as online gradient descent-ascent algorithms for solving min-max problems that are dual to unsupervised learning principles formulated with no explicit reference to neural networks. Here we generalize one such formulation, the correlation game, by replacing a hard constraint with a soft penalty function. Our softened correlation game contains the nonnegative similarity matching principle as a special case. For solving the primal problem, we derive a projected gradient ascent algorithm that achieves speed through sorting. For solving the dual problem, we derive a projected gradient descent-ascent algorithm, the stochastic online variant of which can be interpreted as a neural network algorithm. We prove strong duality when the inhibitory connection matrix is positive definite, a condition that also prohibits multistability of neural activity dynamics. We show empirically that the neural net algorithm can converge when inhibitory plasticity is faster than excitatory plasticity, and may fail to converge in the opposing case. This is intuitively interpreted using the structure of the min-max problem.
Automatic Target Recognition for Low-Resolution SAR Images Based on Super-Resolution Network

Synthetic aperture radar (SAR) automatic target recognition (ATR) is one of the hottest issue in current research because of its wide application value. However, the low-resolution SAR images will decline the recognition accuracy of targets due to its obscure characteristic, and meanwhile it is difficult to acquire a great number of high-resolution SAR images for extracting clear characteristic. To solve these problems, this paper proposes a method of ATR for low-resolution SAR images based on super-resolution network. Super-resolution generative adversarial network (SRGAN) and deep convolutional neural network (DCNN) are utilized for extracting characteristic and classification, respectively. The segmented low-resolution SAR images are enhanced through SRGAN to improve the visual resolution and the feature characterization ability of target in SAR image; Then the enhanced SAR images are classified automatically by DCNN. Finally, the effectiveness and the efficiency are verified on the open data set, moving and stationary target acquisition and recognition (MSTAR).
The GAC Property of a Class of 1‐Resilient Functions with High Nonlinearity

The absolute and sum-of-squares indicators are used to evaluate the Global avalanche characteristics (GAC) of Boolean functions in a global manner. The GAC properties of a class of highly nonlinear 1-resilient Boolean functions are given. We derive new upper bounds of the absolute and sumofsquares indicators for a class of 1-resilient Boolean functions with high nonlinearity. Compared to the known 1-resilient Boolean functions, the constructed functions possess higher nonlinearity and better GAC properties.
QUALITY OF EDUCATIONAL SERVICES

The paper presents issues related to the assessment of the quality of education at universities.The paper defines and describes quality determinants from the student's point of view in the aspect of academic staff assessment and course organization.The results of students' research after completing the first year of studies and after choosing an engineering thesis in the aspect of identifying the gap between the expected and actual state of the educational service are presented.
Preserving Privacy in Convolutional Neural Network: An ∊-tuple Differential Privacy Approach

Recent breakthrough in neural network has led to the birth of Convolutional neural network (CNN) which has been found to be very efficient especially in the areas of image recognition and classification. This success is traceable to the availability of large datasets and its capability to learn salient and complex data features which subsequently produce a reusable output model $(F_{\theta})$ . The $F_{\theta}$ are often made available (e.g. on cloud as-a-service) for others (client) to train their data or do transfer learning, however, an adversary can perpetrate a model inversion attack on the model $F_{\theta}$ to recover training data, hence compromising the sensitivity of the model buildup data. This is possible because CNN as a variant of deep neural network does memorize most of its training data during learning. Consequently, this has pose a privacy concern especially when a medical or financial data are used as model buildup data. Existing researches that proffers privacy preserving approach however suffer from significant accuracy degradation and this has left privacy preserving model on a theoretical desk. In this paper, we proposed an ∊-tuple differential privacy approach that is based on neuron impact factor estimation to preserve privacy of CNN model without significant accuracy degradation. We experiment our approach on two large datasets and the result shows no significant accuracy degradation.