Issues of Biometric Data Processing in Employment

Problematika zpracování biometrických údajů v zaměstnání Abstrakt Tématem práce je problematika zpracování biometrických údajů v zaměstnání. V souvislosti s přijetím nařízení Evropského parlamentu a Rady (EU) 2016/679 o ochraně fyzických osob v souvislosti se zpracováním osobních údajů a o volném pohybu těchto údajů a o zrušení směrnice 95/46/ES (obecné nařízení o ochraně osobních údajů) a rozvojem moderních technologii jde o stále aktuálnější problém se kterým se musí vypořádat nejenom narůstající počet zaměstnavatelů, ale i evropských zákonodárců. Ve své první části práce pojednává o pojmu biometrického údaje a jeho definičních předpokladech. Následně jsou v práci popsány jednotlivé problémy, které se při zpracování biometrických údajů mohou vyskytnout. Tyto problémy jsou přitom zasazeny do pracovněprávního kontextu. Následně, zvláštní část této práce popisuje jednotlivé konkrétní technologie zpracování biometrických identifikátorů v zaměstnání a způsoby jak tyto technologie řeší nebo naopak neřeší nastíněné jednotlivé problémy ochrany osobních údajů. Ve svém závěru tato práce stručně pojednává o případném budoucím legislativním vývoji zpracování biometrických údajů v zaměstnání. Klíčová slova: biometrické údaje, pracovněprávní vztahy, ochrana osobních údajůIssues of Biometric Data Processing in Employment Abstract The topic of the thesis is the Issues of Biometric Data Processing in Employment. In the context of the adoption of Regulation (EU) 2016/679 of the European Parliament and of the Council on the protection of individuals with regard to the processing of personal data and on the free movement of such data and repealing Directive 95/46/EC (General Data Protection Regulation) and development of modern technologies, it has become an increasingly pressing problem which has to be faced by a growing number of employers, but also the European legislators. The first part of this thesis deals with the concept of biometric data and its definition. Subsequently, this thesis describes individual problems that may occur in the context of biometric data processing. These problems are embedded into the labor-law context. Further, the specific part of this thesis describes particular technologies for processing of biometric identifiers in the workplace and the means how these technologies solve or fail to solve the outlined problems of personal data protection. In its conclusion, this thesis briefly discusses the possible future legislative developments in the field of biometric data processing in employment context. Key words: biometric data, employment...Department of Labor Law and Social Security LawKatedra pracovního práva a práva sociálního zabezpečeníFaculty of LawPrávnická fakult

Utilization of Biodiesel By-Products for Biogas Production

This contribution reviews the possibility of using the by-products from biodiesel production as substrates for anaerobic digestion and production of biogas. The process of biodiesel production is predominantly carried out by catalyzed transesterification. Besides desired methylesters, this reaction provides also few other products, including crude glycerol, oil-pressed cakes, and washing water. Crude glycerol or g-phase is heavier separate liquid phase, composed mainly by glycerol. A couple of studies have demonstrated the possibility of biogas production, using g-phase as a single substrate, and it has also shown a great potential as a cosubstrate by anaerobic treatment of different types of organic waste or energy crops. Oil cakes or oil meals are solid residues obtained after oil extraction from the seeds. Another possible by-product is the washing water from raw biodiesel purification, which is an oily and soapy liquid. All of these materials have been suggested as feasible substrates for anaerobic degradation, although some issues and inhibitory factors have to be considered

Brief Announcement: DeadlineAware Scheduling

ABSTRACT This paper presents a novel algorithm for scheduling big data jobs on large compute clusters. In our model, each job is represented by a DAG consisting of several stages linked by precedence constraints. The resource allocation per stage is malleable, in the sense that the processing time of a stage depends on the resources allocated to it (the dependency can be arbitrary in general). The goal of the scheduler is to maximize the total value of completed jobs, where the value for each job depends on its completion time. We design an algorithm for the problem which guarantees an expected constant approximation factor when the cluster capacity is sufficiently high. To the best of our knowledge, this is the first constant-factor approximation algorithm for the problem. The algorithm is based on formulating the problem as a linear program and then rounding an optimal (fractional) solution into a feasible (integral) schedule using randomized rounding

Performance Regression Detection in DevOps

Performance is an important aspect of software quality. The goals of performance are typically defined by setting upper and lower bounds for response time and throughput of a system and physical level measurements such as CPU, memory, and I/O. To meet such performance goals, several performance-related activities are needed in development (Dev) and operations (Ops). Large software system failures are often due to performance issues rather than functional bugs. One of the most important performance issues is performance regression. Although performance regressions are not all bugs, they often have a direct impact on users’ experience of the system. The process of detection of performance regressions in development and operations is faced with challenges. First, the detection of performance regression is conducted after the fact, i.e., after the system is built and deployed in the field or dedicated performance testing environments. Large amounts of resources are required to detect, locate, understand, and fix performance regressions at such a late stage in the development cycle. Second, even we can detect a performance regression, it is extremely hard to fix it because other changes are applied to the system after the introduction of the regression. These challenges call for further in-depth analyses of the performance regression. In this thesis, to avoid performance regression slipping into operation, we first perform an exploratory study on the source code changes that introduce performance regressions in order to understand root-causes of performance regression in the source code level. Second, we propose an approach that automatically predicts whether a test would manifest performance regressions in a code commit. Most of the performance issues are related to configurations. Therefore, third, we propose an approach that predicts whether a configuration option manifests a performance variation issue. To assist practitioners to analyze system performance with operational data, we propose an approach to recovering field-representative workload that can be used to detect performance regression

Statistical Machine Learning Makes Automatic Control Practical for Internet Datacenters

Horizontally-scalable Internet services on clusters of commodity computers appear to be a great fit for automatic control: there is a target output (service-level agreement), observed output (actual latency), and gain controller (adjusting the number of servers). Yet few datacenters are automated this way in practice, due in part to well-founded skepticism about whether the simple models often used in the research literature can capture complex real-life workload/performance relationships and keep up with changing conditions that might invalidate the models. We argue that these shortcomings can be fixed by importing modeling, control, and analysis techniques from statistics and machine learning. In particular, we apply rich statistical models of the application’s performance, simulation-based methods for finding an optimal control policy, and change-point methods to find abrupt changes in performance. Preliminary results running a Web 2.0 benchmark application driven by real workload traces on Amazon’s EC2 cloud show that our method can effectively control the number of servers, even in the face of performance anomalies.

Increasing the effectivity of the antimicrobial surface of carbon quantum dots-based nanocomposite by atmospheric pressure plasma

Preventing nosocomial infections is one of the most significant challenges in modern medicine. The disinfection of medical facilities and medical devices is crucial in order to prevent the uncontrolled spread of bacteria and viruses. Cost-effective, eco-friendly and fast-acting antibacterial coatings are being developed as the prevention of bacteria and viruses' multiplication on various surfaces. One of the possibilities to create such antimicrobial coatings can rely on a photoactive material, that produces singlet oxygen. However, a remote production of the singlet oxygen and disinfection of the desired surface is a time-consuming process. Hence, a coating material that would autonomously produce singlet oxygen employing ambient light will have a significant impact on the shortening of the disinfection time; leading into an increased number of patients that can be cured in one facility. In this work, an ultra-fast and eco-friendly method for decreasing the disinfection time of the photoactive surface is presented. The atmospheric pressure plasma surface treatment on the hydrophobic carbon quantum dots-polydimethylsiloxane nanocomposite is employed. The plasma-treated samples exhibited improved antibacterial properties compared to non-plasma treated samples, with the best results obtained after only 30 seconds of plasma treatment. The short duration and the scalability potential of the here described method open new possibilities of how to improve the already existing antibacterial coatings. © 2020 Elsevier GmbHResearch & Innovation Operational Programme - ERDF; Czech Science FoundationGrant Agency of the Czech Republic [19-16861S]; project Buildingup Centre for Advanced Materials Application of the Slovak Academy of Sciences [313021T081]; [VEGA 2/0051/20]; [APVV-15-0641

Monitoring Alcohol Consumption in Slovak Cities during the COVID-19 Lockdown by Wastewater-Based Epidemiology.

The consumption of alcohol in a population is usually monitored through individual questionnaires, forensics, and toxicological data. However, consumption estimates have some biases, mainly due to the accumulation of alcohol stocks. This study's objective was to assess alcohol consumption in Slovakia during the COVID-19 pandemic-related lockdown using wastewater-based epidemiology (WBE). Samples of municipal wastewater were collected from three Slovak cities during the lockdown and during a successive period with lifted restrictions in 2020. The study included about 14% of the Slovak population. The urinary alcohol biomarker, ethyl sulfate (EtS), was analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). EtS concentrations were used to estimate the per capita alcohol consumption in each city. The average alcohol consumption in the selected cities in 2020 ranged between 2.1 and 327 L/day/1000 inhabitants and increased during days with weaker restrictions. WBE can provide timely information on alcohol consumption at the community level, complementing epidemiology-based monitoring techniques (e.g., population surveys and sales statistics)