Reviewerly/exHarmony huggingface dataset:
Automatic Detection of Sentiment and Theme of English and Ukrainian Song Lyrics

This paper presents the application of natural language processing methods to the detection of emotional charge and theme of English and Ukrainian song lyrics. For this purpose a dataset of 8163 song lyrics have been collected. The main contribution lies in adopting VADER sentiment analyzer for Ukrainian song lyrics analysis. In addition, the lexical richness of English and Ukrainian singers of different genres is determined. The sentiment analysis of song lyrics is viewed as a challenging task due to lingual stylistic characteristics of this type of texts.
Aggregation of the fuzzy logic sets in terms of the functions of the triangular norm and triangular co-norm

Abstract The purpose of this article is to explore the group of the operators, which can be used for aggregation of the fuzzy sets. There were scrutinized operators to be used for the intersection and union such as the triangular norm and triangular co-norm in the article. All of those operators are defined as the binary operations, where there was indicated that the norms and co-norms are two-valued functions of [0, 1]. Furthermore, there were discussed properties of norm and co-norm functions such as symmetry, associativity, neutral entry and null entry properties, monotonicity properties. Moreover, the consideration of the properties of norm and co-norm functions have routed investigation of the norm and co-norm operators to go further to prove theorem, which converts the union into intersection and vice versa in terms to all possible norm and co-norm functions based on the fuzzy logic sets.
Deep Reinforcement Learning for Mobile Robot Navigation

While navigation is arguable the most important aspect of mobile robotics, complex scenarios with dynamic environments or with teams of cooperative robots are still not satisfactory solved yet. Motivated by the recent successes in the reinforcement learning domain, the application of deep reinforcement learning to robot navigation was examined in this paper. In particular this required the development of a training procedure, a set of actions available to the robot, a suitable state representation and a reward function. The setup was evaluated using a simulated real-time environment. A reference setup, different goal-oriented exploration strategies and two different robot kinematics (holonomic, differential) were compared in the evaluation. In a challenging scenario with obstacles at changing locations in the environment the robot was able to reach the desired goal in 93% of the episodes.
IoT Notary: Sensor Data Attestation in Smart Environment

Contemporary IoT environments, such as smart buildings, require end-users to trust data-capturing rules published by the systems. There are several reasons why such a trust is misplaced - IoT systems may violate the rules deliberately or IoT devices may transfer user data to a malicious third-party due to cyberattacks, leading to the loss of individuals' privacy or service integrity. To address such concerns, we propose IoT Notary, a framework to ensure trust in IoT systems and applications. IoT Notary provides secure log sealing on live sensor data to produce a verifiable `proof-of-integrity,' based on which a verifier can attest that captured sensor data adheres to the published data-capturing rules. IoT Notary is an integral part of TIPPERS, a smart space system that has been deployed at UCI to provide various real-time location-based services in the campus. IoT Notary imposes nominal overheads for verification, thereby users can verify their data of one day in less than two seconds.
DOM-based keyword extraction from web pages

We present D-rank, an unsupervised, language and domain independent method for automatically extracting keywords from a single web page. The method does not use any corpus, and relies only on the information and features on the web page including page URL, word frequency, title, hyperlinks, and headers, which are extracted from DOM tree of the page. Different scores are assigned to the words according to their importance that is specified by their positions in the web page. Experimental results on web pages in three different languages show the effectiveness of the proposed method.
Application of Blockchain to Patient-Centric Access Control in Medical Data Management Systems

We proposed a novel solution to the privacy issues in medical data management systems with multi-user data-sharing requirement. Our solution leverages the blockchain technology, in combination with Hierarchical Attribute-Based Encryption (HABE) to improve the access control mechanisms in such systems. While the blockchain security model guarantees the enforcement of patient-specified access policies, the principles of attribute-based encryption enforce those policies through encryption as a secondary mechanism against unauthorized access.
Skype &amp; Type

Voice-over-IP (VoIP) software are among the most widely spread and pervasive software, counting millions of monthly users. However, we argue that people ignore the drawbacks of transmitting information along with their voice, such as keystroke sounds—as such sound can reveal what someone is typing on a keyboard. In this article, we present and assess a new keyboard acoustic eavesdropping attack that involves VoIP, called Skype &amp; Type ( S&amp;T ). Unlike previous attacks, S&amp;T assumes a weak adversary model that is very practical in many real-world settings. Indeed, S&amp;T is very feasible, as it does not require (i) the attacker to be physically close to the victim (either in person or with a recording device) and (ii) precise profiling of the victim’s typing style and keyboard; moreover, it can work with a very small amount of leaked keystrokes. We observe that leakage of keystrokes during a VoIP call is likely, as people often “multi-task” during such calls. As expected, VoIP software acquires and faithfully transmits all sounds, including emanations of pressed keystrokes, which can include passwords and other sensitive information. We show that one very popular VoIP software (Skype) conveys enough audio information to reconstruct the victim’s input—keystrokes typed on the remote keyboard. Our results demonstrate that, given some knowledge on the victim’s typing style and keyboard model, the attacker attains top-5 accuracy of 91.7% in guessing a random key pressed by the victim. This work extends previous results on S&amp;T , demonstrating that our attack is effective with many different recording devices (such as laptop microphones, headset microphones, and smartphones located in proximity of the target keyboard), diverse typing styles and speed, and is particularly threatening when the victim is typing in a known language.
Quality of Education: Pedagogical Aspect

The purpose of this paper is to analyze pedagogical approaches to the concept of the quality in higher education. The personality of a teacher is considered as a key component of quality assurance. The authors analyze the role of the teacher in modern society and teacher’s professional and personal qualities necessary to increase the effectiveness of the educational process. Particular attention is paid to comprehension of quality from the point of view of the main education process participants – students and teachers. The opinions of Russian and foreign teachers in relation to the ways to improve the quality of higher education and the effectiveness of different educational technologies are presented. Using a multidimensional approach to quality, the authors offer their conception of education quality, elements of quality and quality assurance conditions.
Ontologies as nested facet systems for human–data interaction

Irrespective of data size and complexity, query and exploration tools for accessing data resources remain a central linkage for human–data interaction. A fundamental barrier in making query interfaces easier to use, ultimately as easy as online shopping, is the lack of faceted, interactive capabili ties. We propose to repurpose existing ontologies by transforming them into nested facet systems (NFS) to support human–data interaction. Two basic issues need to be addressed for this to happen: one is that the structure and quality of ontologies need to be examined and elevated for the purpose of NFS; the second is that mappings from data-source specific metadata to a corresponding NFS need to be developed to support this new generation of NFS-enabled web-interfaces. The purpose of this paper is to introduce the concept of NFS and outline opportunities involved in using ontologies as NFS for querying and exploring data, especially in the biomedical domain.
Augmented Reality Based Recommendations Based on Perceptual Shape Style Compatibility with Objects in the Viewpoint and Color Compatibility with the Background

Augmented Reality (AR) has been heralded as the next frontier in retail, but so far, has been mostly used to advertise or market products in a gimmicky way and its true potential in digital marketing remains unexploited. In this work, we leverage richer data coming from AR usage to make re-targeting much more persuasive via viewpoint image augmentation. Based on the user's purchase viewpoint visual, we identify relevant objects/products present in the viewpoint along with their style such that products with more style compatibility with those surrounding real-world objects can be recommended. We also use color compatibility with the background of the user's purchase viewpoint to select suitable product textures. We embed the recommended products in the viewpoint at the location of the initially browsed product with similar pose and scale. This makes the recommendations much more personalized and relevant which can increase conversions. Evaluation with user studies show that our system is able to make recommendations better than tag-based recommendations, and targeting using the viewpoint is better than that of usual product catalogs.
Comparison of local solar radiation parameters with data from a typical meteorological year

Precise determination of local climate characteristics has a huge impact on predicting the efficiency of solar energy systems, as well as simulations of energy demand of buildings. Among many weather data, solar conditions are a very relevant parameter, and the solar irradiance is an important part of a Typical Meteorological Year. TMY sets are created by averaging and modeling data, which unfortunately significantly differs from real conditions. The article presents a comparison of locally measured values of solar irradiance, with particular emphasis on the summer period, in relation to TMY data developed in Poland and TMY data from European Photovoltaic Geographical Information System databases. Significant differences between the data were found. The percentage share of solar irradiance in the range of 600 ÷ 1300 W/m2 in summer, for each of the three TMY PVGIS was 19.86%, 23.75%, 22.50% respectively, while for TMY PL only 8.89%. In the measurements for this range in the summer of 2018 this share was 25.14%, and 20.39% in 2006. Despite the fact that TMY databases include average values of individual parameters, TMY PVGIS data do not differ much from those obtained in summer measurements, which contain also extreme values. In addition, the structure similarity indicator used to compare the data sets confirmed these relationships. This suggests the need to update the TMY PL database, for specific locations, especially in relation to the summer season. The analysis also concerned local sunshine duration in various time intervals as well as factors limiting the availability of solar radiation.
Single Image Dehazing using Adaptive Gamma Correction Method

In this paper, an improved single image dehazing method is proposed to remove the haze effect. The proposed algorithm used a very simple and effective approach for haze removal. Many haze removal algorithms have been developed, but halo artifacts and color distortion problem persists. The dark channel prior (DCP) method is the best method for single image dehazing. But, it is applicable only for the small sky region. The dark channel prior (DCP) method still faces the color distortion problem due to the large sky region present in input hazy image. So, we proposed an effective approach that removes the haze from a single input image using the adaptive gamma correction method. In this proposed algorithm, first, we segment the large sky region using the k-means clustering approach and then refined the whole segmented sky region. Here, a guided filter method is used for the refinement of the segmented sky region. It provides edge related information accurately. In most of the dehazed methods, the output haze-free image suffers the brightness level or color distortion problem. So, in the proposed approach gamma correction method is applied to the filtered image. It reduced the color distortion from the hazy image properly.
Single Image Rain Removal via Cascading Attention Aggregation Network on Challenging Weather Conditions

Image rain removal is extremely important since rain streaks can severely degrade the visibility which can decrease accuracy of many current computer vision algorithms. However, many deep-learning methods cannot adapt to the rain streak removal of different density labels, meanwhile retain the background details. To address this problem, we propose a novel image rain removal algorithm (CAAN) based on deep cascaded network and dual-channel attention mechanism. As contextual information is very crucial for rain removal, hence firstly the dilated convolution kernels of different scale branches are used to extract features of different rain streak sizes. Then we applied residual attention module (LMSRAM) to guide the learned features to be discriminative and beneficial. Specifically, a novel cascaded sub-network is developed to propagate information from lower to higher layers and decrease the loss of detailed information. Finally, the dual channel attention mechanism is used to make feature fusion efficiently. The experimental results demonstrate that the proposed method achieves the state-of-the-art results compared to the prevailing methods in terms of both quantitative metrics and visual quality. The source code of our proposed CAAN can download from the personal website. https://github.com/baiyihan/CAAN.
Visual Cues for Disrespectful Conversation Analysis

Toxic, abusive, or disrespectful behavior analysis is a non-trivial problem previously addressed mostly from the language perspective. In this paper, we present a novel video dataset containing disrespect and non-disrespect labels, and introduce such behavior analysis by using visual cues. The dataset is collected from YouTube news show videos of two-party conversations, in which a host and a guest interact through teleconferencing. Each video is labeled by three trained raters to identify disrespect expressed through face and gesture, voice, and language. By resolving confounding factors, we generate the corresponding pairwise samples of non-disrespect. To particularly show the influence of visual cues in disrespectful interactions, we present 222 labeled clips (duration=974.41(s), mean duration=4.39(s)). We extract and analyze the facial action units (AVs) prevalent in disrespectful behavior. Our result shows statistically significant differences after Bonferroni correction for Inner Brow raise (AV01), Lip Corner Depressor (AV15), and Chin Raiser (AV17). For prediction, we build two classifiers using logistic regression and linear Support Vector Machine with 62.61 % and 61.48 % accuracy, respectively. For an in-depth analysis of overall face and gesture features, we conduct a qualitative analysis using theme extraction. Our qualitative analysis provides further insights on leveraging synchronous and asynchronous features, along with combining text and audio data with visual cues to better detect disrespect.
An Interactive Model of Target and Context for Aspect-Level Sentiment Classification

Aspect-level sentiment classification aims to identify the sentiment polarity of a review expressed toward a target. In recent years, neural network-based methods have achieved success in aspect-level sentiment classification, and these methods fall into two types: the first takes the target information into account for context modelling, and the second models the context without considering the target information. It is concluded that the former is better than the latter. However, most of the target-related models just focus on the impact of the target on context modelling, while ignoring the role of context in target modelling. In this study, we introduce an interactive neural network model named LT-T-TR, which divided a review into three parts: the left context with target phrase, the target phrase, and the right context with target phrase. And the interaction between the left/right context and the target phrase is utilized by an attention mechanism to learn the representations of the left/right context and the target phrase separately. As a result, the most important words in the left/right context or in the target phrase are captured, and the results on laptop and restaurant datasets demonstrate that our model outperforms the state-of-the-art methods.
Implementing a just-in-time collection development model in an academic library

This article describes the development of a system for changing a university library's collection development strategy from the traditional “just in case” model to a “just in time” approach. The reasons for such a change included a desire to be more responsive to user requirements, a need to be more flexible in acquiring interdisciplinary resources and new formats, and a substantial cut in the library's budget. The Collection Development librarians envisioned the collections as a service, using a simpler and more versatile accounting structure. The Access-Based Collection Development (ABCD) model emphasizes making materials available at the point of need rather than having them on hand in anticipation of use. ABCD has four overarching themes: to be sustainable, to be holistic and inclusive, to be flexible and scalable, and to use evidence for decision-making. The authors provide details of the implementation, outcomes, evaluation, and future of the model.
Quantitative Logics for Equivalence of Effectful Programs

In order to reason about effects, we can define quantitative formulas to describe behavioural aspects of effectful programs. These formulas can for example express probabilities that (or sets of correct starting states for which) a program satisfies a property. Fundamental to this approach is the notion of quantitative modality, which is used to lift a property on values to a property on computations. Taking all formulas together, we say that two terms are equivalent if they satisfy all formulas to the same quantitative degree. Under sufficient conditions on the quantitative modalities, this equivalence is equal to a notion of Abramsky's applicative bisimilarity, and is moreover a congruence. We investigate these results in the context of Levy's call-by-push-value with general recursion and algebraic effects. For example, the results apply to (combinations of) nondeterministic choice, probabilistic choice, global store, and error.
Parallelism Optimized Architecture on FPGA for Real-Time Traffic Light Detection

In this paper, a portable assistance system is designed to help the visually impaired to detect the traffic light. The designed system is realized on the basis of the AdaBoost algorithm, which is fast and robust in object detections. In order to accelerate the AdaBoost-based approach, a flexible parallel architecture is implemented on the field-programmable gate array (FPGA) platform. The architecture is designed utilizing the parallelism of computations in the AdaBoost-based detection. The computations of the window integral image are implemented in parallel, and the confidences of the weak classifiers are calculated in parallel. The parameters of the weak classifiers are trained by the AdaBoost algorithm with multi-layer features in the MATLAB software, and then are configured on the FPGA platform via the Vivado design suite before the detection process. The parallelism optimized architecture is implemented on an Artix-7 FPGA at 200 MHZ. Experiments show that it can detect the traffic light in videos with a rate of 30 frames per second (fps).
Decision Support System of Teacher Performance Assessment with Smart Method

Abstract In general, the teacher assessment process in Indonesia is still carried out conventionally (this results in the length of time needed for decision making). The criteria used in these study are pedagogic, personality, social, professional. In this research, web-based decision support system application with SMART method will be built. Based on the research conducted, there were significant differences before and after using this application.
TopicSummary: A Tool for Analyzing Class Discussion Forums using Topic Based Summarizations

This Innovative Practice full paper, describes the application of text mining techniques for extracting insights from a course based online discussion forum through generation of topic based summaries. Discussions, either in classroom or online provide opportunity for collaborative learning through exchange of ideas that leads to enhanced learning through active participation. Online discussions offer a number of benefits namely providing additional time to reflect and synthesize information before writing, providing a natural platform for students to voice their ideas without any one student dominating the conversation, and providing a record of the student's thoughts. An online discussion forum provides a repository comprising the discussion threads related to the topics discussed. One approach to extracting useful knowledge from the repository is through generation of concise summaries of the discussion for each topic. This summary information can help both the instructor and the student in being able to focus on the key learning points discussed in the forum threads. The focus of our research is directed towards analysis of online discussion forums (ODFs) and generating topic based summaries that can be viewed by both instructor and the students. We have developed a tool, Topic Based Summarization (TBS), that takes an excel sheet with discussion forum thread posts as input and generates visual reports of summaries that are clustered into different topics. We evaluate the tool using discussion thread posts for an undergraduate course titled “Business Process Modelling and Solutioning”. We also performed qualitative analysis of the tool to investigate the strengths and weakness of various summarization algorithms.
Seminaïve evaluation for a higher-order functional language

One of the workhorse techniques for implementing bottom-up Datalog engines is seminaïve evaluation. This optimization improves the performance of Datalog's most distinctive feature: recursively defined predicates. These are computed iteratively, and under a naïve evaluation strategy, each iteration recomputes all previous values. Seminaïve evaluation computes a safe approximation of the difference between iterations. This can asymptotically improve the performance of Datalog queries. Seminaïve evaluation is defined partly as a program transformation and partly as a modified iteration strategy, and takes advantage of the first-order nature of Datalog code. This paper extends the seminaïve transformation to higher-order programs written in the Datafun language, which extends Datalog with features like first-class relations, higher-order functions, and datatypes like sum types.