Reviewerly/exHarmony huggingface dataset:
FinChat: Corpus and Evaluation Setup for Finnish Chat Conversations on Everyday Topics

Creating open-domain chatbots requires large amounts of conversational data and related benchmark tasks to evaluate them. Standardized evaluation tasks are crucial for creating automatic evaluation metrics for model development; otherwise, comparing the models would require resource-expensive human evaluation. While chatbot challenges have recently managed to provide a plethora of such resources for English, resources in other languages are not yet available. In this work, we provide a starting point for Finnish open-domain chatbot research. We describe our collection efforts to create the Finnish chat conversation corpus FinChat, which is made available publicly. FinChat includes unscripted conversations on seven topics from people of different ages. Using this corpus, we also construct a retrieval-based evaluation task for Finnish chatbot development. We observe that off-the-shelf chatbot models trained on conversational corpora do not perform better than chance at choosing the right answer based on automatic metrics, while humans can do the same task almost perfectly. Similarly, in a human evaluation, responses to questions from the evaluation set generated by the chatbots are predominantly marked as incoherent. Thus, FinChat provides a challenging evaluation set, meant to encourage chatbot development in Finnish.
Efficient MDI Adaptation for n-Gram Language Models

This paper presents an efficient algorithm for n-gram language model adaptation under the minimum discrimination information (MDI) principle, where an out-of-domain language model is adapted to satisfy the constraints of marginal probabilities of the in-domain data. The challenge for MDI language model adaptation is its computational complexity. By taking advantage of the backoff structure of n-gram model and the idea of hierarchical training method, originally proposed for maximum entropy (ME) language models, we show that MDI adaptation can be computed in linear-time complexity to the inputs in each iteration. The complexity remains the same as ME models, although MDI is more general than ME. This makes MDI adaptation practical for large corpus and vocabulary. Experimental results confirm the scalability of our algorithm on very large datasets, while MDI adaptation gets slightly worse perplexity but better word error rate results compared to simple linear interpolation.
Abnormal Behavior Detection and Warning Based on Deep Intelligent Video Analysis for Geriatric Patients

With the rapid growth of the aging population, abnormal detection has become a key issue in the medical and health field. Accurately detecting abnormal events in the monitoring video and giving feedback in real time can effectively reduce injuries even deaths caused by falls in the elderly. A novel Deepgait-based event detection method is proposed in this study for the elderly monitoring application. The classes of this problem contains abnormal behaviors, unusual events and normal daily life activities. To solve the above recognition problem, a novel deep convolutional features combined with joint Bayesian is proposed to model view variance. To evaluate the performance of the proposed model, two open behavioral data sets: Multiple cameras fall dataset and UR Fall Detection Dataset are adopted, where the mAP value of the improved Deepgait algorithm is 2.23% higher than that of Deepgait algorithm, and 5.4% higher than that of energy model (SSD) approach. The results suggest that our proposed monitoring system is an effective method for the elderly monitoring application.
RETRACTED: Thoughts on the Cultivation of Innovative and Entrepreneurial Talents in Colleges and Universities Based on the "Craftsman Spirit” Based on Big Data Analysis

Abstract Craftsmanship is of great significance to the development of the country, enterprises and individuals. Starting from the connotation of "craftsman spirit", this paper uses big data technology to analyze the necessity of introducing innovative talents with "craftsman spirit" into the talent training of colleges and universities. By training high-level innovative teachers with the spirit of craftsmen and building characteristic campus culture, the cultivation of innovative talents for entrepreneurship and the spirit of craftsmen should be organically combined.
Security as an Independent Scientific Discipline - A contribution to a Comprehensive Security Study to Meet the Requirements of the Contemporary Globalized World

The study of security, in all its aspects, traditionally faces a number of problems, from terminology and meaning, to definition and the corpus of knowledge. Only when we resolve these problems can we deliver a successful formulation of security science as an independent discipline. The “Security concept” is often subject to more scrutiny than the general ambit of security science, which would interpret this concept within its own framework. There remains a lack of any unique and comprehensive definition of Security concept, which tends to be viewed as an interaction with the security object. We, attempt to define it through two questions: Security for whom? And Security based on which values? The international reality at the beginning of the 21st century presented a challenge to the academic sector to redefine the dominant existing principles of the security concept. This meant the need for the state to move aside as the long-standing predominant security controller and thus for academics to observe security as a general prerequisite for the functioning of any system, be it a state, database, business, environment or citizen, etc. The field of ​​security is still dominantly studied within the context of some other academic discipline, principally social sciences, such as sociology and criminology. However, there is a growing need to study security in the context of technological sciences, bearing in mind the increasing significance of security of information systems, databases and so on. Research has shown that the security field already has sufficient categories (fields) that would constitute its unique “corpus of knowledge”, as an important prerequisite for qualifying security as an independent science. We also suggest that this corpus of knowledge can be extended to other disciplines to make security studies yet more comprehensive, and also demonstrate elasticity to adapt, as an independent scientific discipline, to the demands of change and new times. This need has been particularly pronounced in the decades that followed the Cold War, in the period of dynamic economic, political and technological globalization, where the security of individuals, social groups, business and institutional systems, has become a dominant aspect in the functioning of modern society. In that sense, the establishment of security science as an independent discipline is necessary not only for the development of a theoretical model, but also because of its wide practical application in modern, globalized world.
Self-Supervised Pre-Training with Acoustic Configurations for Replay Spoofing Detection

Constructing a dataset for replay spoofing detection requires a physical process of playing an utterance and re-recording it, presenting a challenge to the collection of large-scale datasets. In this study, we propose a self-supervised framework for pretraining acoustic configurations using datasets published for other tasks, such as speaker verification. Here, acoustic configurations refer to the environmental factors generated during the process of voice recording but not the voice itself, including microphone types, place and ambient noise levels. Specifically, we select pairs of segments from utterances and train deep neural networks to determine whether the acoustic configurations of the two segments are identical. We validate the effectiveness of the proposed method based on the ASVspoof 2019 physical access dataset utilizing two well-performing systems. The experimental results demonstrate that the proposed method outperforms the baseline approach by 30%.
360$$^{\circ }$$ Real-Time 3D Multi-object Detection and Tracking for Autonomous Vehicle Navigation

This paper presents a real-time 3D Multi Object Detection and Tracking (DAMOT) method proposed for the UAH autonomous electric car. It allows the vehicle to recognize 360 $$^{\circ }$$ surrounding objects and uniquely identify them to be able to follow their trajectory in scene by only receiving a 3D point cloud through ROS framework. First, we describe our proposal of 3D object detector, based on PointPillars [11], processing LiDAR data to locate objects in space obtaining their dimensions and location. Secondly, we use BEV-MOT [7], our Multi-Object Tracking technique in order to uniquely identify each object over a Bird’s-Eye View (BEV) through a combination of 2D Kalman filter and Hungarian algorithm, allowing the ego-vehicle to follow surrounding objects trajectories. A comparison of the performance of our proposal with other state-of-the-art methods is carried out applying KITTI-3DMOT evaluation tool extracted from AB3DMOT [21] on KITTI [5] validation dataset. Finally, we validate our DAMOT in several traffic scenarios implemented in CARLA [4] open-source driving simulator by using AB4COGT tool, designed by authors, studying its performance in a controlled but realistic urban environment with real-time execution, providing several demonstration videos ( https://cutt.ly/3rU113d ).
Bi‐Frequency Illumination: A Quantum‐Enhanced Protocol

Abstract Quantum‐enhanced, idler‐free sensing protocol to measure the response of a target object to the frequency of a probe in a noisy and lossy scenario is proposed. In this protocol, a target with frequency‐dependent reflectivity embedded in a thermal bath is considered. The aim is to estimate the parameter , since it contains relevant information for different problems. For this, a bi‐frequency quantum state is employed as the resource, since it is necessary to capture the relevant information about the parameter. Computing the quantum Fisher information H relative to the parameter λ in an assumed neighborhood of for a two‐mode squeezed state (), and a pair of coherent states (), a quantum enhancement is shown in the estimation of λ. This quantum enhancement grows with the mean reflectivity of the probed object, and is noise‐resilient. Explicit formulas are derived for the optimal observables, and an experimental scheme based on elementary quantum optical transformations is proposed. Furthermore, this work opens the way to applications in both radar and medical imaging, in particular in the microwave domain.
Bringing the QR Code to Canada: The Rise of AliPay and WeChatPay in Canadian e-Commerce Markets

The introduction of QR-based e-payment systems to the Canadian market have created a sub-set of Canadian digital finance that has been dominated by the existing credit-based system. In an increasingly digital world, the QR-code has become a rapidly popular mode of facilitating merchant to customer payments. China has been the dominant producer of QR-code payment systems, with two of the largest e-payment companies worldwide: AliPay and WeChatPay. Partnerships with Canadian companies have introduced the technology to Canadian businesses. This research makes preliminary strides in understanding how this technology presents itself in Canadian commerce. The current presence in large cities demonstrates the demand for QR-based fintech, but the nature of the existing AliPay and WeChatPay markets are closed to non-citizens of the People’s Republic of China. If QR-Code technology is the future of Canadian fintech, local counterparts must be produced in order to establish a widespread presence of QR-Code fintech in Canada.
A Multi-talent Healthcare AI Bot Platform

AI bots have emerged in various industries hoping to simplify customer communication. However, there are a certain set of challenges for such a product in the healthcare domain, including confidentiality and domain knowledge. We discuss the implementation of a secure, multi-task healthcare chatbot built to provide easy, fast, on-demand conversational access to a variety of health-related resources.
Corpus-Based Syntactic-Semantic Graph Analysis

This research exemplifies the corpus-based graph approach to the syntactic-semantic analysis of a concept feeling using the Construction Grammar Conceptual network methodology. by constructing a lexical network from grammatically tagged collocations of the english and the Croatian web corpora, the structure of the semantic domains is revealed as a set of sub-graphs derived from the source lexeme’s friend-of-a-friend graph. the subgraph structures, calculated with the community detection algorithm, are interpreted as the semantic domains associated with the source lexeme’s conceptual matrix. lexical structures are analyzed using a centrality algorithm that determines the overall rank of the salience and semantic relatedness to the source concept feeling . this empirical approach can be used for developing nlP methods and tasks, such as computing semantic similarity, sense disambiguation, sense structuring, as well as for comparative corpus and cross-cultural studies. ConGraCnet has a web application on the page &lt;a target="_blank" rel="nofollow" href="http://emocnet.uniri.hr/congracnet"&gt;http://emocnet.uniri.hr/congracnet&lt;/a&gt;.
Code Complexity Metrics Derived from Software Design: A Framework and Theoretical Evaluation

As software systems continue their exponential growth in size and complexity, measuring this complexity has become fundamental to ensuring longevity and to maximize returns over investment. Modern code complexity methods are agnostic to the intended designs, and are often applied indistinguishably to all software modules. This paper presents novel code complexity metrics that are derived from software intended designs. This approach ensures that the derived metrics are uniquely tailored for the software under development, and that they are customized for each software module. These metrics are derived from the software intended design and their derivation is largely automated. The paper presents both a practical and a theoretical evaluation using Weyuker’s nine properties model.
Detecting Slowly Accumulating Faults Using a Bank of Cumulative Innovations Monitors in Kalman Filters

Extended Kalman filters (EKFs) that monitor innovations over time have been demonstrated to be effective at detecting slowly accumulating measurement faults [1]. This paper first demonstrates that a single cumulative monitor becomes increasingly sensitive to measurement error model uncertainty as the accumulation interval increases. As a result, the monitor’s false alarm and missed detection rates can differ significantly from predefined design parameters. In response, we investigate the use of a bank of cumulative innovations monitors for measurement fault detection in multisensor navigation systems. Focusing on a tightly coupled GPS+inertial EKF, high-fidelity Monte Carlo simulation that includes real-world model uncertainty demonstrates that the monitor bank maintains tighter compliance with specified false alert and missed detection requirements. The simulations also quantify the ability of multiple monitors to reduce time-to-detect. Practical considerations including implications for mitigation are discussed. Simulation results indicate significant improvement in detecting slowly accumulating measurement faults using the monitor bank compared to the single cumulative monitor for a runtime of 30 minutes. We describe a novel covariance analysis extension method and compare the predicted performance of our method to traditional snapshot innovations monitoring and the infinitely accumulating monitor in [1]. Covariance analysis and Monte Carlo simulation results are presented for a variety of fault detection profiles and inertial measurement unit (IMU) qualities (tactical-grade and aviation-grade). Data for the time-to-detect is presented alongside the position-domain bias induced by the fault at the time of detection; generally the monitor bank can detect the presence of faulty measurements after the position-domain bias has reached only tens of meters whereas the other methods may not detect until hundreds of meters.
Internet Usage and Its Effect on the Lifestyle of University Students

With the diversity of internet technology in the world today, usage has spread across every sector of the economy and business operations.Students are not left out from the use of the internet.Most people all over the world are using the internet for scholarly works.Researches on the use of the internet by students have been conducted by many scholars.This research is focused on the various uses of the internet by students in the universities in Ghana and the effects of the usage on their lifestyle.A cross-sectional survey research design was employed.Selfadministered questionnaire was the primary data collection instrument.One hundred and fifty-four (154) students were selected randomly for the study.Most of them were undergraduate students and a few of them were graduate students.Students in the universities in Ghana accessed the internet for two main reasons -for academic activities and for making friends from other parts of the world.The results of the study reveal that most students access the internet daily.They spent several hours on the internet in a day and also for varying days in a week.It was found that students were addicted to the internet and they accessed social media purposely for making friends.They spent varying sums of money on buying internet bundles; even they use their last sum of money to browse the internet.However, with long hours spent on the internet some of them did not make enough time for their academic activities, including visiting the digital libraries.
Defending Against Malicious Reorgs in Tezos Proof-of-Stake

Blockchains are intended to be immutable, so an attacker who is able to delete transactions through a chain reorganization (a malicious reorg) can perform a profitable double-spend attack. We study the rate at which an attacker can execute reorgs in the Tezos Proof-of-Stake protocol. As an example, an attacker with 40% of the staking power is able to execute a 20-block malicious reorg at an average rate of once per day, and the attack probability increases super-linearly as the staking power grows beyond 40%. Moreover, an attacker of the Tezos protocol knows in advance when an attack opportunity will arise, and can use this knowledge to arrange transactions to double-spend. We show that in particular cases, the Tezos protocol can be adjusted to protect against deep reorgs. For instance, we demonstrate protocol parameters that reduce the rate of length-20 reorg opportunities for a 40% attacker by two orders of magnitude. We also observe a trade-off between optimizing for robustness to deep reorgs (costly deviations that may be net profitable because they enable double-spends) and robustness to selfish mining (mining deviations that result in typically short reorgs that are profitable even without double-spends). That is, the parameters that optimally protect against one make the other attack easy. Finally, we develop a method that monitors the Tezos blockchain health with respect to malicious reorgs using only publicly available information.
Interlanguage Analysis on Speech Produced by EFL Learners

Indonesian EFL Learners experience erroneous speech in the process of learning the target language. Interlanguage, the errors that contain linguistic features which neither belong to the first language (L1) nor the target language becomes the focus of this study. The study aims to analyze the native and target language influence on the interlanguage produced by the students in their speech production. This study was designed in qualitative research approach. The data was collected through interviewed of 20 eighth grade students in English using a guided interview technique that contains 15 questions. The oral responses produced by the students were recorded, identified, described, and explained. The results show that in the syntactic and lexicon levels, both native and target language influenced the students’ interlanguage production. Native language influence includes 1) the use of Indonesian syntactic pattern; 2) the use of Indonesian acronym; 3) the use of Indonesian words; 4) omission of –s in plural forms; 5) omission of to be; 6) subject deletion; and 7) the use of verbs. Target language influence is observed in overgeneralization of articles, the use of to be, and the overgeneralization of ending –ed in the past tense.
Style-Based Unsupervised Learning for Real-World Face Image Super-Resolution

Face image synthesis has advanced rapidly in recent years. However, similar success has not been witnessed in related areas such as face single image super-resolution (SISR). The performance of SISR on real-world low-quality face images remains unsatisfactory. In this paper, we demonstrate how to advance the state-of-the-art in face SISR by leveraging style-based generator in unsupervised settings. For real-world low-resolution (LR) face images, we propose a novel unsupervised learning approach by combining style-based generator with relativistic discriminator. With a carefully designed training strategy, we demonstrate our converges faster and better suppresses artifacts than Bulat’s approach. When trained on an ensemble of high-quality datasets (CelebA, AFLW, LS3D-W, and VGGFace2), we report significant visual quality improvements over other competing methods especially for real-world low-quality face images such as those in Widerface. Additionally, we have verified that both our unsupervised approaches are capable of improving the matching performance of widely used face recognition systems such as OpenFace.
Mobile Learning Support in Delivering Distance Education: Perception of Students of University of Cape Coast, Ghana

This paper explored the perception of University of Cape Coast distance education students on the use of mobile technologies to facilitate interactions among students and tutors as a learning-support system. The paper aimed at establishing whether distance education students would accept to be taught using a blend of mobile technologies and biweekly direct face-to-face tutorials on weekends. A questionnaire was used to collect data from a sample of 300 students pursuing various Diploma, Bachelor’s and Master’s degrees by distance at University of Cape Coast using systematic sampling technique. The data was analysed using descriptive statistics. It was found that scheduled face-to-face tutorial sessions of distance education in University of Cape Coast were not supportive enough to address students’ learning needs. Also, all the respondents possessed mobile devices and perceived blending of mobile learning in distance education as an avenue to enhance collaborative learning with faculty and colleagues. It was recommended that curriculum and instructional designers of distance education courses must consider incorporating mobile learning pedagogies in the distance courses and learning experiences to address students’ learning needs using mobile technologies.
An Application of Metacyclic and Miller-Moreno p-Groups to Generalization of Diffie-Hellman Protocol

The results of Ko K., Lee S., are improved and generalized on the basis of metacyclic p-group \( G \) of type Miller-Moreno (minimal non-abelian group). We show that the decomposition problem in this group is intractable. The conjugacy search problem in a group G is the problem of recovering an \( x \in G \) from given \( g \in G \) and \( h \, = \, x^{ - 1} gx \). We show that the conjugacy problem in this group is intractable. The algorithm of generating (designing) of common key in a non-commutative group with two mutually commuting subgroups has been constructed by us. An application of general metacyclic group to control an entropy of key space is considered.