Reviewerly/exHarmony huggingface dataset:
A Laser Projection System for Robot Intention Communication and Human Robot Interaction

In order to deploy service robots in environments where they encounter and/or cooperate with persons, one important key factor is human acceptance. Hence, information on which upcoming actions of the robot are based has to be made transparent and understandable to the human. However, considering the restricted power resources of mobile robot platforms, systems for visualization not only have to be expressive but also energy efficient. In this paper, we applied the well-known technique of laser scanning on a mobile robot to create a novel system for intention visualization and human-robot-interaction. We conducted user tests to compare our system to a low-power consuming LED video projector solution in order to evaluate the suitability for mobile platforms and to get human impressions of both systems. We can show that the presented system is preferred by most users in a dynamic test setup on a mobile platform.
The impact of Pleasure Reading on Academic Success

Discovering ways to improve student academic performance is a common challenge in the modern classroom. This research study examined the reading habits of sixty-five high school juniors, aged fifteen to seventeen years, at a rural Southeast Texas high school. It was theorized that students who engaged in reading self-selected literature for pleasure would average higher grades in English, mathematics, science, and history than their non-reading peers.
Wake Word Detection with Alignment-Free Lattice-Free MMI

Always-on spoken language interfaces, e.g. personal digital assistants, rely on a wake word to start processing spoken input. We present novel methods to train a hybrid DNN/HMM wake word detection system from partially labeled training data, and to use it in on-line applications: (i) we remove the prerequisite of frame-level alignments in the LF-MMI training algorithm, permitting the use of un-transcribed training examples that are annotated only for the presence/absence of the wake word; (ii) we show that the classical keyword/filler model must be supplemented with an explicit non-speech (silence) model for good performance; (iii) we present an FST-based decoder to perform online detection. We evaluate our methods on two real data sets, showing 50%--90% reduction in false rejection rates at pre-specified false alarm rates over the best previously published figures, and re-validate them on a third (large) data set.
Resource reduction for simultaneous generation of two types of continuous variable nonclassical states

We demonstrate experimentally the simultaneous generation and detection of two types of continuous variable nonclassical states from one type-0 phase-matching optical parametric amplifcation (OPA) and subsequent two ring flter cavities (RFCs). The output feld of the OPA includes the baseband ω0 and sideband modes ω0± nωf subjects to the cavity resonance condition, which are separated by two cascaded RFCs. The first RFC resonates with half the pump wavelength ω0 and the transmitted baseband component is a squeezed state. The refected felds of the frst RFC, including the sideband modes ω0± ωf, are separated by the second RFC, construct Einstein–Podolsky–Rosen entangled state. All freedoms, including the filter cavities for sideband separation and relative phases for the measurements of these sidebands, are actively stabilized. The noise variance of squeezed states is 10.2 dB below the shot noise limit (SNL), the correlation variances of both quadrature amplitude-sum and quadrature phase-diference for the entanglement state are 10.0 dB below the corresponding SNL.
Learnability Factors for Investigating the Effectiveness of Augmented Reality Smart Glasses in Smart Campus

Abstract Augmented Reality (AR) has become an emerging platform in the field of learning either in education or industry. Wearable AR smart glasses are the example of new wearable technology devices in AR. AR smart glasses has been introduced as another alternative to enhance the user’s experience in the real world rather than replacing the principle of learning. In education, AR smart glasses is seen as a possible technology to be embedded in the teaching and learning among lecturers and students for achieving smart campus environment. The effectiveness of AR smart glasses in education is a prospect for further study. Thus, this paper aims to identify the factors for investigating the learnability of students while learning using AR smart glasses. Various existing learning factors were reviewed through literature exploration and potential AR smart glasses learning factors are suggested. The results are significance for the development of the AR smart glasses learnability model that will further investigate in the future work.
FEARLESS STEPS Challenge (FS-2): Supervised Learning with Massive Naturalistic Apollo Data

The Fearless Steps Initiative by UTDallas-CRSS led to the digitization, recovery, and diarization of 19,000 hours of original analog audio data, as well as the development of algorithms to extract meaningful information from this multi-channel naturalistic data resource. The 2020 FEARLESS STEPS (FS-2) Challenge is the second annual challenge held for the Speech and Language Technology community to motivate supervised learning algorithm development for multi-party and multi-stream naturalistic audio. In this paper, we present an overview of the challenge sub-tasks, data, performance metrics, and lessons learned from Phase-2 of the Fearless Steps Challenge (FS-2). We present advancements made in FS-2 through extensive community outreach and feedback. We describe innovations in the challenge corpus development, and present revised baseline results. We finally discuss the challenge outcome and general trends in system development across both phases (Phase FS-1 Unsupervised, and Phase FS-2 Supervised) of the challenge, and its continuation into multi-channel challenge tasks for the upcoming Fearless Steps Challenge Phase-3.
LDA filter: A Latent Dirichlet Allocation preprocess method for Weka

This work presents an alternative method to represent documents based on LDA (Latent Dirichlet Allocation) and how it affects to classification algorithms, in comparison to common text representation. LDA assumes that each document deals with a set of predefined topics, which are distributions over an entire vocabulary. Our main objective is to use the probability of a document belonging to each topic to implement a new text representation model. This proposed technique is deployed as an extension of the Weka software as a new filter. To demonstrate its performance, the created filter is tested with different classifiers such as a Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Naive Bayes in different documental corpora (OHSUMED, Reuters-21578, 20Newsgroup, Yahoo! Answers, YELP Polarity, and TREC Genomics 2015). Then, it is compared with the Bag of Words (BoW) representation technique. Results suggest that the application of our proposed filter achieves similar accuracy as BoW but greatly improves classification processing times.
Direct Segmentation Models for Streaming Speech Translation

The cascade approach to Speech Translation (ST) is based on a pipeline that concatenates an Automatic Speech Recognition (ASR) system followed by a Machine Translation (MT) system.These systems are usually connected by a segmenter that splits the ASR output into, hopefully, semantically self-contained chunks to be fed into the MT system.This is specially challenging in the case of streaming ST, where latency requirements must also be taken into account.This work proposes novel segmentation models for streaming ST that incorporate not only textual, but also acoustic information to decide when the ASR output is split into a chunk.An extensive and thorough experimental setup is carried out on the Europarl-ST dataset to prove the contribution of acoustic information to the performance of the segmentation model in terms of BLEU score in a streaming ST scenario.Finally, comparative results with previous work also show the superiority of the segmentation models proposed in this work.
Improving Text Understanding via Deep Syntax-Semantics Communication

Recent studies show that integrating syntactic tree models with sequential semantic models can bring improved task performance, while these methods mostly employ shallow integration of syntax and semantics.In this paper, we propose a deep neural communication model between syntax and semantics to improve the performance of text understanding.Local communication is performed between syntactic tree encoder and sequential semantic encoder for mutual learning of information exchange.Global communication can further ensure comprehensive information propagation.Results on multiple syntax-dependent tasks show that our model outperforms strong baselines by a large margin.In-depth analysis indicates that our method is highly effective in composing sentence semantics.
Diversity can be Transferred: Output Diversification for White- and Black-box Attacks.

Adversarial attacks often involve random perturbations of the inputs drawn from uniform or Gaussian distributions, e.g., to initialize optimization-based white-box attacks or generate update directions in black-box attacks. These simple perturbations, however, could be sub-optimal as they are agnostic to the model being attacked. To improve the efficiency of these attacks, we propose Output Diversified Sampling (ODS), a novel sampling strategy that attempts to maximize diversity in the target model's outputs among the generated samples. While ODS is a gradient-based strategy, the diversity offered by ODS is transferable and can be helpful for both white-box and black-box attacks via surrogate models. Empirically, we demonstrate that ODS significantly improves the performance of existing white-box and black-box attacks. In particular, ODS reduces the number of queries needed for state-of-the-art black-box attacks on ImageNet by a factor of two.
Improvement of a (t,n) Threshold d−Level Quantum Secret Sharing Scheme

Recently, a threshold d−level quantum secret sharing scheme was presented by Song et al. However, Kao et al. showed that in contrary to Song claim, the reconstructor cannot recover the secret without receiving any information from the other participants. To remedy this weakness, we will propose a new version of Song scheme.
A Modular Region and Text Line Layout Analysis System

High quality document layout analysis is fundamental to the accurate processing of handwritten textual material, on both the level of individual lines and higher order zones demarking textual and non-textual content. We present an artificial neural network based approach to prediction of either that is implemented as part of a libre optical character recognition package and highly reconfigurable for a variety of tasks. Experiments on different openly available datasets show competitive results to state-of-the-art methods.
A Performance Analysis of Native JSON Parsers in Java, Python, MS.NET Core, JavaScript, and PHP

JavaScript Object Notation (JSON) has gained prominence over Extensible Markup Language (XML) due to its lightweight and flexible nature. This has also made it a first-class citizen when it comes to choose a data interchange format. All major programming platforms provide native libraries to parse JSON formatted data, but the performance of JSON parsers varies with their implementation. For further processing and analysis of semi-structured JSON data, it is first parsed which is expensive. In this research paper, performance analysis of JSON parsers in the native environment of 5 different programming languages has been done in terms of parsing speed and resource consumption. The experiment data is created using an algorithm written in Java that generates 10 different JSON files with an increasing number of key-value pairs at each level of JSON nesting. The native performance monitor on Windows 10 operating system is used to monitor and measure memory and CPU usage of the running processes. The output of data collector set is saved in the comma separated format for further analysis. The experimental results are discussed and shared at the end.
Coherent Generation of the Complete High-Dimensional Bell Basis by Adaptive Pump Modulation

The Bell basis, a set of maximally entangled biphoton states, is a critical prerequisite towards quantum information processing, and many quantum applications have highlighted the requirement for manipulation of the high-dimensional Bell basis. While Bell states can be created by using ingenious single-photon quantum gates, their implementation complexity in higher dimensions is significantly increased. Here we present an elaborate approach to show that the adaptive pump modulation enables the efficient preparation of the Bell basis in arbitrary-dimensional Hilbert space. A complete set of four-dimensional orbital angular momentum Bell states is experimentally created, yielding high fidelities for certifying the entanglement dimensionality. Our strategy can be simply generalized to prepare more complex forms of quantum states, even exploiting other physical degrees of freedom. Also, it can facilitate the use of high-dimensional entanglement in a variety of quantum protocols, in particular those requiring quantum dense coding.
Improving the accuracy of text classification using stemming method, a case of non-formal Indonesian conversation

Abstract Background Stemming has long been used in data pre-processing to retrieve information by tracking affixed words back into their root. In an Indonesian setting, existing stemming methods have been observed, and the existing stemming methods are proven to result in high accuracy level. However, there are not many stemming methods for non-formal Indonesian text processing. This study introduces a new stemming method to solve problems in the non-formal Indonesian text data pre-processing. Furthermore, this study aims to improve the accuracy of text classifier models by strengthening stemming method. Using the Support Vector Machine algorithm, a text classifier model is developed, and its accuracy is checked. The experimental evaluation was done by testing 550 datasets in Indonesian using two different stemming methods. Findings The results show that using the proposed stemming method, the text classifier model has higher accuracy than the existing methods with a score of 0.85 and 0.73, respectively. These results indicate that the proposed stemming methods produces a classifier model with a small error rate, so it will be more accurate to predict a class of objects. Conclusion The existing Indonesian stemming methods are still oriented towards Indonesian formal sentences, therefore the method has limitations to be used in Indonesian non-formal sentences. This phenomenon underlies the suggestion of developing a corpus by normalizing Indonesian non-formal into formal to be used as a better stemming method. The impact of using the corpus as a stemming method is that it can improve the accuracy of the classifier model. In the future, the proposed corpus and stemming methods can be used for various purposes including text clustering, summarizing, detecting hate speech, and other text processing applications in Indonesian.
Guiding dynamic programing via structural probability for accelerating programming by example

Programming by example (PBE) is an important subproblem of program synthesis, and PBE techniques have been applied to many domains. Though many techniques for accelerating PBE systems have been explored, the scalability remains one of the main challenges: There is still a gap between the performances of state-of-the-art synthesizers and the industrial requirement. To further speed up solving PBE tasks, in this paper, we propose a novel PBE framework MaxFlash. MaxFlash uses a model based on structural probability, named topdown prediction models, to guide a search based on dynamic programming, such that the search will focus on subproblems that form probable programs, and avoid improbable programs. Our evaluation shows that MaxFlash achieves × 4.107− × 2080 speed-ups against state-of-the-art solvers on 244 real-world tasks.
Informative Sample Mining Network for Multi-domain Image-to-Image Translation

The performance of multi-domain image-to-image translation has been significantly improved by recent progress in deep generative models. Existing approaches can use a unified model to achieve translations between all the visual domains. However, their outcomes are far from satisfying when there are large domain variations. In this paper, we reveal that improving the sample selection strategy is an effective solution. To select informative samples, we dynamically estimate sample importance during the training of Generative Adversarial Networks, presenting Informative Sample Mining Network. We theoretically analyze the relationship between the sample importance and the prediction of the global optimal discriminator. Then a practical importance estimation function for general conditions is derived. Furthermore, we propose a novel multi-stage sample training scheme to reduce sample hardness while preserving sample informativeness. Extensive experiments on a wide range of specific image-to-image translation tasks are conducted, and the results demonstrate our superiority over current state-of-the-art methods.
Introduction to NVIDIA Jetson Nano

NVIDIA Jetson Nano is an NVIDIA product that can implement IoT solutions with the power of GPU computation. This board has GPIO pins and a GPU core to help developers, makers, and IT users build programs easily. In this chapter, we will get a brief introduction to NVIDIA Jetson Nano.
Efficient Hardware Implementation of Pseudo-Random Bit Generator Using Dual-CLCG Method

In this work, the architecture of a dual-coupled linear congruential generator (dual-CLCG) for pseudo-random bit generation is proposed to improve the speed of the generator and minimize power dissipation with the optimum chip area. To improve its performance, a new pseudo-random bit generator (PRBG) employing two-operand modulo adder and without shifting operation-based dual-CLCG architecture is proposed. The novelty of the proposed dual-CLCG architecture is the designing of LCG based on two-operand modulo adder rather than a three-operand one and without using shifting operation as compared to the existing LCG architecture. The aim of the work is to generate pseudo-random bits at a uniform clock rate at the maximum clock frequency and achieve maximum length of the random bit sequence. The power dissipation with the optimum chip area of PRBG is also observed for the proposed architecture. The generated sequence passes all the 15 tests of the National Institute of Standards and Technology (NIST) standard. Verilog HDL code is used for the design of the proposed architecture. Its simulation is done on commercially available Spartan-3E FPGA (ISE Design Suite by Xilinx) as well as on 90-nm CMOS technology (Cadence tool).
Dynamic Knowledge Inference Based on Bayesian Network Learning

On the basis of studying datasets of students' course scores, we constructed a Bayesian network and undertook probabilistic inference analysis. We selected six requisite courses in computer science as Bayesian network nodes. We determined the order of the nodes based on expert knowledge. Using 356 datasets, the K2 algorithm learned the Bayesian network structure. Then, we used maximum a posteriori probability estimation to learn the parameters. After constructing the Bayesian network, we used the message-passing algorithm to predict and infer the results. Finally, the results of dynamic knowledge inference were presented through a detailed inference process. In the absence of any evidence node information, the probability of passing other courses was calculated. A mathematics course (a basic professional course) was chosen as the evidence node to dynamically infer the probability of passing other courses. Over time, the probability of passing other courses greatly improved, and the inference results were consistent with the actual values and can thus be visualized and applied to an actual school management system.
A Gold Standard Methodology for Evaluating Accuracy in Data-To-Text Systems

Most Natural Language Generation systems need to produce accurate texts. We propose a methodology for high-quality human evaluation of the accuracy of generated texts, which is intended to serve as a gold-standard for accuracy evaluations of data-to-text systems. We use our methodology to evaluate the accuracy of computer generated basketball summaries. We then show how our gold standard evaluation can be used to validate automated metrics.
Lower Bounds on the Time/Memory Tradeoff of Function Inversion

We study time/memory tradeoffs of function inversion: an algorithm, i.e., an inverter, equipped with an s-bit advice on a randomly chosen function $$f:[n] \mapsto [n]$$ and using q oracle queries to f, tries to invert a randomly chosen output y of f, i.e., to find $$x\in f^{-1}(y)$$ . Much progress was done regarding adaptive function inversion—the inverter is allowed to make adaptive oracle queries. Hellman [IEEE transactions on Information Theory ’80] presented an adaptive inverter that inverts with high probability a random f. Fiat and Naor [SICOMP ’00] proved that for any s, q with $$s^3 q = n^3$$ (ignoring low-order terms), an s-advice, q-query variant of Hellman’s algorithm inverts a constant fraction of the image points of any function. Yao [STOC ’90] proved a lower bound of $$sq\ge n$$ for this problem. Closing the gap between the above lower and upper bounds is a long-standing open question. Very little is known of the non-adaptive variant of the question—the inverter chooses its queries in advance. The only known upper bounds, i.e., inverters, are the trivial ones (with $$s+q= n$$ ), and the only lower bound is the above bound of Yao. In a recent work, Corrigan-Gibbs and Kogan [TCC ’19] partially justified the difficulty of finding lower bounds on non-adaptive inverters, showing that a lower bound on the time/memory tradeoff of non-adaptive inverters implies a lower bound on low-depth Boolean circuits. Bounds that, for a strong enough choice of parameters, are notoriously hard to prove. We make progress on the above intriguing question, both for the adaptive and the non-adaptive case, proving the following lower bounds on restricted families of inverters:
Natural Language Processing-Based Quantication of the Mental State of Psychiatric Patients

Psychiatric practice routinely uses semistructured and/or unstructured free text to record the behavior and mental state of patients. Many of these data are unstructured, lack standardization, and are difficult to use for analysis. Thus, it is difficult to quantitatively analyze a patient's illness trajectory over time and his or her responsiveness to treatment, and it is also difficult to compare different patients quantitatively. In this article, experts in the field of psychiatry, along with machine learning models, have collaboratively transformed patient data available in status assessments generated by physicians into binary vector representations. Data from patients with mental health disorders collected within a real-world clinical setting from one of the largest behavioral electronic health record (EHR) systems in the United States have been used for generating these representations. The binary vector representation of these health records is shown to be useful in various clinical tasks, such as disease phenotyping, characterizing the suicidality of patients, and inferring diagnoses. To summarize, this approach can transform semistructured free-text summaries of patients' status assessments into a structured, quantifiable format, which enriches the data that reside within EHR systems. This allows for effective intra- and interpatient quantifications and comparisons, which are much needed in the field of mental health. With the aid of these binary representations, patients' mental states can be systematically tracked over time, as can their responses to medications at the individual and population levels.