Reviewerly/exHarmony huggingface dataset:
Learning from Monkeys: Authorship Issues Arising from AI Technology

Artificial intelligence is experiencing rapid growth, taking centre stage in R&D investments, policy-making forums and academic literature. The protection of AI generated artwork under copyright law is only one of many policy questions across a range of issues within the intellectual property domain, considering AI as both object and subject of IP protection. However, it has already sparked debates all over the world for the re-examination of copyright legal framework. This Article wishes to contribute to this debate, addressing two research questions: (a) whether works independently created by artificial intelligence systems are eligible for copyright protection under the existing legal framework and (b) whether copyright is actually the best solution for protecting investments in robot-artists.
Unnecessary Requests: Examining Patrons’ Reliance on Interlibrary Loan for Electronically Owned Items at an Academic Library

University of Wisconsin-Stevens Point Library analyzed 4,567 interlibrary loan borrowing requests for articles and other non-returnables. From 2017–2018, nearly 44% of requests were for items that the library owned electronically. Patrons could have accessed the full-text immediately but submitted interlibrary loan requests instead. This prevalence raises questions about user behavior and deficiencies in discovery tools.
Frame Interpolation Using Phase and Amplitude Feature Pyramids

This paper presents a compact neural network for video frame interpolation using phase and amplitude feature pyramids. We design a set of one-dimensional separable complex Gabor filters to extract phase and amplitude feature pyramids for each input image, which is efficient and effective for motion representation. The pyramids are fused and fed into a decoder network to estimate bi-directional optical flow. The interpolated frame is refined by a context-aware synthesis module. We train our model on quintets of frames using motion linear regularization. The proposed network contains much fewer parameters than state-of-the-art approaches. The experiments show that our method outperforms the competing methods. Moreover, our method achieves marked visual improvement in the challenging scenario with lighting changes.
A Two-Stream CNN Framework for American Sign Language Recognition Based on Multimodal Data Fusion

At present, vision-based hand gesture recognition is very important in human-robot interaction (HRI). This non-contact method enables natural and friendly interaction between people and robots. Aiming at this technology, a two-stream CNN framework (2S-CNN) is proposed to recognize the American sign language (ASL) hand gestures based on multimodal (RGB and depth) data fusion. Firstly, the hand gesture data is enhanced to remove the influence of background and noise. Secondly, hand gesture RGB and depth features are extracted for hand gesture recognition using CNNs on two streams, respectively. Finally, a fusion layer is designed for fusing the recognition results of the two streams. This method utilizes multimodal data to increase the recognition accuracy of the ASL hand gestures. The experiments prove that the recognition accuracy of 2S-CNN can reach 92.08 $$\%$$ on ASL fingerspelling database and is higher than that of baseline methods.
VM support for live typing

Live Typing automatically annotates variable types based on the objects assigned to them, and method return types based on the returned objects. IDEs can exploit this information to greatly improve the development tools and the programming experience, bringing some of the benefits of Static Typing tools to Dynamically Typed Languages.
Adaptive Labeling For Hash Code Learning Via Neural Networks

Learning-based hash has been widely used for large-scale similarity retrieval due to the efficient computation and condensed storage of binary representations. In this paper, we propose AdaLabelHash, a hash function learning approach via neural networks. In AdaLabelHash, class label representations are adaptable during the network training. We express the labels as hypercube vertices in a K-dimensional space, and both the network weights and class label representations are updated in the learning process. As the label representations are explored from data, semantically similar categories will be assigned with the label representations that are close to each other in terms of Hamming distance in the label space. The label representations then serve as the desired output of the hash function learning so as to yield compact and discriminating binary hash codes via the network. AdaLabelHash is simple but effective, which can jointly learn label representations and infer compact binary codes from data. It is applicable to both supervised and semi-supervised learning of hash codes. Experimental results on standard benchmarks show the effectiveness of AdaLabelHash.
Adaptive Filtering Fuzzy C-means Image Segmentation with Inclusion Degree

The fuzzy C-means clustering algorithm establishes the uncertain description between sample points and categories, and has the advantage of automatic image segmentation. It can effectively deal with the problems of image blur and distortion in the segmentation process, but it is sensitive to noise and segmentation results are not accurate enough. To solve this problem, this paper proposes an adaptive filtering fuzzy C-means image segmentation algorithm with inclusion degree. The algorithm has characteristics of strong anti-interference ability and high precision for segmentation. The simulation results show that the algorithm can effectively segment noise images and complex background images.
Information System Based on Website Application as Implementation of Disclosure of Public Information at Diskominfo Kabupaten Bandung

This study focuses on the content of public information and news on the local government website, according to the mandate of the KIP (Public Information Openness) Act, is "... information that must be provided and published periodically," at least displayed through the official government website. The KIP Act start effectively on May 1, 2010, and born with a base and objectives that information is basic needs everyone for personality development and environment social and an important part of national security in accordance with clause 28F 1945 constitution. The spread of public information and news in Diskominfo Kabupaten Bandung previously only display in wall magazine in front of the office Diskominfo, so not all people in Kabupaten Bandung know the public information because not all community in Kabupaten Bandung come to Diskominfo. In addition in filing MCAP (mobile community access point) services, people who will submit the MCAP services have to go to Diskominfo and give the submit applications services. To solve the problem, the writer makes a website as a media the public information and filing MCAP service so people have access public information directly and can submit MCAP service from the websitet directly from various devices connected with internet. In designing the website, the research methodology is a prototype where with this method making website will faster. The data collection method used to obtain information from the user (users this system is used by an interview method. With the interview method, needs user and systems walk at the moment will be implemented starting in the new system.
Contribution of Learner Characteristics in the Development of Adaptive Learner Model

Learner models are built to enable adaptive and personalized instructions to prospective learners in Adaptive Intelligent Tutoring Systems (AITS). They are created parallel to the advancement of adaptive intelligent tutoring frameworks and consequently, connected to the system’s advancement. This paper presents the critical literature review from 2002 to 2017 in the research area of learner characteristics in the development of a learner model for the particular field of AITS. The primary aim of this literature review is to answer the two fundamental questions. First, what are the main characteristics of learners to be modeled second, what is the contributions of learner characteristics in the development of the learner model? The learner characteristics model along with a model application and their comparative study has been discussed in ITS. The goal of this paper is to give the gained information to educationalists, developers, researchers, and scholars for making decisions about what the learner’s characteristics are should be followed while developing an effective learner model.
Word Embedding for Small and Domain-specific Malay Corpus

In this paper, we present the process of training the word embedding (WE) model for a small, domain-specific Malay corpus. In this study, Hansard corpus of Malaysia Parliament for specific years was trained on the Word2vec model. However, a specific setting of the hyperparameters is required to obtain an accurate WE model because changing one of the hyperparameters would affect the model's performance. We trained the corpus into a series of WE model on a set of hyperparameters where one of the parameter values was different from each model. The model performances were intrinsically evaluated using three semantic word relations, namely; word similarity, dissimilarity and analogy. The evaluation was performed based on the model output and analysed by experts (corpus linguists). Experts' evaluation result on a small, domain-specific corpus showed that the suitable hyperparameters were a window size of 5 or 10, a vector size of 50 to 100 and Skip-gram architecture.
Learning Super-Resolution Coherent Facial Features Using Nonlinear Multiset PLS for Low-Resolution Face Recognition

Face hallucination (FH) is an effective technique for super-resolving low-resolution (LR) face images. In real-world applications, a face image usually has multiple distinct low resolutions. Most existing FH methods can not effectively deal with multiple LR views simultaneously. To solve this issue, we present a multi-set partial least squares (MPLS) approach and its kernel extension for jointly learning the nonlinear consistency of multi-resolution facial features. With nonlinear MPLS, we present a novel simultaneous super-resolution coherent facial feature method for the face images with multiple LRs, which has capacity of jointly learning the nonlinear relationships between multiple facial resolutions. Experimental results demonstrate the effectiveness and robustness of our proposed FH method.
Real-time Human Detection and Tracking in Infrared Video Feed

Human detection is an attractive field in computer vision. It remains a challenging task in constrained light conditions due to low contrast against the background. Most of the research using visible light cameras have mainly focused on human detection when sufficient light is available. They have used RGB images, which were highly sensitive to changes in visible light conditions. This paper is focused on the infrared video feed, which is less sensitive against visible light illumination changes compared to RGB images. We propose a novel approach to human detection using Deep Convolutional Neural Networks (DCNN) and Kernelized Correlation Filters (KCF). The proposed methodology utilizes the MobileNet, pretrained on ImageNet for frame-wise human detection and KCF to improve the tracking accuracy with motion dynamics model. In addition to that, preprocessing on raw images has been applied to reduce the noise effects. The proposed methodology performed well in infrared image sequence with 89.71% of human detection accuracy, which is higher than the state of the art methods and real-time applicable in currently available surveillance systems.
Formal Verification of Memory Preservation of x86-64 Binaries

Formal verification of a binary can provide high software assurance, even when the source code is unavailable. It is, however, inherently hard due to the low level of abstraction involved; instead of verifying typed and structured source code, one has to verify machine code or reconstructed assembly. This paper presents a semi-automated methodology for formally verifying memory preservation, as well as register preservation, over disassembled binaries. The methodology is based on formal symbolic execution and Floyd-style verification. We show that the methodology is compositional on the function level, which is crucial for scalability. The methodology works for loops, recursion, and both optimized and non-optimized code. It can be used to expose preconditions required for non-exceptional behavior. We demonstrate applicability by verifying a set of functions from the HermitCore unikernel library.
Exploring Non-Human Traffic in Online Digital Advertisements: Analysis and Prediction

An advertisement (ad) click fraud occurs when a user or a bot clicks on an ad with a malicious intent where advertisers need to pay for those fake clicks. Click-fraud is a serious problem for the online advertising industry. Our study demonstrates a hybrid approach using a two-level fingerprint to detect the illegitimate bots targeting ad click fraud. The approach consists of two detection phases: (1) a rule-based phase and (2) a machine learning-based phase. The first level of the fingerprint is used for rule-based detection phase. It is generated using immutable information about the user and traversing a website’s page. The second level of the fingerprint is generated using ad click behavioral patterns. It is used for machine learning-based detection phase. Different traditional classification algorithms were evaluated to be applied in the machine learning-based detection phase. To test our approach, we used a real commercial website for ads called Waseet where the access log of the website server was utilized as a dataset for our experiments. The results of our experiments show that our proposed hybrid approach entails promising results.
Improving DNN Solution using Repeated Training

We present an approach for improving DNN solution by running multiple instances of the same training (where everything is the same except different seed for weight initialization). We show that significant improvements in accuracy can be achieved by using proposed approach. Additionally, we tested two simple stopping criteria that aim to identify best performing networks in the early stage of the training. This allows us to save majority of computational resources as we fully train only one network while terminating other instances of the training in an early phase. We tested combination of 20 repetitions in repeated training with global and gradual stopping rule. Repeated training with global stopping at approx. 1% of average training time can beat average performing network and stopping at approx. 10% of average training time can significantly outperform average network. Furthermore, this approach does not involve additional manual work and requires only small amount of additional computation.
Design and Implementation of Credit Evaluation System for Healthy Aged Service

As the problem of global aging intensifies, the quality of credit for healthy aged service has increasingly received more attention. The traditional evaluation of credit quality of healthy aged service still uses the manual entry way to evaluate the service quality, which cannot well deal with huge data in the evaluation for healthy aged service quality. Therefore, in this paper, a credit quality evaluation system is designed and implemented to predict and evaluate user's credit ratings by a standard operation flow for healthy aged service. In this system, we establish a predictive model to automatically evaluate healthy aged service credit quality by machine learning technique. The credit data is quantified and processed for extracting credit ratings-related features. Machine learning technique is utilized to explore the latent relationship between the credit data and its rating. The credit evaluation model for healthy aged service can be built by supervised learning method for predicting user's credit ratings. The design and implementation of this system provides a reasonable solution for auto-evaluation of credit quality of healthy aged service, which can save more manpower and improve service quality in healthy aged service.
Design of Distributed Control System for the Pick-up Robot Based on CAN Bus

In order to solve the problems that unobvious control architecture, chaotic layout, poor scalability and poor real-time communication of pickup robot in unstructured environment. This paper presents a modular distributed control system based on CAN bus. The hardware platform is built with the embedded system and CAN bus. The software design and the experimental test of control system for visual inspection module, manipulator drive module, robot path planning module and infrared obstacle avoidance module are realized. The above shortcomings are basically solved, the reliability and real-time performance of the pick-up robot is improved in performing the uncertainty task.
Finding Risk Patterns in Cloud System Models

The risk of unauthorized access to confidential data is a major problem in cloud computing. In previous work, the notion of risk patterns was introduced to capture configurations of cloud systems that are prone to data protection issues. In this paper, we devise a program for the automatic detection of risk patterns in cloud system models. Our program makes use of the Eclipse Modeling Framework, the model transformation library Henshin, and the modeling workbench Sirius to (i) enable security experts to describe cloud risk patterns in a compact way, (ii) enable the efficient automatic detection of risk patterns in the model of a cloud system, and (iii) support cloud experts in experimenting with the security implications of different cloud configurations. A case study and experiments demonstrate the applicability and scalability of the proposed approach.
An Ontology-Based Approach for Preventing Incompatibility Problems of Quality Requirements During Cloud SLA Establishment

Cloud computing paradigm has emerged as response to challenges of managing and delivering on-demand services, in which provider and customer are willing to sign a Service Level Agreement (SLA). Even though the SLA is used to specify the level of services provided and the guarantee where negotiated conditions are specified, customers are not always satisfied. In order to promote the use of SLA without any ambiguities in quality requirements definitions, it is useful to develop an ontology-based support tool that automate the process of generating SLA regarding the interaction among the ISO 25010 quality characteristics. We propose an approach that uses ontology to semantically classify the quality requirements into the quality characteristics such as described by ISO 25010 and define their relationships. Taking into account customer requirements and considering their interactions before using cloud service is one of the most important and difficult tasks of SLA establishment. Ontology is used to semantically define diverging quality requirements and their relationships. In order to show the applicability of our approach, a tool for SLA generation is implemented and tested.
Coreference in English OntoNotes: Properties and Genre Differences

The OntoNotes corpus is widely used for training and testing coreference resolution systems, but only little attention has so far been given to the differences between the different genres of language that the corpus is composed of. We are primarily interested in the contrast between spoken and written language, and thus we conducted in-depth analyses of various reference-related properties of the sub-corpora of OntoNotes, which yield several statistically significant differences. We compare these to predictions made in the Linguistics literature, and draw some conclusions for potential genre-specific implementations of coreference resolution.
A Game Model for Proofs with Costs

We look at substructural calculi from a game semantic point of view, guided by certain intuitions about resource conscious and, more specifically, cost conscious reasoning. To this aim, we start with a game, where player defends a claim corresponding to a (single-conclusion) sequent, while player tries to refute that claim. Branching rules for additive connectives are modeled by choices of , while branching for multiplicative connectives leads to splitting the game into parallel subgames, all of which have to be won by player to succeed. The game comes into full swing by adding cost labels to assumptions, and a corresponding budget. Different proofs of the same end-sequent are interpreted as more or less expensive strategies for to defend the corresponding claim. This leads to a new kind of labelled calculus, which can be seen as a fragment of (subexponential linear logic). Finally, we generalize the concept of costs in proofs by using a semiring structure, illustrate our interpretation by examples and investigate some proof-theoretical properties.
Assembly of Multiple Ultrathin Chips on Flexible Foils With High Placement Accuracy by a Simple Transfer Process

This article presents a simple approach for high-accuracy assembly of multiple ultrathin chips on flexible foil substrates. The chips are placed initially in the cavities on a chip carrier substrate and transferred onto a thermally releasable adhesive tape. After application of the epoxy-based adhesive onto the chip surface, the chips are transferred onto the foil substrate. Finally, the epoxy adhesive is cured and the adhesive tape is removed. It was found that a wafer dicing tape with a releasing temperature of 90 °C as thermally releasable adhesive tape and a two-component epoxy adhesive with a curing temperature of 65 °C is suitable for the process. High placement accuracy of the chips of less than 30 μm in the x- and y-orientation as well as rotational misalignment lower than 0.2° were obtained. The process is demonstrated on a polyimide foil with a resulting chip flatness of ±5 μm.
Lightning Protection of 110 kV and 220 kV Transformer Neutral Points in Shenzhen Power Grid

At present, the neutral point of ungrounded transformer in our country is usually protected by gap and/or arrester. The overvoltage including lightning overvoltage and power frequency overvoltage may cause misoperation of protection gap or thermal collapse of arrester, which affects the safe and reliable operation of the system. In recent years, Shenzhen power grid has suffered several faults caused by inappropriate neutral point protection measures or unreasonable parameter configuration of 110kV and 220kV transformers. Therefore, this paper explored two solutions. (1) One is a new lightning protection measure for transformer neutral point. This measure is combined with controllable clearance and metal oxide arrester (MOA). Under lightning overvoltage, the arrester operates. Under power frequency overvoltage, the gap breaks down to protect transformer neutral point insulation and arrester. Such measure can effectively solve the following problems. The arrester is prone to explosion when it operates under power frequency overvoltage. The clearance cannot extinguish arc when it operates under lightning and power frequency overvoltage impulse. The insulation coordination between the clearance and the parallel protection of the arrester is difficult. (2) A new type of relay protection configuration is adopted to avoid possible malfunction of system protection. The above two measures can greatly improve the stability and security of the system, and at the same time, vigorously enhance the level of intelligence of 110 and 220 kV substations, effectively promote the construction of intelligent substations. In the future, more simulation and experimental verification works for the above two measures will be carried out.