Reviewerly/exHarmony huggingface dataset:
Social Media and Scholarly Publication: What Is the Connection?

A recent post on The Scholarly Kitchen website got me thinking about the role of social media in my professional life. I have young adult children who keep me in the loop about social trends to some extent. They instructed me to join Facebook many years ago and, since then, I have remembered friends’ and relatives’ birthdays with much greater success. I joined Twitter and, for a time, had a personal as well as professional account. It was difficult to keep those separate, and I eventually stopped tweeting on the personal account. I only use my professional one now and with greater circumspect (no politics and fewer complaints about hotels and airlines). I have an Instagram account where I follow many chefs, winemakers, and artists, and I enjoy the beautiful images that they post.
Modeling Named Entity Embedding Distribution into Hypersphere

This work models named entity distribution from a way of visualizing topological structure of embedding space, so that we make an assumption that most, if not all, named entities (NEs) for a language tend to aggregate together to be accommodated by a specific hypersphere in embedding space. Thus we present a novel open definition for NE which alleviates the obvious drawback in previous closed NE definition with a limited NE dictionary. Then, we show two applications with introducing the proposed named entity hypersphere model. First, using a generative adversarial neural network to learn a transformation matrix of two embedding spaces, which results in a convenient determination of named entity distribution in the target language, indicating the potential of fast named entity discovery only using isomorphic relation between embedding spaces. Second, the named entity hypersphere model is directly integrated with various named entity recognition models over sentences to achieve state-of-the-art results. Only assuming that embeddings are available, we show a prior knowledge free approach on effective named entity distribution depiction.
Integrating Classical Preprocessing into an Optical Encryption Scheme

Traditionally, cryptographic protocols rely on mathematical assumptions and results to establish security guarantees. Quantum cryptography has demonstrated how physical properties of a communication channel can be leveraged in the design of cryptographic protocols, too. Our starting point is the AlphaEta protocol, which was designed to exploit properties of coherent states of light to transmit data securely over an optical channel. AlphaEta aims to draw security from the uncertainty of any measurement of the transmitted coherent states due to intrinsic quantum noise. We present a technique to combine AlphaEta with classical preprocessing, taking into account error-correction for the optical channel. This enables us to establish strong provable security guarantees. In addition, the type of hybrid encryption we suggest, enables trade-offs between invoking a(n inexpensive) classical communication channel and a (more complex to implement) optical channel, without jeopardizing security. Our design can easily incorporate fast state-of-the-art authenticated encryption, but in this case the security analysis requires heuristic reasoning.
Deep Knowledge Tracing with Side Information

Monitoring student knowledge states or skill acquisition levels known as knowledge tracing, is a fundamental part of intelligent tutoring systems. Despite its inherent challenges, recent deep neural networks based knowledge tracing models have achieved great success, which is largely from models' ability to learn sequential dependencies of questions in student exercise data. However, in addition to sequential information, questions inherently exhibit side relations, which can enrich our understandings about student knowledge states and has great potentials to advance knowledge tracing. Thus, in this paper, we exploit side relations to improve knowledge tracing and design a novel framework DTKS. The experimental results on real education data validate the effectiveness of the proposed framework and demonstrate the importance of side information in knowledge tracing.
Improving Disentangled Representation Learning with the Beta Bernoulli Process

To improve the ability of VAE to disentangle in the latent space, existing works mostly focus on enforcing independence among the learned latent factors. However, the ability of these models to disentangle often decreases as the complexity of the generative factors increases. In this paper, we investigate the little-explored effect of the modeling capacity of a posterior density on the disentangling ability of the VAE. We note that the independence within and the complexity of the latent density are two different properties we constrain when regularizing the posterior density: while the former promotes the disentangling ability of VAE, the latter -- if overly limited -- creates an unnecessary competition with the data reconstruction objective in VAE. Therefore, if we preserve the independence but allow richer modeling capacity in the posterior density, we will lift this competition and thereby allow improved independence and data reconstruction at the same time. We investigate this theoretical intuition with a VAE that utilizes a non-parametric latent factor model, the Indian Buffet Process (IBP), as a latent density that is able to grow with the complexity of the data. Across three widely-used benchmark data sets and two clinical data sets little explored for disentangled learning, we qualitatively and quantitatively demonstrated the improved disentangling performance of IBP-VAE over the state of the art. In the latter two clinical data sets riddled with complex factors of variations, we further demonstrated that unsupervised disentangling of nuisance factors via IBP-VAE -- when combined with a supervised objective -- can not only improve task accuracy in comparison to relevant supervised deep architectures but also facilitate knowledge discovery related to task decision-making. A shorter version of this work will appear in the ICDM 2019 conference proceedings.
Continued Efforts for Transparency and Inclusion

The latest ALA Annual Conference in DC saw the unveiling of the action-oriented research agenda Open and Equitable Scholarly Communications: Creating a More Inclusive Future , developed with leadership from the ACRL Research and Scholarly Environment Committee. It is a key milestone in the association and in the profession by encouraging the community to make the scholarly communications system more open, inclusive, and equitable by outlining trends, encouraging practical actions, and clearly identifying the most strategic research questions to pursue. The report punctuates conversations that the association has been having for the past few years and provides a framework for how to approach institutionalizing and modelling these values.
Personalized Expression Synthesis Using a Hybrid Geometric-Machine Learning Method

Actually, various Geometric and Machine Learning methods are employed to synthesize expressions. The geometric techniques offer high-performance shape deformation but lead to images which are lacking in texture details such as wrinkles and teeth. On the other hand, the machine learning methods (e.g. Generative Adversarial Network GAN) generate photo-realistic expressions and add texture details to the images but the synthesized expressions are not those of the person. In this paper, we propose a hybrid geometric-machine learning approach to synthesize photo-realistic and personalized joy expressions while keeping the identity of the emotion. Our approach combines a geometric technique based on 2D warping method and a generative adversarial network. It aims at benefiting from the advantages of both paradigms and overcoming their own limitations. Moreover, by adding a previous knowledge of the way of smiling of the subject, we personalize the synthesized expressions. Qualitative and quantitative results demonstrate that our person-specific hybrid method can generate personalized joy expressions closer to the ground truth than two generic state-of-the-art approaches.
Architecture Design for Unmanned Aerial Vehicle Mission Planning System

Unmanned aerial vehicle (UAV) mission planning system (MPS) refers to assigning missions to each UAV according to the missions to be accomplished, the number of UAVs and the different mission payloads, and making flight routes through route planning technology.In this paper, starting from the concept of UAV mission planning, the information requirement environment and construction requirement of UAV mission planning system is analyzed.Then the basic architecture and modular design of UAV mission planning is summarized.At last, the problems of UAV mission planning and the development trend of UAV mission planning system is discussed. Concept of UAV Mission PlanningWith the continuous progress of modern information technology and the development of information warfare, UAV's combat mission has developed from single mission to multi-mission.It has developed from target-based to reconnaissance and surveillance missions.At the same time, it has developed to target location and fire negotiation, communication relay, electronic warfare, ground attack, air early warning and other directions.The tactical application of UAVs has also developed from single combat to formation operations between UAVs and hybrid formation operations between UAVs and manned fixed-wing aircraft/armed helicopters.The development of UAV combat mission and the change of tactical application put forward new requirements for mission planning.The requirement of intelligence, effectiveness and real-time decision-making is much higher than before.However, the traditional manual/semi-manual method for UAV mission planning has a long response time and weak processing capacity, which cannot fully utilize the performance of UAV, and it is difficult to meet the operational requirements.UAV mission planning system (MPS) is based on the missions to be accomplished, the number of UAVs and the different mission payloads, assigning missions to each UAV and formulating flight routes through route planning technology.In the process of flight, UAV mission payloads can be controlled in order to complete reconnaissance, attack, evaluation and other missions.The main functions of UAV mission planning are as follows:(1) Mission allocation [1, 2]: Through the mission planning system, reasonable plan is carried out among multi-mission, multi-objective and multi-UAV.The different payloads and performance of UAV is fully considered and the UAV and its payload resources are reasonably allocated.The objective is to minimize the time and cost to achieve and complete the assigned missions.(2) Route planning [3,4]: Formulate the flight path of the take-off, landing, approaching to the monitoring point, reconnaissance of monitoring area, departure from the monitoring point, return flight and emergency flight of the aircraft, which requires that the threat cost of missiles, radar, etc. and fuel consumption cost of the UAV are minimized.
Efektifitas Media Cerita Bergambar (CERGAM) terhadap Pengetahuan, Sikap dan Tindakan Siswa PAUD/TK

&lt;p&gt;The low consumption of vegetables and fruits in the community requires an Eat Vegetables and Fruit Campaign as an effort to reach the Nutrition Conscious Family, especially in school-age children and adolescents as the nation's successors. The research objective was to determine the effectiveness of drawing story media to knowledge, attitude, and behavior of kindergarten. The study used a quasi-experiment study, with a non-randomized control group pre-test - post-test design. Research location in Puskesmas Padang Serai Bengkulu City. The population is kindergarten students. Sampling using the non-probability method with purposive sampling technique totaling 60 samples. Data were analyzed by univariate, bivariate dependent t-test/Wilcoxon test, independent t-test/Mann Whitney and covariance analysis test. The results showed that there were differences in knowledge, attitudes, and behavior in the treatment and control groups, furthermore the drawing story media was effective to knowledge, attitude, and behavior of kindergarten students. Educate by using drawing story media continuously as an effort to increase the knowledge, attitude, and behavior of kindergarten students. Elaborate drawing story media with more interesting storylines and colorful images.&lt;/p&gt;
FLUI-Growth

A number of studies and methods have been proposed to obtain more efficient patterns that meet the requirements of decision makers by considering frequency and utility thresholds, for example, frequent high utility patterns and high utility rare patterns. However, decision makers need to know the patterns that lead to minimal benefits and appear frequently to understand the reasons for low interest rates, as their frequency may be a factor in increasing benefits rates. Therefore, in this paper we propose a new framework for extracting a new model that is important in many cases, named as, frequent inefficient item set (FLUI). In order to extract the proposed patterns, we propose a new algorithm called FLUIMA, which extension of the known UP-Growth algorithm. The new frameworks and algorithm have been evaluated on many benchmark databases. The results show the effectiveness and practicability of the new framework and the new algorithm and its applicability in real life.
Real‐time action feature extraction via fast PCA‐Flow

Summary Action recognition is a research hotspot in the field of Internet of Things (IoT). Currently, local pixel‐domain spatiotemporal feature extraction methods have reached the state‐of‐the‐art action recognition performance on many challenging datasets. However, the poor computational complexity of these approaches prevents them from scaling up to real‐time applications. For solving this problem, we present a novel real‐time video feature extraction technique by exploiting the fast PCA‐Flow algorithm. Firstly, we down‐sample video images in form of grid. Based on the down‐sampling images, PCA‐Flow algorithm is used to calculate optical flow among adjacent images. The PCA‐Flow matrices are then expanded to the original video image size by using efficient gCLSR super‐resolution method to keep the inherent geometric structure of the optical flow. Finally, we compute action descriptors based on original pixel frames and the enlarged PCA‐Flow images. The proposed approach is validated on three challenging datasets: UCF50, Hollywood2, and HMDB51. Experimental results indicate that the proposed method is more efficient in computation and can achieve competitive quality than the state‐of‐the‐art methods.
Truth Diagrams Versus Extant Notations for Propositional Logic

Abstract Truth diagrams (TDs) are introduced as a novel graphical representation for propositional logic (PL). To demonstrate their epistemic efficacy a set of 28 concepts are proposed that any comprehensive representation for PL should encompass. TDs address all the criteria whereas seven other existing representations for PL only provide partial coverage. These existing representations are: the linear formula notation, truth tables, a PL specific interpretation of Venn Diagrams, Frege’s conceptual notation, diagrams from Wittgenstein’s Tractatus, Pierce’s alpha graphs and Gardner’s shuttle diagrams. The comparison of the representations succeeds in distinguishing ideas that are fundamental to PL from features of common PL representations that are somewhat arbitrary.
Deep Learning Based Object Detection Using You Only Look Once

This paper spotlight on the real time detection and recognition model called as YOLO.It uses single convolutional neural network in order to detect and recognize the objects of the images.The model is first trained on COCO dataset and car dataset of achieving a mAP of 91.28% and 70% respectively.YOLO takes 57 FPS to processes the image to detect the objects in Image.Since YOLO takes whole detection pipeline in a single unified network and it helps to increase and optimize the real time object detection with variety of objects.
Blockchain Regulation and Governance in Europe, by Michèle FinckThe Blockchain and the New Architecture of Trust, by Kevin Werbach

Imagine a spreadsheet that creates a permanent record of information while functioning as a distributed ledger enabling users to engage in a wide range of activities without the aid of a trusted intermediary. Inevitably, the development of innovative technologies has never ceased to astound. We now know what this particular form of socio-technical imaginary involves—blockchains! Satoshi Nakamato’s quest to find a pragmatic engineering solution to what he regarded as the problem of institutional mediation is merely the first iteration of that vision – an ‘electronic payment system based on cryptographic proof instead of trust’.1 The idea that human or institutional agency can now be replaced by entrusting responsibility with machines has attracted considerable policy and scholarly interest, particularly during the past two decades. Just as the World Wide Web with its hypertext protocols integrated into the Internet infrastructure facilitated the flow of information from disparate directories, blockchain technologies not only build on these foundations but hold out the promise of decentralized spaces for interaction, participation and innovation. Can a system of cryptographic proof deployed within a peer-to-peer network provide a much-needed solution to the dominant role of trusted third parties in economic transactions?
Proposal of an instrument to evaluate interaction spaces in a VLE

A virtual learning environment is conceived as an interaction space that ease the realization of mediated activities by technology, in this case the internet; besides using multimedia materials, learning objects, social networks, among others; which have changed imminently the traditional education. In this article an instrument is proposed in a checklist format, to evaluate any platform that has interaction spaces such as a Virtual Learning Environment, in this case responding to four spaces or general indicators: information Space, Mediation / Interaction Space, Instructional Design Space and Exhibition Space. Criteria are used according to the interactions and activities carried out by the consultant and virtual student. These, in turn, come up from the analysis and interaction of the advisers achieved in the discussion forums and portfolio activities through collaborative work. It was situated as a qualitative research, with a descriptive nature since it is not limited to data collection only, but also it refers and analyzes the interaction of the advisers achieved in the discussion forums and portfolio activities through the collaborative work of the workshop course "Virtual Learning Environments" developed in a virtual learning environment.
Deep Adversarial Belief Networks

We present a novel adversarial framework for training deep belief networks (DBNs), which includes replacing the generator network in the methodology of generative adversarial networks (GANs) with a DBN and developing a highly parallelizable numerical algorithm for training the resulting architecture in a stochastic manner. Unlike the existing techniques, this framework can be applied to the most general form of DBNs with no requirement for back propagation. As such, it lays a new foundation for developing DBNs on a par with GANs with various regularization units, such as pooling and normalization. Foregoing back-propagation, our framework also exhibits superior scalability as compared to other DBN and GAN learning techniques. We present a number of numerical experiments in computer vision as well as neurosciences to illustrate the main advantages of our approach.
Relating RNN Layers with the Spectral WFA Ranks in Sequence Modelling

We analyse Recurrent Neural Networks (RNNs) to understand the significance of multiple LSTM layers. We argue that the Weighted Finite-state Automata (WFA) trained using a spectral learning algorithm are helpful to analyse RNNs. Our results suggest that multiple LSTM layers in RNNs help learning distributed hidden states, but have a smaller impact on the ability to learn long-term dependencies. The analysis is based on the empirical results, however relevant theory (whenever possible) was discussed to justify and support our conclusions.
The Global Markov Property for a Mixture of DAGs

Real causal processes may contain feedback loops and change over time. In this paper, we model cycles and non-stationary distributions using a mixture of directed acyclic graphs (DAGs). We then study the conditional independence (CI) relations induced by a density that factorizes according to a mixture of DAGs in two steps. First, we generalize d-separation for a single DAG to mixture d-separation for a mixture of DAGs. We then utilize the mixture d-separation criterion to derive a global Markov property that allows us to read off the CI relations induced by a mixture of DAGs using a particular summary graph. This result has potentially far reaching applications in algorithm design for causal discovery.
TBT: Targeted Neural Network Attack with Bit Trojan

Security of modern Deep Neural Networks (DNNs) is under severe scrutiny as the deployment of these models become widespread in many intelligence-based applications. Most recently, DNNs are attacked through Trojan which can effectively infect the model during the training phase and get activated only through specific input patterns (i.e, trigger) during inference. In this work, for the first time, we propose a novel Targeted Bit Trojan(TBT) method, which can insert a targeted neural Trojan into a DNN through the bit-flip attack. Our algorithm efficiently generates a trigger specifically designed to locate certain vulnerable bits of DNN weights stored in main memory (i.e., DRAM). The objective is that once the attacker flips these vulnerable bits, the network still operates with normal inference accuracy with benign input. However, when the attacker activates the trigger by embedding it with any input, the network is forced to classify all inputs to a certain target class. We demonstrate that flipping only several vulnerable bits identified by our method, using available bit-flip techniques (i.e, row-hammer), can transform a fully functional DNN model into a Trojan-infected model. We perform extensive experiments of CIFAR-10, SVHN and ImageNet datasets on both VGG-16 and Resnet-18 architectures. Our proposed TBT could classify 92 % of test images to a target class with as little as 84 bit-flips out of 88 million weight bits on Resnet-18 for CIFAR10 dataset.
Better AI through Logical Scaffolding

We describe the concept of logical scaffolds, which can be used to improve the quality of software that relies on AI components. We explain how some of the existing ideas on runtime monitors for perception systems can be seen as a specific instance of logical scaffolds. Furthermore, we describe how logical scaffolds may be useful for improving AI programs beyond perception systems, to include general prediction systems and agent behavior models.
Interval Type-2 Fuzzy Systems as Deep Neural Network Activation Functions

In this paper, we propose a novel activation function, namely, Interval Type-2 (IT2) Fuzzy Rectifying Unit (FRU), to improve the performance of the Deep Neural Networks (DNNs).The IT2-FRU can generate linear or sophisticated activation functions by simply tuning the size of the footprint of uncertainty of the IT2 Fuzzy Sets.The novel IT2-FRU also alleviates vanishing gradient problem and has a fast convergence rate since it pushes the mean activation to zero by allowing the negative outputs.In order to test the performance of the IT2-FRU, comparative experimental studies are performed on the CIFAR-10 dataset.IT2-FRU is compared with widely used conventional activation functions.Experimental results show that IT2-FRU significantly speeds up the learning and has a superior performance compared to other handled activation functions.