Query: rev.ng: a unified binary analysis framework to recover CFGs and function boundaries

Relevant passages:
Control Flow Integrity for COTS Binaries <=SEP=> Control-Flow Integrity (CFI) has been recognized as an important low-level security property. Its enforcement can defeat most injected and existing code attacks, including those based on Return-Oriented Programming (ROP). Previous implementations of CFI have required compiler support or the presence of relocation or debug information in the binary. In contrast, we present a technique for applying CFI to stripped binaries on x86/Linux. Ours is the first work to apply CFI to complex shared libraries such as glibc. Through experimental evaluation, we demonstrate that our CFI implementation is effective against control-flow hijack attacks, and eliminates the vast majority of ROP gadgets. To achieve this result, we have developed robust techniques for disassembly, static analysis, and transformation of large binaries. Our techniques have been tested on over 300MB of binaries (executables and shared libraries).

SOK: (State of) The Art of War: Offensive Techniques in Binary Analysis <=SEP=> Finding and exploiting vulnerabilities in binary code is a challenging task. The lack of high-level, semantically rich information about data structures and control constructs makes the analysis of program properties harder to scale. However, the importance of binary analysis is on the rise. In many situations binary analysis is the only possible way to prove (or disprove) properties about the code that is actually executed. In this paper, we present a binary analysis framework that implements a number of analysis techniques that have been proposed in the past. We present a systematized implementation of these techniques, which allows other researchers to compose them and develop new approaches. In addition, the implementation of these techniques in a unifying framework allows for the direct comparison of these apporaches and the identification of their advantages and disadvantages. The evaluation included in this paper is performed using a recent dataset created by DARPA for evaluating the effectiveness of binary vulnerability analysis techniques. Our framework has been open-sourced and is available to the security community.

Binary code is not easy <=SEP=> Binary code analysis is an enabling technique for many applications. Modern compilers and run-time libraries have introduced significant complexities to binary code, which negatively affect the capabilities of binary analysis tool kits to analyze binary code, and may cause tools to report inaccurate information about binary code. Analysts may hence be confused and applications based on these tool kits may have degrading quality. We examine the problem of constructing control flow graphs from binary code and labeling the graphs with accurate function boundary annotations. We identified several challenging code constructs that represent hard-to-analyze aspects of binary code, and show code examples for each code construct. As part of this discussion, we present new code parsing algorithms in our open source Dyninst tool kit that support these constructs, including a new model for describing jump tables that improves our ability to precisely determine the control flow targets, a new interprocedural analysis to determine when a function is non-returning, and techniques for handling tail calls. We evaluated how various tool kits fare when handling these code constructs with real software as well as test binaries patterned after each challenging code construct we found in real software.

Firmalice - Automatic Detection of Authentication Bypass Vulnerabilities in Binary Firmware <=SEP=> Embedded devices have become ubiquitous, and they are used in a range of privacy-sensitive and security-critical applications. Most of these devices run proprietary software, and little documentation is available about the software’s inner workings. In some cases, the cost of the hardware and protection mechanisms might make access to the devices themselves infeasible. Analyzing the software that is present in such environments is challenging, but necessary, if the risks associated with software bugs and vulnerabilities must be avoided. As a matter of fact, recent studies revealed the presence of backdoors in a number of embedded devices available on the market. In this paper, we present Firmalice, a binary analysis framework to support the analysis of firmware running on embedded devices. Firmalice builds on top of a symbolic execution engine, and techniques, such as program slicing, to increase its scalability. Furthermore, Firmalice utilizes a novel model of authentication bypass flaws, based on the attacker’s ability to determine the required inputs to perform privileged operations. We evaluated Firmalice on the firmware of three commercially-available devices, and were able to detect authentication bypass backdoors in two of them. Additionally, Firmalice was able to determine that the backdoor in the third firmware sample was not exploitable by an attacker without knowledge of a set of unprivileged credentials.

Who Wrote This Code? Identifying the Authors of Program Binaries <=SEP=> Program authorship attribution—identifying a programmer based on stylistic characteristics of code—has practical implications for detecting software theft, digital forensics, and malware analysis. Authorship attribution is challenging in these domains where usually only binary code is available; existing source code-based approaches to attribution have left unclear whether and to what extent programmer style survives the compilation process. Casting authorship attribution as a machine learning problem, we present a novel program representation and techniques that automatically detect the stylistic features of binary code. We apply these techniques to two attribution problems: identifying the precise author of a program, and finding stylistic similarities between programs by unknown authors. Our experiments provide strong evidence that programmer style is preserved in program binaries.

Importance filtering for image retargeting <=SEP=> Content-aware image retargeting has attracted a lot of interests recently. The key and most challenging issue for this task is how to balance the tradeoff between preserving the important contents and minimizing the visual distortions on the consistency of the image structure. In this paper we present a novel filtering-based technique to tackle this issue, called ”importance filtering”. Specifically, we first filter the image saliency guided by the image itself to achieve a structure-consistent importance map. We then use the pixel importance as the key constraint to compute the gradient map of pixel shifts from the original resolution to the target. Finally, we integrate the shift gradient across the image using a weighted filter to construct a smooth shift map and render the target image. The weight is again controlled by the pixel importance. The two filtering processes enforce to maintain the structural consistency and yet preserve the important contents in the target image. Furthermore, the simple nature of filter operations allows highly efficient implementation for real-time applications and easy extension to video retargeting, as the structural constraints from the original image naturally convey the temporal coherence between frames. The effectiveness and efficiency of our importance filtering algorithm are confirmed in extensive experiments.

PUBLIC-KEY STEGANOGRAPHY BASED ON MODIFIED LSB METHOD <=SEP=> Steganography is the art and science of invisible communication. It is a technique which keeps the existence of the message secret. This paper proposed a technique to implement steganogaraphy and cryptography together to hide the data into an image. This technique describes as: Find the shared stego-key between the two communication parties by applying Diffie-Hellman Key exchange protocol, then encrypt the data using secret stego-key and then select the pixels by encryption process with the help of same secret stegokey to hide the data. Each selected pixel will be used to hide 8 bits of data by using LSB method.

Bayesian Pot-Assembly from Fragments as Problems in Perceptual-Grouping and Geometric-Learning <=SEP=> A heretofore unsolved problem of great archaeological importance is the automatic assembly of pots made on a wheel from the hundreds (or thousands) of sherds found at an excavation site. An approach is presented to the automatic estimation of mathematical models of such pots from 3D measurements of sherds. A Bayesian approach is formulated beginning with a description of the complete set of geometric parameters that determine the distribution of the sherd measurement data. Matching of fragments and aligning them geometrically into configurations is based on matching break-curves (curves on a pot surface separating fragments), estimated axis and profile curve pairs for individual fragments and configurations of fragments, and a number of features of groups of break-curves. Pot assembly is a bottom-up maximum likelihood performance-based search. Experiments are illustrated on pots which were broken for the purpose, and on sherds from an archaeological dig located in Petra, Jordan. The performance measure can also be aposteriori probability, and many other types of information can be included, e.g., pot wall thickness, surface color, patterns on the surface, etc. This can also be viewed as the problem of learning a geometric object from an unorganized set of free-form fragments of the object and of clutter, or as a problem of perceptual grouping.

Drawing lithography for microneedles: a review of fundamentals and biomedical applications. <=SEP=> A microneedle is a three-dimensional (3D) micromechanical structure and has been in the spotlight recently as a drug delivery system (DDS). Because a microneedle delivers the target drug after penetrating the skin barrier, the therapeutic effects of microneedles proceed from its 3D structural geometry. Various types of microneedles have been fabricated using subtractive micromanufacturing methods which are based on the inherently planar two-dimensional (2D) geometries. However, traditional subtractive processes are limited for flexible structural microneedles and makes functional biomedical applications for efficient drug delivery difficult. The authors of the present study propose drawing lithography as a unique additive process for the fabrication of a microneedle directly from 2D planar substrates, thus overcoming a subtractive process shortcoming. The present article provides the first overview of the principal drawing lithography technology: fundamentals and biomedical applications. The continuous drawing technique for an ultrahigh-aspect ratio (UHAR) hollow microneedle, stepwise controlled drawing technique for a dissolving microneedle, and drawing technique with antidromic isolation for a hybrid electro-microneedle (HEM) are reviewed, and efficient biomedical applications by drawing lithography-mediated microneedles as an innovative drug and gene delivery system are described. Drawing lithography herein can provide a great breakthrough in the development of materials science and biotechnology.

Optimization of Cooperative Sensing in Cognitive Radio Networks: A Sensing-Throughput Tradeoff View <=SEP=> In cognitive radio networks, the performance of the spectrum sensing depends on the sensing time and the fusion scheme that are used when cooperative sensing is applied. In this paper, we consider the case where the secondary users cooperatively sense a channel using the k -out-of-N fusion rule to determine the presence of the primary user. A sensing-throughput tradeoff problem under a cooperative sensing scenario is formulated to find a pair of sensing time and k value that maximize the secondary users' throughput subject to sufficient protection that is provided to the primary user. An iterative algorithm is proposed to obtain the optimal values for these two parameters. Computer simulations show that significant improvement in the throughput of the secondary users is achieved when the parameters for the fusion scheme and the sensing time are jointly optimized.

Primary gonadal failure and precocious adrenarche in a boy with Prader-Labhart-Willi syndrome <=SEP=> A 7-year-old boy with Prader-Labhart-Willi syndrome who had precocious adrenarche was found to have primary gonadal failure, as evidenced by appropriate laboratory investigations: elevated basal levels of plasma FSH and LH with exaggerated responses to LH-RH stimulation and unresponsiveness of plasma testosterone to repeated hCG stimulations. The elevated values of plasma DHEA which were found indicate an early activation of the adrenal gland. This patient demonstrates the varibility of pubertal development in the Prader-Labhart-Willi syndrome, with the unusual association of primary gonadal failure and precocious adrenarche.

A survey on virtual machine migration and server consolidation frameworks for cloud data centers <=SEP=> Modern Cloud Data Centers exploit virtualization for efficient resource management to reduce cloud computational cost and energy budget. Virtualization empowered by virtual machine (VM) migration meets the ever increasing demands of dynamic workload by relocating VMs within Cloud Data Centers. VM migration helps successfully achieve various resource management objectives such as load balancing, power management, fault tolerance, and system maintenance. However, being resourceintensive, the VM migration process rigorously affects application performance unless attended by smart optimization methods. Furthermore, a Cloud Data Centre exploits server consolidation and DVFS methods to optimize energy consumption. This paper reviews state-of-the-art bandwidth optimization schemes, server consolidation frameworks, DVFS-enabled power optimization, and storage optimization methods over WAN links. Through a meticulous literature review of state-of-the-art live VM migration schemes, thematic taxonomies are proposed to categorize the reported literature. The critical aspects of virtual machine migration schemes are investigated through a comprehensive analysis of the existing schemes. The commonalties and differences among existing VM migration schemes are highlighted through a set of parameters derived from the literature. Finally, open research issues and trends in the VM migration domain that necessitate further consideration to develop optimal VM migration schemes

Improving Image Classification Using Semantic Attributes <=SEP=> The Bag-of-Words (BoW) model—commonly used for image classification—has two strong limitations: on one hand, visual words are lacking of explicit meanings, on the other hand, they are usually polysemous. This paper proposes to address these two limitations by introducing an intermediate representation based on the use of semantic attributes. Specifically, two different approaches are proposed. Both approaches consist in predicting a set of semantic attributes for the entire images as well as for local image regions, and in using these predictions to build the intermediate level features. Experiments on four challenging image databases (PASCAL VOC 2007, Scene-15, MSRCv2 and SUN-397) show that both approaches improve performance of the BoW model significantly. Moreover, their combination achieves the state-of-the-art results on several of these image databases.

Deceptive Reviews : The Influential Tail <=SEP=> Research in the psycholinguistics literature has identified linguistic cues indicating when a message is more likely to be deceptive and we find that the textual comments in the reviews without prior transactions exhibit many of these characteristics. In contrast, these reviews are less likely to contain expressions describing the fit or feel of the items, which can generally only be assessed by physical inspection of the items.

Process Mining and Fraud Detection <=SEP=> A case study on the theoretical and practical value of using process mining for the detection of fraudulent behavior in the procurement process Abstract This thesis presents the results of a six month research period on process mining and fraud detection. This thesis aimed to answer the research question as to how process mining can be utilized in fraud detection and what the benefits of using process mining for fraud detection are. Based on a literature study it provides a discussion of the theory and application of process mining and its various aspects and techniques. Using both a literature study and an interview with a domain expert, the concepts of fraud and fraud detection are discussed. These results are combined with an analysis of existing case studies on the application of process mining and fraud detection to construct an initial setup of two case studies, in which process mining is applied to detect possible fraudulent behavior in the procurement process. Based on the experiences and results of these case studies, the 1+5+1 methodology is presented as a first step towards operationalizing principles with advice on how process mining techniques can be used in practice when trying to detect fraud. This thesis presents three conclusions: (1) process mining is a valuable addition to fraud detection, (2) using the 1+5+1 concept it was possible to detect indicators of possibly fraudulent behavior (3) the practical use of process mining for fraud detection is diminished by the poor performance of the current tools. The techniques and tools that do not suffer from performance issues are an addition, rather than a replacement, to regular data analysis techniques by providing either new, quicker, or more easily obtainable insights into the process and possible fraudulent behavior. iii Occam's Razor: " One should not increase, beyond what is necessary, the number of entities required to explain anything " iv Contents

Question Answering on Freebase via Relation Extraction and Textual Evidence <=SEP=> Existing knowledge-based question answering systems often rely on small annotated training data. While shallow methods like relation extraction are robust to data scarcity, they are less expressive than the deep meaning representation methods like semantic parsing, thereby failing at answering questions involving multiple constraints. Here we alleviate this problem by empowering a relation extraction method with additional evidence from Wikipedia. We first present a neural network based relation extractor to retrieve the candidate answers from Freebase, and then infer over Wikipedia to validate these answers. Experiments on the WebQuestions question answering dataset show that our method achieves an F1 of 53.3%, a substantial improvement over the state-of-the-art.

Beyond Big and Little : The Four C Model of Creativity <=SEP=> Most investigations of creativity tend to take one of two directions: everyday creativity (also called “little-c”), which can be found in nearly all people, and eminent creativity (also called “Big-C”), which is reserved for the great. In this paper, the authors propose a Four C model of creativity that expands this dichotomy. Specifically, the authors add the idea of “mini-c,” creativity inherent in the learning process, and Pro-c, the developmental and effortful progression beyond little-c that represents professional-level expertise in any creative area. The authors include different transitions and gradations of these four dimensions of creativity, and then discuss advantages and examples of the Four C Model.

Comparing a computer agent with a humanoid robot <=SEP=> HRI researchers interested in social robots have made large investments in humanoid robots. There is still sparse evidence that peoples' responses to robots differ from their responses to computer agents, suggesting that agent studies might serve to test HRI hypotheses. To help us understand the difference between people's social interactions with an agent and a robot, we experimentally compared people's responses in a health interview with (a) a computer agent projected either on a computer monitor or life-size on a screen, (b) a remote robot projected life-size on a screen, or (c) a collocated robot in the same room. We found a few behavioral and large attitude differences across these conditions. Participants forgot more and disclosed least with the collocated robot, next with the projected remote robot, and then with the agent. They spent more time with the collocated robot and their attitudes were most positive toward that robot. We discuss tradeoffs for HRI research of using collocated robots, remote robots, and computer agents as proxies of robots.

Why is image quality assessment so difficult? <=SEP=> Image quality assessment plays an important role in various image processing applications. A great deal of effort has been made in recent years to develop objective image quality metrics that correlate with perceived quality measurement. Unfortunately, only limited success has been achieved. In this paper, we provide some insights on why image quality assessment is so difficult by pointing out the weaknesses of the error sensitivity based framework, which has been used by most image quality assessment approaches in the literature. Furthermore, we propose a new philosophy in designing image quality metrics: The main function of the human eyes is to extract structural information from the viewing field, and the human visual system is highly adapted for this purpose. Therefore, a measurement of structural distortion should be a good approximation of perceived image distortion. Based on the new philosophy, we implemented a simple but effective image quality indexing algorithm, which is very promising as shown by our current results.

Performance indicators for an objective measure of public transport service quality <=SEP=> The measurement of transit performance represents a very useful tool for ensuring continuous increase of the quality of the delivered transit services, and for allocating resources among competing transit agencies. Transit service quality can be evaluated by subjective measures based on passengers’ perceptions, and objective measures represented by disaggregate performance measures expressed as numerical values, which must be compared with fixed standards or past performances. The proposed research work deals with service quality evaluation based on objective measures; specifically, an extensive overview and an interpretative review of the objective indicators until investigated by researchers are proposed. The final aim of the work is to give a review as comprehensive as possible of the objective indicators, and to provide some suggestions for the selection of the most appropriate indicators for evaluating a transit service aspect.

Novel paradigms for advanced distribution grid energy management <=SEP=> The electricity distribution grid was not designed to cope with load dynamics imposed by high penetration of electric vehicles, neither to deal with the increasing deployment of distributed Renewable Energy Sources. Distribution System Operators (DSO) will increasingly rely on flexible Distributed Energy Resources (flexible loads, controllable generation and storage) to keep the grid stable and to ensure quality of supply. In order to properly integrate demand-side flexibility, DSOs need new energy management architectures, capable of fostering collaboration with wholesale market actors and prosumers. We propose the creation of Virtual Distribution Grids (VDG) over a common physical infrastructure, to cope with heterogeneity of resources and actors, and with the increasing complexity of distribution grid management and related resources allocation problems. Focusing on residential VDG, we propose an agent-based hierarchical architecture for providing Demand-Side Management services through a market-based approach, where households transact their surplus/lack of energy and their flexibility with neighbours, aggregators, utilities and DSOs. For implementing the overall solution, we consider fine-grained control of smart homes based on Internet of Things technology. Homes seamlessly transact self-enforcing smart contracts over a blockchainbased generic platform. Finally, we extend the architecture to solve existing problems on smart home control, beyond energy management.

Planar Differential Elliptical UWB Antenna Optimization <=SEP=> A recently proposed optimization procedure, based on the time domain characteristics of an antenna, is exploited to design a planar differential elliptical antenna for ultrawideband (UWB) applications. The optimization procedure aims at finding an antenna not only with low VSWR but also one exhibiting low-dispersion characteristics over the relevant frequency band. Furthermore, since in pulse communications systems the input signal is often of a given form, suited to a particular purpose, the optimization procedure also aims at finding the best antenna for the given input signal form. Specifically, the optimized antenna is designed for high temporal correlation between its electric field intensity signal and the given input signal. The optimization technique followed in this work makes use of genetic algorithm (GA) search concepts. The electromagnetic analysis of the antenna is done by means of a finite-difference time-domain method using the commercially available CST Microwave Studio software

Tracking-based deer vehicle collision detection using thermal imaging <=SEP=> Deer vehicle collision (DVC) is constantly a major safety issue for the driving on rural road. It is estimated that there are over 35,000 DVCs yearly in the US resulting in about 200 deaths and close to 4,000 reported property damages of one thousand dollars or more. This justifies many attempts trying to detect deer on road. However, very little success has been achieved. In order to reduce the number of DVCs, this work focused on the study of using an infrared thermal camera with tracking system to detect the presence of deer to avoid DVCs. The prototype consists of an infrared thermal temperature image grabbing and processing system, which includes an infrared thermal camera, a frame grabber, an image processing system and a motion tracking system, which includes two motors with their motion control system. By analyzing the infrared thermal images which are independent of visible light, the presence of an animal can be determined in either night or day time through pattern recognition and matching.

Beyond One-hot Encoding: lower dimensional target embedding <=SEP=> Target encoding plays a central role when learning Convolutional Neural Networks. In this realm, One-hot encoding is the most prevalent strategy due to its simplicity. However, this so widespread encoding schema assumes a flat label space, thus ignoring rich relationships existing among labels that can be exploited during training. In large-scale datasets, data does not span the full label space, but instead lies in a low-dimensional output manifold. Following this observation, we embed the targets into a lowdimensional space, drastically improving convergence speed while preserving accuracy. Our contribution is two fold: (i) We show that random projections of the label space are a valid tool to find such lower dimensional embeddings, boosting dramatically convergence rates at zero computational cost; and (ii) we propose a normalized eigenrepresentation of the class manifold that encodes the targets with minimal information loss, improving the accuracy of random projections encoding while enjoying the same convergence rates. Experiments on CIFAR-100, CUB200-2011, Imagenet, and MIT Places demonstrate that the proposed approach drastically improves convergence speed while reaching very competitive accuracy rates.

Simba: Efficient In-Memory Spatial Analytics <=SEP=> Large spatial data becomes ubiquitous. As a result, it is critical to provide fast, scalable, and high-throughput spatial queries and analytics for numerous applications in location-based services (LBS). Traditional spatial databases and spatial analytics systems are disk-based and optimized for IO efficiency. But increasingly, data are stored and processed in memory to achieve low latency, and CPU time becomes the new bottleneck. We present the Simba (Spatial In-Memory Big data Analytics) system that offers scalable and efficient in-memory spatial query processing and analytics for big spatial data. Simba is based on Spark and runs over a cluster of commodity machines. In particular, Simba extends the Spark SQL engine to support rich spatial queries and analytics through both SQL and the DataFrame API. It introduces indexes over RDDs in order to work with big spatial data and complex spatial operations. Lastly, Simba implements an effective query optimizer, which leverages its indexes and novel spatial-aware optimizations, to achieve both low latency and high throughput. Extensive experiments over large data sets demonstrate Simba's superior performance compared against other spatial analytics system.

Smart fabrics and interactive textile enabling wearable personal applications: R&D state of the art and future challenges <=SEP=> Smart fabrics and interactive textiles (SFIT) are fibrous structures that are capable of sensing, actuating, generating/storing power and/or communicating. Research and development towards wearable textile-based personal systems allowing e.g. health monitoring, protection & safety, and healthy lifestyle gained strong interest during the last 10 years. Under the Information and Communication Programme of the European Commission, a cluster of R&D projects dealing with smart fabrics and interactive textile wearable systems regroup activities along two different and complementary approaches i.e. “application pull” and “technology push”. This includes projects aiming at personal health management through integration, validation, and use of smart clothing and other networked mobile devices as well as projects targeting the full integration of sensors/actuators, energy sources, processing and communication within the clothes to enable personal applications such as protection/safety, emergency and healthcare. The integration part of the technologies into a real SFIT product is at present stage on the threshold of prototyping and testing. Several issues, technical as well user-centred, societal and business, remain to be solved. The paper presents on going major R&D activities, identifies gaps and discuss key challenges for the future.

Multilingual speech-to-speech translation system for mobile consumer devices <=SEP=> Along with the advancement of speech recognition technology and machine translation technology in addition to the fast distribution of mobile devices, speech-to-speech translation technology no longer remains as a subject of research as it has become popularized throughout many users. In order to develop a speech-to-speech translation system that can be widely used by many users, however, the system needs to reflect various characteristics of utterances by the users who are actually to use the speech-to-speech translation system other than improving the basic functions under the experimental environment. This study has established a massive language and speech database closest to the environment where speech-to- speech translation device actually is being used after mobilizing plenty of people based on the survey on users' demands. Through this study, it was made possible to secure excellent basic performance under the environment similar to speech-to-speech translation environment, rather than just under the experimental environment. Moreover, with the speech-to-speech translation UI, a user-friendly UI has been designed; and at the same time, errors were reduced during the process of translation as many measures to enhance user satisfaction were employed. After implementing the actual services, the massive database collected through the service was additionally applied to the system following a filtering process in order to procure the best-possible robustness toward both the details and the environment of the users' utterances. By applying these measures, this study is to unveil the procedures where multi-language speech-to-speech translation system has been successfully developed for mobile devices.

Missing value imputation for gene expression data: computational techniques to recover missing data from available information <=SEP=> Microarray gene expression data generally suffers from missing value problem due to a variety of experimental reasons. Since the missing data points can adversely affect downstream analysis, many algorithms have been proposed to impute missing values. In this survey, we provide a comprehensive review of existing missing value imputation algorithms, focusing on their underlying algorithmic techniques and how they utilize local or global information from within the data, or their use of domain knowledge during imputation. In addition, we describe how the imputation results can be validated and the different ways to assess the performance of different imputation algorithms, as well as a discussion on some possible future research directions. It is hoped that this review will give the readers a good understanding of the current development in this field and inspire them to come up with the next generation of imputation algorithms.

FML-based feature similarity assessment agent for Japanese/Taiwanese language learning <=SEP=> In this paper, we propose a fuzzy markup language (FML)-based feature similarity assessment agent with machine-learning ability to evaluate easy-to-learn degree of the Japanese and Taiwanese words. The involved domain experts define knowledge base (KB) and rule base (RB) of the proposed agent. The KB and RB are stored in the constructed ontology, including features of pronunciation similarity, writing similarity, and culture similarity. Next, we calculate feature similarity in pronunciation, writing, and culture for each word pair between Japanese and Taiwanese. Finally, we infer the easy-to-learn degree for one Japanese word and its corresponding Taiwanese one. Additionally, a genetic learning is also adopted to tune the KB and RB of the intelligent agent. The experimental results show that after-learning results perform better than before-learning ones.

foo . castr : visualising the future AI workforce <=SEP=> *Correspondence: mmolinas@ic.ac.uk 1Data Science Institute, Imperial College London, London, UK Full list of author information is available at the end of the article Abstract Organization of companies and their HR departments are becoming hugely affected by recent advancements in computational power and Artificial Intelligence, with this trend likely to dramatically rise in the next few years. This work presents foo.castr, a tool we are developing to visualise, communicate and facilitate the understanding of the impact of these advancements in the future of workforce. It builds upon the idea that particular tasks within job descriptions will be progressively taken by computers, forcing the shaping of human jobs. In its current version, foo.castr presents three different scenarios to help HR departments planning potential changes and disruptions brought by the adoption of Artificial Intelligence.