Reviewerly/exHarmony huggingface dataset:
The Impact of FDI Inflows on GDP Growth in Iraq for the period (2004-2017): An applied study

This paper aims to examine the relationship between the changing foreign direct investment inflows and gross domestic product growth using yearly time-series observations for the period (2004-2017). The current study proposed the hypothesis which investigated utilizing the novel econometrics methods such as unit root test, cointegration test, and Granger causality test, the results revealed the existence of a long-run equilibrium relationship between the foreign direct investment inflows and gross domestic product growth during the period of the study, probably due to the little inflow to Iraq in the form to participate in pushing Iraqi economic growth due to the nonoil exports structure where an average of total values of petroleum exports to total Iraqi value of exports reach to (99%) during the study period.
A Decade of Research for Image Compression In Multimedia Laboratory

With the advancement of technology, we have supercomputers with high processing power and affordable prices. In addition, using multimedia expanded all around the world. This caused a vast use of images and videos in different fields. As this kind of data consists of a large amount of information, there is a need to use compression methods to store, manage or transfer them better and faster. One effective technique, which was introduced is variable resolution. This technique stimulates human vision and divides regions in pictures into two different parts, including the area of interest that needs more detail and periphery parts with less detail. This results in better compression. The variable resolution was used for image, video, and 3D motion data compression. This paper investigates the mentioned technique and some other research in this regard.
Intra-Model Collaborative Learning of Neural Networks

Recently, collaborative learning proposed by Song and Chai has achieved remarkable improvements in image classification tasks by simultaneously training multiple classifier heads. However, huge memory footprints required by such multi-head structures may hinder the training of large-capacity baseline models. The natural question is how to achieve collaborative learning within a single network without duplicating any modules. In this paper, we propose four ways of collaborative learning among different parts of a single network with negligible engineering efforts. To improve the robustness of the network, we leverage the consistency of the output layer and intermediate layers for training under the collaborative learning framework. Besides, the similarity of intermediate representation and convolution kernel is also introduced to reduce the reduce redundant in a neural network. Compared to the method of Song and Chai, our framework further considers the collaboration inside a single model and takes smaller overhead. Extensive experiments on Cifar-10, Cifar-100, ImageNet32 and STL-10 corroborate the effectiveness of these four ways separately while combining them leads to further improvements. In particular, test errors on the STL-10 dataset are decreased by $9.28\%$ and $5.45\%$ for ResNet-18 and VGG-16 respectively. Moreover, our method is proven to be robust to label noise with experiments on Cifar-10 dataset. For example, our method has $3.53\%$ higher performance under $50\%$ noise ratio setting.
PEDAGOGICAL ACMEOLOGY-BASED DEVELOPMENT OF SOCIO-PROFESSIONAL MATURITY IN THE PROCESS OF MASTER'S TRAINING

In the article the author shows the peculiarities of the development of socio-professional maturity of the future pedagogue in the context of pedagogical acmeology. Therefore, there is an urgent need to optimize the process of professional training of future lecturers on the basis of a new methodology that would combine personal and professional development in the educational process, direct professional self-determination and self-improvement of the future specialist, promote their self-realization and professional development. We believe that the process of professional training of future pedagogues should be based on the developed acmeogram and professiogram, model the professional and personal development of the specialist in accordance with the content and objectives of professional activity. This will not only direct the process of professional training to a certain ideal model of the specialist, but also to actualize the professional self-determination and self-development of the future pedagogue.Key words: future pedagogue, activity, personality, self-determination, self-improvement, acmeology.
Open-World Classification Algorithm to Product Identification

Classic supervised learning makes the shut world suspicion that the classes found in testing probably showed up in preparing. Be that as it may, this supposition that is frequently disregarded in actual applications. For instance, in a web-based media website, new subjects rise continually furthermore, in internet business, new classes of items show up every day. A model that can't identify new/inconspicuous issues or items is hard to work well in such open conditions. An alluring model using in such situations must have the option to (1) reject models from inconspicuous classes (not showed up in preparing) and (2) gradually become familiar with the new/concealed types to extend the current model. This is called open-world learning (OWL). This paper proposes another OWL technique dependent on meta-learning. The essential oddity is that the model keeps up just a powerful arrangement of seen classes that permits new courses to be included or erased with no requirement for model re-preparing. Each class is spoken to by a little understanding of preparing models.
Global Interactions in English as a Lingua Franca. How Written Communication is Changing under the Influence of Electronic Media and New Contexts of Use. Franca Poppi

Review of Global Interactions in English as a Lingua Franca. How Written Communication is Changing under the Influence of Electronic Media and New Contexts of Use by Franca Poppi
CHANGE-IT @ EVALITA 2020: Change Headlines, Adapt News, GEnerate

We propose a generation task for Italian – more specifically, a style transfer task for headlines of Italian newspapers. This is the first shared task on generation included in the EVALITA evaluation framework. Indeed, one of the reasons to have this task is to stimulate more research on generation within the Italian community. With this aim in mind, we release to the participating teams not only training data, but also a baseline sequence to sequence model that performs the task in order to help everyone get started, even when not accustomed to Natural Language Generation (NLG) approaches. Contextually, we explore the complex issue of automatic evaluation of generated text, which is receiving particular attention in the NLG community.
Improving Blockchain scalability based on one-time cross-chain contract and gossip network

This study proposes a novel solution that provides secure interoperability for blockchains, which improves the overall scalability of the whole blockchain network. In our solution, a cross-chain task will build a one-time cross-blockchain contract. Each blockchain system can follow the contract to complete or this task. The result of tasks is bound with the system, hence can be anchored to all other blockchain systems through the gossip network. This work shows our result can provide linear scalability for the whole system and achieve consistency among honest systems.
Subsystem codes with high thresholds by gauge fixing and reduced qubit overhead

A new approach to implementing quantum error correcting codes reduces their physical resource overhead and improves their tolerance to noise from the environment.
LE METAMORFOSI DELLA POLITICA DI SALUTE MENTALE ITALIANA (1978-2013)/METAMORPHOSIS IN ITALIAN POLICIES OF MENTAL CARE (1978-2013)

The Italian reform law of 1978 is part of a “metamorphosis” which involves western countries’ mental health policies after the Second World War. That is, the questioning of Mental Hospital such place of takes care from people with mental health disorders, which is the basis of Asylums’ birth in 19th century. But in Italy, with the reform law, we have a paradigm shift in psychiatry health care in relation to this confinement paradigm, that had dominated the relationship from mental illness and modern society. The aim of this paper is to analyze the implementation of Italian’s Mental Health policies and the evolution of the politics of psychiatry after the closure of Mental Hospitals. Keywords: Mental Health Policies; Deinstitutionalization; Law n. 180.
A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger's Adversarial Attacks

The Universal Trigger (UniTrigger) is a recently-proposed powerful adversarial textual attack method. Utilizing a learning-based mechanism, UniTrigger generates a fixed phrase that, when added to any benign inputs, can drop the prediction accuracy of a textual neural network (NN) model to near zero on a target class. To defend against this attack that can cause significant harm, in this paper, we borrow the "honeypot" concept from the cybersecurity community and propose DARCY, a honeypot-based defense framework against UniTrigger. DARCY greedily searches and injects multiple trapdoors into an NN model to "bait and catch" potential attacks. Through comprehensive experiments across four public datasets, we show that DARCY detects UniTrigger's adversarial attacks with up to 99% TPR and less than 2% FPR in most cases, while maintaining the prediction accuracy (in F1) for clean inputs within a 1% margin. We also demonstrate that DARCY with multiple trapdoors is also robust to a diverse set of attack scenarios with attackers' varying levels of knowledge and skills. Source code will be released upon the acceptance of this paper.
Content-Augmented Feature Pyramid Network with Light Linear Transformers.

Recently, plenty of work has tried to introduce transformers into computer vision tasks, with good results. Unlike classic convolution networks, which extract features within a local receptive field, transformers can adaptively aggregate similar features from a global view using self-attention mechanism. For object detection, Feature Pyramid Network (FPN) proposes feature interaction across layers and proves its extremely importance. However, its interaction is still in a local manner, which leaves a lot of room for improvement. Since transformer was originally designed for NLP tasks, adapting processing subject directly from text to image will cause unaffordable computation and space overhead. In this paper, we utilize a linearized attention function to overcome above problems and build a novel architecture, named Content-Augmented Feature Pyramid Network (CA-FPN), which proposes a global content extraction module and deeply combines with FPN through light linear transformers. What's more, light transformers can further make the application of multi-head attention mechanism easier. Most importantly, our CA-FPN can be readily plugged into existing FPN-based models. Extensive experiments on the challenging COCO object detection dataset demonstrated that our CA-FPN significantly outperforms competitive baselines without bells and whistles. Code will be made publicly available.
Understanding Neural Networks with Logarithm Determinant Entropy Estimator

Understanding the informative behaviour of deep neural networks is challenged by misused estimators and the complexity of network structure, which leads to inconsistent observations and diversified interpretation. Here we propose the LogDet estimator -- a reliable matrix-based entropy estimator that approximates Shannon differential entropy. We construct informative measurements based on LogDet estimator, verify our method with comparable experiments and utilize it to analyse neural network behaviour. Our results demonstrate the LogDet estimator overcomes the drawbacks that emerge from highly diverse and degenerated distribution thus is reliable to estimate entropy in neural networks. The Network analysis results also find a functional distinction between shallow and deeper layers, which can help understand the compression phenomenon in the Information bottleneck theory of neural networks.
Dialectics as a Stage of Thinking Development

This article presents dialectics not only as an object of study but also as a methodology that needs to consider phenomena in their interrelation and development. Dialectics allow to comprehend the link between thinking and ideas as interaction of proactive and conservative components justifying the conclusion that the state of public consciousness is determined by the nature of thinking and worldview ideas the interrelation of which is the driver of development thereof. That said dialectics are regarded as a new quality of thinking as a stage of development thereof in interrelation with developing worldview ideas. On the basis of the analysis carried out using dialectics the author comes to the conclusion that the concepts of spiritual and material substances are inextricably interconnected dialectical opposites and the opposite forms of idealistic and materialistic worldviews have been formed by linear thinking with its inherent logic of opposing and one-sided nature of links. At the stage of opposition of idealism and materialism dialectics are an achievement of theoretical thinking in the conditions of linear thinking dominance in the public consciousness. The emergence of dialectics as of a new quality of thinking was due to the increase of contradictions between the proactivity of thinking and conservatism of idealistic and materialistic ideas. Dialectics are the opposite of the logic of contrast of the linear nature of thinking related to dualistic worldview and the interrelation thereof as of a thesis and an antithesis takes thinking to a new qualitative level. Comprehension in unity of the spiritual and material substances and of the opposites related thereto: consciousness and matter mind and reality subject and object creates a condition for comprehension of the unity of human and nature however dialectics while removing the contrast of the oppositions do not cover the unity of heterogeneous elements that are not opposites. To comprehend the unity of the world one needs to introduce  the category of systematic unity and to use a systematic approach. The worldview of the world systematic unity is interrelated with the systematic nature of thinking a transfer to which is dialectics.
Pengembangan Modul Ajar Digital Interaktif Metode Collaborative Learning Mata Pelajaran Sistem Komputer Kelas X SMK Muhammadiyah 2 Klaten

This research aims to: 1) to create digital education modules in computer system objects, 2) to understand the qualification of the digital teaching module from the assessments provided by media experts and high school students Muhammadiyah 2 Klaten, 3) the purpose of digital teacher modules is to improve student learning performance. This application is based on Android with Mit App Inventor software. This interactive digital teaching module is worthy of being reviewed by media experts, material experts and students who are testing in the field. This research is a research and development. The results obtained from this study are: 1) The average score of the media expert is 90 (excellent); 2) The average score of material experts is 75 (good); and 3) The average results of student assessments are in the good category. The conclusion of this study is that an interactive digital teaching module is feasible and effective to be used as a teaching module on computer system material with collaborative learning methods.
Getting lost in a layered labyrinth

Review of: Paul Trowler (2019). Accomplishing Change in Teaching and Learning Regimes: HigherEducation and the Practice Sensibility. Oxford University Press. 224 pp.
Learning from Web Data: Improving Crowd Counting via Semi-Supervised Learning

Deep neural networks have been widely used in crowd counting that aims to give the number of objects in images and videos. The performance of crowd counting models is greatly affected by the size of datasets with high quality annotations. However, collecting and annotating large-scale crowd counting dataset is labor-intensive and time-consuming. In this work, we exploit unlabeled web images to boost the performance of crowd counting models in a semi-supervised manner. Based on the observation that the rotation and splitting operations will not change the number of object in image, we design three auxiliary tasks to improve the feature representation ability of deep models. A semi-supervised multi-task learning framework is proposed by introducing three auxiliary tasks with respect to unlabeled data and our framework can be easily extended to other crowd counting models. An unlabeled dataset (Web-Crowd) with 8679 web images are collected for semi-supervised crowd counting. Experiments shows that our semi-supervised multi-task learning framework can effectively boost the performance of crowd counting models on UCF-QNRF dataset and ShanghaiTech dataset.
Corpus Delicti Characteristics Provided By The Criminal Code Of The Russian Federation

The elements of such corpus delicti as cyber fraud (Article 159.6 of the Criminal Code of the Russian Federation) were studied in detail and key features were considered in the paper.The reasons for identifying a special type of fraud, as well as the presence of classification problems were analyzed.The paper considers the possible existence of an objective need to delineate the method of theft of the property of another or acquisition of the right to someone's property by entering computer information from other methods of cyber fraud as a separate method of theft, or it is an integral part of one of the already known methods.The disposition of Article 159.6 of the Criminal Code of the Russian Federation presents methods of cyber fraud, which are mandatory signs of this crime, provided for in the considered article.It is important to note that we face a new form of stealing when dealing with cyber frauds, but without the direct fraud as such related to fraud or abuse of trust.To date, actions and concepts that are related to distortion, introduction or other processes that will change the conditions for information storage facilities, or will be related to changes in the procedure for processing or transmitting computer information through information communication means, have not yet been regulated and explained at the legislative level.
OpenGraphGym-MG: Using Reinforcement Learning to Solve Large Graph Optimization Problems on MultiGPU Systems

Large scale graph optimization problems arise in many fields. This paper presents an extensible, high performance framework (named OpenGraphGym-MG) that uses deep reinforcement learning and graph embedding to solve large graph optimization problems with multiple GPUs. The paper uses a common RL algorithm (deep Q-learning) and a representative graph embedding (structure2vec) to demonstrate the extensibility of the framework and, most importantly, to illustrate the novel optimization techniques, such as spatial parallelism, graph-level and node-level batched processing, distributed sparse graph storage, efficient parallel RL training and inference algorithms, repeated gradient descent iterations, and adaptive multiple-node selections. This study performs a comprehensive performance analysis on parallel efficiency and memory cost that proves the parallel RL training and inference algorithms are efficient and highly scalable on a number of GPUs. This study also conducts a range of large graph experiments, with both generated graphs (over 30 million edges) and real-world graphs, using a single compute node (with six GPUs) of the Summit supercomputer. Good scalability in both RL training and inference is achieved: as the number of GPUs increases from one to six, the time of a single step of RL training and a single step of RL inference on large graphs with more than 30 million edges, is reduced from 316.4s to 54.5s, and 23.8s to 3.4s, respectively. The research results on a single node lay out a solid foundation for the future work to address graph optimization problems with a large number of GPUs across multiple nodes in the Summit.
Dealers of Peaches and Lemons: How Can Used Car Dealers Use Trusted Car Data to Improve their Value Proposition?

The used car market is full of mistrust and uncertainties. Providing a vehicle history with trusted car data increases market transparency but also threatens the market position of used car dealers. But does a vehicle history also provide opportunities for them? Based on interviews with used car dealers, we propose three prototypes addressing current problems. The evaluation results of these prototypes indicate that they could improve the current market situation by easing data access , increasing the efficiency of sales related processes, and by expanding the competences of used car dealers. This paper is one of only few papers analyzing the current situation of used car dealers in the light of emerging new technologies. It indicates that used car dealers do not necessarily become disintermediated by the provision of trusted car data but rather have opportunities to reinvent themselves.
Ensemble Learning based methods for Classification : An Overview

Housing is not a homogeneous commodity, but rather a multidimensional bundle of services. If proliferation of urban slums, shanties and squatters’ settlements is to be drastically minimized or completely eliminated, it would be more beneficial for government and housing suppliers to provide such basic social amenities and important housing accessories.Ensemble approaches in classification is a very popular research area in recent years. An ensemble consists of a set of individually trained classifiers such as neural networks or decision trees whose predictions are combined for classifying new instances. It integrates multiple classifiers to build a classification model, and also used for improving the prediction performance.