Reviewerly/exHarmony huggingface dataset:
SoWaF: Shuffling of Weights and Feature Maps: A Novel Hardware Intrinsic Attack (HIA) on Convolutional Neural Network (CNN)

Security of inference phase deployment of Convolutional neural network (CNN) into resource constrained embedded systems (e.g. low end FPGAs) is a growing research area. Using practices, third party FPGA designers can be provided with no knowledge of initial and final classification layers. In this work, we demonstrate that hardware intrinsic attack (HIA) in such a secure design is still possible. Proposed HIA is inserted inside mathematical operations of individual layers of CNN, which propagates erroneous operations in all the subsequent CNN layers that lead to misclassification. The attack is non-periodic and completely random, hence it becomes difficult to detect. Five different attack scenarios with respect to each CNN layer are designed and evaluated based on the overhead resources and the rate of triggering in comparison to the original implementation. Our results for two CNN architectures show that in all the attack scenarios, additional latency is negligible (<0.61%), increment in DSP, LUT, FF is also less than 2.36%. Three attack scenarios do not require any additional BRAM resources, while in two scenarios BRAM increases, which compensates with the corresponding decrease in FF and LUTs. To the authors' best knowledge this work is the first to address the hardware intrinsic CNN attack with the attacker does not have knowledge of the full CNN.
Recognizing Predictive Substructures with Subgraph Information Bottleneck

The emergence of Graph Convolutional Network (GCN) has greatly boosted the progress of graph learning. However, two disturbing factors, noise and redundancy in graph data, and lack of interpretation for prediction results, impede further development of GCN. One solution is to recognize a predictive yet compressed subgraph to get rid of the noise and redundancy and obtain the interpretable part of the graph. This setting of subgraph is similar to the information bottleneck (IB) principle, which is less studied on graph-structured data and GCN. Inspired by the IB principle, we propose a novel subgraph information bottleneck (SIB) framework to recognize such subgraphs, named IB-subgraph. However, the intractability of mutual information and the discrete nature of graph data makes the objective of SIB notoriously hard to optimize. To this end, we introduce a bilevel optimization scheme coupled with a mutual information estimator for irregular graphs. Moreover, we propose a continuous relaxation for subgraph selection with a connectivity loss for stabilization. We further theoretically prove the error bound of our estimation scheme for mutual information and the noise-invariant nature of IB-subgraph. Extensive experiments on graph learning and large-scale point cloud tasks demonstrate the superior property of IB-subgraph.
Interaction with virtual objects through eye-tracking

In virtual and augmented reality, there has been a lot of research on interaction with virtual objects. In particular, eyetracking techniques are used to achieve hands-free interaction. In this paper, we focus on quick eye movements and intuitive pointing to move a virtual object with the gaze only. This study is basic research about the method for interacting with virtual objects in an augmented reality environment by the gaze. In this paper, we explore interaction by a typical controller and the gaze and investigate each characteristic of the interaction. The results show that the gaze may be useful as same as the controller in pointing to the distant position. To achieve more accurate hands-free interaction using the gaze, it is necessary to understand how to perceive the distance between the virtual object and the real object. Also, we need to examine the gaze interaction that uses other than the closing motion of the eye.
Multi-dialect Neural Machine Translation for 48 Low-resource Japanese Dialects

We present a multi-dialect neural machine translation (NMT) model tailored to Japanese. Although the surface forms of Japanese dialects differ from those of standard Japanese, most of the dialects have common fundamental properties, such as word order, and some also use numerous same phonetic correspondence rules. To take advantage of these properties, we integrate multilingual, syllable-level, and fixed-order translation techniques into a general NMT model. Our experimental results demonstrate that this model can outperform a baseline dialect translation model. In addition, we show that visualizing the dialect embeddings learned by the model can facilitate the geographical and typological analyses of the dialects.
Remote Monitoring of Heart Patients Using Robotic Process Automation (RPA)

After the age of 60 years or older many people are diagnosed with heart related issues to follow up older patients, bedridden, who needs maximum care at their homes implementation of phone monitoring becomes necessary. According to study many heart patients are supposed to have a regular check up to keep a track on their physiological data. Health care is a ground that is rapidly developing in services and technology. A recent development in this region is remote monitoring of patients which has many benefits in a fast-aging world population with increasing health complications. Sensors are used to monitor essentials or vital requirements such as heartbeat, blood pressure, temperature, blood glucose level and many more. Remote monitoring varies for every age group and every aspect. During pandemic times its difficult for many of them to move around, thus remote monitoring of patients helps to have a safer and efficient way to monitor them and also saves time. These new technologies can make a contact-less and monitor illness based on the sensor values. To make remote monitoring more efficient automation places a vital role. In health sector, to make any process automated RPA (Robotic process automation) is used. Using the established application software robots automate the process originally performed by human beings. This paper focuses on easy way to monitor patients who are in a distant place, have a regular check-up without visiting the doctor regularly and in case of emergency contact the doctor within no time.
Development of a Tool to Help Understand Color Spaces and Color Differences

Colors are convenient visual variables in data visualization and play an important role in data representation. However, we frequently see inappropriate color schemes in many different places. Such schemes may be due to the carelessness of the creators and their lack of knowledge of visual representations. The author of this paper has developed a tool to help understand color spaces and color differences. The tool displays cross-sections of color spaces and presents polygons representing color differences in the cross-sections. By using this tool, it is possible to understand the distortion of a color space against human visual characteristics. In this paper, the tool and the methods used in the development of the tool are introduced, and the effectiveness of the tool is discussed.
Entanglement characterization by single-photon counting with random noise

In this article, we investigate the problem of entanglement characterization by polarization measurements combined with maximum likelihood estimation (MLE). A realistic scenario is considered with measurement results distorted by random experimental errors. In particular, by imposing unitary rotations acting on the measurement operators, we can test the performance of the tomographic technique versus the amount of noise. Then, dark counts are introduced to explore the efficiency of the framework in a multi-dimensional noise scenario. The concurrence is used as a figure of merit to quantify how well entanglement is preserved through noisy measurements. Quantum fidelity is computed to quantify the accuracy of state reconstruction. The results of numerical simulations are depicted on graphs and discussed.
Integrating Maps and Location Services

With Power Apps, we can easily build canvas apps that include location-based features. Through mobile and browser-based apps, we can retrieve the location of the current user, and we can also integrate with mapping services.
The Role of Mass Media in the Formation of Media Literacy of the Individual in the Context of Information Warfare

The article defines the role of the media in the formation of media literacy of the individual in the context of information warfare on the basis of theoretical and empirical analysis. It is emphasized that information wars have a long history of development and have been known for a long period of human development. It is noted that the concept of “information warfare” became especially relevant in the second half of the twentieth century. This is due to the active development of scientific and technological progress and the beginning of a new information age of human development. Different interpretations of the concept of “information warfare” are analyzed in detail, the main world concepts of information wars are characterized, in particular: the American, British, French and German ones. The concepts of “media literacy” and “media education” are described. They consist in preparing the young generation for life in modern information conditions. Methods of forming media literacy are analyzed, in particular the activation of independent educational and cognitive activity of students, the formation of knowledge, skills and abilities to use information technologies and the ability to create a media educational platform.
Integral characteristics of psychological readiness of students for volunteering

Presented is an analyzes in features of participation of students in volunteer activity in public life of the country. Shown are results of studying readiness of student volunteers to providing help assistance to population. The analysis of data as to psychological qualities necessary for successful functioning in volunteer activity is carried out. The main conditions for active and long-term participation of student youth in the work of volunteers are considered.
Actitudes y pronunciación en L2. Panorama actual entre teoría y experimentación

This paper offers an overview of the studies focusing on attitudes towards pronunciation in a foreign language and its learning. After defining the object of study –examining conceptual and terminological issues – and providing the justification for its interest which has been put forward, the paradigms that have guided research on pronunciation and, consequently, research on attitudes toward it are presented. The core part of the paper tries to face bibliographic dispersion by elaborating an updated and well-ordered outline of experimental studies, with attention to subject areas and variables handled, which allows visualization of possible future developments.
Dijital Tedarik Zinciri Yönetiminde Artırılmış Gerçeklik Araçlarına İlişkin Performans Değerlendirilmesi: Bulanık Melez Karar Verme Yaklaşımı

The supply chain plays a key role for companies that want to gain an advantage in the competitive market.Most of the companies are want to make their supply chain processes more reliable and sustainable with technological developments.With Industry 4.0, augmented reality is one of the most important technological developments in daily lives and companies' supply chain processes.In this study, the effects of augmented reality tools, which companies will increasingly recognize, are discussed on digital supply chain processes.The purpose of this study is to integrate the increasingly augmented reality tools in the supply chain in the most appropriate way and determine the most suitable one for the supply chain processes among the hardware augmented reality tools.Three augmented reality tools were evaluated with 4 main criteria and 12 sub-criteria.The Fuzzy Analytic Hierarchy Process (AHP) and Fuzzy Technique for Ordering Preference by Similarity to Ideal Solution (TOPSIS) methods are among the most important multi-criteria decision-making methods, are used in the decision-making process.As a result, the obtained results were evaluated and managerial implications were presented.
The Mixed Teaching Mode of College Oral English in the Big Data Era

With the advent of the era of big data, the traditional teaching model has been insufficient to meet the requirements of college students to improve their self-ability in oral English. Hybrid teaching mode is the necessity of the development of the times, which is derived from the information technology era we are in. Generally speaking, it refers mainly to the effective combination of online learning and traditional teaching in the classroom personally, which is very common in life now. A way of learning and training. This document has therefore carried out an investigation, namely the study of the mixed teaching of the English language in the context of the large data. This document conducts research combining the experimental comparison method and the bibliography survey method, Reference to relevant domestic and foreign materials and English teacher conversation in China Current situation summarises mixed oral strategies and methods teaching English at the time of the great data and verifying its relevance by means of an experimental comparison. The results of this article's research are very useful and can be used as a reference for the reform of mixed language teaching on the Chinese base.
A Pattern-Hierarchy Classifier for Reduced Teaching

This paper describes a design that can be used for Explainable AI. The lower level is a nested ensemble of patterns created by self-organisation. The upper level is a hierarchical tree, where nodes are linked through individual concepts, so there is a transition from mixed ensemble masses to specific categories. Lower-level pattern ensembles are learned in an unsupervised manner and then split into branches when it is clear that the category has changed. Links between the two levels define that the concepts are learned and missing links define that they are guessed only. This paper proposes some new clustering algorithms for producing the pattern ensembles, that are themselves an ensemble which converges through aggregations. Multiple solutions are also combined, to make the final result more robust. One measure of success is how coherent these ensembles are, which means that every data row in the cluster belongs to the same category. The total number of clusters is also important and the teaching phase can correct the ensemble estimates with respect to both of these. A teaching phase would then help the classifier to learn the true category for each input row. During this phase, any classifier can learn or infer correct classifications from some other classifier's knowledge, thereby reducing the required number of presentations. As the information is added, cross-referencing between the two structures allows it to be used more widely, where a unique structure can build up that would not be possible by either method separately.
Usability comparison of screen-based and virtual-reality-based virtual museums

This study offers a comparative analysis of user experience and levels of immersion in regards to screen based solutions for virtual museums and virtual reality based (VR) solutions with Head mounted displays (HMD). The aim of this study is to better inform design and research and development of virtual museums using real-time 3DCG for achieving an experience of presence and immersion for viewer that comparable to viewing experiences in an actual museum. We have developed a virtual museum that can be experienced both on a large screen display and also via VR on an HMD. Users can experience the viewing space where paintings, picture scrolls, and 3D-objects are placed in the virtual museum, and the virtual space by freely walking through the virtual environment (VE). The development environment used for the study is the Unity’s game engine, created in-game objects, and introduced output content for outputting as VR in Oculus. Then, Compare the results of the comparison experiment of the virtual museum conducted in Sweden and the comparison experiment of the virtual museum conducted in Japan in the previous research, and evaluate what is close to actual viewing.
Prototype Design of Landslide Early Detection System Using LoRa and IoT

This paper proposes a simple and low-cost landslide early detection system design.This system uses two sensors, namely the HC-SR04 ultrasonic sensor, to detect the soil movement and the YL-69 soil moisture sensor to detect soil moisture levels.Sensor data is processed using Arduino Uno.The soil movement and soil moisture data are sent to the receiving node using the LoRa communication system so that residents around locations with the potential for landslides can access data from the sensor nodes for free.This system is also equipped with an IoT system by using Thingspeak as an IoT platform.Based on the design and testing results, the LoRa system can work well at 250 meters in the channel with many buildings and trees.This research also designs an application to access sensor data via the Thingspeak web server using an Android device.
Role of Immersive Simulation and Cyber Technology based Approaches in Supporting Learning and Curriculum Innovation

This paper focuses on the emergence of cyber technologies and immersive simulation-based approaches in supporting curriculum innovation and learning. A discussion of three thrusts involving immersive simulation and cyber technologies is presented. The first thrust focuses on innovative learning activities involving space systems concepts that revolved around the creation of 3D VR based immersive simulation environments for NASA's Deep Space Mission process contexts. The second thrust involves the need to expose and encourage students towards entrepreneurial activities involving information-centric and cyber manufacturing principles. The third thrust involves the creation of 3D Virtual Learning Environments (VLEs) to support autistic students to learn science and engineering concepts interactively using both immersive and haptic interfaces.
The iPad as a Musical Instrument!

When used to make music, the tablet device is a musical instrument and has several things in common with traditional wind and string instruments: It can be played well, making wonderful music, and it can be played poorly, producing sounds that are wanting in terms of musicality. First, and foremost, a tablet will make no sound until a human touches it. Second, practice is required to perform correctly on a tablet, and the performer must build technique. The tablet, like an oboe or violin, will do nothing musical until a human being interacts with it, develops technique through practice, and makes musical decisions using it. It is possible to use tablets in live performance by examining real-life examples by an iPad ensemble that model learner-centered pedagogical principles. Taking into account the social and musical aspects of the musicians, the chapter discusses possible approaches to rehearsals, what performances might involve, including collaborations with other artists, and breaking down the artificial fourth wall that too often is placed between musicians and audience.
A Central Limit Theorem for Differentially Private Query Answering

Perhaps the single most important use case for differential privacy is to privately answer numerical queries, which is usually achieved by adding noise to the answer vector. The central question, therefore, is to understand which noise distribution optimizes the privacy-accuracy trade-off, especially when the dimension of the answer vector is high. Accordingly, extensive literature has been dedicated to the question and the upper and lower bounds have been matched up to constant factors [BUV18, SU17]. In this paper, we take a novel approach to address this important optimality question. We first demonstrate an intriguing central limit theorem phenomenon in the high-dimensional regime. More precisely, we prove that a mechanism is approximately Gaussian Differentially Private [DRS21] if the added noise satisfies certain conditions. In particular, densities proportional to $\mathrm{e}^{-\|x\|_p^α}$, where $\|x\|_p$ is the standard $\ell_p$-norm, satisfies the conditions. Taking this perspective, we make use of the Cramer--Rao inequality and show an "uncertainty principle"-style result: the product of the privacy parameter and the $\ell_2$-loss of the mechanism is lower bounded by the dimension. Furthermore, the Gaussian mechanism achieves the constant-sharp optimal privacy-accuracy trade-off among all such noises. Our findings are corroborated by numerical experiments.
Customized Data Extraction and Effective Text Data Preprocessing Technique for Hydroxychloroquin Related Twitter Data

Coronavirus Disease 2019 (COVID-19) has become a significant worldwide issue with a rising the number of infected people and increases in mortality.Among every single helpful methodologies, contentions have raised about hydroxychloroquine (HCQ) viability in the treatment of COVID-19.The utilization of hydoxychloroquine is acknowledged as commonly accepted for patients with malaria and autoimmune diseases, however its utilization where not demonstrated and without clinical management can cause genuine results and ought to be maintained a strategic distance.This research carried out a sentiment analysis regarding the effectiveness of hydroxychloroquine in the treatment of COVID-19.Sentimental Analysis is the way toward recognizing concept from text written based on Natural Language Processing the element it is alluding to.Twitter is an informal community that allows clients to post their suppositions about current issues, share their get-togethers, and associate with others.Twitter has now gotten probably the biggest wellspring of information, with more than 200 million dynamic clients month to month.The technique concentrates and investigations sentimental data from microblogs to forecast the patient's assessment of hydroxychloroquine.In this work, a pre-handling strategy for assessment mining is executed and will be used for examining patients' remarks on Twitte' social media about hydroxychloroquine.The different content pre-handling strategies have been used on the dataset to accomplish a sufficient standard text.
Physical reservoir computing with origami: a feasibility study

In the field of soft robotics, harnessing the nonlinear dynamics of soft and compliant bodies as a computational resource to enable embodied intelligence and control is known as morphological computation. Physical reservoir computing (PRC) is a true instance of morphological computation wherein; a physical nonlinear dynamic system is used as a fixed reservoir to perform complex computational tasks. These dynamic reservoirs can be used to approximate nonlinear dynamical systems and even perform machine learning tasks. By numerical simulation, this study illustrates that an origami meta-material can also be used as a dynamic reservoir for pattern generation, output modulation, and input sensing. These results could pave the way for intelligently designed origami-based robots that interact with the environment through a distributed network of sensors and actuators. This embodied intelligence will enable the next generations of soft robots to autonomously coordinate and modulate their activities, such as locomotion gait generation and limb manipulation while resisting external disturbances.