Reviewerly/exHarmony huggingface dataset:
Distributed Data Protection and Liability on Blockchains

Blockchains and the GDPR pursue similar objectives where they seek to grant users greater control over their personal data. While the latter pursues this goal by imposing duties of care to centralized controllers and collectors of data, blockchains go a step beyond by trying to eliminate these stakeholders and the need to trust them. Nevertheless, the rules set out by the GDPR apply whenever personal data are at stake, and various actors of the blockchain ecosystem risk liability for controlling of processing data in violation of privacy requirements. A possible solution is to re-contextualize the concepts of data controlling and responsibility, as framed by the GDPR, in light of blockchains' enhanced individual autonomy. In this paper, we set the framework for a further inquiry on the role of users as both data subjects and data controllers of distributed ledgers.
Co-design of Business and IT Services - A Tool-Supported Approach

Service modeling is an important step in designing service-oriented systems. There are multiple levels of design because service science includes both the business rationale and the IT implementation of the services. As business and IT perspectives differ, the modeling techniques are different, and often the respective modeling languages are disconnected or ad-hoc. We propose a new service-modeling approach for connecting the business modeling and the web service modeling by presenting these two perspectives in a single model. We present a multi-stage modeling process for capturing different perspectives and creating models iteratively by working with levels of abstraction from higher to lower. The model is then used as an input in order to generate a REST API specification in the OpenAPI format to feed the next stages of the service life-cycle.
Fine-Grained Human-Centric Tracklet Segmentation with Single Frame Supervision

In this paper, we target at the Fine-grAined human-Centric Tracklet Segmentation (FACTS) problem, where 12 human parts, e.g., face, pants, left-leg, are segmented. To reduce the heavy and tedious labeling efforts, FACTS requires only one labeled frame per video during training. The small size of human parts and the labeling scarcity makes FACTS very challenging. Considering adjacent frames of videos are continuous and human usually do not change clothes in a short time, we explicitly consider the pixel-level and frame-level context in the proposed Temporal Context segmentation Network (TCNet). On the one hand, optical flow is on-line calculated to propagate the pixel-level segmentation results to neighboring frames. On the other hand, frame-level classification likelihood vectors are also propagated to nearby frames. By fully exploiting the pixel-level and frame-level context, TCNet indirectly uses the large amount of unlabeled frames during training and produces smooth segmentation results during inference. Experimental results on four video datasets show the superiority of TCNet over the state-of-the-arts. The newly annotated datasets can be downloaded via http://liusi-group.com/projects/FACTS for the further studies.
Towards Self-Defense of Non-Stationary Systems

One of the major trends in research on Intrusion Response Systems is to use a model of the system to be protected and/or a model of the attacker to predict the evolution of the system and of the strategy of the attacker. However, very often, modeled systems exhibit a non-stationary behavior due to changes in their configuration, in the software base and in the users behavior. If not properly captured by the system model, such a non-stationary behavior could lead to divergences between the expected and the actual behaviors, thus invalidating the model-based approach. In this paper, we introduce a model-free technique for self-defense of non-stationary systems based on Q-Learning. We experimentally show that the proposed approach is able to effectively capture the dynamics of the underlying system and quickly adapts to changes in the environment.
Influence of rewards and recognition on employees� motivation and job performance: path analysis approach

Objective: The aim of smart home is to create an environment that is aware of the activities of elderly, disabled people within home and then predicting their behavior which aids for further actions like alerts.Predictive Intelligence environment gathers information from Wireless Sensor Networks (WSN) from various parts of the home which includes daily activities, interactions with the objects within the monitoring environment.Methods/Statistical Analysis: Assistance independent living of the elderly helps them to lead the daily life independently in a self-regulating way.Based on the key daily living activity like preparing food, showering, walking, sleeping, watching television, reading books etc., their routine and their wellness can be tracked.Behavior of occupant of smart home different times using prediction methods is collected based on which the extraction of patterns is done leading to classification of activity and rating the activity as normal or abnormal.A novel behavior prediction model for daily activity and analysis in monitoring has been designed and developed.Findings: The datasets are being experimented with the support vector machines and deep learning networks.Based on the best performance results, deep learning network with SVM linear kernel is capable of making correct classification of datasets accurately with the average accuracy of 88.20% and the prediction time is 5.178 sec.The results show that daily normal and abnormal patterns can be identified with behavioral changes.Application/Improvements: The deep learning model of smart home system is a useful approach for learning the mobility habits at the home environment, with the potential to detect behavior changes that occur due to health problems.A deep learning based system can successfully identify and predict the activity of the elder people.
The Study of the Permeability of the Upper Crust Part on the Photo Image of the Surface in the Area of the Nalychevo Field of Thermomineral Waters (Kamchatka)

The study of tectonic break of the upper layer of the earth's crust in the area of the Nalychevo field of thermomineral waters was made. The source material was the results of decryption of aerial-and space images. The specific length of the lineaments (SLL) of the supposed faults was determined up to the depth of 3 km and a 3D matrix was created. The voxel image of the SLL of the investigated block with areas of increased crust permeability was constructed. The Nalychevo field is located at the intersection of the north-western, latitudinal and submeridional zones of increased permeability, which are distinguished in the voxel model by the maximum values of the SLL.
Quasi-Dyadic Girault Identification Scheme

Zero-knowledge identification schemes allow a prover to convince a verifier that a certain fact is true, while not revealing any additional information. In this paper, we propose a scheme whose security relies on the hardness of the Quasi-Dyadic Subcode Equivalence and the Quasi-dyadic syndrome decoding problems. Our code-based scheme is an improvement of the code-based identification scheme devised by Girault. Our construction uses quasi-dyadic subcode with a cheating probability of 1/2. Using quasi-dyadic subcode allows to reduce matrix size and also the communication cost by sending lower data.
Consistency Constrained Reconstruction of Depth Maps from Epipolar Plane Images

In this paper, we propose a method of reconstructing the depth map of a set of multiview images from the epipolar plane images (EPIs) of multiview Images. Our method involves two steps: finding support points and estimating depth. First, we propose to include a consistency term and a smoothness term in the objective function for edge point detection, where the consistency term is used to identify edge points and the smoothness term is applied to mitigate false edge detection due to light density variations caused by viewpoint changes. Then, based on the detected edge points, a depth map can be estimated by solving a energy minimization problem, in which a line uniformness term and a matching error term are introduced to ensure the line traces estimated from EPIs for depth estimation match the colors of edge points well. The depths of non-edge points are then estimated by introducing an additional prior term. In order to speed up our algorithm, the depth estimation problem is aggregated by a winner-take-all strategy. Experiments show that our method outperforms the state-of-the-art schemes in reconstructing depth map with fine details.
Geothermal Exploration in Some Interest Geothermal Area in Republic of Yemen

The first geothermal area (Al Lisi–Isbil Geothermal field) is located about (100) km from Sana'a capital city of Republic of Yemen, and the second geothermal area (Alsyani – Algandeah Geothermal field) is located about (250) km from Sana'a capital city of Republic of Yemen. In addition, the third geothermal area (Red Sea geothermal area) is located about (290) km from Sana'a capital city of Republic of Yemen. The geothermal mapping was carried out in (Al Lisi and Isbil and Alsyani – Algandeah Geothermal field) in the western area of Yemen.
A Constrained Randomization Approach to Interactive Visual Data Exploration with Subjective Feedback

Data visualization and iterative/interactive data mining are growing rapidly in attention, both in research as well as in industry. However, while there are a plethora of advanced data mining methods and lots of works in the field of visualization, integrated methods that combine advanced visualization and/or interaction with data mining techniques in a principled way are rare. We present a framework based on constrained randomization which lets users explore high-dimensional data via `subjectively informative' two-dimensional data visualizations. The user is presented with `interesting' projections, allowing users to express their observations using visual interactions that update a background model representing the user's belief state. This background model is then considered by a projection-finding algorithm employing data randomization to compute a new `interesting' projection. By providing users with information that contrasts with the background model, we maximize the chance that the user encounters striking new information present in the data. This process can be iterated until the user runs out of time or until the difference between the randomized and the real data is insignificant. We present two case studies, one controlled study on synthetic data and another on census data, using the proof-of-concept tool SIDE that demonstrates the presented framework.
A Comparative Analysis of Traditional and Modern Data Compression Schemes for Large Multi-Dimensional Extendible Array

Data analysis and mining in scientific domains involve storage of large-scale multi-dimensional datasets for scientific, statistical & engineering applications in multidimensional online analytical processing (MOLAP) databases. Because of the size of the datasets is increasing and the degree of data-sparsity is being high, it is important to find the suitable and efficient compression scheme for storing data at a minimal scheme. This paper represents a comparative analysis of Traditional and Modern Data Compression Schemes for Multi-Dimensional data ranging from dimension 1 to 3. The main idea is to compare the space savings of four different & significant compressions schemes i.e. Bit Map, Header Compression, Compressed Row Storage (CRS) & Extendible Array Based Compression Scheme (EaCRS). The results from experiments show that EaCRS scheme is better than the other schemes in case of space complexity especially for higher data density.
Reliable Rowhammer Attack and Mitigation Based on Reverse Engineering Memory Address Mapping Algorithms

Rowhammer attacks intentionally induce bit flips to corrupt victim’s data whose integrity must be guaranteed. To perform sophisticated rowhammer attacks, attackers need to repeatedly access the neighboring rows of target data. In DRAM, however, the physical addresses of neighboring rows are not always contiguous even if they are located before or after a target row. Hence, it is important to know the mapping algorithm which maps between physical addresses and physical row indexes not only for an attack but also for protection. In this paper, we introduce a method to reverse engineer the exact mapping algorithm and demonstrate that the assumption in previous rowhammer work is faulty. In addition, we introduce a novel and efficient rowhammer method and improve existing mitigations that has a security hole caused by the faulty assumption. Finally, we evaluate the effectiveness of the proposed attack and show that the proposed mitigation almost perfectly defends against rowhammer attacks.
Gigabit Switches Considered Harmful

Objective: Authentic originals and the Ethernet have collected incredible enthusiasm from the two futurists and scholars over the most recent quite a long while. In this work, we demonstrate the development of Boolean rationale, which encapsulates the instinctive standards of calculations. We investigate new stable systems (Suet), refuting that the acclaimed community-oriented calculation for the examination of enormous multiplayer online pretending diversions keeps running in Ω(n!) time. Methods/Statistical Analysis: We expect that every part of Suet permits the investigation of connected records, autonomous of every other segment. Even though cyberneticists, for the most part, accept the correct inverse, Suet depends on this property for right conduct. Findings: While we know about the same examinations on superblocks, a couple of undertakings have been labeled and watched essentially. Likewise, blunder bars have been omitted, since a large portion of our information focuses on 95 standard deviations from watched implies. This is evidential to the accomplishment of our work. Application: Further, we have an unrivaled perceiving how thwarts can be associated with the examination of the Internet. We described that e-business and entries are usually incongruent. Keywords: Boolean Logic, Ethernet, Super Locks
Proposal on Mutual Cooperation Between Simulation Research and Field Research in Archaeology

In this paper, we propose a methodology to collaborate the research using agent simulation and the research using conventional method (field research) in archaeology. The main stream researches using Agent-Based Simulation (ABS) are unidirectional cooperation with the results of field research as input and the results of simulation research as output. In our proposed method, by presenting the hypothesis verification method from ABS result, the simulation result can become the input of field research. As an application example of proposed method, we discuss the problem of whether native Jomon people or Chinese-Korean immigrants played the major role of agricultural culture in Yayoi period by ABS.
Quasi Black Hole Effect of Gradient Descent in Large Dimension: Consequence on Neural Network Learning

The gradient descent to a local minimum is the key ingredient of deep neural networks learning techniques. We consider a function L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> (.) in dimension n with a random set of m absolute minima. When log m = o(n), we show that a gradient descent from an initial random point quasi always ends on a unique local minimum approximately at the centroid of the absolute minima. This fake minimum acts like an absorbing node, but its value by function L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> (.) can be far above the values obtained by L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> (.) on the absolute minima and sometimes gives very bad coefficients for the neural network. Fortunately in most cases the fake minimum leads to a neural network with not so bad prediction, with an error rate of order n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1/4</sup> . The only way to escape the fake minimum is to start a new gradient descent from a new random point and we show that finding a good initial point takes in average time which is at least proportional to e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bn</sup> /mn <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for some b > 0.
A Quality Attribute-based Evaluation of Time-series Databases for Edge-centric Architectures

Edge computing is unlocking the potential of Industrial Internet of Things (IIoT) to deliver business value and impact. Reliable data processing and guarantee of services within the IIoT sphere mandates the use of local storage within the device. The choice of database is also crucial for the performance of applications deployed on this resource-constrained hardware. Yet, there has not been any defined quality-attribute based approach for selecting databases for edge deployments. Much of the work done in the past focus on evaluating databases for applications deployed on high computing servers and cloud-first architectures.
Accurate Target Annotation in 3D from Multimodal Streams

Accurate annotation is fundamental to quantify the performance of multi-sensor and multi-modal object detectors and trackers. However, invasive or expensive instrumentation is needed to automatically generate these annotations. To mitigate this problem, we present a multi-modal approach that leverages annotations from reference streams (e.g. individual camera views) and measurements from unannotated additional streams (e.g. audio) to infer 3D trajectories through an optimization. The core of our approach is a multi-modal extension of Bundle Adjustment with a cross-modal correspondence detection that selectively uses measurements in the optimization. We apply the proposed approach to fully annotate a new multi-modal and multi-view dataset for multi-speaker 3D tracking.
BYOD Collaborative Storytelling in Tangible Technology-Enhanced Language Learning Settings

Research on pedagogical and didactic advancements in the context of new mobile technologies is currently gaining importance. This paper presents a novel pedagogical framework, in which augmented interactions with the surrounding environment provide the students an enhanced learning experience through the gadgets they own. Our pedagogical model employs QR codes that are easily accessible through smartphone apps and stimulates the efficient development of essential communication skills. Hence, students can use their own devices to interact with the educational content, share information, and help each other while working collaboratively on different assignments. More specifically, we introduce and explore a new BYOD-based T-TEL setup that supports the creation and management of storytelling activities and fosters the development of communication skills through m-CSCL. This approach could be extended to environments specifically crafted for individuals with special needs.
Towards Cross-modality Topic Modelling via Deep Topical Correlation Analysis

The cross-modality topic detection in social media retains as an open problem mainly due to the difficulty of dealing with modality independence and modality missing. In this paper, we present a novel Deep Topical Correlation Analysis (DTCA) approach, which achieves robust and accurate topic detecting for micro-blogs and handles aforementioned challenges simultaneously. In particular, bidirectional recurrent neural networks and convolutional neural networks are used to learn deep textual and visual features, respectively. Then a Canonical Correlation Analysis based fusion scheme is proposed, which has two innovations to deal with both two problems mentioned above. We further release a large-scale cross-modal twitter dataset for topic detection. Extensive and quantitative evaluations are conducted with comparisons to several state-of-the-arts and alternative approaches on this dataset. Significant performance gains are reported to demonstrate the merits of proposed approach.
Building a Better Decision Tree by Delaying the Split Decision

Binary decision trees are non-linear prediction models dating back to Breiman's work in the 1980's. The standard algorithm for building a decision tree is a greedy approach whereby all variables and levels of the variable are cycled through until the partition is found that minimizes the impurity of the partition. Our approach delays the split decision one or more steps. We have found that delaying the split decision just one level often provides a tree with better statistical properties and better predictive capability.
Framework for Knee Joint Movement Analysis with Inertial Sensors and Recursive Filters

Health and sports monitoring based on wireless low power and low range communication devices have been proposed by a number of commercial and scientific solutions. Low cost and small form devices allow data transfer and processing without cumbersome cabling. Inertial Measurement Units (IMU), that include an accelerometer, a gyroscope and sometimes may include one magnetometer, are used to determine body movement profile. Despite their inherent inaccuracy, these sensors are used for computing three-dimensional space object orientation. These computations are usually implemented with sensor fusion algorithms within devices that have enhanced computational capabilities. This paper presents a work that aims at estimating the limb and body trunk movements of a pedaling user/patient in a bicycle like device. The presented study has been undertaken within the Exobike project. The Exobike is a bicycle like exoskeleton where the lower limbs and the trunk of a user/patient are to be monitored using IMU data transferred through a radio frequency Bluetooth Low Energy protocol. This paper focuses on the use of IMU sensors and a type of recursive filters to estimate movement profile.
kFHCO: Optimal VM Consolidation via k -Factor Horizontal Checkpoint Oversubscription

Consolidation is one pivotal practice for virtual machine (VM) placement in compute cloud to reduce the cost and energy consumption. Oversubscription is often used by cloud service provider (SP) to fulfill this consolidation goal. But too much consolidation may overload cloud servers, which adversely impact the Quality of Service (QoS) of cloud services. Actions need to be taken by the cloud management platform to maximize consolidation while reducing the risk of overload. There are mainly four approaches to mitigate overload: Ballooning technique (i.e., resource cooperation), VM live migration, optimized placement of VMs and proactive machine learning. But still those works suffer from some disadvantages such as incompatibility for cross-platform cloud services, inability to handle overload radically or being limited to single physical machine (PM) scenario. In this paper, we propose a VM checkpoint placement strategy k-Factor Horizontal Checkpoint Oversubscription (kFHCO) which maximizes consolidation while constraining the chance of overhead. Since the consolidation problem is usually NP-hard like Bin Packing, we first determine the optimal k and target VMs/PMs using a probability-based trigger for the checkpoints. We then formulate the problem as an optimization problem and solve it through an approximation algorithm. We compared our approach with state-of-art solutions using different strategies of VM migration. Simulation results show that our solution can achieve better consolidation level and save cost in various constraints compared with state of the arts.