Miracle-dz/UniFAR-FTUs huggingface dataset:
A Semantic Support for Testing Activities of Safety-Critical Embedded Systems

Testing is a relevant activity for the development life-cycle of Safety Critical Embedded systems. In particular much effort is spent for design of test cases, where natural language description of system requirements must be translated into test scripts, which must automatically executed to check correctness and achievement of target service levels. Semantic and text analysis are used here to support developers for the management of test documents to speedup and improve the testing definition phase. Facilities for both advanced classification, indexing and discovery of test documents are provided.
Approaches to adaptively reduce processing effort for LTE Cloud-RAN systems

Cloud-RAN is a novel architecture for LTE networks, where antennas with only limited processing capabilities are deployed in the field and all baseband and higher layer processing of the base stations is pooled in a central office. It has been shown that the centralization leads to multiplexing gains for signal processing hardware. When a system is able to efficiently cope with overload of the compute resources, significant savings are possible. This allows to install less resources (saving Capital Expenditure, CAPEX) and to switch off more resources during low-load periods (saving Operational Expenditure, OPEX). In this publication, the resource allocation of a system of LTE base stations is formulated as an optimization problem. The formulation includes the reduction of interference by not using radio resources for transmission and the flexibility of using different MIMO modes. The results of optimization runs are evaluated. They show that about 50% of the compute resources can be saved without impacting the system performance. In overload situations, only 20% of the resources are sufficient to deliver 87% of the system performance. The most efficient approach to reduce processing effort is to adapt the MIMO mode and to reduce the number of virtual transmit antennas.
Computer Vision for the Car Industry

This report provides a brief and informal introduction into stereo and motion analysis for driver assistance. Stereo and motion analysis play a central role in computer vision [10]. Many algorithms in this field have been proposed and carefully studied; see, for example, [2, 14] and the website vision.middlebury. edu for stereo and optic flow algorithms. In short, a stereo pair of images allows to identify pairs of corresponding points, and those allow to calculate the distance between the projected point in the three-dimensional world and the recording cameras. Fig. 1. A stereo pair captured by two cameras, installed on the windscreen of an ego-vehicle. The vehicle which is used to host the stereo camera platform for capturing stereo image sequences is called the ego-vehicle. Figure 1 illustrates an example of a stereo video sequence, recorded and processed at Daimler AG, Germany. After years of research on ego-motion estimation [1], the automobile industry has all the tools for producing rectified (i.e., geometrically corrected) stereo image sequences. Calculated distance values can then be visualized in a distance map, where gray values represent various distance levels. Figure 2 illustrates two depth maps
Firms’ Clustering and SEE Export Performance: Lessons from the Italian Experience

Like all transition economies, South Eastern Europe (SEE) countries stand to gain most from an export-led growth. Unfortunately, though, productive structure in these countries is largely made up of small and medium enterprises (SMEs) that, due to their limited size, may face obstacles to gain access to international markets. A possible way out of the conundrum is for SMEs to cluster together and, by sharing the costs of internationalization, jointly tap foreign markets. This approach has been at the heart of the successful export performance of SMEs clustered within Italy’s industrial districts. In this paper we use historical data on Italian exports (in 1971 and 1961) to quantify the boosting effect due to firms’ clustering. In particular, we use detailed data on export classified by sector and by destination country to estimate a panel gravity model on which we compute the firms’ clustering effect. Next, building on the Italian experience we design four scenarios for firms’ clustering in three SEE economies (Bulgaria, Romania and Slovenia). By means of these scenarios and applying the estimated coefficient for firms’ clustering in Italy, we simulate the firms’ clustering effect for the three SEE economies and obtain the gain in export growth over the benchmark case. Results show that an additional export growth between 3% and 11% over a five- year period could be expected.
Distinct Roles for Ceramide and Glucosylceramide at Different Stages of Neuronal Growth

Sphingolipids (SLs) are important structural and regulatory components of neuronal plasma membranes. Previous studies using fumonisin B1, an inhibitor of the synthesis of ceramide, the precursor of all SLs, demonstrated that ceramide synthesis is required to sustain axonal growth in hippocampal neurons (Harel and Futerman, 1993; Schwarz et al., 1995) and dendritic growth in cerebellar Purkinje cells (Furuya et al., 1995). We now show that ceramide plays distinct roles at different stages of neuronal development. (1) During axon growth, ceramide must be metabolized to glucosylceramide (GlcCer) to sustain growth. Thus, whereasd-erythro-ceramide, which is metabolized to GlcCer, is able to antagonize the disruptive effects of fumonisin B1 on axon growth,l-threo-ceramide, which is not metabolized to GlcCer, is ineffective. (2) The formation of minor processes from lamellipodia can be stimulated by incubation with short-acyl chain analogs of ceramide that are active in ceramide-mediated signaling pathways, or by generation of endogenous ceramide by incubation with sphingomyelinase. However, GlcCer synthesis is not required for this initial stage of neuronal development. (3) During minor process formation and during axon growth, incubation with high concentrations of ceramide or sphingomyelinase, but not dihydroceramide, induces apoptosis. Together, these observations are consistent with the possibility that minor process formation and apoptosis can be regulated by ceramide-dependent signaling pathways and that the decision whether to enter these diametrically opposed pathways depends on intracellular ceramide concentrations. In contrast, axonal growth requires the synthesis of GlcCer from ceramide, perhaps to support an intracellular transport pathway.
Mining Social and Affective Data for Recommendation of Student Tutors

This paper presents a learning environment where a mining algorithm is used to learn patterns of interaction with the user and to represent these patterns in a scheme called item descriptors. The learning environment keeps theoretical information about subjects, as well as tools and exercises where the student can put into practice the knowledge gained. One of the main purposes of the project is to stimulate collaborative learning through the interaction of students with different levels of knowledge. The students' actions, as well as their interactions, are monitored by the system and used to find patterns that can guide the search for students that may play the role of a tutor. Such patterns are found with a particular learning algorithm and represented in item descriptors. The paper presents the educational environment, the representation mechanism and learning algorithm used to mine social-affective data in order to create a recommendation model of tutors.
Transactional Orc

Transactions provide a language construct that programmers can use to specify that operations execute atomically, in isolation from all computation outside the transaction. Transactions permit optimistic concurrency for accesses to shared data, which may improve performance over locking mechanisms. Transactions also allow programmers to specify atomic regions in a composable way, which locks do not permit. Although transactions are theoretically capable of these performance and programmability improvements, researchers have found it difficult to exploit these benefits in practice via transactional memory. Most transactional memory implementations provide a single transaction management policy. Which policy is desirable, however, often differs with the memory location. Some objects are highly contended and may benefit from deferred updates, while others seldom suffer from contention and may benefit from inplace update, for instance. By implementing transactions in Orc, a language that separates computation from concurrency, we allow different transaction management policies to be used in a single program, and even within a single transaction. Although composability is one of the key programmability goals of transactions, most nesting solutions also suffer from high overheads, unnecessary restrictions, unintuitive nesting policies, or excessive aborts at runtime that may limit gains in programmability. Orc provides nested parallel transactions, and allows computations and data structures to decide how, and whether, they will support nested transactions.
Genome-wide analysis reveals recurrent structural abnormalities of TP 63 and other p 53-related genes in peripheral T-cell lymphomas Running head : Genome-wide analysis of T-cell lymphomas

Peripheral T-cell lymphomas (PTCLs) are aggressive malignancies of mature T lymphocytes with 5-year overall survival rates of only ~35%. Improvement in outcomes has been stymied by poor understanding of the genetics and molecular pathogenesis of PTCL, with a resulting paucity of molecular targets for therapy. We developed bioinformatic tools to identify chromosomal rearrangements using genome-wide, nextgeneration sequencing analysis of mate-pair DNA libraries and applied these tools to 16 PTCL patient tissue samples and 6 PTCL cell lines. Thirteen recurrent abnormalities were identified, of which five involved p53-related genes (TP53, TP63, CDKN2A, WWOX, and ANKRD11). Among these abnormalities were novel TP63 rearrangements encoding fusion proteins homologous to ΔNp63, a dominant-negative p63 isoform that inhibits the p53 pathway. TP63 rearrangements were seen in 11/190 (5.8%) of PTCLs and were associated with inferior overall survival; they also were detected in 2/164 (1.2%) diffuse large B-cell lymphomas. As TP53 mutations are rare in PTCL compared to other malignancies, our findings suggest that a constellation of alternate genetic abnormalities may contribute to disruption of p53-associated tumor suppressor function in PTCL. For personal use only. on April 21, 2017. by guest www.bloodjournal. org From
Flexible entity search on surfaces

Surface computing allows flexible search interaction where users can manipulate the representation of entities recommended for them to create new queries or augment existing queries by taking advantage of increased screen estate and almost physical tactile interaction. We demonstrate a search system based on 1) Direct Manipulation of Entity Representation on Surfaces and 2) Entity Recommendation and Document Retrieval. Entities are modeled as a knowledge-graph and the relevances of entities are computed using the graph structure. Users can manipulate the representation of entities via spatial grouping and assigning preferences on entities. Our contribution can help to design effective information exploration systems that take advantage of large surfaces.
An Educational Data Mining Tool to Browse Tutor-Student Interactions : Time Will Tell !

A basic question in mining data from an intelligent tutoring system is, “What happened when. ” We identify requirements for a tool to help answer such questions by finding occurrences of specified phenomena and browsing them in human-understandable form. We describe an implemented tool and how it meets the requirements. The tool applies to MySQL databases whose representation of tutorial events includes student, computer, start time, and end time. It automatically computes and displays the temporal hierarchy implicit in this representation. We illustrate the use of this tool to mine data from Project LISTEN’s automated Reading Tutor.
SYNZIP Protein Interaction Toolbox: in Vitro and in Vivo Specifications of Heterospecific Coiled-Coil Interaction Domains

The synthetic biology toolkit contains a growing number of parts for regulating transcription and translation, but very few that can be used to control protein association. Here we report characterization of 22 previously published heterospecific synthetic coiled-coil peptides called SYNZIPs. We present biophysical analysis of the oligomerization states, helix orientations, and affinities of 27 SYNZIP pairs. SYNZIP pairs were also tested for interaction in two cell-based assays. In a yeast two-hybrid screen, >85% of 253 comparable interactions were consistent with prior in vitro measurements made using coiled-coil microarrays. In a yeast-signaling assay controlled by coiled-coil mediated scaffolding, 12 SYNZIP pairs were successfully used to down-regulate the expression of a reporter gene following treatment with α-factor. Characterization of these interaction modules dramatically increases the number of available protein interaction parts for synthetic biology and should facilitate a wide range of molecular engineering applications. Summary characteristics of 27 SYNZIP peptide pairs are reported in specification sheets available in the Supporting Information and at the SYNZIP Web site [http://keatingweb.mit. edu/SYNZIP/].
Keyword Search over Dynamic Categorized Information

Consider an information repository whose content is categorized. A data item (in the repository) can belong to multiple categories and new data is continuously added to the system. In this paper, we describe a system, CS*, which takes a keyword query and returns the relevant top-K categories. In contrast, traditional keyword search returns the top-K documents (i.e., data items) relevant to a user query. The need to dynamically categorize new data and also update the meta-data required for fast responses to user queries poses interesting challenges. The brute force approach of updating the meta-data by comparing each new data item with all the categories is impractical due to (i) the large cost involved in finding the categories associated with a data item and (ii) the high rate of arrival of new data items. We show that a sampling based approach which provides statistical guarantees on the reported results is also impracticable. We hence develop the CS* approach whose effectiveness results from its ability to focus on a strategically chosen subset of categories on the one hand and a subset of new data on the other. Given a query, CS* finds the top-K categories with high accuracy even in time-constrained situations. An experimental evaluation of the CS* system using real world data shows that it can easily achieve accuracy in excess of 90%, whereas other approaches demand at least 57% more resources (i.e., processing power), for providing similar results. Our experimental results also show that, contrary to expectations, if the rate of arrival of data items doubles, whereas CS* continues to provide high accuracy without a significant increase in resources, other approaches require more than double the number of resources.
Evolving Blackjack Strategies Using Cultural Learning in Multi – Agent Systems

This paper examines a new approach to the evolution of blackjack strategies, that of cultural learning. Many traditional machine learning approaches have concentrated on reinforcement learning approaches and report satisfactory results. Populations of neural network agents evolve using genetic algorithms (population learning) and at each generation the best performing agents are selected as teachers. Cultural learning is implemented through a hidden layer in each teacher’s neural network that is used to produce utterances which are imitated by its pupils during many games of blackjack. Results show that the cultural learning approach outperforms previous work as well as the best known non–card counting human approaches.
Data Mining of High Accuracy for the Efficiency in the Task of Massive Printing

Random forests are known to be robust for missing and erroneous data as well as irrelevant features. Moreover, even though the forests have many trees, they can utilize the fast building property of decision trees, so they do not require much computing time. In this paper an efficient procedure that utilizes random forests to predict the cylinder bands in rotogravure printing is shown. Even though several research results have been published already to find better prediction accuracy based on other methods, a new and very good result has been found with the suggested method having appropriate parameters of random forests.
A Rule-Based Natural Language Interface to Data Warehouse

Data Warehouse Systems are used for decisionmaking. It is difficult task for casual users to write their request in technical language because they may not have knowledge of technical structure of Database or Data Warehouse. Writing questions in user’s natural languages are easy. In this work, a Natural Language Interface to Data Warehouse has been presented. A set of rules are going to be proposed to understand accurate aggregations in user input natural language question. With the help of rules, the aggregation elements are precisely identified from the user query. Finally, empirical analysis has been carried out to evaluate the proposed system. With the help of this mechanism, system can easily find the aggregate elements from the user input query. Keywords— Natural Language Interface, Aggregate functions, Data Dictionary, Data Warehouse and Mapping Rules
Are They Ready to Teach With Technology? An Investigation of Technology Instruction in Music Teacher Education Programs

The purpose of this research was to investigate the type, quantity, and effects of technology instruction currently being provided to undergraduate music education majors. Undergraduate participants (n = 46) at 10 degree-granting institutions completed an online survey on the technology instruction received during their undergraduate degree programs and their plans for future technology implementation in their classrooms. Participants’ responses indicated that 63% had taken a required technology course during their undergraduate degree program. Required technology courses were most commonly taught by the college/school/department of music, with music notation software and sound mixing and editing applications being the most common topics covered. Participants indicated that more technology instruction in instructional and student-centered applications was desired, with 43% of participants indicating that they did not feel prepared to effectively use technology in their future teaching positions.
LHCDS: A Novel Deployment Strategy of Proxy Caches for P2P Traffic in ISP Networks

Peer-to-Peer (P2P) systems have brought two serious problems for Internet Services Providers (ISPs): traffic surges and network congestion. Proxy caching for P2P traffic is an effective means of easing the burden imposed by P2P traffic on ISPs. The deployment strategy of proxy caches has a significant impact on their effect on ISPs. It has two important components: one is cache operation that is what P2P traffic caches monitor and serve, the other is cache deployment algorithm that is where to deploy caches. In this paper, we propose a deployment strategy of proxy caches, called LHCDS (Cache Deployment Strategy for Lower and Higher networks), including a new type of cache operation and its corresponding deployment algorithm. It can serve P2P traffic not only from the local lower access network to the higher transit ISP's network but also within the higher transit ISP's network itself. In LHCDS, caches are deployed at or near core routers of the transit ISP. We develop several deployment algorithms based on the new cache operation. We then evaluate the algorithms, and conclude that the proposed greedy algorithm is a viable and practical algorithm whose performance is close to optimal. Experiments also show that ISPs can achieve more P2P traffic decrease on links using LHCDS than previous strategies.
A Batteryless Beacon Based on Dual ISM-Band RF Harvesting with Solar-Biasing Current

We propose a Bluetooth Low Energy (BLE) beacon that operates entirely on harvested energy from dual ISM-band RF sources with aid from photovoltaics. Indoor RF power can be harvested from Wi-Fi devices in both 2.4 GHz and 5 GHz bands. Indoor photovoltaic power, while often too low to be considered useful, can be used as biasing current to improve the efficiency of the RF harvester. The proposed harvester performs impedance matching in two bands and maximum power point tracking and stores energy in a solid-state battery and a supercapcitor. Our implementation of the energy-neutral BLE beacon can broadcast every 45 seconds at the input power of 15 dBm. In terms of harvested power, our proposed system harvests more power from both bands in a miniature, low-cost circuit than the state-of-the-art from three channels in a single band. This work represents a step towards enabling a class of IoT systems to operate entirely on harvested indoor RF power.
Frequency of occurrence of idiotypes associated with anti-p- azophenylarsonate antibodies arising in mice immunologically suppressed with respect to a cross-reactive idiotype

Inoculation of rabbit anti-idiotypic (anti-id) antibodies suppresses the subsequent appearance of a cross-reactive idiotype (CRI) associated with the anti-p-azophenylarsonate (anti-Ar) antibodies of A/J mice. Such suppressed mice produce normal concentrations of anti-Ar antibodies which lack the CRI, but against which anti-id antisera can be prepared. The anti-Ar antibodies of an individual, suppressed mouse do not in general share idiotype with anti-Ar antibodies of other A/J mice, either suppressed or nonsuppressed. The present experiments were undertaken to quantitate several "private idiotypes" in a large number of hyperimmunized A/J mice. Anti-Ar antibodies of three mice, suppressed for the CRI, were labeled with 125I and subjected to isoelectric focusing. Four single peaks, that were over 90% reactive with autologous antiid, were randomly selected for use as ligands in a radioimmunoassay, and ascitic fluids containing anti-Ar antibodies from 181 A/J mice were tested as inhibitors. Two of the four idiotypes could not be detected in any mouse other than the donor. The concentration of the idiotype was less than 1 part in 1,250 to less than 1 part in 25,000 of the anti-Ar antibody population; these are minimum values. A third idiotype was detected in 3 of the 181 mice, but at very low concentrations. The fourth idiotype was present in 28% of the mice, again at a low concentration. The data support the existence of a very large repertoire of anti-Ar antibodies in the A/J strain and are consistent with a process of random somatic mutation for generating diversity in hypervariable regions. It is proposed that the cross- reactive idiotype may be controlled by a germ line gene or a gene related to a germ line gene through a small number of somatic mutations; and that the idiotypes that were not detectable in other mice were the products of genes that had undergone extensive mutations, with a low probability of recurrence in other mice.