Reviewerly/exHarmony huggingface dataset:
Examination of Correction Method of Long-term Solar Radiation Forecasts of Numerical Weather Prediction

To optimize operation plans for storage batteries with solar power systems, it is necessary to predict solar radiation for up to one week with high accuracy. However, it is known that the further a prediction system forecasts into the future, the lower the prediction accuracy. In this paper, we report on the results of testing a method for correcting the solar radiation prediction value statistically from a numerical weather prediction model by using the tendency of past error occurrence for predicting seven days ahead.
Detection and Prevention of Profile Cloning in Online Social Networks

In today's world, online social networking sites are becoming quite popular. They influence the way people communicate with each other. Social Sites like Facebook, Twitter, LinkedIn, and Google have tens or hundreds of millions of users across the globe that generates billions of personal data content. With such a huge amount of data available there is always a fear of this data being stolen or misused by some kind of adversaries or attackers. Hence such huge amount of data generates security and privacy threats to the users worldwide. These can be Identity Theft Attack (profile cloning) or may be some kind of Malware Attacks or Structural Attacks. Profile cloning is a kind of stealing identity of existing user to duplicate or to create a duplicate profile of a user using these credentials. In this paper an attempt has been made to propose a methodology for detection of profile cloning in various social networks. Also, we tried to suggest some measures that can be followed by the users and OSN providers to protect the users from these kinds of attacks.
CLEVis: A Semantic Driven Visual Analytics System for Community Level Events

Community-level event (CLE) datasets, such as police reports of crime events, contain abundant semantic information of event situations, and descriptions in a geospatial-temporal context. They are critical for frontline users, such as police officers and social workers, to discover and examine insights about community neighborhoods. We propose CLEVis, a neighborhood visual analytics system for CLE datasets, to help frontline users explore events for insights at community regions of interest, namely fine-grained geographical resolutions, such as small neighborhoods around local restaurants, churches, and schools. CLEVis fully utilizes semantic information by integrating automatic algorithms and interactive visualizations. The design and development of CLEVis are conducted with solid collaborations with real-world community workers and social scientists. Case studies and user feedback are presented with real-world datasets and applications.
Fast Planning Over Roadmaps via Selective Densification

We propose the Selective Densification method for fast motion planning through configuration space. We create a sequence of roadmaps by iteratively adding configurations. We organize these roadmaps into layers and add edges between identical configurations between layers. We find a path using best-first search, guided by our proposed estimate of remaining planning time. This estimate prefers to expand nodes closer to the goal and nodes on sparser layers. We present proofs of the path quality and maximum depth of nodes expanded using our proposed graph and heuristic. We also present experiments comparing Selective Densification to bidirectional RRT-connect, as well as many graph search approaches. In difficult environments that require exploration on the dense layers we find Selective Densification finds solutions faster than all other approaches.
Towards story-based classification of movie scenes

Humans are entertained and emotionally captivated by a good story. Artworks, such as operas, theatre plays, movies, TV series, cartoons, etc., contain implicit stories, which are conveyed visually (e.g., through scenes) and audially (e.g., via music and speech). Story theorists have explored the structure of various artworks and identified forms and paradigms that are common to most well-written stories. Further, typical story structures have been formalized in different ways and used by professional screenwriters as guidelines. Currently, computers cannot yet identify such a latent narrative structure of a movie story. Therefore, in this work, we raise the novel challenge of understanding and formulating the movie story structure and introduce the first ever story-based labeled dataset-the Flintstones Scene Dataset (FSD). The dataset consists of 1, 569 scenes taken from a manual annotation of 60 episodes of a famous cartoon series, The Flintstones, by 105 distinct annotators. The various labels assigned to each scene by different annotators are summarized by a probability vector over 10 possible story elements representing the function of each scene in the advancement of the story, such as the Climax of Act One or the Midpoint. These elements are learned from guidelines for professional script-writing. The annotated dataset is used to investigate the effectiveness of various story-related features and multi-label classification algorithms for the task of predicting the probability distribution of scene labels. We use cosine similarity and KL divergence to measure the quality of predicted distributions. The best approaches demonstrated 0.81 average similarity and 0.67 KL divergence between the predicted label vectors and the ground truth vectors based on the manual annotations. These results demonstrate the ability of machine learning approaches to detect the narrative structure in movies, which could lead to the development of story-related video analytics tools, such as automatic video summarization and recommendation systems.
A Low-Complexity Color Image Compression Algorithm Based on AMBTC

In this paper, a new color image compression algorithm based on Absolute Moment Block Truncation Coding (AMBTC) and entropy coding is proposed. The AMBTC improves the compression performance of Block Truncation Coding (BTC) obviously. A novel technique by using eight different types of bitmap tables is developed for the proposed algorithm. In addition, an entropy coding technique combining prediction and Huffman Coding approach is included in the proposed algorithm and decreased the data amount of luminance images. The experimental results show that more than 82% data amount can be compressed by using the eight different types of novel bitmap tables. Compared with previous studies, this work has better compression performance than previous 4*4 BTC, JPEG, and JPEG-LS algorithms.
Binary Relevance Model for Activity Recognition in Home Environment using Ambient Sensors

One of the most important applications of the smart home environment is health monitoring and assistance by analysing activities of daily living and here Human Activity Recognition (HAR) plays a major role. The HAR problem, basically a temporal classification problem has been modelled in the past with various methods such as Bayesian Networks, Hidden Markov Model, Conditional Random Field, etc. Here, we propose the Binary Relevance Method of the multi- label classification to tackle the multi-resident activity recognition problem on real world dataset. Through the results obtained by the evaluation metrics namely accuracy, precision and hamming loss, it can be inferred that the model not only computes competitive results to previous works but also signifies the importance of the baseline Binary Relevance method to solve multi-label problems.
A Perspective view of Resource-based Capacity planning in Cloud computing

In current scenario, a web based organization are highly attained a day to day demand of executing server computation and responding very fast in nature. They faced a big booming of productivity in IT market. Cloud Computing is a new era in web based computing, it includes resource like memory, Processing cycle, set of connections, application and software services are delivered as a service. Cloud computing offers services, which access to a variety of diverse resources. It has a collection of virtualized end users and they are dynamically getting single or more integrated computing resources. These middleware, resources and software are measured from the cloud via the internet and their corresponding services. The customers are in creating time to time and their usage and query also higher. So, to sustain the client needs, we are ready to serve a better resource configured to manage planning. Especially in the cloud, each service is indeed of own resource requirement. So, resource capacity planning is a base for capacity plans and virtualized resource management in Cloud computing. This work aimed to propose a survey of resource based capacity planning methods, issues and their similarities/dissimilarities. The work observed to identify the benchmark functionalities and resource ceilings in capacity planning.
Lean Training Data Generation for Planar Object Detection Models in Unsteady Logistics Contexts

Supervised deep learning has become the state of the art method for object detection and is used in many application areas such as autonomous driving, manufacturing industries or security systems. The acquisition of annotated data sets for the training of neural networks is highly time-consuming and error-prone. Thus, the supervised training of such object detection models is not feasible in some cases. This holds for the task of logistics transport label detection, as this use-case stands out by requiring highly specialized, quickly adapting models whilst allowing for little manual efforts in the data preparation and training process. We propose an easy training data generation method enabling the fully automated training of specialized models for the task of logistics transport label detection. For data synthesis, we stitch instances of the transport labels to be detected into background images whilst using image degradation and augmentation methods. We evaluate the employment of both use-case-specific, carefully selected background images and randomly selected real-world background images. Further, we compare two different data generation approaches: one generating realistically looking images and a simpler one making do without any manual image annotation. We examine and evaluate the introduced method on a new and publicly available example data set relevant for logistics transport label detection. We show that accurate models can be trained exclusively on synthetic training data and we compare their performance to models trained on real, manually annotated images.
Managing library www-resources

Ongoing and methodologically rigorous management of resources in the information and library web-environment affects its efficient advance. The author examines the problems of the new stage of management in the area. She provides general characteristics and definition of the term “iManagement” (Internet-management), substantiates the scholarly and applied potential of analytical instruments of library web-resources. She also emphasizes that the competent use of the analytical instruments facilitates efficient analysis of web-resources in the digital environment. The selection of analytical instruments depends upon the resource type (official www-site, account, blog, conference www-site or library event web-page, etc.) and upon the target goals. Acquiring and analyzing webometrics with analytical instruments make one of the basic reorganization methods. The paper goal is to study ways of managing www-site contents with webometrical indicators and to design the generic cycle for managing library web-resources. The advantages of the single system of library web-resources management are identified. The stages of management cycle are characterized in brief, as well as their sequence, i. e. goal setting, monitoring, analysis, planning, control, and decision making.
The impact of preprocessing in natural language for open source intelligence and criminal investigation

Underground forums serves as gathering place for like-minded cyber criminals and are an continued threat to law and order. Law enforcement agencies can use Open-Source Intelligence (OSINT) to gather valuable information to proactively counter existing and new threats. For example, by shifting criminal investigation's focus onto certain cyber criminals with large impact in underground forums and related criminal business models. This paper presents our study on text preprocessing requirements and document construction for the topic model algorithm Latent Dirichlet Allocation (LDA). We identify a set of preprocessing requirements based on literature review and demonstrate them on a real-world forum, similar to those used by cyber criminals. Our result show that topic modelling processes needs to follow a very strict procedure to provide significant result that can be useful in OSINT. Additionally, more reliable results are produced by tuning the hyper-parameters and the number of topics for LDA. We demonstrate improved results by iterative preprocessing to continuously improve the model, which provide more coherent and focused topics.
Effectiveness of Usability &amp; Performance Features for Web Credibility Evaluation

Unreliable web pages, such as fake news, have become an unavoidable problem. To tackle this problem, recent researches have adopted both content and social features to predict the credibility of the web pages; however, the accuracy is almost saturated. In this paper, we propose the adoption of Google Lighthouse features to predict web page credibility. Our experimental results show that the proposed method achieves an increased accuracy of 7.9% in comparison with state-of-the-art methods.
Face Recognition Based on Global and Local Feature Fusion

In the training process of face images, the traditional convolutional neural network does not fuse the information of high and low convolutional layers. In order to make full use of the feature information of each layer of image, a convolutional neural network model based on global and local feature fusion of MobileNet network is proposed. The algorithm fuses the local features of the first layer of the network with the global features after the principal component analysis. Thus, the expression of shallow features is increased, the extraction effect of deep features is strengthened, and the information extracted by the improved MobileNet network is more complete.
Exact Learning

A major problem in computational learning theory is whether the class of formulas in conjunctive normal form (CNF) is efficiently learnable. Although it is known that this class cannot be polynomially learned using either membership or equivalence queries alone, it is open whether the CNF class can be polynomially learned using both types of queries. One of the most important results concerning a restriction of the CNF class is that propositional Horn formulas are polynomial time learnable in Angluin’s exact learning model with membership and equivalence queries. In this work, we push this boundary and show that the class of multivalued dependency formulas (MVDF), which non-trivially extends propositional Horn, is polynomially learnable from interpretations. We then provide a notion of reduction between learning problems in Angluin’s model, showing that a transformation of the algorithm suffices to efficiently learn multivalued database dependencies from data relations. We also show via reductions that our main result extends well known previous results and allows us to find alternative solutions for them.
Local Search for Group Closeness Maximization on Big Graphs

In network analysis and graph mining, closeness centrality is a popular measure to infer the importance of a vertex. Computing closeness efficiently for individual vertices received considerable attention. The NP-hard problem of group closeness maximization, in turn, is more challenging: the objective is to find a vertex group that is central as a whole and state-of the-art heuristics for it do not scale to very big graphs yet.In this paper, we present new local search heuristics for group closeness maximization. By using randomized approximation techniques and dynamic data structures, our algorithms are often able to perform locally optimal decisions efficiently. The final result is a group with high (but not optimal) closeness centrality. We compare our algorithms to the current state-of-the-art greedy heuristic both on weighted and on unweighted real-world graphs. For graphs with hundreds of millions of edges, our local search algorithms take only around ten minutes, while greedy requires more than ten hours. Overall, our new algorithms are between one and two orders of magnitude faster, depending on the desired group size and solution quality. For example, on weighted graphs and k =10, our algorithms yield solutions of 12.4% higher quality, while also being 793. 6× faster. For unweighted graphs and k =10, we achieve solutions within 99.4% of the state-of-the-art quality while being 127. 8× faster.
Scalable Neural Dialogue State Tracking

A Dialogue State Tracker (DST) is a key component in a dialogue system aiming at estimating the beliefs of possible user goals at each dialogue turn. Most of the current DST trackers make use of recurrent neural networks and are based on complex architectures that manage several aspects of a dialogue, including the user utterance, the system actions, and the slot-value pairs defined in a domain ontology. However, the complexity of such neural architectures incurs into a considerable latency in the dialogue state prediction, which limits the deployments of the models in real-world applications, particularly when task scalability (i.e. amount of slots) is a crucial factor. In this paper, we propose an innovative neural model for dialogue state tracking, named Global encoder and Slot-Attentive decoders (G-SAT), which can predict the dialogue state with a very low latency time, while maintaining high-level performance. We report experiments on three different languages (English, Italian, and German) of the WOZ2.0 dataset, and show that the proposed approach provides competitive advantages over state-of-art DST systems, both in terms of accuracy and in terms of time complexity for predictions, being over 15 times faster than the other systems.
Librany - A Face Recognition and QR Code Technology based Smart Library System

Libraries play the most important role when it comes to books and reading. Today, libraries have become a great source of information. If we look upon the procedure that most of the conventional libraries in the world follow, we see a great amount of drudgery involved. In this paper, we propose a smart library system, which eliminates most of this drudgery in the conventional library management systems. Using face recognition and QR code based technology, the system automates and eases the tasks of Book Issue, Book Return and Book Search. It resembles `Just walk out technology' implemented by Amazon in the Amazon Go stores. The proposed system enjoins self-issue and self-return procedures along with the dynamic search feature which provides the real-time location of the books. The proposed system significantly expedites the process of issue, return and search thus avoiding long queues at the reception counter/librarian's desk.
Why Rectified Linear Neurons Are Efficient: A Possible Theoretical Explanation

Traditionally, neural networks used a sigmoid activation function. Recently, it turned out that piecewise linear activation functions are much more efficient—especially in deep learning applications. However, so far, there have been no convincing theoretical explanation for this empirical efficiency. In this paper, we provide such an explanation.
Interleaving Loidreau’s Rank-Metric Cryptosystem

We propose and analyze an interleaved variant of Loidreau's rank-metric cryptosystem based on rank multipliers. We analyze and adapt several attacks on the system, propose design rules, and study weak keys. Finding secure instances requires near-MRD rank-metric codes which are not investigated in the literature. Thus, we propose a random code construction that makes use of the fact that short random codes over large fields are MRD with high probability. We derive an upper bound on the decryption failure rate and give example parameters for potential key size reduction.
Towards Practical Deployment of Post-quantum Cryptography on Constrained Platforms and Hardware-Accelerated Platforms

Most of the cryptographic constructions deployed in practical systems today, in particular digital signatures and key-establishment schemes, are vulnerable to attacks using quantum computers. Post-quantum cryptography (PQC) deals with the design and implementation of cryptographic algorithms that are resistant to these attacks. In this paper, we evaluate the NIST’s PQC competition candidates with respect to their suitability for the implementation on special hardware platforms. In particular, we focus on the implementability on constrained platforms (e.g., smart cards, small single-board computers) on one side and on the performance on very fast hardware-accelerated platforms (i.e., field-programmable gate arrays - FPGAs) on the other side. Besides the analysis of the candidates’ design features affecting the performance on these devices and security aspects, we present also the practical results from the existing implementation on contemporary hardware.
CrossOIE: Cross-Lingual Classifier for Open Information Extraction

Open information extraction (Open IE) is the task of extracting open-domain assertions from natural language sentences. Considering the low availability of datasets and tools for this task in languages other than English, recently it has been proposed that multilingual resources can be used to improve Open IE methods for different languages. In this work, we present the CrossOIE, a multilingual publicly available relation tuple validity classifier that scores Open IE systems’ extractions based on their estimated quality and can be used to improve Open IE systems and assist in the creation of Open IE benchmarks for different languages. Experiments show that our model trained using a small corpus in English, Spanish, and Portuguese can trade recall performance for up to 27% improvement in precision. This result was also archived in a zero-shot scenario, demonstrating a successful knowledge transfer across the languages.
Rule-Based Inquiry Service to Elderly at Home for Efficient Mind Sensing

To support in-home long-term care, we are studying techniques of Mind Sensing, which externalizes internal states of elderly people as words through conversations with agents or robots. We have previously developed a prototype system of Mind Sensing, integrated with an activity recognition system and an LINE chatbot. However, the system was tightly coupled with the fixed systems, it was difficult to add or change the setting of questions from the chatbot to individual elderly people.