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Since 2013 an Armenian - Italian archaeological mission has been working in the Kotayk Province in Armenia with multiple purposes. One of these is the systematic investigation of the valley of the River Marmarik, the main tributary of the River Hrazdan, which is the main watercourse in the Central Armenia. The Marmarik valley is an area of great interest, both for its geographical characteristics and for the archaeological remains that have been found there over the years. In fact the area is characterised by the presence of gold, and some of the most important ores in Armenia, including iron and copper. This contribution presents and discusses the archaeological evidence relating to the first protohistoric phase, the Early Bronze Age, in the River Marmarik valley.

Recently, Distributed Denial of Service(DDOS) attacks have been on the rise and come in very many forms costing many technology firms a lot of time and money. In this study, deep learning models were compared in terms of performance, to solve the problem of detecting these attacks. The first step to mitigating DDOS attacks is by first identifying them, which serves as a toll order. This report used two deep learning models: the Deep Feed Forward (DFF) algorithm and a hybrid containing a CNN with BiLSTM (bidirectional long short-term memory). To compare these algorithms, the “DDoS Botnet Attack on IoT a71a0b42-4” dataset available on Kaggle was chosen. The dataset was undergone various evaluations to find out the performance metrics between the two algorithms. From the simulations conducted, DFF was found to have an accuracy of 87.2% with detecting the time of 0.8 seconds, while the CNN-Bi-LSTM was found to have an accuracy of 94.6% with detecting the time of 1.4 seconds.

OverviewDaily data collection during archaeological fieldwork forms the basis for later interpretation and analysis. Across the world, we observe a wide variety of digital data collection methods and tools employed during fieldwork. Here, we detail the daily practices at four recent survey and excavation projects in the South Caucasian country of Armenia. As archaeology continues to become ever more digital, it is useful to consider these day-to-day recording processes at a typical field project. We provide details on both the types of data collected and the ways they are collected so as to foreground these topics. Finally, we reflect on how our work is currently impacted by digital changes and how it may continue to change in the future.

Objective. Speech decoding, one of the most intriguing brain-computer interface applications, opens up plentiful opportunities from rehabilitation of patients to direct and seamless communication between human species. Typical solutions rely on invasive recordings with a large number of distributed electrodes implanted through craniotomy. Here we explored the possibility of creating speech prosthesis in a minimally invasive setting with a small number of spatially segregated intracranial electrodes. Approach. We collected one hour of data (from two sessions) in two patients implanted with invasive electrodes. We then used only the contacts that pertained to a single stereotactic electroencephalographic (sEEG) shaft or an electrocorticographic (ECoG) stripe to decode neural activity into 26 words and one silence class. We employed a compact convolutional network-based architecture whose spatial and temporal filter weights allow for a physiologically plausible interpretation. Main results. We achieved on average 55% accuracy using only six channels of data recorded with a single minimally invasive sEEG electrode in the first patient and 70% accuracy using only eight channels of data recorded for a single ECoG strip in the second patient in classifying 26+1 overtly pronounced words. Our compact architecture did not require the use of pre-engineered features, learned fast and resulted in a stable, interpretable and physiologically meaningful decision rule successfully operating over a contiguous dataset collected during a different time interval than that used for training. Spatial characteristics of the pivotal neuronal populations corroborate with active and passive speech mapping results and exhibit the inverse space-frequency relationship characteristic of neural activity. Compared to other architectures our compact solution performed on par or better than those recently featured in neural speech decoding literature. Significance. We showcase the possibility of building a speech prosthesis with a small number of electrodes and based on a compact feature engineering free decoder derived from a small amount of training data.

The area encompassing the modern Republic of Armenia lies within the Armenian highlands and is situated at the very core of a dynamic corridor between Africa and Eurasia. As such, Armenia proves critical for understanding the initial stages of human settlement and the formation of ancient civilisations in the Near East and beyond. Stone Age artefacts have been known to exist within the territory of Armenia since the end of the 19th century, and they indicate that the area attracted a variety of Stone Age populations, from early hominids to early complex societies of the Chalcolithic. Presented work is the first attempt to summarise the results and achievements of the Stone Age archaeology in Armenia which is counting its history more than a century.

The Kotayk Survey Project is an Armenian–Italian expedition aimed at the archaeological study of Kotayk Region, Armenia. The research project is developed under a Memorandum of Understanding signed between Institute of Archaeology and Ethnography of National Academy of the Sciences of the Republic of Armenia (IAE NAS RA) and ISMEO – International Association of Mediterranean and Oriental Studies. The project is co-funded by ISMEO, IAE NAS RA and MAECI (Ministry of Foreign Affairs and International Cooperation of Italy). The project combines an extensive survey in the region, the subsequent selection of important sites for test trenches, and a major excavation in the Urartian fortress of Solak-1/Varsak. The activities started in 2013 with a preliminary analysis of the territory through the study of satellite images of the area, followed by a field survey over an extensive area, which covers about 100,000 ha. During the survey activities, 114 archaeological sites have been identified, spanning from Palaeolithic to Late Medieval. The research is still in progress with two main aims: on one side the excavation, conservation and touristic fruition of the Urartian fortress of Solak-1/Varsak, and on the other, the creation of archaeological maps of the region, that will illuminate the massively exploited and partially still unknown area of Kotayk Region. The work of the Kotayk Survey Project is part of a long tradition of cooperation between Armenian and Italian archaeological institutions and develops from the history of prior investigations in the region, that first recognised the important heritage of this territory.

The Vayots Dzor Project (VDP) is an Armenian-Italian archaeological research project involving both survey and excavation activities, conducted in accordance with an agreement between IAE NAS RA (Institute of Archaeology and Ethnography NAS RA) and ISMEO-International Association of Mediterranean and Oriental Studies, co-funded by IAE NAS RA, Gfoeller Renaissance Foundation of USA, ISMEO and MAECI (Italian Ministry of Foreign Affairs and International Cooperation). The work is carried out in cooperation with the Yeghegnadzor Regional Museum. The project started in summer 2016, after a preliminary visit to the region – renowned for its vineyards and high-quality wine production – in 2015, with a view to its archaeological exploration. It is particularly focused on the investigation of the area surrounding the valleys of the River Arpa and its tributaries.

Background: Brain-computer interfaces (BCIs) decode neural activity and extract from it information that can be meaningfully interpreted. One of the most intriguing opportunities is to employ BCIs for decoding speech, a uniquely human trait, which opens up plentiful applications from rehabilitation of patients to a direct and seamless communication between human species. To decipher neuronal code complex deep neural networks furnish only limited success. In such solutions an iffy performance gain is achieved with uniterpretable decision rules characterised by thousands of parameters to be identified from a limited amount of training data. Our recent experience shows that when applied to neural activity data compact neural networks with trainable and physiologically meaningful feature extraction layers [1] deliver comparable performance, ensure robustness of the learned decision rules and offer the exciting opportunity of automatic knowledge discovery. Methods: We collected approximately one hour of data (from two sessions) where we recorded stereotactic EEG (sEEG) activity during overt speech (6 different randomly shuffled phrases and rest). We have also recorded synchronized audio speech signal. The sEEG recording was carried out in an epilepsy patient implanted for medical reasons with an sEEG electrode passing through Broca area with 6 contacts spaced at 5 mm. We then used a compact convolutional network-based architecture to recover speech mel-cepstrum coefficients followed by a 2D convolutional network to classify individual words. We then interpreted the former network weights using the theoretically justified approach devised by us earlier [1]. Results: We achieved on average 44% accuracy in classifying 26+ 1 words (3.7% chance level) using only 6 channels of data recorded with a single minimally invasive sEEG electrode. We compared the performance of our compact convolutional network to that of the DenseNet-like architecture that has recently been featured in neural speech decoding literature and did not find statistically significant performance differences. Moreover, our architecture appeared to be able to learn faster and resulted in a stable, interpretable and physiologically meaningful decision rule successfully operating over a contiguous data segment no-overlapping with the training data interval. Spatial characteristics of neuronal population pivotal to the task corroborate the results of active speech mapping procedure and frequency domain patterns show primary involvement of the high frequency activity. Conclusions: Most of the speech decoding solutions availabel to date either use potentially harmful intracortical electrodes or rely on the data recorded with impractically massive multielectrode grids covering large cortical area. Here we for the first time achieved practically usable decoding accuracy for the vocabulary of 26 words + 1 silence class backed by only 6 channels of cortical activity sampled with a single sEEG shaft. The decoding was implemented using a compact and interpretable architecture which ensures robustness of the solution and requires small amount of training data. The proposed approach is the first step towards minimally invasive implantable BCI solution for restoring speech function.

The Vayots Dzor Fortress Lansdscapes Project investigates the rise and fall of fortress culture in southern Armenia, focusing on the Late Bronze/Early Iron and medieval periods. The paper summarises the findings of the inaugural field season in 2017. The team performed extensive and intensive survey in a high alpine valley that was a key transportation corridor between Lake Sevan in the north and Nakhichevan and northwest Iran to the south. The project employed digital mapping, drone photogrammetry and digital storytelling in support of digital humanities approaches. Important findings include the documentation of Late Bronze/Early Iron cyclopean fortress architecture situated along roads as well as evidence for Urartian control of the valley. Furthermore, medieval structures and monuments are prominent features in the archaeological record of the valley.

In 2013 an Urartian tomb has been identified by chance during the construction of a house in the village of Aghavnadzor in Vayots Dzor Region, Armenia. Despite the tomb was heavily damaged, archaeologists were able to dig it and document it before its destruction. The multiple burial showed the contemporary coexistence of inhumation and incineration, according to a funerary practice well known in Urartian times. A good amount of grave goods have been recovered and restored. Most of it shows typical Urartian features with some interesting exceptions that refer to contemporary Assyrian models. The grave has been dated back to the 8th century thanks to 14C. In the present poster, the materials of the grave are presented and discussed. This discovery is particularly important because gives new information on the Urartian occupation of this part of the Armenian Highlands and, in particular, of the Vayots Dzor Region. The tomb will be discussed in the wider frame of the important Urartian evidence already known in the region, like the so-called ‘tomb of Yeghegnadzor’.

The paper deals with the results of the discovery of three new archaeological sites in the Kotayk region of modern Republic of Armenia dated to the 3th millennium BC. The archaeological materials collected during the survey are indeed referable to the Kura-Araxes culture that developed in those areas during the Early Bronze Age. The three sites add important information for the knowledge of central Armenia during the 3th millennium BC.

In this work, we motivate and present a novel compact CNN. For the architectures that combine the adaptation in both space and time, we describen a theoretically justified approach to interpreting the temporal and spatial weights. We apply the proposed architecture to Berlin BCI IV competition and our own datasets to decode electrocorticogram into finger kinematics. Without feature engineering our architecture delivers similar or better decoding accuracy as compared to the BCI competition winner. After training the network, we interpret the solution (spatial and temporal convolution weights) and extract physiologically meaningful patterns.

Objective. Brain–computer interfaces (BCIs) decode information from neural activity and send it to external devices. The use of Deep Learning approaches for decoding allows for automatic feature engineering within the specific decoding task. Physiologically plausible interpretation of the network parameters ensures the robustness of the learned decision rules and opens the exciting opportunity for automatic knowledge discovery. Approach. We describe a compact convolutional network-based architecture for adaptive decoding of electrocorticographic (ECoG) data into finger kinematics. We also propose a novel theoretically justified approach to interpreting the spatial and temporal weights in the architectures that combine adaptation in both space and time. The obtained spatial and frequency patterns characterizing the neuronal populations pivotal to the specific decoding task can then be interpreted by fitting appropriate spatial and dynamical models. Main results. We first tested our solution using realistic Monte-Carlo simulations. Then, when applied to the ECoG data from Berlin BCI competition IV dataset, our architecture performed comparably to the competition winners without requiring explicit feature engineering. Using the proposed approach to the network weights interpretation we could unravel the spatial and the spectral patterns of the neuronal processes underlying the successful decoding of finger kinematics from an ECoG dataset. Finally we have also applied the entire pipeline to the analysis of a 32-channel EEG motor-imagery dataset and observed physiologically plausible patterns specific to the task. Significance. We described a compact and interpretable CNN architecture derived from the basic principles and encompassing the knowledge in the field of neural electrophysiology. For the first time in the context of such multibranch architectures with factorized spatial and temporal processing we presented theoretically justified weights interpretation rules. We verified our recipes using simulations and real data and demonstrated that the proposed solution offers a good decoder and a tool for investigating motor control neural mechanisms.

This article describes some of the archaeological activities carried out as part of the Vayots Dzor Project (VDP), an Armenian – Italian archaeological expedition aimed at a detailed investigation of Vayots Dzor Region. The project is based on a Memorandum of Understanding between the Institute of Archaeology and Ethnography of the National Academy of Sciences of the Republic of Armenia (IAE NAS RA) and ISMEO – International Association of Mediterranean and Oriental Studies. The expeditions are co-financed by Gfoeller Renaissance Foundation of USA, ISMEO and MAECI (Ministry of Foreign Affairs and International Cooperation of the Italian Republic, ARC-001548).

A large area of the Ararat plain has been heavily deformed by modern agricultural activity, including the cultivation of land and the construction of a water channel. These activities have made prehistoric sites that originally bore non-pronounced landforms less visible in the current landscape. As a result of such heavy land deformations, reconstruction of the Ararat plain’s prehistoric landscape is becoming increasingly difficult. In this situation, it is worthwhile to gather fragmental information on prehistoric sites in the Ararat plain that were investigated during previous unpublished research, and then evaluate them through current scientific methods.

The Eo-Holocene Lernagog-1 site (also known as the Lower or Early Holocene, between 10,300 BP and 7500 BP, approximately equating to the last quarter of the 9th millennium BC to the middle of the 6th millennium BC) is a significant period in the Near East as encompasses the transition from hunting-gathering to agriculture. Early farming villages, also known as Pre-pottery Neolithic communities, also appeared at this time and developed in some areas of the Near East.

The Kotayk Survey Project (ksp) started in the summer of 2013. This is a joint Armenian—Italian project involving the study of the upper Hrazdan river valley, located in the northern part of the Kotayk region in the Armenian Republic. This article presents the results of the third fieldwork season, which involved both excavation and survey. In particular, the text describes the excavation of the Solak 1 fortress and discusses its role in the framework of Middle Iron Age/Urartian archaeology.

CMOS scaling affects to sub-threshold leakage, gate leakage and device variations growth leakage current. Static Random Access Memory (SRAM) cell is engaged the most area of Integrated Circuit (IC). It has been implemented FinFET bitcell, which leakage becomes the significant key factor in SRAM. In addition, proposed to implement 8T SRAM cell using diode connected NMOS/PMOS transistors to reduce leakage current. Different approaches were used for leakage reduction in the proposed circuit. Consequently, leakage power in the SRAM bitcell is reduced and runs better performance. The proposed SRAM bitcell showed an improvement of about 3 times leakage power over the 6T SRAM bitcell. Read and Write access times of the proposed SRAM bitcell increases about 18 %. Power also reduced about 10%. The proposed techniques of leakage reduction have been implemented by using Synopsys Armenia Educational Department’s SAED 14 nm technology for FinFET SRAM.

The southern Caucasus is a critical region for those interested in Palaeolithic research because of its varied topography and location at the crossroads of Europe, Africa, and Asia. Modern Armenia sits at the heart of this area, but has until now played a small role in broader debates, largely because of its paucity of well-excavated and well-dated sites. To improve this situation, a survey was conducted for Palaeolithic sites along the valley of the Debed River (Lori Depression, northeastern Armenia). Twenty-three open-air sites, spanning the Lower through the Upper Palaeolithic periods, were identified. Most of the lithic material is of Middle Palaeolithic manufacture. Upper Palaeolithic material is also well represented, but only a handful of Lower Palaeolithic artifacts have been identified. Test excavations at several sites suggest that they preserve in situ deposits that may help us to understand the role of the southern Caucasus in the Palaeolithic occupation of Eurasia.