Reviewerly/exHarmony huggingface dataset:
BMCLeech: Introducing Stealthy Memory Forensics to BMC

Several system management technologies have been introduced that leverage additional devices on the main board to asynchronously access and control the host's computing resources. One such prominent technology for server systems is the Baseboard Management Controller (BMC), a co-processors with some firmware that allows an administrator to monitor and administer a server remotely. This paper introduces BMCLeech, the first software that brings forensic memory acquisition onto the BMC which makes it very useful for incident response teams. BMCLeech is based on the open source BMC implementation OpenBMC and internally leverages the power of PCILeech, a well-known framework for memory acquisition via DMA.
AuthDNA: An Adaptive Authentication Service for any Identity Server

Adaptive authentication refers to the way that configures two factors or multi-factor authentication, based on the user's risk profile. One of the most pressing concerns in modern days is the security of credentials. As a solution, developers have introduced the multifactor authentication. The multi-factor authentication has an adverse effect on user experience. This paper proposes a novel adaptive authentication mechanism which tries to eradicate the negative user experience of the traditional multi factor authentication systems. Adaptive authentication gathers information about each user and prevents fraudulent attempts by validating them against the created profiles. This approach will increase the usability, user-friendliness by introducing multi-factor authentication only when its necessary using a risk based adaptive approach. Furthermore, the solution ensures security by authenticating the legitimate user through collectively analyzing the properties, behavior, device and network related information. In the creation of the user profile, the adaptive authentication system will gather and analyze the user typing behaviors using a unique recurrent neural network algorithm named LSTMs with 95.55% accuracy and mouse behaviors using SVMs with 95.48% accuracy. In device-based authentication, a fingerprint is generated to the browser and to the mobile device which is utilized in the analysis of the accuracy rate of the authentication. Blacklisting and whitelisting of the networks and geo velocity of the authentication request are captured under the geolocation and network-based authentication. All the accuracy rates are fed to the risk-based authentication which helps the decision of re-authentication or in the grant of access to the system by analyzing the risk score generated for the authentication request.
MissIt: Using Missed Calls for Free, Extremely Low Bit-Rate Communication in Developing Regions

Mobile devices have become the primary mode for Internet access in developing countries. Yet typical data plans and SMS costs can be overwhelming for low income users in these countries. In this paper, we explore the design and usability of a free but extremely low bit rate communication channel to address this challenge. We propose, a data communication channel that uses to transmit messages between phones, thereby sacrificing performance in exchange for low cost. While the data rate of is extremely low (<1 bps), our prototype implementation and small scale user studies explore the feasibility of this idea for different types of messaging scenarios. Our results show that could be a viable option for messaging scenarios that require short, pre-determined responses (e.g., survey questions) while for traditional SMS-style messaging, a suitable user interface and other customizations are likely required to make it a viable option for users.
Monte Carlo Tree Search for Generating Interactive Data Analysis Interfaces

Interactive tools like user interfaces help democratize data access for end-users by hiding underlying programming details and exposing the necessary widget interface to users. Since customized interfaces are costly to build, automated interface generation is desirable. SQL is the dominant way to analyze data and there already exists logs to analyze data. Previous work proposed a syntactic approach to analyze structural changes in SQL query logs and automatically generates a set of widgets to express the changes. However, they do not consider layout usability and the sequential order of queries in the log. We propose to adopt Monte Carlo Tree Search(MCTS) to search for the optimal interface that accounts for hierarchical layout as well as the usability in terms of how easy to express the query log.
Interdisciplinary knowledge‐based implicit emotion recognition

Summary Detecting different emotions is a key step toward intelligent recommendation and affective computing, which plays an increasingly important role in guiding decisions. There have been a large number of previous work of machine learning in this topic. However, most existing methods neglect the help of long‐term accumulated and precious interdisciplinary knowledge, which cover both computer science and psychology. In this work, we distill part of the psychological knowledge as following: (i) The emotions with the same polarity co‐occur frequently, while emotions with contrary polarity co‐exist hardly; (ii) The relations between emotions are variable and complicated; (iii) The number of emotions that can be aroused in the same moment is limited. In this paper, we propose two novel modules to execute above prior knowledge, by adding external constraints to our proposed model Double Attention Convolutional Neural Network. In the first module, the emotion relationships are captured by an automatically extracted emotion template. In the second module, uncertainty punishment is applied to limit the too frequent emotions. Our proposed model, which employs interdisciplinary knowledge, performs potentially and get state‐of‐the‐art result when compared with models without above constraints.
Optimal Key Generation for Data Sanitization and Restoration of Cloud Data: Future of Financial Cyber Security

Cloud security in finance is considered as the key importance, taking account of the aspect of critical data stored over cloud spaces within organizations all around the globe. They are chiefly relying on cloud computing to accelerate their business profitability and scale up their business processes with enhanced productivity coming through flexible work environments offered in cloud-run working systems. Hence, there is a prerequisite to contemplate cloud security in the entire financial service sector. Moreover, the main issue challenged by privacy and security is the presence of diverse chances to attack the sensitive data by cloud operators, which leads to double the user’s anxiety on the stored data. For solving this problem, the main intent of this paper is to develop an intelligent privacy preservation approach for data stored in the cloud sector, mainly the financial data. The proposed privacy preservation model involves two main phases: (a) data sanitization and (b) data restoration. In the sanitization process, the sensitive data is hidden, which prevents sensitive information from leaking on the cloud side. Further, the normal as well as the sensitive data is stored in a cloud environment. For the sanitization process, a key should be generated that depends on the new meta-heuristic algorithm called crossover improved-lion algorithm (CI-LA), which is inspired by the lion’s unique social behavior. During data restoration, the same key should be used for effectively restoring the original data. Here, the optimal key generation is done in such a way that the objective model involves the degree of modification, hiding rate, and information preservation rate, which effectively enhance the cyber security performance in the cloud.
Implementation of Digitization and Blockchain Methods in the Oil and Gas Sector

Blockchain technologies are now as familiar in many areas of society as television, mobile communications or the Internet. However Blockchain technologies are not only unused, but they are not even seriously thought about in the industrial sectors of the economy and at many industrial enterprises. At the same time, Blockchain technologies have great potential for application in industry, and their implementation is not something too complicated and expensive. Currently, as part of the modernization of ordinary business processes, there is a total digitalization and the transformation of each company and each business process into “data-driven processes”, creating data streams, including data on the registration of the state/origin of physical objects, which is the place of application of blockchain protocols. The use of Blockchain technology is already on the agenda for many Russian companies, if they want to maintain their competitiveness not only in world markets, but even in national and local ones.
ExTuNe: Explaining Tuple Non-conformance

In data-driven systems, we often encounter tuples on which the predictions of a machine-learned model are untrustworthy. A key cause of such untrustworthiness is non-conformance of a new tuple with respect to the training dataset. To check conformance, we introduce a novel concept of data invariant, which captures a set of implicit constraints that all tuples of a dataset satisfy: a test tuple is non-conforming if it violates the data invariants. Data invariants model complex relationships among multiple attributes; but do not provide interpretable explanations of non-conformance. We present ExTuNe, a system for Explaining causes of Tuple Non-conformance. Based on the principles of causality, ExTuNe assigns responsibility to the attributes for causing non-conformance. The key idea is to observe change in invariant violation under intervention on attribute-values. Through a simple interface, ExTuNe produces a ranked list of the test tuples based on their degree of non-conformance and visualizes tuple-level attribute responsibility for non-conformance through heat maps. ExTuNe further visualizes attribute responsibility, aggregated over the test tuples. We demonstrate how ExTuNe can detect and explain tuple non-conformance and assist the users to make careful decisions towards achieving trusted machine learning.
Identification of tumor nodule in soft tissue: An inverse finite‐element framework based on mechanical characterization

Identification and characterization of nodules in soft tissue, including their size, shape, and location, provide a basis for tumor identification. This study proposes an inverse finite-element (FE) based computational framework, for characterizing the size of examined tissue sample and detecting the presence of embedded tumor nodules using instrumented palpation, without a priori anatomical knowledge. The inverse analysis was applied to a model system, the human prostate, and was based on the reaction forces which can be obtained by trans-rectal mechanical probing and those from an equivalent FE model, which was optimized iteratively, by minimizing an error function between the two cases, toward the target solution. The tumor nodule can be identified through its influence on the stress state of the prostate. The effectiveness of the proposed method was further verified using a realistic prostate model reconstructed from magnetic resonance (MR) images. The results show the proposed framework to be capable of characterizing the key geometrical indices of the prostate and identifying the presence of cancerous nodules. Therefore, it has potential, when combined with instrumented palpation, for primary diagnosis of prostate cancer, and, potentially, solid tumors in other types of soft tissue.
Collision Detection or Nearest-Neighbor Search? On the Computational Bottleneck in Sampling-based Motion Planning

The complexity of nearest-neighbor search dominates the asymptotic running time of many sampling-based motion-planning algorithms. However, collision detection is often considered to be the computational bottleneck in practice. Examining various asymptotically optimal planning algorithms, we characterize settings, which we call NN-sensitive, in which the practical computational role of nearest-neighbor search is far from being negligible, i.e., the portion of running time taken up by nearest-neighbor search is comparable to, or sometimes even greater than the portion of time taken up by collision detection. This reinforces and substantiates the claim that motion-planning algorithms could significantly benefit from efficient and possibly specially-tailored nearest-neighbor data structures. The asymptotic (near) optimality of these algorithms relies on a prescribed connection radius, defining a ball around a configuration q, such that q needs to be connected to all other configurations in that ball. To facilitate our study, we show how to adapt this radius to non-Euclidean spaces, which are prevalent in motion planning. This technical result is of independent interest, as it enables to compare the radial-connection approach with the common alternative, namely, connecting each configuration to its k nearest neighbors (K-NN). Indeed, as we demonstrate, there are scenarios where using the radial connection scheme, a solution path of a specific cost is produced ten-fold (and more) faster than with K-NN.
Ultrasonic Sensor Application As A Performance Enhancement Of Robot Two Wheels

Abstract In this paper, Robot Two Wheels are developed further to improve performance or intelligence. A Robot Two Wheels is a robot that can move and balance itself on two wheels, maintain its position upright of the earth’s surface on a flat plane. The design includes the Arduino Mega 2560, accelerometer and gyroscope sensors, as well as proportional Integral Derivative (PID) control as the controlling method. Proportional Integral Derivative control is used to determine the magnitude of the DC Motor’s speed and rotary direction as the driver, based on the robotic body’s tilt angle to the surface of the flat plane. The angle of inclination is taken into consideration and the robot moves either in the forward or backward direction to balance itself. When operated, the self-balancing robot continuously rotates its motor in its efforts to balance at upright position. The self-balancing robot movement is not mapped to its range, where the robot will still move randomly to achieve balanced conditions. The concept of this paper is expand the ability of the robot to sense objects around it which is by adding an ultrasonic sensor as an object detector around it, including obstacles or walls around self balancing robots. Detection of obstructions by ultrasonic sensors will maintain the movement of the robot in the safe area to avoid the impact that can occur when the robot is trying to maintain balance.
SchemaTree: Maximum-Likelihood Property Recommendation for Wikidata

Wikidata is a free and open knowledge base which can be read and edited by both humans and machines. It acts as a central storage for the structured data of several Wikimedia projects. To improve the process of manually inserting new facts, the Wikidata platform features an association rule-based tool to recommend additional suitable properties. In this work, we introduce a novel approach to provide such recommendations based on frequentist inference. We introduce a trie-based method that can efficiently learn and represent property set probabilities in RDF graphs. We extend the method by adding type information to improve recommendation precision and introduce backoff strategies which further increase the performance of the initial approach for entities with rare property combinations. We investigate how the captured structure can be employed for property recommendation, analogously to the Wikidata PropertySuggester. We evaluate our approach on the full Wikidata dataset and compare its performance to the state-of-the-art Wikidata PropertySuggester, outperforming it in all evaluated metrics. Notably we could reduce the average rank of the first relevant recommendation by 71%.
In-hospital Mortality Prediction for ICU Patients on Large Healthcare MIMIC Datasets Using Class Imbalance Learning

The problem of class imbalance in the in-hospital mortality prediction for ICU patients is presented. We propose to build a novel predicting model using the balanced random forest (BRF) algorithm, and tune the hyper parameters using a better performance measure, i.e., adjusted geometric-mean. The performance of the model is evaluated using the data derived from the publicly available Medical Information Mart for Intensive Care (MIMIC-III) database. Our results show that the recall rate of the death class of ICU patients was significantly improved compared with the benchmarking model.
Does Menzerath–Altmann Law Hold True for Translational Language: Evidence from Translated English Literary Texts

Menzerath–Altmann Law (MAL) is regarded as one of the fundamental laws of language due to its extensive validity for different languages at various linguistic levels and applicability for register differentiation. However, whether MAL holds true for translational language remains to be answered. Translational language, different from both the source language and target original (non-translated) language, is viewed as ‘the third code’. This study delves into the validity of MAL for translated English literary texts and its comparable original texts by exploring the relationship between the sentence length (in number of clauses) and the clause length (in number of words). Results of the study corroborate that MAL held true for both original and translated texts. In addition, both a and b, the fitting parameters of MAL formula, could differentiate the translational language from the original, thus justifying the uniqueness of translational language as ‘the third code’ in its own right. This finding suggests that the fitting parameters might be viable indicators for typological differentiation in translation studies. Further, exploring the dynamic relations between a language construct and its constituents may shed some light on the translating process.
'That's Not Me': Surprising Algorithmic Inferences

Online platforms such as Google and Facebook make inferences about users based on data from their online and offline behavior that can be used for various purposes. Though some of these inferences are available for users to view, there exists a gap between what platforms are actually able to infer from collected data and what inferences users are expecting or believe to be possible. Studying users' reactions to inferences made about them, especially what surprises them, allows us to better understand this gap. We interviewed users of Google and Facebook to learn their current beliefs and expectations about how these platforms use their data to make inferences, and identified four common sources of surprise for participants: irrelevant inferences, outdated inferences, inferences with no connection to online activity, and inferences related to friends or family. We discuss the implications for designing inference-generating systems.
High Performance Queries Using Compressed Bitmap Indexes

Data that often contain unchanging records is becoming increasingly important. Many data sources, such as historical archives, sensor readings, health systems, and machine logs, do not change frequently but are constantly increasing. For this reason, the need to process such datasets more quickly has emerged. Bitmap index that can benefit from multicore and multiprocessor systems is designed to process data that has grown over time but does not change frequently. It has a well-known advantage, particularly in low cardinality data queries. Data such as gender, age, marital status, postal code and even date with low cardinality occupy an important place in datasets. Furthermore, the bitmap index using the compression algorithm can be applied efficiently even if the data has a high cardinality. In this study, bitmap index is introduced to improve queries and it has been shown to perform up to 20x faster queries with an appropriate encoding for data containing frequently unchanging records in a performance comparison against a commonly used relational database system.
A Genetic Programming Method for Scale-Invariant Texture Classification

Texture offers an effective characterization of image shape and orientation. Thus, a predominant task is to detect and extract texture features that discriminate accurately images within different semantic classes. The challenge resides in making these features invariant to several changes, such as affine transformation and viewpoint change, in order to ensure their robustness. Besides, the training phase requires a large number of images. To deal with these issues, Genetic Programming (GP) is adopted in this work with the intention of classifying precisely texture images using some training images per class. In fact, in order to automatically generate a descriptor that is invariant to illumination, rotation and scale; the proposed method combines GP with the scale extraction technique involved by SIFT. The performance of the proposed method is validated on five challenging datasets of non-scaled as well as scaled texture images. Results show that the method is robust not only to scale but also to rotation, while achieving significant performance compared to the state of the art methods.
Artificial Intelligence in Learning Skills Assessment

Competency-based learning assessment has recently undergone an important development in the field of education. Its objective lies on offering learners the necessary skills to follow the enormous development of their environment, and to acquire the essentials capacities for various forms of interaction. This type of assessment is characterized by a diagnosis, remediation and improvement of the learners' skills without being limited to the assessment of knowledge. In this work, we propose an evaluation approach which is based on skills assessment strategy with interesting precision. Thus, the intelligent system detects the degree of learning skills C1 (Appropriate), C2 (Analyze and Reason), C3 (Realize), C4 (Validate) and C5 (communicate). In fact, the system used detects the level of the class in the different skills mentioned above, and offers educational situations to remedy the shortcomings that are encountered. Likewise, it offers the necessary support for each student by offering pedagogical situations whose objective is to increase the level of each learner skills (individualized assessment). The system also helps the teacher to make decisions to provide adequate supports. This device allows complete training for students, and it contributes to the development of a balanced (harmonized) personality in learning skills. This artificial intelligence that is involved allows performance while permanently monitoring each student. In fact, it allows a judicious evaluation which follows the enormous progress by adopting an automated method which takes into account the competence level of each pupil.
Interactive View Recommendation

Existing view recommendation approaches proposed a variety of utility functions in selecting useful views. Even though each utility function might be suitable for specific scenarios, identifying the most appropriate ones, along with their tunable parameters, which represent the user's intention during an exploration, is a challenge for both expert and non-expert users. This paper presents an attempt towards interactive view recommendation that automatically discovers the utility function composition during an exploration that best matches the user's intentions and exploration task.