Reviewerly/exHarmony huggingface dataset:
Study on 2D Feature-Based Hash Learning

Hashing is an important topic in image processing, as it can help save a considerable amount of storage and computational cost. Recently, inspired by 2D strategies employed in other areas of image processing, such as feature extraction, some 2D-based hashing methods were proposed. Related papers have shown that these methods may have better image retrieval performance in terms of both effectiveness and efficiency. However, the difference in the retrieval performances of hashing methods resulting from different forms of input (1D or 2D) has not been previously studied. Whether the widely used bilinear strategy in 2D-based hashing can truly help improve the retrieval precision has not been investigated in existing research. In this paper, we conduct a comparison study on 1D and 2D feature-based hashing methods and attempt to theoretically and experimentally analyse the differences in using 1D and 2D features in hashing. Furthermore, two new hashing methods are proposed for conducting the comparison experiments. Through a comprehensive study, we obtain three main conclusions in this paper: 1) Linear projection on 1D features and bilinear projection on 2D features are essentially the same. 2) 2D-based hashing methods are obviously more efficient than 1D-based methods for analysing high-dimensional input features. 3) 2D-based hashing methods show generally better performance for solving small sample size problems.
A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning

In this paper we consider the problem of how a reinforcement learning agent that is tasked with solving a sequence of reinforcement learning problems (a sequence of Markov decision processes) can use knowledge acquired early in its lifetime to improve its ability to solve new problems. We argue that previous experience with similar problems can provide an agent with information about how it should explore when facing a new but related problem. We show that the search for an optimal exploration strategy can be formulated as a reinforcement learning problem itself and demonstrate that such strategy can leverage patterns found in the structure of related problems. We conclude with experiments that show the benefits of optimizing an exploration strategy using our proposed framework.
News From the Background to the Foreground

People are the designers and curators of their own news and information ecosystems, due to the disruption of the news industry and developments in media technology. To understand how people use technology to manage their news consumption, we conducted a two-week diary study with 14 participants, focusing on how people transition between news content and behaviors via different media, sources, platforms and devices. We used an inductive, qualitative analysis of the diary study data to analyze the news behaviors and their underlying motivations and found that people frequently shift their focus between ambient background news streams and active foreground news behaviors. Although people often passively consume news content as a background activity, they also actively manage background news habits to increase the chances of relevant foreground experiences. People manage news consumption by developing routines that are often supported by technology use and social interactions. We encourage product designers to treat backgrounding as an essential part of news consumption behavior and suggest new design directions that employ ubiquitous computing technologies---such as context sensing and routine modeling---to more effectively attend to background-to-foreground behaviors and transitions.
An LSTM Adaptation Study of (Un)grammaticality

We propose a novel approach to the study of how artificial neural network perceive the distinction between grammatical and ungrammatical sentences, a crucial task in the growing field of synthetic linguistics.The method is based on performance measures of language models trained on corpora and finetuned with either grammatical or ungrammatical sentences, then applied to (different types of) grammatical or ungrammatical sentences.The results show that both in the difficult and highly symmetrical task of detecting subject islands and in the more open CoLA dataset, grammatical sentences give rise to better scores than ungrammatical ones, possibly because they can be better integrated within the body of linguistic structural knowledge that the language model has accumulated.
Personality Evaluation and CV Analysis using Machine Learning Algorithm

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Solve the IRP problem with an improved discrete differential evolution algorithm

The inventory -routing problem is a NP hard problem. It is difficult to find the optimal solution in polynomial time. Many scholars have studied it in many years. This paper analyses the inventory-routing optimisation problem, and comprehensive differential evolution algorithm is good performance in solving combinatorial optimisation problems. The differential evolution algorithm was improved to make it be suitable for solving discrete combination optimisation problems. In order to improve the performance of the differential evolution algorithm to solve the inventory routing problem, this paper puts forward dynamic adjustment of mutation factor and crossover factor of the differential evolution. It is proved by numerical experiments that the proposed algorithm has certain performance advantages, and it also proves that the improved algorithm can improve the performance of the algorithm by dynamic adjustment of the mutation factor and crossover factor.
Ground state excitation of an atom strongly coupled to a free quantum field

This paper presents a nonperturbative treatment of strong-coupling induced effects in atom-field systems which cannot be seen in traditional perturbative treatments invoking compromising assumptions such as the Born-Markov, rotating wave, or Fermi Golden rule. We consider an atom whose internal degrees of freedom are modeled by a harmonic oscillator which is bilinearly coupled to a scalar quantum field, representing one of the two polarizations of an electromagnetic field. Because the whole system is Gaussian we can solve this problem exactly. Using the open quantum system conceptual framework and the influence functional formalism we derive the dynamics of the reduced density matrix for the atom which enables the calculation of atomic transition probability and other relevant physical quantities. Finding an exact solution to this problem has the distinct advantage of enabling one to capture fully the strong coupling regime and discover interesting effects such as spontaneous ground state excitation [R. Passante, T. Petrosky, and I. Prigogine, Long-time behaviour of self-dressing and indirect spectroscopy, Physica (Amsterdam) 218A, 437 (1995).] which is unfathomable in perturbative treatments. The conventional description of atomic-optical activities is predicated on the assumption that the state of the total atom-field system is a product state of the atomic excitations and the photon number states, an assumption which is valid only for vanishingly weak coupling. The correct energy eigenfunctions to use should be that of the Hamiltonian of the combined atom-field system. Other features associated with finite to strong coupling effects such as resonance peak broadening and transition from a gapped to a gapless spectrum can all be understood from this perspective. Finally, to put the issues in a proper perspective we take the perturbative limit of the exact results and compare them with those from conventional time-dependent perturbation theory (TDPT). This enables one to pin-point where the deficiencies of TDPT lie as one removes the ultraweak coupling assumption.
Identifying and Resisting Adversarial Videos Using Temporal Consistency

Video classification is a challenging task in computer vision. Although Deep Neural Networks (DNNs) have achieved excellent performance in video classification, recent research shows adding imperceptible perturbations to clean videos can make the well-trained models output wrong labels with high confidence. In this paper, we propose an effective defense framework to characterize and defend adversarial videos. The proposed method contains two phases: (1) adversarial video detection using temporal consistency between adjacent frames, and (2) adversarial perturbation reduction via denoisers in the spatial and temporal domains respectively. Specifically, because of the linear nature of DNNs, the imperceptible perturbations will enlarge with the increasing of DNNs depth, which leads to the inconsistency of DNNs output between adjacent frames. However, the benign video frames often have the same outputs with their neighbor frames owing to the slight changes. Based on this observation, we can distinguish between adversarial videos and benign videos. After that, we utilize different defense strategies against different attacks. We propose the temporal defense, which reconstructs the polluted frames with their temporally neighbor clean frames, to deal with the adversarial videos with sparse polluted frames. For the videos with dense polluted frames, we use an efficient adversarial denoiser to process each frame in the spatial domain, and thus purify the perturbations (we call it as spatial defense). A series of experiments conducted on the UCF-101 dataset demonstrate that the proposed method significantly improves the robustness of video classifiers against adversarial attacks.
Granular Evolving Min-Max Fuzzy Modeling

The paper addresses a novel evolving functional fuzzy modeling algorithm using hyperboxes and min-max fuzzy granulation.Data space granulation is done as data are input, and undergoes never ending adaptation using expansion and reduction operations to encompass new information.A fuzzy rule is assigned to each hyperbox using Gaussian membership functions in the rule antecedents, and affine functions in the rule consequents.The algorithm is fast, simple, and interpretable.Computational evaluation using time series modeling and nonlinear system identification experiments shows that the granular evolving min-max fuzzy modeling algorithm outperforms current state of the art evolving algorithms counterparts.
Designer-driven add-to-cart recommendations

Although real-time dynamic recommender systems have been applied successfully by e-commerce and technology companies for more than a decade, we at IKEA Group have just started our journey into this exciting field. At IKEA, customer experience is at our heart, and a key principle for any machine learning algorithm that we design to improve this experience is that it should act as an extension to the home-furnishing expertise that our co-workers have developed and fine-tuned for more than 75 years. In this talk, we discuss a particular recommendation strategy that projects the inspirational shopping experience of our blue boxes onto our digital touch points by defining a notion of style from our vast collection of inspirational content.
Event Structure Representation: Between Verbs and Argument Structure Constructions

This paper proposes a novel representation of event structure by separating verbal semantics and the meaning of argument structure constructions that verbs occur in.Our model demonstrates how the two meaning representations interact.Our model thus effectively deals with various verb construals in different argument structure constructions, unlike purely verb-based approaches.However, unlike many constructionally-based approaches, we also provide a richer representation of the event structure evoked by the verb meaning.
An Accurate Image Measurement Method Based on a Laser-Based Virtual Scale

Image measurement methods have been widely used in broad areas due to their accuracy and efficiency. However, current techniques usually involve complex calibration, an elaborate optical design, or sensitivity to the test environment. In this paper, a simple optical device was designed to emit parallel beams to obtain a virtual scale for measurement purposes. The proposed theory ensures the robustness of the system when obtaining each scale in the presence of uncertainty. The scale creates a mapping from image coordinates to world coordinates. By using the moving least squares (MLS) method, a full-field scale map can be reconstructed to achieve high-precision measurement at the sub-pixel level. Experimental verifications are carried out, showing that the proposed method provides very accurate and reliable results. The proposed approach is simple in terms of equipment, and the scale can be automatically calculated. Therefore, the system proposed in this paper is a promising candidate as a tool for non-contacting measurements (e.g., the crack development, geometric size) in the inaccessible structures such as high-rise buildings and long-span bridges.
Students’ Continuance Intention to Experience Virtual and Remote Labs in Engineering and Scientific Education

Virtual and Remote Labs (VRLs) had been widely used in Engineering and Scientific Education in recent years. The purpose of this study was to explore the students’ satisfaction of VRLs and their continuance intention to experience VRLs in future study. The VRL developed by Unity3D PRO was integrated into an undergraduate course named “Introduction to computer science”. 240 college students from China Three Gorges University took the course and had the virtual and remote laboratory experience for 16 weeks. Students’ satisfaction and continuance intention were investigated by a three-part questionnaire based on the Expectation-Confirmation Model and Flow Theory. SPSS 19.0 was used for the quantitative analysis. The results indicated that the flow experience of students in VRL was at general level while the female students’ perception of flow was higher than the males. Correlation analysis found that the flow experience had an extremely significant correlation with the students’ perceived usefulness, confirmation, satisfaction and continuous intention. Stepwise regression analysis found that flow experience, perceived usefulness and satisfaction were all positive predictor of the students’ continuance intention. Finally, some suggestions for integrating VRLs in engineering and scientific education were provided.
Analysis of Trouble Ticket System Using COBIT 5 Framework (A Case Study Approach)

Trouble Ticket System is an application used to deal with customer complaints related to interference from telecommunication networks. The object used in this research is trouble ticket system in telecommunications infrastructure provider. The goals of IT Management of Trouble Ticket System on information security are having ISO/ IEC 27001 certification and structured governance that provides not only the details of the processes involved in each area focusing on effectiveness and efficiency, but also insights into how business practices can produce sustainable results. It is therefore necessary to analyze IT governance, especially for information security. The maturity level will analyze the difference between the company's performance objectives and the company's current capabilities. COBIT 5 combined with CMMI used to analyze the information security of trouble ticket system. Interview and questionnaire were also used in this research. The analysis results show that the target capacity level for the 5 domains is 3. The results of the obtained assessments on EDM03, APO12 and BAI06 are 0, which means the process is incomplete. The value of APO13 capacity is 1 (Process executed) and the value of the capacity DSS05 is 3 (Process established), but not completely reached. There is a difference in the target and current level of capacity that underpins the author to propose recommendations, such as complete the output (work products) at level 1 and generic practices at level 2 and 3.
English Conversation Class (ECC) Untuk Menciptakan English Environment Di SMA Islam Nusantara (SMAINUS)

The teaching of English in Indonesia has been started from elementary school until university level because students are expected to have good knowledge of English as the international language. In order to compete with people all over the world, English learners should be able to use English both in written and spoken form. The teaching of English in SMAINUS has been conducted in a fun way supported by the innovative teacher and interactive technique. Consequently, students have positive perceptions toward English and are more motivated to improve their English. However, there is no any English club, especially on English Conversation Club (ECC) that can facilitate students to do speaking practice outside the class to improve their productive skill rapidly. Therefore, this program is the starting point of ECC in SMAINUS. This paper aims at exploiting students’ perceptions of ECC program to create English environment.
Support Vector Machine for Sentiment Analysis of Nigerian Banks Financial Tweets

The rise of social media paves way for unprecedented benefits or risks to several organisations depending on how they adapt to its changes.This rise comes with a great challenge of gaining insights from these big data for effective and efficient decision making that can improve quality, profitability, productivity, competitiveness and customer satisfaction.Sentiment analysis is the field that is concerned with the classification and analysis of user generated text under defined polarities.Despite the upsurge of research in sentiment analysis in recent years, there is a dearth in literature on sentiment analysis applied to banks social media data and mostly on African datasets.Against this background, this study applied machine learning technique (support vector machine) for sentiment analysis of Nigerian banks twitter data within a 2-year period, from 1st January 2017 to 31st December 2018.After crawling and preprocessing of the data, LibSVM algorithm in WEKA was used to build the sentiment classification model based on the training data.The performance of this model was evaluated on a pre-labelled test dataset generated from the five banks.The results show that the accuracy of the classifier was 71.8367%.The precision for both the positive and negative classes was above 0.7, the recall for the negative class was 0.696 and that of the positive class was 0.741 which shows the prediction did better than chance in addition to other measures.Applying the model in predicting the sentiments of the five Nigerian banks twitter data reveals that the number of positive tweets within this period was slightly greater than the number of negative tweets.The scatter plots for the sentiments series indicated that, majority of the data falls between 0 and 100 sentiments per day, with few outliers above this range.
“Hacking in” Wikipedia Zero in Angola

Background A few years ago, a number of people in Angola were banned from editing Wikipedia Zero because they were concealing large files in the Wikimedia feature, from where the files could be shared in a private Facebook Zero group. Analysis To interpret this case study, the concept of “hacking in” is proposed as part of an open media and technological infrastructure. Conclusions and implications This short case study focuses on the ways in which users shape media infrastructure. These users tried to bypass the restricted conditions imposed on them first by a technology and a media ecology that increasingly resembles a walled-off garden and second by Wikipedia Zero’s associated discourse, which reinforced its designer’s intentions for the technology.
A Multi-Class Fisher Linear Discriminant Approach for the Improvement in the Accuracy of Complex Texture Discrimination

Texture segmentation has a wide spectrum of applications in diverse fields.This paper presents an elaborated Fisher Linear Discriminant (FLD) based semi-supervised approach for improving the accuracy of segmentation of multi-class complex fine textures.Gabor filter and local statistics (local variance) are used for feature extraction of texture images.Texture segments in the image are separated using K-means clustering.The results obtained using K-means clustering are refined by multi-class Fisher Linear Discriminant (MFLD).The algorithm is tested on wide varieties of several hundred homogenous and complex textures from five texture databases viz.Outex texture database, vision texture database (Vistex), Brodatz textures, Prague textures and Pertex texture database.Fisher distance (FD) is a measure of texture separability.Segmentation of complex textures is relatively a difficult task.The improvement in the segmentation accuracy of complex textures is achieved simply by the termination of MFLD based algorithm when Fisher distance (FD) ceases to increase with the increasing iterations of MFLD.After a quantitative analysis of the experimentation, it is concluded that the segmentation accuracy of complex textures and the combination of complex and homogeneous fine textures (with small texture primitives) increases as high as 29.83% with the increasing iterations of MFLD resulting in a significant improvement at the boundaries.Detailed results are provided in the experimentation and results section of the paper.The results achieve the second rank for 21 benchmark images among the ten state-of-the-art algorithms.
A nonlinear fourth-order PDE for multi-frame image super-resolution enhancement

The multiframe super-resolution (SR) techniques are considered as one of the active research fields. More precisely, the construction of the desired high resolution (HR) image with less artifacts in the SR models, which are always ill-posed problems, requires a great care. In this paper, we propose a new fourth-order equation based on a diffusive tensor that takes the benefit from the diffusion model of Perona-Malik in the flat regions and the Weickert model near boundaries with a high diffusion order. As a result, the proposed SR approach can efficiently preserve image features such as corner and texture much better with less blur near edges. The existence and uniqueness of the proposed partial differential equation (PDE) are also demonstrated in an appropriate functional space. Finally, the given experimental results show the effectiveness of the proposed PDE compared to some competitive methods in both visually and quantitatively.