Reviewerly/exHarmony huggingface dataset:
Robust and Fast Scene Recognition in Robotics Through the Automatic Identification of Meaningful Images

Scene recognition is still a very important topic in many fields, and that is definitely the case in robotics. Nevertheless, this task is view-dependent, which implies the existence of preferable directions when recognizing a particular scene. Both in human and computer vision-based classification, this actually often turns out to be biased. In our case, instead of trying to improve the generalization capability for different view directions, we have opted for the development of a system capable of filtering out noisy or meaningless images while, on the contrary, retaining those views from which is likely feasible that the correct identification of the scene can be made. Our proposal works with a heuristic metric based on the detection of key points in 3D meshes (Harris 3D). This metric is later used to build a model that combines a Minimum Spanning Tree and a Support Vector Machine (SVM). We have performed an extensive number of experiments through which we have addressed (a) the search for efficient visual descriptors, (b) the analysis of the extent to which our heuristic metric resembles the human criteria for relevance and, finally, (c) the experimental validation of our complete proposal. In the experiments, we have used both a public image database and images collected at our research center.
Using Krakatoa for Teaching Formal Verification of Java Programs

In this work, we present a study of different support tools to teach formal verification of Java programs and show our experience with Krakatoa, an automatic theorem prover based on Hoare logic which allows students to interactively visualize the different steps required to prove the correctness of a program, to think about the used reasoning and to understand the importance of verification of algorithms to improve the reliability of our programs.
Detecting Manipulations in Video

This chapter presents the techniques researched and developed within InVID for the forensic analysis of videos, and the detection and localization of forgeries within User-Generated Videos (UGVs). Following an overview of state-of-the-art video tampering detection techniques, we observed that the bulk of current research is mainly dedicated to frame-based tampering analysis or encoding-based inconsistency characterization. We built upon this existing research, by designing forensics filters aimed to highlight any traces left behind by video tampering, with a focus on identifying disruptions in the temporal aspects of a video. As for many other data analysis domains, deep neural networks show very promising results in tampering detection as well. Thus, following the development of a number of analysis filters aimed to help human users in highlighting inconsistencies in video content, we proceeded to develop a deep learning approach aimed to analyze the outputs of these forensics filters and automatically detect tampered videos. In this chapter, we present our survey of the state of the art with respect to its relevance to the goals of InVID, the forensics filters we developed and their potential role in localizing video forgeries, as well as our deep learning approach for automatic tampering detection. We present experimental results on benchmark and real-world data, and analyze the results. We observe that the proposed method yields promising results compared to the state of the art, especially with respect to the algorithm’s ability to generalize to unknown data taken from the real world. We conclude with the research directions that our work in InVID has opened for the future.
Mapping Derivative Relationships from RDA to BIBFRAME 2

Semantic interoperability between Resource Description and Access (RDA) and BIBFRAME models is of great interest to the library community. In this context, this work investigates the mapping of cor...
Comparing incomplete sequences via longest common subsequence

Inspired by scaffold filling, a recent approach for genome reconstruction from incomplete data, we consider a variant of the well-known longest common subsequence problem for the comparison of two sequences. The new problem, called Longest Filled Common Subsequence, aims to compare a complete sequence with an incomplete one, i.e. with some missing elements. Longest Filled Common Subsequence (LFCS), given a complete sequence A, an incomplete sequence B, and a multiset M of symbols missing in B, asks for a sequence B⁎ obtained by inserting the symbols of M into B so that B⁎ induces a common subsequence with A of maximum length. We investigate the computational and approximation complexity of the problem and we show that it is NP-hard and APX-hard when A contains at most two occurrences of each symbol, and we give a polynomial time algorithm when the input sequences are over a constant-size alphabet. We give a 35−approximation algorithm for the Longest Filled Common Subsequence problem. Finally, we present a fixed-parameter algorithm for the problem, when it is parameterized by the number of symbols inserted in B that “match” symbols of A.
On Unsupervised and Supervised Discretisation in Mining Stylometric Features

Writing styles can be described by stylometric features. They are quantitative in nature, often continuous, which either limits techniques used in mining to those capable of calculations on this form, or adds discretisation to initial pre-processing of data. The paper describes research on unsupervised and supervised discretisation applied in the stylometric domain for the task of authorship attribution. The recognition of authorship is executed as classification performed by chosen inducers capable of operating on both continuous and categorical attributes.
Modeling concepts and their relationships for corpus-based query auto-completion

Abstract Query auto-completion helps users to formulate their information needs by providing suggestion lists at every typed key. This task is commonly addressed by exploiting query logs and the approaches proposed in the literature fit well in web scale scenarios, where usually huge amounts of past user queries can be analyzed to provide reliable suggestions. However, when query logs are not available, e.g. in enterprise or desktop search engines, these methods are not applicable at all. To face these challenging scenarios, we present a novel corpus-based approach which exploits the textual content of an indexed document collection in order to dynamically generate query completions. Our method extracts informative text fragments from the corpus and it combines them using a probabilistic graphical model in order to capture the relationships between the extracted concepts. Using this approach, it is possible to automatically complete partial queries with significant suggestions related to the keywords already entered by the user without requiring the analysis of the past queries. We evaluate our system through a user study on two different real-world document collections. The experiments show that our method is able to provide meaningful completions outperforming the state-of-the art approach.
Research on the Application of the Improved Assessment Model Based on the Separation of Teaching and Examination in C Programming

Since the 1990s, Chinese universities have greatly expanded their enrollment, and major universities have made corresponding reforms in teaching and assessment. In 2019, the government called on Higher Vocational Colleges to enroll 1 million people, which indicates that vocational education will undergo changes, including enrollment system, teaching system and assessment methods, in order to ensure that higher vocational colleges have connotation and quality to expand their scale. Based on the analysis of the advantages and disadvantages of the traditional “integration of teaching and examination” and the “separation of teaching and examination” proposed in recent years, this paper puts forward an assessment method suitable for C programming in Higher Vocational colleges, which has been tested in our higher vocational colleges since September 2017. From the feedback and achievements of students in these two years, the effect is good.
Adaptive RGB Color Lexicographical Ordering Framework Using Statistical Parameters From the Color Component Histogram

In image filtering, the classical lexicographical ordering is a popular method that cannot be directly applied for ordering colors in RGB color images. This is due to the fact that each color has similar importance and no order can be defined trivially a priori. In this work we propose an adaptive color lexicographical ordering framework for RGB color images where a color pixel is transformed into a real number. This transformation is weighted by statistical parameters from each color component histogram and used as the main component for color comparison. This approach seeks to avoid the arbitrariness since the order of the color component priorities is defined by the information extracted from the image itself. The proposed approach was tested by applying a median filter to reduce noise and a morphological approach to local contrast enhancement. In noise reduction, we compare our method with classical ordering techniques on images with different noise levels. Results show that our proposal outperformed the state-of-the art methods according to the Mean Absolute Error (MAE) measure, especially in those scenarios with higher noise levels. In contrast enhancement, the proposed framework outperformed the classical lexicographical ordering method according to Contrast Improvement Ratio (CIR) metric, especially when increasing the contrast factor. Our proposal generates less distortion than the state-of-the art methods ordering.
Human Face Expressions from Images

Several computer algorithms for recognition of visible human emotions are compared at the web camera scenario using CNN/MMOD face detector. The recognition refers to four face expressions: smile, surprise, anger, and neutral. At the feature extraction stage, the following three concepts of face des cription are confronted: (a) static 2D face geometry represented by its 68 characteristic landmarks (FP68); (b) dynamic 3D geometry defined by motion parameters for eight distinguished face parts (denoted as AU8) of personalized Candide-3 model; (c) static 2D visual description as 2D array of gray scale pixels (known as facial raw image). At the classification stage, the performance of two major models are analyzed: (a) support vector machine (SVM) with kernel options; (b) convolutional neural network (CNN) with variety of relevant tensor processing layers and blocks of them. The models are trained for frontal views of human faces while they are tested for arbitrary head poses. For geometric features, the success rate (accuracy) indicate nearly triple increase of performance of CNN with respect to SVM classifiers. For raw images, CNN outperforms in accuracy its best geometric counterpart (AU/CNN) by about 30 percent while the best SVM solutions are inferior. For F-score the high advantage of raw/CNN over geometric/CNN and geometric/SVM is observed, as well. We conclude that contrary to CNN based emotion classifiers, the generalization capability wrt human head pose for SVM based emotion classifiers, is worse too.
REVIEW ON TASK SCHEDULING IN UBIQUITOUS CLOUDS

The cloud being a prominent area for research, is ubiquitous as it serves the client needs irrespective of time and place. It has become the most preferred network due to its guaranteed service rendering and cost effectiveness. The increase in the capability of the cloud services has increased the number of users to adapt to cloud. The increase in the adaption towards cloud in turn results with insufficiency in the proper and the simultaneous allocation of the resources according to the requisitions. So the task scheduling and resource allocation for the cloud becomes essential. As the conventional methods of task scheduling arrive to a local optima solution that are less-effective, the paper surveys the meta-heuristic optimization based task scheduling and the resource allocation for the ubiquitous cloud environment, that arrives to an more optimal solution at a faster rate and at ease. The paper presents the survey of the optimization techniques of task scheduling available for the cloud and the discussion of the improvement of the performance metrics in terms of make-span, throughput, cost, latency and successful service provisioning compared to the other conventional methods.
Sustainable Cloud Encoding for Adaptive Bitrate Streaming over CDNs

Video streaming is the most popular application on today's Internet. Millions of people around the globe access video contents using various end user devices, such as smart phones, tablets, laptops, and TVs. To meet the requirements of different end user devices and variable network conditions, videos need to be encoded into different quality versions before delivery to the clients. Such large-scale encoding tasks consume significant amounts of energy. In this paper, we investigate to what extent the realtime video encoding clouds can be powered by renewable energy sources. We show that video encoding tasks are suitable for execution on clouds that are powered by a combination of renewable and grid energy sources. With the use of our power management policies, grid energy usage can be reduced by 73-83%, which leads to electricity cost reductions of 14-28% compared to unlimited non-renewable power.
Adversarial Campaign Mitigation via ROC-Centric Prognostics

Machine learning models are vulnerable to adversarial inputs that induce seemingly unjustifiable errors. As automated classifiers are increasingly used in industrial control systems and machinery, these adversarial errors could grow to be a serious problem. Despite numerous studies over the past few years, the field of adversarial ML is still considered alchemy, with no practical unbroken defenses demonstrated to date, leaving PHM practitioners with few meaningful ways of addressing the problem. We introduce “turbidity detection” as a practical superset of the adversarial input detection problem, coping with adversarial campaigns rather than statistically invisible one-offs. This perspective is coupled with ROC-theoretic design guidance that prescribes an inexpensive domain adaptation layer at the output of a deep learning model during an attack campaign. The result aims to approximate the Bayes optimal mitigation that ameliorates the detection model’s degraded health. A proactively reactive type of prognostics is achieved via Monte Carlo simulation of various adversarial campaign scenarios, by sampling from the model’s own turbidity distribution to quickly deploy the correct mitigation during a real-world campaign.
A Real-time Drone Surveillance System Using Pixel-level Short-time Fourier Transform

In this study we propose a novel method for drone surveillance that can simultaneously analyze time-frequency responses in all pixels of a high-frame-rate video. The propellers of flying drones rotate at hundreds of Hz and their principal vibration frequency components are much higher than those of their background objects. To separate the pixels around a drone's propellers from its background, we utilize these time-series features for vibration source localization with pixel-level short-time Fourier transform (STFT). We verify the relationship between the number of taps in the STFT computation and the performance of our algorithm, including the execution time and the localization accuracy, by conducting experiments under various conditions, such as degraded appearance, weather, and defocused blur. The robustness of the proposed algorithm is also verified by localizing a flying multi-copter in real-time in an outdoor scenario.
Development of Mobile Application for Incident Reporting

The study entitled "Development of Mobile Application for Incident Reporting" is an application that aimed to report and respond to crime incidents that may occur in the Philippines community. It includes two main applications such as Crime Reporting, Crime Responder and the website. The Crime Reporting application used by the victim or witness, to submit an incident report with photos that are then coordinated to either the nearest barangay or police station. The Crime Responder application used by the barangay or police officials for notification and immediate response on the given report. The website for incident demographics. The application runs in Android phones with a 4.4 to 6.0 version and has an internet connection. The 30 participants, including 10 end-users, 5 police officers, 5 barangay officers, 7 IT teachers, and 3 Android programmers using the ISO 25010 Model as an assessment tool, assessed it.
Enhancing QoE for Video Streaming Considering Congestion: A Fault Tolerance Approach

Since tremendous amount of traffic is generated in modern networks as a result of the mobile devices and video streaming, the congestion issue is faced more frequently in the networks. Accordingly, failures and performance losses in networks due to congestion result in deteriorated Quality of Experience (QoE) from the end user perspective that may cause financial and reputation loss for the service provider. Even though the new video streaming paradigm, Dynamic Adaptive Streaming over HTTP (DASH), is proposed as a solution for the changing condition of the networks, it is not sufficient considering the heavily loaded links that show the symptoms of link failures. Therefore, the flexible implementation of the data plane fault tolerance scheme that can be applied for other problems like congestion in networks is crucial. Thus, in this study, we apply the data plane fault tolerance approach in the Software-Defined Network to improve the QoE of DASH clients in the case of congestion rather than the failure. To detect the congestion in the network level, we use the Bidirectional Forwarding Protocol (BFD) that is originally implemented for link failures. In our experiments, we investigate the effect of the BFD interval, video segment size, and traffic load on QoE parameters. Our results show that if the fault tolerance approach is applied using a small BFD interval with a large segment size, QoE parameters are noticeably enhanced considering the non-applied case.
An Efficient File Hierarchy Attribute Based Encryption Using Optimized Tate Pairing Construction in Cloud Environment

One of the most preferred encryption techniques for data sharing in cloud environment is Ciphertext-Policy Attribute Based Encryption (CP-ABE). An efficient File Hierarchy CP-ABE (FH-CP-ABE) scheme using an integrated access structure was proposed by Wang and colleagues. Their working construction was based on symmetric pairing. In this article, we propose an efficient Asymmetric File Hierarchy Ciphertext-Policy Attribute Based Encryption (AFH-CP-ABE) which is based on an asymmetric pairing construction using denominator elimination and twist mapping. We apply this optimized Tate pairing in the proposed AFH-CP-ABE to find out its computational costs for encryption and decryption using gmp 6.0.0 library and then compared with existing schemes.
Global and Local Spatial-Attention Network for Isolated Gesture Recognition

In this paper, we focus on isolated gesture recognition from RGB-D videos. Our main idea is to design an algorithm that can extract global and local information from multi-modality inputs. To this end, we propose a novel attention-based method with 3D convolutional neural network (CNN) to recognize isolated gesture recognition. It includes two parts. The first one is a global and local spatial-attention network (GLSANet), which takes into account the global information that focuses on the context of the frame and the local information that focuses on the hand/arm actions of the person, to extract efficient features from multi-modality inputs simultaneously. The second part is an adaptive model fusion strategy to fuse the predicted probabilities from multi-modality inputs. Experiments demonstrate that the proposed method has achieved state-of-the-art performance on the IsoGD dataset.
Development of High-Performance and Flexible Protocol Handler for International Web Accesses

In this big data era, a high-speed web access technique is expected for sharing a variety of large-scale data, especially over an international network. There are several high-speed web access techniques, such as data caching, data compression, data visualization, and intelligent data processing. In addition, hypertext transfer protocol (HTTP)/1.1 provides a persistent connection for better throughput than the HTTP/1.0. However, using these techniques developed so far, there are many users who are not satisfied with the current web access speed. In this paper, we propose a novel protocol handler for international web accesses based on a high-speed data transfer protocol, namely high-performance and flexible protocol (HpFP). We first design our protocol handler on the basis of HTTP, and then implement it for the Internet Explorer browser in Windows operating system (OS). Using a laboratory experiment, we evaluate the performance of our protocol handler in terms of throughput on a real-time web for weather satellite data. The results show that the proposed protocol handler achieves higher throughput than the HTTP for website requests on international networks. The improvement gets more significant as either the latency or the packet loss ratio becomes higher. This implies that the proposed protocol handler especially devotes to international web accesses for big data.
Application of Virtual Simulation Platform in Basic Medical Teaching

In the context of educational informationization, virtual simulation technology has been applied in medical experimental teaching due to its openness, sharing, interactivity, and low cost. This paper researched the function of the virtual simulation experimental platform and focused on the construction concept, content, function, and teaching characteristics of the platform. The basic medical virtual simulation experimental platform provides a new way for the cultivation of medical students’ clinical skills and also opens up new ideas for improving the informationization of medical education.
Goal-directed autonomous navigation of mobile robot based on the principle of neuromodulation

Autonomous navigation in dynamic environment is aprerequisite of the mobile robot to perform tasks, and numerous approaches have been presented, including the supervised learning. Using supervised learning in robot navigation might meet problems, such as inconsistent and noisy data, and high error in training data. Inspired by the advantages of the reinforcement learning, such as no need for desired outputs, many researchers have applied reinforcement learning to robot navigation. This paper presents anovel method to address the robot navigation in different settings, through integrating supervised learning and analogical reinforcement learning into amotivated developmental network. We focus on the effect of the new learning rate on the robot navigation behavior. Experimentally, we show that the effect of internal neurons on the learning rate allows the agent to approach the target and avoid the obstacle as compounding effects of sequential states in static, dynamic, and complex environments. Further, we compare the performance between the emergent developmental network system and asymbolic system, as well as other four reinforcement learning algorithms. These experiments indicate that the reinforcement learning is beneficial for developing desirable behaviors in this set of robot navigation– staying statistically close to its target and away from obstacle.