Reviewerly/exHarmony huggingface dataset:
Trash classification using quantum transfer learning

Trash classification is an important activity which helps in identification of waste. In this paper, a Classical- Quantum Transfer learning model, namely TrashQNet is proposed to classify trash into two classes, Organic and Recyclable trash. Classical-Quantum Transfer learning is a combination of machine learning and quantum computing. The proposed TrashQNet model uses a pre-trained DenseNet169 network for the feature extraction process and a variational quantum circuit as a classifier. The performance of TrashQNet is compared with classical machine learning models K- Nearest Neighbour & Support Vector Machine, deep learning model - Convolutional Neural Network and transfer learning model. TrashQNet outperforms all these models, it achieves an accuracy of 94% on test dataset.
Sound feedback control model for live electronic performance in the Ecos Study

Through the Ecos Study we investigate possibilities for musical structuring based on theories of emergence, finding a fertile territory for experimentations within Agostino Di Scipio's paradigm of Audible Ecosystems. The inclusion of the acoustic environment as an active component of the musical system led us to rethink classical techniques of sound synthesis and modelling, some traditional models of electroacoustic performance, and to criticize the very role of interactions in a qualitative perspective. A positive feedback control model has been developed aiming at understanding the behaviour and the structure of the ecosystem supporting the implementation of the Spatial Instrument, a core element implemented through MAX/MSP patch and that offers a logical rationale for organizing speakers and microphones in the acoustic space. We introduce the underlying theoretical debate and present the feedback control mechanism as the basis from which the Spatial Instrument is developed.
Fine-Grained Classification of Colposcopic Images with Multi-Modality

Computer aided diagnosis (CAD) has gained popularity in medical image processing over the last decade. As with image based manual diagnosis, CAD system should base its results on specific lesions. In the case of colposcopic images, two modalities are collected for diagnosis, namely Lugol’s Iodine (LI) and Acetic Acid (A). In practice, classification performance may suffer from barely distinguishable lesions in mono-modality. To tackle this problem, we present in this paper a framework with fine-grained strategy and further boost its performance with multi-modality. Specifically, our proposed framework consists of two branches, Local Lesion Branch and Global Branch, to implement a fine-grained strategy. Local Lesion Branch indicates existence and severity of lesion in different modalities on patch level. Global Branch extracts features on whole image level, and uses our proposed Cross Stage Modality Fusion (CSMF) Blocks to enable feature exchange and fusion between two modalities. Features from two branches are then concatenated for final prediction. We conduct experiments on clinical dataset of colposcopic images. The result shows that our method achieves an accuracy of 83.39%, outperforming mono-modal baseline model by over 7.24%.
Analysis of Password Protected Documents Using Statistical Approaches on High Performance Computing

Password-based validation frameworks are as yet the most regularly utilized components for ensuring the data regardless of being helpless against dictionary reference-based attacks. Password breaking is the way towards speculating or recuperating a secret key from put away areas or from an information transmission framework. Best state-of-the-art password analysing methods like HashCat, John the Ripper and rainbow crack empower clients to check billions of passwords each second against the secret key hashes. This paper discusses various techniques including traditional, probabilistic and statistical methods for cracking the password protected files. Further, experimental evaluation, rationale and performance analysis on some sample password protected files are presented in this paper. The findings in this paper will also help understanding of both password-composition policies and metrics for quantifying password security.
Decidable and Undecidable Problems for First-Order Definability and Modal Definability

The core of this paper is Chagrova’s Theorems about first-order definability of given modal formulas and modal definability of given elementary conditions. We consider classes of frames for which modal definability is decidable and classes of frames for which first-order definability is trivial. We give a new proof of Chagrova’s Theorem about modal definability and sketches of proofs of new variants of Chagrova’s Theorem about modal definability.
Training clusters in modern engineering education

Reducing term of bachelor training at the technical university is compensated by improvement in the inde-pendent work. However, using its previous forms is not leading to a decrease in the dropped-out student number. The proposed training technology was called cluster, as were production associations of the same name actively created nowadays to solve problems in specific areas of engineering practice. Simulating the content of a training cluster includes generalization of fundamental philosophical, natural, scientific, engineering and physical compilations, being a methodology of innovative research and theoretical knowledge in scientific discipline, as the basis of sector industrial practice, specific subject activity, as the development of social experience in the industry. Training clusters have practice-oriented engineering content, typical subject and production situations, work with reference and other technical information. Distinctive feature of a training cluster is a set of personal tasks, which make it possible to concretize the student cognitive activity, direct it and execute it step by step directly in the classroom. Essential basis of the cluster type education is created by the subject educational and cognitive student activity and the tutor participation in it. Cluster training fundamentally changes the form of education turning a monologue into a dialogue of communication, into a joint activity to achieve a common educational goal. Thanks to the on-line tutor assistance in the classroom, any student performing a cluster task immediately sharpens understanding of theory and masters the activity subject and practical experience. This is a fundamentally new type of training in modern higher education, where cognitive activity of students rises to the self-education level, to the level of independent self-training under control and assistance provided by the tutor.
Shedding Light on the Fate of Antibiotics in Forward Osmosis Through Experimental Investigation and Machine Learning Modeling

Antibiotic rejection during Forward osmosis (FO) is a complicated process. Mechanistic models have been developed to describe antibiotic rejection by FO membrane under ideal conditions but cannot be a pplied to real wastewater. Herein, the effects of draw concentration, pH, and solute type on the fate of tetracycline during FO was investigated by combining experimentation, factor analysis, and artificial neural network (ANN) modeling. High draw concentrations led to high convective diffusion that favored antibiotic diffusion. Low draw pH helped reject antibiotics due to the decreased tortuosity and pore size of the FO membrane. When different draw solutes were tested, both convective diffusion and electrostatic interaction exerted effects on antibiotic retention on the FO membrane surface, and steric hindrance could further affect the amount of tetracycline in the draw solution. Exploratory factor analysis showed that tetracycline rejection was a combined result of convective diffusion, steric hindrance, and electrostatic interactions. 8 representative input variables were selected from 13 observed explanatory variables using redundancy analysis, based on which an ANN was trained and successfully predicted antibiotic diffusion and transfer through the FO membrane. These results have provided practical and predictive insights in the development of FO processes for efficient treatment of antibiotic-rich wastewater
Functional diversity: Needs for technological accessibility for learning

Since the pandemic beginning, the students’ problems became visible, particularly those who have a disability when changing their way of working; in the same way, teachers do not know about technological tools to work through the virtual modality. This lived reality has positive and negative results. Among the negatives are: the disengagement from the educational system, the withdrawal of new knowledge and the breakdown of their relationship with classmates and teachers. On the positive side, it can be mentioned that this pandemic has been an opportunity for both teachers and students, it has made it possible to search for learning and teaching strategies and methodologies based mainly on the virtual modality. This article describes an investigation carried out in Ecuador, in which the techno-logical accessibility conditions, the required competencies and the needs from the functional diversity of 212 children and adolescents with disabilities were identified. Subsequently, a Technological Accessibility Plan for Students with Disabilities (TAPSD) was developed, based on person-centered planning (PCP) and universal design for learning (UDL), in which the use of different technological applications is proposed depending on the students’ needs and skills).
Analysis and Research on All Elements of Teacher Training Effectiveness Based on Linear Regression

Abstract: In the context of today's big data era, China's education informatization has entered a new stage, which requires further improving the ability of college teachers to deeply integrate information technology and teaching innovation. In the teacher training system of colleges and universities, teacher training is an important carrier to realize the improvement of teachers' comprehensive ability enhancement. In this paper, the effectiveness of teacher training in colleges and universities is comprehensively analyzed from five elements, including knowledge effectiveness, skill effectiveness, ability effectiveness, education concept effectiveness and education informatization ability effectiveness. Meanwhile, this paper uses big data mining analysis technology to select the dataset of China's Ministry of Education and China's National Bureau of Statistics from 2015-2019, and uses OLS and 2SLS linear regression models to analyze the influence effect of college teacher training effectiveness and college graduates' employment, and the results show that college teacher training effectiveness has a significant positive driving The results show that the effectiveness of teacher training in colleges and universities has a significant positive effect on the employment of college graduates.
Range Compensation Based Cooperative Online Path Planning for Multiple Fixed-Wing UAV System

In order to finish the assembly mission for multiple fixed-wing Unmanned Aerial Vehicles (UAVs) cooperatively, in this paper, a range compensation based cooperative path planning method is proposed by dividing the planning path process into two parts: Fast Generation of Initial Path and Range Adjustment. To provide a preferable initial condition for the Initial Path part, Dubins path method is firstly utilized for achieving heading angle consistency. Subsequently, a time based online cooperative range adjustment method is developed to ensure that the path range of each UAV to reach synchronization. Consequently, the fixed-wing UAVs are able to reach the terminal point simultaneously, thus completing the assembly mission successfully. The simulation results show that this proposed method can achieve time coordination efficiently by using range adjustment mechanism and generate the cooperative path for fixed-wing UAVs.
A study of using gaming technologies in teaching russian in technical universities

This article discusses the benefits of using a number of games in teaching Russian to increase students’ vocabulary. A real way to preserve cognitive motifs is for students to “include activities in mastering the Russian language in an activity that has a certain personal meaning”. The advantage of this game is that it is based on the situation. This game is not only useful for the study of science, but also helps to develop mental abilities. As a result, interest in the Russian language may fade. It is even necessary to start the process of greeting them in an unconventional way, for example, with the arrival of the teacher, an effective way to start the lesson with a greeting Russian song.
Processing of Null-subject Sentences in Chinese-English Sight Translation

Sight translation is an extremely complicated bilingual activity.Particularly, syntactic processing is integral to satisfactory translation quality.In recent years, quite a few scholars have investigated the syntactic processing, transfer and reformulation from the perspective of cognitive psychology.Nevertheless, few studies have focused on Chinese null-subject sentences or thoroughly analyzed the translation techniques adopted by interpreters or translators.Thus, the present empirical study aimed to supplement previous studies by systematically exploring sight translators' processing of null-subject sentences and their strategies for reducing cognitive efforts.Eleven Chinese MTI students sight translated 15 null-subject sentences and 15 sentences with subjects and immediately completed mini questionnaires.Based on the findings, the sentence type under study did not significantly influence translation duration, but detrimentally influenced the quality of sight translation.Furthermore, the sight translators deemed null-subject sentences more challenging than those with subjects and revealed the difficulty mainly occurred during the phrase of syntactic reformulation.However, several strategies were adopted by the participants to reduce cognitive burden.
Uncertainty Quantification with Extreme Learning Machine

On the one hand, UQ is crucial to assess the prediction quality of an ML model. It is particularly true in the previous chapter setup, where a relatively small number of points are modelled in a high-dimensional space. On the other hand, ELM is a universal approximator for non-linear regression problems that benefit from numerous advantages inherited from linear regression. It is very fast, easy to implement, has very few hyper-parameters and has a one-step optimisation process with a unique analytical solution. This chapter presents original and rigorous UQ developments of ELM.
Geological, Geophysical and Surface Geochemical Guides for Gold Exploration in the Falémé Volcanic Belt, West African Craton, Senegal

Abstract The Paleoproterozoic sequences of the Kédougou-Kéniéba Inlier, West African Craton, contain major gold deposits. New geological, geophysical and surface multi-element geochemistry data allow us to improve our understanding of the gold distribution within the Boboti intrusion and its vicinity in the Falémé Volcanic Belt (FVB) and the Dialé-Daléma host rocks. On the one hand, the geological study shows variability of lithologies hosting gold mineralization in the FVB, including intermediate to felsic intrusions, andesite, quartz veins and sedimentary rocks. On the other hand, geophysical and surface multi-element geochemistry study, handled with regionalized Principal Component Analysis with kriging of gold factor unveil a change of the gold metal association from the Boboti intrusion (association of Au, Ni, Co, Cu and Pb) toward the FVB (association of Au and Zn), and the Dialé-Daléma sedimentary host rocks, where Au is only associated with As.Such variability of metal association can be used as an exploration guide to characterize gold-rich zones, whether located in the Dialé-Daléma Series, within the Boboti metaluminous pluton, or in the FVB areas. Furthermore, a comparison between soil and termite sampling shows that the latter is a good and more effective and suitable exploration guide in areas of high weathering profile and with intense surface mining activities, such as the FVB area. The continuity of gold anomalies from surface to the subsurface levels is confirmed by drilling data. This study has allowed us to better constrain the gold geochemical anomalies in high weathering environment such as the southeastern part of Senegal.
Photonic Computing and Communication for Neural Network Accelerators

Conventional electronic Artificial Neural Networks (ANNs) accelerators focus on architecture design and numerical computation optimization to improve the training speed. Optical technology with low energy consumption and high transmission speed are expected to play an important role in the next generation of computing architectures. To provide a better understanding of optical technology used in ANN acceleration, we present a comprehensive review for the optical implementations of ANNs accelerator in this paper. We propose a classification of existing solutions which are categorized into optical computing acceleration and optical communication acceleration according to optical effects and optical architectures. Moreover, we discuss the challenges for these photonic neural network acceleration approaches to highlight the most promising future research opportunities in this field.
Situational awareness gaps and opportunities for cyber security

Security practitioners feel the need to improve cyber situational awareness (SA), but capabilities and assessments have not been matched. SA is an essential component of cyber security for everyone, from individuals to companies, response teams and threat exchange. In this field note, we highlight existing research and our field observations, a recent review of cyber security research literature, and call on the research community to help address three research challenges in situational awareness for cyber security. Gaps suggest the need to (1) understand what cyber is - SA from the perspective of human operators, and then (2) measure it so that (3) the community can know if SA has meaningful pathways to cyber security and if methods, technology, or other solutions are used would improve SA and thus improve would these results.
Explicit Dynamic Coordination Reinforcement Learning Based on Utility

Multi-agent systems often need to achieve the goal of learning more effectively for a task through coordination.Although the introduction of deep learning has addressed the state space problems, multi-agent learning remains infeasible because of the joint action spaces.Largescale joint action spaces can be sparse according to implicit or explicit coordination structure, which can ensure reasonable coordination action through the coordination structure.In general, the multi-agent system is dynamic, which makes the relations among agents and the coordination structure are dynamic.Therefore, the explicit coordination structure can better represent the coordinative relationship among agents and achieve better coordination between agents.Inspired by the maximization of social group utility, we dynamically construct a factor graph as an explicit coordination structure to express the coordinative relationship according to the utility among agents and estimate the joint action values based on the local utility transfer among factor graphs.We present the application of such techniques in the scenario of multiple intelligent vehicle systems, where state space and action space are a problem and have too many interactions among agents.The results on the multiple intelligent vehicle systems demonstrate the efficiency and effectiveness of our proposed methods.
Development of Comfortable Umbrella with Fan by Using Solar Energy

Heat waves often occur during sunny days in tropical regions during summer season, with temperatures sometimes reaching over 42 degrees Celsius. Because overheat waves can cause heatstroke for human beings who work outside, umbrellas are usually used to cut off sunshine to protect them. In the paper, an umbrella prototype is designed, which can drive a fan to provide cooling effect for persons who work outside in tropical regions. The designed umbrella uses solar cell attached on the above surface of the umbrella to convert solar energy into electricity, which is used to drive a fan or charge a battery. Moreover, the battery can also drive the fan when sunshine intensity is weak, or provide a charging port with 5 voltages for portable devices, such as mobile phones. The key component to realize these functions is an electronic control module, which includes charging circuit and discharging circuit. The former increases output voltage from the solar cell to the desired voltage to charge the battery; the latter can control electricity from the battery to drive the DC motor fan, LED indicator, and charging port.
Contrasting and Analyzing Machine and Human Translation: A Case Study on Red Sorghum

The quality of machine translation is an important part in machine language competence.Currently, there are still many differences between machine translation and human translation.The quality of machine translation still could not reach the quality of human translation.We take a Chinese literary text, Mo Yan's Red Sorghum, as the material to translate, and used LIWC2015 to analyze the differences between machine and human translation.Also, we make a linguistical attribution analysis to find out the reasons behind the errors machines make.For the engines of machine translation, we choose Baidu, Youdao and Google as machine translate engines.By analyzing the results from LIWC2015 and error analysis, we find that compared to human translators, machine translate engines tend to use simpler words and often fail to recognize discoursal connection; the errors machine translate engines make are usually caused by long sentences, Chinese names, encyclopedic knowledge, unique expressions and constructions in Chinese and literary expression.
DESIGNING THE TOOLKIT TO ASSESS THE RESEARCH ACTIVITY PRODUCTS OF PEDAGOGICAL MASTER’S DEGREE STUDENTS

The research activity is a systemic vector of professional education of a pedagogical master’s degree student that aims to train a teacher capable of scientifically substantiated solution complex and diverse professional tasks. The significance of the research activity in future teacher training determines, in many respects, the necessity for its organizational and methodological support, an integral part of which is the assessment of the process and the research activity results. The subject of this study is the performance aspect of the research activity products assessment considered in the context of the research project life cycle. The main task of the research is the development of assessment tools relevant to the research activity nature at different stages of the project life cycle. The solution of the assigned task is necessary to ensure constructive and productive feedback within the educational process, which provides the possibility of timely adjustment of the student’s research activity and, on the whole, flexible transitions between the stages of the research project life cycle. To solve this task, the authors describe the actual state and the prospects of studying the research activity as a support object; sum up the principles of the author’s concept of organizational and methodological support of the research activity; consider the stages of the research project life cycle; propose the pedagogical assessment interpretation; prove the requirements for pedagogical assessment; describe the procedure to design pedagogical assessment tools. The assessment cards for the products of analytical, design, formative, and presentational research activity are the study results. The proposed cards can form the base for more detailed development of assessment tools for evaluating products of the future teachers’ research activity.