Reviewerly/exHarmony huggingface dataset:
A Lightweight Deep Autoencoder-Based Approach for Unsupervised Anomaly Detection

Unsupervised anomaly detection is an important area of research to find abnormal behavior and integral part of many systems. In this research, a lightweight deep autoencoder based approach is presented to detect anomalies in unsupervised manner. It has the ability to learn the model over the normal patterns and any deviation is considered as an anomaly. Consequently, it can relax the condition to have anomalous data patterns during the training phase of the model. In this work, we examine lightweight autoencoder for anomaly detection task in order to show that simple architecture can show good performance in terms of training, testing time, number of parameters and metrics. We apply autoencoder for binary classification problem (i.e., each data point considered either normal or abnormal). The reconstruction error is used to detect anomalies. The experiments are carried out over the particular class of cyber security domain known as intrusion detection systems. We evaluated our model on standard publicly available benchmarks of KDD-99, NSL-KDD and UNSW-NB15 and achieved F1-score of 0.96, 0.88 and 0.95, respectively. It outperforms by a considerable margin when compared to state-of-the-art methods.
Eye Movement Analysis of Digital Learning Content for Educational Innovation

Eye movement technology is highly valued for evaluating and improving digital learning content. In this paper, an educational innovation study of eye movement behaviors on digital learning content is presented. We proposed three new eye movement metrics to explain eye movement behaviors. In the proposed method, the digital content, which were slide-deck-like works, were classified into page categories according to the characteristics of each page. We interpreted the subjects’ eye movement behaviors on the digital slide decks. After data regularization and filtering, the results were analyzed to give directions for how to design an attractive digital learning content from the viewpoint of eye movement behaviors. The relationships between the subjects’ evaluation scores, page categories, and eye movement metrics are discussed. The results demonstrated that the proposed fixation time percentage (FTP) was a representative, strong, and stable eye movement metric to measure the subjects’ interest. Moreover, a reasonable portion of semantic content had a positive influence on the subjects’ interest.
A Syllable-Based Technique for Uyghur Text Compression

To improve utilization of text storage resources and efficiency of data transmission, we proposed two syllable-based Uyghur text compression coding schemes. First, according to the statistics of syllable coverage of the corpus text, we constructed a 12-bit and 16-bit syllable code tables and added commonly used symbols—such as punctuation marks and ASCII characters—to the code tables. To enable the coding scheme to process Uyghur texts mixed with other language symbols, we introduced a flag code in the compression process to distinguish the Unicode encodings that were not in the code table. The experiments showed that the 12-bit coding scheme had an average compression ratio of 0.3 on Uyghur text less than 4 KB in size and that the 16-bit coding scheme had an average compression ratio of 0.5 on text less than 2 KB in size. Our compression schemes outperformed GZip, BZip2, and the LZW algorithm on short text and could be effectively applied to the compression of Uyghur short text for storage and applications.
Vulnerability market as a public-good auction with privacy preservation

Exploitations of zero-day vulnerabilities cause enormous damages to organizations. Hence, organizations would invest in buying zero-day vulnerabilities to patch their systems. On the other hand, hackers are interested in buying zero-day vulnerabilities to exploit their targets. Considering such a market, the vulnerability finder decides whether to sell the vulnerability information to the organizations or to the hackers in the black market. In this paper, we model the vulnerability market as a public-good auction where the organizations collaboratively bid for the vulnerability information. In this case, an organization determines how much to invest in the vulnerability information to maximize its payoff. First, we characterize the auction and study the bidding strategies in centralized and decentralized approaches, and then, we compare the efficiency of the coalition. Moreover, as the bidding value in such an auction is sensitive information, we present a novel privacy-preserving mechanism based on cryptographic primitives to protect the organizations’ bidding value. Our mechanism can also be applicable in other public-good auctions. Security analysis and performance evaluation are conducted showing the practicality of our proposed mechanism.
Hierarchical Visual-aware Minimax Ranking Based on Co-purchase Data for Personalized Recommendation

Personalized recommendation aims at ranking a set of items according to the learnt preferences of the user. Existing methods optimize the ranking function by considering an item that the user has not bought yet as a negative item and assuming that the user prefers the positive item that he has bought to the negative item. The strategy is to exclude irrelevant items from the dataset to narrow down the set of potential positive items to improve ranking accuracy. It conflicts with the goal of recommendation from the seller's point of view, which aims to enlarge that set for each user. In this paper, we diminish this limitation by proposing a novel learning method called Hierarchical Visual-aware Minimax Ranking (H-VMMR), in which a new concept of predictive sampling is proposed to sample items in a close relationship with the positive items (e.g., substitutes, compliments). We set up the problem by maximizing the preference discrepancy between positive and negative items, as well as minimizing the gap between positive and predictive items based on visual features. We also build a hierarchical learning model based on co-purchase data to solve the data sparsity problem. Our method is able to enlarge the set of potential positive items as well as true negative items during ranking. The experimental results show that our H-VMMR outperforms the state-of-the-art learning methods.
TRAP: Two-level Regularized Autoencoder-based Embedding for Power-law Distributed Data

Recently, autoencoder (AE)-based embedding approaches have achieved state-of-the-art performance in many tasks, especially in top-k recommendation with user embedding or node classification with node embedding. However, we find that many real-world data follow the power-law distribution with respect to the data object sparsity. When learning AE-based embeddings of these data, dense inputs move away from sparse inputs in an embedding space even when they are highly correlated. This phenomenon, which we call polarization, obviously distorts the embedding. In this paper, we propose TRAP that leverages two-level regularizers to effectively alleviate the polarization problem. The macroscopic regularizer generally prevents dense input objects from being distant from other sparse input objects, and the microscopic regularizer individually attracts each object to correlated neighbor objects rather than uncorrelated ones. Importantly, TRAP is a meta-algorithm that can be easily coupled with existing AE-based embedding methods with a simple modification. In extensive experiments on two representative embedding tasks using six-real world datasets, TRAP boosted the performance of the state-of-the-art algorithms by up to 31.53% and 94.99% respectively.
Constrained Private Mechanisms for Count Data

Concern about how to aggregate sensitive user data without compromising individual privacy is a major barrier to greater availability of data. Differential privacy has emerged as an accepted model to release sensitive information while giving a statistical guarantee for privacy. Many different algorithms are possible to address different target functions. We focus on the core problem of count queries, and seek to design mechanisms to release data associated with a group of n individuals. Prior work has focused on designing mechanisms by raw optimization of a loss function, without regard to the consequences on the results. This can lead to mechanisms with undesirable properties, such as never reporting some outputs (gaps), and overreporting others (spikes). We tame these pathological behaviors by introducing a set of desirable properties that mechanisms can obey. Any combination of these can be satisfied by solving a linear program (LP) which minimizes a cost function, with constraints enforcing the properties. We focus on a particular cost function, and provide explicit constructions that are optimal for certain combinations of properties, and show a closed form for their cost. In the end, there are only a handful of distinct optimal mechanisms to choose between: one is the well-known (truncated) geometric mechanism; the second a novel mechanism that we introduce here, and the remainder are found as the solution to particular LPs. These all avoid the bad behaviors we identify. We demonstrate in a set of experiments on real and synthetic data which is preferable in practice, for different combinations of data distributions, constraints, and privacy parameters.
AutoNav: Evaluation and Automatization of Web Navigation Policies

Undesired navigation in browsers powers a significant class of attacks on web applications. In a move to mitigate risks associated with undesired navigation, the security community has proposed a standard that gives control to web pages to restrict navigation. The standard draft introduces a new navigate-to directive of the Content Security Policy (CSP). The directive is currently being implemented by mainstream browsers. This paper is a first evaluation of navigate-to, focusing on security, performance, and automatization of navigation policies. We present new vulnerabilities introduced by the directive into the web ecosystem, opening up for attacks such as probing to detect if users are logged in to other websites or have active shopping carts, bypassing third-party cookie blocking, exfiltrating secrets, as well as leaking browsing history. Unfortunately, the directive triggers vulnerabilities even in websites that do not use the directive in their policies. We identify both specification- and implementation-level vulnerabilities and propose countermeasures to mitigate both. To aid developers in configuring navigation policies, we develop and implement AutoNav1, an automated black-box mechanism to infer navigation policies. AutoNav leverages the benefits of origin-wide policies in order to improve security without degrading performance. We evaluate the viability of navigate-to and AutoNav by an empirical study on Alexa’s top 10,000 websites.
Android application development of exploration career based on Multiple Intellegence: A model hypothetical

Abstract The purpose of this study is to develop and design an android application of exploration career based on multiple intelligence that is used for high school students to provide information exploration career based on multiple intelligence. Data collection is done to find out the problems faced and to design solutions. The techniques for collecting data through literature studies. Exploration career based on multiple intelligence itself is a form of individual effort to find, obtain and manage various kinds of appropriate career information based on consideration of the intelligence possessed by each individual. Intelligence is the ability possessed by someone who includes knowledge, skills and expertise in solving problems. Basically, each individual is unique and has some intelligence such as linguistic, logical-mathematical, visual-spatial, kinaesthetic, interpersonal, intrapersonal, musical, and naturalist intelligence. By understanding that each individual is born and has different types of intelligence, here the author is interested in developing an android application about exploration of careers based on multiple intelligence-. This android application will provide an interesting description and design while at the same time helping individuals, especially high school students, in understanding the intelligence they have with the right career for their future, so it is expected that the development of this android application can provide a good understanding to each individual especially in understanding their career in accordance with their intelligence.
Sentiment Analysis and Prediction of Election Results 2018

Social media is becoming very popular throughout the planet. People feel very comfortable in expressing their views freely. It was decided to take the help from this fact for sentiment analysis of the discussion and their free comments with regard to the forthcoming assembly elections in India. The social media data is analyzed by applying different mining techniques to predict the possible outcomes of the assembly elections and watching the positions of various political parties in India. An attempt has been made to analyze the user behavior on social media in order to find out the prediction of political parties' position in election.
In Opinion Holders’ Shoes: Modeling Cumulative Influence for View Change in Online Argumentation

Understanding how people change their views during multiparty argumentative discussions is important in applications that involve human communication, e.g., in social media and education. Existing research focuses on lexical features of individual comments, dynamics of discussions, or the personalities of participants but deemphasizes the cumulative influence of the interplay of comments by different participants on a participant's mindset. We address the task of predicting the points where a user's view changes given an entire discussion, thereby tackling the confusion due to multiple plausible alternatives when considering the entirety of a discussion.
Negative Purchase Intent Identification in Twitter

Social network users often express their discontent with a product or a service from a company on social media. Such a reaction is more pronounced in the aftermath of a corporate scandal such as a corruption scandal or food poisoning in a chain restaurant. In our work, we focus on identifying negative purchase intent in a tweet, i.e. the intent of a user of not purchasing any product or consuming any service from a company. We develop a binary classifier for such a task, which consists of a generalization of logistic regression leveraging the locality of purchase intent in posts from Twitter. We conduct an extensive experimental evaluation against state-of-the-art approaches on a large collection of tweets, showing the effectiveness of our approach in terms of F1 score. We also provide some preliminary results on which kinds of corporate scandals might affect the purchase intent of customers the most.
Consistency Ensured Bi-directional GAN for Anomaly Detection

Anomaly detection is a challenging and fundamental issue in computer vision tasks. In recent years, GAN (Generative Adversarial Networks) based anomaly detection methods have achieved remarkable results. But the instability of training of GAN could be considered that decreases the anomaly detection score. In particular, Bi-directional GAN has the following two causes that make the training difficult: the lack of consistency of the mutual mapping between the image space and the latent space, and the difficulty in conditioning by the latent variables of the image. Here we propose a novel GAN-based anomaly detection model. In our model, we introduce the consistency loss for ensuring mutual mappings. Further, we propose introducing the projection discriminator as an alternative of concatenating discriminator in order to perform efficient conditioning in the Bi-directional GAN model. In experiments, we evaluate the effectiveness of our model in a simple dataset and real-world setting dataset and confirmed that our model outperforms the conventional anomaly detection methods.
Enhancing Students' English Speaking Skills through Web-Based Teaching

Teachers play a very important role in the teaching and learning process. They should utilize effective strategies that are included in classroom management. In this digital era, the teachers should adjust their methods in delivering or explaining the materials for the students. One of the technology is the web. The aim of this study is to investigate the teacher’s perceptions of using the web to enhance students’ English speaking skills. This study was under phenomenological qualitative research and characterized by an interdisciplinary approach, especially education and technology. The direct personal interview was utilized to obtain the information from the teacher. The survey encompasses twelve questions that become different categories and was done in eight grades of class B in SMP Negeri 3 Pleret. The results showed that teaching speaking using the web can effectively enhance the students’ English speaking skills and the students give positive reactions regarding this method.
Proposal and testing goals-guided interaction for occasional users

Abstract The latest shifts in technology have brought about new kinds of users who occasionally access unfamiliar systems in new scenarios. This way of use should not request any learning curve. There have been many attempts to help this kind of users: agents, floating help, tooltips, direct video demonstrations, etc., elements that support the appealing direct manipulation style (DM), but add indeed an extra layer of supporting material over it. In this paper, we describe an analyse goals - guided Interaction , GGI, as the basis for a different approach. We do not expect the user to understand or use a metaphor but guide them throughout the whole interaction process. This help system is integrated in the interface. The user will be closely guided on the “what to do” and on the “how to do,” always according to their current goal. This no-previous-knowledge-required way of interaction shortens significantly the learning curve. In order to support this proposal, a complete study based on user testing is performed. The results show that, for non-expert occasional users, GGI is more efficient in time and less error prone than the classic Direct Manipulation. Participants also showed a strong preference for GGI. This research offers an alternative way of interaction for designers and developers of human-centric interactive systems who have occasional users as their main target users.
Automated Algebraic Reasoning for Collections and Local Variables with Lenses

Lenses are a useful algebraic structure for giving a unifying semantics to program variables in a variety of store models. They support efficient automated proof in the Isabelle/UTP verification framework. In this paper, we expand our lens library with (1) dynamic lenses, that support mutable indexed collections, such as arrays, and (2) symmetric lenses, which allow partitioning of a state space into disjoint local and global regions to support variable scopes. From this basis, we provide an enriched program model in Isabelle/UTP for collection variables and variable blocks. For the latter, we adopt an approach first used by Back and von Wright, and derive weakest precondition and Hoare calculi. We demonstrate several examples, including verification of insertion sort.
Simulation of the Guidance and Control Systems for Underactuated Vessels

Intelligent and/or autonomous vehicle technologies are rapidly growing to meet the needs of marine safety and transport efficiency. One of the requirements to manage autonomous vehicles includes the integration between route planning and automatic motion control. In the authors’ opinion, the latter could be sketched in three different layers: obstacle detection, planning and actuation. Moreover, the three layers should be able to interact in real-time. Dealing with such a challenging task, one of the best techniques to develop and test the logic is the use of the time-domain simulation. In the present work, a simulation model, integrating a path planning algorithm in the presence of obstacles with a track keeping controller, is developed. The path planning is based on a modified version of the Rapidly-exploring Random Tree (RRT*) algorithm. The track keeping is based on the Line-of-Sight (LOS) waypoints navigation for underactuated vessels. To achieve more reliable results, a detailed ship simulation model is used as a benchmark. Different scenarios and navigation modes are successfully tested, and the results are presented and analysed.
Game theoretic approach applied in cybersecurity information exchange framework

In CYBersecurity information EXchange (CYBEX) framework, Cyber Threat Intelligence (CTI) is shared among multiple organizations with a view of creating situational awareness. But there is a possibility that malicious organizations coexist with regular ones in this framework, which can get hold of the threat data shared by other organizations and can use it for carrying out malicious activities. We formulate the aforementioned problem as an incomplete information game assuming that whenever CYBEX receives any information, it processes the information for anomaly detection. We find the mixed strategy Nash equilibrium probabilities and corresponding Bayesian belief updates. We simulate the game to find the best response strategies with which regular and malicious organizations can play to increase their payoffs. Based on the best response strategies of organizations, we analyze that achieving more anomaly detection rate while keeping the processing rate minimum is the best action strategy with which CYBEX can play to increase the gain of both CYBEX and regular organizations over malicious organizations. We also find the approximate average minimum processing rate and anomaly detection rate with which CYBEX can play in order to maintain the payoff of itself and regular organizations higher than the malicious ones.
The Use of VR in Journalism: Current Research and Future Opportunities

Virtual reality (VR) is more and more used in journalism since 2015 and research is trying to keep the pace. This paper gives an overview of the existing academic research on the topic. First, key concepts are defined, as some terms, such as immersion and presence, are used in various fields with slightly different meaning. Second, the existing research on the use of VR is presented. Research on production mainly analyses challenges of the new technology and ethical issues, while studies on audience are rather empirical, looking at the users’ reactions to the new medium, compared to the classical TV and print. Finally, the state-of-the art is discussed and some research gaps are pointed out, followed by recommendations for further research.
Combined PSO Methods for UAVs Swarm Modelling and Simulation

In this paper selected methods used for Unmanned Aerial Vehicles (UAVs) swarm simulation are presented. Proposed approach is based on Particle Swarm Optimization algorithm applied for a search problem in a large-scale terrain with enhancements like collision avoidance, leadership or formation arrangement mechanisms. The uniqueness of proposed work relies on appliance these mechanisms (separately or in combined way) to the PSO algorithm, whereas they have not been designed in the presented manner. Moreover, the mechanisms are meant to keep the PSO algorithm idea and its simplicity. The UAVs swarm simulation is performed in original distributed simulation environment (built completely from the scratch) with the alternative communication provided by DIS or HLA protocols. The environment consists of a constructive component (responsible for multiagent and multiresolution modelling) for UAVs behaviours simulation and the VBS3 virtual simulator handling 3D visualizations and another objects control.