Reviewerly/exHarmony huggingface dataset:
34.2 A 28-nm Bulk-CMOS IC for Full Control of a Superconducting Quantum Processor Unit-Cell

While large-scale fault-tolerant quantum computers promise to enable the solution to certain classes of problems for which no other efficient approach is known, such a machine is believed to require over a million performant qubits. Scaling today's 0(100) qubit superconducting (SC) quantum computers to this extent while also improving performance carries many daunting challenges, including control of such a large quantum processor (QP). Integrating the control electronics at an intermediate temperature stage within the cryostat is an attractive option, e.g., due to the inherent thermal stability of the cryogenic environment and the feasibility of connecting to the QP via dense low-loss/high-thermal-isolation SC lines [1], [2]. Several cryo-CMOS quantum controllers have been reported, with examples used to control spin [3] and transmon [1], [2] qubits. To date, IC-based quantum control experiments have focused on resonant RF control, but baseband signals are often central to the execution of gates. Here, we report the design and system characterization of a cryo-CMOS IC for generating both the RF and baseband signals required for full control of a SC QP unit-cell, and show its ability to implement the components of a high-fidelity gate set that is universal for quantum computing.
Cooperative quantum information erasure

We demonstrate an information erasure protocol that resets <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> qubits at once. The method displays exceptional performances in terms of energy cost (it operates nearly at Landauer energy cost <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi><mml:mi>T</mml:mi><mml:mi>ln</mml:mi><mml:mo>&amp;#x2061;</mml:mo><mml:mn>2</mml:mn></mml:math>), time duration (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>&amp;#x223C;</mml:mo><mml:mi>&amp;#x03BC;</mml:mi><mml:mi>s</mml:mi></mml:math>) and erasure success rate (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>&amp;#x223C;</mml:mo><mml:mn>99</mml:mn><mml:mo>,</mml:mo><mml:mn>9</mml:mn><mml:mi mathvariant="normal">&amp;#x0025;</mml:mi></mml:math>). The method departs from the standard algorithmic cooling paradigm by exploiting cooperative effects associated to the mechanism of spontaneous symmetry breaking which are amplified by quantum tunnelling phenomena. Such cooperative quantum erasure protocol is experimentally demonstrated on a commercial quantum annealer and could be readily applied in next generation hybrid gate-based/quantum-annealing quantum computers, for fast, effective, and energy efficient initialisation of quantum processing units.
Supply chain logistics with quantum and classical annealing algorithms

Noisy intermediate-scale quantum (NISQ) hardware is almost universally incompatible with full-scale optimization problems of practical importance which can have many variables and unwieldy objective functions. As a consequence, there is a growing body of literature that tests quantum algorithms on miniaturized versions of problems that arise in an operations research setting. Rather than taking this approach, we investigate a problem of substantial commercial value, multi-truck vehicle routing for supply chain logistics, at the scale used by a corporation in their operations. Such a problem is too complex to be fully embedded on any near-term quantum hardware or simulator; we avoid confronting this challenge by taking a hybrid workflow approach: we iteratively assign routes for trucks by generating a new binary optimization problem instance one truck at a time. Each instance has [Formula: see text] quadratic binary variables, putting it in a range that is feasible for NISQ quantum computing, especially quantum annealing hardware. We test our methods using simulated annealing and the D-Wave Hybrid solver as a place-holder in wait of quantum hardware developments. After feeding the vehicle routes suggested by these runs into a highly realistic classical supply chain simulation, we find excellent performance for the full supply chain. Our work gives a set of techniques that can be adopted in contexts beyond vehicle routing to apply NISQ devices in a hybrid fashion to large-scale problems of commercial interest.
Sentiment and Emotion on Twitter: The Case of the Global Consumer Electronics Industry

Sentiment analysis is a new tool on new social media platforms, locations very attractive to the global consumer industry to investigate, due to their relevance and increased consumption in a pandemic. This study aims to determine the predominant sentiment and emotions on Twitter through a sentiment analysis in the consumer electronics industry, according to the top 30 companies of the Consumer Electronic Show 2020, by analyzing 96,000 tweets with a total of 273,221 words. The methodology used is quantitative, of a descriptive type, that integrates the study of emotions and sentiment through a statistical analysis of the tweets with R. The main results identify that the predominant sentiment is of positive assessment and the emotions of anticipation and confidence were the most representative. The contribution of this research is to provide empirical evidence of the global consumer electronics industry for correct decision-making through a data language analysis procedure on Twitter.
Predicting choice behaviour in economic games using gaze data encoded as scanpath images

Eye movement data has been extensively utilized by researchers interested in studying decision-making within the strategic setting of economic games. In this paper, we demonstrate that both deep learning and support vector machine classification methods are able to accurately identify participants' decision strategies before they commit to action while playing games. Our approach focuses on creating scanpath images that best capture the dynamics of a participant's gaze behaviour in a way that is meaningful for predictions to the machine learning models. Our results demonstrate a higher classification accuracy by 18% points compared to a baseline logistic regression model, which is traditionally used to analyse gaze data recorded during economic games. In a broader context, we aim to illustrate the potential for eye-tracking data to create information asymmetries in strategic environments in favour of those who collect and process the data. These information asymmetries could become especially relevant as eye-tracking is expected to become more widespread in user applications, with the seemingly imminent mass adoption of virtual reality systems and the development of devices with the ability to record eye movement outside of a laboratory setting.
A New Approach to Artificial Intelligent Based Three-Way Decision Making and Analyzing S-Box Image Encryption Using TOPSIS Method

In fuzzy artificial intelligent decision support systems, three-way intelligent-decision making (TWIDM) has played a very important role in ranking objects under the double hierarchy linguistic variable (DHLV). The 8 × 8 S-boxes are very important for image encryption in secure communication. Therefore, the aim of the present study is to develop a new approach to artificial intelligent three-way decision making via DHLV and apply it to S-box image encryption. Artificial intelligent based three-way decision-making problems with double hierarchy hesitant linguistic terms are developed. The first and second hierarchy hesitant linguistic term sets make up the double hierarchy hesitant linguistic term set, which allows for more flexible expressions of doubt and fuzziness. First, we define the Einstein operational laws, score function, and Einstein aggregation operators; i.e., double hierarchy hesitant linguistic Einstein weighted averaging and weighted geometric operators. First, the unknown weight vector for decision experts is determined by using aggregation operators and entropy measures for DHLV. Then, we find the weight vector for our criteria by using the distance measure. In TWIDM, conditional probability is determined by using the extended TOPSIS method for evaluating the S-boxes for image encryption. The expected losses are then computed by aggregating the loss functions with the help of Einstein-weighted averaging aggregation operators. Finally, we apply the minimum-loss decision rules for the selection of S-box to image encryption. The proposed decision technique has been compared with existing three-way decisions and the result of proposed three-way decision making for analyzing and ranking the S-box is very good and reliable for decision making.
A robust document image watermarking scheme using deep neural network

Watermarking is an important copyright protection technology which generally embeds the identity information into the carrier imperceptibly. Then the identity can be extracted to prove the copyright from the watermarked carrier even after suffering various attacks. Most of the existing watermarking technologies take the nature images as carriers. Different from the natural images, document images are not so rich in color and texture, and thus have less redundant information to carry watermarks. This paper proposes an end-to-end document image watermarking scheme using the deep neural network. Specifically, an encoder and a decoder are designed to embed and extract the watermark. A noise layer is added to simulate the various attacks that could be encountered in reality, such as the Cropout, Dropout, Gaussian blur, Gaussian noise, Resize, and JPEG Compression. A text-sensitive loss function is designed to limit the embedding modification on characters. An embedding strength adjustment strategy is proposed to improve the quality of watermarked image with little loss of extraction accuracy. Experimental results show that the proposed document image watermarking technology outperforms three state-of-the-art methods in terms of the robustness and image quality.
Knowledge graph assisted end-to-end medical dialog generation

Medical dialog systems have the potential to assist e-medicine in improving access to healthcare services, improving patient treatment quality, and lowering medical expenses. In this research, we describe a knowledge-grounded conversation generation model that demonstrates how large-scale medical information in the form of knowledge graphs can aid in language comprehension and generation in medical dialog systems. Generic responses are often produced by existing generative dialog systems, resulting in monotonous and uninteresting conversations. To solve this problem, we combine various pre-trained language models with a medical knowledge base (UMLS) to generate clinically correct and human-like medical conversations using the recently released MedDialog-EN dataset. The medical-specific knowledge graph contains broadly 3 types of medical-related information, including disease, symptom and laboratory test. We perform reasoning over the retrieved knowledge graph by reading the triples in each graph using MedFact attention, which allows us to use semantic information from the graphs for better response generation. In order to preserve medical information, we employ a policy network, which effectively injects relevant entities associated with each dialog into the response. We also study how transfer learning can significantly improve the performance by utilizing a relatively small corpus, created by extending the recently released CovidDialog dataset, containing the dialogs for diseases that are symptoms of Covid-19. Empirical results on the MedDialog corpus and the extended CovidDialog dataset demonstrate that our proposed model significantly outperforms the state-of-the-art methods in terms of both automatic evaluation and human judgment.
Arbitrary Spatial Trajectory Reconstruction Based on a Single Inertial Sensor

Compared with visible, infrared rays, and ultrasonic positioning technologies, the signal acquisition of portable inertial sensors is not affected by the external environment, such as light and occlusion. Therefore, motion tracking based on inertial signals is a promising complement. However, accurate trajectory reconstruction based on inertial sensors is a great challenge due to intrinsic and measurement errors, especially the drift error that exacerbates the accumulative error in trajectory calculation. To address this challenge, we propose a new trajectory reconstruction method, geometric dynamic segmental reconstruction (GDSR), where we treat the movement trajectory as a combination of basic trajectories. To this end, we design a temporal and spatial interaction segmentation approach to decompose the trajectory into basic segments by combining the dynamic feature of inertial measurement unit (IMU) signals with the spatial morphological feature of motion. Accordingly, we design a geometrical model library with undetermined parameters to match these segments. For precise parameter prediction, we propose an extra-supervised learning method that integrates different prediction tasks into one framework, which can not only expand training samples, but also enable different subtasks to compete with each other, thus improving the parameter prediction accuracy of each subtask, thereby accurately approximating the trajectory segments. To quantify the trajectory reconstruction accuracy, we propose the Fréchet spline sliding error (FSSE) and length error ratio (LER) to evaluate curve similarity. The range of FSSE is [0], [2], where 0 means that the two curves have the same shape. The range of LER is [1, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$+\infty $ </tex-math></inline-formula> ], where 1 means that the two curves have the same length. We test different IMUs in two experimental scenarios and one public available dataset. In all three tests, the FSSE of the GDSR method is less than 0.09, and the LER is less than 1.31, which is significantly better than all the comparison methods.
Advancements in Arabic Text-to-Speech Systems: A 22-Year Literature Review

Although there are several speech synthesis models available for different languages tailored to specific domain requirements and applications, there is currently no readily available information on the latest trends in Arabic language speech synthesis. This can make it challenging for beginners to research and develop text-to-speech (TTS) systems for Arabic languages. To address this issue, this article provides a comprehensive overview of several scholars’ contributions to the field of Arabic TTS, along with an examination of the unique features of the Arabic language and the corresponding challenges in creating TTS systems. Reporting only on papers discussing Arabic TTS, this systematic review evaluated the available literature published between 2000 and 2022. We conducted a systematic review in six databases using preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines to identify studies that addressed Arabic Text-to-Speech systems. Of a total of 3719 articles identified, only 36 (0.96%) articles met our search criteria. Bibliometric analyses of these studies were conducted and reported. The results highlight the main types of speech synthesis techniques used in TTS systems: concatenative, formant, deep neural network (DNN), hybrid models, and multiagent. The corpora used to develop these systems, as well as the diacritization techniques incorporated, evaluation techniques, and the results of the performance of the systems are reported. Subjective evaluation using the mean opinion score is most applied to measure the accuracy of systems. This study also identifies gaps in the literature and makes recommendations for future research directions.
Quantum reservoir computing in finite dimensions

Most existing results in the analysis of quantum reservoir computing (QRC) systems with classical inputs have been obtained using the density matrix formalism. This paper shows that alternative representations can provide better insight when dealing with design and assessment questions. More explicitly, system isomorphisms are established that unify the density matrix approach to QRC with the representation in the space of observables using Bloch vectors associated with Gell-Mann bases. It is shown that these vector representations yield state-affine systems previously introduced in the classical reservoir computing literature and for which numerous theoretical results have been established. This connection is used to show that various statements in relation to the fading memory property (FMP) and the echo state property (ESP) are independent of the representation and also to shed some light on fundamental questions in QRC theory in finite dimensions. In particular, a necessary and sufficient condition for the ESP and FMP to hold is formulated using standard hypotheses, and contractive quantum channels that have exclusively trivial semi-infinite solutions are characterized in terms of the existence of input-independent fixed points.
Mutual information harvested by uniformly accelerated particle detectors

We investigate the mutual information harvesting protocol for two uniformly accelerated particle detectors. We numerically show that, while a single detector responds as if it is immersed in a thermal bath, the quantum mutual information between two accelerating detectors behaves differently than that of two inertial detectors in a thermal bath. This is due to the fact that while the Wightman function along the trajectory of a single uniformly accelerating detector is the same as that of as a detector in a thermal bath, a pair of detectors in the same respective cases will have different Wightman functions.
Effectiveness of Virtual Reality on Chinese as a second language vocabulary learning: perceptions from international students

This study explores the effectiveness of Virtual Reality (VR) in Chinese as a second language (CSL) vocabulary learning from the international students’ perspective. A new theoretical framework of VR-based vocabulary learning was designed and verified to serve the investigation. A total of 30 international students learning CSL in a Chinese university participated in the study. Data were collected through vocabulary tests, a VR-based vocabulary learning questionnaire, and semi-structured interviews. Findings indicate that the VR approach can effectively facilitate students’ vocabulary learning and retention, as well as a positive learning experience. VR provides an authentic and repetition-enabled learning context. In an immersive environment, a higher rate of engagement in interactions and multimodal, immediate, and learner-friendly scaffolding benefit students’ VR-based vocabulary learning. Results also indicate that VR can motivate students to be actively involved in learning and build their language skills despite some challenges. Investigation of a wider range of learners with different backgrounds and language levels, and the differences between VR-based learning and traditional learning is recommended for future research. Additionally, CSL teachers’ perceptions are worthy of consideration.
Explainable GeoAI: can saliency maps help interpret artificial intelligence’s learning process? An empirical study on natural feature detection

AbstractImproving the interpretability of geospatial artificial intelligence (GeoAI) models has become critically important to open the ‘black box’ of complex AI models, such as deep learning. This paper compares popular saliency map generation techniques and their strengths and weaknesses in interpreting GeoAI and deep learning models’ reasoning behaviors, particularly when applied to geospatial analysis and image processing tasks. We surveyed two broad classes of model explanation methods: perturbation-based and gradient-based methods. The former identifies important image areas, which help machines make predictions by modifying a localized area of the input image. The latter evaluates the contribution of every single pixel of the input image to the model’s prediction results through gradient backpropagation. In this study, three algorithms—the occlusion method, the integrated gradients method, and the class activation map method—are examined for a natural feature detection task using deep learning. The algorithms’ strengths and weaknesses are discussed, and the consistency between model-learned and human-understandable concepts for object recognition is also compared. The experiments used two GeoAI-ready datasets to demonstrate the generalizability of the research findings.Keywords: XAIartificial intelligencedeep learningvisualizationGeoAI Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe data and codes that support the findings of this study are available at https://github.com/ASUcicilab/explainable-geoai. Instructions on how to use the data and codes are provided in the README file.Additional informationFundingThis work is supported in part by the National Science Foundation under [awards 2120943, 2230034, 1853864].Notes on contributorsChia-Yu HsuChia-Yu Hsu is a research professional at Arizona State University. His research interests include artificial intelligence, computer vision, spatiotemporal data analysis, and their applications in climate change and terrain research.Wenwen LiWenwen Li is a professor in geographic information science at Arizona State University (ASU). Her research interests are cyberinfrastructure, big data, GeoAI and their applications in data- and computation-intensive environmental and social sciences. At ASU, she directs the Cyberinfrastructure and Computational Intelligence Lab (http://cici.lab.asu.edu/) and serves as the Research Director for the Spatial Analysis Research Center.
Emergence of robotic technologies: implications for Nigerian academic libraries

Purpose Libraries may become obsolete in the 21st century unless they begin to harness new technology and improve information and service delivery. This paper aims to examine the emergence of robotic technologies with its implications to academic libraries in Nigeria. Design/methodology/approach The research, which was conducted on a desk, examined how the adoption of robotic technologies may affect library services and operations. Findings It is believed that robot adoption in libraries can enhance library services and give users with reliable information that can foster growth and development in the information age. Originality/value This paper is the original idea from the authors and does not reflect on any copyrighted materials. The paper recommended that academic libraries should collaborate with their parent institutions or organisations to create a strategy plan for cutting-edge technology, such as robots; federal and state governments should strengthen their alliances with robot vendors to take advantage of direct support; and open communication between library management boards and librarians should be encouraged, particularly when it comes to the acceptance and use of robots in libraries.