Reviewerly/exHarmony huggingface dataset:
View-Action Representation Learning for Active First-Person Vision

In visual navigation, a moving agent equipped with a camera is traditionally controlled by an input action and the estimation of the features from a sensory state (i.e. the camera view) is treated as a pre-processing step to perform high-level vision tasks. In this paper, we present a representation learning approach that, instead, considers both state and action as inputs. We condition the encoded feature from the state transition network on the action that changes the view of the camera, thus describing the scene more effectively. Specifically, we introduce an action representation module that generates decoded higher dimensional representations from an input action to increase the representational power. We then fuse the output from the action representation module with the intermediate response of the state transition network that predicts the future state. To enhance the discrimination capability among predictions from different input actions, we further introduce triplet ranking loss and $N$ -tuplet loss functions, which in turn can be integrated with the regression loss. We demonstrate the proposed representation learning approach in reinforcement and imitation learning-based mapless navigation tasks, where the camera agent learns to navigate only through the view of the camera and the performed action, without external information.
Static job scheduling for environments with vertical elasticity

Summary In virtualized environments, such as Clouds, allocating a fixed amount of resources to a job a priori, may result in underutilization of the shared host. Meanwhile, vertical elasticity can be adopted to reduce the impact by resizing virtual machines (VMs) dynamically, in conjunction with suspension and/or migration before the host had been overloaded. In order to reduce the number of such events, but still avoid overloading the host, it is important to find an effective initial VM allocation. To achieve this, the scheduler must work in unison with the local host elasticity controllers to reduce interference. This work proposes and evaluates a framework for job scheduling in elastic memory managed virtualized environments. The memory elasticity management in clouds (MEMiC) framework is a two‐tier VM scheduler for batch jobs which attempts to predict the impact caused by competition for the memory of a host in shared Cloud‐like environments for harnessing VM allocation, suspension, and migration. Evaluations show that the MEMiC achieves a reduction of approximately 19% in the total time for jobs execution in comparison to other approaches, by reducing the degree of interference.
Genetic Programming Based Evolvement of Models of Models

The main idea of this paper is to use Simple Symbolic Formulas generated offline with the help of the deterministic function extraction algorithm as building blocks for Genetic Programming. This idea comparison to Automatically Defined Functions approach was considered. A possibility to take into consideration an expert’s knowledge about the problem in hand has been reviewed. In this work a map of building block’s set is generated by means of clustering. All distances between blocks are calculated offline by using a special metric for symbolic expressions. A mutation operator in Genetic Programming was modified for work with this kind of nodes. The effectiveness of this approach was evaluated on benchmark as well as on real world problems.
Blockchain Readiness

Blockchain technology is an emerging innovation, and it is viewed as a better approach to help the necessities of people and institutions in terms of record management. Using the Delphi technique and the PERM model, this chapter seeks to present the perspectives of experts in the field of blockchain on the readiness of the institutions in developing economies to adopt and implement it. The findings suggest that the understanding of the technology, knowledge on how to use it, availability of skilled personnel, availability of technical components, risk, capital, management support, business process, policies and regulations, and government initiatives on technology are key influencing factors assessing the readiness to adopt blockchain in a developing economy. It is expected that these findings will enlighten practitioners on the prospects of the application of blockchain in all sectors. The originality of this study lies in the fact that it is a maiden exploratory study that examines the factors that influence the readiness to adopt and implement blockchain technology in a developing economy.
Using Entropy and Marr Wavelets to Automatic Feature Detection for Image Matching

Image matching, also refereed as feature point matching, is a fundamental issue in computer vision. In this paper, we propose to use local entropy based on Marr wavelets within scale-interaction to improve the accuracy of automatic feature detection in the context of image matching. The goal is to improve the accuracy of the feature matching step while exhibiting a highly representative set of features of the objects within both images. To improve reliability, we propose to exploit local entropy under a mesh division strategy in combination with a sensitive feature selection stage. Experimental results show that this algorithm can outperform some of the conventional feature extraction algorithms with higher subsequent matching recall rate of image matching.
Visual Linguistic Model and Its Applications in Image Captioning

Image captioning is a well-known task of generating textual description of a given image. Research work on this problem statement requires efforts in both computer vision and natural language processing domains to obtain better quality image descriptions. In this paper, we are proposing a new deep learning approach to generate image captions. In this approach, we generate a sequence of visual embeddings for objects and their relationships present in the image. These visual embeddings are arranged in a particular manner and are then supplied to the encoder part of an attention-based sequence-to-sequence model. In the final step, we receive the generated image captions from the decoder part of our sequence-to-sequence model. We tested its performance on MSCOCO Dataset, and the obtained results suggested that our model generates better image captions for MSCOCO testing dataset.
Distributed Evolution of Deep Autoencoders

Autoencoders have seen wide success in domains ranging from feature selection to information retrieval. Despite this success, designing an autoencoder for a given task remains a challenging undertaking due to the lack of firm intuition on how the backing neural network architectures of the encoder and decoder impact the overall performance of the autoencoder. In this work we present a distributed system that uses an efficient evolutionary algorithm to design a modular autoencoder. We demonstrate the effectiveness of this system on the tasks of manifold learning and image denoising. The system beats random search by nearly an order of magnitude on both tasks while achieving near linear horizontal scaling as additional worker nodes are added to the system.
Semantic Mapping of Component Framework Interface Ontologies for Interoperability of Vehicle Applications

Over the past few years, ontology merging, and ontology semantic alignment has gained significant interest as research topics in automotive application domain for finding solutions to semantic data heterogeneity. To accomplish the complex and novel vehicle service requirements such as autonomous driving, V2X (Vehicle-to-Vehicle communication), etc. the automotive applications involve collaborations of several platform-specific data from heterogeneous enterprises component frameworks and consequently there has been increase in data interoperability issues. At the application component level, data interoperability relies on the semantic alignment or mapping between the various component framework interfaces data models represented as XML schemas (XSD). With the XML schemas being the preferred standard for the interface description exchange between most of the automotive application domain components, however, the data interoperability between the semantically equivalent but structurally different data constructs of multiple heterogeneous XSDs stands as a challenge in the absence of an ontology-based approach. To confront this crucial requirement for data interoperability and to increase in effect the reuse of existing components through their interfaces, we propose an approach to semantically map the various component framework interface data models when expressed as ontology schemas, based on the exploration of semantic synergies. The transformation between XSD and RDF (Resource Description Framework) schema representations and the use of queries over the ontology schemas for semantic mapping are demonstrated including a real-world case study.
Light-Weight Visual Feature Based Labeling (LVFL) for Unsupervised Person Re-identification

Person re-identification is a vital problem in smart video surveillance environment. Performing person reidentification with an unlabeled dataset is challenging. Even though certain labeling mechanisms are available in the literature, the computation overhead prevents the system to perform re-identification task dynamically. To overcome this issue, we propose a Light-weight Visual Feature based Labeling (LVFL) method to label the person re-identification images and reduce the computation overhead than the state-of-themethods. The computation overhead is reduced at three stages namely model initialization, neural network utilization and algorithmic complexity through evaluation of the cluster quality and Cumulative Match Curve (CMC) scores. The proposed method reports a reduced computation complexity than the traditional unsupervised person re-identification methods by determining a tight bound fine-tuning with a very less CMC score trade-off. Experimental results tested on three major benchmark datasets namely DukeMTMC re-id, Market1501 and CUHK03 show that the proposed LVFL produces a decent matching performance with a computation overhead reduction of about 29 % to 41 %.
Invasive Ductal Carcinoma Detection by A Gated Recurrent Unit Network with Self Attention

Representing around 80% of breast cancer, Invasive Ductal Carcinoma is the most common type of breast cancer. In this work, we have proposed a self-attention GRU model to detect Invasive Ductal Carcinoma. Self-attention is a way to motivate the architecture paying the attention to different locations of the sequence generated by an image effectively mapping regions of the image. The model was used to discriminate between cancerous samples and non-cancerous samples through training on the breast cancer specimens. The ability of discriminative representation has been improved using the self-attention mechanism. We have achieved the best average accuracy of 86%, a mean f1 score of 86% from our proposed model (It should be noted that we used 1:1 train-test split to achieve this score). We also experimented with a baseline CNN, ResNets (ResNet-18, ResNet-34, ResNet-50) and RNN variants (LSTM, LSTM + Attention). Our simple recurrent architectures with the attention mechanism outperformed Convolutional Networks which are traditional choices for image classification tasks. We have demonstrated how the scale of data can play a big role in model selection by studying different RNN, CNN variations for breast cancer detection scheme. This result is expected to be helpful in the early detection of breast cancer.
A Sign Language Interactive System based on Multi-feature Fusion

The sign language interaction system not only helps improve the communication mode of deaf-mutes, but also helps us explore gesture interaction technology. In this paper, the MYO armband was used to collect 6-channel inertial signals and 8-channel sEMG signals, and 8 subjects were invited to establish 35 Chinese sign language data sets, and a complete sign language interactive system was developed with a mobile application. In the aspect of algorithm, a segmentation method based on the comprehensive judgment of average energy and volatility is proposed to extract the starting and ending points of active segments more accurately. In addition, a multi-feature fusion method is proposed in feature selection, and the accuracy of the best subset is 98.12% verified by SVM classifier. At the same time, it is determined that the classification effect is better when the feature dimension is about 30.
Development of a Hybrid PV-Thermoelectric System

The solar photovoltaic energy is one of the main renewable energy alternatives for countries to achieve a greater diversity in their energy matrix. It benefits from high levels of incident solar irradiance. However, the increase in temperature that comes with it, allied to other environmental variables lowers its overall yield. One cooling option is to use thermoelectric, or Peltier cells. This paper aims to study a hybrid system that seeks an increase of a photovoltaic module yield through its active cooling by a thermoelectric cell. Research strategies include evaluation of controlled temperature, number of thermoelectric cells for a given photovoltaic module, use of maximum power tracking algorithms together with control of the thermoelectric cells power supply to achieve an optimum point of operation. This study uses mathematical models, and simulations to show that the use of thermoelectric cells to cool off the photovoltaic module can increase its yield by up to 7% in the cases studied.
Appraisal of Two Arabic Opinion Summarization Methods: Statistical Versus Machine Learning

Abstract In this paper, we propose to overcome the challenge of digesting opinions in a news article. Our objective is to provide a summary of opinions delivered by many sources about a main topic in an Arabic news article. In literature, several studies addressed issues related to opinion summarization. However, we noticed a lack of studies that address this problem in Arabic language. So, we have proposed two different methods: multi-criteria and machine learning-based methods. We proceed by comparing the results provided by the proposed methods for opinionated sentence extraction. The proposed methods were evaluated using two feature types: text-based features and opinion-specific features. Experimental results show the robustness of machine learning method to extract opinionated sentences with consideration of two sets of features.
Initial Design of Two-Stage Acoustic Vehicle Detection System for High Traffic Roads

As the adoption of Intelligent Transport Systems (ITS) grows worldwide, so does the need for lost-cost, fast-deployment vehicle detection systems. SAVeD is a low-cost acoustic detection system developed by the authors which works by fitting a curve indicating vehicle passage to a sound map depicting the difference in arrival time of a passing vehicle's sound at two microphones installed on the roadside. This paper expands on the SAVeD method by proposing a Two-Stage Acoustic Vehicle Detection System for use in high-traffic environments, where multiple simultaneously and successively passing vehicles cause interference in the detection process. To solve this problem, the sound map fitting process is divided into two stages: the detection range is narrowed based on information estimated during the Pre-Fitting stage, and neighborhood point extraction is performed during the Post-Fitting stage to improve vehicle detection accuracy. Initial evaluation performed on a four-lane, two-direction road showed a vehicle detection F-measure of 0.63, a 12-point increase over SAVeD.
Highly Automated Formal Proofs over Memory Usage of Assembly Code

Abstract We present a methodology for generating a characterization of the memory used by an assembly program, as well as a formal proof that the assembly is bounded to the generated memory regions. A formal proof of memory usage is required for compositional reasoning over assembly programs. Moreover, it can be used to prove low-level security properties, such as integrity of the return address of a function. Our verification method is based on interactive theorem proving, but provides automation by generating pre- and postconditions, invariants, control-flow, and assumptions on memory layout. As a case study, three binaries of the Xen hypervisor are disassembled. These binaries are the result of a complex build-chain compiling production code, and contain various complex and nested loops, large and compound data structures, and functions with over 100 basic blocks. The methodology has been successfully applied to 251 functions, covering 12,252 assembly instructions.
E-payment for Jakarta Smart Public Transportation, Using the Point System for E-Commerce

Abstract The ease of using transportation is one of the most critical things in the city with a significant population like Jakarta. The growth of the population in Jakarta is increased rapidly. The wage that Many transportations are causing a traffic jam in Jakarta. The government suggests that people use public transportation for their mobility. However, people choose to use their vehicles rather than public transportation. The main reason is that public transportation cannot guarantee the arriving time, whether it is on time or not. Many people move on to online transportation services. However, the massive growth of online transportation is still a contradiction as public transportation. People in Jakarta need faster mobility to go. This research is trying to make a system for public transport without any delay and hard to use it. This problem can be solved by building a new system. This system required e-wallet for payment in public transportation. This E-Wallet will search the possible route from the nearest location to the destination — only the public transport where there is a station that can use it. By using QR-Code generated by e-wallet on the mobile phone, people can scan it directly to a machine located in the station. This method will make a transaction faster than ever. Moreover, people can enjoy another feature like cashback and redeem the prize by a point system. In this era, e-commerce is dominating the market for purchasing something. This is also the attraction of the system. This system is also required excellent facilities from the government so that people can enjoy it.
Information Retrieval in Computing Model

Information Retrieval is a process through which we obtain and present related information from a very large collection of information resources according to user's query. Information Retrieval can vary according to user's wish, it can be an information retrieval for documents, music, pictures, etc. In a classic search model we have a user who has to fulfil a task. And for that to happen that user needs some information and to search the information needed user should make a query based on the information. So that query will act as an input in a search box, and on the basis of that query our information will be retrieved. But it is required that no misconception happens to user which may lead user to believe that the information needed can be wrong. And if that happens it may lead to improper formation of the query, meaning the query formed on the basis of information can be wrong thus resulting in a wrong result through information retrieval. So it is important for us to pay proper attention to every step that is going to take place during information retrieval. This paper presents a basic IR model and describes how an information retrieval is done following user's query. This paper also addresses how Information Retrieval and Data Retrieval are two different things and are often confused to be one.
Adaptive Hyper-Feature Fusion for Visual Tracking

In this work, we propose a robust tracking algorithm based on context-aware correlation filter framework. In order to improve the richness of the feature representation, the proposed hyper-feature which contains linearly weighted mixture of hand-crafted features (such as HOG, color histogram) and hierarchical deep convolutional features (such as VGGNet). The final output response map is optimized by the Gaussian constrained optimization method to control the response map follow the Gaussian distribution, which gain the robustness to target appearance variations. In addition, in terms of model update, an efficient adaptive model updating method is proposed to suppress the model noises significantly. Extensive experimental results on well-known tracking benchmark datasets to evaluate the proposed algorithm. Experimental results demonstrate that the proposed algorithm performs favorably against many state-of-the-art methods in terms of success rate, accuracy, and robustness.
Achieving Trust using RoT in IaaS Cloud

The cloud computing technology is raPIDly increasing day by day as it provides hardware, storage, network as well as platforms and software. There is a risk of various attacks like data breaches, data loss, denial of service etc which creates various kind of security issues. Multiple tenants store their private and confidential data in the cloud. So, cloud security is playing an important role in it. RoT is a very new and useful perspective to accomplish security in the cloud. By applying RoT in the cloud, new trust layer can be generating for the users so that users can deploy their sensitive information without hesitation. In this research, the main objective is to work on achieving RoT. RoT defined with parameters as authentication, identification, privacy, confidentiality, integrity, access control, authorization. The target is to deploy RoT in the cloud using three parameters that are authentication, access control, and confidentiality. For that three proposed models are characterize in this paper. Thus, intruders can't access the cloud data and security can be achieved.
Word Sense Disambiguation by Context Detection

Word Sense Disambiguation (WSD) refers to the process of finding the appropriate sense of an ambiguous word i.e. a word with multiple senses. It is an important problem in natural language processing that enables a machine to disambiguate and understand human natural language. In this paper, a method for WSD is proposed based on the context of the word using WordNet as a knowledge base. First, the input paragraph is preprocessed before disambiguating operation starts. The disambiguation process determines the appropriate senses of the ambiguous words using context information of each word in the paragraph. The radix tree data structure used to store context information for efficient searching.
Hierarchical Temporal Memory with Reinforcement Learning

Nowadays our knowledge of the brain is actively getting wider. Hierarchical Temporal Memory is the technology that arose due to new discoveries in neurobiology, such as research on the structure of the neocortex. One of the most popular applications of this technology is image recognition and anomaly detection. Nevertheless, both in the neocortex and in hierarchical temporal memory an image is recognized by its parts. Therefore, there is a problem of choosing the most meaningful parts of an image in order to perform fast and effective recognition. In this work we propose the architecture that unites Hierarchical Temporal Memory and Reinforcement Learning in order to find the optimal way of image exploration. Besides, we prove by experiments that this architecture is effective, and the quality of the resulting movement pattern is high.
Salton Award Keynote

This article is a lightly edited text of the author's Salton Award Keynote: Information Interaction in Context, presented at the 41st SIGIR conference in Ann Arbor, July 9th, 2019. It first gives some personal background and then discusses some important areas of information seeking and IR in the author's research work. These include task-based information behavior and interaction, natural language processing to improve document ranking in mono- and cross-language IR, and IR evaluation metrics. Finally, the article proposes a way to organize research on information interaction.