Reviewerly/exHarmony huggingface dataset:
Comparison between Top-Ranked Iranian Medical Universities and Top-Ranked World Universities based on the Website Analysis

Evaluating the quality and quantity of information presented by universities on their website is the main strategy to ensure that the university web-based activities are in the correct order. In this regard, there are several ranking systems that evaluate the quality and quantity of web-based activities of universities and accordingly rank them. Comparing the universities based on web activity measurement is a suitable tool to find out the weaknesses of the university for presenting itself on World Wide Web. In this study, top-ranked Iranian medical universities were compared with top-ranked world universities according to the website traffic data. For this purpose, the website traffic information was acquired from “Similarweb” website. The results showed that the total traffic size, bounce rate and backlinks from referrals and social networks were significantly different between Iranian medical and world top-ranked universities. Furthermore, the results of this analysis revealed that the quality of website, which expectedly increased the visit duration and visited pages, was not significantly correlated with total traffic size for nor top-ranked world and Iranian medical universities. In addition, the results showed that the total traffic size of top-ranked world universities was significantly correlated with backlinks from direct access and search engines, while for Iranian medical universities such correlation could not be found. In conclusion, the main gap between top-ranked Iranian medical universities and top-ranked world universities was due to the search engine optimization issues and the link between social networks and universities, that both need more enhancement.
Towards Sentiment Analysis: A powerful technique for Data Analytics

With the advancement of cyberspace of social networking, blogging and micro-blogging websites increasing popularity, leads to an enormous increase in the amount of informal subjective text statements which are made available online for better understanding. Sentiment Analysis is the mindset, emotion for individual analytical research. It has become more important for the growth of the business, political issues, and for different customer. The data mining is the most common field of computer science for analysis of data. In this paper, various approaches have been described and also discussed the different principles and techniques that are used for sentiment analysis using a tool called Weka. This dataset is used to optimize classification of data using different types of the algorithm (Naïve Bayes, Support Vector Machine, Decision tree etc.) to findout the performance analysis of each algorithm to get best and accurate results.
A Features Determination of an Augmented Reality Application with Regards to Commercial Perspective

Regarding a commercial perspective, the cost of production of any application always one of the main concerns. In terms of an augmented reality application, both the aesthetic part from the graphic design team and the logical part from the developer team are defined in the early pre-production process for numerous factors. This paper presents a framework aim at decreasing production time with a case study of `Canvas Kids,' a 3D augmented reality application for kids. By focusing on features determination of the application, the effort estimated with production working days from 5 experts in this field for each feature was compared with the satisfaction score from 42 target users. The satisfaction was studied in five perspectives, including aesthetic, joyful, time spent using the application, the number of functions used, and overall impression. The result shows that with our proposed framework, an augmented reality production team could decrease the production time by features preselected.
DoubtNet: Using Semantic Context to Enable Adaptive Inference for the IoT

While techniques such as model pruning and precision reduction have improved the efficiency of Deep Neural Network (DNN) inference, DNN-based object detection still involves substantial computation, thereby limiting the scope of DNN inference on compute-limited platforms, such as many IoT devices. Proposed methods for differentiating easy versus difficult classification cases, such as early exit, can reduce the average amount of compute incurred but are limited by the accuracy to which they can correctly predict the level of effort needed for classification based only upon early stage features. In this work, we propose an alternative approach to predicting necessary effort that leverages semantic context. Namely, in any image there will usually be several objects present that can provide context for the plausibility of the set of classifications as a whole, with greater effort being applied only to outliers. Rather than relying on co-location of objects within training data, we derive our plausibility model from WordNet, a large pre-existing database of semantic relationships among objects, allowing classifier training to be performed in a traditional, relationship-agnostic fashion. We demonstrate the effectiveness of our approach, DoubtNet, using MobileNet as the initial low-power classifier, ResNet as the high-powered classifier, and developing an outlier detection module that is independent of both networks. DoubtNet increases the mAP of our base classifier by as much as 22% and consistently outperforms prior, training-set-based context models.
Holger Diessel, The grammar network: How linguistic structure is shaped by language use. Cambridge: Cambridge University Press, 2019. Pp. xvii + 289. ISBN 9781108671040.

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Collaborative Enhanced System for Heterogeneous IoT Devices

With the pace by which IoT devices are created nowadays, there will be numerous IoT devices in any environment, be it home or industry or anywhere else. With this rapid pace, it has become more and more difficult for device manufactures to expect that their end users have a unified homogeneous ecosystem (where every device comes from the same manufacture and are carefully developed to work in harmony so that they work with each other without any hassle) which is not in most of the cases. Thus, a solution is being proposed which allows devices from different manufactures which are meant to perform different tasks, to collaborate and use each other's knowledge to give a unified experience to the end-user, even if the actual network doesn't consist of homogeneous devices. In our proposed system, there exists a central node (central server) in every local network which communicates with each IoT device present in that network. Here the central node acts as a mediator for IoT devices to communicate with each other and share their data. Plus, any IoT device connected to the central server can also utilize its power to perform certain high-end tasks which they can't perform on their own in real-time.
Self Adaptive Learning – Great Deluge Based Hyper-heuristics for Solving Cross Optimization Problem Domains

In the literature, almost all optimization problems in NP-hard class are solved by meta-heuristics approach. However, this approach has the drawback of requiring tuning parameters for each different problem domain and different instances of the same problem. This approach is considered less effective in resolving these problems. Therefore, a new approach is needed, namely the hyper-heuristics approach that is able to solve cross-domain problems. Hyper-heuristic is one of the approximate search methods which is able to provide solutions to NP-hard problems in polynomial time, as well as giving fairly good and acceptable results. This method has two properties of search space, namely the selection of LLH and the acceptance of solutions (move acceptance). This approach works in barrier domains rather than directly working in problem domains. With these properties, hyper-heuristic is able to solve problems in different domains. In addition, hyper-heuristics has a learning mechanism through feedback from previously generated solutions. This final project tries to apply a hyperheuristic algorithm in six combinatorial optimization problem domains, namely SAT, Bin Packing, Flow Shop, Personnel Scheduling, TSP, and VRP. The method that will be used in this final project is Self Adaptive - Great Deluge (SAD-GED). The Self Adaptive mechanism is used to make LLH selection to be used, while the Great Deluge is used in determining the acceptance of solutions (move acceptance) in a hyperheuristic framework. The application of the SAD-GED algorithm is expected to be able to provide better results than the existing algorithm used previously, namely Simple Random - Simulated Annealing.
Operational Quantum Annealers are Cursed by their Qubits Interconnection Topologies

Recent experimental works have studied how the quantum annealing approach, as implemented in D-Wave devices, deals with pathological instances of the (polynomial) bipartite matching problem known to be hard for classical simulated annealing algorithms and shown that the quantum approach also performed rather poorly on such instances. In this paper, we investigate the extent to which the qubits interconnection topologies explains these latter experimental results. In particular, we provide evidences that the sparsity of these topologies which, as such, lead to QUBO problems of artificially inflated sizes can partly explain the aforementioned disappointing observations. Therefore, this paper hints that denser interconnection topologies are necessary to unleash the potential of the quantum annealing approach.
Encouraging Data Sharing for Safe Autonomous Driving

More information can be gathered when autonomous or self-driving vehicles can connect to and communicate with other vehicles and their environments, improving safe self-driving. However, we need an effective scheme that encourages drivers to share state information voluntarily while driving because the information can be shared only by their permission. Therefore, this paper proposes an incentive scheme to boost information sharing and hence improve autonomous driving safety.
Security Analysis of the First Certificateless Proxy Signature Scheme Against Malicious-But-Passive KGC Attacks

Abstract Recently, Yang et al. proposed the first certificateless proxy signature scheme against malicious-but-passive key generation center (MKGC) attacks. They proved that their scheme can resist the MKGC attacks in the standard model. In this paper, we point out that their scheme cannot achieve this security because the adversary can forge valid signatures.
Web-based and interactive Italian blended learning course: embedded apps and tools in a structured learning process

‘Web-based and Interactive Italian’ is a detailed and progressive programme developed by the author for the Maastricht University Language Centre. The course started in 2013, with the intention of catering for the variability in the number of students following the regular courses, as well as broadening the language offer using blended learning. The eight interactive tutor-led Skype sessions are preceded by 80 hours of self-study per level. Starting from a flipped classroom approach, it is structured in three consecutive learning steps. It makes use of existing language apps and tools, like Babbel2 and Quizlet3, and of a manual book used at the intermediate levels, as well as bespoke web-based and interactive learning materials as preparation for the oral sessions. The course is still running successfully and represents an effective alternative to traditional courses, offering distance learners the possibility of completing the whole study programme from A1 to B2.
A Mixed Reality Interface for Handheld 3D Scanners

The user interface is an essential part of handheld 3D scanners. During the scanning process, it provides feedback to the user to help him operate the scanner in an efficient way. For instance, it generally displays the reconstructed 3D model in real-time to let the user know which parts of the object have been captured and which have not. Traditionally, this type of information is displayed on a 2D screen, via a graphical user interface. Instead, we propose to use a mixed reality headset. We claim that this technology is better suited for handheld 3D scanning because it allows the reconstructed 3D model to be blended into the user’s perception of the real world. To validate this claim, we developed a prototype that uses the HTC Vive Pro headset as an interface for a handheld 3D scanner based on a Primesense Carmine RGB-D Camera.
A Leap from Randomized to Quantum Clustering with Support Vector Machine - A Computation Complexity Analysis

Supervised machine learning deals with developing complex non-linear models, which can be used later to predict the output for a known input. Clustering is usually treated as an unsupervised machine learning task, but we can formulate a solution to a clustering problem by using a supervised classification algorithm [1]. However, these classification algorithms are highly computationally intensive in nature, so the overall complexity in designing a clustering solution is often very costly from an implementation point of view. The more data we use, the more computational power is required too. Recent advancements in quantum computing show promising advantages in dealing with this kind of computational issues we face while training a complex machine-learning algorithm. In this paper, we do a theoretical investigation on the runtime complexity of algorithms, from classical to randomized, and then to quantum frameworks, when designing a clustering algorithm. The analysis shows significant computational advantages with a quantum framework as compared to the classical and randomized versions of the implementation.
CRITERIA AND LEVELS OF DEVELOPMENT OF PROFESSIONAL SELF-REFLECTION OF FUTURE LEADER OF AN INSTRUMENTAL BAND

This article examines the state and criteria of development of professional self- reflection of future leaders of an instrumental bands. The qualitative characteristic of levels of development of professional self-reflection is defined and four levels of development of this skill are outlined. The value and role of professional self-reflection of the leader of an instrumental band in the context of collective performance management is disclosed. It is noted that the reflexive method makes it possible to comprehend one’s own and team members’ activities, to predict one’s own actions, to form a new thinking style, to detect and anticipate problems in one’s own professional activities and improve the quality of work. We also believe that professional reflection is a necessary component of the professional education of the future leader of the instrumental ensemble and the cornerstone principle of successful management of any ensemble performance in particular.
Numerical control press design based on quantitative evaluation of appearance area features

Abstract The appearance characteristics of numerical control presses were quantitatively evaluated using eye-tracking technology, and a numerical control press design method was proposed to provide guidance for the design of such mechanical products. By investigating and analyzing the appearance of numerical control presses locally and abroad, the modeling characteristics of these products are summarized. The interest areas of the press are divided using the hot zone map data of the eye movement experiment, and people’s knowledge of each area of the press is quantitatively analyzed. Data generated by multiple eye movement experiments are combined with a subjective questionnaire to identify the mechanics of the press. The front and work areas are the main features of the press design. Based on the experiment result, the press design is conducted based on color and modeling elements. We used the Jiangsu CPTEK numerical control press as the actual case. We also re-matched the color, optimized the details, and developed an improved design scheme, thus verifying the proposed design method.
Location based Human Behaviour Analysis on Products or Events

From corporate decisions like calculating the perfect target audience to political decisions like designing an act for citizens of a state, it has always been tried to predict how people of a certain community or location will react to a particular product or event. People of different regions react to a similar situation in a different manner; a topic may be a sensitive issue in one location and may seem to be like a normal one in other. Several crimes, protests and product failures can be avoided if they could simply predict the reaction of the target audience (population of a particular location) in a certain situation. In this scenario, this research work aims to analyze the behavior of people based on a different location to analyze the impact of the products/events which are to be launched. Hence, architecture is proposed to analyze how people of a given location respond to a particular event/product and this is achieved by integrating sentiment analysis, location-based data analysis, natural language processing techniques, and machine learning approach. In the end, the analysis results are used to optimize decision making and problem-solving.
Vulnerability Analysis Against Fault Attack in terms of the Timing Behavior of Fault Injection

Security of implemented cryptographic systems has been faces with the serious concerns in recent years. Nowadays, side channel attacks especially differential fault attacks are known as very power threats. Therefore, it is essential to have a method for assessing the vulnerability of a hardware against these kind of attacks. Fault attack is known as a powerful attack for accessing the secret data from crypto-systems. This paper introduces the concept of Fault Confidence Window (FCW) to vulnerability analysis of a general system against the fault attack. FCW investigates the conditions that a transient fault can be exploitable by attackers. Our analyses determines the properties of fault and victim circuits that tend to have an effective attack. Result of the proposed analysis can help the designers to harden their system against transient fault attacks.
Multi Stage Directional LMMSE Estimation Based Video Denoising for Poisson Noise: A Frame &amp; Localized Frame Based Approach

In multimedia technology, video processing is a challenging task when it comes to deal with random noise such as poisson noise which is highly pixel dependent. A video affected by such noise shows unpleasant visual effects and thus it is difficult to access the true contents of the frames. The cause of photon noise is due to the discrete nature of photons which adversely affects the signal to noise ratio and amounts to loss of features. Many techniques in the past literature are able to reduce the effect of poisson noise at the cost of edge loss. The work proposed in this paper aims to improve the perceptual quality of each individual frame by reducingnoise and also preserving the contents over the edges as much as possible. A compromise is made between signal to noise ratio and the quality with respect to features in the frames. The advantage of high correlation between adjacent frames is effectively used for estimating the near approximate value of the individual pixel. The current frame (N) blocks (3x3) is localized in the successive frame (N+1) using a novel Block matching technique over a 7x7 window in the successive frame. No thresholding has been considered for block matching. Thus a new localized frame is obtained from the successive frame with reference to the spatial coordinates of the current frame. The Linear minimum mean square error estimate using directional denoising is used over the average of denoised current frame and the denoised localized frame. Directional denoising uses simple or local statistics and thus reduces the computational complexity of the proposed method. Three estimates are evaluated in horizontal/vertical, diagonal and center directions of the noisy pixel and finally fused for final estimate. It is seen that when directional denoising is used over single noisy frame, the signal to noise ratio is better but it is unable to preserve the edges and a blurred image is seen for each frame. Also it is unable to preserve the chroma when applied on individual R, G and B frames. Whereas, when directional denoising over the average of two denoised frames (the current and the localized frame) is used, it is able to improve the video quality by preserving edges and maintaining a low but nearer value of signal to noise ratio to the former case.
Deliver Insights, Not Information: The Power of Data Visualization to Improve Care Delivery and Patient Experience

Would you prefer a 20-page report with columns and rows, or would you rather see that information in a simple pie or bar chart? Data presented in a visually simple format is easy to comprehend. From the giant screens that display your favorite coffee to the information on your phone screens, we all interact with visual information. Our lives today are filled with technology and data, and visualized data is an integrated component of the technologies we interact with. Although most industries have adopted data visualization for several decades, it was only the past decade that saw healthcare starting to use it to communicate information to patients. Leveraging data visualization helps us to deliver clinical insights and help with critical decision making in a quicker and more accurate manner. Healthcare undoubtedly benefits from leveraging these tools to identify trends and opportunities to optimize systems thereby improving the quality of care.
Accuracy-Relevance trade-off in Transfer Learning for Object Detection

This paper addresses the problem of accuracy while using different datasets to train the parent model while using transfer learning. We show how the training time and accuracy of the custom object detector depend on the parent model's dataset. We show comprehensive empirical evidence that there is a strong dependence of datasets, while there is a possibility to train any model up to the desired accuracy if trained for a longer time, or if the relevance with the custom dataset is increased. The more the relevance, the faster the training and more is the accuracy of the custom detector. The difference in accuracy, given the same time to train, but different parent dataset is 10-12%. When using Ava as parent dataset we obtain 68.4%. By merely changing the parent dataset to COCO, we achieved a 20% increase in the mAP score.
Image Processing Based Human Following Cart Using 360° Camera

This study involves a robot that aims to follow a human target after detection, recognition, and tracking. The system has used 360 ° cameras to provide solution to the limitation of the tracking zone of a conventional camera. The image captured is in fisheye format and was then converted into a panoramic image to be processed by You Only Look Once (yOLO) Detection Algorithm to detect the human target and Channel and Spatial Reliability Tracking (CSRT) algorithm to track the target. The proponents included a 10-second target recognition methodology after detection so the system does not need to find the target from time to time.