Reviewerly/exHarmony huggingface dataset:
Data Science Education by Remote Method with Satellite Data Platform

In the experiments performed in the advanced course of the National Institute of Technology, Numazu College, data science education was conducted remotely by using asynchronous and synchronous interactive methods. The Tellus platform with satellite and terrestrial data was used to analyze data in a cloud environment. We adopted the development of data engineering, data analysis, and value creation skills as basic educational guidelines. From the answers to the questionnaire, we conclude that 1) the asynchronous interactive method made us aware of many benefits, 2) the synchronous interactive methods gave us some issues to be solved, and 3) the three guidelines were successfully achieved.
A Novel Method for the Automatic Generation of JOP Chain Exploits

Jump-Oriented Programming (JOP) is a seldom studied form of advanced code-reuse attacks, very different from return-oriented programming (ROP). JOP identifies snippets of code ending in an indirect jump or indirect call (gadgets), and these are chained together to construct exploits. All applications contain gadgets in executable memory. In this paper we present a mature tool, JOP ROCKET, to facilitate JOP gadget discovery and classification. Additionally, it automates generation a complete JOP chain to bypass Data Execution Prevention (DEP), using a limited virtual machine with emulation. The JOP chain generation utilizes a novel variation to the approach to JOP. Automating JOP chain generation can help provide for automatic detection of vulnerabilities in an application prior to being released, allowing for remediation.
Developing adult motivation for continuous training

Traditional education focused primarily on the moment of teaching, on the moment of transmitting information, knowledge, and only then on aspects related to ethical, motivational, moral nature. Contemporary society in a continuous remodeling and change, no longer coincides, no longer accepts such a system. Traditional learning does not keep pace with contemporary society. The aim of this study is to investigate the motivation of learning in adulthood. In the motivational structure of the investigated adults, extrinsic reasons predominated, indirectly related to the learning activity, such as: the need to advance professionally, to keep up with the times, to obtain a social status as high as possible, to meet current requirements, to earn the respect of others, for a diploma, a change of job. Among the intrinsic reasons invoked by the study participants, directly related to the learning activity, we mention: the desire for knowledge and personal development, curiosity, the desire to teach others. In conclusion, we want to offer a better perspective and a well-understanding of motivational factors implicated in adult learning, ensuring the ease of triangulation of sources of information collection, thus improving the credibility of findings.
Video classification using transfer learning techniques for human-machine interface

Video classification is a crucial aspect when we discuss human-machine interface as it helps to analyze various activities. Using transfer learning techniques can help us in making predictions accurately. The dataset used for research is a subdivision of the UCF101-Action Recognition Dataset, consisting of 10 classes in total, where each class contains more than 120 videos. Each video is converted into a series of frames at a frame rate of 5. Feature extraction is performed on these frames using InceptionV3. The fine-tuned model architecture is composed of 4 dense layers. These layers are built using “relu” activation function with 1024, 512, 256 and 128 neurons respectively and another dense layer is built using “softmax” activation function with 10 neurons so as to predict 10 classes. This technique finds a huge range of applications related to human-machine interface such as helping the visually challenged people in classifying various activities.
Case Study of Sequential Indicator Simulation with Data of Different Support

This chapter illustrates the application of Sequential Indicator Simulation with data of different support to measure the geological complexity of a mineral deposit. We define the geological complexity based on the number of rock types and how connected they are in a mining operation. For example, an ore formation that contains a single rock type has minimum geological complexity. In contrast, an ore formation that contains five different rock types intermingled has high complexity.
PSO with Coupled Map Lattice and Worker Ant’s Law

In this study, we present two types of approaches for developing of particle swarm optimization (PSO). Firstly, we introduce coupled map lattice (CML) that is used a logistic map, to the moving equation of PSO. We consider that the PSO obtains two parts which are moving according to random vector and moving according similar vector by the CML phenomena. Secondly, we introduce Worker ant's law to the moving equation of PSO. The Worker ant's law is a variant of the Pareto's law. The ratio of ants is 2:6:2 that include hard working ants, normal ants, and not working ants, respectively. We give the different momentum term for each swarm that is divided according to Worker ant's law. It is considered that the search range can be expanded by the different momentum term without the divergence of solution search. We confirm that the CML and Worker ant's law improve the solution searching ability of PSO by comparison with the general PSOs.
Political Participation, Voting, and Chaos: Electronic Voting and an Assessment of Turkey

Political participation stands for the attitudes and behaviours that a citizen assumes towards the political system that they exist in. Held in specific periods in democratic systems, elections are one of the means of political participation. For more than 30 years, electronic voting has been practiced in a number of countries across the globe. The system in question has changed upon the developments in technology and the inclusion of the internet technology in our lives. The systems, named e-voting or i-voting, are being implemented today as well. It is known that a large number of studies have been conducted in Turkey over the recent years, in regards to electronic voting, and electronic voting machines. This study will discuss what the electronic voting system is, along with its use in the countries, and it also involves a discussion on how it could affect the political participation in the event that it is used in Turkey, and on any probability of chaotic situations that it could lead to.
Intelligent User Interface to Control a Powered Wheelchair Using Infrared Sensors

This paper presents a new system to steer a powered wheelchair using a Sharp IR sensor and a Raspberry Pi. Interviews with occupational therapists, helpers and carers at Chailey Heritage Foundation/School revealed that clicking noises generated from closing switch contact used to operate powered wheelchairs disturbed the attention and reduced the focus of young wheelchair users having cognitive or physical disability. Also switches often slipped away and became unreachable. The new system replaced lever-switches used to steer powered wheelchairs by an electronic circuit. The circuit consisted of a Sharp IR sensor, Analogue to Digital converter, relays, and a Raspberry Pi. The sharp IR sensor detected movement in its range and the Raspberry Pi interpreted the data and generated commands to steer a powered wheelchair. Two modes were used to overcome the problem of sensors slipping from position: Click to Calibrate and Auto-Calibrate. A technical User Interface was created to modify sensitivity, user and detection settings. Practical testing showed the system behaved satisfactorily. It detected users’ voluntary movements and used them to steer a powered wheelchair and overcome the problem of switches slipping from position. Clinical trials will be conducted at Chailey Heritage Foundation.
A Type-2 Fuzzy Logic Based Explainable AI Approach for the Easy Calibration of AI models in IoT Environments

Internet of things is projected to make its way into all spheres of human life in the near future. This has been compounded with the growing demand for contactless solutions in the wake of the recent pandemic. A potential solution could involve a privacy-preserving gesture-based control system that could control a wide range of appliances. Implementing such gesture-based control systems is mainly conducted using opaque box Artificial Intelligence (AI) models. Systems based on such opaque box AI models have shown high-performance metrics on in-distribution data in a lab environment. However, they are prone to failure when exposed to real-world out-of-distribution data where they cannot be tuned or calibrated due to their complexity and opaqueness. Interval Type-2 Fuzzy Logic-based explainable AI models offer an alternative to opaque box models showing comparable performance on lab in-distribution data. In contrast, in the real world, out-of-distribution data, the type-2 fuzzy models could be easily calibrated and tuned (thanks for their explainability) to provide similar performance to those achieved on the lab in-distribution data.
Face Detection in Images Using an FPGA

Algorithms for face detection in an image find their usage in a wide range of industrial applications. In this paper, a popular Viola-Jones algorithm for face detection is described using Very High Speed Integrated Circuit Hardware Description Language (VHDL). The proposed digital design is synthesized and implemented in ZYBO ZYNQ 7010 System on Chip (SoC) FPGA. The proposed design can process different image resolutions with minimal changes in the design. The evaluation of the proposed solution confirms the correctness of the implementation and possibility to use the proposed design in many applications requiring cost-efficient and real-time face detection.
A Framework for Semantic Knowledge Representation of Al-Quran Based on Word Dependencies

A variety of applications have been built in recent years with the aim to extract knowledge from Al-Quran. Current knowledge representations of Al-Quran give attention primarily on conceptual ontology models that describe the semantic relations between the Quranic concepts or entities. There seems to be minimal effort towards recognizing the semantic relations between words in Quranic text, which is relatively more complex. This paper aims to present a framework for semantic knowledge representation of Al-Quran using dependency relations between words, in an attempt to boost the retrieval accuracy for Al-Quran. The semantic analysis is performed on Quranic verses according to word dependency relations using dependency parsing. Based on parsed dependencies, a set of rules are formulated to build a semantic graph of Surah Ali Imran of Al-Quran. The efficiency of the semantic representation was tested by developing a prototype question answering system. The framework was evaluated using precision and recall, First Hit Success, First Answer Reciprocal Rank and Total Reciprocal Rank by comparing the retrieved and actual answers. The results indicate that the performance of the proposed framework using word dependencies improves the semantic representation of knowledge.
Towards Industry 4.0 Through Cloud Resource Management

Cloud Computing delivers on-demand metered services to its consumers which include computing power, servers, storage, networking, and intelligence over the Internet. The cloud computing technology acts as a critical enabler for Industry 4.0, as the popular cloud business model depends upon the provisioning of resources by the service provider to satisfy user demands from different geographical regions. This multitenant environment of the cloud further promotes a manufacturing ecosystem that paves the path for Industry 4.0 as it aids in the manufacturing process by providing customized products based on demand. To provision resources, in such an environment, various resource provisioning techniques are designed that abides service-level agreements (SLA). The performance of these techniques can be evaluated by different performance parameters, and achieving adequate results for these performance parameters assures the efficient management of resources. The chapter enlightens the concept of resource management in cloud computing within Industry 4.0. Different RM techniques, along with associated challenges, advantages, disadvantages, and performance parameters have been explained in detail.
Educating Well-Rounded Students through Science of Craftsmanship

Japanese sword is one of Japan’s world-class traditional craft technologies, and is also called “art of iron”. Currently, the history, technique, and art of Japanese sword are being handed down through daily studies/ disciplines (trainings) by swordsmiths, sword polishers and so on. With these backgrounds, Kanazawa Institute of Technology signed a partnership agreement with the Sakaki town (Nagano prefecture), which is known as “Iron town” in 2015, and has been developing various projects. One of these projects is a collaborative project with the “Iron exhibition hall” in the Sakaki town, and the authors and our students participate in this collaborative project and try to design novel Japanese swords with the science of “craftsmanship” as the keyword. It is thought that these activities will contribute to the engineering education of students based on “developing new ideas based on study of the past”. In this paper, we discuss the contents and results of educating well-rounded students through the science of craftsmanship by this collaborative project in which the authors have performed with our laboratory students for six years. As a result, it was clarified that it was possible to provide students with valuable experiences, including many failures and successes through this project. Therefore, these activities are one essential of as a new effective method of engineering education
Financial Distress Detection and Interpretation with Semi-supervised System

Financial distress prediction is of great importance for companies to take timely measures. It is also useful for banks and investors to avoid potential risk. Mining potential distressful companies in this fast-changing business environment is crucial for timely intervention, but it is also very challenging. Because in real scenario, only few companies have been assessed to be financial distressful, while most companies remain unlabeled. Traditional supervised learning and unsupervised learning are no longer suitable under such circumstances. Since this problem can be viewed as an anomaly detection problem with partially observed anomalies, this paper proposes a semi-supervised learning framework adopting PU-learning method and unsupervised method combined with improved feature selection procedure. The proposed system makes full use of limited observed data and is robust to unknown novel anomalies at the same time. This system outperforms traditional supervised and unsupervised methods as well as some other semi-supervised methods. Meanwhile, the framework provides explanation for the detected anomalous companies which is useful for further analysis.
Small Object Recognition Based on the Generative Adversarial Network and Multi-instance Learning

In recent years, object recognition has experienced impressive progress. Despite these improvements, there is still a significant gap in the performance between the detection of small and large objects. We analyze that the limitation of existing algorithms for small target detection, such as: (1) the high computational overhead of image resolution increase and (2) the non-semantic data augmentation of small-object- copy-based strategy, leading a worse result in mAP. So, we figure out that the limited number of semantic training samples is a key impediment for this task due to the high cost of collecting and labelling nature images. In this paper, we propose a simple but effective framework for small object recognition. With an improved generative model, we propose a multiply instance learning detector based on CNN, which jointly learns from the labeled nature datasets and unlabeled generated images. Our method shows a state-of-the-art performance for small objects, obtained by Mask R-CNN, on MS COCO.
Research on Image Recognition Based on Neural Network

Abstract With the rapid development of Internet and mobile Internet, the image data in the network shows explosive growth. Image data is simple and intuitive, and contains rich information, which is widely used as a carrier of information exchange. Content-based image recognition can start from the image itself, first extract significant features from the image, and then recognize the image according to the differences of features, with good recognition effect. Neural network plays a very important role in artificial intelligence. Neural network is the key to realize artificial intelligence by learning data through algorithms and then making predictions and decisions. The image recognition of neural network is based on the image content. Neural network starts from the image data itself, extracts the image features of the bottom layer from the data, and then establishes the relationship between the image semantics of the bottom layer and the top layer through the algorithm of neural network, thus realizing the image recognition.
Integration of Information Systems Through a Process-Based Approach

The aim of the work is to develop methodological foundations and approaches to the design of a modern integration platform based on a process-oriented approach.The importance of integration as a constantly arising task, especially within the framework of modern digitalization programs, is determined by the joint operation of various software and hardware technologies, digital transformation of business processes, as well as digitalization drivers: miniaturization of electronic components, convergence of communication means, development of industry-specific production technologies, etc.Typical problems of enterprises determining the necessity, urgency and complexity of integration are formulated: heterogeneity of information systems, the need for constant modification of their interaction, the diversity of the integrated systems themselves, a large number of informalized knowledge, a variety of data standards, protocols, and interfaces.Based on the formulated tasks that need to be solved during integration, the following methodical elements of the integration platform functioning have been defined: formation of a unified information space (UIS), consisting of a dictionary of entities and a mechanism for integrating user data, which implements elements of a semantic approach to data integration; creation of business processes in BPMN 2.0 notation allowing to graphically describe regulations and instructions in a human-friendly way; the idea of minimizing coding and automatically adapting user interfaces to changes in data models and processes in order to support evolutionary development of processes; simplicity, adaptability and development of cross-platform user interfaces.The developed methodological approaches to the design and use of the integration platform based on ESB and BPM-machine allow solving the tasks of process automation using information and computing resources provided by different participants (including automated systems, industrial equipment and IoT elements).
Application of Deep Learning Methods for the Identification of Partially Observable Subgraphs Under the Conditions of a Priori Uncertainty and Stochastic Disturbances (Using the Example of the Problem of Recognizing Constellations)

This paper demonstrates the effective capabilities of deep neural networks in solution of the problem of structural identification on graphs in conditions of a priori uncertainty, incomplete observability and stochastic disturbances which is also knows as subgraph detection or recovery. The problem of identification of observed constellations in a photo of the night sky was considered as a test. The solution with quality of 0.927 $$F_1$$ is obtained. In this work we synthesized original ResNet architecture of the convolution neural network with 26 trainable layers, 415 193 configurable parameters, carried out statistical analysis of structural characteristics of the dataset and adapted the standard binary cross entropy loss function, developed a special strategy for learning the neural network. Moreover, an adequate criterion of observability of the constellation in the image was formed. We also studied the influence of noise on the quality and stability of the received solutions.
A Non-Autoregressivee Network for Chinese Text to Speech and Voice Cloning

Text to speech (TTS) has been evolving rapidly these years. Researchers have successfully converted English text into speech which sounds like natural speaker, proposing numerous models from RNN to non-autoregressive network. However, the migration of these models to Chinese TTS is still an issue because of its prosodic phrasing problems and large character set, not to mention the disappointing outcomes of those successfully-migrated models, most of which are autoregressive. In this paper, we successfully migrate FastSpeech2 to the field of Chinese TTS with generative adversarial network (GAN) as its discriminator for training to enhance the outcome. Postnet of Tactron2 is also applied to fine-tune the mel-spectrogram. We also use x-vector-based voiceprint extraction model to extract voiceprint to achieve voice cloning. The experiment is operated on both models which offers results of 3.83 mean opinion score (MOS) in terms of naturalness and 3.82 MOS in terms of similarity.
Abstractive Summarization with Word Embedding Prediction and Scheduled Sampling

Abstractive summarization models based on the encoder-decoder framework have made great advances over the recent years. Since most summarization datasets only provide a reference summary for each article, and encoder-decoder models typically adopt the negative log likelihood loss function, the predicted synonym of the target word is equally punished as other semantically dissimilar words. To mitigate the problem, we train the summarization model to additionally predict the word embedding of the target word. A loss function calculated from the distance between the predicted embedding and target embedding is then integrated into the training loss. Besides, ground truth words are provided during training, but they are not available during inference and the model has to use predicted words instead. The discrepancy can yield errors that accumulate quickly along the generated summary. To bridge the gap, we apply the scheduled sampling strategy that partially uses the generated words during training phase. Experiments on the mainstream CNN/Daily Mail dataset demonstrate that word embedding prediction and scheduled sampling can consistently improve the pointer-generator baseline.