Reviewerly/exHarmony huggingface dataset:
An automated approach to fix buffer overflows

Buffer overflows are one of the most common software vulnerabilities that occur when more data is inserted into a buffer than it can hold. Various manual and automated techniques for detecting and fixing specific types of buffer overflow vulnerability have been proposed, but the solution to fix Unicode buffer overflow has not been proposed yet. Public security vulnerability repository e.g., Common Weakness Enumeration (CWE) holds useful articles about software security vulnerabilities. Mitigation strategies listed in CWE may be useful for fixing the specified software security vulnerabilities. This research contributes by developing a prototype that automatically fixes different types of buffer overflows by using the strategies suggested in CWE articles and existing research. A static analysis tool has been used to evaluate the performance of the developed prototype tools. The results suggest that the proposed approach can automatically fix buffer overflows without inducing errors.
Merging Large Ontologies using BigData GraphDB

Nowaday the massive and growing production of digital data is the natural consequence of the use of ICT technologies in every aspects of human life. The benefits carried out by the digital revolution are innumerable both for users involved with the retrieval of useful and rich information and for companies which tries to have profits from data processing knowledge discovery. For this reason, data have a crucial role both from technology and economic-financial point of view. In fact, it is often not trivial to extract meaningful and reusable information from the large amount of available data for different reasons (e.g. unstructured data, lack of ability to process the size of data, standardless representation of data leading to heterogeneity). One of the most serious issues to consider in this context is heterogeneity, which can arise at different levels of abstraction, preventing a richer and more exhaustive retrieval of information. In our opinion, the matching and merging of various information sources become crucial process in order to create a comprehensive knowledge base smart applications. The use of pure formalisms, i.e. ontologies, is a good strategy to limit the gap originating from different sources. In this paper we propose a novel technique for matching and merging ontologies, which aims to create an integrated ontology based on standard representations of abstract concepts related to a particular domain. Then we present a real use of our methodology by means a use case implementing a graph-based knowledge base related to a complex domain related to cultural heritage.
An EpiDoc ontological perspective: the epigraphs of the Castello Ursino Civic Museum of Catania via CIDOC CRM

The rich epigraphic heritage of the Castello Ursino Civic Museum of Catania has been studied by the EpiCUM project that encoded it in EpiDoc TEI XML, an XML based standard digital representation for cultural heritage contents. The project made the epigraphic heritage available in a digital museum: under the guise of the ‘Voci di Pietra’ exhibition, a selection of epigraphs were presented, implementing innovative presentation modalities thanks to a smart use of technological and digital means. Information contained in the epigraphs was semantically reorganized in a unique homogeneous container, the EpiONT ontology, constructed according to the Linked Open Data paradigm and to consolidated international standards. The encoding of the ancient texts, by the TEI standard and its EpiDoc subset, is wedded to the paradigmatic semantic web model for museums and cultural heritage. The EpiONT ontology is currently populated by 580 epigraphs collected in the Castello Ursino Civic Museum. Designed according to the CIDOC CRM standard, it makes use of the SKOS vocabularies of the EAGLE project concerning material, execution technique, type of inscription, and type of support of an epigraph. The EpiONT ontology additionally can handle any uncertainty in the origin and place of discovery of the epigraphs.
Hardware Architecture Proposal for TEDA Algorithm to Data Streaming Anomaly Detection

The amount of data in real-time, such as time series and streaming data, available today continues to grow.Being able to analyze this data the moment it arrives can bring an immense added value.However, it also requires a lot of computational effort and new acceleration techniques.As a possible solution to this problem, this paper proposes a hardware architecture for Typicality and Eccentricity Data Analytic (TEDA) algorithm implemented on Field Programmable Gate Arrays (FPGA) for use in data streaming anomaly detection.TEDA is based on a new approach to outlier detection in the data stream context.The suggested design has a full parallel input of N elements and a 3-stage pipelined architecture to reduce the critical path and thus optimize the throughput.In order to validate the proposals, results of the occupation and throughput of the proposed hardware are presented.The design reached a speed of up to 693x, compared to other software platforms, with a throughput of up to 10.96 MSPs (Mega Sample Per second), using a small portion of the target FPGA resources.Besides, the bit accurate simulation results are also presented.This work is a pioneer in the hardware implementation of the TEDA technique in FPGA.The project aims to Xilinx Virtex-6 xc6vlx240t-1ff1156 as the target FPGA.
Dark Channel Processing for Medical Image Enhancement

The images obtained through biomedical instruments are not always satisfactory. They are often blurred or vague. Doctors or researchers undergo a lot of problems because of these artefacts. Our research concentrated on image processing system which enhance these biomedical images. Dark channel used for this purpose followed by transmission maps which are calculated for every image. The radiance in the image is recovered and soft matting is used. Satisfactory results were obtained which showed clearer biomedical images. Further SSIM values and correlation factor are calculated to compare the results. The results were tested for ultrasound and microscopic medical images.
Impact of Virtual Reality on Modern Education

Virtual reality in education is at its center stage. For students to gain relevant skills there is a need to introduce a virtual world so that one can achieve the practical aspect needed in the workplace environment. Memorizing facts makes students bored; hence, the need for virtual reality that will help students gain expertise. The paper seeks to discuss the impact of virtual reality in modern education. To understand the effects of virtual reality in the education sector, the theoretical approach and quantitative research have been used. The results of the study show that virtual reality assists students with special education needs and ensure skills are obtained. Virtual reality improves student self-esteem.
Open IOT Service Platform Technology with Semantic Web

This paper centers around how innovations adds to enhancing interoperability between IoT gadgets, and making effectively utilization of IoT gadgets.The proposed stage innovation gives semantic-based IoT data administrations, and semantic interoperability of IoT gadgets.This administration stage can be material to a great deal of semantic IoT administrations: gathering imperceptible data in genuine condition by brilliant gadgets, giving keen life benefits by sharing, taking an interest, circulating open detecting data.
‘The Tireless Selling-Machine’ – Commercial Deployment of Social Bots during Black Friday Season on Twitter

In recent years, mainstream E-commerce platforms have changed the way consumer goods are merchandized online. While such platforms increasingly discover social elements to drive sales, social media start implementing E-commerce features themselves. This emerging intersection also provides space for bot activity in a Social Commerce (SC) context, which is sparsely understood. In this short paper, we investigate the deployment of social bots during a large-scale commercial event. To this end, we applied bot detection techniques to the Twitter communication during the 2018 Black Friday season. Using three distinct metrics, we identified 42 bot-like accounts. A manual classification of 11,889 tweets found those bots to be primarily deployed in pre-transactional phases of SC to promote third party products and to initiate external transactions. Our results further suggest adapting detection metrics to event-specific user behavior. Further research aims at the dynamics, impact, and network positions of SC bots.
Blockchain, intellectual property and fashion

Definition: A blockchain is a distributed ledger that can record all sorts of transactions between users (Distributed Ledger Technology). This ledger is not centralized in one computer but distributed onto the computers of participants, also known as ‘nodes’. It is safe, transparent and operates outside any central control body. Different types of blockchain: the network is more or less open, depending on the type of blockchain. There are three types of blockchain: The choice of a blockchain does not only constitute a technical challenge but a governance issue: in the case of private blockchains, the protocol and conditions of use are defined by the network administrator. Disintermediation?: This technology is disruptive as it does not require a central authority. It does not require the involvement of a third party (eg banks, public registries, etc) to transfer value or secure the transaction. In traditional databases, a transaction needs...
Blockchain-based Power Trading Process

Recently, the paradigm of the power industry has been digitalization with a focus on renewable energy.With this shift toward the energy-ICT convergence accelerator, there will also be many changes in effect energy policies.In particular, in terms of energy demand management, there will be regional-centric, self-reliant decentralization, and the activation of Distributed Energy Resources (DERs) including renewable energy will result in the deployment of microgrid-type virtual power plants on a region-wide basis.This paper designs a blockchain-based power transaction process in which individuals (producers) can produce and use power themselves or sell the remaining power to others, rather than transmitting and using the developed power from the existing centralized power.
Photovoltaic Geographical Information System module for the estimation of solar electricity generation: A comparative study in Khyber Pakhtunkhwa Pakistan

Renewable energy resources are safe and easy to access giving solution to the problems caused by non-renewable energy resources (fossil fuels etc.) and can balance the increasing demand of the electricity generation in Pakistan. The aim of this study was to utilize PVGIS module to estimate the solar electricity generation at six districts (such as Dir, Mansehra, Peshawar, Buner, Dera Ismail Khan (DIK) and Chitral) of Khyber Pakhtunkhwa province of Pakistan. PVGIS is a web based interactive GIS system provided database (based on meteorological geostationary satellites e.g., Meteosat &amp;amp; GOES) which include monthly and yearly average values of the global solar irradiation on horizontal and inclined surfaces, as well as climatic parameters (i.e.; Linke atmospheric turbidity, the ratio of diffuse to global irradiation). Based on the PVGIS module, monthly averages of global irradiation were utilized to estimate the solar electricity generation at six locations of Khyber Pakhtunkhwa. The results showed that the daily as well as monthly electricity production (Ed and Em) and global irradiance (Hd and Hm) for all the districts are maximum in the months of March until October and minimum during January, February, November and December. The acquired results of the estimated electricity production (kWh) and the estimated irradiations (kwh/m2) per year from a PV system for the six districts exhibited that Dera Ismail Khan (DIK) district has highest estimated electricity production as well as estimated irradiations followed by Buner, Dir and Peshawar districts. It is concluded that PVGIS datasets can be used for the estimation of solar electricity generation and as a support system for the policy-making in the region.
ENTROTUNER: A Computational Method Adopting the Musician’s Interaction With the Instrument to Estimate its Tuning

Archeomusicologists commonly use methods based on the physical properties and the relative tuning system of a musical instrument in order to estimate its tones.However, because the musician often alters the tones' frequency, for example, while playing in wind instruments by means of embouchure or by stressing the string in string instruments, the current methods that neglect the musician's interaction with the instrument cannot provide solid results.In this work, we introduce ENTROTUNER, a computational method, based on mathematical optimization, to more accurately estimate the generated tones by considering: the instrument as a sound production mechanism, the relevant musical scale(s), and the musician's interaction with the instrument.We simulate this interaction as a system that, by following tuning rules, aims to maximize the partials' overlap (harmonicity), coded as entropy's minimization of the aggregated tones' spectrum.Last, we put ENTROTUNER into practice for the ancient Greek wind instrument Aulos.The results reveal that, compared with the traditional methods, ENTROTUNER highlights increased harmonicity (entropy decreased by 0.341bits), eleven additional consonant intervals, as well as 47.8% more tuning quality for the musical instrument.
A Case Study on Economic Development of Tanzania

The United Republic of Tanzania is one of the East African countries landlocked by 8 different nations has a literacy rate of almost 70% and is growing by 7% of GDP per year. It is a $ 80 billion economy with a per capita income of almost $1700. The nation which has almost 15% of agriculture land offers 80% employment in agriculture area only rest 20% is employed in industries and services. Except agriculture main industries are mining and wood products. The presence of mineral resources, tourism and natural gas has led to an increase in the attraction of foreign investors into the country by 14.5 percent. FDI has covered almost half of the country’s current account deficit and 6.5 percent of its GDP. Investors are attracted due to proper implementation of macroeconomic policies, effectiveness in privatization and amount of natural resources available in Tanzania. The main obstacles to why some investors tend to withdraw capital inflows are lack of transparency, infrastructure, and low development in industrial sector. The following case will highlight the economic development and hindrance faced by Tanzania and how is has overcome and growing rapidly.
Identifying and assessing factors affecting farmers’ markets Electronic Benefit Transfer sales in Hawai‘i

Abstract Objective: Electronic Benefit Transfer (EBT) placement at farmers’ markets can reduce access disparities for low-income consumers. However, resources needed to operate EBT programs may challenge markets’ business models. A conceptual model of factors impacting EBT program success was developed from literature, and an exploratory study conducted to assess the impact of model variables on market EBT sales. Design: Annual EBT sales data were obtained for all Hawai‘i farmers’ markets with EBT programs ( n 22). Key informant interviews ( n 19), along with records review, were performed to gather data on model variables. Exploratory analysis was conducted to estimate the impact of individual model variables on EBT sales. Setting: Farmers’ markets accepting EBT in the state of Hawai‘i. Participants: Market managers and EBT program partners ( n 19). Results: Markets engaging in community partnerships $\left( {{\mkern 1mu} {\mkern 1mu} \Delta \overline x = \$ 852} \right)$ , consumer education $\left( {{\mkern 1mu} {\mkern 1mu} \Delta \overline x = \$ {\rm{598}}} \right)$ , social media promotion $\left( {{\mkern 1mu} {\mkern 1mu} \Delta \overline x = \$ {\rm{732}}} \right)$ or EBT incentives $\left( {{\mkern 1mu} {\mkern 1mu} \Delta \overline x = \$ {\rm{5}}0{\rm{9}}} \right)$ averaged higher sales than markets not reporting these practices. Sales increased by $3 for every ten additional SNAP-participating households and decreased by $35 for each competing EBT-accepting supermarket, grocery or farmers’ market within the market’s access area. Sales increased by $137/vendor for each additional hour/week the market was open. Conclusion: Factors suggested by the model, particularly community engagement and partnership, marketing methods, consumer base and competition for EBT sales in the market area substantively affected EBT sales. Assessing these factors may identify markets with the greatest chance of EBT success and suggest ways to strengthen struggling EBT programs.
Modification of Initialization Vector for Parallel CMAC Algorithm

The growing number of threats and attacks on communication systems has encouraged researchers to identify methods for providing security for data communication. Besides the encryption and authentication algorithms provide data security services for communication systems. However, the recent attacks on communication systems reveal that the attacks utilize the analysis of plaintext/ciphertext to breach the security of communication systems. Therefore, the plaintext should be randomized to prevent correlation between ciphertext. In this work, an algorithm is proposed for increasing ciphertext randomness in Parallel Cipher-based Message Authentication Code (PCMAC) Algorithm. The proposed algorithm utilizes the high throughput of PCMAC authenticated encryption algorithm for providing high throughput and more randomized ciphertext. The proposed algorithm is implemented for generation of Initialization Vector (IV) using the GEFFE generator and right shift operation for creating randomness in the ciphertext. The comparisons result show that the proposed implementation is more randomized as compared to the previous implementation.
Location-Based Cryptographic Techniques for Data Protection

Data protection has become one of the significant issues in cloud computing when end users must rely on their cloud providers for security purposes. Cloud providers never guarantee the security of data. One of the best ways to overcome this issue is to have the data encrypted before it reaches the cloud servers. Cryptographic becomes a common mechanism used to transmit data securely in open networks, but there are also some challenges to implement encryption internally due to key management. Either the keys are vulnerable towards any security attacks such as HTTP-focused-brute-force attack during transmit over the network, in case of the keys are kept at the user site, or keys are missing if the device is stolen. Thus, location-based encryption adds another layer of security on top of existing encryption method. Location-based encryption enhances security by implementing location information into the encryption and decryption process to counter the possible problems. Location coordinates are used as a key for encrypting the data and the cipher text can only be decrypted if and only the decrypted location is matched with the location that has been computed with the key. In this paper discuss on location-based encryption technique that has been implemented in previous works which are asymmetric, symmetric and hybrid algorithm followed by its strengths and weaknesses. This paper finds that majority of the researchers used hybrid algorithm to implement their location-based encryption instead of using the asymmetric algorithm or hybrid algorithm alone because the hybrid algorithm has fast computation of symmetric algorithm and high security of asymmetric dual keys
Online Speaker Adaptation Using Memory-Aware Networks for Speech Recognition

In our previous work, we introduced our attention-based speaker adaptation method, which has been proved to be an efficient online speaker adaptation method for real-time speech recognition. In this paper, we present a more complete framework of this method named memory-aware networks, which consists of the main network, the memory module, the attention module and the connection module. A gate mechanism and a multiple-connections strategy are presented to connect the memory with the main network in order to take full advantage of the memory. An auxiliary speaker classification task is provided to improve the accuracy of the attention module. The fixed-size ordinally forgetting encoding method is used together with average pooling to gather both short-term and long-term information. Furthermore, instead of only using traditional speaker embeddings such as i-vectors or d-vectors as the memory, we design a new form of memory called residual vectors, which can represent different pronunciation habits. Experiments on both the Switchboard and AISHELL-2 tasks show that our method can perform online speaker adaptation very well with no additional adaptation data and with only a relative 3% increase in decoding computation complexity. Under the cross-entropy criterion, our method achieves a relative word error rate reduction of 9.4% and 8.3% compared to that of the speaker-independent model on the Switchboard task and the AISHELL-2 task, respectively, and approximately 7.0% compared to that of the traditional d-vector-based speaker adaptation method.
Multi-task Learning for Acoustic Modeling Using Articulatory Attributes

In addition to the phone sequences, articulatory attributes in spoken utterances have demonstrated salient cues for supervised training of acoustic models in automatic speech recognition (ASR). In this paper, a multi-task learning (MTL) scheme for neural network-based acoustic modeling is proposed. It aims to simultaneously minimize the cross-entropy losses of the triphone-states and articulatory attributes, given their corresponding true alignments. Supposing the articulatory information associated with the physical process is not as abstract and composite as the phonetic descriptions, the layer-wise neuron sharing occurs only in the first few layers. Moreover, instead of the fully-connected feed-forward networks (FFNs), the well-known structure of time-delay neural networks (TDNNs) is adopted to efficiently model the long-term contexts of each acoustic input frame. The results of experiments on the MATBN Mandarin Chinese broadcast news corpus show that our proposed framework achieves relative character error rate reductions of 3.3% and 5.7% over the non-MTL TDNN-based system and the MTL-FFN-based system, respectively.
Fair Data Transactions Across Private Databases

In this era of big data, data transactions are becoming more frequent and bringing new benefits for both data buyers and providers.However, there are no data transaction schemes to satisfy the special security and business requirements of data transactions.In this paper, we introduce the requirements in practical data transaction processes and compare the differences of requirements and techniques between data transaction and traditional data sharing.Next, we propose and implement the first data transaction scheme based on a newly defined concept probabilistic commutative encryption, which makes the decryption order of a probabilistic encryption algorithm and a deterministic encryption algorithm commutative.Since the security concerns of both sides can be achieved in a malicious model, our scheme is fair to both data buyers and data providers from a business perspective.Furthermore, our scheme allows data transaction to be partly outsourced to a semi-honest cloud storage platform.