Reviewerly/exHarmony huggingface dataset:
Combine Relu with Tanh

Activation function is an integral part of convolutional neural networks. Through many experiments we find that there are some complementary properties between Relu activation function and Tanh activation function. The output of Tanh function could increase the values activated by Relu units and decrease the values clipped by Relu units. By changing Relu activation function into the weighted sum of Relu activation function and Tanh activation function, the networks could obtain a great improvement. We conduct a series of experiments on some datesets, the results show that our method could improve the accuracy of ResNet and Inception by a large margin with only two parameters added every convolutional layer.
Anticipating Averted Gaze in Dyadic Interactions

We present the first method to anticipate averted gaze in natural dyadic interactions. The task of anticipating averted gaze, i.e. that a person will not make eye contact in the near future, remains unsolved despite its importance for human social encounters as well as a number of applications, including human-robot interaction or conversational agents. Our multimodal method is based on a long short-term memory (LSTM) network that analyses non-verbal facial cues and speaking behaviour. We empirically evaluate our method for different future time horizons on a novel dataset of 121 YouTube videos of dyadic video conferences (74 hours in total). We investigate person-specific and person-independent performance and demonstrate that our method clearly outperforms baselines in both settings. As such, our work sheds light on the tight interplay between eye contact and other non-verbal signals and underlines the potential of computational modelling and anticipation of averted gaze for interactive applications.
Time-sync comments denoising via graph convolutional and contextual encoding

Time-Sync Comments (TSC), which is a new kind of textual comments on online video websites, has showed its great potential in fine-grain video analysis. However, as a crowd-sourced resource, there are many low quality comments in TSC data and this is an impediment to make full use of TSC. Thus a denoising method is necessary when we are dealing with these comments. In this study, we propose GCCED, a graph convolutional and contextual encoding denoising model for TSC semantic denoising problem. A TSC graph is built on the whole corpus and semantic embedding of words are learned through graph convolution. Moreover, we exploit the relations between TSC and its context and design an embedding method based on the word graph. Experiments on real world dataset are conducted and the result demonstrate the proposed model outperforming other baselines in almost all classification metrics.
Resource scheduling of green communication network for large sports events based on edge computing

The operation of large-scale sports events requires strong support from the network foundation. At the same time, network resource scheduling in the era of big data not only determines the operating efficiency of communication network systems, but also plays an important role in effectively reducing greenhouse gas emissions. In order to improve the green energy-saving effect of large-scale sports events network operation, this study analyzes the operation process of its network system and uses the time slot method to perform task routing decision processing. Moreover, this research uses Lyapunov's optimization technology for algorithm design and algorithm construction for service chain cache and routing problems. In addition, this study combines large-scale sports event communication networks to conduct controlled trial design and performs algorithm performance analysis through simulation analysis. The research shows that the algorithm proposed in this paper can obtain a solution with better performance and can provide theoretical references for subsequent related research.
Using Modularity Metrics as Design Features to Guide Evolution in Genetic Programming

Genetic Programming has advanced the state of the art in the field of software synthesis. However, it has still not been able to produce some of the more complex programs routinely written by humans. One of the heuristics human programmers use to build complex software is the organization of code into reusable modules. Ever since the introduction of the concept of Automatically Defined Functions (ADFs) by John Koza in the 1990s, the genetic programming community has also expressed the need to evolve modular programs, but despite this interest and several subsequent innovations, the goal of evolving large-scale software built on reusable modules has not yet been achieved. In this chapter, we first discuss two modularity metrics—Reuse and Repetition—and describe the procedure for calculating them from program code and corresponding execution traces. We then introduce the concept of design features, which can be used alongside error measures to guide evolution. We also demonstrate the use of modularity design features in parent selection.
A primer on data visualization in infection prevention and antimicrobial stewardship

Data visualization refers to the techniques used to communicate information by encoding it as visual objects (eg, points, lines, or bars) contained in graphics. The recent acceleration in informatics technology has made it possible to obtain and process large amounts of data. Although data visualization can provide insights from large datasets, it can also help simplify messaging, making information more accessible for healthcare stakeholders. The field of data visualization is constantly evolving, and new techniques are frequently being created. However, evidence regarding the best way to visualize data in the fields of infection prevention and antimicrobial stewardship is limited. We provide an overview of data visualization theory and history, as well as recommendations for creating graphs for infection prevention and antimicrobial stewardship.
Interactive Learning Technology for Overcoming Academic Adaptation Barriers

The authors focus on the problem of overcoming barriers to students’ academic adaptation at a university using ICT. The objective of the paper is to analyze the psychological and pedagogical conditions needed for academic adaptation, as well as to create and test interactive learning technology aimed at overcoming first-year students’ adaptation barriers at a technical university. Introduction of interactive learning technology into the educational process has proved to be useful for those students who experience difficulties in the transition period. The paper formulates the key theoretical positions for overcoming academic adaptation barriers. The developed diagnostic tools provide timely assessment of the first-year students’ adaptation level. The research results will provide an important basis for enriching the higher education theory and social knowledge about overcoming academic adaptation barriers. As well, they can be applied in constructing various academic practices aimed at using interactive learning technology to manage the adaptation processes at a university. The research material could be useful for teachers engaged in students’ adaptation to the university environment; for heads of educational institutions and students themselves.
Voices of the Social Sector

The prevalence of HCI research carried out with nonprofit organizations has increased dramatically over the past 35 years. Despite this history and our accumulating understanding of social sector organizations and their diverse stakeholders’ use of technology, there have not yet been any systematic reviews of this body of research. Before making more holistic claims about what this body of knowledge might suggest for a research agenda moving forward, we carry out a systematic literature review to characterize the systemic biases that are present as a result of the varying degrees of inclusion or exclusion of the voices of different groups of stakeholders. Building off of the results of our analysis, we contribute a framework of Analytic Charismas for more broadly engaging in reflexivity as a discipline.
Layer Embedding Analysis in Convolutional Neural Networks for Improved Probability Calibration and Classification

In this project, our goal is to develop a method for interpreting how a neural network makes layer-by-layer embedded decisions when trained for a classification task, and also to use this insight for improving the model performance.To do this, we first approximate the distribution of the image representations in these embeddings using random forest models, the output of which, termed embedding outputs, are used for measuring how the network classifies each sample.Next, we design a pipeline to use this layer embedding output to calibrate the original model output for improved probability calibration and classification.We apply this two-steps method in a fully convolutional neural network trained for a liver tissue classification task on our institutional dataset that contains 20 3D multi-parameter MR images for patients with hepatocellular carcinoma, as well as on a public dataset with 131 3D CT images.The results show that our method is not only able to provide visualizations that are easy to interpret, but that the embedded decision-based information is also useful for improving model performance in terms of probability calibration and classification, achieving the best performance compared to other baseline methods.Moreover, this method is computationally efficient, easy to implement, and robust to hyper-parameters.
Certificate Verification System Using Blockchain

The large number of counterfeit certificates in circulation is a huge problem that has been prevalent for a long time. Issuing of such certificates has become a business stemming from the need/want of the people for employment. Hard working people with legitimate degrees/certificates have to suffer the consequences of this phenomenon since they are denied what could have been theirs by the holders of these fake credentials. It a lot of cases this can prove extremely dangerous for example, consider a surgeon operating on the basis of a fake degree. It is situations like these that highlight the need for a system which can provide verification and authentication of the certificates, its issuer and the holder. In this paper, leveraging the tamperproof and non-repudiation property of blockchain, we propose and implement a certificate issuing and verifying model.
Whats Trending? An Efficient Trending Research Topics Extractor and Recommender

Finding trending research topics can be of great importance to many domains. However, despite its importance, the works that concentrated on this task are very limited. Furthermore, they did not consider important criteria to detect trending scientific topics. Furthermore, the extraction of trending topics need to be integrated with a recommender system, as it would be difficult for users to browse all the extracted trending scientific topics. In this work, we propose an efficient trending scientific topics extractor and an integrated recommender system. The contribution of this work lies in three points. First, improving the performance of the trending topic extractors by using two important criteria that were not used by the literature. Second, integrating a recommender system with the trending topic extractor to provide intelligent results based on the user profile. Third, insisting on the importance of this field as it deserves more works in this direction. Experimental results showed that the system is efficient in terms of updating time, trending topic extraction time, extraction recall and precision, and recommendation time.
Denotational semantics for guarded dependent type theory

Abstract We present a new model of guarded dependent type theory (GDTT), a type theory with guarded recursion and multiple clocks in which one can program with and reason about coinductive types. Productivity of recursively defined coinductive programs and proofs is encoded in types using guarded recursion and can therefore be checked modularly, unlike the syntactic checks implemented in modern proof assistants. The model is based on a category of covariant presheaves over a category of time objects, and quantification over clocks is modelled using a presheaf of clocks. To model the clock irrelevance axiom , crucial for programming with coinductive types, types must be interpreted as presheaves internally right orthogonal to the object of clocks. In the case of dependent types, this translates to a lifting condition similar to the one found in homotopy theoretic models of type theory, but here with an additional requirement of uniqueness of lifts. Since the universes defined by the standard Hofmann–Streicher construction in this model do not satisfy this property, the universes in GDTT must be indexed by contexts of clock variables. We show how to model these universes in such a way that inclusions of clock contexts give rise to inclusions of universes commuting with type operations on the nose.
Survey on Various Conversational Systems

Conversation agents or chatbots are software’s which can have conversations with human beings as if the conversation is between two humans. Such bots use artificial intelligence to understand the input given by human and accordingly respond back. In today’s world, a lot of people use the smartphones most of the time. They may use chatbots for their personal assistance and to extract information from the internet. Medical/Health, Agriculture and educational domains are important domains to pay attention to. Today, Chatbots can perform tasks such as Minimized Agent Transfers, Quicker Issue Resolution, improved Self-Service, etc. with no waiting time and cost optimization. Other chatbots perform prediction tasks (especially in the medical domain) which is possible today with advancements in AI and Data Mining Techniques.The goal of this paper is to conduct a survey to compare already existing chatbots with respect to platforms used, algorithms applied, tools and software used etc.
Mechanical Shape Correlation: A novel integrated digital image correlation approach

Mechanical Shape Correlation (MSC) is a novel Integrated Digital Image Correlation (IDIC) based technique used for parameter identification. Digital images taken during an experiment are correlated and coupled to a Finite Element model of the specimen, in order to find the correct parameters in this numerical model. In contrast to regular IDIC techniques, where the images consist of a grayscale speckle pattern applied to the sample, in MSC the images are projections based on the contour lines of the test specimen only. This makes the technique suitable in cases where IDIC cannot be used, e.g., when large deformations and rotations cause parts of the sample to rotate in or out-of-view, or when the speckle pattern degrades due to large or complex deformations, or when application of the pattern is difficult because of small or large specimen dimensions. The method targets problems for which the outline of the specimen is large with respect to the volume of the structure and changes significantly upon deformation. The technique is here applied to virtual experiments with stretchable electronic interconnects, for identification of both elastic and plastic properties. Furthermore, attention is paid to the influence of algorithmic choices and experimental issues. The method reveals good convergence and adequate initial guess robustness. The results are promising and indicate that the method can be used in cases of either large, complex or three-dimensional displacements and rotations on any scale.
Hybrid Symbiotic Organism Search algorithms for Automatic Data Clustering

Cluster analysis is an essential tool in data mining. Several clustering algorithms have been proposed and implemented for which most are able to find the good quality or optimal clustering solutions. However, most of these algorithms still depend on the number of a cluster being provided a priori. In dealing with real-life problems, the number of clusters is unknown and determining the optimal number of clusters for a large density and high dimensionality dataset is quite a difficult task to handle. This paper, therefore, proposes five new hybrid symbiotic organism search algorithms to automatically partition datasets without any prior information regarding the number of clusters. Furthermore, the hybrid algorithms will be evaluated in terms of solution quality using the Davies-Bouldin clustering validity index. The simulation results show that the performance of the hybrid symbiotic organisms search particle swarm optimization algorithm is superior to the other proposed hybrid algorithms.
How to Determine Minimum Support in Association Rule

The growth of increasingly complex data now raises new challenges in the world of technology. Large volumes of data store a lot of knowledge that can help in the decision-making process. One way to find knowledge in big data is by the Association Rule. The association rule is a technique in data mining that can produce rules based on the frequency of items appearing from a transactional database. One thing that is critical in the association rule is the determination of the minimum support value used to determine which items will be included in the formation of rules. If the minimum support value that is set is too small, it causes too many items to be involved in establishing the rules. Conversely, if the minimum support is too large, the number of items involved in forming the rule is too small. The problem in determining the minimum support value greatly affects the accuracy of the resulting rule. In this paper, various methods will be discussed to determine the minimum value of support through study literature based on related research. In addition, it explains research opportunities that can be done in the future about the minimum value of support determination in the association rule.
Traffic Sign Detection and Recognition Using Novel Center-Point Estimation and Local Features

Traffic sign detection is one of the critical technologies in the field of intelligent transportation systems (ITS). The difficulty of traffic sign detection mainly lies in detecting small objects in a wide and complex traffic scene quickly and accurately. In this paper, we regard traffic sign detection as a region classification problem and propose a two-stage CNN-based approach to solve it. At the first stage, we design an efficient network which is built with improved fire-modules to generate object proposals quickly. The network up-samples and merges the feature maps of different scales to attain a high-resolution fused feature map which contains semantically strong features of multi-scale objects. Specially, the prediction is made on the fuse feature map and based on the novel center-point estimation. With the overall designs, our region proposal network can achieve high recall value while using low-resolution images. At the second stage, a separate classification network is proposed. The bottleneck of the classification performance is generally caused by the greatly similar appearances between traffic signs. Therefore, we further explore local regions with critical differences between traffic signs to obtain fine-grained local features which help to improve classification. Finally, we evaluate our method on a challenge benchmark Tsinghua-Tencent 100K which provides many large images with small traffic sign instances. The experiment result shows that our method has better performance and faster detection speed than many state-of-the-art traffic sign detection methods.
A Machine Learning Based Adult Content Detection Using Support Vector Machine

In the era of internet, recognizing pornographic images is of great significance for protecting children's physical and mental health. With small kids surfing over the internet they are just one click away from getting access to pornographic images. However, this task is very challenging as the key pornographic contents (example. breast, private part) in an image often lie in local regions of small size. The proposed model is based on supervised learning-based Support Vector Machine (SVM) algorithm which returns whether an image is safe or unsafe. The proposed model not only differentiates the image between safe/unsafe but also blurs/colors the exposed skin portion completely black if the image is found to be unsafe (i.e. pornographic image) using image processing technique. So, that the end user won't be able to see exposed private parts in an image. When tested on our newly-collected large scale dataset demonstrates the effectiveness of the proposed method, achieving an accuracy of ~91 % when tested 4k pornographic images and 4k normal images.
Towards a Blockchain Contract-for-Difference Financial Instrument for Hedging Renewable Electricity Transactions

Contract-for-Difference financial instruments are available to renewable electricity generators in day-ahead electricity markets to allow them to hedge against revenue risk. Traditional CfDs while designed to hedge revenue risk, introduce other new risks such as counterparty credit, margining and third-party risks. We therefore propose a novel financial instrument - an Ethereum blockchain-based dual escrow smart contract, to serve as the mediator in a CfD agreement between a renewable electricity generator and supplier. This financial instrument addresses hedging related risks that result from traditional CfD agreements in day-ahead electricity markets. In this paper, we design the logic of the financial instrument, translate this logic to smart contract codes and demonstrate its expected performance. Overall, the proposed financial instrument has the benefits of reducing hedging related risks inherent in traditional CfDs. Likewise, it enables secure, efficient, cost-effective, consistent, reliable, transparent and frictionless transactions between contracting parties in a CfD agreement.
Bringing Order to Chaos: The Case of Collision-Resistant Chameleon-Hashes

Chameleon-hash functions, introduced by Krawczyk and Rabin at NDSS 2000, are trapdoor collision-resistant hash-functions parametrized by a public key. If the corresponding secret key is known, arbitrary collisions for the hash function can be efficiently found. Chameleon-hash functions have prominent applications in the design of cryptographic primitives, such as lifting non-adaptively secure signatures to adaptively secure ones. Recently, this primitive also received a lot of attention as a building block in more complex cryptographic applications ranging from editable blockchains to advanced signature and encryption schemes. We observe that in latter applications various different notions of collision-resistance are used, and it is not always clear if the respective notion does really cover what seems intuitively required by the application. Therefore, we revisit existing collision-resistance notions in the literature, study their relations, and—using the example of the recent redactable blockchain proposals—discuss which practical impact different notions of collision-resistance might have. Moreover, we provide a stronger, and arguably more desirable, notion of collision-resistance than what is known from the literature. Finally, we present a surprisingly simple and efficient black-box construction of chameleon-hash functions achieving this strong notion.