Query: New local edge binary patterns for image retrieval

Relevant passages:
Histograms of oriented gradients for human detection <=SEP=> We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.

Completed Local Binary Count for Rotation Invariant Texture Classification <=SEP=> In this brief, a novel local descriptor, named local binary count (LBC), is proposed for rotation invariant texture classification. The proposed LBC can extract the local binary grayscale difference information, and totally abandon the local binary structural information. Although the LBC codes do not represent visual microstructure, the statistics of LBC features can represent the local texture effectively. In addition, a completed LBC (CLBC) is also proposed to enhance the performance of texture classification. Experimental results obtained from three databases demonstrate that the proposed CLBC can achieve comparable accurate classification rates with completed local binary pattern.

Description of interest regions with local binary patterns <=SEP=> This paper presents a novel method for interest region description. We adopted the idea that the appearance of an interest region can be well characterized by the distribution of its local features. The most well-known descriptor built on this idea is the SIFT descriptor that uses gradient as the local feature. Thus far, existing texture features are not widely utilized in the context of region description. In this paper, we introduce a new texture feature called center-symmetric local binary pattern (CS-LBP) that is a modified version of the well-known local binary pattern (LBP) feature. To combine the strengths of the SIFT and LBP, we use the CS-LBP as the local feature in the SIFT algorithm. The resulting descriptor is called the CS-LBP descriptor. In the matching and object category classification experiments, our descriptor performs favorably compared to the SIFT. Furthermore, the CS-LBP descriptor is computationally simpler than the SIFT.

A comparative study of texture measures with classification based on featured distributions <=SEP=> -This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently. For classification a method based on Kullback discrimination of sample and prototype distributions is used. The classification results for single features with one-dimensional feature value distributions and for pairs of complementary features with two-dimensional distributions are presented Texture analysis Classification Feature distribution Kullback discriminant Performance evaluation Brodatz textures l. I N T R O D U C T I O N Texture is an important characteristic for the analysis of many types of images. A wide variety of measures for discriminating textures have been proposed, c1'2~ Comparative studies to evaluate the performance of some texture measures have been carried out by Weszka et al., ~3~ Du Bufet al. ~4~ and Ohanian and Dubes, ~s) for

Survey on LBP based texture descriptors for image classification <=SEP=> The aim of this work is to find the best way for describing a given texture using a local binary pattern (LBP) based approach. First several different approaches are compared, then the best fusion approach is tested on different datasets and compared with several approaches proposed in the literature (for fair comparisons, when possible we have used code shared by the original authors). Our experiments show that a fusion approach based on uniform local quinary pattern (LQP) and a rotation invariant local quinary pattern, where a bin selection based on variance is performed and Neighborhood Preserving Embedding (NPE) feature transform is applied, obtains a method that performs well on all tested datasets. As the classifier, we have tested a stand-alone support vector machine (SVM) and a random subspace ensemble of SVM. We compare several texture descriptors and show that our proposed approach coupled with random subspace ensemble outperforms other recent state-of-the-art approaches. This conclusion is based on extensive experiments conducted in several domains using six benchmark databases. 2011 Elsevier Ltd. All rights reserved.

ADLs and dynamic architecture changes <=SEP=> Existing ADLs typically support only static architecture specification and do not provide facilities for the support of dynamically changing architectures. This paper presents a possible solution to this problem: in order to adequately support dynamic architecture changes, ADLs can leverage techniques used in dynamic programming languages. In particular, changes to ADL specifications should be interpreted. To enable interpretation, an ADL should have an architecture construction component that supports explicit and incremental specification of architectural changes, in addition to the traditional architecture description facilities. This will allow software architects to specify the changes to an architecture after it has been built. The paper expands upon the results from an ongoing project -building a development environment for CZstyle architectures.’

The Language of Place: Semantic Value from Geospatial Context <=SEP=> There is a relationship between what we say and where we say it. Word embeddings are usually trained assuming that semantically-similar words occur within the same textual contexts. We investigate the extent to which semantically-similar words occur within the same geospatial contexts. We enrich a corpus of geolocated Twitter posts with physical data derived from Google Places and OpenStreetMap, and train word embeddings using the resulting geospatial contexts. Intrinsic evaluation of the resulting vectors shows that geographic context alone does provide useful information about semantic relatedness.

Spherical CNNs <=SEP=> Convolutional Neural Networks (CNNs) have become the method of choice for learning problems involving 2D planar images. However, a number of problems of recent interest have created a demand for models that can analyze spherical images. Examples include omnidirectional vision for drones, robots, and autonomous cars, molecular regression problems, and global weather and climate modelling. A naive application of convolutional networks to a planar projection of the spherical signal is destined to fail, because the space-varying distortions introduced by such a projection will make translational weight sharing ineffective. In this paper we introduce the building blocks for constructing spherical CNNs. We propose a definition for the spherical cross-correlation that is both expressive and rotation-equivariant. The spherical correlation satisfies a generalized Fourier theorem, which allows us to compute it efficiently using a generalized (non-commutative) Fast Fourier Transform (FFT) algorithm. We demonstrate the computational efficiency, numerical accuracy, and effectiveness of spherical CNNs applied to 3D model recognition and atomization energy regression.

A novel method for providing relational databases with rich semantics and natural language processing <=SEP=> Purpose – With the development of systems and applications, the number of users interacting with databases has increased considerably. The relational database model is still considered as the most used model for data storage and manipulation. However, it doesn’t offer any semantic support for the stored data which can facilitate data access for users. Indeed, a large number of users are intimidated when retrieving data because they are non-technical or have little technical knowledge. To overcome this problem, researchers are continuously developing new techniques for Natural Language Interfaces to Databases (NLIDB). Nowadays, the usage of existing NLIDB’s is not wide-spread due to their deficiencies in understanding natural language queries. In this sense, this paper proposes a novel method for intelligent understanding of natural language queries using semantically enriched database sources. Design/Methodology/Approach – First a reverse engineering process is applied to extract relational database hidden semantics. In the second step, the extracted semantics are enriched further using a domain ontology. After this, all semantics are stored in the same relational database. The phase of processing natural language queries uses the stored semantics to generate a semantic-tree. Findings – The evaluation part of our work show the advantages of using a semantically enriched database source to understand natural language queries. Additionally, enriching a relational database has given more flexibility to understand contextual and synonymous words that may be used in a natural language query. Originality/value – Existing NLIDB’s aren’t yet a standard option for interfacing a relational database due their lack for understanding natural language queries. Indeed, the techniques used in the literature have their limits. This paper handles those limits by identifying natural language elements by their semantic nature in order to generate a semantic-tree. This last is a key solution toward intelligent understanding of natural language queries to relational databases.

Teaching a lay theory before college narrows achievement gaps at scale. <=SEP=> Previous experiments have shown that college students benefit when they understand that challenges in the transition to college are common and improvable and, thus, that early struggles need not portend a permanent lack of belonging or potential. Could such an approach-called a lay theory intervention-be effective before college matriculation? Could this strategy reduce a portion of racial, ethnic, and socioeconomic achievement gaps for entire institutions? Three double-blind experiments tested this possibility. Ninety percent of first-year college students from three institutions were randomly assigned to complete single-session, online lay theory or control materials before matriculation (n > 9,500). The lay theory interventions raised first-year full-time college enrollment among students from socially and economically disadvantaged backgrounds exiting a high-performing charter high school network or entering a public flagship university (experiments 1 and 2) and, at a selective private university, raised disadvantaged students' cumulative first-year grade point average (experiment 3). These gains correspond to 31-40% reductions of the raw (unadjusted) institutional achievement gaps between students from disadvantaged and nondisadvantaged backgrounds at those institutions. Further, follow-up surveys suggest that the interventions improved disadvantaged students' overall college experiences, promoting use of student support services and the development of friendship networks and mentor relationships. This research therefore provides a basis for further tests of the generalizability of preparatory lay theories interventions and of their potential to reduce social inequality and improve other major life transitions.

Experts ’ Views on Digital Parenting Strategies <=SEP=> American teenagers are spending much of their time online. Online communities offer teens perspective, acceptance, connection, and education that were once more limited by geography. However, the online world also opens teens to risks. Adults must decide how to guide teen online behavior in a space that they may not understand. We interviewed 16 experts about teen online behavior, related benefits and risks, and risk mitigation methods. We found that experts agree on certain mitigation methods, especially communication and education about online behavior, but are divided over monitoring. We propose a set of possible solutions that promote online safety while preserving teens’ privacy.

Using Machine Learning Methods and Linguistic Features in Single-Document Extractive Summarization <=SEP=> Extractive summarization of text documents usually consists of ranking the document sentences and extracting the top-ranked sentences subject to the summary length constraints. In this paper, we explore the contribution of various supervised learning algorithms to the sentence ranking task. For this purpose, we introduce a novel sentence ranking methodology based on the similarity score between a candidate sentence and benchmark summaries. Our experiments are performed on three benchmark summarization corpora: DUC-2002, DUC2007 and MultiLing-2013. The popular linear regression model achieved the best results in all evaluated datasets. Additionally, the linear regression model, which included POS (Part-of-Speech)-based features, outperformed the one with statistical features only.

How effective is the IEEE 802.11 RTS/CTS handshake in ad hoc networks <=SEP=> IEEE 802.11 MAC mainly relies on two techniques to combat interference: physical carrier sensing and RTS/CTS handshake (also known as “virtual carrier sensing”). Ideally, the RTS/CTS handshake can eliminate most interference. However, the effectiveness of RTS/CTS handshake is based on the assumption that hidden nodes are within transmission range of receivers. In this paper, we prove using analytic models that in ad hoc networks, such an assumption cannot hold due to the fact that power needed for interrupting a packet reception is much lower than that of delivering a packet successfully. Thus, the “virtual carrier sensing” implemented by RTS/CTS handshake cannot prevent all interference as we expect in theory. Physical carrier sensing can complement this in some degree. However, since interference happens at receivers, while physical carrier sensing is detecting transmitters (the same problem causing the hidden terminal situation), physical carrier sensing cannot help much, unless a very large carrier sensing range is adopted, which is limited by the antenna sensitivity. In this paper, we investigate how effective is the RTS/CTS handshake in terms of reducing interference. We show that in some situations, the interference range is much larger than transmission range, where RTS/CTS cannot function well. Then, a simple MAC layer scheme is proposed to solve this problem. Simulation results verify that our scheme can help IEEE 802.11 resolve most interference caused by large interference range.

DC Motor Drive with P, PI, and Particle Swarm Optimization Speed Controllers <=SEP=> This paper implements a Particle Swarm Optimization (PSO) speed controller for controlling the speed of DC motor. Also traditional Proportional (P), Proportional-Integral (PI) controller have been developed and simulated using MATLAB/SIMULINK. The simulation results indicate that the PI controller has big overshoot, sensitivity to controller gains. The optimization with particle swarm verified the reduction of oscillations as well as improve the steady state error, rise time and overshot in speed response. The simulation results confirmed the system is less sensitive to gain.

Learning recursion: Multiple nested and crossed dependencies <=SEP=> Language acquisition in both natural and artificial language learning settings crucially depends on extracting information from ordered sequences. A shared sequence learning mechanism is thus assumed to underlie both natural and artificial language learning. A growing body of empirical evidence is consistent with this hypothesis. By means of artificial language learning experiments, we may therefore gain more insight in this shared mechanism. In this paper, we review empirical evidence from artificial language learning and computational modeling studies, as well as natural language data, and suggest that there are two key factors that help determine processing complexity in sequence learning, and thus in natural language processing. We propose that the specific ordering of non-adjacent dependencies (i.e. nested or crossed), as well as the number of non-adjacent dependencies to be resolved simultaneously (i.e. two or three) are important factors in gaining more insight into the boundaries of human sequence learning; and thus, also in natural language processing. The implications for theories of linguistic competence are discussed.

Validity and Utility of Alternative Predictors of Job Performance <=SEP=> Meta-analysis of the cumulative research on various predictors of job performance shows that for entry-level jobs there is no predictor with validity equal to that of ability, which has a mean validity of .53. For selection on the basis of current job performance, the work sample test, with mean validity of .54, is slightly better. For federal entry-level jobs, substitution of an alternative predictor would cost from $3.12 billion (job tryout) to $15.89 billion per year (age). Hiring on ability has a utility of $15.61 billion per year, but affects minority groups adversely. Hiring on ability by quotas would decrease this utility by 5%. A third strategy—using a low cutoff score—would decrease utility by 83%. Using other predictors in conjunction with ability tests might improve validity and reduce adverse impact, but there is as yet no data base for studying this possibility.

Information fusion for wireless sensor networks: Methods, models, and classifications <=SEP=> Wireless sensor networks produce a large amount of data that needs to be processed, delivered, and assessed according to the application objectives. The way these data are manipulated by the sensor nodes is a fundamental issue. Information fusion arises as a response to process data gathered by sensor nodes and benefits from their processing capability. By exploiting the synergy among the available data, information fusion techniques can reduce the amount of data traffic, filter noisy measurements, and make predictions and inferences about a monitored entity. In this work, we survey the current state-of-the-art of information fusion by presenting the known methods, algorithms, architectures, and models of information fusion, and discuss their applicability in the context of wireless sensor networks.

Enhancing Cross-Device Interaction Scripting with Interactive Illustrations <=SEP=> Cross-device interactions involve input and output on multiple computing devices. Implementing and reasoning about interactions that cover multiple devices with a diversity of form factors and capabilities can be complex. To assist developers in programming cross-device interactions, we created DemoScript, a technique that automatically analyzes a cross-device interaction program while it is being written. DemoScript visually illustrates the step-by-step execution of a selected portion or the entire program with a novel, automatically generated cross-device storyboard visualization. In addition to helping developers understand the behavior of the program, DemoScript also allows developers to revise their program by interactively manipulating the cross-device storyboard. We evaluated DemoScript with 8 professional programmers and found that DemoScript significantly improved development efficiency by helping developers interpret and manage cross-device interaction; it also encourages testing to think through the script in a development process.

An alginate-antacid formulation (Gaviscon Double Action Liquid) can eliminate or displace the postprandial 'acid pocket' in symptomatic GERD patients. <=SEP=> BACKGROUND
Recently, an 'acid pocket' has been described in the proximal stomach, particularly evident postprandially in GERD patients, when heartburn is common. By creating a low density gel 'raft' that floats on top of gastric contents, alginate-antacid formulations may neutralise the 'acid pocket'.


AIM
To assess the ability of a commercial high-concentration alginate-antacid formulation to neutralize and/or displace the acid pocket in GERD patients.


METHODS
The 'acid pocket' was studied in ten symptomatic GERD patients. Measurements were made using concurrent stepwise pH pull-throughs, high resolution manometry and fluoroscopy in a semi-recumbent posture. Each subject was studied in three conditions: fasted, 20 min after consuming a high-fat meal and 20 min later after a 20 mL oral dose of an alginate-antacid formulation (Gaviscon Double Action Liquid, Reckitt Benckiser Healthcare, Hull, UK). The relative position of pH transition points (pH >4) to the EGJ high-pressure zone was analysed.


RESULTS
Most patients (8/10) exhibited an acidified segment extending from the proximal stomach into the EGJ when fasted that persisted postprandially. Gaviscon neutralised the acidified segment in six of the eight subjects shifting the pH transition point significantly away from the EGJ. The length and pressure of the EGJ high-pressure zone were minimally affected.


CONCLUSIONS
Gaviscon can eliminate or displace the 'acid pocket' in GERD patients. Considering that EGJ length was unchanged throughout, this effect was likely attributable to the alginate 'raft' displacing gastric contents away from the EGJ. These findings suggest the alginate-antacid formulation to be an appropriately targeted postprandial GERD therapy.

Anonymous CoinJoin Transactions with Arbitrary Values <=SEP=> Bitcoin, the arguably most popular cryptocurrency to date, allows users to perform transactions using freely chosen pseudonymous addresses. Previous research, however, suggests that these pseudonyms can easily be linked, implying a lower level of privacy than originally expected. To obfuscate the links between pseudonyms, different mixing methods have been proposed. One of the first approaches is the CoinJoin concept, where multiple users merge their transactions into one larger transaction. In theory, CoinJoin can be used to mix and transact bitcoins simultaneously, in one step. Yet, it is expected that differing bitcoin amounts would allow an attacker to derive the original single transactions. Solutions based on CoinJoin therefore prescribe the use of fixed bitcoin amounts and cannot be used to perform arbitrary transactions.In this paper, we define a model for CoinJoin transactions and metrics that allow conclusions about the provided anonymity. We generate and analyze CoinJoin transactions and show that with differing, representative amounts they generally do not provide any significant anonymity gains. As a solution to this problem, we present an output splitting approach that introduces sufficient ambiguity to effectively prevent linking in CoinJoin transactions. Furthermore, we discuss how this approach could be used in Bitcoin today.

Model compression via distillation and quantization <=SEP=> Deep neural networks (DNNs) continue to make significant advances, solving tasks from image classification to translation or reinforcement learning. One aspect of the field receiving considerable attention is efficiently executing deep models in resource-constrained environments, such as mobile or embedded devices. This paper focuses on this problem, and proposes two new compression methods, which jointly leverage weight quantization and distillation of larger networks, called “teachers,” into compressed “student” networks. The first method we propose is called quantized distillation and leverages distillation during the training process, by incorporating distillation loss, expressed with respect to the teacher network, into the training of a smaller student network whose weights are quantized to a limited set of levels. The second method, differentiable quantization, optimizes the location of quantization points through stochastic gradient descent, to better fit the behavior of the teacher model. We validate both methods through experiments on convolutional and recurrent architectures. We show that quantized shallow students can reach similar accuracy levels to state-of-the-art full-precision teacher models, while providing up to order of magnitude compression, and inference speedup that is almost linear in the depth reduction. In sum, our results enable DNNs for resource-constrained environments to leverage architecture and accuracy advances developed on more powerful devices.

Expectancy-Value Theory of Achievement Motivation. <=SEP=> We discuss the expectancy-value theory of motivation, focusing on an expectancy-value model developed and researched by Eccles, Wigfield, and their colleagues. Definitions of crucial constructs in the model, including ability beliefs, expectancies for success, and the components of subjective task values, are provided. These definitions are compared to those of related constructs, including self-efficacy, intrinsic and extrinsic motivation, and interest. Research is reviewed dealing with two issues: (1) change in children's and adolescents' ability beliefs, expectancies for success, and subjective values, and (2) relations of children's and adolescents' ability-expectancy beliefs and subjective task values to their performance and choice of activities. Copyright 2000 Academic Press.

Smart street light system looking like usual street lights based on sensor networks <=SEP=> Currently, in the whole world, enormous electric energy is consumed by the street lights, which are automatically turn on when it becomes dark and automatically turn off when it becomes bright. This is the huge waste of energy in the whole world and should be changed. This paper discusses a smart street light system, whose concept is proposed by Fujii et al. The main aim of smart street light systems is that lights turn on when needed and light turn off when not needed. Moreover, the smart street light system in this paper behaves like usual street lights that turn on all night. The ideal behavior of the smart street light system is that no one finds turn-off of street lights at night. Whenever someone see street lights, they turn on and whenever no one see street lights, they turn off. The smart street light system consists of LED lights, brightness sensors, motion sensors and short-distance communication networks. The lights turn on before pedestrians and vehicles come and turn off or reduce brightness when there is no one. It will be difficult for pedestrians and drivers of vehicles to distinguish our smart street lights and the conventional street lights because our street lights all turn on before they come.

On the Consistency of Top-k Surrogate Losses <=SEP=> The top-k error is often employed to evaluate performance for challenging classification tasks in computer vision as it is designed to compensate for ambiguity in ground truth labels. This practical success motivates our theoretical analysis of consistent top-k classification. To this end, we define top-k calibration as a necessary and sufficient condition for consistency, for bounded below loss functions. Unlike prior work, our analysis of top-k calibration handles non-uniqueness of the predictor scores, and extends calibration to consistency – providing a theoretically sound basis for analysis of this topic. Based on the topk calibration analysis, we propose a rich class of top-k calibrated Bregman divergence surrogates. Our analysis continues by showing previously proposed hinge-like top-k surrogate losses are not top-k calibrated and thus inconsistent. On the other hand, we propose two new hinge-like losses, one which is similarly inconsistent, and one which is consistent. Our empirical results highlight theoretical claims, confirming our analysis of the consistency of these losses.

Genetic studies of human diversity in East Asia. <=SEP=> East Asia is one of the most important regions for studying evolution and genetic diversity of human populations. Recognizing the relevance of characterizing the genetic diversity and structure of East Asian populations for understanding their genetic history and designing and interpreting genetic studies of human diseases, in recent years researchers in China have made substantial efforts to collect samples and generate data especially for markers on Y chromosomes and mtDNA. The hallmark of these efforts is the discovery and confirmation of consistent distinction between northern and southern East Asian populations at genetic markers across the genome. With the confirmation of an African origin for East Asian populations and the observation of a dominating impact of the gene flow entering East Asia from the south in early human settlement, interpretation of the north-south division in this context poses the challenge to the field. Other areas of interest that have been studied include the gene flow between East Asia and its neighbouring regions (i.e. Central Asia, the Sub-continent, America and the Pacific Islands), the origin of Sino-Tibetan populations and expansion of the Chinese.

A Secured System for Information Hiding in Image Steganography using Genetic Algorithm and Cryptography <=SEP=> Encryption is used to securely communicate data in open networks. Each type of data has its own structures; therefore according to the data the different techniques should be used to defend confidential data. Among them digital images are also very popular to carry confidential information in untrusted networks. . For pleasing the defense of data hiding and communication over network, the proposed system uses cryptographic algorithm along with Steganography. In the proposed system, the file which user want to make secure is firstly compressed to shrink in size and then the compressed data is altered into cipher text by using AES cryptographic algorithm and then the encrypted data is concealed in the image. In order to hide the information over the image in complex manner the genetic algorithm based technique is implemented which is used to evaluate the valuable pixels where the data can be hide in a secure manner. In addition of that, for hiding the information in images, the LSB (least significant bits) based steganographic method is used after the selection of eligible pixels. The implementation of the anticipated technique is performed using JAVA technology and for performance evaluation the time and space complexity is computed. In addition of that a

Generating multi-fingered robotic grasps via deep learning <=SEP=> This paper presents a deep learning architecture for detecting the palm and fingertip positions of stable grasps directly from partial object views. The architecture is trained using RGBD image patches of fingertip and palm positions from grasps computed on complete object models using a grasping simulator. At runtime, the architecture is able to estimate grasp quality metrics without the need to explicitly calculate the given metric. This ability is useful as the exact calculation of these quality functions is impossible from an incomplete view of a novel object without any tactile feedback. This architecture for grasp quality prediction provides a framework for generalizing grasp experience from known to novel objects.

The role of visual attention in saccadic eye movements. <=SEP=> The relationship between saccadic eye movements and covert orienting or visual spatial attention was investigated in two experiments. In the first experiment, subjects were required to make a saccade to a specified location while also detecting a visual target presented just prior to the eye movement. Detection accuracy was highest when the location of the target coincided with the location of the saccade, suggesting that subjects use spatial attention in the programming and/or execution of saccadic eye movements. In the second experiment, subjects were explicitly directed to attend to a particular location and to make a saccade to the same location or to a different one. Superior target detection occurred at the saccade location regardless of attention instructions. This finding shows that subjects cannot move their eyes to one location and attend to a different one. The result of these experiments suggest that visuospatial attention is an important mechanism in generating voluntary saccadic eye movements.

Training Products of Experts by Minimizing Contrastive Divergence <=SEP=> It is possible to combine multiple latent-variable models of the same data by multiplying their probability distributions together and then renormalizing. This way of combining individual expert models makes it hard to generate samples from the combined model but easy to infer the values of the latent variables of each expert, because the combination rule ensures that the latent variables of different experts are conditionally independent when given the data. A product of experts (PoE) is therefore an interesting candidate for a perceptual system in which rapid inference is vital and generation is unnecessary. Training a PoE by maximizing the likelihood of the data is difficult because it is hard even to approximate the derivatives of the renormalization term in the combination rule. Fortunately, a PoE can be trained using a different objective function called contrastive divergence whose derivatives with regard to the parameters can be approximated accurately and efficiently. Examples are presented of contrastive divergence learning using several types of expert on several types of data.

Artificial General Intelligence <=SEP=> Against the backdrop of increasing progresses in AI research paired with a rise of AI applications in decision-making processes, security-critical domains as well as in ethically relevant frames, a largescale debate on possible safety measures encompassing corresponding long-term and short-term issues has emerged across different disciplines. One pertinent topic in this context which has been addressed by various AI Safety researchers is e.g. the AI alignment problem for which no final consensus has been achieved yet. In this paper, we present a multidisciplinary toolkit of AI Safety strategies combining considerations from AI and Systems Engineering as well as from Cognitive Science with a security mindset as often relevant in Cybersecurity. We elaborate on how AGI “Self-awareness” could complement different AI Safety measures in a framework extended by a jointly performed Human Enhancement procedure. Our analysis suggests that this hybrid framework could contribute to undertake the AI alignment problem from a new holistic perspective through security-building synergetic effects emerging thereof and could help to increase the odds of a possible safe future transition towards superintelligent systems.