Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic cognitive assessment: Combining sparse datasets with disparate cognitive scores

Automatic prediction of cognitive assessment scores through analysis of speech is a challenging task not least due to the lack of available data; this is exacerbated by datasets often being accompanied by disparate cognitive scores as diagnostic practices vary across the world. The ADReSSo 2021 challenge aimed at supporting research in this area and defined a number of tasks including a regression task (predicting Mini-Mental State Examination (MMSE) scores). It saw the successful introduction of a number of BERT-based models including our winning classification approach that successfully applied data augmentation using ASR-generated hypotheses. In this paper, we port this approach to the regression task and further present an investigation into the effect of combining smaller datasets with disparate cognitive scores. In particular, we combine the ADReSSo data with our in-house IVA dataset, which is associated with a different type of cognitive assessment: the Addenbrooke’s Cognitive Examination (ACE-III). We show improved performance by converting ACE-III to MMSE scores thus enabling us to combine the two datasets. By selecting good hyper-parameters, the RMSE reduces from 4.45 to 4.40 on the ADReSSo task. Likewise, using the ADReSSo dataset to boost the IVA regression model, decreases RMSE from 3.50 to 3.00 .
What Quality Control Mechanisms do We Need for High-Quality Crowd Work?

Crowd sourcing and human computation has slowly become a mainstay for many application areas that seek to leverage the crowd in the development of high quality datasets, annotations, and problem solving beyond the reach of current AI solutions. One of the major challenges to the domain is ensuring high-quality and diligent work. In response, the literature has seen a large number of quality control mechanisms each voicing (sometimes domain-specific) benefits and advantages when deployed in largescale human computation projects. This creates a complex design space for practitioners: it is not always clear which mechanism(s) to use for maximal quality control. In this article, we argue that this decision is perhaps overinflated and that provided there is “some kind” of quality control that this obviously known to crowd workers this is sufficient for “high-quality” solutions. To evidence this, and provide a basis for discussion, we undertake two experiments where we explore the relationship between task design, task complexity, quality control and solution quality. We do this with tasks from natural language processing, and image recognition of varying complexity. We illustrate that minimal quality control is enough to repel constantly underperforming contributors and that this is constant across tasks of varying complexity and formats. Our key takeaway: quality control is necessary, but seemingly not how it is implemented.
Stock Market Prediction of NIFTY 50 Index Applying Machine Learning Techniques

ABSTRACT The stock market is viewed as an unpredictable, volatile, and competitive market. The prediction of stock prices has been a challenging task for many years. In fact, many analysts are highly interested in the research area of stock price prediction. Various forecasting methods can be categorized into linear and non-linear algorithms. In this paper, we offer an overview of the use of deep learning networks for the Indian National Stock Exchange time series analysis and prediction. The networks used are Recurrent Neural Network, Long Short-Term Memory Network, and Convolutional Neural Network to predict future trends of NIFTY 50 stock prices. Comparative analysis is done using different evaluation metrics. These analysis led us to identify the impact of feature selection process and hyper-parameter optimization on prediction quality and metrics used in the prediction of stock market performance and prices. The performance of the models was quantified using MSE metric. These errors in the LSTM model are found to be lower compared to RNN and CNN models.
XCVI. Theory of barium titanate

Summary The theory of the dielectric and crystallographic properties of barium titanate is considered. By expanding the free energy as a function of polarization and strain and making reasonable assumptions about the coefficients, it is found possible to account for the various crystal transitions. Calculations are made of the dielectric constants, crystal stains, internal energy, and self polarization as functions of temperature. Finally relations are obtained between the coefficients in the free energy and the ionic force constants. These are used to estimate some of the coefficients which are not completely determined by experimental data.
Negative Guided Abstractive Dialogue Summarization

The goal of the abstractive dialogue summarization task is to generate a shorter form of a long conversation while retaining its most salient information, which plays an important role in speech. Unlike the well-structured text, such as scientific articles and news, dialogues often comprise of utterances coming from multiple interlocutors, where the conversations are often informal, verbose, repetitive, and sprinkled with false-starts, backchanneling, reconfirmations, hesitations as well as speaker interruptions, which might introduce much noisy information and thus brings new challenges of summarizing dialogues. In this work, we extend the widely-used sequence-to-sequence summarization framework with a negative guided mechanism, which allows models to explicitly perceive the unnecessary pieces (i.e., noise) of a dialogue and thus focus more on the salient information. Specifically, the negative guided mechanism has two main components, negative example construction and negative guided loss. We explore two different ways to constructing the negative examples and further calculate the negative loss. Extensive experiments on the benchmark datasets demonstrate that our method significantly outperforms the baselines with regard to both semantic matching and factual consistent based metrics. We also elicit the human efforts to prove the performance gains.
Robust Pitch Estimation Using Multi-Branch CNN-LSTM and 1-Norm LP Residual

Pitch and voicing determination are important in many speech and audio signal processing applications. Even in the clean signal case their estimation can pose problems, and more so when noise is present. In this paper we propose a Multi-Branch CNN-LSTM based Temporal Neural Network for pitch and voicing determination. In addition, rather than using the raw waveform, we use the ℓ 1 -norm based LP residual as the input signal. These changes have made the proposed method more robust to SNR degradation, i.e., even though there is a slight fall in accuracy in the clean signal case, there is a 2 . 9% absolute increase in RPA for the 0 dB case when compared with the CREPE algorithm. More importantly, when the RPA tolerance is tightened, the fall in accuracy is smaller. This robustness has been achieved with only 1.79M parameters, which is an order of magnitude less than what is used in CREPE.
Universal Chiplet Interconnect Express (UCIe): An Open Industry Standard for Innovations With Chiplets at Package Level

Universal Chiplet Interconnect Express (UCIe) is an open industry standard interconnect for developing an open chiplet ecosystem, where chiplets from any supplier can be packaged anywhere in an interoperable manner. This article delves into the architectural, circuit, channel, and packaging aspects that we developed that has been adopted in the UCIe 1.0 Specification. We present our results based on our channel and circuit implementation studies.
Deliberating the Constitutional Supremacy from Legal Perspective in Palestine

The system of government considers constitutional supremacy in which the freedom of the legislature of parliamentary supremacy relinquishes to the requirements of a constitution. This article examines the constitutional supremacy of basic law from the legal and judicial perspective, particularly concerning the Palestinian situation. In addition, constitutions differentiate according to whether they are codified or not into written constitutions and unwritten constitutions. Besides, constitutions differ in how they are amended into flexible and rigid constitutions. Palestinian Basic Law of 2003 is not explicitly provided with any legal provision or article in the Basic Law about the principle of constitutional supremacy or the supremacy of the basic law. However, some elements and legal provisions or articles could make the Basic Law supreme. The article aims to clarify the decline of the concept of constitutional supremacy or the supremacy of the basic law from the legal and judicial perspective, with particular reference to the Palestinian situation. Moreover, constitutional oversight secured the supremacy of the Basic Law of 2003. These main findings show that the Palestinian Basic Law of 2003 has adopted the principle of supremacy of the Basic Law of 2003 over ordinary laws and subsidiaries within the state of Palestine. The article also emphasises the need to explicitly mention the principle of constitutional supremacy or the supremacy of the basic law by amending the Basic Law of 2003
Cross-Lingual Transfer Learning Approach to Phoneme Error Detection via Latent Phonetic Representation

Extensive research has been conducted on CALL systems for Pronunciation Error detection to automate language improvement through self-evaluation. However, many of these previous approaches have relied on HMM or Neural Network Hybrid Models which, although have proven to be effective, often utilize phonetically labelled L2 speech data which is expensive and often scarce. This paper discusses a ”zero-shot” transfer learning approach to detect phonetic errors in L2 English speech by Japanese native speakers using solely unaligned phonetically labelled native language speech. The proposed method introduces a simple base architecture which utilizes the XLSR-Wav2Vec2.0 model pre-trained on unlabelled multilingual speech. Phoneme mapping for each language is determined based on difference of articulation of similar phonemes. This method achieved a Phonetic Error Rate of 0.214 on erroneous L2 speech after fine-tuning on 70 hours of speech with low resource automated phonetic labelling, and proved to additionally model phonemes of the native language of the L2 speaker effectively without the need for L2 speech fine-tuning.
Modeling, Simulation and Implementation of All Terrain Adaptive Five DOF Robot

The ability of an off-road robot to traverse obstacles determines whether the robot can complete complex environmental tasks. In order to improve the off-road ability of off-road robots, this paper proposes a new design idea, in which four hub motors are the power system of the robot, the steering system of the robot is composed of a steering machine and a stepping motor, and a five degree of freedom robot model is established. The body structure is designed according to the characteristics of arthropods. The body structure is divided into three modules, and the connecting rod is used as the joint system of the robot to connect the three parts. The body can deform when facing complex obstacles, so as to adapt to different terrains. Then the body structure is simplified, and a mathematical model is established to describe the mathematical relationship between body joint changes. In order to verify the ability of the adaptive all-terrain cross-country robot to traverse obstacles, the load-bearing experiment and obstacle-crossing simulation experiment were carried out through Adams software, and the continuous traversing performance at low obstacles and the ability to break through high obstacles were tested, respectively. The experimental results prove that the designed adaptive all-terrain off-road robot is feasible, has good carrying capacity, and has good passability in the face of low obstacles and high obstacles. Using Ansys software to perform finite element analysis on the wheel connection, the experimental results show that the strength meets the material strength requirements. Finally, a real vehicle test is carried out to verify the correctness of the simulation results.
Principles and procedures of numerical analysis

Preliminaries. - Approximation and Interpolation of Functions. - Numerical Differentiation and Integration. - General Theory for Iteration Methods. - Solution of Nonlinear Equations. - The Solution of Simultaneous Linear Equations. - The Solution of Matrix Eigenvalue Problems. - The Numerical Solution of Ordinary Differential Equations. - The Numerical Solution of Partial Differential Equations.
The Poisson Scale-Space: A Unified Approach to Phase-Based Image Processing in Scale-Space

In this paper we address the topics of scale-space and phase-based signal processing in a common framework. The involved linear scale-space is no longer based on the Gaussian kernel but on the Poisson kernel. The resulting scale-space representation is directly related to the monogenic signal, a 2D generalization of the analytic signal. Hence, the local phase arises as a natural concept in this framework which results in several advanced relationships that can be used in image processing.
The Effect of Deep Dialogue/Critical Thinking Model on Students’ Conceptual Understanding Ability

Most students in Indonesia find it difficult to understand the concepts of mathematics correctly. This is caused by the factor of teacher who doesn’t master the learning method according to the class situation and the factor of the students who are less interested in learning mathematics. The DDCT is suitable in this case, the purpose of this study is to find out the effect of the DDCT and how much it affects the students' ability to understand concepts at SMPN 7 Ambon. This research is quantitative looking for the relationship between variables, with a pre-experimental design of the type of one-shot case study. The samples taken were students of class X-1 (experimental) and X-2 (control), each of which amounted to 15 people at SMPN 7 Ambon. Data were collected on concept understanding ability tests and questionnaires used for student responses during learning using DDCT. Data were analyzed through hypothesis testing and determinant coefficients using SPSS 22.0. The results showed that the average value of the experimental class was 88.67 and the control class was 60, so there was a difference in the average value of 28.67. From the results of the analysis obtained tcount = 0.862 ttable = 0.05, it is concluded that there is an effect of DDCT on students' understanding of mathematical concepts. The findings during the research showed that DDCT: 1) can be used to train students to more easily understand a material, and 2) deep dialogue/critical thinking emphasizes attitude and mental emotional so that students learn with fun.
Multi-Objective Optimization Framework of a Radial-Axial Hybrid Excitation Machine for Electric Vehicles

This paper proposes a multi-objective optimization framework for a radial-axial hybrid excitation machine (RAHEM) to provide higher average torque, better flux regulation ability and smaller torque ripple, which are applied to electric vehicles (EVs). The design variables related to multiple-objective are analyzed by sensitivity stratification. Non-dominated sorting genetic algorithm II (NSGA-II) based on response surface model (RSM) is adopted for the high sensitivity layer variable. The advantages are selected with the pareto optimal solutions (POS), while the low sensitivity layer variables are optimized by sensitivity ranking for single parameter scanning. The optimization function compares the two sensitive layers results to obtain the optimal design. Three-dimensional (3-D) finite element analysis (FEA) is used to compare the electromagnetic performance of initial and optimal designs. Finally, a prototype is manufactured to verify the effectiveness of the proposed framework.
Unsupervised domain adaptation for speech recognition with unsupervised error correction

The transcription quality of automatic speech recognition (ASR) systems degrades signiﬁcantly when transcribing audios coming from unseen domains. We propose an unsupervised error correction method for unsupervised ASR domain adaption, aiming to recover transcription errors caused by domain mismatch. Unlike existing correction methods that rely on transcribed audios for training, our approach requires only unlabeled data of the target domains in which a pseudo-labeling technique is applied to generate correction training samples. To reduce over-ﬁtting to the pseudo data, we also propose an encoder-decoder correction model that can take into account additional information such as dialogue context and acoustic features. Experiment results show that our method obtains a signiﬁcant word error rate (WER) reduction over non-adapted ASR systems. The correction model can also be applied on top of other adaptation approaches to bring an additional improvement of 10% relatively.
Robust Petri Net Controllers for Flexible Manufacturing Systems With Multitype and Multiunit Unreliable Resources

It is inevitable that resource failures occur in real production processes, and sometimes many different types of unreliable resource failures may occur. Once there are resource failures, the stagnation states of production caused by these failures, called as failure blockings (FBs), tend to appear. Hence, both deadlocks and FBs can arise to reduce production efficiency sharply. Designing robust control policies for such an automated system is thus very important. Our work focuses on a novel robust Petri net controller of flexible manufacturing systems with multitype and multiunit of unreliable resources. First, under the assumption that all resources are reliable, these systems are modeled by a class of Petri nets called systems of simple sequential processes with resources (S3PRs). For each operation place that is a holder of an unreliable resource, the corresponding repair place and related transitions are added to the net and an S3PR with unreliable resources (S3PRu) is developed. For an S3PRu, the formal definition of FBs is then proposed. Such an FB is characterized by a maximal perfect resource-transition circuit (MPC). Next, the concept of a critical set of MPCs is introduced. For any two adjacent MPCs in such a critical set, one input transition of the former and one output transition of the latter are connected by passing through resource places. A $\xi $ -resource is a one-unit resource shared by two or more MPCs that do not contain each other. For an S3PRu without $\xi $ -resources and critical sets of MPCs, a control place corresponding to an MPC is added to the net with its output arcs to the input transitions of the MPC. However, for an S3PRu with $\xi $ -resources or critical sets of MPCs, the output arcs of each control place are added to the source transitions of the original net. Thus, a novel robust controller of an S3PRu is synthesized. Such a robust controller can guarantee that as long as at least one unit of each type of unreliable resources can work normally, all kinds of parts can be processed to complete smoothly their tasks through any one of their process routes. Finally, three examples are provided to illustrate the efficiency of the proposed robust controller.
Mandarin nasal place assimilation revisited: an acoustic study

Two types of nasal place assimilation (NPA) have been proved to take place in Mandarin Chinese (MC) disyllabic words: categorical NPA and gradient NPA. The former mainly happens in /n.C/ clusters and the latter /ŋ. C/ clusters. Previous studies show that in fast speech, the nasal coda may be completely lost, indicating a possible effect of speech rate on NPA patterns. Previous studies on NPA in other languages show that not only does the consonant have anticipatory effect on the preceding nasal, but the nasal also has carryover effect on the following consonant. However, no agreement has been reached concerning speech-rate effect on NPA patterns. This study aims to examine whether the nasal coda in MC N.C clusters has carryover effect on C and how speech rate affects N.C coarticulatory patterns. A production experiment was carried out and results show that (i) coda /n/ does not have carryover effect on Cs, but the fronted /a/ does; (ii) coda /ŋ/ does have carryover effect on the consonantal onset; (iii) speech rate does not have significant effect on the overall N.C coarticulatory patterns, but does for certain Cs. The results were explained in light of the degree of articulatory constraint (DAC) model.
Information innovations and effectiveness of the academic library: The example of Belgrade School of Engineering Management library

Data management innovations in business and educational systems are especially reflected in the work of academic libraries. This paper shows how the application of new technologies and use of scientific databases affect the library activities, in relation to teaching and scientific activities. Data analysis indicates the connection between the needs of library users in higher education and the possibilities of library services, and thus explores the use value of library information resources and effectiveness of library operations in higher education. Case study of the Belgrade School of Engineering Management Library shows the response of users through the process of introducing innovations. Data on the use of the fund, access to scientific databases, monitoring the researchers' bibliographies show that activities at the academic library change and develop according to current user needs. As library and information resources became more comprehensive, the basis for education and scientific research becomes stronger. Traditional and modern approaches are not mutually exclusive, but complement each other. Leadership support and communication with teachers play key roles in planning innovations and activities at the library. In order to access current and reliable data, the paper emphasizes cooperation with related institutions, development and connection of information systems.
Linguistically Informed Post-processing for ASR Error correction in Sanskrit

We propose an ASR system for Sanskrit, a low-resource language, that effectively combines subword to-kenisation strategies and search space enrichment with linguistic information. More specifically, to address the challenges due to the high degree of out-of-vocabulary entries present in the language, we first use a subword-based language model and acoustic model to generate a search space. The search space, so obtained, is converted into a word-based search space and is further enriched with morphological and lexical information based on a shallow parser. Finally, the transitions in the search space are rescored using a supervised morphological parser proposed for Sanskrit. Our proposed approach currently reports the state-of-the-art results in Sanskrit ASR, with a 7.18 absolute point reduction in WER than the previous state-of-the-art.
On the theory of the superfluid Fermi liquid

In this study we use the Fermi-liquid approach based on the idea of generalization of the self-consistent field method to superfluid systems. The Fermi-liquid theory of both the normal and the superfluid states is constructed using the general expression for the energy as a functional of the normal and abnormal fermion distribution functions. The symmetries of the energy functional essential in the description of a superfluid are studied. The principle of extremal entropy as a functional of the normal and abnormal distribution functions is used to obtain the self-consistent equation for the equilibrium distribution function in matrix form. In the approximation of the weak Fermi-liquid interaction, this equation leads to the results of the BCS--Bogolyubov theory. The ideal hydrodynamics of the superfluid Fermi liquid is constructed without assuming Galilean invariance of the system.