Reviewerly/exHarmony huggingface dataset:
Innovation Centers and the Information Schools: The Influence of LIS Faculty

As universities worldwide develop centers for innovation intended to encourage a culture of technological innovation among their students, what role should LIS faculty play? What is the relationship between technological innovation and LIS education? How can LIS educators help students develop their ability to innovate with emerging technologies? This article presents a case study of an undergraduate course offered by the School of Information at Florida State University that brought together students from different majors in a custom-built innovation classroom simulating the innovation centers that many universities are creating for their students. It evaluates a teaching philosophy that offered students ownership over the structure of the semester, safe opportunities to take risks, and the opportunity to solve real-life problems with technology. The results document the connections between innovation centers and the information schools and show how LIS faculty can influence the development of innovation centers on campus.
Learning to predict with supporting evidence

The impact of machine learning models on healthcare will depend on the degree of trust that healthcare professionals place in the predictions made by these models. In this paper, we present a method to provide individuals with clinical expertise with domain-relevant evidence about why a prediction should be trusted. We first design a probabilistic model that relates meaningful latent concepts to prediction targets and observed data. Inference of latent variables in this model corresponds to both making a prediction and providing supporting evidence for that prediction. We present a two-step process to efficiently approximate inference: (i) estimating model parameters using variational learning, and (ii) approximating maximum a posteriori estimation of latent variables in the model using a neural network, trained with an objective derived from the probabilistic model. We demonstrate the method on the task of predicting mortality risk for patients with cardiovascular disease. Specifically, using electrocardiogram and tabular data as input, we show that our approach provides appropriate domain-relevant supporting evidence for accurate predictions.
Analysis on Entropy Sources based on Smartphone Sensors

Random number generator (RNG) is the basic primitive in cryptography. The randomness of random numbers generated by RNGs is the base of the security of various cryptosystems implemented in network and communications. With the popularization of smart mobile devices (such as smartphones) and the surge in demand for cryptographic applications of such devices, research on providing random number services for mobile devices has attracted more and more attentions. As the important components of smartphones, sensors are used to collect data from user behaviors and environments, and some data sources have the non-deterministic properties. Currently, some work focuses on how to design sensor-based RNG towards smartphones, since no additional hardware is required by this method. It is critical to evaluate the quality of entropy sources which is the main source of randomness for RNGs. However, as far as we know, there is no work to systematically analyze the feasibility for utilizing the raw sensor data to generate random sequences, and how much the entropy contained in the data is. In this paper, we aim to providing an analysis method for quantifying the entropy in the raw data captured by sensors embedded in smartphones, and studying the feasibility of generating random numbers from the data. We establish several data collection models for some typical sensors with different scenarios and data sampling frequencies. Furthermore, we propose a universal entropy estimation scheme for multivariate data to quantify the entropy of the sensor data, and apply it on a type of Android smartphones. The experiments demonstrate that the raw data collected by the sensors has a considerable amount of entropy, and the ability of different sensors to provide entropy has a certain relationship with the usage scenarios of smartphones and the sampling frequency of sensor data. Particularly, when in a static scenario and the sampling frequency is 50Hz, we get a conservative entropy estimation for our testing smartphones based on the min-entropy, which is about 189bits/s, 13bits/s and 254bits/s for the accelerometer, gyroscope, and magnetometer respectively. While the randomness of sensor data in dynamic scenarios will increase compared to static scenarios, because the environment and the way that the user uses the smartphones actually exist differences each time, parts of which are unknowable to the attacker.
Students training for numerical control machines programming by means of computer-aided manufacturing tools

Abstract The article describes the analysis of computer-aided manufacturing systems introduction for the control program machines preparation with the numerical control into the technical students’ educational process. The main stages of teaching students the basics of control machines programming by means of addressing to manual programming and CAM-systems are considered. Recommendations for the control programs testing implementation without a numerical control machine, by means of numerical control machine simulators introduction into the educational process are given.
Poster: Project-Based Learning in the Mathematics Course for First Year Students at a Technical University

Project activity is a form of educational and cognitive activity of students, which consists in motivational achievement of a consciously set goal. Project-based learning is increasingly being used in the various academic disciplines around the world. However, the question of when to start using this method effectively remains open. Therefore, the purpose of this article is to study the possibility of using project-based learning in the study of mathematics in the first semester in the first year at a technical University. To study the effectiveness of this method, we conducted a statistical test of the hypothesis of equality of average scores in the intermediate testing, scored by students who studied using the project method and the traditional one. The result of a survey of students about their knowledge of the project method before using it in a mathematics course at the University is presented. It is established that the use of the project-based learning can increase the interest of students in the learning process. The project-based learning has increased the average score of students at the end of the semester by 16.19% compared to the control group.
News Categorization using Hybrid BiLSTM-ANN Model with Feature Engineering

There is an exponential increase in the number of articles across diverse topics published in various media around the globe. A digital management system would help users to find the relevant topics of discussion in the system and help to find articles of their interest. This project implements a categorization model that uses a hybrid model consisting of BiLSTM and ANN that performs the classification of news articles into chosen topics by using the hypernyms and hyponyms of the words present in them. The BiLSTM model maps the semantic structure of the articles and the ANN model maps the semantic meaning of the article with the help of hypernyms and hyponyms. The performance of the hybrid classification model for news articles spread across six categories is measured over different chosen levels of hypernyms and hyponyms was analyzed. The model performed the best when the hybrid combination of Level 1-2-3 of hypernyms along with the words of the articles is used, achieving a high testing accuracy of 61.33%.
Semantic Recommendation System for Bilingual Corpus of Academic Papers

We tested four methods of making document representations cross-lingual for the task of semantic search for the similar papers based on the corpus of papers from three Russian conferences on NLP: Dialogue, AIST and AINL. The pipeline consisted of three stages: preprocessing, word-by-word vectorisation using models obtained with various methods to map vectors from two independent vector spaces to a common one, and search for the most similar papers based on the cosine similarity of text vectors. The four methods used can be grouped into two approaches: 1) aligning two pretrained monolingual word embedding models with a bilingual dictionary on our own (for example, with the VecMap algorithm) and 2) using pre-aligned cross-lingual word embedding models (MUSE). To find out, which approach brings more benefit to the task, we conducted a manual evaluation of the results and calculated the average precision of recommendations for all the methods mentioned above. MUSE turned out to have the highest search relevance, but the other methods produced more recommendations in a language other than the one of the target paper.
A Survey on Different Deep Learning Architectures for Image Captioning

Vision plays an important part which helps us to look at the world and perceive information about our surroundings. A human perceives information by looking at an object or the surrounding on the whole and tries to map visual features and attributes and by summarizing these features we can describe or tell about our surroundings. The way the human brain does this is still a huge mystery. But, For a machine/computer this task is what is called as Image Captioning. The computer or machine is fed with images from which they learn to extract features i.e pixel information, object position, geometry, etc. Using these features the machine tries to map it to a sentence word by word or on a whole which summarizes the information of the image. Due to the advancements in recent Computer Vision Methods and Deep Learning architectures, Computers have been able to correctly summarize images which have been fed to them. In this paper, we present a survey on the new types of architectures and the datasets which are being used to train such architectures. Furthermore, we have discussed future methods that can be implemented.
DUGRA: Dual-Graph Representation Learning for Health Information Networks

With the rapidly growing volume and variety of Electronic Health Records (EHR) data, deep-learning models exhibit state-of-the-art performance for many predictive tasks in the health domain. To overcome the challenge of high dimensionality in EHR data, many representation learning methods have been proposed to learn low-dimensional diagnosis representations. Another challenge is how to effectively incorporate the domain knowledge, such as the International Classification of Diseases (ICD) medical ontology, into the learned embeddings. Albeit the medical ontology is a knowledge graph, none of the existing methods take advantage of Graph Neural Network (GNN), which has demonstrated its ability in other domains. The problem is that a GNN with multiple hidden layers, which are required to propagate information from the leaf of the medical ontology graph to the root, dilutes the differences among the nodes, degrading the quality of the learned embeddings. In this paper we introduce a densely connected graph derived from the original ontology graph to tackle the problem. Furthermore, to model the information in patient records, we construct a single co-occurrence graph based on the co-occurrence of diagnoses and a patient's diagnosis history. Experimental results show that the diagnosis embeddings learned from our model, DUal-GRAph Representation Learning (DUGRA), outperform the current state-of-the-art models in terms of diagnosis prediction accuracy.
Towards Training Robust Private Aggregation of Teacher Ensembles Under Noisy Labels

Deep learning models trained on large-scale data have achieved encouraging performance in many real-world tasks. Meanwhile, publishing those models trained on sensitive datasets, such as medical records, could pose serious privacy concerns. To counter these issues, one of the current state-of-the-art approaches is Private Aggregation of Teacher Ensembles, or PATE, which achieved promising results in preserving the utility of the model while providing a strong privacy guarantee. PATE combines an ensemble of "teacher models" trained on sensitive data and transfers the knowledge to a "student" model through the noisy aggregation of teachers' votes for labeling unlabeled public data which the student model will be trained on. However, the knowledge or voted labels learned by the student are noisy due to private aggregation. Learning directly from noisy labels can significantly impact the accuracy of the student model. In this paper, we propose the PATE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">++</sub> mechanism, which combines the current advanced noisy label training mechanisms co-teaching(+) with the original PATE framework to enhance its accuracy. A novel structure of Generative Adversarial Nets with one generator and two discriminators is developed in order to integrate them effectively. Furthermore, we discuss the intrinsic limitations of the "update-by-disagreement" method in the co-teaching <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sub> mechanism and develop a novel noisy label detection mechanism for semi-supervised model training to further improve student model performance when training with noisy labels. We evaluate our method on Fashion-MNIST and SVHN to show the improvements on the original PATE on all measures.
Application of Exchanging Monte Carlo Method to Sample Deep Boltzmann Machines

There is a method to learn the parameters of Boltzmann Machines such that using contrastive divergence, or parallel tempering. However, none of them can be applied to multilayered BM in general way. To this problem, we introduce a new sampling method based on Exchanging Monte Carlo to estimate the distribution of BM with combining crystallizing effect of simulated annealing on Boltzmann machines.
Adversarial shared-private model for cross-domain clinical text entailment recognition

The recognition of textual entailment (RTE) as the main text understanding task is crucial to the application in biomedical and clinical field, however, the developing of which has been hindered, due to the scarcity of the data annotation. In this work, we propose a domain adaptation framework for the cross-domain clinical RTE. We first construct a hierarchical feature encoder architecture for fully exploring the interactions between the input sentence pair. We then establish shared and private feature extractors based on the feature encoder, for capturing both the domain-specific and domain-invariant features. We further introduce a domain discriminator with the adversarial training algorithm for enhancing the cross-domain transferring. Based on the real-world Chinese dataset, our framework achieves significantly enhanced performances against baseline domain adaptation methods, on the few-shot and zero-shot transferring settings. Further analysis reveals that our model is effective for the cross-domain clinical RTE.
Reinforcement learning methods based on GPU accelerated industrial control hardware

Abstract Reinforcement learning is a promising approach for manufacturing processes. Process knowledge can be gained automatically, and autonomous tuning of control is possible. However, the use of reinforcement learning in a production environment imposes specific requirements that must be met for a successful application. This article defines those requirements and evaluates three reinforcement learning methods to explore their applicability. The results show that convolutional neural networks are computationally heavy and violate the real-time execution requirements. A new architecture is presented and validated that allows using GPU-based hardware acceleration while meeting the real-time execution requirements.
Phishing detection on tor hidden services

Phishing is the act of impersonating another party to attack a user, usually stealing information or money. In darknets, where participants are usually anonymous, phishing is a huge problem. We describe the current state of phishing in darknets, especially the Tor network. We analyse what techniques attackers can use to impersonate other services as well as develop some metrics to automatically detect phishing pages. Existing solutions against phishing are presented and phishing detection in the clearnet is examined on the transferability to darknets.
Att-FPA: Boosting Feature Perceive for Object Detection

The deep convolutional networks have a great success in vision classification tasks. For object detection, tasks are divided into two subtasks: localization and classification. The detectors scan the whole image to generate object proposals relying on the predefined anchors or points, then classify and fine trim the proposals. The localization task plays an important role in object detection. The foreground objects and background can be easily confused under complex scenes in the existing approaches. Thus, the localization task results in a bad influence on the performance of classification problems. In order to enhance the object localization perceived, a novel method called Attentional Feature Perceive and Augmentation (Att-FPA) is constructed, which devotes setting up a feature dual perceive for localization and classification. Firstly, an attention mask branch (with an attention mask) is imported to check out if the detecting area is the background or containing one or more objects. By maintaining the feature representation of background, the attention mask is employed to strengthen the feature representation of foreground objects, which significantly enhances the localization perceived ability of objects. Moreover, Att-FPA supplies a novel way on feature re-extraction and augmentation, which further promotes the performance significantly. In the task of Ms COCO object detection, Att-FPA achieves an outstanding promotion(3.0+ mAP) than baseline (Faster-Rcnn) method. It establishes a new efficient method of feature representation, which outperforms the state-of-the-art models in object detection.
A forensically-sound methodology for advanced data acquisition from embedded devices at-scene

This paper presents a methodology for advanced extraction of data from embedded devices such as Internet routers. The use of different access techniques are considered, in order to gain access to device memory memory, and an investigative methodology is proposed. Lessons learnt from “hardware hacking” are considered and presented. Preparatory steps are discussed to maximise efficiency and likelihood of success for data acquisition. At scene actions and practice are discussed. The results show that, under the right circumstances, a full ‘filesystem’ and a full 'physical' acquisition of the device’s internal flash memory can be achieved. That this data can be decoded and extracted into a format which may subsequently be examined in industry-standard digital forensic tools is also presented and explored.
Surrogate-assisted firefly algorithm for breast cancer detection

Breast cancer is a severe disease for women health, however, with expensive diagnostic cost or obsolete medical technique, many patients are hard to obtain prompt medical treatment. Thus, efficient detection result of breast cancer while lower medical cost may be a promising way to protect women health. Breast cancer detection using all features will take a lot of time and computational resources. Thus, in this paper, we proposed a novel framework with surrogate-assisted firefly algorithm (FA) for breast cancer detection (SFA-BCD). As an advanced evolutionary algorithm (EA), FA is adopted to make feature selection, and the machine learning as classifier identify the breast cancer. Moreover, the surrogate model is utilized to decrease computation cost and expensive computation, which is the approximation function built by offline data to the real object function. The comprehensive experiments have been conducted under several breast cancer dataset derived from UCI. Experimental results verified that the proposed framework with surrogate-assisted FA significantly reduced the computation cost.
The Implementation of User Centered Design Methods on Public Service Mapping Websites

Abstract Public services are basically held to increase the level of community satisfaction. To achieve the satisfaction, a city / country will be demanded the quality of professional public services. One of them is the city of Tegal. Tegal is one of the cities in Indonesia. In order to implement a smart city, the city government of Tegal strives to provide services to the public quickly, effectively and efficiently. The public services of Tegal City Government have been running quite well, but information on services has not been integrated between Regional-Owned Enterprises (BUMD) and the private sector. Therefore a website-based mapping media was created in the form of clustering of Tegal city public services, both organized by the government, BUMD and the private sector are integrated each other and can be accessed and used easily by the people of Tegal city. This website can provide the closest route and is equipped with a comparison chart of public service categories in the city of Tegal. This application was built with the User Centered Design method so that it can be adjusted to the needs of the user and speed up the completion time, which only takes 105 days or 15 weeks. The results of this mapping website test were carried out by using Black Box Testing with the equivalence partitions method, which is the effectiveness value for all ID Test results is 83%, and the user test results are carried out by using the Usability Testing method that applied the SUS scale, with a value of 89% or excellent.
Projection: a mechanism for human-like reasoning in Artificial Intelligence

Artificial Intelligence systems cannot yet match human abilities to apply knowledge to situations that vary from what they have been programmed for, or trained for. In visual object recognition, methods of inference exploiting top-down information (from a model) have been shown to be effective for recognising entities in difficult conditions. Here a component of this type of inference, called ‘projection’, is shown to be a key mechanism to solve the problem of applying knowledge to varied or challenging situations, across a range of AI domains, such as vision, robotics, or language. Finally, the relevance of projection to tackling the commonsense knowledge problem is discussed.
Design and implementation of electronic circuit virtual laboratory based on virtual reality technology

Virtual reality technology has been applied in virtual labs and remote experiment teaching. This paper designs an electronic circuit virtual laboratory based on virtual reality technology, and relevant experimental teaching content. The system uses Unity 3D and 3ds max tools to build a three-dimensional model of general instrumentation, electronic components, and laboratory scenes in electronic circuit experiments. The C# is used to develop general virtual instrumentation functions and electronic circuit simulation, the electronic circuit virtual reality laboratory based on VR equipment was realized. We have developed VR electronic circuit virtual laboratory on PC and web. Students can use VR electronic circuit virtual laboratory to learn basic electronic circuit knowledge and carry out electronic circuit virtual experiment immersively.