Reviewerly/exHarmony huggingface dataset:
Principled Unearthing of TCP Side Channel Vulnerabilities

Recent work has showcased the presence of subtle TCP side channels in modern operating systems, that can be exploited by off-path adversaries to launch pernicious attacks such as hijacking a connection. Unfortunately, most work to date is on the manual discovery of such side-channels, and patching them subsequently. In this work we ask "Can we develop a principled approach that can lead to the automated discovery of such hard-to-find TCP side-channels?" We identify that the crux of why such side-channels exist is the violation of the non-interference property between simultaneous TCP connections i.e., there exist cases wherein a change in state of one connection implicitly leaks some information to a different connection (controlled possibly by an attacker). To find such non-interference property violations, we argue that model-checking is a natural fit. However, because of limitations with regards to its scalability, there exist many challenges in using model checking. Specifically, these challenges relate to (a) making the TCP code base self-contained and amenable to model checking and (b) limiting the search space of model checking and yet achieving reasonable levels of code coverage. We develop a tool that we call SCENT (for Side Channel Excavation Tool) that addresses these challenges in a mostly automated way. At the heart of SCENT is an automated downscaling component that transforms the TCP code base in a consistent way to achieve both a reduction in the state space complexity encountered by the model checker and the number and types of inputs needed for verification. Our extensive evaluations show that SCENT leads to the discovery of 12 new side channel vulnerabilities in the Linux and FreeBSD kernels. In particular, a real world validation with one class of vulnerabilities shows that an off-path attacker is able to infer whether two arbitrary hosts are communicating with each other, within slightly more than 1 minute, on average.
Senpy: A framework for semantic sentiment and emotion analysis services

Senpy is a framework to develop, evaluate and publish web services for sentiment and emotion analysis in text. The framework is aimed towards both developers and users. For developers, it is a means to evaluate their classifiers and easily publish them as web services. For users, it is a way to consume sentiment analysis from different providers through the same interface. This is achieved through a combination of an API aligned with the NLP Interchange Format (NIF) service specification, the use of semantic formats and a series of well established vocabularies. The framework is Open Source, and has been used extensively in several projects. As a result, several Senpy Open Source services are available for use and download.
Do Internal Software Quality Tools Measure Validated Metrics?

Internal software quality determines the maintainability of the software product and influences the quality in use. There is a plethora of metrics which purport to measure the internal quality of software, and these metrics are offered by static software analysis tools. To date, a number of reports have assessed the validity of these metrics. No data are available, however, on whether metrics offered by the tools are somehow validated in scientific studies. The current study covers this gap by providing data on which tools and how many validated metrics are provided. The results show that a range of metrics that the tools provided do not seem to be validated in the literature and that only a small percentage of metrics are validated in the provided tools.
Meaningful Data Sampling for a Faithful Local Explanation Method

Data sampling has an important role in the majority of local explanation methods. Generating neighborhood samples using either the Gaussian distribution or the distribution of training data is a widely-used procedure in the tabular data case. Generally, this approach has several weaknesses: first, it produces a uniform data which may not represent the actual distribution of samples; second, disregarding the interaction between features tends to create unlikely samples; and third, it may fail to define a compact and diverse locality for the sample being explained. In this paper, we propose a sampling methodology based on observation-level feature importance to derive more meaningful perturbed samples. To evaluate the efficiency of the proposed approach we applied it to the LIME explanation method. The conducted experiments demonstrate considerable improvements in terms of fidelity and explainability.
A Novel Timing-based Network Covert Channel Detection Method

Abstract Network stealth events are endless, and covert timing channel is one of the most difficult means to prevent. In order to further improve the detection rate of covert timing channel, several typical network covert timing channel construction algorithms are analyzed. On the basis of the above analysis, a detection method based on IPDs multidimensional features was proposed in this paper. IPDs of covert timing channels from three dimensions: shape, change rule and data statistics are analyzed. Respectively, polarization feature, autocorrelation feature, clustering feature are proposed, and the three features are unified into a model. The threshold method is used to determine whether the channel to be detected is a normal channel. Experiments show that the method can detect the existing covert timing channel with less time cost and compared with the traditional detection method has a certain rate of improvement.
Situated Storytelling with SLAM Enabled Augmented Reality

This paper addresses the feasibility of situated storytelling using Simultaneous Localisation and Mapping (SLAM) enabled augmented reality (AR) on a mobile phone. We specifically focus on storytelling in the heritage context as it provides a rich environment for stories to be told in. We conducted expert interviews with several museum and heritage sites to identify major themes for storytelling in the heritage context. These themes informed the development of an AR based storytelling application for a mobile phone. We evaluated the application in a user study and gained further insight into the factors that users appreciate in AR based storytelling. From these insights we derive several high level design guidelines that may inform future system development for situated storytelling, especially in the heritage context.
Prediction of Press-Fit Quality via Data Mining Techniques and Artificial Intelligence

Determining the press-fit quality of pieces in advance is of the utmost importance because it enables the reduction of the time that is invested in the process and the prevention of material losses. High predictive accuracy is essential in a classification model; however, several studies have shown that the class category of a new instance may be insufficient information for decision making. To provide additional information to the user, this study presents a novel system that is based on a hybrid model, which, in addition, to using a classifier, extracts a set of class association rules that enable the determination of which patterns influence the new instance to belong to a class category. To select the classifier, the accuracy, recall and F-measure metrics were utilized. The rules were obtained with the Apriori algorithm to show that this knowledge is automatically represented in an ontological scheme with the objective of applying the Pellet reasoner.
Polymorphic Session Processes as Morphisms

The study of expressiveness of concurrent processes via session types opens a connection between linear logic and mobile processes, grounded in the rigorous logical background of propositions-as-types. One such study includes a notion of parametric session polymorphism, which connects session typed processes with rich higher-order functional computations. This work proposes a novel and non-trivial application of session parametricity – an encoding of inductive and coinductive session types, justified via the theory of initial algebras and final co-algebras using a processes-as-morphisms viewpoint. The correctness of the encoding (i.e. universality) relies crucially on parametricity and the associated relational lifting of sessions.
Chicken Farm Monitoring System Using Sensors and Arduino Microcontroller

Chicken meat production is in demand now a days because of the increasing population around the world. Monitoring of a chicken farm could be very helpful to produce a great number of chicken meat and to meet the demand of the people. However, there are different environmental factors that are affecting the growth of a chicken that also affects the production of chicken meat for food consumption. This study aimed to determine the different sensors and materials that can be included in the system architecture for smart chicken farming which focus on the monitoring of the environmental parameter literature survey. The result of the literature survey was use as basis in the design of the system architecture for a Smart Chicken Farming. Based on the literature survey, there are different sensors and materials that could be used to monitor the environmental parameter in a chicken farm. Thus, the following factors areconsidered such as low cost, effectiveness and positive used of the sensors and materials that is the reason that they are included in the system architecture.
Enabling Opportunistic Users in Multi-Tenant IoT Systems using Decentralized Identifiers and Permissioned Blockchains

In this work, we leverage advances in decentralized identifiers and permissioned blockchains to build a flexible user authentication and authorization mechanism that offers enhanced privacy, achieves fast revocation, and supports distributed "policy decision points'' executed in mutually untrusted entities. The proposed solution can be applied in multi-tenant "IoT hubs'' that interconnect diverse IoT silos and enable authorization of "guest'' users, i.e., opportunistic users that have no trust relationship with the system, which has not encountered or known them before.
Weighed query-specific distance and hybrid NARX neural network for video object retrieval

Abstract The technical revolution in the field of video recording using the surveillance videos has increased the amount of the video databases that caused the need for an efficient video management system. This paper proposes a hybrid model using the nearest search algorithm (NSA) and the Levenberg–Marquardt (LM)-based non-linear autoregressive exogenous (NARX) neural network for performing the video object retrieval using the trajectories. Initially, the position of the objects in the video are retrieved using NSA and NARX individually, and they are averaged to determine the position of the object. The positions determined using the hybrid model is compared with the original database, and the trajectories of the objects are retrieved based on the minimum distance, which depends on the weighed query-specific distance. Experiments have been carried out using seven videos taken from the CAVIAR dataset, and the performance of the proposed method is compared with the existing methods. This proposed method found to be better than the existing method with respect to multiple object tracking precision (MOTP), multiple object tracking accuracy (MOTA), average tracking accuracy (ATA), precision, recall and F-measure that results a greater MOTP rate of 0.8796, precision rate of 0.8154, recall rate of 0.8408, the F-measure at a rate of 0.8371, MOTA of 0.8459 and ATA of 0.8324.
Comparison study of SMART and AHP method for paddy fertilizer recommendation in decision support system

Abstract Decision support systems are used as evidence-based decision-making tools in agriculture to provide relevant information. The implementation is to recommend the provision of fertilizer for rice plants, to support plant growth based on location specifics. This study aims to analyze the final results of two SMART and AHP methods by comparing the final results in evaluating the best alternatives. The criteria analyzed are; location, the yield of rice productivity, Leaf Color, Planting Season, Nutrient Status of P, nutrient status of K soil, Fertilizer Cage, Organic Straw Material, Use of Compost Fertilizer. For alternative solutions in the form of rice fertilizer dosage N, P, K. The results of the analysis of the two methods show that both have similarities in determining location-specific rice fertilizer recommendations, which are measured by comparing alternative final results of the two methods with recommendations from experts. Tests were carried out in ten cases with different locations. The accuracy of the two methods was 70%.
Evolutionary learning of HMM with Gaussian mixture densities for Automatic speech recognition

Automatic speech recognition (ASR) is a very important field that can be used in many applications such as facilitate a physically handicapped person to command and control a machine, telecommunication systems, investigations, voice email and management. ASR systems are becoming more and more important today. In speech recognition, the training method plays a very important role. When a good practice model is obtained for speech mode, it means that it increases the speed of recognition significantly. In this paper, we explore the possibility of using Genetic Algorithms (GAs) for optimizing the parameter learning of hidden Markov models (HMM) in automatic speech recognition systems. This approach has been tested on samples from different users with different environments (with/without adding noise) and achieves a high degree of accuracy during recognition.
Fearing Disengagement from the Real World

With the adoption of mobile head mounted displays (HMDs) amongst non-experts outside of lab settings, it becomes increasingly important to understand what factors influence a holistic mobile virtual reality (MVR) user experience. We present the results of a field study (N=34), in which we used three methods - a drawing task, a storytelling exercise, and the technology acceptance questionnaire (TAM) - to explore factors, beyond technical capability, that influence the user experience of HMDs. Our analysis (1) highlights factors that designers and researchers can adopt to create and evaluate socially acceptable MVR systems for non-expert users outside a lab context, and (2) puts these factors in context with existing research from industry and academia.
A continuous-variable quantum-inspired algorithm for classical image segmentation

The probabilistic nature of quantum particles, state space, and the superposition principle are among the important concepts in quantum mechanics. A framework was previously developed by the authors that allowed to take advantage of these quantum aspects in the field of image processing. This was done by modeling each image’s pixel by a two-state quantum system which allowed efficient single-object segmentation. However, the extension of the framework to multi-object segmentation would be highly complex and computationally expensive. In this paper, we propose a classical image segmentation algorithm inspired by the continuous-variable quantum theory that overcomes the challenges in extending the framework to multi-object segmentation. By associating each pixel with a quantum harmonic oscillator, the space of coherent states becomes continuous. Thus, each pixel can evolve from an initial state to any of the continuous coherent states under the influence of an external resonant force. The Hamiltonian operator is designed to account for this force and is derived from the features extracted at the pixel. Therefore, the system evolves from an initial ground state to a final coherent state depending on the image features. Finally by calculating the fidelity between the final state and a set of reference states representing the objects in the image, the state with the highest fidelity is selected. The collective states of all pixels produce the final segmentation. The proposed method is tested on a database of synthetic and natural images, and compared with other methods. Average sensitivity and specificity of 97.86% and 99.61% were obtained respectively indicating the high segmentation accuracy of the algorithm.
A Comparison of Human and Machine-Generated Voice

This study investigates the influence of a virtual human (VH) with recorded human voice vs VH with a machine-generated voice (text-to-speech) on building trust and working alliance. We measured the co-presence perception to understand the impact of VH's perception on building the human-VH relationship. The results revealed no differences between the two types of voices on co-presence perception, trust or working alliance.
Sentiment Analysis using Unlabeled Email data

Sentiment Analysis (SA) in the context of text mining is an automated process to detect subjectivity information, such as opinions, attitudes, emotions and feeling. Most prior work in SA view it as a text classification problem which needs labeled data to train the model. However, it is tough to get a labeled dataset. Most of the times we will need to do it by hand. Another issue is that the lack of portability across different domains makes it hard to use the same labeled data in different applications. Thus, we need to create labeled data for each domain manually. In this paper, we will use sentiment analysis to analyze the Enron email dataset. This work aims to find the best techniques to label the dataset automatically and avoid manual labeling. The training data is used to build a classifier using a supervised machine learning algorithm. In the labeling phase, we compare the lexicon labeling with k-mean labeling. Lexicon labeling gave better and reliable results. We used this labeled dataset to train the classifier. We used TF-IDF for feature extraction, to train Naïve Bayes and Support vector machine (SVM) classifiers.
Cost Aware Resource Sizing and Scaling of Microservices

Microservices are small, independent, loosely coupled components which provide flexibility, agility, and scalability to an application. While these are aimed for scalability, achieving it needs judicious trade-offs between size, number and cost of provisioning. In this architecture, sizing in both homogeneous and heterogeneous resources plays a key role to balance application performance and resource requirement, as workload demand varies. This paper provides insights where the importance of considering workload characterization to decide a homogeneous or heterogeneous scaling strategy for a microservice is discussed. The work exploits the correlation of workload characterization, predicted workload demand and selection of right-sized microservice to minimize resource costs. Size of microservice is referred with respect to the amount of resources allocated to the microservice. This work also evaluates trade-offs between considering the entire predicted workload demand for resource cost optimization against algorithmic computational complexity and designs a heuristic to reduce such complexity. Evaluation of results demonstrate two important outcomes. Firstly, workload characterization helps to choose between homogeneous or heterogeneous sizing for different microservices. And secondly, by considering workload demand prediction beyond the current scheduling interval, allows to make scaling decision in the current cycle keeping in view whether the demand is going to increase or decrease. The paper also details on how to use the insights of application characterization and workload trend for choosing an appropriate scaling strategy.
Comparison Study of the Adaptability of Layered and Stream Replication Variants of the WebRTC Simulcast

WebRTC uses two simulcast schemes: the older, based on the replication of streams encoded to different qualities of the original signal (typically: video), and the newer, fully introduced this year to one of the more popular browsers, based on layered encoding. While the first solution has a well-established position in the WebRTC technology, the layered simulcast still remains in an early stage of development. The goal of this paper was to compare behavior of the two WebRTC simulcast schemes in terms of the key feature of the simulcast, i.e. its adaptability to network conditions. To achieve this goal, we wrote the WebRTC application (a simple videomonitoring software) that act as simulcast sender. The adaptability of the simulcast schemes was investigated for the case of video traffic transmitted in a heterogeneous test network (wired and wireless). As adaptability indicators, such as the throughput and packet error rate were used. Results of the simulcasting of 4K video show that in the case of infrastructural limitations only there are no significant differences between the adaptability of the stream replication simulcast and the layered simulcast. Adaptability for dynamic limitations (introduced by background traffic) was better in the case of the layered simulcast.
Access Control Based on Ciphertext Attribute Authentication and Threshold Policy for the Internet of Things

The development of the Internet of Things has led to great development of data sharing and data interaction, which has made security and privacy more and more a concern for users. How to ensure the safe sharing of data, avoid the leakage of sensitive information, and protect the privacy of users is a serious challenge. Access control is an important issue to ensure the trust of the Internet of Things. This paper proposes an access control scheme based on ciphertext attribute authentication and threshold policy, which uses the identity authentication of hidden attributes and divides the user's permission grade by setting the threshold function with the user's attributes. Users obtain different permission grades according to attribute authentication and access data of different sensitivity grades to achieve fine-grained, flexible and secure access to data in the cloud server while protecting personal privacy issues. In addition, when the resource is acquired, the identity and permission joint authentication method is adopted to avoid the collusion attack of the illegal member, which makes the resource access control more secure.
Enhancing Query Expansion Method Using Word Embedding

Recently, many query expansion methods have been proposed to improve the results of search applications. However, many of these search applications still lack better results and many attributed due to query expansion issues. This paper enhanced the query expansion method based on unigram model with Okapi BM25and word embedding using Glove. A Glove model captured the semantic similarity by mapping various words based on unigram with Okapi BM25 results. The results indicate that our proposed method based on Glove model word embedding can significantly improve query expansion methods using Arberry dataset.