Reviewerly/exHarmony huggingface dataset:
From scattered sources to comprehensive technology landscape : A recommendation-based retrieval approach

Mapping the technology landscape is crucial for market actors to take informed investment decisions. However, given the large amount of data on the Web and its subsequent information overload, manually retrieving information is a seemingly ineffective and incomplete approach. In this work, we propose an end-to-end recommendation based retrieval approach to support automatic retrieval of technologies and their associated companies from raw Web data. This is a two-task setup involving (i) technology classification of entities extracted from company corpus, and (ii) technology and company retrieval based on classified technologies. Our proposed framework approaches the first task by leveraging DistilBERT which is a state-of-the-art language model. For the retrieval task, we introduce a recommendation-based retrieval technique to simultaneously support retrieving related companies, technologies related to a specific company and companies relevant to a technology. To evaluate these tasks, we also construct a data set that includes company documents and entities extracted from these documents together with company categories and technology labels. Experiments show that our approach is able to return 4 times more relevant companies while outperforming traditional retrieval baseline in retrieving technologies.
Database description: Russian fricatives recorded in 198 real speech sentences from 59 speakers

This speech dataset is primarily designed to investigate linguistic and speaker information in fricative sounds in Russian. Acoustic recordings were obtained from 59 students (30 females and 29 males) between 18 and 30 years. Eighteen participants were recorded in a second session. The participants were born and lived since their early childhood in St. Petersburg. The participants did not report any speech or hearing impairment. The recording sessions were conducted at the phonetic laboratory of the Phonetic Institute in St. Petersburg, in an audiometric booth using the recording program Speech-Recorder version 3.28.0 at a sample rate of 44.1 kHz (16-bit encoding). During the recordings, a clip-on microphone (Sennheiser MKE 2-P) was placed at a distance of 15cm from the speakers' mouth and connected through an audio interface (Zoom U-22) to a laptop computer. The participants were instructed to read 198 randomized sentences from a computer screen. The fricatives [f], [s], [ʃ], [x], [v], [z], [ʒ], [sʲ], [ɕ], [vʲ], [zʲ] were embedded into those sentences. Two sentence structures were designed to obtain each real-word lexemes produced in three different contexts. The first type of sentence is a so-called carrier sentence with the structure of "She said "X" and not "Y" ". Minimal pairs of real words, containing one of the 11 tested fricatives were placed in both "X" and "Y" positions. The second type of pre-designed sentence was a natural language sentence including each of the lexemes. All raw audio files were first automatically pre-processed by applying the online tool Munich Automatic Segmentation system. Then, the files of the first recording session were filtered below 80 and above 20050 Hz, and the boundaries were manually corrected using Praat. The dataset consists of 22,561 fricative tokens. The number of observations per sound differs across categories, because of their natural distribution. The dataset is made available as a collection of audio files in wav format along with companion Praat TextGrid files for each sentence. Target fricatives are furthermore available as individual wav files. The whole dataset can be accessed with the DOI https://doi.org/10.48656/4q9c-gz16. Additionally, the experimental design allows the investigation of other sound categories. The number of speakers recorded gives further possibilities for phonetic-oriented speaker identification studies.
The role of digital technologies in the organisation of training sessions for future teachers

The article analyses the practice of giving training sessions of future teachers on the basis of digital technologies, the importance and effectiveness of which have increased significantly in the period of digitalisation of society. The main purpose of the research is to study the peculiarities of training students in higher education programmes for teaching activities on the basis of the potential of their self-independent work. The authors justify the need to use digital technologies in the educational process, contributing to a deeper immersion of post-graduates in the features of teaching activities using the creative potential of students, an independent active search. The article analyses various types of digital technologies, including students’ self-independent work with interactive learning material, written assignments aimed at developing practical skills.&#x0D; Methods. A special emphasis in the article is made on the use of simulators for acquiring skills in conducting various types of classes. In analysing the existing practice of conducting training sessions with postgraduates, theoretical (analysis of pedagogical literature, comparative analysis) and empirical (conversations, observations, evaluation and self-assessment of post-graduates and teachers) methods were used.&#x0D; Conclusion. The conclusion states that in the context of digitalisation of society, quality training of future teachers will require revision of available technologies, types of training sessions organisation based on active involvement of post-graduates in the process of organisation and conducting training sessions with the use of digital technologies.
Professional training in modern adult education

Introduction. Under the conditions of dehumanization of the social sphere, collapse of intergenerational links, loss of culturally appropriate meanings of self-realisation in professional activity, the content of the concept «professional training» requires rethinking. The correlation between its teaching and educating components is to be realized. Following the logic of passport specialty 5.8.7 «Methodology and Technology of Professional Education», it is necessary to make an «anthropological turn» in professional training, the conceptual description of which today is reduced to the extremely practice-oriented process of&#x0D; equipping a student with knowledge, skills and abilities necessary for a particular work activity. A humanistic-anthropological methodology is proposed by the author of this article as the basis for this process.&#x0D; Research methods: analysis of scientific and pedagogical literature, conceptual analysis, historical and pedagogical analysis, hermeneutic procedure.&#x0D; Research results. The historical and pedagogical analysis made it possible to identify the value constant of the national culture of understanding of labor and professional activity. It consists in the adult taking personal and professional position based on the awareness of the inseparability of personal and social significance of his own work. This tradition was interrupted in the post-perestroika period, when the utilitarian and material approach to the choice and implementation of professional activity prevailed.&#x0D; This was particularly detrimental to the professions dedicated to serving the community (teachers, doctors, the military, including the police). The humanitarian-anthropological approach allows designing the professional training as a support of human potential of a student by creating conditions for the development of his subjectivity in the educational environment of the eventful professional community through reflective practices. The methodological logic of designing a particular subject-specific professional training programme on the basis of the structure of interactive training cycle proposed by M.V. Clarin is described.
Propagation of Ultrashort Optical Pulses in Fractal Objects

In this paper, we study the features of the evolution of an electromagnetic pulse in fractal structures. Different fractal structures were considered, with different symmetry and different generators. Based on the electron dispersion law for fractal objects, an expression for the electric current density in the system under study was obtained. It was found that the fracton dimension does not significantly affect the dynamics of ultrashort optical pulses. Thus, ultrashort optical pulses do not feel the symmetry of fractal objects.
Analysis of Pre-service Teachers' Perceptions of the 2022 Revised Practical Arts Education Curriculum Technology Area Achievement Standards

This study was conducted to examine the need for pre-service teachers to be considered in the education stage of the 2022 revised practical arts curriculum in order for the curriculum to effectively reflect the educational field. In order to achieve this purpose, researchers analyzed pre-service teachers' perceptions of technical area achievement standards in the 2022 revised practical course.&#x0D; This study’s results revealed several key findings. Firstly, pre-service teachers recognized the importance of educational content and the ability of teachers to educate students in reaching technology achievement standards related to the use of artificial intelligence, computers, digital devices, and robotics. Secondly, pre-service teachers’ perception of educational performance ability on achievement standards was relatively high as it related to cyberspace, digital devices, and plants. Thirdly, the Matrix analysis confirmed the need for intensive education of pre-service teachers to assist them in developing theoretical and practical skills in robotics, computing, and artificial intelligence. It was also found that high interest was needed in pre-service teacher education in intellectual property, digital devices, robotics, computing, and artificial intelligence.&#x0D; This study and analysis led to three conclusions. Firstly, in order for pre-service teachers to apply the 2022 revised practical arts curriculum to the educational field, it is necessary to actively educate them in the areas of artificial intelligence, computing, digital devices, robotics, and intellectual property rights. Secondly, pre-service teachers must be instructed in the value of achievement standards related to agriculture, along with other subjects that have been newly introduced to the curriculum, such as transportation technology and construction technology.&#x0D; Thirdly, the educational content to be emphasized in the pre-service teacher education stage should be carefully selected, focusing on the importance of achievement standards and the abilities of pre-service teachers.
Automatic Feedback During Coding Exams: Curse or Blessing?

Automatic feedback during a coding exam can be very useful for both the students and the examiners. It can provide a quick and objective evaluation of the code written by the students, which can save a lot of time for the examiners and ensure consistency in grading.
Being Agile in the Software Engineering Classroom: Using Agile Approaches Instead of Plan-driven Approaches

We had an outdated, unsuitable pair of courses covering software engineering over an academic year, which were rewritten last summer. Out went the plan-driven project approach of GANNT charts, and a belief that 'better estimates' would save the day. In came a lightweight focus on a mix of extreme programming and scrum to incrementally, and iteratively build products.
BIG DATA FROM SOCIAL MEDIA PERSPECTIVE: A CASE STUDY WITH FACEBOOK

Thanks to the development of internet science and the ability of computers to bring individuals, institutions, and communities together as a communication tool, social media platforms have gone beyond being used as a communication tool and have become perception management and manipulation tools of institutions. Using digital communication tools, user data was used to identify target users and create strategies. In the current age, the concept of data shows its importance frequently. The Economist's article "The world's most valuable resource is no longer oil, but data" shows that the concept of data has reached a striking point. The process of determining user profiles, behaviours and habits based on user information takes place thanks to cumulatively increasing huge data stacks. These massive stacks of data are called ‘Big Data’. For Big Data, social media tools are used to obtain up-to-date, huge data stacks covering users from all walks of life. The role of big data in social media platforms is discussed in the study. The concepts of big data and social media and their development processes, the advantages and disadvantages of big data and data breaches will be discussed, and data breaches will be examined through the Facebook platform. The study aims to create a discussion point about data breaches and the limits of big data.
Inclusive Group Work Assessment for Cybersecurity

This poster presents an ongoing study that takes a diverse and inclusive approach to designing a practical group work assessment for an undergraduate cybersecurity course delivered to third year university cohorts. Students were given the choice to either work individually or as part of a group to complete the assignment in virtual laboratories. The study evaluates how student grouping preferences change when the teaching structure adapts in response to the the Covid-19 pandemic and how grouping preference impacts upon academic performance. Students reflected positively on the assignment and demonstrated a preference for treating group information as private. No disputes regarding group marks or contributions from different group members arose despite the number of groups involved. Students have taken responsibility for their choices and have accepted the outcomes of their teamwork.
Transformer-based feature interactor for person re-identification with margin self-punishment loss

Person re-identification aims to retrieve specific pedestrians from different cameras and scenes, in which extracting robust and discriminative features is crucial for this task. To explore the potential interactions among images and learn more robust representations, this paper proposes Transformer-based Feature Interactor(TFI) and improved Margin Self-punishment Softmax loss(MS-Softamx). The Transformer-based Feature Interactor consists of Group Channel Pyramid Attention(GCPA) and Neighbor Interaction Modeling(NIM). Firstly, the Group Channel Pyramid Attention module provides prior information for high-level semantics via low-level semantics. The attention information is gradually stacked from coarse to fine to obtain enhanced hierarchical multi-scale features. Then, Neighbor Interaction Modeling effectively model the input and similar neighbors to produce a more robust and discriminative image representation. To make TFI more focused on intra-class embedding learning, we also propose Margin Self-punishment Softmax guide deep network learning, which obtains a tighter custom classification boundary by pushing the inter-class threshold and minimizing the intra-class variance. The proposed method is verified on four datasets, and this achieves 92.8%/95.6% mAP/Rank-1 on Market1501, 86.1%/90.8% mAP/Rank-1 on DukeMTMC, 64.4%/ 81.2% mAP/Rank-1 on MSMT17, 79.7%/80.8% mAP/Rank-1 on CUHK03-detected and 81.8%/81.9% mAP/Rank-1 on CUHK03-labeled. Extensive experiments demonstrate that the proposed method achieves competitive performance with other state-of-the-art methods.
Lightweight mediated quantum key distribution

Inspired by the semi-quantum protocols, this paper defines the lightweight quantum security protocols, in which lightweight participants can only perform at most two out of four very lightweight quantum operations. Subsequently, this study proposes a Lightweight Mediated Quantum Key Distribution (LMQKD) protocol as an example to show the feasibility and advantage of the lightweight quantum protocol. In the proposed protocol, a dishonest third party (TP) with complete quantum capabilities helps two lightweight quantum users establish a secure key. The lightweight quantum users are allowed to perform only: (1) unitary operations and (2) reflecting qubits without disturbance. The proposed protocol has been showed to be robust under the collective attack.
Should i really do that? Using quantile regression to examine the impact of sanctions on information security policy compliance behavior

Deterrence theory is one of the most commonly used theories to study information security policy non-compliance behavior. However, the results of studies in the information security field are ambiguous. To further address this heterogeneity, various influencing factors have been considered in the context of deterrence theory. However, a current challenge with these findings is that recent studies that quantitatively assess the effectiveness of deterrence have relied predominantly on methods that analyze the underlying data, starting from a regression-based approach. By applying quantile regression, we estimate the overall effect of deterrents, and uncover how their effect differs among employees with different inclinations toward ISP compliance behavior – a critical insight for determining security measures for specific employee groups. Based on longitudinal data gathered in the U.S., our findings show significantly different effects in the analyzed quantiles for both aspects of sanctions, namely certainty and severity.
Instance segmentation algorithm based on fine-grained feature perception and cross-path aggregation

Most existing instance segmentation methods are based on object detection models, and their segmentation performance is limited by the accuracy of the detection boxes. To overcome the limitation of detection boxes, we propose an instance segmentation algorithm based on fine-grained feature perception and cross-path aggregation. Our approach aims to find strongly distinguished feature regions without relying on detection boxes and to perform precise local segmentation. Therefore, a fine-grained feature perception module is designed in the decoding stage by compressing the number of channels to aggregate scattered feature responses and then using an activation function to obtain a weight matrix. Finally, the weight matrix is used to find feature maps that focus on different mask regions to perform precise local segmentation. In addition, we also design a new cross-path aggregation structure in the encoding stage to reduce the loss of foreground instance information during network propagation. Experiments on the MS-COCO and PASCAL VOC 2012 datasets validate the effectiveness of our proposed algorithm and show that our method is competitive compared with state-of-the-art methods.
Citation Prediction via Influence Representation Using Temporal Graphs

Predicting the impact of publications has become an important research area, which is useful in various scenarios such as technology investment, research direction selection, and technology policymaking. Citation trajectory prediction is one of the most popular tasks in this area. One major challenge of this task is to quantify the influence of publications with integrated structural and temporal features from evolutionary citation graphs. Recent machine learning approaches are based on the aggregation of metadata features from citation graphs. However, richer information on the handling of temporal and attributes remains to be explored. In this paper, we propose CPIR, a new citation trajectory prediction framework that is able to represent the influence (the momentum of citation) of new or existing publications using the history information of all their attributes. Our framework consists of three modules: difference-preserved graph embedding, fine-grained influence representation, and learning-based trajectory calculation. To test the effectiveness of our framework in more situations, we collect and construct a new temporal graph dataset from the real world, named AIPatent, which stems from global patents in the field of artificial intelligence. Experiments are conducted on both the APS academic dataset and our contributed AIPatent dataset. The results demonstrate the strengths of our approach in the citation trajectory prediction task.
A Close-Up on the AI Radiologist Software

Proper computer-assisted detection (CADe) based on machine learning (ML) is a hot research topic in healthcare. Thousands of studies are published yearly focusing on enhancing the performance of ML-based models, but few tackle the challenge of deploying them efficiently. This paper focuses on designing an effective graphical user interface (GUI) tool, AI Radiologist, that clinicians can use during pre-operative planning to segment different liver tissues (parenchyma, tumors, and vessels). The tool employs convolutional neural networks (ConvNets) for liver tissue segmentation. This helps increase the success rate of any operation through meticulous pre-planning that allows surgeons to prepare well and plan for worst-case scenarios that might occur during surgery. AI Radiologist, an offline system application, utilizes three ConvNet models trained to segment all liver tissues. We use the PyQt5 Python module for the GUI to create a single-page application. The output of the AI Radiologist application is the liver, tumors, and vessels 2D slices and the 3D interpolation in .obj and .mtl format. The 3D interpolation can be visualized as a 3D liver object on any 3D-friendly software or 3D printed. Creating the AI Radiologist provides clinicians with a user-friendly GUI tool for liver tissues’ segmentation and 3D interpolation, employing state-of-the-art models for all tissues’ segmentation processes. Clinicians can select the volume(s) and the pre-trained models, and the AI Radiologist will take care of the rest.
Siamese Network with Gabor Filter for Recognizing Handwritten Digits

Even though significant progress has been made in the field of pattern recognition in recent years, handwritten digit recognition remains an exciting challenge. Due to its widely possible application, this issue has attracted much attention. Nowadays, different methods are available for handwritten digit recognition. The current demand is for researchers to find new techniques for better performing handwritten digit recognition problems. Therefore, this paper aims to propose a new approach, the Siamese network with Gabor filter, for handwritten digit recognition. Inspired by several studies on the Siamese network and Gabor filter separately, which have already achieved superb performances, this research will bring out the best qualities of their fusion. The computational experiments have been conducted on a handwritten digit image of the MNIST dataset. Empirically, the results implied that the proposed Siamese network with the Gabor filter algorithm outperformed the classical Siamese network and other existing methods in terms of accuracy.
Mitigating Backdoor Attacks Using Prediction of Model Update Trends

Federated Learning (FL) is a novel distributed machine learning technique that enables multiple data owners to train a superior model without sharing the original data. Nevertheless, FL is susceptible to attacks from malevolent clients due to distributed training and looser censorship of client data. Backdoor attacks are one of the attack methods against FL, in which a malicious client embeds a backdoor in the uploaded model updates through local training to compromise the global model. In order to counter backdoor attacks, we propose a defense method that uses Recurrent Neural Networks(RNN) to anticipate model update vectors to screen out malicious clients. Through experiments on three datasets, the results indicate that our approach effectively safeguards the global model by detecting and eliminating malicious clients.