Reviewerly/exHarmony huggingface dataset:
A Multi-Scale Detector Based on Attention Mechanism

After the two-stage detector is first introduced and popularized by R-CNN, two-stage detectors have achieved great performance, but there are still many problems. 1. FPN tries to use different level features to deal with the scale variance problem in object detection, but lacks the screening of information, leading to important information not protruding and introducing interference; 2. Although CNN realizes the automation of feature extraction, there are still many components that need manual design, such as loss functions, etc., how to choose suitable loss function for different stages are still to be explored. To overcome these issues, we introduce attention mechanism to FPN and propose a more effective feature fusion method for it. Besides, we explore the selection criteria about choosing loss function in each stage and find combining Smooth L1 loss function with the new loss function focused on inliers such as Balanced Smooth L1 yield better results than only using a single loss function. Based on them, we propose Attention Feature Pyramid Networks(AFPN) Detector and train with different loss functions. Experiments show that our method achieves 1.1 points AP improvement than FPN Faster R-CNN on MS-COCO.
Research on Website Group Data Monitoring System Based on Sensitive Words Filtering

Taking Chongqing Three Gorges Medical College as the research object, we design and implement a website group data monitoring system based on sensitive words filtering. The monitoring target of the system is the website group data, and this system is designed using C/S architecture, and using broadband or mobile network working mode. The programming language is Python, and the database is MySQL. This system can provide effective support for monitoring the data of Chongqing Three Gorges Medical College, and can also provide references for the relevant units.
The Design of 4C/ID in Teacher Training Course and Its Empirical Research

4C/ID (Four Component Instructional Design) is an integrated design theory, consisting of four basic steps, following the zigzag design path, which has certain advantages in guiding the design of professional training courses of the short and medium terms. The research carried out the design of teachers' training course based on the 4C/ID, and tested the improvement effect of teachers' TPACK before the course beginning and after the course accepted. And the conclusion is drawn: The design of teachers' training courses under the guidance of the 4C/ID can improve the level of the seven sub-dimensions of TPACK and its comprehensive level. In addition, teachers with strong beliefs in the teaching of IT have markedly improved their TPACK comprehensive level after the course.
Learning Between Real and Virtual. Narrative and Cognitive Elaborations of University Teaching

Cognitive styles express the preferential way of engaging personal resources in learning processes and represent one of the most important predictors of success in academic paths. The mental representations of university students related distance and presence learning are in some way related to these styles, which orient personal dispositions towards one or another mode of teaching organization. The present research work explores these representations and identifies the variables that students take as a reference point to establish the personal meaning attributed to the experiences of learning in presence and at at distance.
Data Protection for Files and Logs in Fog Cloud Storage Using 3DES

Nowadays cloud computing is being used extensively in daily life. With these new computing and communication technologies, new data security challenges arise. Privacy, integrity, and availability are the primary goals of the security structure. Many authentication schemes have been introduced over the years to ensure better security and to provide a wider range of services with a faster perspective. Each type of data is stored in the cloud and can be easily accessed anytime, anywhere. But, it lags due to location awareness when it comes to privacy in cloud computing. Improved cloud computing uses a fog-centric secure plan to ensure logs and information against unapproved access, change, and extinction. To obstruct unauthorized entry, the current scheme uses a technology called Xor-Combination to hide data. Besides, Xor-Combination results in preventing malicious recovery and ensuring better recovery capacity in case of data loss and Intermediate Fog does not provide server security. Cloud server and log files have been proposed and improved to provide higher security and intermediate security differently compared to the existing system for storing data on the fog server. Log files also contain personal information with equally critical protection and confidentiality of log data as an alternative scheme for storing logs in a cloud environment. Our proposed security to protect log files and data files on the main server on the Intermediate Fog server. We use an enhanced 3DES security method that provides better security than the Xor-combination technique.
A salient object detection algorithm based on RGB-D images

To solve the problem lack of RGB-D dataset for training, a salient object detection algorithm by cross dataset training only using RGB dataset is proposed. First, a simple convolutional neural network is designed to prediction foreground and background trained on RGB dataset MSRA10k. Then, the SLIC superpixel segmentation method is used to fuse the depth image information and cluster pixels, which can segment the edge of salient object more accurately. Finally, based on the global distribution characteristics of salient objects, superpixels are labeled using kernel probability density estimation. In order to verify the effectiveness, the proposed algorithm is compared with three newer algorithms, which has achieved better detection results in terms of PR curve, AUC and F-Measure. Experimental results show that the proposed method can improve the salient object detection of RGB-D image in the absence of RGB-D images for training.
Image Recognition based on Convolution Neural Network

This paper introduces the development of image recognition technology, summarizes several classic traditional algorithms, and then introduces the use of convolution neural network for image recognition, and summarizes the application and development of CNN in various fields in recent years, and proposes to recognize images by deep learning in complex or high-risk environment similar to substation Don't take the place of manpower to detect the working state of instruments and equipment, which has a development prospect. In the end, it puts forward some suggestions on the direction of image recognition using deep learning. In the future, we need to improve and optimize the model, intelligent algorithm and data set.
A Modern Interpreter of Predicate Linear Logic Formulas

This paper describes the design, implementation and possibilities of using a new interpreter of resource-oriented logical formulas. Predicate linear logic is an apparatus that captures the dynamics of a changing world uniquely -resources are consumed after their use. Although this logic system is quite well researched in the world due to its unique features, its most widespread computer implementations are woefully outdated and unsuitable for current research needs. The interpreter described in this article was created after a detailed study of the principles of predicate linear logic as well as existing interpreters of this logic. The new interpreter will be used not only in teaching the subject Logic for Informatics at the Technical University in Košice, but we also plan to expand its possibilities and build on the ongoing (so far only) theoretical research at our university.
Computing the Winner of 2-Player TANHINMIN

DAIHINMIN, which means Grand Pauper, is a popular playing-card game in Japan. TANHINMIN is a simplified variant of DAIHINMIN, which was proposed by Nishino in 2007 in order to investigate the mathematical properties of DAIHINMIN. In this paper, we consider a 2-player generalized TANHINMIN, where the deck size is arbitrary n. We present a linear-time algorithm that determines which player has a winning strategy after all cards are distributed to the players.
Expanding Penn State’s Office Delivery Service to Include Graduate Students

AbstractThe Office Delivery service at the Pennsylvania State University Libraries’ University Park campus allows faculty and staff to request and receive libraries owned and borrowed materials in their campus offices. This service is operated by the Interlibrary Loan team and was created in the summer of 2013. The service has grown since its inception, and in December 2017, the Interlibrary Loan team took steps to expand the service to graduate students. This article presents a brief history of the office delivery service, including procedures followed to request materials and steps taken to offer the service to graduate students. The article will also describe how staff solicited feedback from pilot users of the graduate delivery service, and plans for the future.Keywords: Delivery servicesgraduate studentsinterlibrary loanoffice delivery Additional informationNotes on contributorsMeg MasseyMeg Massey, MLS, Clarion University, became Manager of Interlibrary Loan at Pennsylvania State University Libraries in September 2017. Her research interests include outreach and marketing of interlibrary loan services, collaboration across library units, and the management of delivery services.
Teaching and learning in the COVID-19 Era: The experience of an Italian primary school class

The authors present a case study, where digital technologies were implemented for online education (particularly, mathematics education) at Italian primary school level Results of this study confirm that the quick transition to the online form of education, due to the COVID-19 pandemic, was successful and provided experience which may be useful in future didactic activities
Parity Check Based Descriptor for Hand Gesture Detection and Recognition

Feature extraction is one of the most important technique in many pattern recognition applications. More specifically, the performance of a hand gesture detection and recognition system depends on the robustness of the designed feature descriptor. In this paper, we propose a parity check based descriptor (PCBD) for hand gesture recognition. The descriptor extracts the intensity variations by establishing the bit-plane relationship between the neighboring pixels. The bit level thresholding is used to encode the patterns and extracted features trained by SVM classifier with HGRI database that improves the efficiency of hand gesture recognition with more discriminability with low memory storage. The experimental results show better performance of the proposed method as compared to existing state-of-the-art approaches.
An Efficient Image Interpolation Using Edge-error Based Sharpening

Image up-scaling is a technique for converting low resolution (LR) images to high resolution (HR) images of different size. While performing interpolation, there is some blur occurring in the high frequency (HF) region of image. So to overcome the above problem, a novel pre-processing technique based on the concept of unsharp masking is proposed. Here, the LR image is up-scaled using Lanczos interpolation method. Using the laplacian filter, the edge of the LR image and up-scaled images are calculated correspondingly. Then, the blurred HR edge is downsample and subtracted from LR edge, to produce a sharpened edge-error image. The LR image is added to sharpened edge-error image using the concept of unsharp masking. Again, this sharpened LR image is up-scaled to give a natural look HR image. It can be observed from the result that the proposed method performs better both subjectively and objectively.
A Fast Recovery of Encrypted Message Database of WeChat On GPU

WeChat is one of the most popular instant-messaging applications in the world. The recovery of WeChat's encrypted local database is concerned by forensic community. This paper studies a fast recovery method in a special situation that we only have the encrypted database without any helpful information. The new method uses a fast implementation of pbkdf2 function which is two times faster than before. The new method could successfully recover the database in about 30 seconds on our GPU system, while the previous method will take about 10 days on a desktop CPU.
DynOcc: Learning Single-View Depth from Dynamic Occlusion Cues

Recently, significant progress has been made in singleview depth estimation thanks to increasingly large and diverse depth datasets. However, these datasets are largely limited to specific application domains (e.g. indoor, autonomous driving) or static in-the-wild scenes due to hardware constraints or technical limitations of 3D reconstruction. In this paper, we introduce the first depth dataset DynOcc consisting of dynamic in-the-wild scenes. Our approach leverages the occlusion cues in these dynamic scenes to infer depth relationships between points of selected video frames. To achieve accurate occlusion detection and depth order estimation, we employ a novel occlusion boundary detection, filtering and thinning scheme followed by a robust foreground/background classification method. In total our DynOcc dataset contains 22M depth pairs out of 91K frames from a diverse set of videos. Using our dataset we achieved state-of-the-art results measured in weighted human disagreement rate (WHDR). We also show that the inferred depth maps trained with DynOcc can preserve sharper depth boundaries.
Sacrificing Privacy in the Fight Against Pandemics: How Far Is Too Far? Examples from Bosnia and Herzegovina and Montenegro

The pandemic of COVID-19 has put many governments to a test for which we have no precedent. In fighting to protect public health and economics, authorities worldwide have implemented various measures with one common denominator—significant limitations to a wide specter of human rights. While many governments have opted for various forms of high-tech surveillance and intrusions on the privacy of citizens, the governments of Bosnia and Herzegovina and the Republic of Montenegro have implemented more orthodox and rudimental measures. Various levels of governments in the two countries have been publishing lists of citizens who have been ordered to self-isolation, with some of them publishing the data of all of these individuals and some of them publishing the data of only those individuals who have breached self-isolation. While the purpose of these measures is unclear, and thus their benefits questionable, all the measures represent undoubtedly a significant interference with the right to private life. With both Bosnia and Herzegovina and the Republic of Montenegro being Member States of the Council of Europe and States Parties to the European Convention of Human Rights, and neither of them deciding to derogate from their obligations under this Convention, an important question arises: Would the measures undertaken by the authorities pass the standards established by the European Court of Human Rights? This paper attempts to provide answers by analyzing measures through the jurisprudence of the European Court of Human Rights.
Knowledge Distillation with Contrastive Inter-Class Relationship

Abstract Due to the high computational cost, the application of deep neural networks (DNNs) to the real-time tasks has been limited. A possible solution is to compress the size of the model so that the demand for computation resources can be decreased. A popular method is called knowledge distillation (KD). The basic philosophy behind KD is to transfer the information extracted from the larger teacher network to the smaller student network. The general knowledge transfer strategy is to match the one-to-one logit or intermedia layers between the teacher and student networks, correspondingly. This objective may neglect the informative relationship information between different samples. In this paper, we borrow the idea of metric learning to transfer the contrastive relationship information learned from teacher network to the student. Specifically, we use the well-known Triplet loss to regularize the training of the student network. By the modified negative selection strategy, our Contrastive Knowledge Distillation (CKD) method can efficiently improve the performance of the student network compared with the traditional KD methods. Empirical experiments on KD benchmarks and real-world datasets also demonstrate the superiority of CKD.
Applying Multiple Models to Improve the Accuracy of Prediction Results in Neural Networks

The neural network is being widely applied to solve various types of real-life problems. Continuous researches are needed to improve the accuracy of the neural network for practical application. In this paper, the method of applying multiple neural network models as a way to improve the accuracy of the conventional neural network is proposed. Each of multiple neural networks is trained independently with each other, and each generated model is integrated to produce a single prediction result. The proposed prediction model with multiple neural network models is evaluated as compared to the conventional neural network model in terms of accuracy. The results of the experiment showed that the accuracy of the proposed methods was higher than that of the conventional neural network model. The use of neural networks is expected to be continuously increasing. The proposed prediction model with multiple neural network models is expected to be applied in prediction problems in real-life to improve the reliability of the prediction results.
Chinese Named Entity Recognition for Hazard And Operability Analysis Text Based on Albert

HAZOP plays an important role in chemical safety, in the project of Chinese named entity recognition of HAZOP, In response to the problem of low accuracy of entity recognition and low efficiency of model training in the past methods, this paper proposes Albert-BiLSTM-CRF model. In the process of text pre-training, we use Albert model instead of word2vec, Bert, CNN and other traditional structures to train word vectors, and get word embedding at the character level related to context, which improves the ability of text feature extraction. The experiments show that: on the basis of the accuracy of entity recognition, the F1-score of this model on HAZOP test corpus reaches 91.09%, which is 2.27% and 2.36% higher than that of BiLSTM-CRF model and CNN-BiLSTM-CRF model, and is equivalent to Bert-BiLSTM-CRF model, with F1-score exceeding 90%. on the basis of the speed of model training, the average training time of each epoch is only 162 seconds, which is faster than the training speed of other models under the same configuration environment. The results reflect that the Albert-BiLSTM-CRF model can better solve the shortcomings of the previous models, not only to ensure high accuracy of entity recognition, but also to improve the training speed of the model. This model is more suitable for HAZOP in named entity recognition project.
Ontological Dialogue as Synergetic Model of Musical Dramaturgy Arrangement (by the Material of L. Dychko’s “Triumphant Liturgy”)

The article analyses L. Dychko’s “Triumphant Liturgy”, a symbolic composition of the early ХХІ century, through the lens of the “ontological dialogue” category, one of the key categories of musical analysis. Ontology of music reveals spiritual essence of a musical composition taking into account its genre specificity (secular or liturgical music). The authors propose a model of musical composition in which ontological parameters system integrates hierarchical elements through interaction of the human’s image and worldview, elements of musical language and spatial-temporal arrangement. Choice of L. Dychko’s composition is conditioned by the fact that it presents synthesis of ontological systems on the basis of synergy. The research findings are as follows: introduction of the “ontological dialogue” category allows interpreting musical composition dramaturgy as a spiritual vertical integrating different genres and styles. The synergetic type of musical dramaturgy arrangement is determined by specificity of liturgy as an archetype of the Christian culture.
Decentralized Multi-authority Anonymous Authentication for Global Identities with Non-interactive Proofs.

We propose a decentralized multi-authority anonymous authentication scheme in which a prover and a verifier are non-interactive. When a prover wants authorities to issue private attribute certificates, the authorities simply generate digital signatures on her global identity string. We give two security definitions; resistance against collusion attacks that cause misauthentication, and anonymity for privacy protection. Then we give a concrete construction by employing the structure-preserving signature scheme and the Groth-Sahai non-interactive proof system, the both of which are based on bilinear groups. We give security proofs in the standard model, which reduce to the security of the building blocks.
Iterative Collective Classification for Visual Focus of Attention Prediction

Identifying the visual focus of attention (VFOA) in multi-person discussions is related to many types of social interactions such as dominance/deference, like/dislike, and trust/distrust relationships. However, identifying the VFOA of a person is challenging since it changes rapidly in dynamic discussions. We propose ICAF (Iterative Collective Attention Focus), a system that simultaneously tracks the VFOA and speaking probabilities of all people. In order to apply the system previously unseen videos, we propose a lightly supervised technique to train the model in ICAF with performance which is only slightly worse than in the fully supervised case. Our system can visualize the predicted VFOA and speaking probabilities and interaction networks in videos.
A Survey on Personality Trait Recognition from Handwriting: Approach and Applications

An individual's personality is the true mirror of an individual's thoughts and qualifications as well as skill, physical strength, character, etc. Judging someone is the prime characteristic that happens when met someone or communicate with someone, it's human nature. The identification of such a personality relates to a person's handwriting. Writing is a good way of communication and the study of this field is included in graphology. This study shows that a person's mind concentrates more when he or she is writing. Handwriting is an expression of the subconscious mind or through of individual's mind. There is a relation between handwriting and personality which is presented in this study. It also discusses the extracted features, challenges, application of handwriting analysis.
Study of Factors Influencing the Formation of the Modern Social Situation in the Environment of Youth in a Multicultural Region

The modern stage of society’s development is characterized by the accelerated pace of globalization, rapid development of the world information space, which leads to a quick change in working conditions, public attitudes and beliefs. The upbringing of the younger generation is of particular importance. The article considers objective and subjective factors that generate a special state of individual consciousness, the social basis of destructive behavior. For the purpose of its prevention, it is necessary to strengthen the moral, spiritual and religious values that are common to all peoples and faiths.