Reviewerly/exHarmony huggingface dataset:
Clustering-Based Adaptive Dropout for CNN-Based Classification

Dropout has been widely used to improve the generalization ability of a deep network, while current dropout variants rarely adapt the dropout probabilities of the network hidden units or weights dynamically to their contributions on the network optimization. In this work, a clustering-based dropout based on the network characteristics of features, weights or their derivatives is proposed, where the dropout probabilities for these characteristics are updated self-adaptively according to the corresponding clustering group to differentiate their contributions. Experimental results on the databases of Fashion-MNIST and CIFAR10 and expression databases of FER2013 and CK+ show that the proposed clustering-based dropout achieves better accuracy than the original dropout and various dropout variants, and the most competitive performances compared with state-of-the-art algorithms.
Model Development of Accounting Internship Efforts in Dealing Industrial Revolution 4.0

This study aims to develop a model of an internship in the accounting department of Polines in order to achieve competence level KKNI and SKKNI. The basic of this model development is based on the purpose of the Polines Accounting Department interns, students and Company. This research can be categorized as research and development. The data in this study using quantitative and qualitative data such as advice and inputs provided by the students, supervising internships and companies. Quantitative and qualitative data using a questionnaire to assess the feasibility of the proposed model of apprenticeship. The next stage is the development of the model, at this stage derived from theoretical models that sought input from stakeholders through the FGD. The last stage is the implementation of the model is limited. The subjects were 43 students taking internships, industrial 5 and 20 people from higher education institutions. The object of research is the implementation of the accounting department intern program which will be reviewed by: monitoring the implementation of the internship, the duration of the apprenticeship, internship mentor in the field, the suitability of the curriculum to the needs of the world of work, and the skills of students. The results showed that the aspects that need to be improved is the design of the curriculum, internships and assessment management. Results of the model development internship in the Polines accounting department consisting of: (1) model of apprenticeship curriculum development; (2) the management model of apprenticeship; (3) implementing structural model of apprenticeship; (4) models and assessment procedures intern and (5) Industrial Attachment guide.
DIGITAL LITERACY AND ICT IN LEARNING AND INCLUSION - ECUADOR

reason or another have limitations for access to ICT, is among the main objectives of Cuban society. The present chapter ventures into this issue and systematically exposes achievements, projections and barriers still existing for the gradual digital inclusion of the disabled, elderly, women, children and young people from rural schools in remote areas. Educational experiences that have been obtained are addressed, as well as pedagogical and technological challenges that still need to be resolved in order to reduce the digital divide in Cuba.
DEVELOPING INTERACTIVE MULTIMEDIA FOR TEACHING DESCRIPTIVE TEXTS BASED ON PALEMBANG LOCAL CULTURE

The purpose of this study was to find out the validity, practicality, and potential effect of the developed interactive multimedia for teaching descriptive texts based on Palembang local culture. With Research and Development (R&amp;D) study, this research procedure was adapted from ADDIE model consisted of five phases, i.e. analysis, design, development, implementation, and evaluation. In evaluation phase, formative evaluation which consisted of self-evaluation, expert review, one to one evaluation, small group evaluation, and field test. Formative evaluation was conducted to see the validity, practicality, and potential effect of the product. Validity was evaluated in expert review phase for its content, construct, and media. Based on experts review the result showed that the average score of all aspects of the developed product was 3.58 which can be categorized as very highly valid. The practicality was evaluated in one to one evaluation and small group evaluation. The result showed that the product was very highly practical since the average score in both phases were 3.69 and 3.74 respectively. The potential effect was evaluated in the field test. The product also had very high potential effect after being tried out in the field test as 100 % of the students achieved the minimum mastery criterion which was 75. In conclusion, the developed product was potentially effective to be applied for the tenth grade students.Keywords: development research; interactive multimedia; descriptive texts; Palembang local culture
Document Management using Artificial Neural Network

Document Classification is the problem of assigning documents into predefined category making it easier to manage and sort documents. There are several algorithms using which the problem of classification can be solved. We use feed forward neural network with multiple layers (also called multilayer perceptron) trained using backpropagation algorithm for the problem for categorization of resume to make management of these documents easier. Neural networks can learn and model non-linear and complex relationships and model generalize solutions that can predict on unseen data. Applications of neural networks are character recognition, image processing and stock market prediction. Backpropagation algorithm which trains the neural net is a supervised learning approach which learns from errors in order to minimize it, thus, requires a lot of training and storage to produce efficient results.
INTERNALIZATION OF TEACHER EDUCATION AS A PART OF A UNIVERSITY INTERNALIZATION STRATEGY

The internationalization of University teacher education is very important today since teachers have to prepare students for living and working in the new global world. To prepare students for the globalization of the society, university teachers should have the opportunity to gain international experience – from traditional studies and internships to summer schools and degree theses abroad, blended learning formats, buddy programmes, international field trips to preparing English-language degree programmes together with international colleagues. In this paper the authors describe the IDUTE programme (International Dimension of University Teacher Education) designed to enhance the international dimensions of various components of University teacher education programmes, to transform courses in order to facilitate change and improvement in reaching the internationalization goals of their work and to help provide University teachers with greater global competence. This paper details the nature and specifics of the IDUTE programme and provides details of the programme’s outcomes and results. The authors have done extensive research to determine both the existing international content and programme practices for University teacher education and perceived needs for improvement. The IDUTE programme promotes internationalisation among technical teachers and students and expands intercultural competence into their studies and career through creation and development of separate 16/36/72-hour modules on international activities of University Teachers.
A Parallel Neural Network-based Scheme for Radar Emitter Recognition

Passive radar systems are used in the military for intelligence gathering, threat detection and as a support to electronic attack systems. Therefore, radar emitter recognition is a crucial task of reconnaissance systems for accurately identification of hostile threats. However, this problem is challenging due to the complicated noisy electromagnetic environment as well as the increasing complexity of modern radar signals. In this paper, we introduce a novel deep neural network-based scheme, named ParallelNet for the recognition of different radar types. In our approach, I/Q samples and radar pulse features extracted from received wideband signal are inputs of two parallel sub-neural networks. The outputs of sub-networks are subsequently combined to deduce the classification result. We realize extensive simulations to show that ParallelNet achieves an outstanding performance in terms of recognition accuracy and robustness in severely noisy conditions.
Formalizing Data Deletion in the Context of the Right to be Forgotten

The right of an individual to request the deletion of their personal data by an entity that might be storing it -- referred to as the right to be forgotten -- has been explicitly recognized, legislated, and exercised in several jurisdictions across the world, including the European Union, Argentina, and California. However, much of the discussion surrounding this right offers only an intuitive notion of what it means for it to be fulfilled -- of what it means for such personal data to be deleted. In this work, we provide a formal definitional framework for the right to be forgotten using tools and paradigms from cryptography. In particular, we provide a precise definition of what could be (or should be) expected from an entity that collects individuals' data when a request is made of it to delete some of this data. Our framework captures several, though not all, relevant aspects of typical systems involved in data processing. While it cannot be viewed as expressing the statements of current laws (especially since these are rather vague in this respect), our work offers technically precise definitions that represent possibilities for what the law could reasonably expect, and alternatives for what future versions of the law could explicitly require. Finally, with the goal of demonstrating the applicability of our framework and definitions, we consider various natural and simple scenarios where the right to be forgotten comes up. For each of these scenarios, we highlight the pitfalls that arise even in genuine attempts at implementing systems offering deletion guarantees, and also describe technological solutions that provably satisfy our definitions. These solutions bring together techniques built by various communities.
Games Applied to Children with Motor Impairment using the Myo Wearable Device

The characteristic of immersion, involvement and motivation have made serious games an important tool to be used in the medical field. However, there are people that, having some sort of physical disability, are not able to, or do not feel motivated to play. This work presents a strategy to support human- computer interactions for children with a disability in their upper limbs through a wearable device, with the objective of improving access to digital games for the target audience. For the development of this work, the wearable device Myo was used to control a puzzle game as a means of providing the interaction between the individual and the game. In this context, the game is controlled with the movement of the user's upper limb that presents the disability, through the Myo device. For the validation of this research, the game developed was made available for individuals presenting a disability in the upper limbs, from an age range between five and fifteen years old, of the Association of Assistance to the Disabled Child (AACD). Soon, it was noticed that the participants of the research soon could play while utilizing the deficient limb with Myo's support and felt more motivated to play. On the long run, it is expected to contribute to the motivation of patients through technological innovation, so that the process of body consciousness is made more interactive.
On the (29, 5)-Arcs in PG(2, 7) and Some Generalized Arcs in PG(2, q)

Using an exhaustive computer search, we prove that the number of inequivalent ( 29 , 5 ) -arcs in PG ( 2 , 7 ) is exactly 22. This generalizes a result of Barlotti (see Barlotti, A. Some Topics in Finite Geometrical Structures, 1965), who constructed the first such arc from a conic. Our classification result is based on the fact that arcs and linear codes are related, which enables us to apply an algorithm for classifying the associated linear codes instead. Related to this result, several infinite families of arcs and multiple blocking sets are constructed. Lastly, the relationship between these arcs and the Barlotti arc is explored using a construction that we call transitioning.
Case Study: Feasibility Analysis of Close-in Proximity Operations Using Angles-only Navigation

The feasibility of angles-only navigation (AON)-based close-in proximity operation is studied for application to upcoming small satellite-based uncooperative rendezvous missions. For the relative motion equations described by Yamanaka-Ankersen, the Square-Root Unscented Kalman Filter (SRUKF) is adapted for relative position estimations, considering low-volume/mass, lower-power, simple optical/infrared instruments together with Lidar measurements. Considering simplicity and achievements in engineering, the multi-pulse glideslope guidance law is utilized. Using a linear covariance technique, a complete set of analytical functions are deduced for closed-loop true dispersions and estimated dispersion analysis. Monte Carlo simulation proves the offset observation model proposed provides a good solution for the range-observability dilemma: the range estimation error decreases from an initial decameter-level to a final decimeter-level. Two proximity operation mission trajectories are designed: direct v-bar quasi-linear glide approach for robotic arm capture or net capture, and glide approach (i.e., circumnavigate–glide approach for capture with attitude requirement). Using a well-designed relative approaching guidance profile, the camera operational range can be extended. This enables the operation time of the Lidar to be shortened, or even replaced by an optimal/infrared camera. This is very helpful in saving mass and power for the chaser. The variable-structure SRUKF proposed leads to a more robust trajectory: true dispersion is improved by two orders in the 100 m range, and more accurate covariance prediction (i.e., all sampled trajectories are inside the ellipse) when compared to the standard SRUKF. The analysis method proposed, which could raise analytical closed-loop linear covariance, is applicable for onboard maneuver planning and real-time closed-loop control error estimation.
How Improved Attribution in Cyber Warfare Can Help De-Escalate Cyber Arms Race

Cyber warfare is a critical component of nation-states' military arsenals, and a cyber arms race has emerged in the absence of international agreements (norms and confidence-building measures) to limit the use of cyber warfare.One key impediment to building consensus on cyber norms and confidence-building measures is a lack of transparency in cyber weapons development and poor attribution of attack perpetrators.Recently, there has been an improvement in attribution capabilities based on better data collection and the profiling of known hackers and nation states by intelligence agencies, and this should give impetus to efforts to establish confidence-building measures and cyber norms.This article discusses the need for and challenges associated with attribution, recent advances that will lead to better attribution, and the collective responsibility of nation states in addressing these challenges.It suggests several initiatives to reduce chances of cyber conflict, as well as to prevent cyber conflicts from escalating, such as defining clear processes for attribution, creating neutral bodies for incident analysis, and limiting the scope of retaliation based on the confidence in attribution.
APPLICATION OF THERMAL IMAGING CAMERAS IN CRIME DETECTION

The application of thermal imaging cameras in crime detection has raised the question of the legality of their use, as well as the procedural value of thus obtained information. With regard to these questions, the standpoint of the U.S. court practice has shown diametrically opposite views. The earliar U.S. courts rulings took the position that the application of thermal imaging cameras was not subject to the fulfillment of any particular conditions and that it was encompassed by police discretionary decisions. The position of later rulings was that the application of new technologies, including thermal imaging cameras, was subject to basic conditions required for searching, i.e. mandatory obtaining of the court order with the purpose of protecting the right to privacy. As the application of thermal imaging cameras in the Republic of Serbia is prescribed neither by laws nor by by-laws, it could be governed by general regulations on the use of technical means in implementing operational tactical measures and actions, as well as gathering of evidence. Therefore, thermal imaging cameras might be used in police actions, such as police observation, covert surveillance and recording. In the course of covert surveillance and recording, as part of theevidence gathering process, the use of thermal imaging cameras would be regulated by the same conditions by which the undertaken actions are regulated. Since the possibility of the application of thermal imaging cameras while performing police observation is not explicitly provided for, dilemmas with regard to their use still remain, as well as the issues concerning their procedural value.
A new case-based CAD scheme using a hierarchical SSIM feature extraction method to classify between malignant and benign cases

The purpose of this study is to assess feasibility of developing a new case-based computer-aided diagnosis (CAD) scheme of mammograms based on a tree-based analysis of SSIM characteristics of the matched bilateral local areas of left and right breasts to predict likelihood of cases being malignant. We assembled a dataset involving screening mammograms acquired from 1000 patients. Among them, 500 cases were positive with cancer detected and verified, while other 500 cases had benign masses. Both CC and MLO view of the mammograms were used for feature extraction in this study. A CAD scheme was applied to preprocess the bilateral mammograms of the left and right breasts, generate image maps in the special domain, compute SSIM-based image features between the matched bilateral mammograms, and apply a support vector machine model to classify between malignant and benign cases. For performance evaluation, CAD scheme was trained and tested using a 10-fold cross-validation method. The area under a receiving operating characteristic curve (AUC) was computed as an index of performance evaluation. Using the poll of 12 extracted SSIM features, the CAD scheme yielded a performance level of AUC = 0.84&plusmn;0.016, which is significantly higher than using each individual SSIM feature for the classification purpose (<sub>p</sub> &lt; 0.05), and an odds ratio of 19.0 with 95% confidence interval of [15.3, 29.8]. Thus, this study supports the feasibility of applying an innovative method to develop a new case-based CAD scheme without lesion segmentation and demonstrates higher performance of new CAD scheme to classify between malignant and benign mammographic cases.
Innovation and Practice of Teaching Reform in Higher Vocational Colleges under Artificial Intelligence Environment

Abstract With the great success of machine learning algorithms represented by deep learning in the fields of machine vision and speech recognition, as well as the abundant and continuous data resources provided by cloud computing and big data, artificial intelligence has entered an unprecedented period of rapid development. Deeply changing all walks of life, the state requires corresponding intelligent education, but the artificial intelligence + education road is how to go, there is still no systematic discussion. In view of this article, artificial intelligence is used to promote the research of teaching reform, in order to enlighten the application of artificial intelligence teaching and promote education. The intelligent development of teaching.
Study on optimization of rake head density of suction hopper dredger based on bat algorithm and extreme learning machine

The dredging output of suction dredger mainly comes from the suction density of the rake head. Accurate prediction of suction density is of great significance to improve the dredging output of suction dredger. In order to overcome the shortcomings of low accuracy and poor real-time performance of the current inhalation density prediction methods, a bat algorithm is proposed to optimize the inhalation density prediction method of extreme learning machine. The bat algorithms for optimizing extreme learning machines prediction model is constructed based on the measured construction data of “Xinhaifeng” Yangtze Estuary, and compared with other prediction models. Finally, the bat algorithms for optimizing extreme learning machines model is used to build the output simulator of inhalation density. Compared with the actual construction, the selection of control parameters is analyzed when the output of inhalation density is the best. Experients show that bat algorithms for optimizing extreme learning machines prediction has high accuracy and good stability, and can provide scientific and effective reference for yield prediction and construction guidance.
Keyphrase Generation With CopyNet and Semantic Web

Keyphrases provide core information for users to understand the document. Most previous works utilize machine learning based methods for keyphrases extraction and achieve promising performance. However, these methods focus on identify keyphrases from the input text, and can not extract keyphrases that do not appear in the text. In this paper, we present an encoder-decoder framework, which incorporating copying mechanism, to generate keyphrases for the given text. This framework (CopyNet) integrates the generation part and copying part. The generation part generates the keyphrase from the predefined vocabulary, and the copy part gets the keyphrases from the source text. Furthermore, we improve the CopyNet by using different probability of the two parts. To incorporate more related information for keyphrase generation, the automatically built keyphrase semantic web is merged into the dataset to participate in the training process of the neural network. Semantic similarity based and word co-occurrence based methods are used for keyphrase semantic web construction. We build a large-scale biomedical keyphrase dataset to evaluate the system performance. Experiments show that our improved CopyNet can achieve better performance with different portions of the generation and copying part, and the incorporation of the semantic web also effectively improves the keyphrase generation.
Exploration and practice of training mode of engineering talents based on open online courses

As Chinese economy changes from rapid-speed growth to superior-quality development mods, cultivating the talents for the emerging industries must keep up with The Times to make corresponding adjustments, as well as based on information technology, adopting the multi-paradigm training mode to cultivate amounts of the new engineering talents with cross-knowledge system and innovation.This paper discusses how to take advantage of the creative knowledge map of hourglass to rapidly promote students’ professional ability and the cultivation of their innovation in virtue of online courses, and finally comes to an exploration model.