Reviewerly/exHarmony huggingface dataset:
Quantum-Inspired Digital Annealing for Join Ordering

Finding the optimal join order (JO) is one of the most important problems in query optimisation, and has been extensively considered in research and practise. As it involves huge search spaces, approximation approaches and heuristics are commonly used, which explore a reduced solution space at the cost of solution quality. To explore even large JO search spaces, we may consider special-purpose software, such as mixed-integer linear programming (MILP) solvers, which have successfully solved JO problems. However, even mature solvers cannot overcome the limitations of conventional hardware prompted by the end of Moore's law. We consider quantum-inspired digital annealing hardware, which takes inspiration from quantum processing units (QPUs). Unlike QPUs, which likely remain limited in size and reliability in the near and mid-term future, the digital annealer (DA) can solve large instances of mathematically encoded optimisation problems today. We derive a novel, native encoding for the JO problem tailored to this class of machines that substantially improves over known MILP and quantum-based encodings, and reduces encoding size over the state-of-the-art. By augmenting the computation with a novel readout method, we derive valid join orders for each solution obtained by the (probabilistically operating) DA. Most importantly and despite an extremely large solution space, our approach scales to practically relevant dimensions of around 50 relations and improves result quality over conventionally employed approaches, adding a novel alternative to solving the long-standing JO problem.
An Efficient Transfer Learning Based Configuration Adviser for Database Tuning

In recent years, a wide spectrum of database tuning systems have emerged to automatically optimize database performance. However, these systems require a significant number of workload runs to deliver a satisfactory level of database performance, which is time-consuming and resource-intensive. While many attempts have been made to address this issue by using advanced search optimizers, empirical studies have shown that no single optimizer can dominate the rest across tuning tasks with different characteristics. Choosing an inferior optimizer may significantly increase the tuning cost. Unfortunately, current practices typically adopt a single optimizer or follow simple heuristics without considering the task characteristics. Consequently, they fail to choose the most suitable optimizer for a specific task. Furthermore, constructing a compact search space can significantly improve the tuning efficiency. However, current practices neglect the setting of the value range for each knob and rely on a large number of workload runs to select important knobs, resulting in a considerable amount of unnecessary exploration in ineffective regions. To pursue efficient database tuning, in this paper, we argue that it is imperative to have an approach that can judiciously determine a precise space and search optimizer for an arbitrary tuning task. To this end, we propose OpAdviser, which exploits the information learned from historical tuning tasks to guide the search space construction and search optimizer selection. Our design can greatly accelerate the tuning process and further reduce the required workload runs. Given a tuning task, OpAdviser learns the geometries of search space, including important knobs and their effective regions, from relevant previous tasks. It then constructs the target search space from the geometries according to the on-the-fly task similarity, which allows for adaptive adjustment of the target space. OpAdviser also employs a pairwise ranking model to capture the relationship from task characteristics to optimizer rankings. This ranking model is invoked during tuning and predicts the best optimizer to be used for the current iteration. We conduct extensive evaluations across a diverse set of workloads, where OpAdviser achieves 9.2% higher throughput and significantly reduces the number of workload runs with an average speedup of ~3.4x compared to state-of-the-art tuning systems.
Fitting Algorithms for Conjunctive Queries

A fitting algorithm for conjunctive queries (CQs) is an algorithm that takes as input a collection of data examples and outputs a CQ that fits the examples. In this column, we propose a set of desirable properties of such algorithms and use this as a guide for surveying results from the authors' recent papers published in PODS 2023, IJCAI 2023, and Inf. Proc. Letters 2024. In particular, we explain and compare several concrete fitting algorithms, and we discuss complexity and size bounds for constructing fitting CQs with desirable properties.
STUDY OF THE IMPACT OF A TEACHER’S PSYCHOLOGICAL HEALTH ON PSYCHOLOGICAL READINESS FOR PROFESSIONAL ACTIVITY

The article is devoted to establishing the regularities of the impact of a teacher's psychological health on his/her readiness for creative professional activity. The author identifies the structure of psychological health and the psychological readiness of teachers for professional activity. Psychological health is defined as a multi-level formation that represents a harmonious state of the individual, based on the balance between the spiritual, social, intellectual, emotional, volitional and bodily substructures of the personality, as well as harmony between the individual and the world. The structure of psychological health is represented by a hierarchy of spiritual-semantic, social, mental and psychosomatic components. The psychological readiness of a teacher is a psychological quality based on the unity of pedagogical motivation and pedagogical abilities. The components of psychological readiness are: value-motivational, moral-communicative, creative-cognitive and emotional-volitional. The article describes the features of the state of psychological health and psychological readiness of modern teachers of the New Ukrainian School (NUS). The study revealed that 70% of the NUS teachers have a high level of psychological readiness for creative pedagogical activity. However, 17% of them have a low level of psychological health. Using the methods of mathematical statistics, the author established the patterns of the impact of the state of psychological health of teachers on their readiness for professional creativity. The results of this survey pointed to the following: the higher the level of psychological health of a person, the more pronounced his/her psychological readiness for pedagogical activity; the lower the level of psychological health, the less capable the teacher is of professional and pedagogical creativity. An important factor of development in a teacher’s psychological readiness is the flexibility of thinking and a positive emotional state. The researcher has experimentally proved that the psychological health of a teacher is the basis for the effectiveness of his/her professional activity.
Ways to implement higher education strategies as a factor in the formation of student’s professional orientations

In the realities of modern Russian society, the question arises of the relevance of a particular profession, a set of knowledge, skills, which is given in educational institutions of higher education, or as it is commonly called now, a set of competencies or competency maps of a future employee. Therefore, the agenda raises the question of how the current education system is currently ready to implement this practice. Or the level of professional development and competence is now more dependent on the personal perceived quality of life of the individual, his cultural capital, and all the symbolic accumulated capital is secondary. Here the question arises of the public recognition of this or that profession, this or that institution of education. The article discusses approaches to understanding the profession and professional choice in domestic and foreign scientific thought. The key issues of the study of professional strategies and professional choice are presented in a historical retrospective. As an empirical basis for the study of this article, students of leading educational institutions of higher education, who have been assigned a special status through the establishment of a specific legal status and educational activity, will be considered — the Federal state budgetary educational institution of higher education “Moscow State University named after M.V. Lomonosov” and the Federal state budgetary educational institution of higher education “St. Petersburg State University”. Based on the empirical data of a sociological study conducted in February 2023, the article analyzes the strategies of universities in terms of the production of knowledge (through the exchange of scientific and teaching staff, scientific events, additional training programs) for the professional self-determination of a student (through his desire to work in this profession and desire to continue their education in this profession).
Tertiary Education Trust Fund (TETFund) Contribution to the Staff Improvement and Research and Publication of Higher Institutions in Kebbi State, Nigeria, from 2015 to 2020

This study is on the assessment of the Tertiary Education Trust Fund (TETFund) contribution to the staff improvement and research and publication of higher institutions in Kebbi State, Nigeria from 2015-2020. The study adopted a survey research design to enable the researcher to assess the TETFund contribution for the improvement of research and publication of higher institutions in Kebbi State. The study selected 161 out of 1900 academic staff total of the population and 196 out of the 2323 on non-academic staff of the entire population of the study using the Research Advisors Table (2006) for determining the sample size. The study used a questionnaire as an instrument for collecting data. The method of data analysis was frequency count and percentage. The study found that TETFund contributed to staff improvement through TETFund normal intervention by sponsoring of local and international training to academic staff and sponsoring local seminars and conferences or workshops to academic and non-academic staff of Kebbi State higher institutions and TETFund contributed to the improvement of research and publication through normal intervention by sponsoring of journal publications and researches and book publications of higher institutions in Kebbi State. It is also recommended that TETFund should live up to its expectations in terms of normal intervention on staff improvement that would aid the effective improvement of higher institutions in Kebbi State and that publishers should complement the effort of TETFund in assisting the Kebbi State higher institutions staff in the publication of their books and journals without payment. Such publishers should be encouraged by the State Government by granting them tax relief and other incentives for smooth operations.
Face Recognition Based Attendance System Using Histogram of Oriented Gradients and Linear Support Vector Machine

In the 21st century, modern technology is playing an important role in providing innovative on traditional challenges across various domains or sectors. One such challenging task is of daily attendance marking and tracking. Manual attendance requires efforts and it is time-consuming. Sometimes attendance cannot be mark due to human errors. Relying on voice, iris, or fingerprint recognition, increases the complexity and the hardware infrastructure of the system and also increases the cost. To effectively address such issues, we have developed a “Camera based Attendance System”. This system encompasses several crucial stages, including data entry, dataset of multiple people. It is an image-based face recognition system for marking attendance on the SQL database. It excels in detecting and recognizing multiple individuals faces from image and comparing it with the dataset for accurately marking the attendance. This makes the attendance marking process fully automatic. Remarkably, our proposed system attains an impressive recognition and provides the accuracy of approximately 95%. With this solution, daily attendance marking and recording becomes effortless and the stored attendance record can be also used in future if require, eliminating the risk of attendance not getting marked due to human error.
Bayesian inference of local government audit outcomes

This chapter presents the first-in-literature application of Bayesian inference to predict financial statement audit outcomes of South African local government entities. The audit outcomes are modelled using BLR with financial ratios as input features. The inference is performed using the MHMC, S2HMC, random walk MH, MALA and HMC methods. We employ BLR with ARD to identify the features that are most important when modelling audit outcomes. Most of the considered algorithms agree on which financial ratios are the most relevant for modelling audit opinions. Our analysis shows that the repairs and maintenance as a percentage of total assets ratio, current ratio, debt to total operating revenue, net operating surplus margin and capital cost to total operating expenditure ratio are the five most important features when predicting local government audit outcomes. These results could be of use for various stakeholders, including the AG-SA, as focusing on these ratios can speed up the detection of fraudulent behaviour in municipal entities and improve the speed and overall quality of the audit.
GRAPHIC AND ORTHOGRAPHIC FEATURES OF ENGLISH AND GEORGIAN-LANGUAGE INTERNET DISCOURSE (BASED ON YOUTUBE VIDEO COMMENTS)

The present paper is devoted to the investigation of the linguistic (mainly graphic and orthographic) features of Internet Discourse, as a new type of language. Graphic and orthographic features of Internet Discourse are investigated on the basis of English and Georgian YouTube video comments. The analysis showed that Internet Discourse is characterized by specific graphic and orthographic features: the usage of emoticons, unusual usage of punctuation marks, multiple punctuation, unconventional spelling, capitalization, etc.
Reasons for the Fuzziness of Legislative Language

As we all know, there is nothing that does not contain contradiction, and without contradiction, there is no world. Chinese ancients said as early as in Zhouyi·Xici that “One Yin and one Yang is Taoism”. Linguistics is full of contradictions. Accuracy and fuzziness are a pair of contradictions. We have already been familiar with the importance of accuracy, but we should not ignore the other side of the contradiction due to the importance of accuracy. While meeting the requirement of accuracy, fuzzy rhetoric often has rich connotation and implicit speech style.
Design and development of accessible video chat application for people with disabilities

Communication has been a struggle for everyone since the covid outbreak and in the aftermath, people have had to get accustomed to video conferencing applications. However people with physical or mental limitations are still unable to use video conferencing apps and their interfaces. This necessitates the development of web-based video chat applications. These applications can aid those who are unable to communicate verbally and/or operate using standard mouse and keyboard inputs, but yet need to feel close to others when they are apart. The proposed application incorporates various accessibility features such as speech-to-text and text-to-speech, gaze tracking and pictorial speech interfaces. It enables individuals with disabilities to participate in virtual meetings on an equal footing with their peers. The goal is to remove barriers and promote inclusiveness in remote work and collaboration for all users, regardless of their abilities using this application.
SpeechDocs - A voice activated document editing software

SpeechDocs is a web application that aims to solve the difficulties faced by users with unsupportable conditions such as Arthritis in the hand, Parkinson's, Carpal Tunnel Syndrome, or Essential tremors, who have trouble using traditional keyboard and mouse-based software. With its fully-featured voice-controlled document editing system, SpeechDocs enables users to perform any operation such as creating, editing, opening, writing, or replacing words in a document, simply by using their voice. It uses speech-to-text converter and natural language processing technology to allow users to manage documents efficiently. The system also includes features such as facial recognition authentication, document history, and a favorites tab for easy access to frequently used documents. Although some challenges exist, such as accurately capturing speech in noisy environments, SpeechDocs has the potential to greatly improve the lives of users who have disabilities or other conditions that limit their ability to use traditional word processing software.
Digital Filter Design For Experimental Continuous-Variable Quantum Key Distribution

We explore the effect of digital filter design on enhancing the performance of an experimental CV-QKD system. The filtering procedure is anticipated to enable greater than 20Mbaud/s at 20km fiber length.
Optical Network Diagnostics Using Graph Neural Networks and Natural Language Processing

We propose an AI-powered network diagnostic strategy including alarm clustering and fault localization with >98% accuracy for up-to 16-degree ROADMs and demonstrate the advantages of using NLP in encoding.
A Parallel ANS Coder with Reduced Decoding Error Diffusion

The Asymmetrical Numeral Systems (ANS) coding has received significant attention in well-integrated compression systems. One particularly insightful technique that has emerged is the interleaved ANS coder (IAC), which substantially enhances the throughput by using multiple encoders and decoders. However, IAC has a limitation that even a single bit error in the compressed bitstream may diffuse across multiple decoders, resulting in many symbols to be incorrectly decoded. In this paper, we propose a parallel method to mitigate the diffusion for a class of error patterns, by limiting the errors to affect only a single decoder. The simulation results show that compared with IAC, our proposal can reduce the average number of incorrectly decoded symbols by about 50% while maintaining comparable encoding and decoding throughput.
Optical Switching will Innovate Intra Data Center Networks

This tutorial will discuss how optical switching technologies can innovate future data center networks. The two core technologies, large-port-count optical switches that have pay-as-you-grow scalability, and simple and fast optical switch network control are reviewed.
Secure Unrepeated Fiber Transmission with Quantum Deliberate Signal Randomization on Y-00 Protocol

We demonstrate security-enhanced 10-Gbit/s PSK Y-00 cipher transmission with deliberate signal randomization driven by a quantum random number generator in a 362 km ultralow-loss fiber link without optical amplifiers. High security is achieved at high optical powers.
On-Chip Universal Linear Optics using a 4x4 Silicon Photonic Coherent Crossbar

We demonstrate the first on-chip and fidelity-restorable universal linear optical circuit that relies on a novel 4×4 silicon photonic coherent crossbar architecture. Its experimental characterization yields a fidelity of 99.93±0.06%, calculated over 10,000 arbitrary matrices.
Multi-User Entanglement Routing for Quantum Mesh Networks

Multipath routing for sharing entanglement between multiple devices was simulated on real network topologies and compared against single-path routing. Results show improved entanglement rates, especially for topologies with a higher average nodal degree. © 2022 The Author(s)
Multiple Description Audio Coding for Wireless Low-Frequency Sound Zones

We present a joint design of sound zone control filters and robust audio coding for wireless low frequency sound zones. The audio signal is filtered using sound zone control filters and encoded using a multiple-description coder. The control filters and the multiple-description coder are combined in a nested loop. The inner loop performs filtering for sound zone control and generates multiple descriptions using oversampling and closed-loop prediction. The outer loop performs noise shaping and guarantees a trade-off between robustness and quality of the descriptions. A closed-form expression for the optimal sound-zone control filters are provided, and a simulation study demonstrates that even at moderate packet loss rates, a significant gain is possible compared to not using multiple descriptions.
Error-Tolerant Integrated Optical Unitary Processor based on Multi-Plane Light Conversion

We experimentally demonstrate error-tolerant optical unitary processors with multiport directional couplers. Thanks to the inherent redundancy of the multi-plane light conversion scheme, equivalent performance is obtained in the presence of large fabrication errors.
SiP Architecture For Accelerating Collective Communication in Distributed Deep Learning

We present a silicon photonic architecture for accelerating collective communications in distributed deep learning. We demonstrate a 22% job completion time improvement in a small-scale testbed and 1.4 to 5.9× improvement in large-scale simulations.
Practical Implementations of Compressed RAM

Given a string S over an alphabet of size $\sigma$, we consider practical implementations of extended compressed RAM on S, which supports access, replace, lnsert, and delete operations on S while maintaining S in compressed form. In this paper, we proposed two implementations where each of them is based on the compressed RAM of Jansson et al. [ICALP 2012], and Grossi et al. [ICALP 2013], respectively. Experimental results show that our implementations support the operations efficiently while keeping the space proportional to the entropy of the input during the updates.
SnappyR: A New High-Speed Lossless Data Compression Algorithm

We propose a high-speed lossless data compression algorithm, named SnappyR. Improved upon Snappy, we design new structures of the literal and the match tokens to achieve better compression ratio than Snappy. Numerous benchmarks are conducted on different sets of data corpus. The evaluations consistently show that SnappyR provides a better compression ratio comparing to Snappy, as well as LZ4, and better than LZO in most cases. Although a little slower than Snappy, the compression and decompression speeds of SnappyR are still much higher than entropy encoding based compression algorithms, such as ZSTD, deflate or Zlib. Thus, SnappyR can become another viable replacement or alternative to Snappy, LZ4 or LZO for computing and storage systems and applications, where high-speed lossless data compression is needed. More details about SnappyR algorithm design and performance evaluation can be found at [1].
Confidential Detection of Multiple Failures in Optical Networks: an Experimental Evaluation

This paper presents a Machine Learning technique based on Principal Component Analysis (PCA) combined with telemetry data scrambling to detect multiple types of failure in optical networks while preserving data confidentiality. Experiments in an optical testbed show the effectiveness of the proposed solution.
Optimization of Classical Light Wavelengths Coexisting with C-band Quantum Networks for Minimal Noise Impact

We investigate the optimal coexisting classical light wavelengths to use alongside C-band quantum networks to minimize noise from spontaneous Raman scattering and discuss techniques for optimizing coexisting time synchronization systems for teleportation and entanglement swapping.
Incoherent Fiber-based Optical Neuromorphic Computing Circuit

We present novel photonic neuromorphic computing scheme working with incoherent light while capable implementing negative weighting for the neural network and obtaining reliable/accurate computing of the linear multiply-accumulate function necessary for neural networks applications.