Reviewerly/exHarmony huggingface dataset:
Implicit Linking of Food Entities in Social Media

Dining is an important part in people’s lives and this explains why food-related microblogs and reviews are popular in social media. Identifying food entities in food-related posts is important to food lover profiling and food (or restaurant) recommendations. In this work, we conduct Implicit Entity Linking (IEL) to link food-related posts to food entities in a knowledge base. In IEL, we link posts even if they do not contain explicit entity mentions. We first show empirically that food venues are entity-focused and associated with a limited number of food entities each. Hence same-venue posts are likely to share common food entities. Drawing from these findings, we propose an IEL model which incorporates venue-based query expansion of test posts and venue-based prior distributions over entities. In addition, our model assigns larger weights to words that are more indicative of entities. Our experiments on Instagram captions and food reviews shows our proposed model to outperform competitive baselines.
On Finer Control of Information Flow in LSTMs

Since its inception in 1995, the Long Short-Term Memory (LSTM) architecture for recurrent neural networks has shown promising performance, sometimes state-of-art, for various tasks. Aiming at achieving constant error flow through hidden units, LSTM introduces a complex unit called a memory cell, in which gates are adopted to control the exposure/isolation of information flowing in, out and back to itself. Despite its widely acknowledged success, in this paper, we propose a hypothesis that LSTMs may suffer from an implicit functional binding of information exposure/isolation for the output and candidate computation, i.e., the output gate at time $$t-1$$ is not only in charge of the information flowing out of a cell as the response to the external environment, but also controls the information flowing back to the cell for the candidate computation, which is often the only source of nonlinear combination of input at time t and previous cell state at time $$t-1$$ for cell memory updates. We propose Untied Long Short Term Memory (ULSTM) as a solution to the above problem. We test our model on various tasks, including semantic relatedness prediction, language modeling and sentiment classification. Experimental results indicate that our proposed model is capable to at least partially solve the problem and outperform LSTM for all these tasks. Code related to this paper is available at: https://github.com/HangGao/ULSTM.git .
Introduction to the special issue on architecting autonomous and smart systems

Software protection techniques are used to protect valuable software assets against man-at-the-end attacks. Those attacks include reverse engineering to steal confidential assets, and tampering to break the software’s integrity in unauthorized ways. While their ultimate aims are the original assets, attackers also target the protections along their attack path. To allow both humans and tools to reason about the strength of available protections (and combinations thereof) against potential attacks on concrete applications and their assets, i.e., to assess the true strength of layered protections, all relevant and available knowledge on the relations between the relevant aspects of protections, attacks, applications, and assets need to be collected, structured, and formalized. This paper presents a software protection meta-model that can be instantiated to construct a formal knowledge base that holds precisely that information. The presented meta-model is validated against existing models and taxonomies in the domain of software protection, and by means of prototype tools that we developed to help non-modelling-expert software defenders with populating a knowledge base and with extracting and inferring practically useful information from it. All discussed tools are available as open source, and we evaluate their use as part of a software protection work flow on an open source application and industrial use cases.
A Gulf College Case Study: Technology Enhanced Students’ Learning Geared with Mobile Devices

Over the decades, the world has become increasingly hyper-connected. The people and businesses can communicate with each other instantly where machines are equally interconnected. Through the help of the internet and its associated services, information is accessible and immediate for every-one. In education, the utilization of information and communication technologies helps improve teaching pedagogy and enhanced students’ learning. The use of technology affects the quality of education, and more specifically it can result in better student performance and enhance the student learning experience. Today, mobile devices are widely used for communication, education, entertainment, research collaboration and social interaction. Research issues and challenges related to technology-enhanced learning are discussed for classroom learning at a distance, digital libraries, online learning, and collections of various online information, and e-collaboration. Nowadays, the learning world has used mobile devices intensively for various day-to-day activities. However, some technology developers, teaching pedagogical experts and teachers are still tied to the old world of desktop computers for software and application development. The proposed research aims to investigate the various methodologies, framework and approaches adopted to demonstrate the role of technology in enhancing the students’ learning experience in which enhancement is conceived and the use of various technologies in empowering mobile devices as an innovative tool to an effective learning environment. The aim would be in developing a framework (like teachers) to tailor their learning materials and using different educational technologies in mobile devices to substantiate claims about technology enhanced learning. Qualitative and quantitative data analyses are used to examine the relationships explored between the aims of technology improved learning interventions. The evidence presented, and the ways in which enhancement is conceived and how large it has effects on the student’s learning and teacher pedagogical approaches are highlighted.
Improvement of Privacy and Security in Hybrid Cloud with Attribute Group Based Access Control

Cloud computing enables users to accumulate their sensitive data into cloud service providers to achieve scalable services on-demand. Outstanding security requirements arising from this means of data storage and management include data security and privacy. Attribute-based Encryption (ABE) is an efficient encryption system with fine-grained access control for encrypting out-sourced data in cloud computing. Since data outsourcing systems require flexible access control approach Problems arises when sharing confidential corporate data in cloud computing. User-Identity needs to be managed globally and access policies can be defined by several authorities. Data is dual encrypted for more security and to maintain De-Centralization in Multi-Authority environment.
Development and Evaluation of Methodology for Personal Recommendations Applicable in Connected Health

In this paper, a personal recommendation system of outdoor physical activities using solely user’s history data and without application of collaborative filtering algorithms is proposed and evaluated. The methodology proposed contains four phases: data fuzzification, activity usefulness calculation, estimation of most useful activities, activities classification. In the process of classification several data mining techniques were compared such as: decision trees algorithms, decision rules algorithm, Bayes algorithm and support vector machines. The proposed algorithm has been experimentally validated using real dataset collected in a certain period of time from a community of 1000 active users. Recommendations generated by the system were related to weight loss. The results show that our generated recommendations have high accuracy, up to 95%.
Artificial intelligence and jobs of the future

Humans, the most intelligent species on planet Earth, have always found a way to adapt to their dynamic environment and survive in a consistent manner. We have made tremendous progress in evolving from primitive men and women in small, dreary caves to modern men and women in large, bustling cities. Our passionate strive to survive and affinity to adapt is similar to that shown by mosquitoes; regardless of the continuous development and massive deployment of insect repellent products, these undeniably irking insects, which cause a plethora of life-threatening diseases, always make a striking comeback (Stanczyk, Brookfield, Field, &amp; Logan, 2013).
Resolving Ambiguous Types in Haskell by Checking Uniqueness of Type Variable Assignments under Type Class Constraints

The type class mechanism, which introduces ad-hoc polymorphism into programming languages, is commonly used to realize overloading. However, this forces programmers to write many type annotations in their programs to resolve ambiguous types. Haskell's type defaulting rules reduce requirements for annotation. Furthermore, the widely used Glasgow Haskell Compiler (GHC) has an ExtendedDefaultRules (EDR) extension that facilitates interactive sessions so that the programmer avoids problems that frequently occur when using values like [] and Nothing. However, the GHC EDR extension sometimes replaces type variables with inappropriate types, so that, for example, the term show.read that is determined to have type String -> String under the GHC EDR extension does not exhibit any meaningful behavior because the function read in the term is considered to have type String -> (). We present a flexible way of resolving ambiguous types that alleviates this problem. Our proposed method does not depend on default types defined elsewhere but rather assigns a type to a type variable only when the candidate is unique. It works with any type and type class constraints. The type to be assigned is determined by scanning a list of existing type class instances that meet the type class constraints. This decision is lightweight as it is based on operations over sets without using algorithms that require backtracking. Our method is preferable to using the GHC EDR extension since it avoids the use of unnatural type variable assignments. In this paper, we describe the details of our method. We also discuss our prototype implementation that is based on the GHC plugins, and the feasibility of modifying GHC to incorporate our method.
The CO.R.E. Project. An Integrated Security Approach to Self-Monitoring and Medical Record Keeping

The widespread use of electronic Personal Health Records is considered of great importance, however, until today, there is no widely adopted application paradigm for the functional specifications of a modern ePHR due to absence of trust, inadequate data completeness and overall use complexity and “unfriendliness”. CO.R.E. (COnsolidation &amp; Routing Engine) is an innovative approach towards the development of a health data consolidation and cloud access provision infrastructure, taking under consideration both the needs for wide adoption and the application of mission critical technologies in real production environments. The CO.R.E. infrastructure provides an environment for deploying medical record applications with central storage and individually controlled distributed access, ensuring: a) the absence of readable identifiers in any network communication among the involved systems and b) the inability (as much as modern cryptographic methods offer) of anyone - even the engineers working on the system - to correlate the stored medical data with their owner/physical person.
Case Study

Recruitment process takes place based on needed data while certain limiting factors are ignored. The objective of the chapter is to recruit best employees while taking care of limiting factors from the cluster for resource management and scheduling. Various parameters of the recruits have been selected to find the maximum score achieved by them. Recruitment process makes a database as cluster in the software environment perform the information retrieval on the database and then perform data mining using genetic algorithm while taking care of the positive values in contrast to limiting values received from the database. A bigger level recruitment process finds required values of a person, so negative points are ignored earlier in the recruitment process because there is no direct way to compare them. Genetic algorithm will create output in the form of chromosomal form. Again, apply information retrieval to get actual output. Major application of this process is that it will improve the selection process of candidates to a higher level of perfection in less time.
Complexity of translations from resolution to sequent calculus

Resolution and sequent calculus are two well-known formal proof systems. Their differences make them suitable for distinct tasks. Resolution and its variants are very efficient for automated reasoning and are in fact the theoretical basis of many theorem provers. However, being intentionally machine oriented, the resolution calculus is not as natural for human beings and the input problem needs to be pre-processed to clause normal form. Sequent calculus, on the other hand, is a modular formalism that is useful for analysing meta-properties of various logics and is, therefore, popular among proof theorists. The input problem does not need to be pre-processed, and proofs are more detailed. However, proofs also tend to be larger and more verbose. When the worlds of proof theory and automated theorem proving meet, translations between resolution and sequent calculus are often necessary. In this paper, we compare three translation methods and analyse their complexity.
Monitoring Emergency First Responders’ Activities via Gradient Boosting and Inertial Sensor Data

Emergency first response teams during operations expend much time to communicate their current location and status with their leader over noisy radio communication systems. We are developing a modular system to provide as much of that information as possible to team leaders. One component of the system is a human activity recognition (HAR) algorithm, which applies an ensemble of gradient boosted decision trees (GBT) to features extracted from inertial data captured by a wireless-enabled device, to infer what activity a first responder is engaged in. An easy-to-use smartphone application can be used to monitor up to four first responders’ activities, visualise the current activity, and inspect the GBT output in more detail.
4WD Robot Posture Estimation by Radial Multi-View Visual Odometry

This chapter presents a four-wheel robot’s trajectory tracking model by an extended Kalman filter (EKF) estimator for visual odometry using a divergent trinocular visual sensor. The trinocular sensor is homemade and a specific observer model was developed to measure 3D key-points by combining multi-view cameras. The observer approaches a geometric model and the key-points are used as references for estimating the robot’s displacement. The robot’s displacement is estimated by triangulation of multiple pairs of environmental 3D key-points. The four-wheel drive (4WD) robot’s inverse/direct kinematic control law is combined with the visual observer, the visual odometry model, and the EKF. The robot’s control law is used to produce experimental locomotion statistical variances and is used as a prediction model in the EKF. The proposed dead-reckoning approach models the four asynchronous drives and the four damping suspensions. This chapter presents the deductions of models, formulations and their validation, as well as the experimental results on posture state estimation comparing the four-wheel dead-reckoning model, the visual observer, and the EKF with an external global positioning reference.
Development of a Mobile Learning System based on the Industry-school Education Process for Skill Succession for Fisheries and Maritime Technology

We developed a mobile learning system for fishery and ocean techniques using video contents. The system aims to improve the current situation in the field of education on fishery and ocean for skill succession. The proposal is based on the industry-school education process for skill succession. By accessing the web site and watching practical videos, the users can learn methodologies on fishery and ocean. As a result, we showed that the system is effective for sharing and succession of techniques for the learner. Application of the proposed system in the class showed improvement in the students' autonomous learning.
Markov Chain Monte Carlo for Effective Personalized Recommendations

This paper adopts a Bayesian approach for finding top recommendations. The approach is entirely personalized, and consists of learning a utility function over user preferences via employing a sampling-based, non-intrusive preference elicitation framework. We explicitly model the uncertainty over the utility function and learn it through passive user feedback, provided in the form of clicks on previously recommended items. The utility function is a linear combination of weighted features, and beliefs are maintained using a Markov Chain Monte Carlo algorithm. Our approach overcomes the problem of having conflicting user constraints by identifying a convex region within a user’s preferences model. Additionally, it handles situations where not enough data about the user is available, by exploiting the information from clusters of (feature) weight vectors created by observing other users’ behavior. We evaluate our system’s performance by applying it in the online hotel booking recommendations domain using a real-world dataset, with very encouraging results.
Case Study of Improvement for Internal Quality Assurance (IQA) in Tokyo KOSEN at the last JABEE Accreditation in 2017

One of the most importance of JABEE accreditation is giving higher education to have a great opportunity for improving the internal quality assurance system in engineering education. The internal quality assurance mechanism is believed to complement the quality of higher education by including the aspects which relate to the stakeholders inclusive of the lecturers, graduates and also employers, in terms of meeting the requirement in accordance with JABEE standards. In 2017, Tokyo KOSEN has an opportunity of JABEE accreditation, and has many important suggestions about our future improvement. According to the accreditation, improvement for internal quality assurance was considered in order to realize school administrative competencies associate with implementation in internal quality assurance of KOSEN education. As a system of the internal quality assurance, study achievement meeting where all faculties attend for discussing outcomes of each student was started.
Sharing Device Resources in Heterogeneous CPS Using Unique Identifiers with Multi-site Systems Environments

Cyber-physical systems environments offer value-added infrastructure by incorporating heterogeneous capabilities that provide a variety of data and processes over diverse and independent final devices. This can often leads to a complex setup that underuse the global capacities of the devices by preserving it in a single service process. By creating several identifiers for each service using specific devices, we can reuse multiple devices for several high layer individual services and processes. This paper proposes an architecture model and a division based on the different levels that are required to add the reutilization of the resources. This includes service replication schemes and device identification processes to the system. In addition, a first approximation of the methodology for providing unique identifiers is presented, in order to allow sharing device resources between different services.
Geometric Filtering of Matches Between Points in Bands of Hyperspectral Cubes

Hyperspectral images can present low contrast, noisy pixels, and illumination variation among bands, which complicates the extraction of interest points and reduces the number of reliable image matches affecting subsequent tasks as band registration and bundle adjustment. Once matched points have been determined, a technique to select correct matches in sets with outliers is required, as well as to fix mismatches. In this letter, we apply a filtering technique that uses a majority voting algorithm combined with a 2-D Helmert geometric transformation to identify consistent matches. The correct matches also allow the estimation of parameters of a geometric transformation, which enables point transfer between images. Thus, mismatches can be fixed to their correct positions. Experiments were performed with the proposed technique using hyperspectral images that were collected with a lightweight camera using the time-sequential principle, while onboard an unmanned aerial vehicle. Scale-invariant feature transform was used for both keypoint extraction and image matching. Reliable matches were extracted from the sets with outliers, and incorrect matches were fixed. The results of the technique were compared with an algorithm based on random sample consensus. In the comparison, the proposed technique was efficient in extracting a larger number of correct matches. In addition, 85% of the incorrect matches were recovered, which significantly increased the density of matched pairs.
Multi-identity IBFHE and Multi-attribute ABFHE in the Standard Model

The notion of multi-identity IBFHE is an extension of identity based fully homomorphic (IBFHE) encryption. In 2015, Clear and McGoldrick (CRYPTO 2015) proposed a multi-identity IBFHE scheme that is selectively secure in the random oracle model under the hardness of Learning with Errors (LWE). At TCC 2016, Brakerski et al. presented multi-target ABFHE in the random oracle where the evaluator should know the target policy. In this paper, we present a multi-identity IBFHE scheme and a multi-attribute ABFHE scheme in the standard model. Our schemes can support evaluating circuits of unbounded depth but with one limitation: there is a bound N on the number of ciphertexts under the same identity or attribute involved in the computation. The bound N could be thought of as a bound on the number of independent senders. Our schemes allow N to be exponentially large so we do not think it is a limitation in practice. Our construction combines fully multi-key FHE and leveled single-identity IBFHE or single-attribute ABFHE, both of which have been realized from LWE, and therefore we can instantiate our construction that is secure under LWE. Moreover, our multi-attribute ABFHE is non-target where the public evaluator do not need to know the policy.