Reviewerly/exHarmony huggingface dataset:
Recovering purity with comonads and capabilities

In this paper, we take a pervasively effectful (in the style of ML) typed lambda calculus, and show how to extend it to permit capturing pure expressions with types. Our key observation is that, just as the pure simply-typed lambda calculus can be extended to support effects with a monadic type discipline, an impure typed lambda calculus can be extended to support purity with a comonadic type discipline. We establish the correctness of our type system via a simple denotational model, which we call the capability space model. Our model formalizes the intuition common to systems programmers that the ability to perform effects should be controlled via access to a permission or capability, and that a program is capability-safe if it performs no effects that it does not have a runtime capability for. We then identify the axiomatic categorical structure that the capability space model validates, and use these axioms to give a categorical semantics for our comonadic type system. We then give an equational theory (substitution and the call-by-value $\beta$ and $\eta$ laws) for the imperative lambda calculus, and show its soundness relative to this semantics. Finally, we give a translation of the pure simply-typed lambda calculus into our comonadic imperative calculus, and show that any two terms which are $\beta\eta$-equal in the STLC are equal in the equational theory of the comonadic calculus, establishing that pure programs can be mapped in an equation-preserving way into our imperative calculus.
Phishing Email Detection Based on Hybrid Features

As an attack of social engineering, phishing email has caused tremendous financial loss to recipients. Therefore, there is an urgent need for phishing email detection with high accuracy. In this paper, we proposed phishing emails detection based on hybrid features. By analysing the email-header structure, email-URL information, email-script function and email psychological features, we extracted 18 hybrid features. Then we chose Support Vector Machine (SVM) classifier to evaluate our experiments. Experiments are performed on a dataset consisting of 500 legitimate emails and 500 phishing emails. The proposed approach achieved overall true-positive rate of 99%, false-positive rate of 9%, precision of 91.7% and accuracy of 95.00%. Furthermore, we evaluated the effectiveness of our proposed psychological features. The results showed that psychological features can improve the accuracy of detection and reduce the false-positive rate. Our proposed method has a good performance in detecting phishing emails.
Keyword-Based Semi-Supervised Text Classification

Industrial organizations generate massive volumes of data during their routine business and production activities. Such data may be structured (numerical or categorical), or it may be unstructured and textual. Both structured and unstructured data contain a wealth of knowledge that can help organizations improve their operations. Organizations find it easy to automatically extract knowledge from structured data. Unstructured data, however, must be mined and interpreted manually which is cumbersome, error-prone and time consuming. This paper focuses on how to automatically analyze unstructured text data to extract important business value. It proposes a semi-supervised natural language (NL) approach to analyze a corpus of documents associated with accounts receivable disputes at a large corporation. The name semi-supervised derives from the philosophy underlying the methodology, where a set of categories and the keywords associated with these categories are defined in consultation with the domain experts. Subsequently, these categories and their associated keywords are supplied as input to the algorithm, which classifies the disputes automatically into these predefined categories. The performance of the semi-supervised methodology is very comparable to that of the random forest, which is a supervised learning approach. The paper discusses the benefits of the semi-supervised approach over supervised learning; namely, a considerable reduction in the manual effort to analyze, understand and label training data set, without any noticeable degradation in performance.
LENTA: Longitudinal Exploration for Network Traffic Analysis From Passive Data

In this paper, we present longitudinal exploration for network traffic analysis (LENTA), a system that supports the network analysts in the identification of traffic generated by services and applications running on the Web. In the case of URLs observed in operative network, LENTA simplifies the analyst's job by letting her observe few hundreds of clusters instead of the original hundred thousands of single URLs. We implement a self-learning methodology, where the system grows its knowledge, which is used in turn to automatically associate traffic to previously observed services, and identify new traffic generated by possibly suspicious applications. This approach lets the analysts easily observe changes in network traffic, identify new services, and unexpected activities. We follow a data-driven approach and run LENTA on traces collected both in ISP networks and directly on hosts via proxies. We analyze traffic in batches of 24-h worth of traffic. Big data solutions are used to enable horizontal scalability and meet performance requirements. We show that LENTA allows the analyst to clearly understand which services are running on their network, possibly highlighting malicious traffic and changes over time, greatly simplifying the view and understanding of the network traffic.
Exploring speculative methods

This article explores approaches to propagating interspecies understanding and examines the most appropriate ways to investigate the topic as a form of research. It addresses making, or Research through Design (RtD), as a more appropriate research method to generate new knowledge around interspecies embodied experience and to help audiences consider what it might be like to be a nonhuman animal than more traditional forms of scholarship. It presents a range of approaches to exploring interspecies understanding and then situates this knowledge in context with reference to a series of prototypes and design artifacts which constitute the body of work Equine Eyes. The Equine Eyes project consists of a mixed-reality headset, which uses immersive technology to help the user adopt the “point of view” of a horse. The work and the knowledge it produces is experiential in that it requires the audience to wear the headset which simulates horse-like vision to consider how tacit knowledge can be explored through making. The project adopts a RtD method to explore how speculative design artifacts, and play, can be utilised to help foster interspecies thinking and understanding and generate new speculative methods for interspecies design practice. It emphasizes the importance of developing usable speculative design artifacts that can be experienced by users to enact the speculation as an embodied experience.
APCP-NET: Aggregated Parallel Cross-Scale Pyramid Network for CMR Segmentation

Cardiac magnetic resonance (CMR) segmentation has been a glaring medical imaging issue for the past decades. The existing convolutional networks used for CMR segmentation lack adequate communication between semantic and spatial information. In this paper, we propose an aggregated parallel cross-scale pyramid network (APCP-Net) for CMR segmentation, which adopts parallel cross-scale spatial pyramid to enhance the ability of multi-scale feature fusion. To alleviate the computational complexity of the network without losing performance and increasing time consumption, we present a convolution decomposition strategy which decomposes a regular convolutional layer into several lightweight ones. Furthermore, deep aggregation is used among parallel spatial pyramids to promote the representation ability of our APCP-Net. We evaluate our APCP-Net on the segmentation dataset of ACDC 2017 Challenge. Experimental results demonstrate that our APCP-Net is comparable to the state-of-the-art methods in the case where the parameter amount is 54 times less than the existing methods.
Model-Based Multi-objective Reinforcement Learning with Unknown Weights

This paper describes solving multi-objective reinforcement learning problems where there are multiple conflicting objectives with unknown weights. Reinforcement learning (RL) is a popular algorithm for automatically solving sequential decision problems and most of them are focused on single-objective settings to decide a single solution. In multi-objective reinforcement learning (MORL), the reward function emits a reward vector instead of a scalar reward. A scalarization function with a vector of n weights (weight vector) is a commonly used to decide a single solution. The simple scalarization function is linear scalarization such as weighted sum. The main problem of previous MORL methods is a huge learning cost required to collect all Pareto optimal policies. Hence, it is hard to learn the high dimensional Pareto optimal policies. To solve this, this paper proposes the novel model-based MORL method by reward occurrence probability (ROP) with unknown weights. There are two main features. The first feature is that the average reward of a policy is defined by inner product of the ROP vector and the weight vector. The second feature is that it learns ROP in each policy instead of Q-values. Pareto optimal deterministic policies directly form the vertices of a convex hull in the ROP vector space. Therefore, Pareto optimal policies are calculated independently with weights and just once. The experimental results show that our proposed method collected all optimal policies under four dimensional Pareto optimal policies, and it takes a small computation time though previous MORL methods learn at most two or three dimensions.
Temporal data exchange

Data exchange is the problem of transforming data that is structured under a source schema into data structured under another schema, called the target schema, so that both the source and target data satisfy the relationship between the schemas. Many applications such as planning, scheduling, medical and fraud detection systems, require data exchange in the context of temporal data. Even though the formal framework of data exchange for relational database systems is well-established, it does not immediately carry over to the settings of temporal data, which necessitates reasoning over unbounded periods of time. In this work, we study data exchange for temporal data. We first motivate the need for two views of temporal data: the concrete view, which depicts how temporal data is compactly represented and on which the implementations are based, and the abstract view, which defines the semantics of temporal data as a sequence of snapshots. We first extend the chase procedure for the abstract view to have a conceptual basis for the data exchange for temporal databases. Considering non-temporal source-to-target tuple generating dependencies and equality generating dependencies, the chase algorithm can be applied on each snapshot independently. Then we define a chase procedure (called c-chase) on concrete instances and show the result of c-chase on a concrete instance is semantically aligned with the result of chase on the corresponding abstract instance. In order to interpret intervals as constants while checking if a dependency or a query is satisfied by a concrete database, we will normalize the instance with respect to the dependency or the query. To obtain the semantic alignment, the nulls (which are introduced by data exchange and model incompleteness) in the concrete view are annotated with temporal information. Furthermore, we show that the result of the concrete chase provides a foundation for query answering. We define naïve evaluation on the result of the c-chase and show it produces certain answers.
Study of Eye Gaze and Presence Effect in Virtual Reality

This study explores the characteristics of eye gaze in an immersive virtual environment. The study investigated the eye gaze data of users by using VR stimuli and analyzing users’ concentration. The goal of the study was to investigate how eye movements are affected by the story of an animation. In the study, users’ level of concentration as well as presence effect were measured with 53 participants by analyzing their eye gaze movements and gaze fixations.
Developing a Communication Application System for Chamwino Small-Scale Farmers in Tanzania: A Participatory Design Research

Small-scale farmers in Chamwino Tanzania, lack reliable means of communication for facilitating information flow between farmers themselves and other crop farming stakeholders. This study, therefore, sought to develop a mobile phone application system to support farming decision-making. The study employed a participatory design approach that used interviews, focus group discussions, and brainstorming for data collection and a software developer for the creation of a prototype. Thematic analysis was conducted, and the findings show that farmers require information on farming input, weather, transportation, market, crop farming policies and on agricultural research reports. Thereafter, convergent thinking facilitated the development of a mobile phone application system (mobileCHAPONA). The system enables two-way farming information flow among stakeholders (farmers, extension officers, advisory services and mediators). The system assists in the coordination of stakeholders towards improving production. Also, it is easily usable and adoptable by stakeholders aged between 24 to 44 years old, while elders above 45 years old may require a long run training to cope with innovation. It is expected that in the future, software developers, development practitioners, and policy experts will use the system, and improve it further in a manner that keeps into consideration the ecological perspectives in Sub-Saharan Africa.
Using Machine Learning to Find Anomalies in Field Bus Network Traffic

Devices for building automation are often connected by field buses. Typically no encryption and authentication is available, hence the transmitted data can be read by anyone connected to the bus. This problem gave rise to the idea of developing an intrusion detection system. Due to the lack of information about previous attacks on building automation it is not possible to use a pattern-based IDS. Unsupervised machine learning algorithms should be able to find anomalies automatically and trigger an alarm in case of intrusion. A concept how to create such an IDS is hereby presented. For the analysis of the feature space local outlier factor, support vector machines and entropy analysis were used. The occurring addresses were also monitored. Some of the tested attack scenarios could be detected. Attacks injecting traffic massively got found by nearly all four tested modules, while more cautious ones haven’t been detected.
Application of constrained learning in making deep networks more transparent, regularized, and biologically plausible

Constrained learning has numerous applications and advantages, especially in circumstances in which hardware implementation imposes some constraints on us by biological justifications. Making neural network comprehensible, faster convergence and learning general properties are of other advantages of constrained learning. In this article we have tried to use constrained learning to reach more plausibility biologically. We will demonstrate that not only does the proposed model have advantages of the previously proposed models potentially, but also it can be used as a technique for regularization of neural network weights and faster convergence. Finally, having used an ensemble method of different networks, the result for MNIST dataset with data augmentation is 99.81% and the results for CIFAR-10 and SVHN datasets without data augmentation are 93.4% and 98.19% respectively.
Scaling Gain and Eyeheight While Locomoting in a Large VE

Virtual Environments (VEs) presented through head-mounted displays (HMDs) are often explored on foot. This type of exploration is useful since the inertial cues of physical locomotion aid spatial awareness. However, the size of the VE that can be explored on foot is limited to the dimensions of the tracking space of the HMD unless locomotion is somehow manipulated. This paper presents a system for exploring a large VE on foot when the size of the physical surroundings is small by leveraging people’s natural ability to maintain spatial awareness using their own locomotion. We examine two strategies to increase the explorable size of the virtual space: scaling the translational gain of walking and scaling eyeheight. Translational gain is scaled by changing the relationship between physical and visual translation so that one step forward in physical space corresponds to several steps forward in virtual space. To scale gain higher than ten, it becomes necessary to investigate ways to minimize distracting small physical head motions. We present such a method here. We examine a range of scaling factors and find that we can expect to scale translational gain by a factor of 50. In addition to this finding, this paper also investigates whether scaling eyeheight proportionally to gain increases spatial awareness. We found that providing a map-like overview of the environment does not increase the user’s spatial orientation in the VE.
Research on Sentiment Analysis of Chinese E-Commerce Comments Based on Deep Learning

Abstract The analysis of emotional polarity of Chinese phrases in complex contexts has always been a problem in the field of machine learning. The emergence of Artificial Neural Networks has opened a window to solve this problem. Sentiment analysis has also become a hotspot in the research. This paper aims to solve this problem by introducing Attention mechanism to the Temporal Convolutional Nets (TCN) model. This paper chooses the Chinese commodity comment phrase on the E-commerce platform as the research object, to automatically analyse and judge the polarity (positive and negative) of each comment phrase. This paper optimized the design and constructed of the TCN Attention model, which made the TCN Attention model show superior performance in the area of sentiment analysis, and improved significantly compared with other models.
Discrete Fireworks Algorithm for Welding Robot Path Planning

Abstract Welding robots are widely used in manufacturing industries, and reasonable welding path planning is an important issue in production efficiency. Welding robot path planning aims to arrange the sequence of weld joints and find the optimal welding path for the robot, which is essentially a combinatorial optimization problem. With the development of artificial intelligence, swarm intelligence algorithms shed new light on this problem. Fireworks algorithm (FWA) is a newly proposed swarm intelligence algorithm, simulating the process of fireworks explosion producing sparks to find the optimal solution. It has shown excellent performance in continuous optimization problems. In this paper, a discrete fireworks algorithm (DFWA), is proposed to solve the welding robot path planning problem. We introduce some operations to the framework of traditional FWA. In DFWA, 2-opt local search and crossover operator are applied on the explosion sparks. A new way of fireworks quality judgment is designed. Mutation operator is implemented for the generation of Gaussian sparks, and the selection strategy is improved. Simulation experiments have been made to verify our method and compare its performance with other current swarm intelligence algorithms. Experimental results show that the proposed method performs well with good convergence, stability and accuracy. It is effective for welding robot path planning.
Request Balancing Among Users in Multiple Autonomous Cloud Provider Environments

As an increasing number of providers are offering cloud service reservations, an increasingly competitive market has emerged. While most of these providers charge users for metered usage based on fixed prices, they cannot make a timely response to demand changes and may risk losing users. Hence, we highlight the importance of an autonomous provider who self-adjusts the price according to the latest demands of the cloud market. Extensive research has shown that this is not a problem for a single provider. However, when it comes to multiple providers, the main challenge is coordinating the interactions between users and users, providers and providers, as well as users and providers. In this paper, we focus on request balancing among users in the multiple-autonomous-provider environment. Since the request strategy and payment of each user are affected by decisions of other users, we take game theory to address this problem and formulate it into a noncooperative game among multiple users, without sharing specific strategies. Each user tries to maximize its utility, which combines the net profit with time efficiency. The variational inequality theory is employed and proved that there exists a Nash equilibrium solution set for the game. Then, an iterative proximal algorithm is proposed to find the Nash equilibrium solution, and its convergence is proven. Simulation results demonstrate that the proposed scheme makes requests balanced and increases users' utilities, and also shifts demands from peak to off-peak periods.
Online judge MySQL for learning process of database practice course

Abstract Database course is the main courses in the department of informatics and computer science. This course aims to provide knowledge to students to build and manage data on Database Management Systems (DBMS) like MySQL. Therefore, the implementation of database processing practices in one of the DBMS such as MySQL is very important as basic skills in the field of informatics. With the growing number of informatics students, lectures cannot correct the results of student exercises quickly. For that, we need a system that can help lecturers to make corrections of query exercise on MySQL automatically, which is called Online Judge MySQL. This application was developed using NodeJS to execute queries that users input and ReactJS to build its interfaces. Testing is done by using this application for the online exam in several classes simultaneously. The results show that this application can correct the test results quickly and lightly.
Pupil Localization for Ophthalmic Diagnosis Using Anchor Ellipse Regression

Recent developments of deep neural networks, such as Mask R-CNN, have shown significant advances in simultaneous object detection and segmentation. We thus apply deep learning to pupil localization for ophthalmic diagnosis and propose a novel anchor ellipse regression approach based on region proposal network and Mask R-CNN for detecting pupils, estimating pupil shape parameters, and segmenting pupil regions at the same time in infrared images. This new extension of anchor ellipse regression for Mask R-CNN is demonstrated to be effective in size and rotation estimations of elliptical objects, as well as in object detections and segmentations, by experiments. Temporal pupil size estimations by using the proposed approach for normal and abnormal subjects give meaningful indices of pupil size changes for ophthalmic diagnosis.