Reviewerly/exHarmony huggingface dataset:
Authenticating Deeds/Organizing Society Considerations for Blockchain-Based Land Registries

For years now the hype surrounding block-chain as a new and more decentralized architecture to facilitate transactions has led people to search for cases of application for this emerging technology. Before thematizing the hype that has been propelling block-chain on the global stage, it is useful to mention the main novelty that block-chain brings: authentication of uniqueness of data in digital settings or “native authentication,” i.e. without relying on an external/non-digital intermediary. Without this property, it could not be used for digital currency, which, of course, must be unforgeable. Other relevant, and related properties are: decentralization (even if no unique guarantor is often substituted by clusters of miners), immutability (records cannot be deleted), auditability (mathematically provable), and pseudonymity (via key-pair identification).
Perceiving Social Computing via Social Networks on Cloud

Online social networks growing rapidly in the industry as well as in the academia. Nowadays, it is easy for people to share their facilities, data, and resources through social networks. Social network made a drastic change in the method of interaction and communication. The digital communities can represent, document, and explore the social relationship by establishing and contributing. With this development of social networking, computational applications also further develop social interaction and collaboration beyond personal computing. The compute networks facilitate the intersection of social studies and human associated which is the part of information technology further administered by social computing. The key findings of this work converse for the service of social networks control by social computing also demonstrate the model of social computing of the social networks which clarifies the allocation of resources in the cloud.
Certain Inverstigations on Privacy-Preserving Data Mining Using Three-Dimensional Spin Revolution (3DSR) Scheme

The information disquiets are attractive in information extraction for safeguarding confidentiality. The key problem in information disquiets is to portray the equalization of two differing features as confidentiality safeguarding and information usage. The intention is to design a symmetrical information disquiets scheme by employing information disquiets and three-dimensional spins. Here the elements are segregated into clusters of three where each and every element is revolved about three pairs of alignments. The angle of revolution is chosen based on the difference in terms of confidentiality parameters which makes the actual reformation of information quiet intricate. Several information extraction schemes such as categorization and grouping are regular to symmetrical disquiets, and the information usage is conserved in the designed scheme. The results of the designed schemes offer improved confidentiality safeguarded outcomes and information usage against the conventional schemes.
Semi-supervised Keypoint Localization

Knowledge about the locations of keypoints of an object in an image can assist in fine-grained classification and identification tasks, particularly for the case of objects that exhibit large variations in poses that greatly influence their visual appearance, such as wild animals. However, supervised training of a keypoint detection network requires annotating a large image dataset for each animal species, which is a labor-intensive task. To reduce the need for labeled data, we propose to learn simultaneously keypoint heatmaps and pose invariant keypoint representations in a semi-supervised manner using a small set of labeled images along with a larger set of unlabeled images. Keypoint representations are learnt with a semantic keypoint consistency constraint that forces the keypoint detection network to learn similar features for the same keypoint across the dataset. Pose invariance is achieved by making keypoint representations for the image and its augmented copies closer together in feature space. Our semi-supervised approach significantly outperforms previous methods on several benchmarks for human and animal body landmark localization.
Learning Disconnected Manifolds: Avoiding The No Gan's Land by Latent Rejection

Standard formulations of GANs, where a continuous function deforms a connected latent space, have been shown to be misspecified when fitting disconnected manifolds. In particular, when covering different classes of images, the generator will necessarily sample some low quality images in between the modes. Rather than modify the learning procedure, a line of works aims at improving the sampling quality from trained generators. Thus, it is now common to introduce a rejection step within the generation procedure. Building on this, we propose to train an additional network and transform the latent space via an adversarial learning of importance weights. This idea has several advantages: 1) it provides a way to inject disconnectedness on any GAN architecture, 2) the rejection avoids going through both the generator and the discriminator saving computation time, 3) this importance weights formulation provides a principled way to estimate the Wasserstein's distance to the true distribution, enabling its minimization. We demonstrate the effectiveness of our method on different datasets, both synthetic and high dimensional, and stress its superiority on highly disconnected data.
Metodi e tecniche di trattamento automatico della lingua per l'estrazione di conoscenza dalla documentazione scolastica

In its daily activities, the school produces large amounts of textual data in response to different needs, ranging from planning activities, to supporting internal and external communication and self-assessment. These data represent a vast and varied information asset which needs to be profitably analysed to monitor and study ongoing phenomena in the field of education. To profile the contents conveyed by this continuously growing documenta¬tion, we propose advanced methods and techniques for knowledge extraction based on language technologies. The paper illustrates the first and promis¬ing results of the proposed methodology for monitoring educational strategies through time, space and different types of schools, starting from free texts. The methodology has been tested within two scenarios focusing on i) the analysis of the strategic choices and actions implemented by schools to achieve improvement and innovation objectives, and ii) the monitoring of the technical-professional and soft skills developed in School-Work Alternation experiences. Although preliminary, achieved results show that Natural Language Processing enabled methods and techniques can lead to effective and exhaustive school profiling.
Using Move software by geological field works

Summary This paper is written about Field Move Clino and FieldMove programs and their capabilities. We describe the features of processing in field works. Advantages and disadvantages of using gadgets during geological field camp are indicated. These thesis demonstrate that Move software is the right solution for field geological work.
Preprocessing Techniques to Improve CNN based Face Recognition System

In this work, deep CNN based model have been suggested for face recognition. CNN is employed to extract unique facial features and softmax classifier is applied to classify facial images in a fully connected layer of CNN. The experiments conducted in Extended YALE B and FERET databases for smaller batch sizes and low value of learning rate, showed that the proposed model has improved the face recognition accuracy. Accuracy rates of up to 96.2% is achieved using the proposed model in Extended Yale B database. To improve the accuracy rate further, preprocessing techniques like SQI, HE, LTISN, GIC and DoG are applied to the CNN model. After the application of preprocessing techniques, the improved accuracy of 99.8% is achieved with deep CNN model for the YALE B Extended Database. In FERET Database with frontal face, before the application of preprocessing techniques, CNN model yields the maximum accuracy of 71.4%. After applying the above-mentioned preprocessing techniques, the accuracy is improved to 76.3%.
Data Augmentation Techniques for the Video Question Answering Task

Video Question Answering (VideoQA) is a task that requires a model to analyze and understand both the visual content given by the input video and the textual part given by the question, and the interaction between them in order to produce a meaningful answer. In our work we focus on the Egocentric VideoQA task, which exploits first-person videos, because of the importance of such task which can have impact on many different fields, such as those pertaining the social assistance and the industrial training. Recently, an Egocentric VideoQA dataset, called EgoVQA, has been released. Given its small size, models tend to overfit quickly. To alleviate this problem, we propose several augmentation techniques which give us a +5.5% improvement on the final accuracy over the considered baseline.
Research on prediction of concrete frost resistance based on random forest

Poor frost resistance of concrete will accelerate the deterioration of concrete structure and shorten the service life of concrete. This paper predicts the frost resistance of concrete and selects the initial index of factors that affect the frost resistance of concrete. The random forest algorithm is introduced to remove the unimportant indicators in the initial indicator system of the concrete mix ratio and determine the optimal indicator combination. The relative dynamic elastic modulus is used as the output index of random forest to predict the frost resistance of concrete. The proposed random forest model provides a reasonable and effective method for concrete frost resistance prediction.
PEMANFAATAN TEKNOLOGI INFORMASI DAN KOMUNIKASI DALAM MATA KULIAH BAHASA INDONESIA DI PERGURUAN TINGGI

AbstractThis study aims to describe the role of information and communication technology in Indonesian language learning in tertiary institutions. The research method is in the form of literature review or literature study. This research examines critically and deeply the theoretical sources associated with the problems in research. Based on the research results, the following conclusions can be drawn. (1) Information and communication technology is related to communication and information, computers, and a set of other technology-based tools that are experiencing rapid development and affecting education in Indonesia, especially in Indonesian language learning in tertiary institutions. Information and communication technology can be used as a means for compiling data and so on relating to education, especially in learning; (2) Learning has a meaning as a process to gain an experience in accordance with the purpose of a lesson, in learning Indonesian, an attractive medium is needed so that students are more interested in learning. Information and communication technology can be used as a means to increase student interest in learning Indonesian; (3) information and communication technology can be utilized in the four competencies that exist in Indonesian, namely writing, speaking, listening, and reading. Through the use of information and communication technology, it can improve the quality of lessons, student quality, student interest, and at the same time improve the quality of lecturers in technology. Keywords: Information and Communication Technology, Indonesian Language Learning, Higher Education
Two-step artificial neural network to estimate the solar radiation at Java Island

&lt;span&gt;The availability of information about solar radiation characteristics, particularly solar radiation predictions, is important for efficiently designing solar energy systems. Solar radiation information is not available in Indonesia because official measurements have not been conducted by the Indonesian Meteorological, Climatology, and Geophysical Agency (BMKG). In this study, a new two-step artificial neural network (ANN) is proposed to estimate both the daily average and hourly solar radiation at Java Island, Indonesia. The input parameters for the daily average solar radiation estimation are the location and time required, along with five selected monthly meteorological parameters that BMKG predicts for the subsequent month. The selected meteorological parameters are temperatures, relative humidity, and precipitation. The estimated daily average solar radiation is then used as the input parameter of the hourly solar radiation estimation along with the local time and location. The ANN training was conducted using two years of data, 2018 and 2019, from Surabaya and Jakarta, while the validation was performed in the same cities for January through July 2020. The accuracy of the proposed method is comparable to previous studies with an average R2 of 98.70% for the daily average solar radiation estimate and 97.44% for the hourly solar radiation estimate.&lt;/span&gt;
Analysis of Meta-Learning Approaches for TCGA Pan-cancer Datasets

Cancer has been characterized as a heterogeneous disease, and the classification of cancer subtypes has become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients and provide clinical decision support for clinicians. With the advance of machine learning in the last decade, many researchers employ machine learning to tackle the cancer classification problem. Importantly, traditional machine learning algorithms require a large amount of annotated data for model training. However, collection of large amounts of annotated data is time-consuming and expensive and may not be realistic in real-world activities. Facing data scarcity, meta-learning is proposed to tackle this problem. Meta-learning utilizes prior knowledge learned from related tasks and generalizes to new tasks of limited supervised experience, and it has been applied in many fields to tackle scarce annotated data problem, such as few-shot image classification, drug discovery, etc. As data scarcity is common in cancer research and diagnosis studies, and there are only few previous studies that classify cancers based on limited annotated data. We explore the meta-learning algorithm (MAML) to tackle the scenario where only limited annotated data are available. In this work, our objective is to comprehensively compare MAML among few-shot learning methods (matching network and prototypical network) and traditional machine learning methods (random forest and K-nearest neighbor). Experimental results on The Cancer Genome Atlas (TCGA) cancer patient data demonstrates the effectiveness and superiority of MAML over other methods, including its ability to outperform the other methods using 4.5-fold fewer features.
Synergy of Intelligent Algorithms for Efficient Child-Robot Interaction in Special Education: A Feasibility Study

In this paper the problem of algorithms’ interoperability in the context of an intelligent child-robot interaction for special education is studied. Given a technically demanding intervention scenario designed by experts, the set of algorithms that needed to implement the scenario are defined explicitly. Following a test case implementation involving a NAO robot, a feasibility analysis of the algorithms’ synergy, with respect to the adopted architecture consisting of the robot, its sensory modules and the timing performance of the incorporated algorithms is presented. The developed algorithms have been tested both in laboratory conditions as well as in a pilot study involving three children. The experiments reveal the hardware and software requirements for the implementation of such an intervention scenario with high intelligent child-robot interaction capabilities and highlight the need for an alternative system architecture.
Decoupling Global and Local Representations via Invertible Generative Flows

In this work, we propose a new generative model that is capable of automatically decoupling global and local representations of images in an entirely unsupervised setting, by embedding a generative flow in the VAE framework to model the decoder. Specifically, the proposed model utilizes the variational auto-encoding framework to learn a (low-dimensional) vector of latent variables to capture the global information of an image, which is fed as a conditional input to a flow-based invertible decoder with architecture borrowed from style transfer literature. Experimental results on standard image benchmarks demonstrate the effectiveness of our model in terms of density estimation, image generation and unsupervised representation learning. Importantly, this work demonstrates that with only architectural inductive biases, a generative model with a likelihood-based objective is capable of learning decoupled representations, requiring no explicit supervision.
Learnable Bag Similarity Based Deep Multi-Instance Network for Breast Cancer Diagnosis

Computer-aided diagnosis for breast cancer is an important and challenging research problem in medical image analysis. The main difficulty lies in that only image-level labels rather than fine-grained patch-level labels can be gotten for medical images in general. This situation fits well with the settings of Multi-Instance Learning (MIL). Following this line of research, Bag Similarity Network (BSN) uses the inter-bag similarities to learn the relationship between bags (images) and achieves a good performance in automatic diagnosis of breast cancer. Nevertheless, the inter-bag similarities are pre-defined rather than learnable. In this paper, we propose a Learnable Bag Similarity Network for deep MIL, called LBSN, to aid breast cancer diagnosis. To implement automatic similarity learning, LBSN first extracts fixed numbers of global representations of each bag using the attention mechanism, and then employs channel and spatial attention on similarity matrices for learning the relation of bags and that of instances, respectively. The experimental results on a publicly available breast cancer data set demonstrate that the proposed LBSN outperforms BSN by a large margin in terms of classification accuracy.
Understand Watchdogs: Discover How Game Bot Get Discovered

The game industry has long been troubled by malicious activities utilizing game bots. The game bots disturb other game players and destroy the environmental system of the games. For these reasons, the game industry put their best efforts to detect the game bots among players' characters using the learning-based detections. However, one problem with the detection methodologies is that they do not provide rational explanations about their decisions. To resolve this problem, in this work, we investigate the explainabilities of the game bot detection. We develop the XAI model using a dataset from the Korean MMORPG, AION, which includes game logs of human players and game bots. More than one classification model has been applied to the dataset to be analyzed by applying interpretable models. This provides us explanations about the game bots' behavior, and the truthfulness of the explanations has been evaluated. Besides, interpretability contributes to minimizing false detection, which imposes unfair restrictions on human players.
Seismic Monitoring and Data Processing at the National Institute for Earth Physics – Romania

Constantin Ionescu, Mihaela Popa, Cristian Neagoe and Daniela Veronica Ghica report on seismic monitorig at the National Institute for Earth Physics in Măgurele, Romania, for the Summary of the Bulletin of the ISC.
An Efficient Protocol for Distributed Column Subset Selection in the Entrywise ℓp Norm

We give a distributed protocol with nearly-optimal communication and number of rounds for Column Subset Selection with respect to the entrywise {l1} norm (k-CSS1), and more generally, for the lp-norm with 1≤p<2. We study matrix factorization in l1-norm loss, rather than the more standard Frobenius norm loss, because the l1 norm is more robust to noise, which is observed to lead to improved performance in a wide range of computer vision and robotics problems. In the distributed setting, we consider s servers in the standard coordinator model of communication, where the columns of the input matrix A∈Rd×n (n≫d) are distributed across the s servers. We give a protocol in this model with O~(sdk) communication, 1 round, and polynomial running time, and which achieves a multiplicative k1p−12\poly(log⁡nd)-approximation to the best possible column subset. A key ingredient in our proof is the reduction to the lp,2-norm, which corresponds to the p-norm of the vector of Euclidean norms of each of the columns of A. This enables us to use strong coreset constructions for Euclidean norms, which previously had not been used in this context. This naturally also allows us to implement our algorithm in the popular streaming model of computation. We further propose a greedy algorithm for selecting columns, which can be used by the coordinator, and show the first provable guarantees for a greedy algorithm for the l1,2 norm. Finally, we implement our protocol and give significant practical advantages on real-world data analysis tasks.
Connecting Stakeholders through Educational Technology for Effective and Digitalised Higher Education Environments

The idea of “Education for All” is already explained in detail in the corresponding UNESCO World Declaration in the form of a “Framework for Action to Meet Basic Learning Needs” in 1990. Interestingly, challenges for global access to education are being worked out that are still relevant today: Universalising access and promoting equity, focusing on leaning acquisition, broadening the means and scope of basic education, enhancing the environment of learning, strengthening partnerships. Since then, many things have been turned to good, much remains to be done. However, today's omnipresent digitization offers completely new opportunities to live up to this vision. Serious changes and disruptive innovations are pushed by digital transformations. (UNESCO, 1990)
Inaccessible Island vs Urban Education in Greece. Knowledge-Based Summarization of Educational Videos by Analyzing Students’ Watching Patterns

During the last decade, the problem of inequality in education has been studied by several researchers, especially regarding urban and rural (inaccessible) areas. Most studies conclude that funding and adoption of new technologies are necessary to reduce inequalities. However they do not propose any specific means to overcome these inequalities. Towards this direction, in this paper a specific method is proposed that contributes to the reduction of inequalities, without demanding any funding. The method is designed especially for inaccessible areas (including isolated islands), which face network connectivity issues or Internet speed becomes very slow. In particular knowledge-based video summarization is introduced, to overcome boredom, frustration, anger etc. when students try to watch online educational videos, using limited throughput channels. The main question is: which information is less important so that it can be excluded or roughly described ? In this paper students play the role of human sensors, by letting recording and analyzing their watching behaviors. Then average watching patterns are extracted and used for video summarization, which is accomplished by a keyframes extraction algorithm that detects uncorrelated content. Finally, results are presented, to delimit the potential of such applications as well as to set the bases for future work.