Reviewerly/exHarmony huggingface dataset:
The crowd against the few: Measuring the impact of expert recommendations

A large amount of research on recommender systems has focused on improving the accuracy of suggestions in offline settings. However, this focus and the commonly used techniques can lead to a "filter bubble", severely limiting the diversity of content discovered by users. Several offline studies show that this can be mitigated by using experts for recommendation. In contrast to standard recommender systems, experts are able to generate more diverse recommendations and increase the novelty of given suggestions. They can be used in missing-data or cold-start scenarios and reduce noise in the users' ratings. This paper examines the impact of employed experts' recommendations on user behavior for a real-world recommender system on a popular video-on-demand website, provided by a large television network. We study whether the potential benefits of experts lead to differences in user behavior, user perceptions and properties of given recommendations (e.g., diversity). We find that enriching a state-of-the-art system with the suggestions of employed experts can significantly increase platform use. Even though expert recommendations are used less frequently and are less successful than expected, users watch a greater number of clips, use more recommendations, and come back to the website more frequently when they receive expert suggestions. When searching for other influencing factors, we find that experts generate more diverse recommendations and improve the taste coverage of the system keeping user satisfaction unaffected. In summary, our results show large benefits of using employed experts and have implications for the design and use of recommender systems in real-world scenarios.
Secure Sharing of IOT Data in Cloud Environment Using Attribute-Based Encryption

Storage of data security has emerged as a basic necessity for both large- and small-scale industries. Cloud computing is internet-based computing technology in which people can work with their application with a high level of security in Internet of Things (IoT) enhanced devices. Cloud computing also provides privilege to store data that are collected from the devices in server side. Confidentiality and integrity of data play a major issue in cloud computing while preserving the IoT data. In this paper, the security of data is achieved by performing clustering and further applying cryptographic technique to the clustered data gathered from the devices that connected to the internet. Security of data can be improved by using a prominent cryptographic technique Attribute Based Encryption (ABE). Initially, the documents are clustered using the EM algorithm and clustered results are stored in various parts of the cloud. These documents are secured and can be accessed by the user who satisfies the attributes. In some cases, if the users’ attributes are not satisfied with the documents, it cannot be accessed by the corresponding user. Thus, the data from various devices are encrypted and maintained by the owner securely.
Offline Signature Verification Using Convolutional Neural Network

Offline handwritten signature verification is widely used important form of biometrics. It is a challenging task due to time-variant nature of signature. To address the above difficulty, a new approach is proposed in this paper to compute the features of signatures. The proposed approach is divided into two parts: 1) writer-independent approach, 2) writer-dependent approach. Writer-independent approach is utilized for fine-tuning of VGG16 convolutional neural network (CNN). In writer-dependent approach, this fine-tuned CNN is utilized to extract the features from the signature. The signature is passed through this fine-tuned CNN and the vector obtained at first fully connected layer (after last convolutional layer) is used as feature vector. To obtain the accurate features for classification of signatures, the pixels of thinned signature image are replaced by their Gaussian Weighting Based Tangent Angle (GWBTA) in both writer-independent and writer-dependent approach. The computed features which are obtained in writer-dependent approach are fed to Support Vector Machine (SVM) classifier to classify the signature into genuine or forgery class. The performance results obtained on different databases of offline handwritten signatures confirms the validity of proposed methodology for offline signature verification.
Rewritable and Sustainable 2D Barcode for Traceability Application in Smart IoT Based Fault-Tolerant Mechanism

With the development of the Internet of Things (IoT) technology, two-dimensional (2D) barcodes are widely used in smart IoT applications as a perception portal. In industries with many circulations and testing links like traceability, since the existing 2D barcode cannot be changed once it is printed, it can only be replaced with more expensive radio frequency identification (RFID) labels or new 2D barcodes, causing a waste of human resources and costs. For better circulation efficiency and resource utilization, we propose a new design of the rewritable and sustainable 2D barcode based on the fault-tolerance mechanism. The ability to add new information in the 2D barcode can be achieved through data encryption and the insertion of a rewritable layer. It means the message of 2D barcodes could be changed, and increases the flexibility and liquidity of the 2D barcode application. Besides, the encoding and decoding method of the proposed 2D barcode is presented. Experimental results have illustrated the superiority of rewritable and sustainable 2D barcodes in the traceability of herbal medicine compared with the conventional 2D barcodes, and demonstrated the feasibility of the design. The findings show the potential for significant application in the field of traceability in smart IoT, as well as in the manufacturing industry and logistics.
Categorization of YouTube Videos by Video Sampling and Keyword Processing

Many students benefit from a wide variety of educational content on YouTube. It is a challenging task to watch only educational videos without any other distracting entertaining video catching our eye. Classifying the videos as educational and non-educational helps students to focus on learning without distraction. This paper describes a two-level automated mechanism to categorize videos into Educational and Non-Educational videos. The first level of classification is done by Video Processing. The feature vectors of the frames of the video are extracted using Inception V3, which are then processed using deep learning techniques. The second level of classification is done by Text Processing of the keywords available in the metadata of YouTube Videos. These keywords are used in the searching for YouTube videos. Our work brings novelty to the approach by classifying videos using both Computer Vision and Natural Language Processing techniques using the keywords from metadata. This method is faster and efficient to create a distraction-free environment where students can learn. The proposed approach has successfully classified videos into educational and non-educational.
An intelligent navigation experimental system based on multi-mode fusion

At present, most experimental teaching systems lack guidance of an operator, and thus users often do not know what to do during an experiment. The user load is therefore increased, and the learning efficiency of the students is decreased. To solve the problem of insufficient system interactivity and guidance, an experimental navigation system based on multi-mode fusion is proposed in this paper. The system first obtains user information by sensing the hardware devices, intelligently perceives the user intention and progress of the experiment according to the information acquired, and finally carries out a multi-modal intelligent navigation process for users. As an innovative aspect of this study, an intelligent multi-mode navigation system is used to guide users in conducting experiments, thereby reducing the user load and enabling the users to effectively complete their experiments. The results prove that this system can guide users in completing their experiments, and can effectively reduce the user load during the interaction process and improve the efficiency.
Ensuring Data Privacy Using Machine Learning for Responsible Data Science

With the advancement use of computers extensively the use of data has also grown to big data level. Nowadays data is collected without any specific purpose, every activity of a machine or a human being is recorded, If needed in the future then the data will be analyzed. But here the question of trust arises as the data will go through many phases for the analysis by different parties. The data may contain some sensitive or private information which can be misutilized by the organizations involved in the analysis stages. So it is needed for the hour to consider the data privacy issues very seriously. Different types of methods have been proposed in this paper to ensure data privacy and also different machine learning algorithms have been discussed which have been used to design the proposed methods to ensure data privacy.
Machine learning-based classification of viewing behavior using a wide range of statistical oculomotor features

Since the seminal work of Yarbus, multiple studies have demonstrated the influence of task-set on oculomotor behavior and the current cognitive state. In more recent years, this field of research has expanded by evaluating the costs of abruptly switching between such different tasks. At the same time, the field of classifying oculomotor behavior has been moving toward more advanced, data-driven methods of decoding data. For the current study, we used a large dataset compiled over multiple experiments and implemented separate state-of-the-art machine learning methods for decoding both cognitive state and task-switching. We found that, by extracting a wide range of oculomotor features, we were able to implement robust classifier models for decoding both cognitive state and task-switching. Our decoding performance highlights the feasibility of this approach, even invariant of image statistics. Additionally, we present a feature ranking for both models, indicating the relative magnitude of different oculomotor features for both classifiers. These rankings indicate a separate set of important predictors for decoding each task, respectively. Finally, we discuss the implications of the current approach related to interpreting the decoding results.
Incorporating Synonym for Lexical Sememe Prediction: An Attention-Based Model

Sememe is the smallest semantic unit for describing real-world concepts, which improves the interpretability and performance of Natural Language Processing (NLP). To maintain the accuracy of the sememe description, its knowledge base needs to be continuously updated, which is time-consuming and labor-intensive. Sememes predictions can assign sememes to unlabeled words and are valuable work for automatically building and/or updating sememeknowledge bases (KBs). Existing methods are overdependent on the quality of the word embedding vectors, it remains a challenge for accurate sememe prediction. To address this problem, this study proposes a novel model to improve the performance of sememe prediction by introducing synonyms. The model scores candidate sememes from synonyms by combining distances of words in embedding vector space and derives an attention-based strategy to dynamically balance two kinds of knowledge from synonymous word set and word embedding vector. A series of experiments are performed, and the results show that the proposed model has made a significant improvement in the sememe prediction accuracy. The model provides a methodological reference for commonsense KB updating and embedding of commonsense knowledge.
A Low-Cost High-Speed Object Tracking VLSI System Based on Unified Textural and Dynamic Compressive Features

Real-time object tracking is demanded in versatile embedded computer vision applications. To this end, a low-cost high-speed VLSI system is proposed for object tracking, based on robust and computationally simple unified textural and dynamic compressive sensing features and simple elliptic matching with fast online template updating capability. The system introduces a memory-centric architectural paradigm, multiple-level pipelines and parallel processing circuits to achieve high frame rate while consuming as few hardware resources as possible. We implemented an FPGA prototype of the proposed VLSI tracking system. Under a 100 MHz clock frequency, the prototype achieves over 600 frame/s processing speed under <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$320\times240$ </tex-math></inline-formula> image resolution and obtains robust tracking results.
LSTM-Based Speech Segmentation Trained on Different Foreign Languages

This paper describes experiments on speech segmentation by using bidirectional LSTM neural networks. The networks were trained on various languages (English, German, Russian and Czech), segmentation experiments were performed on 4 Czech professional voices. To be able to use various combinations of foreign languages, we defined a reduced phonetic alphabet based on IPA notation. It consists of 26 phones, all included in all languages. To increase the segmentation accuracy, we applied an iterative procedure based on detection of improperly segmented data and retraining of the network. Experiments confirmed the convergence of the procedure. A comparison with a reference HMM-based segmentation with additional manual corrections was performed.
KEAN: Knowledge Embedded and Attention-based Network for POI Recommendation

In recent years, with the rapid development of location-based social networks (LBSN) in the Internet, point of interest (POI) recommendation has become a hot spot. Most existing researches make use of contextual information to model users' interest preferences. However, the existing methods for extracting various auxiliary information still need to be improved, such as how to treat the users' social relations equally. In order to obtain users' actual preferences more accurately, in POI recommendation, we propose a deep learning framework KEAN (Knowledge Embedded and Attention Based Network) based on knowledge graph and attention model. The framework includes knowledge-graph embedding method, preference extraction network based on attention mechanism and recommendation network. Our study used knowledge-graph embedding method to get the embedding of each user and POI. In addition, an LSTM network based on attention mechanism was proposed, which uses LSTM network to learn the user's preferences according to the user's check-in sequence. Besides, the attention mechanism was used to extract friends' preferences and merge them with the user's preferences to generate end-user preferences. Finally, our model use fully-connected neural networks to realize recommendations. The effectiveness of the model was proved by the experimental results based on real LBSN datasets.
XAI-Based Fuzzy SWOT Maps for Analysis of Complex Systems

The classical SWOT methodology and many of the tools based on it used so far are very static, used for one stable project and lacking dynamics [1]. This paper proposes the idea of combining several SWOT analyses enriched with computing with words (CWW) paradigm into a single network. In this network, individual analysis of the situation is treated as the node. The whole structure is based on fuzzy cognitive maps (FCM) that have forward and backward chaining, so it is called fuzzy SWOT maps. Fuzzy SWOT maps methodology newly introduces the dynamics that projects are interacting, what exists in a real dynamic environment. The whole fuzzy SWOT maps network structure has explainable artificial intelligence (XAI) traits because each node in this network is a "white box"-all the reasoning chain can be tracked and checked why a particular decision has been made, which increases explainability by being able to check the rules to determine why a particular decision was made or why and how one project affects another. To confirm the vitality of the approach, a case with three interacting projects has been analyzed with a developed prototypical software tool and results are delivered.
(De)Constructing Bridges for Development and Innovation: Intercultural Concerns Regarding ICT4D

The purpose of this paper is to offer some critical comments about the collected articles, by introducing a point of view inspired by cultural psychology concerning information and communication technologies for development (ICT4D) research issues. This conception of ICTs highlights three fundamental aspects: the role of artifacts in mediating action that are culturally meaningful; the agency of people, and thus their responsibility as social actors; and the need for highly contextualized analysis. In the article, I will read these three points through the lenses of cultural psychology.
Morphologically motivated word classes for very large vocabulary speech recognition of Finnish and Estonian

We study class-based n-gram and neural network language models for very large vocabulary speech recognition of two morphologically rich languages: Finnish and Estonian. Due to morphological processes such as derivation, inflection and compounding, the models need to be trained with vocabulary sizes of several millions of word types. Class-based language modelling is in this case a powerful approach to alleviate the data sparsity and reduce the computational load. For a very large vocabulary, bigram statistics may not be an optimal way to derive the classes. We thus study utilizing the output of a morphological analyzer to achieve efficient word classes. We show that efficient classes can be learned by refining the morphological classes to smaller equivalence classes using merging, splitting and exchange procedures with suitable constraints. This type of classification can improve the results, particularly when language model training data is not very large. We also extend the previous analyses by rescoring the hypotheses obtained from a very large vocabulary recognizer using class-based neural network language models. We show that despite the fixed vocabulary, carefully constructed classes for word-based language models can in some cases result in lower error rates than subword-based unlimited vocabulary language models.
Numerical Abstract Persuasion Argumentation for Expressing Concurrent Multi-Agent Negotiations

A negotiation process by 2 agents $$e_1$$ and $$e_2$$ can be interleaved by another negotiation process between, say, $$e_1$$ and $$e_3$$ . The interleaving may alter the resource allocation assumed at the inception of the first negotiation process. Existing proposals for argumentation-based negotiations have focused primarily on two-agent bilateral negotiations, but scarcely on the concurrency of multi-agent negotiations. To fill the gap, we present a novel argumentation theory, basing its development on abstract persuasion argumentation (which is an abstract argumentation formalism with a dynamic relation). Incorporating into it numerical information and a mechanism of handshakes among members of the dynamic relation, we show that the extended theory adapts well to concurrent multi-agent negotiations over scarce resources.
Adversarially Robust Learning via Entropic Regularization

In this paper we propose a new family of algorithms, ATENT, for training adversarially robust deep neural networks. We formulate a new loss function that is equipped with an additional entropic regularization. Our loss function considers the contribution of adversarial samples that are drawn from a specially designed distribution in the data space that assigns high probability to points with high loss and in the immediate neighborhood of training samples. Our proposed algorithms optimize this loss to seek adversarially robust valleys of the loss landscape. Our approach achieves competitive (or better) performance in terms of robust classification accuracy as compared to several state-of-the-art robust learning approaches on benchmark datasets such as MNIST and CIFAR-10.
A Real-Time Pedestrian Counting System Based on RGB-D

Pedestrian counting is an important task in visual surveillance. Existing computer vision based pedestrian counting methods usually require high image quality and simple background, hence their applications are limited. In this paper, we introduce an innovative RGB-D based system for real-time pedestrian counting, which is easy to install and robust to work under various conditions. The system uses a RGB-Time Of Flight (TOF) camera to capture depth and RGB images simultaneously, then detects and tracks pedestrians based on fusion of both images. We propose a Deep Convex Convolution Filtering (DCCF) algorithm for pedestrian detection in depth images in order to overcome the problem of parameter sensitiveness in traditional methods. Experimental results highlight the effectiveness and efficiency of our designed system. Our proposed system has already been put to work in public places of multiple cities successfully.
Secure File Sharing System in Cloud Using AES and Time Stamping Algorithms

Abstract In recent days, the growth of digital data has been increased explosively due to data-intensive applications like healthcare, education and electronic commerce. Due to the huge volume of data created and the utilization of data in emerging applications, a local storage device has to be deployed. Cloud storage services allow both the individuals and commercial users for the outsourcing of data to the cloud server and to access the data remotely with the help of the internet, so the demand for cloud storage has been raised. However, one of the critical problems of cloud data storage is security. Some researchers have used third party auditor for verifying the data stored in the cloud. This paper introduces a novel method for cloud storage and for protecting the organizations’ data. The proposed system strengthens the level of authentication with the help of AES and time-stamping algorithms. The experimental results demonstrate the efficiency of the proposed method when auditing the shared data integrity
Towards Data-Driven Multilinear Metro Maps

Traditionally, most schematic metro maps as well as metro map layout algorithms adhere to an octolinear layout style with all paths composed of horizontal, vertical, and $$45^\circ $$ -diagonal edges. Despite growing interest in more general multilinear metro maps, generic algorithms to draw metro maps based on a system of $$k \ge 2$$ not necessarily equidistant slopes have not been investigated thoroughly. We present and implement an adaptation of the octolinear mixed-integer linear programming approach of Nöllenburg and Wolff (2011) that can draw metro maps schematized to any set $$\mathcal {C}$$ of arbitrary orientations. We further present a data-driven approach to determine a suitable set $$\mathcal {C}$$ by either detecting the best rotation of an equidistant orientation system or by clustering the input edge orientations using a k-means algorithm. We demonstrate the new possibilities of our method in a real-world case study.