Reviewerly/exHarmony huggingface dataset:
A novel negotiation approach for requirements engineering in a cooperative context

The requirement engineering process aims to provide a requirements specification document that defines the system to be developed. In this paper, we focus on the negotiation phase for the requirements engineering process in cooperative information systems (CIS). However, inter-enterprise cooperatio n can generate conflicts and contradictions among enterprise objectives due to the heterogeneity of their interests. The negotiation includes the discussion of requirements conflict and the search for a compromise approved by all stakeholders. In order to improve this phase, we propose a new negotiation approach for requirements engineering in a cooperative context, which overcomes the following problems: requirements could be in conflict, and the existing conflicts cannot be explicitly well expressed, i.e. conflicts are often unknown. Our approach has two steps, where the first one treats conflict detection by using a matrix. The second step consists on applying heuristic and contract net protocols related to the multi-agent systems for the resolution of the conflicts under a negotiation process. The principal quality of CIS is to interoperate physical sites by respecting their autonomy in this perspective, the multi-agents paradigm propose concepts particularly interesting for the development of CIS, such as dynamic organization, autonomy of control, decentralization, and interaction that includes cooperation, coordination, and negotiation. Finally, we apply our approach on a case study to demonstrate its advantages in reducing the complexity and problems of the requirements engineering.
Spread Embedding Technique in LSB Image Steganography based on Chaos Theory

Spread spectrum technique is one way to increase the security and imperceptibility of messages embedded in stego images. The way this method works is by embedding it spreadly and randomly to all images with certain random patterns. Generally, random patterns are implemented using modulation and PN sequence generators. In this research, spread spectrum embedding techniques are implemented with chaotic encryption. Generally, chaotic encryption is done on the message image, but in this research, chaos encryption is applied to the cover image with the aim of spreading the message. Chaos encryption is chosen because this method only randomizes pixel coordinates without changing pixel values. In this way, the message embedding on the smallest bit of the cover image is carried out in a spread with chaotic patterns so that the attacker is not easily guessed. Chaotic patterns will be formed from the Arnold chaotic map formula with parameters determined from password processing, the goal being that patterns are formed more dynamically. In addition, during the embedding process, the message is encrypted with the XOR operation based on the MSB value. Based on testing the proposed method proved to be safer and slightly more imperceptible compared to the previous method.
Multi-camera Temporal Grouping for Play/Break Event Detection in Soccer Games

Many current deep learning approaches to action recognition focus on recognizing concrete (e.g., single actor) actions in trimmed videos from datasets such as UCF-101 and HMDB-51. However, high-level semantic analysis of sports videos often requires recognizing more abstract events or situations involving multiple players with longer time-scale context. This paper builds upon inflated 3D (I3D) ConvNets for video action recognition to detect and differentiate six abstract categories of events in untrimmed videos of soccer games from multiple fixed cameras: normal play, plus breaks in play due to kick-offs, free kicks, throw-ins, and goal and corner kicks. Raw video unit classifications by variants of the basic I3D network are post-processed by two novel and efficient grouping methods for localizing the boundaries of events. Our experiments show that the proposed methods can achieve 84.2% weighted precision for event categories at the level of video units, and boost event temporal localization mean average precision at 0.5 tIoU (mAP@0.5) to 62.0%.
Big RDF Data Storage, Computation, and Analysis: A Strawman's Arguments

RDF data is big and continues to grow rapidly. RDF data sets are typically viewed as heterogeneous graph data sets with complex correlations and multifaceted heterogeneity. Although there have been flurry of research on processing and analyzing RDF data, efficient storage, computation, analysis of big and growing RDF data continue to challenge multiple computer science disciplines, ranging from systems, network computing, data management to data analytics. In this paper, we make use of strawman arguments on big RDF data challenges with respect to storage, computation, analysis, focusing on identifying the grand challenges in developing high performance RDF storage, parallel computation, efficient distribution, and smart RDF analytics. We attempt to answer a number of important and frequently asked questions: (1) Do we really need RDF-specific storage techniques and algorithms for building efficient and high performance RDF stores? (2) How hard can it be to effectively parallelize big RDF data for high performance storage, computation, and mining? (3) What type of distribution models can we employ to scale RDF processing models and algorithms for real-time querying (subgraph pattern matching) and mining of big RDF data? (4) Can machine learning algorithms be leveraged to effectively mine and dive into RDF data? We attempt to answer these questions by sharing some of our results, our unique experiences, and our lessons learned from a collection of research projects in RDF systems research and development. We will also discuss our ongoing research endeavors, potential applications and avenues for future work.
Empirical Analysis of Fixed Point Precision Quantization of CNNs

Image classification, speech processing, autonomous driving, and medical diagnosis have made Convolutional Neural Networks (CNN) mainstream. Due to their success, many deep networks have been developed such as AlexNet, VGGNet, GoogleNet, ResidualNet [1]-[4],etc. Implementing these deep and complex networks in hardware is a challenge. There have been many hardware and algorithmic solutions to improve the throughput, latency and accuracy. Compression and optimization techniques help reduce the size of the model while maintaining the accuracy. Traditionally, quantization of weights and inputs are used to reduce the memory transfer and power consumption. Quantizing the outputs of layers can be a challenge since the output of each layer changes with the input. In this paper, we use quantization on the output of each layer for AlexNet and VGGNET16 sequentially to analyze the effect it has on accuracy. We use Signal to Quantization Noise Ratio (SQNR) to empirically determine the integer length (IL) as well as the fractional length (FL) for the fixed point precision. Based on our observations, we can report that accuracy is sensitive to fractional length as well as integer length. For AlexNet we observe deterioration in accuracy as the word length decreases. The results are similar in the case of VGGNET16.
Investigating the Feasibility of Digital Repositories in Private Clouds

Installing and configuring a digital repository toolkit for an organisation is a non-trivial task for which many organisations now seek external third-party service providers. Some of these service providers offer a cloud-hosted environment. However, universities increasingly have such cloud infrastructure in-house to support internal systems, and to maintain control and custody of data and systems. This study investigated the feasibility of using a private cloud internal to an organisation for the management of digital repositories. The results show that private cloud environments can run institutional repositories with negligible performance degradation as the number of virtual machine instances in the cloud are increased. A usability study of the prototype tool received positive feedback. Participants in the study were able to install and customise their own DSpace repositories.
Automatic Modulation Classification for DVB-S2 System in Fading Channels

The modulation recognition problem has been researched extensively in the past, but the classification in the presence of an unknown channel still exists a number of open issues. Interestingly, the number of the papers for the automatic modulation classification that are recommended in the digital video broadcasting - satellite - second generation standard is very limited. Motivated by this, the automatic modulation classification technique using the high order statistic approach was present in the paper. Signal model with raised cosine filter was employed for the modulation recognition. The classifier using the K-nearest neighbor algorithm with fourth- and sixth-order cumulants of the signal are investigated and analyzed. The performance of the algorithm is evaluated at a signal-to-noise ratio of 5 dB in additive white Gaussian noise and fading channels. Simulation results showed that the recognition rates were strongly affected by fading channels.
Transformation of Solar Energy to Electricity with Photovoltaic Systems, Reduction of Electrical Consumption and Installation of LED Lamps in the ITSAO

In this work of technological development and research is presented the work done in the ITSAO to contribute to the use of clean energy such as photovoltaics and the implementation of activities to reduce electricity consumption by the technological community, the monitoring of electricity consumption is made on a monthly basis and an annual report is prepared in which the consumption monitoring and the reduction percentage thereof are shown by tables and graphs.The students of electronic Engineering carry out activities of optoelectronics and sustainable development where they intervene with the design and installation of photovoltaic systems that are part of their academic formation.It has a photovoltaic system that produces 2.16 kW-h / d and another will be installed to have photovoltaic energy in the tables of the ITSAO planters; these actions are part of the operational control related to the energy of the Integral Management System (SGI).
Adaptive Confidence Evaluation Scheme for Periodic Activity Recognition in Smart Home Environments

The concept of Internet of Things (IoT) and related applications have developed rapidly worldwide in recent years. Application such as smart home has made it possible to monitor, control and notify alerts in a home. It also able to improve an individual's quality of life by discovering periodic human activity to enable home automations. Furthermore, the selection of appropriate set of sensors and correct preprocessing of sensor data are all important elements to correctly identify user's daily activities. However, these application usually ignore the spatial information that each sensor may provide, and identify user activities simply based on the data collected from sensors. Moreover, the confidence which indicates the probability that activities are correctly recognized is also an important issue. To enhance the result of periodic activity recognition, only the results with high confidence should be included when training sensor data. Thus, this paper proposes an adaptive confidence evaluation scheme that evaluate activities dynamically to enhance the result of periodic activity recognition. Experiment results showed the proposed method with adaptive confidence evaluation scheme may reduce the number of discovered activities at an average of 17.87% and improve the accuracy of find the occurrence of activities compared with proposed method without adaptive confidence evaluation scheme. This indicate that the proposed method with adaptive confidence evaluation scheme can effectively applied to smart home environments.
Probabilistic analysis of QoS‐aware service composition with explicit environment models

In service composition, quality-of-service (QoS) represents a crucial indicator for the policy adoption. Existing composition strategies rarely address the influence of the environment, which may influence QoS and thus lead to sub-optimal composition policies in a dynamic environment. In this study, a model-based service composition approach is proposed. Given the user request, it is possible to first find a set of matching abstract web services (AWSs), and then pull relevant concrete web services (CWSs) based on the AWSs. The set of CWSs can be modelled as a Markov decision process (MDP). In addition, the authors model the environment as a fully probabilistic system, capturing changes of environment probabilistically. The environment model can be further composed of the MDP from the service models, obtaining a monolithic MDP. They demonstrate how the probabilistic verification techniques can be used to find the optimal service selection strategy against their QoS and the environment change. A distinguishing feature of their approach is that the QoS, as well as the dynamic of environment change, is made parametric so that the formal analysis is adaptive to the environment which is of paramount importance for autonomous and self-adaptive systems. Examples and experiments confirm the feasibility of their approach.
Flush Code Eraser: Fast Attack Response Invalidating Cryptographic Sensitive Data

Fault-injection attacks against cryptographic devices have been studied in great detail. As a countermeasure to such attacks, the flush code eraser (FCE) has been proposed. The FCE realizes a new attack reaction in which sensitive data in a cryptographic device are quickly invalidated by redistributing the electrons in the circuits. In this letter, we discuss the abilities of the FCE by evaluating the data invalidation period with experiments using a test chip. As a result, the invalidation period is estimated to be approximately 80 ps, which enhances the security of the cryptographic device and also prevents the possibility of the future attacks.
IPSO: A Scaling Model for Data-Intensive Applications

Today's data center applications are predominantly data-intensive, calling for scaling out the workload to a large number of servers for parallel processing. Unfortunately, the existing scaling laws, notably, Amdahl's and Gustafson's laws are inadequate to characterize the scaling properties of dataintensive workloads. To fill this void, in this paper, we put forward a new scaling model, called In-Proportion and Scale-Out-induced scaling model (IPSO). IPSO generalizes the existing scaling models in two important aspects. First, it accounts for the possible in-proportion scaling, i.e., the scaling of the serial portion of the workload in proportion to the scaling of the parallelizable portion of the workload. Second, it takes into account the possible scaleout-induced scaling, i.e., the scaling of the collective overhead or workload induced by scaling out. IPSO exposes scaling properties of data-intensive workloads, rendering the existing scaling laws its special cases. In particular, IPSO reveals two new pathological scaling properties. Namely, the speedup may level off even in the case of the fixed-time workload underlying Gustafson's law, and it may peak and then fall as the system scales out. Extensive MapReduce and Spark-based case studies demonstrate that IPSO successfully captures diverse scaling properties of dataintensive applications. As a result, it can serve as a diagnostic tool to gain insights on or even uncover counter-intuitive root causes of observed scaling behaviors, especially pathological ones, for data-intensive applications. Finally, preliminary results also demonstrate the promising prospects of IPSO to facilitate effective resource provisioning to achieve the best speedup-versuscost tradeoffs for data-intensive applications.
Identification of Difficult English Words for Assisting Hearing Impaired Children in Learning Language

Hearing impairment is a partial or total inability to hear. Children cannot learn speech and develop vocabulary at a regular pace due to which process of language development in such children is slow. For hearing impaired children, reading and writing English language text is considered to be a complex task. A Text is always composed of sentences and the sentences are made by the combination of complex and simpler words. Extensive use of complex words in a text (stories, essays, study material, or any text available on web stores) makes it difficult to read, write, understand and interpret. This causes more language inconsistency and learning problems for hearing impaired children as compared to the normal hearing peers. Proposed methodology in this paper focuses on the identification of difficult words from the English text for assisting hearing impaired children in learning the English language in a less complex way. Developed system uses C45 decision tree learning model to classify words as difficult or not difficult from the given text after the extraction of lexical and semantic features of words based on linguistic rules specific to hearing impaired children. Proposed methodology, is a full fledge learning tool that not only helps a child to improve and learn reading but also assists the therapist in conducting academic exercises and conclude that which words are difficult or which are not. Results indicate improved accuracy as compared to previous techniques used for the respective research.
MoPo Sane—Mobility Portal for Health Care Centers

In the era of advanced Telemedicine (Projekt-Telepflege Homepage []), outpatient and inpatient stay in health care facilities will continue to be indispensable in the frame of health care and services of general interest. Essential facilities of the Health services (clinics, medical specialists, etc.) are located at the middle-order and/or higher-order regional centers. The project “MoPo gesund—Mobilitätsportal für das Gesundheitswesen” (MoPo sane—mobility portal for the health care centers) as an example for a smart mobility system provides a solution for flexible and networked mobility in order to improve the accessibility to health centers, especially for rural areas. A web-based mobility portal will be used to collect the mobility needs of patients, visitors and employees with no or restricted individual mobility options (older people, young people without driving licenses, socially disadvantaged people) and connect them to sustainable mobility alternatives. The portal will offer a clearly defined regional backdrop with “mobility endpoints” that can be clearly located from a geographical location. The consideration of such system boundaries has significant advantages. Providing the necessary mobility services would be difficult if the sources and destinations of the paths covered by rural residents are weakly concentrated or widely dispersed. This project will concentrate the mobility endpoints onto various health care facilities.
On Verification of Remote Computing on Potentially Untrusted Nodes

Verifying remote computing environments, such as computing nodes in fog and edge computing, has gained considerable attention. This poster extends an existing remote attestation method so that it can verify that obtained results are generated by trusted computing nodes as well as remote computing nodes are trusted.
Comparative Analysis of Three Obstacle Detection and Avoidance Algorithms for a Compact Differential Drive Robot I N V-Rep

The aim of this research is to build a compact differential drive robot using the Virtual Robotics Experimentation Platform. Sensors are embedded in the Pioneer 3-dx mobile robot to provide necessary data from the real world to the robot. The main purpose of the mobile robot is its ability to arrive at a given destination (goal) precisely and astutely, hence, significantly reducing the risk of human mistakes. Many existing algorithms like obstacle detection, lane detection is combined to provide the essential and basic control functionalities to the car. The mobile robot controller model runs on a series of benchmark tasks, and its performance is compared to conventional controllers. During the scope of this project, comparisons between different algorithms, hardware and tools have been made to choose the best-fit for the project. The results are obstacle detection algorithms and a terrain handling feature, that works very well in simulations and real-life situations. The major tailbacks during the development of this project were limitations caused by low hardware computational power, the presence of stronger processors would exponentially increase the throughput and consequently improve the accuracy of the scene objects and the obstacle detection algorithms.
Real-time 3D Route Planning based on Modified Rapidly Exploring Random-tree Algorithm

In this paper, the real-time Unmanned aerial vehicle(UAV) three-dimensional route planning problem is firstly modeled, and the improved fast-expanded random tree algorithm (M-RRT) is used to optimize the route. Simulation results show that M-RRT has good real-time and complete performancein solving real-time three-dimensional route planning problem, and can quickly find routes that can avoid all threat areas and meet the error range, Meanwhile, due to the greedy strategy and random sampling of the algorithm, M-RRT algorithm has strong stability.
Development of Machine Learning-Based Text Processing Tool for Abstract of Physical Chemistry Papers

We have developed a text processing tool consisting of several machine learning-related programs such as TreeTagger and Doc2Vec. This system was applied to a number of abstracts of a couple of physical chemistry journals (Chemical Physics Letters and American Journal of Physics). The degree of similarity of contents was then made evaluatable as a guide for papers to be checked for a certain topic.
Detection of Target by Means of Weibull/CFAR Circuit Modified CA LIN/CFAR Circuit

I have observed sea clutter containing a target (ship) using an S-band radar with a frequency of 3.05GHz, a beamwidth of 1.8°, a pulsewidth of 0.5µs, and a pulse-repetition frequency of 1,600Hz. To investigate the sea clutter amplitude statistics, I use partial Q-Q plot. I have found that the sea clutter amplitudes obey the Weibull distribution with the shape parameter of c=3.18 for entire data and the Weibull and log-Weibull distributions with the shape parameters of c=3.05 to 4.36 and c=11.84 to 11.95, respectively, for data within the beam width of an antenna. The number of the largest log-likelihood of the Weibull and log-Weibull distributions to data are 9 and 2, respectively, from 11 range sweep numbers. I have used the Weibull/CFAR (Constant False Alarm Rate) circuit which is modified a Cell-Averaging (CA) LIN/CFAR circuit. It is found that ship is detected and sea clutter is suppressed with improvement of 40.82dB by Weibull/CFAR.
Meal Preference Extraction and Its Rating Scale for Diet Analysis Using Associative Mining

In this paper, we proposed a method to obtain the meal preference for diet requirement analysis targeted for restaurant customers and evaluate them using the proposed recommendation scale. We applied associative mining and filter the mining result and extracted the preferred meal in form of workflows from a universal workflow. The workflow represents dietary preferences in terms of combination of meals taken in different categories. Once we extracted a workflow, we evaluated the workflow with a rating scale to show the recommendation strength quantitatively. Finally, we gave a comparison of the method effectiveness by comparing the extraction preference rating scale with a conventional menu which is based on frequently ordered meals.