Reviewerly/exHarmony huggingface dataset:
The Big Digital Contact Tracing Experiment<sup>1</sup>

Abstract Over a third of the world’s countries have launched contact‐tracing apps in response to the COVID‐19 pandemic, generating much hype about the prospect of automating contact‐tracing to manage outbreaks and to alleviate costly and labour‐intensive ‘manual’ contact tracing. Despite the stringent scientific criteria applied to the development of vaccines and other ‘biomedical countermeasures’ in responding to the pandemic, digital contact tracing has been widely recommended despite being an unproven public health intervention. Evidence for its effectiveness is thin, based on theoretical mathematical modelling and unsystematic observations across diverse country contexts. Establishing evidence of effectiveness is complicated by the fact that two private corporations, Apple and Google, own the technology upon which most government contact‐tracing apps are based and which, by design, restricts access to needed data on privacy grounds. In this policy insight, we make the case for why public health authorities urgently need to establish the effectiveness and societal implications of this big digital contact‐tracing experiment.
Data-driven Approaches for Discovery and Prediction of User-preferred Picture Settings on Smart TVs

We discover user-preferred picture settings on smart TVs and investigate whether it is possible to predict the users’ picture setting preferences through machine learning methods. We first perform K-means clustering on large-scale smart TV usage log data to understand how users fine-tune the factory default picture settings. Clustering results recognize 3–4 user groups who have reasonably different preferences toward the default settings. By characterizing these user preferences, we come up with new user-preferred picture settings. We perform an in-depth analysis of the newly discovered picture settings regarding diverse TV device characteristics. We also perform lab experiments to demonstrate how these new settings deliver different picture quality than the default. Next, we construct a deep learning-based classifier that learns and predicts the picture setting preferences of the users. The final trained model shows 86% accuracy in predicting users’ decisions to choose a specific picture setting out of four available options.
The use of innovative technologies to enhance the cognitive activity of students

The modern educational system has a peculiarity: acting as a user, it simultaneously stimulates the development of information and digital technologies. This characteristic is especially come out in the higher education system. At the same time, despite the objective and subjective attractiveness of modern information and digital technologies, it is necessary to realize that their adaptation is, although effective, but still an auxiliary tool in the organization and implementation of the educational process. The introduction of information technologies into the educational process makes it possible to significantly enrich the process of transferring knowledge from teacher to student, to facilitate understanding of complex topics, to level the repetitive monotony in the classroom, to increase the motivation of educational activities, to intensify the activities of students in the classroom, to form a conscious attitude to the educational process, etc. At the same time, the use of information and digital technologies should be justified, logically expedient, designed to complement (and not replace) the teacher's activities in the classroom.
CONVERTING DEGRADED IMAGES TO HIGH RESOLUTION IMAGES USING CONVOLUTIONAL NEURAL NETWORK

During uncertain times like natural disaster, satellite imagery plays a key role in navigating the places to view the most affected places, monitor the forthcoming situation and get better insights from the images. To achieve this, the quality of the satellitery image should satisfy the standard image quality. Presently, a wide range of approaches are proposed to restore the good quality image from distorted image using different Deep learning techniques. To solve the problem with degraded images, different methods like Full resolution convolutional neural network, Deep resolution convolution neural network and Very deep resolution neural network and PSNR (Peak signal to noise ratio) are utilized. Among all, PSNR provides better colors. But PSNR cannot provide better quality of textures. To overcome this problem, a model has been proposed to restore the image quality by using SRCNN (Super Resolution Convolution Neural Network). Super resolution is one of the image restoration techniques, where it will split each layer in order to perform end-to-end mapping in each layer to obtain a high-quality image from a diminished image. This proposed system will restore the better texture for an image when the input is given as degraded image and it further yields the similar scope image as output with High tenacity.
Intelligent System Construction and Data Analysis of Social Service Platform in Vocational Colleges

Intelligent system construction and data analysis of social service platform in vocational colleges is studied. There are many theories and methods related to data preprocessing, and they have been applied in many fields. In the process of data collection, it will be affected by the environment, usage and other aspects, resulting in the collected data can not be directly processed as an ideal sample. Hence, the PCA is applied for the data dimensional reduction, and the data distribution pattern is then considered for the comprehensive modelling. The application scenario is selected as the social service platform. The data is studied based on the integration of the theoretical model. The accuracy and efficiency are both improved.
ASIDL : A method for publishing missing data based on decomposition and slicing

In data release, data mining and analysis technologies can fully tap the value of data and promote the development of related industries. However, maliciously mining data seriously threatens users' private information. Therefore, the privacy protection data release technology that realizes data security and effectiveness has developed rapidly, and corresponding research results have been obtained. However, the current research directions are mostly related to the general protection needs, and the data release area for special protection needs still needs attention. Aiming at the problem of reconstruction errors that are almost unavoidable in the reconstruction method of missing data, this paper proposes the ASIDL method based on decomposition and slicing. The TBBL (Tuple Bucket Build based on 1-diversity) algorithm clusters the QI attributes and randomly extracts the top 1 Tuples with different sensitive values form buckets, and more tuples formed by permutation and combination are used to protect their identity. At the same time, the TBSL (Tuple Bucket Split based on 1-diversity) algorithm is used to split the existing tuple buckets under the premise of satisfying 1-diversity, so as to achieve smaller buckets and ensure information validity. This paper compares with existing algorithms, and the results show that the method proposed in this paper has lower information loss and higher efficiency.
Deep Semantic Hashing Using Pairwise Labels

Data hashing has been widely used to approximate large-scale similarity searches. Original text data can be represented using compact binary codes through hashing. Recent advances in neural network architecture have demonstrated the effectiveness of this method and its ability to learn hash functions more accurately. Most previous studies have been focused on encoding explicit supervised features, such as pointwise labels. Owing to the special nature of textual data, previous semantic text hashing approaches have only utilized pointwise label information. The purpose of the learning hash code developed in the present study is to make similar or related text have similar hash codes. Separate label learning for each datum is the easiest means of achieving this objective, but some inconsistencies remain. However, pairwise label information reflects the similarity more intuitively than pointwise label data. This paper proposes a supervised semantic text hashing method that utilizes pairwise label information. Several different methods based on the variational auto-encoder model are employed to calculate the pairwise similarity of text pairs. Because the similarity calculation process does not require additional parameters, the entire learning process is faster and more efficient than those in the existing methods. The experimental results obtained using public datasets show that the proposed method can exploit pairwise label information sufficiently well to outperform previous state-of-the-art hashing approaches. This report also describes variants involving different technique combinations, presents analyses of the efficiencies of these approaches, and discusses methods of improving their efficiencies.
Analysis of Risks to Data Privacy for Family Units in Many Countries

Ground-breaking research by Sweeney a number of years ago demonstrated the vulnerability of personal information that can be relatively easily discovered, allowing a malicious attacker or hacker to be able to recover sensitive information about an individual. In particular, the Sweeney research showed that close to 90% of the individuals in the United States can be identified uniquely using only three easily discoverable data points: postal code, gender, and birthdate including year. Our current research has shown that in most United Nation member countries, including the United States, almost 90% of family units at the same residence can be identified uniquely using only two easily found data points: postal code and birthdate including year of one family member.
Internet of Things Based Gesture Controlled Wheel Chair for Physically Disabled

A gesture-controlled wheelchair is beneficial to a physically disabled person. Though there is an incredible leap in the field of wheelchair technology, these advances haven’t been able to help steer wheelchair unaided—the wheelchair which can be operated by effortless hand signals. The user can control the wheelchair using the have movements and construes the motion calculated by the user and directs the wheelchair accordingly. It also aims at making the system wireless by using ZigBee that creates a personal area network for transfer of data wirelessly.
Alive+: A Private Cloud Messaging System for Android Devices

Conventional private messaging systems over the network often focus on the privacy problem but pay little attention to important performance qualities such as system reliability, scalability and fault tolerance. The existing systems usually meet the security requirements using verifiable shuffles to prevent a message loss or by generating an efficient noise across many servers using bloom filters. This paper introduces Alive+ system as a private cloud messaging to forward messages over the Android devices securely. Alive+ employs a cryptography scheme to guarantee the confidentiality of messaging process. Messages are encrypted and decrypted over the cloud server using AES algorithm and SHA-256 hash function to generate unique secret keys, thus strengthening Alive+ against possible network attacks. Alive+ is connected to a reliable cloud-based database designed to handle the user requests and communications efficiently. We evaluate the performance of Alive+ in terms of reliability, latency, and scalability under various scenarios and settings. The performance of Alive+ is also evaluated in terms of fault tolerance, CPU utilization, memory usage, and network traffic. The experimental results show that Alive+ is secure, reliable, and error-tolerant with high scalability levels, thereby serving a large number of users simultaneously.
Texture classification using improved ResNet based on multi-scale attention mechanism

Aiming at the problem of inaccurate classification of textures at different scales in traditional texture classification methods, this paper proposed a deep convolutional neural network (CNN) based on improved residual blocks to increase the accuracy of texture classification. First, the two convolution layers in the original residual block were replaced with two dilated convolution layers, and a spatial attention module after the second dilated convolution layers was inserted. Thereafter, the residual connections were used for feature fusion to obtain a greater receptive field and attention-enhanced features. Second, based on the improved residual blocks, a multi-scale texture classification CNN was stacked in a way of increasing the number of block channels. The experiment was performed on a 64-class texture dataset. Experiments show that, compared with the state-of-the-art methods, the proposed method achieved a higher classification accuracy of 99.17%.
Development of inquiry skills at lower secondary school level

Abstract As part of a national IT Academy project, our goal is to increase pupils’ interest in studying Informatics and STEM subjects in general. We consider the changes in education focused on targeted use of the IBSE approach to be key. We monitor the development of pupils’ skills and the degree of conceptual understanding of key concepts and phenomena. To introduce the necessary innovations in every STEM subject, a team of experts has proposed 60 innovative lessons (methodologies), for each subject. The methodologies were piloted and evaluated by experienced teachers, modified and re-entered in the verification. Teachers have the opportunity to participate in continuous education, where they gain experience with inquiry teaching approach, formative assessment and strategies for developing pupils’ skills. One of the key conditions for teachers to begin to trust in innovation is to gain confidence in their importance and benefits. In order to motivate teachers, we used initial measurements of selected skills of pupils. Our goal is to prove the necessity of systematic monitoring and targeted development of skills through the offered educational activities. Within the national project IT academy, a multiple choice test has been developed to assess skills to making predictions and following directions. The tasks are inspired by the real life situations pupils either have experienced before or it is possible they will experience. The test items have a form of an on-line assignment. The pre testing was conducted at the lower secondary schools joined into national project. Pre-test results are made available to schools to respond by introducing innovative methodologies into their school curricula. In the paper we present selected results of the entrance skills test, analysis of questions focused on the development of individual skill levels and comparison of results depending on the school year of pupils.
Extended Instruction Set Design for Transcoding Application Using RISC-V ISA

RISC-V is a coeval, refined and flexible open-source instruction set architecture (ISA) basically designed for computer architecture research. It presents a conventional and open base ISA and competitive reference hardware designs that anybody can freely use as a base for further innovation. This makes RISC-V unambiguously suitable for the later phase of development in microprocessor hardware architectures. Transcoding plays a crucial role in a multimedia streaming service. Content owners and publishers require transcoders to modify their data to different bitrates, formats and qualities before streaming them to end users with the best quality of service. In this paper, improving the performance of transcoding application using RISC–V ISA is explained by adding an instruction to RISC-V instruction set architecture. The three stages of a pipeline architecture, i.e., fetch, decode, and execute are verified. Each stage except multiplication operations taking one clock cycle used in DCT operation and the designed system is synthesized using Spartan 6 XC6SLX45 fgg484 device.
Hegel, Hölderlin and we, the ukrainians, or: is it worse to accept recommendations of the german idealism?

The article represents the explication of some Hegel’s ideas with coherence to Hölderlin’s philosophical reflections, all of which are dealing with the problem of making more significant the philosophy for the social life especially in the period of the national self-statement and radical cultural transformations on the crossing of the XVIII-th and XIX-th centuries. The communication between Hegel and Hölderlin shows the different ways of making philosophy more available for lower classes of population. Hölderlin’s considering on this problem was to make philosophical thinking more sensuous through its transforming into a new rational religion. Hegel perceived solving of this problem in education and teaching without reducing standards of philosophy as a science. The university philosophy with its abstract concepts and systems was criticized by Hölderlin and accepted by Hegel, because his aim was to activate the social and orientation functions of philosophical ideas. The popular philosophy with its metaphorical language was regarded by him as preparing for fundamental philosophical studies. The role of the philosophical lyric in the evolution of German Idealism is analyzed with its impulses for the renewal of philosophical thinking. Hegel shows some risks of symbiosis of the lyric and philosophy such as losing its freedom and turning into a new mythology with eclipse of reason. Hegel emphasizes the role of the grammar of philosophical thinking: its concepts and categories. The dialectical potential of concepts is illustrated on their cunning possibility what might be regarded as opening of closed systems. The adaptation possibilities of this theoretical and practical experience to the Ukrainian social and cultural contexts are regarded. Hegel’s interpretation of the dialectical interconnection existing between the philosophical creativity and the philosophical education for the foundation of the philosophical inquiring culture are actual today as well his demand to search for the truth. The causes for explaining Hegel’s intuitions about the concept’s and reason’s cunning are stating in their validity for developing strategies and practices of the spiritual renewal of the Ukrainian society.
Object Detection with Convolution Neural Networks and Custom-SmallerVGGNet

Computer vision has reached significant levels of Object detection. Whether performed via computer vision or deep learning, there are three main results that we to wish obtain, including a list of bounding boxes (or x and y coordinators) for each object in the image, a class label linked with each bounding label, and the probability or confidence score associated with each bounding box and class label. Object detection is becoming even more popular with the breakthrough of deep learning, and especially Convolutional Neural Networks (CNN) which are comprised of two elements, namely convolution layers and pooling layers. With these successes in deep learning, we have more choice on selecting a method and technology to experiment and improve accuracy. There are several datasets such as MNIST, Animal, CIFAR-10, SMILE and ImageNet; these datasets would be a based foundation for researcher to do the experiment. However, there is an approach which can achieve to get a dataset quickly. This paper will introduce two methodologies for object detection and determine the future research directions of researching with object detection in deep dive. First, the traditional object detection pipeline, which will focus on these techniques such as sliding windows, image pyramid and classification. Second, the based network of an object detection framework, this will used certain classification network as a based network in a deep learning object detection such as SmallerVGGNet, Faster R-CNN, SSD or YOLO. Lastly, we experimented using custom SmallerVGGNet and received a positive outcome in so far as greater than 95% of accuracy.
Investigating Materiality as a Frame for Supporting Creative and Organic Relations with Shape-changing Artifacts in Everyday Settings Over Time

The HCI and C&C communities have developed many unique shape-changing artifacts for supporting novel interactions and experiences. However, little research has been initiated to designing for supporting long-term relations with dynamic shape change. Recently, Wiberg has proposed the notion of the materiality of interaction as a new ontology for tangible computing. There is an opportunity to investigate how such a theory can frame creative and organic experiences with designed shape-changing artifacts in everyday settings over time. To do so, I aim to craft three different shape-changing artifacts as resources for supporting deployment studies. Reflecting on design processes and accumulating empirical data may contribute to further explorations on creativity, materiality, and temporality in interaction design and HCI fields.
Privacy Preserving System and Data Integrity Using Distributed Hash Table in Blockchain

With an expanding number of information focused on assaults on cloud-based workers, the hope is to actualize a blockchain backend so as to build security and diminish weaknesses. A customary cloud worker is unified consequently powerless to being a solitary purpose of disappointment. Utilizing certificateless cryptography with blockchain as a stage, the backend becomes decentralized wiping out the previously mentioned issue. Information would be put away in distributed hash tables (DHT) while the pointers to the DHT would be put away in the blockchain. Utilizing this technique can fulfill the three fundamental boundaries of certifiability, transparency, and availability. Edge computing would be actualized to deal with the IoT information at the organization edge rather than a distant cloud. Clients at that point do not have to stress over unapproved endeavors to get to information as vindictive endeavors can undoubtedly be identified. The whole framework is then discernible and responsible.
Research on the Model of College English Network Learning Platform Based on Education Big Data

With the development of society and the progress of science, network technology is changing the living state of modern people little by little. It also brings some challenges and opportunities to the field of education in China. Nowadays, the network learning platform of Big Data provides a lot of convenience for English teaching in colleges and universities in China. At present, many colleges and universities do not realize the importance of online teaching mode to learning English. Many teachers still adhere to the traditional idea and adopt the teaching mode of full hall irrigation, which leads to the extremely low quality of English teaching and the low interest of students in learning, so it is difficult to achieve the ideal learning effect. Therefore, under the background of Big Data, it is necessary to construct the model of college English network platform reasonably to help students improve their learning efficiency. This paper briefly summarizes the model of Big Data and network learning platform, and analyzes the application of Big Data in the field of education. The principle of model construction of college English online learning platform based on Big Data is deeply studied. At the same time, the problems of college English education in China are put forward, and the way of constructing English network learning platform model under the background of educational Big Data is analyzed.